1 Introduction

Colon cancer remains a significant global health challenge, being one of the leading causes of cancer-related mortality worldwide. The advent of high-throughput sequencing technologies has enabled the generation of vast amounts of omics data, including genomics, transcriptomics, proteomics, and metabolomics. These multi-omics datasets provide a comprehensive view of the molecular underpinnings of cancer, offering unprecedented opportunities for biomarker discovery and personalized medicine (Tong et al., 2020a; Chai et al., 2021; Li et al., 2022).

Traditional single-omics approaches often fall short in capturing the complexity of cancer biology, as they provide a limited perspective on cellular functions. Integrating multi-omics data can overcome these limitations by offering a holistic view of the molecular interactions and pathways involved in cancer progression (Zhao et al., 2020; Menyhárt and Győrffy, 2021). This integrative approach has shown promise in improving the accuracy of cancer prognosis, diagnosis, and subtype classification, thereby enhancing the potential for early detection and targeted therapies (Figure 1) (Tong et al., 2020b; Yin et al., 2020; Li and Sun, 2022).

This study aims to explore the role of multi-omics data integration in identifying early biomarkers for colon cancer and to provide a comprehensive understanding of how multi-omics data integration can revolutionize the early detection and treatment of colon cancer, ultimately contributing to better patient outcomes and personalized healthcare strategies.

2 Colon Cancer: An Overview

Colon cancer, also known as colorectal cancer, is one of the most common malignancies worldwide and a leading cause of cancer-related deaths. It originates in the colon or rectum and is often grouped together due to their similar characteristics. The American Cancer Society estimates that in 2024, there will be approximately 106 590 new cases of colon cancer and 46 220 new cases of rectal cancer in the U.S., with an estimated total of 53 010 deaths (Siegel et al., 2024). In addition, it is estimated that there are 376.3 new cases of colorectal cancer diagnosed per 100 000 people each year in China (Chen et al., 2015). The development and progression of colon cancer involve a complex interplay of genetic, epigenetic, and environmental factors.

2.1 Pathogenesis and progression of colon cancer

The pathogenesis of colon cancer is a multistep process that involves the accumulation of genetic and epigenetic alterations. The transformation from normal colonic epithelium to invasive carcinoma takes several years, with the most common features being the accumulation of genetic mutations, adenoma formation, and subsequent carcinogenesis (adenoma-carcinoma sequence) (Gajendran et al., 2019; Sokic-Milutinovic, et al., 2019; Xiao et al., 2019). Genetic and epigenetic alterations lead to the transformation of normal colonic epithelium into adenomatous polyps and eventually into invasive carcinoma. The transition from normal colonic epithelium to dysplasia involves the accumulation of genetic changes over time, ultimately leading to carcinoma. Colorectal cancer can develop through three main genetic pathways: chromosomal instability (CIN), mismatch repair (MMR) deficiencies, and CpG island methylator phenotype (CIMP). These pathways are not mutually exclusive but exhibit significant overlap. Key genetic changes include mutations in oncogenes, tumor suppressor genes, and genes involved in DNA repair mechanisms. For instance, mutations in the APC gene, KRAS, and TP53 are commonly observed in colon cancer (Sun et al., 2021).

Epigenetic modifications, such as DNA methylation and histone modifications, also play a crucial role in the progression of colon cancer. Aberrant DNA methylation patterns can lead to the silencing of tumor suppressor genes and activation of oncogenes, contributing to tumorigenesis (Coppede, 2014). Apolipoprotein C1 (APOC1) promotes tumor progression in colorectal cancer through the MAPK signaling pathway (Ren et al., 2019). MicroRNAs (miRNAs) can also alter cellular signaling pathways and act as either oncogenes or tumor suppressors, contributing to tumorigenesis or recurrence (Lovat et al., 2011; Saliminejad et al., 2019). With advances in gene sequencing, miRNAs are considered potential biomarkers not only for early cancer detection but also for prognostic prediction (Nassar et al., 2017; Dieckmann et al., 2019; Moya et al., 2019). Additionally, the tumor microenvironment, including interactions with immune cells and stromal cells, influences cancer progression and metastasis.

2.2 Importance of early detection

Early detection of colon cancer significantly improves the prognosis and survival rates of patients. When detected at an early stage, colon cancer is often curable with surgical resection and adjuvant therapies. Brenner et al. found that the 5-year survival rate for patients with early-diagnosed CRC is greater than 90% (Berger and Mardis, 2018). However, the majority of cases are diagnosed at advanced stages, where the prognosis is poor and treatment options are limited. In metastatic cases, the 5-year survival rate is much lower, approximately 13% (Siegel et al., 2017). Although the incidence of this disease has been decreasing in Western countries, mainly due to the widespread use of colonoscopy, the incidence is rising among younger people, making early detection of colorectal cancer critically important (Stoffel and Murphy, 2020).

Early detection allows for the identification and removal of precancerous polyps before they progress to invasive cancer. This can be achieved through regular screening programs, which have been shown to reduce the incidence and mortality of colon cancer. Colorectal cancer screening can be performed using various methods. Screening initiation and follow-up guidelines vary among different organizations (Smith et al., 2019). The diagnosis of colorectal cancer requires a tissue biopsy, typically obtained through a colonoscopy. All newly diagnosed colorectal cancers should undergo screening for common genetic mutations, complete colonoscopy, and baseline carcinoembryonic antigen (CEA) testing. Most patients with invasive cancer require baseline chest and abdominopelvic computed tomography (CT) scans (Benson et al., 2021). Moreover, early detection facilitates the implementation of personalized treatment strategies, improving patient outcomes.

2.3 Current diagnostic methods and their limitations

Current diagnostic methods for colon cancer include colonoscopy, fecal occult blood tests (FOBT), and imaging techniques such as CT colonography. The initial diagnosis may involve a barium enema or CT colonography. Ultimately, a colonoscopy is required for tissue diagnosis (Dawson et al., 2019; Grimm and McGill, 2019; Manjunath et al., 2019). Colonoscopy is considered the gold standard for detecting and removing polyps and early-stage cancers. However, it is an invasive procedure that requires bowel preparation and sedation, which can be uncomfortable for patients.

FOBT is a non-invasive test that detects hidden blood in the stool, which can be an early sign of colon cancer. Randomized controlled trials and large cohort studies have confirmed that overall survival (OS) is improved in patients whose cancer was detected through FOBT screening compared to those whose cancer was not detected through screening (Hardcastle et al., 1996; Mandel et al., 2000; Lindholm et al., 2008; Scholefield et al., 2012; Shaukat et al., 2013; Ananda et al., 2016). While it is less invasive than colonoscopy, FOBT has lower sensitivity and specificity, leading to false positives and negatives. CT colonography, also known as virtual colonoscopy, is a less invasive imaging technique that provides detailed images of the colon. However, it may miss small polyps and still requires bowel preparation.

Despite these available methods, there are significant limitations in the early detection of colon cancer. Many cases are diagnosed at advanced stages due to the lack of symptoms in the early stages and the limitations of current screening methods. Therefore, there is a critical need for the development of more sensitive and specific diagnostic tools, such as those based on multi-omics data integration, to improve early detection and patient outcomes.

3 Multi-Omics Data in Cancer Research

3.1 Definition and types of omics data

Multi-omics data integration involves the comprehensive analysis of various types of omics data to gain a holistic understanding of biological systems and disease mechanisms. The primary types of omics data include genomics, transcriptomics, epigenomics, proteomics, and metabolomics.

3.1.1 Genomics

Genomics is the study of the complete set of DNA within an organism, including all of its genes. It involves the sequencing and analysis of genomes to identify genetic variations and mutations that may contribute to cancer development. Genomic data provides insights into the genetic basis of cancer and helps in identifying potential driver mutations (Zhao et al., 2020; Menyhárt and Győrffy et al., 2021). The sequencing of the human genome has rapidly advanced. In recent years, numerous publications have described a large number of newly sequenced human genomes, including specific population cohorts from Iceland (Jónsson et al., 2017; Kehr et al., 2017) , Denmark (Maretty et al., 2017), Sweden (Eisfeldt et al., 2020), Papua New Guinea (Jacobs et al., 2019), Mongolia (Bai et al., 2018), and Africa (Gurdasani et al., 2015; Mathias et al., 2016a; Choudhury et al., 2017) , as well as large-scale surveys across the world (Auton et al., 2015; Sudmant et al., 2015; Mallick et al., 2016b; Telenti et al., 2016). By incorporating cancer genomics into diagnostic medicine, the precision of clinical care for cancer patients is improving. Over the past decade, large-scale parallel sequencing or next-generation sequencing (NGS) has been applied to extensive cancer genomics discovery projects, revealing remarkable new information about the underlying genomic drivers of cancer development and progression across multiple anatomical sites (Berger and Mardis, 2018). The application of NGS technology in characterizing human tumors has provided unprecedented opportunities to understand the biological foundations of different cancer types, develop targeted therapies and interventions, discover genomic biomarkers of drug response and resistance, and guide clinical decisions related to patient treatment (Garraway and Lander, 2013; Hyman et al., 2017).

3.1.2 Transcriptomics

Transcriptomics involves the study of the complete set of RNA transcripts produced by the genome under specific circumstances or in a specific cell. This type of data helps in understanding gene expression patterns and how they change in response to cancer. In recent decades, transcriptome analysis has rapidly gained popularity in cancer research, providing remarkable insights into the field of cancer immunotherapy. The revolution from bulk RNA sequencing to single-cell RNA sequencing (scRNA-seq) has made transcriptome analysis more accurate and powerful. The transcriptomic profiles of millions of individual cells have deepened our understanding of cancer heterogeneity and the tumor microenvironment (Lei et al., 2021). microRNAs (miRNAs) can also influence cell signaling pathways, acting as oncogenes or suppressors involved in tumor initiation or recurrence. With advancements in gene sequencing, miRNAs are viewed as potential biomarkers, not only for early cancer detection but also for predicting prognosis (He et al., 2019).Transcriptomic data is crucial for identifying differentially expressed genes and understanding the regulatory mechanisms at play(Sathyanarayanan et al., 2020).

3.1.3 Epigenomics

Epigenomics is the study of the complete set of epigenetic modifications on the genetic material of a cell. These modifications, such as DNA methylation and histone modification, do not change the DNA sequence but can affect gene expression. Epigenetic mechanisms, including cytosine base methylation, play a crucial role in regulating gene expression during normal mammalian development. However, disruption of these regulatory mechanisms can lead to hypermethylation or hypomethylation of gene promoter regions, resulting in the silencing of critical tumor suppressor functions (Baylin, 2005). Epigenomic data is essential for understanding how epigenetic changes contribute to cancer progression and for identifying potential epigenetic biomarkers.

3.1.4 Proteomics

Proteomics is the large-scale study of proteins, particularly their structures and functions. Since proteins are the functional molecules in cells, proteomic data provides direct insights into the functional state of the cell. This type of data is valuable for identifying protein biomarkers and understanding the molecular mechanisms of cancer.

Proteomics is the large-scale study of proteins, including their expression levels, post-translational modifications, and protein-protein interactions, with a particular focus on their structure and function. It provides a comprehensive understanding of disease development, cellular metabolism, and other processes at the protein level. Since proteins are functional molecules within cells, proteomic data offer direct insights into cellular functional states. By comparing proteomes between normal and pathological individuals, we can identify 'disease-specific protein molecules' that could serve as molecular targets for new drug design or provide molecular markers for early diagnosis of diseases. Such data are highly valuable for identifying protein biomarkers and understanding the molecular mechanisms of cancer.

3.1.5 Metabolomics

Metabolomics, inspired by the research approaches of genomics and proteomics, involves the quantitative analysis of all metabolites within an organism and seeks to identify the relationships between metabolites and physiological or pathological changes. Metabolites are small molecules that are intermediates and products of metabolism. Metabolomic data helps in understanding the metabolic alterations in cancer cells and can provide insights into the metabolic pathways that are dysregulated in cancer.

3.2 Advantages of multi-omics approaches

The integration of multi-omics data offers several advantages in cancer research. The Cancer Genome Atlas (TCGA) provides sequencing data for various cancers from different platforms, including gene expression, DNA methylation, and copy number data (Network, 2012; Qiu et al., 2020; Qiu et al., 2021a; Qiu et al., 2021b). These different types of data alone cannot fully describe the molecular mechanisms of cancer, but they complement each other and cover highly organized molecular and cellular events. Some cancer subtype prediction models integrate different omics data to capture the complexity of phenotypes and the heterogeneity of biological processes (Wang et al., 2014; Ritchie et al., 2015). Compared to models using single omics data (such as gene expression), models utilizing multi-omics data offer a more comprehensive understanding of the molecular mechanisms underlying specific biological processes or complex diseases (de Hijas-Liste et al., 2014). By combining different types of omics data, researchers can gain a more comprehensive understanding of the molecular mechanisms underlying cancer. This holistic approach allows for the identification of novel biomarkers and therapeutic targets that may not be apparent when analyzing a single type of omics data.

Multi-omics approaches also improve the accuracy of cancer prognosis and diagnosis. For instance, integrating genomics, transcriptomics, and epigenomics data can enhance the prediction of cancer outcomes and the identification of cancer subtypes (Kim et al., 2014). Additionally, multi-omics data integration can reveal complex interactions between different molecular layers, providing deeper insights into the pathogenesis of cancer (Nicora et al., 2020). At the same time, advancements in technology and decreasing costs have enabled international collaborations such as the International Cancer Genome Consortium and The Cancer Genome Atlas (TCGA) to perform multi-platform sequencing of thousands of tumors, facilitating the transition to integrated multi-omics cancer research (Hudson et al., 2010; Tomczak et al., 2015). Additionally, large projects such as GTEx (Consortium, 2020), ENCODE (Consortium, 2012) , ROADMAP (Kundaje et al., 2015) , and the Human Cell Atlas (Regev et al., 2017) have publicly released whole-genome and tissue-specific molecular maps. These large, publicly available datasets allow researchers to study disease-related tissues across various biological and multi-omics layers (Hasin et al., 2017) and provide deep insights into the connections between risk factors and diseases.

3.3 Challenges in multi-omics data integration

Despite its advantages, multi-omics data integration poses several challenges. One major challenge is the heterogeneity of the data, as different types of omics data have different characteristics and scales. Integrating these diverse datasets requires sophisticated computational methods and tools. Additionally, as analytical methods continue to evolve, the approaches for integrating multi-omics datasets are becoming increasingly diverse. New multi-omics integration tools are continuously being developed, making the task of selecting the most suitable integration tool from the numerous available options both complex and time-consuming.

Another challenge is the high dimensionality of omics data, which can lead to issues with data sparsity and noise. Effective data integration methods must be able to handle these issues to extract meaningful insights (Silverbush et al., 2019). Additionally, the uneven maturity of different omics technologies can hinder the translation of multi-omics approaches into clinical practice. Due to the uneven maturity of different omics technologies and the high dimensionality of omics data, integrated multi-omics datasets may not always have significant research value.

Finally, the complexity of biological systems means that multi-omics data integration must account for intricate interactions between different molecular layers : from genomics and epigenomics to transcriptomics, proteomics, and metabolomics, and back to genomics and epigenomics. This requires advanced modeling techniques and a deep understanding of the underlying biology (Wang et al., 2016).

In conclusion, while multi-omics data integration holds great promise for advancing cancer research, it also presents significant challenges that must be addressed to fully realize its potential.

4 Integrative Analysis Techniques

4.1 Data preprocessing and quality control

Data preprocessing and quality control are critical steps in multi-omics data integration to ensure the reliability and accuracy of downstream analyses. High-throughput sequencing technologies generate vast amounts of data, which often contain noise and biases. Effective preprocessing involves normalization, imputation of missing values, and batch effect correction. For instance, a Denoising Autoencoder was employed to handle data noise and extract robust features from multi-omics data, enhancing the accuracy of cancer prognosis prediction (Chai et al., 2021).

4.2 Statistical and computational methods for data integration

4.2.1 Correlation-based approaches

Correlation-based approaches are fundamental in identifying relationships between different omics layers. These methods often involve calculating correlation coefficients to determine the strength and direction of associations between variables. For example, Zhao et al. (2020) utilized network analysis to identify genes with broad correlations across various cancers, demonstrating the utility of correlation-based methods in multi-omics data integration. Xu et al. (2019) proposed a method called High-Order Pathway Elucidation Similarity (HOPES) to identify cancer subtypes by querying multi-omics data simultaneously. They used this method to identify gene expression (GE), DNA methylation (DM), and mutation (ME) data across five TCGA cancers and further validated its reliability and clinical significance (Xu et al., 2019).

4.2.2 Machine learning techniques

Machine learning techniques have become indispensable in multi-omics data integration due to their ability to handle high-dimensional data and uncover complex patterns. Various machine learning algorithms, such as clustering, classification, and regression, are employed to integrate and analyze multi-omics data. In research of Reel et al., machine learning methods were reviewed for their effectiveness in integrating omics data to discover new biomarkers and improve disease prediction and patient stratification (Reel et al., 2021). Additionally, Chai et al. highlighted the use of deep learning techniques, such as Autoencoders, to integrate multi-omics data for accurate cancer prognosis prediction (Chai et al., 2021). Wang et al. used Similarity Network Fusion (SNF) to combine mRNA expression, DNA methylation, and microRNA (miRNA) expression data from five cancer datasets. SNF significantly outperformed single data type analyses and established integrative methods in identifying cancer subtypes and effectively predicting survival rates (Wang et al., 2014). Chu employed 10 clustering algorithms to synthesize multi-omics data from patients with muscle-invasive urothelial carcinoma (MUC), and combined this with 10 machine learning algorithms to identify high-resolution molecular subgroups and develop a robust consensus machine learning-driven signature (CMLS) with strong prognostic prediction capabilities (Chu et al., 2023).

4.2.3 Network-based integration

Network-based integration methods leverage the interconnected nature of biological systems to integrate multi-omics data. These approaches construct networks where nodes represent biomolecules, and edges represent interactions or correlations. Constructing networks can provide a more comprehensive understanding of specific diseases and their biological processes, which is beneficial for identifying cancer subtypes and prognostic biomarkers. For instance, Wang et al. proposed a multiplex network-based approach to integrate heterogeneous omics data, achieving high performance in identifying cancer subtypes (Wang et al., 2016). Similarly, Zhao et al. used network analysis to identify prognostic biomarkers with broad correlations across different cancers (Zhao et al., 2020).

4.2.4 Multi-omics data fusion

Multi-omics data fusion involves combining different types of omics data to create a comprehensive view of the biological system. This approach can enhance the identification of biomarkers and improve disease prognosis. Kim et al. (2013) conducted a series of studies using the TCGA dataset to identify interactions between multi-omics data and link these interactions to cancer clinical outcomes (Kim et al., 2013; Kim et al., 2014; Kundaje et al., 2015). Pan-cancer studies and integrative analyses were also performed (Hoadley et al., 2014; Hoadley et al., 2018). These studies demonstrate that multi-scale or multi-platform genomic research is superior to single-scale research in cancer studies. In research of Tong et al., an integrative prognostic model for colon cancer was developed by combining clinical and multi-omics data, resulting in improved prognostic performance (Tong et al., 2020). Additionally, Li and Sun (2022) proposed a novel method for integrating gene expression, copy number variation, DNA methylation, and miRNA data to identify cancer biomarkers, demonstrating the effectiveness of data fusion in multi-omics studies.

4.3 Tools and software for multi-omics data integration

Several tools and software have been developed to facilitate the integration and analysis of multi-omics data. These tools often provide user-friendly interfaces and robust algorithms to handle the complexity of multi-omics datasets. For example, Subramanian et al. reviewed various tools and methods for multi-omics data integration, highlighting their applications in disease subtyping, biomarker prediction, and data interpretation (Table 1). Additionally, Yang et al. introduced the MDICC model, which integrates new affinity matrix and network fusion methods for clustering and identifying cancer subtypes, demonstrating the effectiveness of specialized software in multi-omics data integration.

In summary, the integration of multi-omics data is a powerful approach to uncovering the complex mechanisms underlying diseases such as colon cancer. By employing various statistical and computational methods, including correlation-based approaches, machine learning techniques, network-based integration, and data fusion, researchers can enhance the identification of early biomarkers and improve disease prognosis. The development and utilization of specialized tools and software further facilitate these integrative analyses, paving the way for advancements in precision medicine.

5 Identification of Early Colon Cancer Biomarkers

5.1 Genomic alterations as biomarkers

Genomic alterations, including mutations and somatic copy number variations, play a crucial role in the identification of early colon cancer biomarkers. For instance, the integration of multi-omics data has revealed specific gene mutations such as TERT and ERBB4, which are associated with improved survival in immunotherapy-treated colon cancers (Elsayed et al., 2022). Ge et al. (2019) identified a four-gene signature (ACVR2A, APC, DOCK2, and POLE) as a strong predictor of survival in high-mutant colorectal cancer (CRC) and found it to be particularly effective in stage II and III colon cancer and MSI-H CRC. Huang et al. (2019) discovered five prognostic genes (MMP1, ACSL6, SMPD1, PPARGC1A, and HEPACAM2), which could provide valuable insights for further research and clinical treatment. Additionally, genes like SLK, which exhibit high missense mutation rates, have been identified as potential prognostic biomarkers across various cancers, including colon cancer (Zhao et al., 2020). In addition, the CNV profiles of 159 genes could be used to predict prognosis of colon cancer patients (Yang et al., 2020).

5.2 Transcriptomic signatures

Transcriptomic analysis at the single-cell level has identified differentially expressed genes that serve as marker genes for various colon cancer subtypes. These marker genes show significant specificity compared to normal colon cells, particularly those that are upregulated in tumors (Sun et al., 2021). Deng identified a novel long non-coding RNA (PiHL, p53 Inhibitory LncRNA) as a negative regulator of p53, which is significantly upregulated in colorectal cancer (CRC) and serves as an independent predictor of poor prognosis in CRC (Deng et al., 2020). Moreover, transcriptomic profiles, including mRNA and miRNA expression, have demonstrated high prognostic performance in colon cancer, often outperforming other omics profiles (Zhu et al., 2017). Jacob et al. (2017) used miRNA expression profiles as biomarkers for prognosis in stage II and III colon cancer and identified a 16-miRNA signature as a reliable prognostic marker for stratifying stage II and III colon cancer patients into low and high recurrence risk groups. We also identified 268 genes that are highly expressed in colon cancer tissue, and 13 genes that are associated with colon cancer patient prognosis (Figure 2).

5.3 Epigenetic changes

5.3.1 DNA methylation

DNA methylation patterns are critical in the regulation of gene expression and have been identified as significant biomarkers in colon cancer. Dysregulated methylation at the single-cell level has been linked to the expression of marker genes that are specific to different colon cancer subtypes (Sun et al., 2021). Huang et al. (2022) mentioned 21 5mC regulatory factors such as TET3 and DNMT1 in their study. And our analysis showed that the 5mC regulatory factors expression was significantly different in normal and tumor samples (Figure 3). 

High methylation of the O6-methylguanine-DNA methyltransferase (MGMT) gene promoter has been observed in the normal tissues of CRC patients, and it is associated with mutations in p53 and KRAS, indicating its relevance to CRC progression (Guo et al., 2017). Other high-methylation genes reported in normal tissues of CRC patients include SFRP2, TFPI2, NDRG4, BMP3, and ADAMTS14 (Alonso et al., 2015). He et al. (2018) found that APC2 is in a hypermethylated state and could serve as a tumorigenic biomarker for CRC patients in China (He et al., 2018). Additionally, DNA methylation profiles have shown substantial prognostic value, often ranking just below mRNA and miRNA expression profiles in terms of performance (Figure 4) (Zhu et al., 2017).

5.3.2 Histone modifications

Histone modifications, although less frequently studied compared to DNA methylation, also contribute to the epigenetic landscape of colon cancer. These modifications can influence gene expression and have been implicated in the regulation of various oncogenic pathways. Cancer cells exhibit altered histone modification patterns at the single-gene and single-cell mononuclear level (Seligson et al., 2009). Assessments of histone tail modifications and transcriptional analyses during colorectal cancer progression reveal an overall decrease in H3K4me3 activity (Triff et al., 2017).The integration of histone modification data with other omics layers can provide a more comprehensive understanding of the epigenetic changes driving colon cancer. In the SW480 cell line, the states of H3K27me3, H2BK5ac, H2BK15ac, H3K9ac, H3K18ac, and H4K8ac was correlates with the alteration of RGC-32, which can cause cell cycle changes of SW480 cells (Cao et al., 2024).

5.4 Proteomic and metabolomic biomarkers

Proteomic and metabolomic analyses have identified several biomarkers that are crucial for the early detection and prognosis of colon cancer. For example, proteins such as PREX1 and RAD50, along with specific miRNAs and oncogenic pathways, have been correlated with antitumor immune signatures in colon cancer (Elsayed et al., 2022). Additionally, the integration of transcriptomics and metabolomics has led to the identification of prognostic signatures that capture relevant molecular alterations in cancer tissues, including those related to cellular signaling and immune system modulation (Xu et al., 2022). Additionally, epidemiological data suggest that abnormalities in the biosynthesis and metabolism of sex steroid hormones are associated with the development of various cancers, including colorectal cancer (Kennelly et al., 2008; Figueroa and Brinton, 2012).

5.5 Integrated biomarker discovery: case studies

The integration of multi-omics data has proven to be a powerful approach for the discovery of early colon cancer biomarkers. For instance, a study utilizing multi-omics data integration identified essential prognostic features such as EPB41, PSMA1, and FGFR3, which were validated through independent datasets and shown to distinguish the prognosis of colon cancer patients effectively (Yin et al., 2020). This study systematically analyzes the prognosis of colorectal cancer based on four omics data types from COAD samples: gene expression, exon expression, DNA methylation, and somatic mutations. It performs functional annotation of prognosis-related features through protein-protein interaction (PPI) networks and cancer-related pathways. Another study demonstrated the utility of multi-omics integration in identifying cancer subtypes and prognostic biomarkers, highlighting the effectiveness of methods like MDICC in clustering and survival analysis (Yang et al., 2022).

In summary, the integration of genomic, transcriptomic, epigenetic, proteomic, and metabolomic data provides a comprehensive framework for identifying early colon cancer biomarkers. This multi-omics approach not only enhances the specificity and sensitivity of biomarker discovery but also offers valuable insights into the underlying molecular mechanisms driving colon cancer.

6 Clinical Application of Multi-Omics Biomarkers

6.1 Biomarker validation and verification

The validation and verification of biomarkers identified through multi-omics data integration are crucial steps in translating these findings into clinical practice. Multi-omics approaches, which combine data from genomics, transcriptomics, proteomics, and metabolomics, provide a comprehensive view of the molecular underpinnings of cancer. This comprehensive approach can identify novel biomarkers and therapeutic targets that may not be revealed through single-omics data analysis. For instance, the integration of various omics data types has been shown to improve the identification of cancer driver genes and pathways, thereby enhancing the robustness of potential biomarkers (Silverbush et al., 2019; Zhao et al., 2020). The use of advanced computational methods, such as deep learning and autoencoders, further refines the selection of candidate biomarkers by reducing noise and bias in the data (Chai et al., 2021; Li and Sun, 2022). These validated biomarkers can then be subjected to rigorous clinical testing to ensure their efficacy and reliability in predicting cancer prognosis and treatment outcomes (Hristova and Chan, 2019).

6.2 Development of diagnostic tests

The development of diagnostic tests based on multi-omics biomarkers involves translating the complex data into practical tools that can be used in clinical settings. Multi-omics data integration has been instrumental in creating more accurate and reliable diagnostic tests for various cancers. For example, the integration of clinical and multi-omics data has led to the development of prognostic models that significantly improve the prediction of colon cancer outcomes (Tong et al., 2020). Additionally, methods like ModulOmics, which integrate multiple data types simultaneously, have been successful in identifying cancer driver modules that can be targeted for diagnostic purposes (Silverbush et al., 2019). These advancements highlight the potential of multi-omics approaches to revolutionize cancer diagnostics by providing more precise and individualized assessments of disease status.

6.3 Translational challenges and solutions

Despite the promising potential of multi-omics biomarkers, several translational challenges remain. One major obstacle is the uneven maturity of different omics technologies, which can hinder their integration into routine clinical practice (Menyhárt and Győrffy, 2021). Additionally, the high dimensionality and complexity of multi-omics data pose significant challenges in data analysis and interpretation (Subramanian et al., 2020). To address these issues, researchers have developed various computational tools and methods that facilitate the integration and interpretation of multi-omics data. For instance, the use of denoising autoencoders and other machine learning techniques can help mitigate data noise and improve the robustness of biomarker identification (Chai et al., 2021). Furthermore, collaborative efforts between researchers, clinicians, and bioinformaticians are essential to bridge the gap between discovery and clinical application, ensuring that multi-omics biomarkers can be effectively translated into clinical practice (Hristova and Chan, 2019; Vlachavas et al., 2021).

6.4 Case studies of successful biomarker implementation

Several case studies demonstrate the successful implementation of multi-omics biomarkers in clinical settings. For example, a study integrating gene expression, DNA methylation, and miRNA data identified a set of biomarkers that significantly improved the prognostic prediction of colon cancer (Tong et al., 2020). Another study applied a novel method for multi-omics data integration to identify cancer subtypes, leading to better patient stratification and personalized treatment plans (Yang et al., 2022). Additionally, the use of multi-omics data to identify prognostic markers for breast cancer has shown promising results, with several identified genes being validated through independent datasets (Chai et al., 2021). These case studies underscore the potential of multi-omics approaches to enhance cancer diagnosis, prognosis, and treatment, ultimately improving patient outcomes (Zhao et al., 2020; Li and Sun, 2020).

By leveraging the power of multi-omics data integration, researchers can uncover novel biomarkers that provide deeper insights into cancer biology, paving the way for more effective and personalized clinical applications.

7 Future Perspectives and Research Directions

7.1 Emerging technologies in multi-omics

The integration of multi-omics data has revolutionized cancer research, providing a comprehensive understanding of the molecular mechanisms underlying cancer progression. Emerging technologies such as deep learning and advanced computational methods are enhancing the accuracy and efficiency of multi-omics data integration. For instance, the use of denoising autoencoders has shown promise in improving cancer prognosis prediction by extracting robust features from noisy data (Chai et al., 2021). Additionally, novel methods like ModulOmics, which integrate various omics data types into a single probabilistic model, have demonstrated superior performance in identifying cancer driver pathways (Silverbush et al., 2019). These advancements are paving the way for more precise and reliable biomarker discovery and cancer subtype identification.

7.2 Integrating single-cell omics

Single-cell omics technologies have emerged as powerful tools for dissecting the heterogeneity of cancer at an unprecedented resolution. By profiling genomics, transcriptomics, epigenomics, and proteomics at the single-cell level, researchers can gain deeper insights into the cellular and molecular mechanisms of tumorigenesis, evolution, and metastasis (Peng et al., 2020). In colon cancer research, single-cell multi-omics integration has been used to identify marker genes and improve diagnostic accuracy (Sun et al., 2021). The ability to analyze RNA expression, single nucleotide polymorphisms, and protein abundance simultaneously allows for a more comprehensive understanding of gene expression regulatory mechanisms and their impact on cancer progression.

7.3 Spatial omics in colon cancer research

Spatial omics technologies are transforming our understanding of the tumor microenvironment by enabling the spatial mapping of molecular features within tissue sections. Techniques such as multiplexed immunofluorescence and spatial transcriptomics allow for the simultaneous detection of multiple protein and RNA targets within their spatial context, providing valuable insights into cellular interactions and tumor biology (Chatterjee et al., 2022). In colon cancer research, spatial multi-omics analysis can reveal the spatial distribution of biomarkers, aiding in the identification of tumor subtypes and the development of targeted therapies. The integration of spatial omics with other multi-omics data types holds great potential for advancing our understanding of colon cancer and improving patient outcomes.

7.4 Personalized medicine and multi-omics

The integration of multi-omics data is crucial for the advancement of personalized medicine in cancer treatment. By leveraging the multi-scale molecular information of patients, researchers can develop more accurate and individualized treatment strategies. For example, integrating gene expression, DNA methylation, miRNA expression, and copy number variations has been shown to improve the prediction of overall survival for breast and ovarian cancer patients (Tong et al., 2021). Similarly, the identification of prognostic biomarkers through multi-omics data integration can guide the development of targeted therapies and improve clinical outcomes. As multi-omics technologies continue to evolve, their application in personalized medicine will become increasingly important, offering new opportunities for precision oncology.

8 Concluding Remarks

The integration of multi-omics data has shown significant promise in identifying early biomarkers for colon cancer. Various studies have demonstrated that combining different types of omics data, such as gene expression, DNA methylation, miRNA expression, and proteomics, can enhance the detection and prognostic accuracy of biomarkers. For instance, the integration of clinical and multi-omics data has been shown to improve prognostic performance for colon cancer, with better concordance indices compared to using clinical data alone. Additionally, novel methods for multi-omics data integration have been developed to identify cancer subtypes and prognostic biomarkers, which have shown better performance than traditional single-omics approaches. The use of multi-omics technologies has also led to the discovery of new biomarkers in various types of samples, including blood, stool, and tissue, which are crucial for early detection and monitoring of colorectal cancer.

The findings from these studies have significant implications for the early detection of colon cancer. The integration of multi-omics data allows for a more comprehensive understanding of the molecular mechanisms underlying colon cancer, which can lead to the identification of more specific and sensitive biomarkers. This can potentially improve current screening programs, which primarily rely on stool tests and colonoscopy, by incorporating new biomarkers that can detect cancer at earlier stages. Moreover, the development of prognostic models that combine clinical and multi-omics data can help in better stratifying patients based on their risk, leading to more personalized and effective treatment strategies. The use of multi-omics approaches also holds promise for identifying novel therapeutic targets, which can further enhance the clinical management of colon cancer.

In conclusion, the integration of multi-omics data represents a significant advancement in the field of cancer biomarker discovery and early detection. The studies reviewed highlight the potential of multi-omics approaches to improve the accuracy and reliability of colon cancer biomarkers, which can lead to better patient outcomes. However, there are still challenges to be addressed, such as the need for standardized methods for data integration and the translation of these technologies into routine clinical practice. Future research should focus on refining these methods and validating the identified biomarkers in larger, independent cohorts. Additionally, efforts should be made to make multi-omics technologies more accessible and cost-effective for widespread clinical use. With continued advancements in this field, multi-omics data integration has the potential to revolutionize the early detection and treatment of colon cancer, ultimately reducing the burden of this disease.

Acknowledgments

The authors extend sincere thanks to two anonymous peer reviewers for their feedback on the manuscript of this study.

Funding

This research was funded by Natural Science Foundation of Shandong Province (ZR2023QC028).

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Alonso, S., Dai, Y., Yamashita, K., Horiuchi, S., Dai, T., Matsunaga, A., Sánchez-Muñoz R., Bilbao-Sieyro C., Díaz-Chico J.C., Chernov A.V., Strongin A.Y., and Perucho M., 2015, Methylation of MGMT and ADAMTS14 in normal colon mucosa: biomarkers of a field defect for cancerization preferentially targeting elder African-Americans, Oncotarget, 6(5), 3420-3431.

https://doi.org/10.18632/oncotarget.2852

Ananda S., Wong H., Faragher I., Jones I.T., Steele M., Kosmider S., Desai, J., Tie J., Field K., Wong R., Tran B., Bae S., and Gibbs P., 2016, Survival impact of the Australian national bowel cancer screening programme, Internal Medicine Journal, 46(2): 166-171.

https://doi.org/10.1111/imj.12916

Auton A., Abecasis G., Altshuler D., Durbin R., Bentley D., Chakravarti A., Clark A., Donnelly P., Eichler E., Flicek P., Gabriel S., Gibbs R., Green E., Hurles M., Knoppers B., Korbel J., Lander E., Lee C., Lehrach H., Mardis E., Marth G., McVean G., Nickerson D., Schmidt J., Sherry S., Wang J., Wilson R., Barnes K., Beiswanger C., Burchard E., Bustamante C., Cai H., Cao H., Gerry N., Gharani N., Gignoux C., Gravel S., Henn B., Jones D., Jorde L., Kaye J., Keinan A., Kent A., Kerasidou A., Li Y., Mathias R., Moreno-Estrada A., Ossorio P., Parker M., Resch A., Rotimi C., Royal C., Sandoval K., Su Y., Sudbrak R., Tian Z., Tishkoff S., Toji L., Tyler-Smith C., Via M., Wang Y., Yang H., Yang L., Zhu J., Brooks L., Felsenfeld A., Mcewen J., Vaydylevich Y., Duncanson A., Dunn M., Schloss J., Garrison E., Kang H., Marchini J., and McCarthy S., 2015, A global reference for human genetic variation, Nature, 526(7571): 68-74.

https://doi.org/10.1038/nature15393

Bai, H., Guo, X., Narisu, N., Lan, T., Wu, Q., Xing, Y., Zhang Y., Bond S.R., Pei S., Zhang Y., Zhang D., Jirimutu J., Zhang D., Yang X., Morigenbatu M., Zhang L., Ding B., Guan B., Cao J., Lu H., Liu Y., Li W.S., Dang Y., Jiang M., Wang S., Xu H., Wang D., Liu C., Luo X., Gao Y., Li X, Wu Z., Yang L., Meng F., Ning X., Hashenqimuge H., Wu K., Wang B., Suyalatu S., Liu Y., Ye C., Wu H., Leppälä K., Li L., Fang L., Chen Y., Xu W., Li T., Liu X., Xu X., Gignoux C.R., Yang H., Brody L.C., Wang J., Kristiansen K., Burenbatu B., Zhou H., and Yin Y., 2018, Whole-genome sequencing of 175 Mongolians uncovers population-specific genetic architecture and gene flow throughout North and East Asia, Nat. Genet, 50(12): 1696-1704.

https://doi.org/10.1038/s41588-018-0250-5

Baylin S.B., 2005, DNA methylation and gene silencing in cancer, Nat. Clin. Pract. Oncol., 2(1): S4-11.

https://doi.org/10.1038/ncponc0354

Benson A.B., Venook A.P., Al-Hawary M.M., Arain M.A., Chen Y.J., Ciombor K.K., Cohen S., Cooper H., Deming D., Farkas L., Garrido-Laguna I., Grem J., Gunn A., Hecht J., Hoffe S., Hubbard J., Hunt S., Johung K., Kirilcuk N., Krishnamurthi S., Messersmith W., Meyerhardt J., Miller E., Mulcahy M., Nurkin S., Overman M., Parikh A., Patel H., Pedersen K., Saltz L., Schneider C., Shibata D., Skibber J., Sofocleous C., Stoffel E., Stotsky-Himelfarb E., Willett C., Gregory K., and Gurski L.A., 2021, Colon cancer, version 2.2021, NCCN clinical practice guidelines in oncology, Journal of the National Comprehensive Cancer Network, 19(3): 329-359.

https://doi.org/10.6004/jnccn.2021.0012

Berger M.F. and Mardis E.R., 2018, The emerging clinical relevance of genomics in cancer medicine, Nat. Rev. Clin. Oncol.,15(6): 353-365.

https://doi.org/10.1038/s41571-018-0002-6

Cao Q., Tian Y., Deng Z., Yang F., and Chen E., 2024, Epigenetic alteration in colorectal cancer: potential diagnostic and prognostic implications, International Journal of Molecular Sciences, 25(6): 3358.

https://doi.org/10.3390/ijms25063358

Chai H., Zhou X., Zhang Z., Rao J., Zhao H., and Yang Y., 2021, Integrating multi-omics data through deep learning for accurate cancer prognosis prediction, Computers in biology and medicine, 134: 104481.

https://doi.org/10.1016/j.compbiomed.2021.104481

Chen W., Zheng R., Baade P., Zhang S., Zeng H., Bray F., Jemal A., Yu X., and He J., 2016, Cancer statistics in China, 2015, CA: A Cancer Journal for Clinicians, 66(2): 115-132.

https://doi.org/10.3322/caac.21338

Chatterjee G., Vezeau G., Lazova M., Sismour M., and Manesse M., 2022, Abstract 3866: Spatial multi-omics analysis targeting protein and RNA biomarkers on a single FFPE tissue section using an integrated staining workflow, Cancer Research, 82(12): 3866-3866.

https://doi.org/10.1158/1538-7445.AM2022-3866

Choudhury A., Ramsay M., Hazelhurst S., Aron S., Bardien S., Botha G., Chimusa E., Christoffels A., Gamieldien J., Sefid-Dashti M., Joubert F., Meintjes A., Mulder N., Ramesar R., Rees J., Scholtz K., Sengupta D., Soodyall H., Venter P., Warnich L., and Pepper M., 2017, Whole-genome sequencing for an enhanced understanding of genetic variation among South Africans, Nature Communications, 8(1): 2062.

https://doi.org/10.1038/s41467-017-00663-9

Chu G., Ji X., Wang Y., and Niu H., 2023, Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for muscle-invasive urothelial cancer, Molecular Therapy-Nucleic Acids, 33: 110-126.

https://doi.org/10.1016/j.omtn.2023.06.001

Consortium G., 2020, The GTEx Consortium atlas of genetic regulatory effects across human tissues, Science, 369(6509): 1318-1330.

https://doi.org/10.1126/science.aaz1776

Consortium E.P., 2012, An integrated encyclopedia of DNA elements in the human genome, Nature, 489(7414): 57-74.

https://doi.org/10.1038/nature11247

Coppede F., 2014, Epigenetic biomarkers of colorectal cancer: focus on DNA methylation, Cancer Lett., 342(2): 238-247.

https://doi.org/10.1016/j.canlet.2011.12.030

Dieckmann K., Radtke A., Geczi L., Matthies C., Anheuser P., Eckardt U., Sommer J., Zengerling F., Trenti E., Pichler R., Belz H., Zastrow S., Winter A., Melchior S., Hammel J., Kranz J., Bolten M., Krege S., Haben B., Loidl W., Ruf C., Heinzelbecker J., Heidenreich A., Cremers J., Oing C., Hermanns T., Fankhauser C., Gillessen S., Reichegger H., Cathomas R., Pichler M., Hentrich M., Eredics K., Lorch A., Wülfing C., Peine S., Wosniok W., Bokemeyer C., and Belge G., 2019, Serum levels of microRNA-371a-3p (M371 Test) as a new biomarker of testicular germ cell tumors: results of a prospective multicentric study, Journal of Clinical Oncology, 37(16): 1412-1423.

https://doi.org/10.1200/JCO.18.01480

Dawson H., Kirsch R., Messenger D., and Driman D., 2019, A review of current challenges in colorectal cancer reporting, Archives of pathology & laboratory medicine, 143(7): 869-882.

https://doi.org/10.5858/arpa.2017-0475-RA

de Hijas-Liste G.M., Klipp E., Balsa-Canto E., and Banga J.R., 2014, Global dynamic optimization approach to predict activation in metabolic pathways, BMC Systems Biology, 8: 1.

https://doi.org/10.1186/1752-0509-8-1

Deng X., Li S., Kong F., Ruan H., Xu X., Zhang X., Wu Z., Zhang L., Xu Y., Yuan H., Peng H., Yang D., and Guan M., 2020, Long noncoding RNA PiHL regulates p53 protein stability through GRWD1/RPL11/MDM2 axis in colorectal cancer, Theranostics, 10(1): 265-280.

https://doi.org/10.7150/thno.36045

Eisfeldt J., Mårtensson G., Ameur A., Nilsson D., and Lindstrand A., 2019, Discovery of novel sequences in 1 000 swedish genomes, Molecular Biology and Evolution, 37(1): 18-30.

https://doi.org/10.1093/molbev/msz176

Elsayed I., Elsayed N., Feng Q., Sheahan K., Moran B., and Wang X., 2022, Multi-OMICs data analysis identifies molecular features correlating with tumor immunity in colon cancer, Cancer Biomarkers: Section A of Disease Markers, 33(2): 261-271.

https://doi.org/10.3233/CBM-210222

Figueroa J.D., and Brinton L.A., 2012, Unraveling genes, hormones, and breast cancer, J. Natl. Cancer Inst., 104(9): 641-642.

https://doi.org/10.1093/jnci/djs193

Gajendran M., Loganathan P., Jimenez G., Catinella A.P., Ng N., Umapathy C., Ziade N., and Hashash J.G., 2019, A comprehensive review and update on ulcerative colitis, Disease-a-month, 65(12): 100851.

https://doi.org/10.1016/j.disamonth.2019.02.004

Garraway L.A., and Lander E.S., 2013, Lessons from the cancer genome, Cell, 153(1): 17-37.

https://doi.org/10.1016/j.cell.2013.03.002

Gevaert O., 2015, MethylMix: an R package for identifying DNA methylation-driven genes, Bioinformatics, 31(11): 1839-1841.

https://doi.org/10.1093/bioinformatics/btv020

Ge W., Cai W., Bai R., Hu W., Wu D., Zheng S., and Hu H., 2019, A novel 4-gene prognostic signature for hypermutated colorectal cancer, Cancer Management and Research, 11: 1985-1996.

https://doi.org/10.2147/CMAR.S190963

Grimm I.S., and McGill S.K., 2019, Look, but don't touch: what not to do in managing large colorectal polyps, Gastrointest Endosc., 89(3): 479-481.

https://doi.org/10.1016/j.gie.2018.10.008

Guo H., Zeng W., Feng L., Yu X., Li P., Zhang K., Zhou Z., and Cheng S., 2017, Integrated transcriptomic analysis of distance-related field cancerization in rectal cancer patients, Oncotarget, 8(37): 61107-61117.

https://doi.org/10.18632/oncotarget.17864

Gurdasani D., Carstensen T., Tekola-Ayele F., Pagani L., Tachmazidou I., Hatzikotoulas K., Karthikeyan S., Iles L., Pollard M., Choudhury A., Ritchie G., Xue Y., Asimit J., Nsubuga R., Young E., Pomilla C., Kivinen K., Rockett K., Kamali A., Doumatey A., Asiki G., Seeley J., Sisay-Joof F., Jallow M., Tollman S., Mekonnen E., Ekong R., Oljira T., Bradman N., Bojang K., Ramsay M., Adeyemo A., Bekele E., Motala A., Norris S., Pirie F., Kaleebu P., Kwiatkowski D., Tyler-Smith C., Rotimi C., Zeggini E., and Sandhu M., 2015, The african genome variation project shapes medical genetics in Africa, Nature, 517(7534): 327-332.

https://doi.org/10.1038/nature13997

Hasin Y., Seldin M., and Lusis A., 2017, Multi-omics approaches to disease, Genome Biol., 18(1): 83.

https://doi.org/10.1186/s13059-017-1215-1

Hardcastle J., Chamberlain J., Robinson M., Moss S., Amar S., Balfour T., James P., and Mangham C., 1996, Randomised controlled trial of faecal-occult-blood screening for colorectal cancer, Lancet, 348(9040): 1472-1477.

https://doi.org/10.1016/S0140-6736(96)03386-7

He K., Li W. X., Guan D., Gong M., Ye S., Fang Z., Huang J., and Lu A., 2019, Regulatory network reconstruction of five essential microRNAs for survival analysis in breast cancer by integrating miRNA and mRNA expression datasets, Functional & Integrative Genomics, 19(4): 645-658.

https://doi.org/10.1007/s10142-019-00670-7

He Y., Sun L., Wang J., Gong R., Shao Q., Zhang Z., Ye Z., Wang H., Xu R., and Shao J., 2018, Hypermethylation of APC2 is a predictive epigenetic biomarker for chinese colorectal cancer, Disease Markers, (1): 8619462.

https://doi.org/10.1155/2018/8619462

Hoadley K., Yau C., Hinoue T., Wolf D., Lazar A., Drill E., Shen R., Taylor A., Cherniack A., Thorsson V., Akbani R., Bowlby R., Wong C., Wiznerowicz M., Sánchez-Vega F., Robertson A., Schneider B., Lawrence M., Noushmehr H., Malta T., Stuart J., Benz C., and Laird P., 2018, Cell-of-origin patterns dominate the molecular classification of 10 000 tumors from 33 types of cancer, Cell, 173(2): 291-304.

Hoadley K., Yau C., Wolf D., Cherniack A., Tamborero D., Ng S., Leiserson M., Niu B., McLellan M., Uzunangelov V., Zhang J., Kandoth C., Akbani R., Shen H., Omberg L., Chu A., Margolin A., Veer L., López-Bigas N., Laird P., Raphael B., Ding L., Robertson A., Byers L., Mills G., Weinstein J., Waes C., Chen Z., Collisson E., Benz C., Perou C., and Stuart J., 2014, Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin, Cell, 158(4): 929-944.

https://doi.org/10.1016/j.cell.2014.06.049

Hristova V.A., and Chan D.W., 2019, Cancer biomarker discovery and translation: proteomics and beyond, Expert Rev Proteomics, 16(2): 93-103.

https://doi.org/10.1080/14789450.2019.1559062

Huang Y., Huang H., Wang Y., Liu H., and Huang Y., 2022, 5mC-related lncRNAs as potential prognostic biomarkers in colon adenocarcinoma, Biology, 11(2): 231.

https://doi.org/10.3390/biology11020231

Huang Z., Yang Q., and Huang Z., 2018, Identification of critical genes and five prognostic biomarkers associated with colorectal cancer, Med. Sci. Monit., 24: 4625-4633.

https://doi.org/10.12659/MSM.907224

Hudson T., Anderson W., Artez A., Barker A., Bell C., Bernabé R., Bhan M., Calvo F., Eerola I., Gerhard D., Guttmacher A., Guyer M., Hemsley F., Jennings J., Kerr D., Klatt P., Kolar P., Kusada J., Lane D., Laplace F., Youyong L., Nettekoven G., Ozenberger B., Peterson J., Rao T., Remacle J., Schafer A., Shibata T., Stratton M., Vockley J., Watanabe K., Yang H., Yuen M., Knoppers B., Bobrow M., Cambon-Thomsen A., Dressler L., Dyke S., Joly Y., Kato K., Kennedy K., Nicolàs P., Parker M., Rial-Sebbag E., Romeo-Casabona C., Shaw K., Wallace S., Wiesner G., Zeps N., Lichter P., Biankin A., Chabannon C., Chin L., Clement B., Álava E., Degos F., Ferguson M., Geary P., Hayes D., Johns A., Kasprzyk A., Nakagawa H., Penny R., Piris M., Sarin R., Scarpa A., Vijver M., Futreal P., Aburatani H., Bayés M., Botwell D., Campbell P., Estivill X., Grimmond S., Gut I., Hirst M., López-Otín C., Majumder P., Marra M., McPherson J., Ning Z., Puente X., Ruan Y., Stunnenberg H., Swerdlow H., Velculescu V., Wilson R., Xue H., Yang L., Spellman P., Bader G., Boutros P., Flicek P., Getz G., Guigó R., Guo G., Haussler D., Heath S., Hubbard T., Jiang T., Jones S., Li Q., López-Bigas N., Luo R., Muthuswamy L., Ouellette B., Pearson J., Quesada V., Raphael B., Sander C., Speed T., Stein L., Stuart J., Teague J., Totoki Y., Tsunoda T., Valencia A., Wheeler D., Wu H., Zhao S., Zhou G., Lathrop M., Thomas G., Yoshida T., Axton M., Gunter C., Miller L., Zhang J., Haider S., Wang J., Yung C., Cros A., Liang Y., Gnaneshan S., Guberman J., Hsu J., Chalmers D., Hasel K., Kaan T., Lowrance W., Masui T., Rodriguez L., Vergely C., Bowtell D., Cloonan N., deFazio A., Eshleman J., Etemadmoghadam D., Gardiner B., Kench J., Sutherland R., Tempero M., Waddell N., Wilson P., Gallinger S., Tsao M., Shaw P., Petersen G., Mukhopadhyay D., DePinho R., Thayer S., Shazand K., Beck T., Sam M., Timms L., Ballin V., Lu Y., Ji J., Zhang X., Chen F., Hu X., Yang Q., Tian G., Zhang, L., Xing X., Li X., Zhu Z., Yu Y., Yu J., Tost J., Brennan P., Holcatova I., Zaridze D., Brazma A., Egevard L., Prokhortchouk E., Banks R., Uhlén M., Viksna J., Pontén F., Skryabin K., Birney E., Borg Å., Børresen-Dale A., Caldas C., Foekens J., Martin S., Reis-Filho J., Richardson A., Sotiriou C., Thoms G., Veer L., Birnbaum D., Blanché H., Boucher P., Boyault S., Masson-Jacquemier J., Pauporté I., Pivot X., Vincent-Salomon A., Tabone E., Theillet C., Treilleux I., Bioulac-Sage P., Decaens T., Franco D., Gut M., Samuel D., Zucman-Rossi J., Eils R., Brors B., Korbel J., Korshunov A., Landgraf P., Lehrach H., Pfister S., Radlwimmer B., Reifenberger G., Taylor M., Kalle C., Majumder P., Pederzoli P., Lawlor R., Delledonne M., Bardelli A., Gress T., Klimstra D., Zamboni G., Nakamura Y., Miyano S., Fujimoto A., Campo E., Sanjosé S., Montserrat E., González-Díaz M., Jares P., Himmelbauer H., Beà S., Aparicio S., Easton D., Collins F., Compton C., Lander E., Burke W., Green A., Hamilton S., Kallioniemi O., Ley T., Liu E., and Wainwright B., 2010, International network of cancer genome projects, Nature, 464(7291): 993-998.

https://doi.org/10.1038/nature08987

Hyman D.M., Taylor B.S., and Baselga J., 2017, Implementing genome-driven oncology, Cell, 168(4): 584-599.

https://doi.org/10.1016/j.cell.2016.12.015

Jacobs G., Hudjashov G., Saag L., Kusuma P., Darusallam C., Lawson D., Mondal M., Pagani L., Ricaut F., Stoneking M., Metspalu M., Sudoyo H., Lansing J., and Cox M., 2019, Multiple deeply divergent denisovan ancestries in papuans, Cell, 177(4): 1010-1021.

https://doi.org/10.1016/j.cell.2019.02.035

Jacob H., Stanisavljević L., Storli K., Hestetun K., Dahl O., and Myklebust M., 2017, Identification of a sixteen-microRNA signature as prognostic biomarker for stage II and III colon cancer, Oncotarget, 8(50): 87837-87847.

https://doi.org/10.18632/oncotarget.21237

Jónsson H., Sulem P., Kehr B., Kristmundsdottir S., Zink F., Hjartarson E., Hardarson M., Hjorleifsson K., Eggertsson H., Gudjonsson S., Ward L., Arnadottir G., Helgason E., Helgason H., Gylfason A., Jonasdottir A., Jónasdóttir Á., Rafnar T., Besenbacher S., Frigge M., Stacey S., Magnusson O., Thorsteinsdóttir U., Másson G., Kong A., Halldórsson B., Helgason A., Gudbjartsson D., and Stefánsson K., 2017, Whole genome characterization of sequence diversity of 15 220 Icelanders, Sci. Data, 4: 170115.

https://doi.org/10.1038/sdata.2017.115

Kehr B., Helgadóttir A., Melsted P., Jónsson H., Helgason H., Jonasdottir A., Jónasdóttir Á., Sigurdsson A., Gylfason A., Halldorsson G., Kristmundsdottir S., Thorgeirsson G., Olafsson I., Hólm H., Thorsteinsdóttir U., Sulem P., Helgason A., Gudbjartsson D., Halldórsson B., and Stefánsson K., 2017, Diversity in non-repetitive human sequences not found in the reference genome, Nat. Genet, 49(4): 588-593.

https://doi.org/10.1038/ng.3801

Kennelly R., Kavanagh D., Hogan A., and Winter D., 2008, Oestrogen and the colon: potential mechanisms for cancer prevention, Lancet. Oncol., 9(4): 385-391.

https://doi.org/10.1016/S1470-2045(08)70100-1

Kim D., Shin H., Sohn K., Verma A., Ritchie M., and Kim J., 2014, Incorporating inter-relationships between different levels of genomic data into cancer clinical outcome prediction, Methods, 67(3): 344-53.

https://doi.org/10.1016/j.ymeth.2014.02.003

Kim D., Li R., Dudek S., and Ritchie M., 2013, ATHENA: Identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network, BioData Min., 6(1): 23.

https://doi.org/10.1186/1756-0381-6-23

Kundaje A., Meuleman W., Ernst J., Bilenky M., Yen A., Moussavi A., Kheradpour P., Zhang Z., Wang J., Ziller M., Amin V., Whitaker J., Schultz M., Ward L., Sarkar A., Quon G., Sandstrom R., Eaton M., Wu Y., Pfenning A., Wang X., Claussnitzer M., Liu Y., Coarfa C., Harris R., Shoresh N., Epstein C., Gjoneska E., Leung D., Xie W., Hawkins R., Lister R., Hong C., Gascard P., Mungall A., Moore R., Chuah E., Tam A., Canfield T., Hansen R., Kaul R., Sabo P., Bansal M., Carles A., Dixon J., Farh K., Feizi S., Karlić R., Kim A., Kulkarni A., Li D., Lowdon R., Elliott G., Mercer T., Neph S., Onuchic V., Polak P., Rajagopal N., Ray P., Sallari R., Siebenthall K., Sinnott-Armstrong N., Stevens M., Thurman R., Wu J., Zhang B., Zhou X., Beaudet A., Boyer L., Jager P., Farnham P., Fisher S., Haussler D., Jones S., Li W., Marra M., McManus M., Sunyaev S., Thomson J., Tlsty T., Tsai L., Wang W., Waterland R., Zhang M., Chadwick L., Bernstein B., Costello J., Ecker J., Hirst M., Meissner A., Milosavljevic A., Ren B., Stamatoyannopoulos J., Wang T., and Kellis M., 2015, Integrative analysis of 111 reference human epigenomes, Nature, 518(7539): 317-330.

https://doi.org/10.1038/nature14248

Lai Y.P., Wang L.B., Wang W.A., Lai L.C., Tsai M.H., Lu T.P., and Chuang E.Y., 2017, iGC-an integrated analysis package of gene expression and copy number alteration, BMC Bioinformatics, 18(1): 35.

https://doi.org/10.1186/s12859-016-1438-2

Leday G.G., and van de Wiel M.A.,, 2013, PLRS: a flexible tool for the joint analysis of DNA copy number and mRNA expression data, Bioinformatics, 29(8): 1081-1082.

https://doi.org/10.1093/bioinformatics/btt082

Lindholm E., Brevinge H., and Haglind E., 2008, Survival benefit in a randomized clinical trial of faecal occult blood screening for colorectal cancer, Journal of British Surgery, 95(8): 1029-1036.

https://doi.org/10.1002/bjs.6136

Li P., and Sun B., 2022, Integration of multi-omics data to identify cancer biomarkers, Journal of Information Technology Research (JITR), 15(1): 1-15.

https://doi.org/10.4018/JITR.2022010105

Li X., Ma J., Leng L., Han M., Li M., He F., and Zhu Y., 2022, MoGCN: a multi-omics integration method based on graph convolutional network for cancer subtype analysis, Frontiers in Genetics, 13: 806842.

https://doi.org/10.3389/fgene.2022.806842

Lei Y., Tang R., Xu J., Wang W., Zhang B., Liu J., Yu X., and Shi S., 2021, Applications of single-cell sequencing in cancer research: progress and perspectives, Journal of Hematology & Oncology, 14(1): 91.

https://doi.org/10.1186/s13045-021-01105-2

Lock E.F., and Dunson D.B., 2013, Bayesian consensus clustering, Bioinformatics, 29(20): 2610-2616.

https://doi.org/10.1093/bioinformatics/btt425

Lovat F., Valeri N., and Croce C.M., 2011, MicroRNAs in the pathogenesis of cancer, Semin. Oncol., 38(6): 724-733.

https://doi.org/10.1053/j.seminoncol.2011.08.006

Louhimo R., and Hautaniemi S., 2011, CNAmet: an R package for integrating copy number, methylation and expression data, Bioinformatics, 27(6): 887-888.

https://doi.org/10.1093/bioinformatics/btr019

Mallick S., Li H., Lipson M., Mathieson I., Gymrek M., Racimo F., Zhao M., Chennagiri N., Nordenfelt S., Tandon A., Skoglund P., Lazaridis I., Sankararaman S., Fu Q., Rohland N., Renaud G., Erlich Y., Willems T., Gallo C., Spence J., Song Y., Poletti G., Balloux F., Driem G., Knijff P., Romero I., Jha A., Behar D., Bravi C., Capelli C., Hervig T., Moreno-Estrada A., Posukh O., Balanovska E., Balanovsky O., Karachanak-Yankova S., Sahakyan H., Toncheva D., Yepiskoposyan L., Tyler-Smith C., Xue Y., Abdullah M., Ruiz-Linares A., Beall C., Rienzo A., Jeong C., Starikovskaya E., Metspalu E., Parik J., Villems R., Henn B., Hodoğlugil U., Mahley R., Sajantila A., Stamatoyannopoulos G., Wee J., Khusainova R., Khusnutdinova E., Litvinov S., Ayodo G., Comas D., Hammer M., Kivisild T., Klitz W., Winkler C., Labuda D., Bamshad M., Jorde L., Tishkoff S., Watkins W., Metspalu M., Dryomov S., Sukernik R., Singh L., Thangaraj K., Pääbo S., Kelso J., Patterson N., and Reich D., 2016, The simons genome diversity project: 300 genomes from 142 diverse populations, Nature, 538(7624): 201-206.

https://doi.org/10.1038/nature18964

Mandel J., Church T., Bond J., Ederer F., Geisser M., Mongin S., Snover D., and Schuman L., 2000, The effect of fecal occult-blood screening on the incidence of colorectal cancer, The New England Journal of Medicine, 343(22): 1603-1607.

https://doi.org/10.1056/NEJM200011303432203

Manjunath K.N., Siddalingaswamy P., and Prabhu G.K., 2019, Domain-based analysis of colon polyp in CT colonography using image-processing techniques, Asian. Pac. J. Cancer Prev., 20(2): 629-637.

https://doi.org/10.31557/APJCP.2019.20.2.629

Mathias R., Taub M., Gignoux C., Fu W., Musharoff S., O’Connor T., Vergara C., Torgerson D., Pino-Yanes M., Shringarpure S., Huang L., Rafaels N., Boorgula M., Johnston H., Ortega V., Levin A., Song W., Torres R., Padhukasahasram B., Eng C., Mejia-Mejia D., Ferguson T., Qin Z., Scott A., Yazdanbakhsh M., Wilson J., Marrugo J., Lange L., Kumar R., Avila P., Williams L., Watson H., Ware L., Olopade C., Olopade O., Oliveira R., Ober C., Nicolae D., Meyers D., Mayorga A., Knight-Madden J., Hartert T., Hansel N., Foreman M., Ford J., Faruque M., Dunston G., Caraballo L., Burchard E., Bleecker E., Araújo M., Herrera-Paz E., Gietzen K., Grus W., Bamshad M., Bustamante C., Kenny E., Hernandez R., Beaty T., Ruczinski I., Akey J., Ga M., Campbell M., Chavan S., Foster C., Gao L., Horowitz E., Ortiz R., Potee J., Gao J., Hu Y., Hansen M., Deshpande A., Locke D., Grammer L., Kim K., Schleimer R., Vega F., Szpiech Z., Oluwole O., Arinola G., Correa A., Musani S., Chong J., Nickerson D., Reiner A., Maul P., Maul T., Martínez B., Meza C., Ayestas G., Landaverde-Torres P., Erazo S., Martinez R., Mayorga L., Ramos H., Saenz A., Varela G., Vasquez O., Samms-Vaughan M., Wilks R., Adegnika A., Ateba-Ngoa U., and Barnes K., 2016, A continuum of admixture in the Western Hemisphere revealed by the African Diaspora genome, Nat. Commun., 7: 12522.

https://doi.org/10.1038/ncomms12522

Maretty L., Jensen J., Petersen B., Sibbesen J., Liu S., Villesen P., Skov L., Belling K., Have C., Izarzugaza J., Grosjean M., Bork-Jensen J., Grove J., Als T., Huang S., Chang Y., Xu R., Ye W., Rao J., Guo X., Sun J., Cao H., Ye C., Beusekom J., Espeseth T., Flindt E., Friborg R., Halager A., Hellard S., Hultman C., Lescai F., Li S., Lund O., Løngreen P., Mailund T., Matey-Hernandez M., Mors O., Pedersen C., Sicheritz-Pontén T., Sullivan P., Syed A., Westergaard D., Yadav R., Li N., Xu X., Hansen T., Krogh A., Bolund L., Sørensen T., Pedersen O., Gupta R., Rasmussen S., Besenbacher S., Børglum A., Wang J., Eiberg H., Kristiansen K., Brunak S., and Schierup M., 2017, Sequencing and de novo assembly of 150 genomes from Denmark as a population reference, Nature, 548(7665): 87-91.

https://doi.org/10.1038/nature23264

Menyhárt O., and Győrffy B., 2021, Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis, Comput Struct Biotechnol J., 19: 949-960.

https://doi.org/10.1016/j.csbj.2021.01.009

Moya L., Meijer J., Schubert S., Matin F., and Batra J., 2019, Assessment of miR-98-5p, miR-152-3p, miR-326 and miR-4289 expression as biomarker for prostate cancer diagnosis, Int. J. Mol. Sci., 20(5): 1154.

https://doi.org/10.3390/ijms20051154

Mo Q., and Shen R., 2018, 2-18, iClusterPlus: Integrative clustering of multi-type genomic data, Bioconductor R Package Version, 2018: 1.

Nassar F.J., Nasr R., and Talhouk R., 2017, MicroRNAs as biomarkers for early breast cancer diagnosis, prognosis and therapy prediction, Pharmacol. Ther., 172: 34-49.

https://doi.org/10.1016/j.pharmthera.2016.11.012

Network C.G.A., 2012, Comprehensive molecular characterization of human colon and rectal cancer, Nature, 487(7407): 330-337.

https://doi.org/10.1038/nature11252

Nicora G., Vitali F., Dagliati A., Geifman N., and Bellazzi R., 2020, Integrated multi-omics analyses in oncology: a review of machine learning methods and tools, Frontiers in Oncology, 10: 1030.

https://doi.org/10.3389/fonc.2020.01030

Peng A., Mao X., Zhong J., Fan S., and Hu Y., 2020, Single-cell multi-omics and its prospective application in cancer biology, Proteomics, 20(13): 1900271.

https://doi.org/10.1002/pmic.201900271

Qiu Y., Ching W.K., and Zou Q.J.B.i.B., 2021a, Prediction of rna-binding protein and alternative splicing event associations during epithelial-mesenchymal transition based on inductive matrix completion, Briefings in Bioinformatics,22(5): bbaa440.

https://doi.org/10.1093/bib/bbaa440

Qiu Y., Ching W.K., and Zou Q.J.B.i.B., 2021b, Matrix factorization-based data fusion for the prediction of RNA-binding proteins and alternative splicing event associations during epithelial-mesenchymal transition, Briefings in Bioinformatics, 22(6): bbab332.

https://doi.org/10.1093/bib/bbab332

Qiu Y., Lyu J., Dunlap M., Harvey S.E., and Cheng C., 2020, A combinatorially regulated RNA splicing signature predicts breast cancer EMT states and patient survival, RNA, 26(9): 1257-1267.

https://doi.org/10.1261/rna.074187.119

Reel P.S., Reel S., Pearson E., Trucco E., and Jefferson E., 2021, Using machine learning approaches for multi-omics data analysis: a review, Biotechnology Advances, 49: 107739.

https://doi.org/10.1016/j.biotechadv.2021.107739

Regev A., Teichmann S., Lander E., Amit I., Benoist C., Birney E., Bodenmiller B., Campbell P., Carninci P., Clatworthy M., Clevers H., Deplancke B., Dunham I., Eberwine J., Eils R., Enard W., Farmer A., Fugger L., Göttgens B., Hacohen N., Haniffa M., Hemberg M., Kim S., Klenerman P., Kriegstein A., Lein E., Linnarsson S., Lundberg E., Lundeberg J., Majumder P., Marioni J., Merad M., Mhlanga M., Nawijn M., Netea M., Nolan G., Pe’er D., Phillipakis A., Ponting C., Quake S., Reik W., Rozenblatt-Rosen O., Sanes J., Satija R., Schumacher T., Shalek A., Shapiro E., Sharma P., Shin J., Stegle O., Stratton M., Stubbington M., Theis F., Uhlén M., Oudenaarden A., Wagner A., Watt F., Weissman J., Wold B., Xavier R., and Yosef N., 2017, The Human Cell Atlas, eLife, 6: e27041.

Ren H., Chen Z., Yang L., Xiong W., Yang H., Xu K., Zhai E., Ding L., He Y., and Song X., 2019, Apolipoprotein C1 (APOC1) promotes tumor progression via MAPK signaling pathways in colorectal cancer, Cancer Manag Res., 11: 4917-4930.

https://doi.org/10.2147/CMAR.S192529

Ritchie M., Holzinger E., Li R., Pendergrass S., and Kim D., 2015, Methods of integrating data to uncover genotype-phenotype interactions, Nature Reviews Genetics, 16(2): 85-97.

https://doi.org/10.1038/nrg3868

Rohart F., Gautier B., Singh A., and Cao K., 2017, mixOmics: an R package for ‘omics feature selection and multiple data integration, PLoS Computational Biology, 13(11): e1005752.

https://doi.org/10.1371/journal.pcbi.1005752

Saliminejad K., Khorshid H., Fard S., and Ghaffari S., 2018, An overview of microRNAs: Biology, functions, therapeutics, and analysis methods, Journal of Cellular Physiology, 234(5): 5451-5465.

https://doi.org/10.1002/jcp.27486

Sathyanarayanan A., Gupta R., Thompson E., Nyholt D., Bauer D., and Nagaraj S., 2019, A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping, Briefings in Bioinformatics, 21(6): 1920-1936.

https://doi.org/10.1093/bib/bbz121

Scholefield J., Moss S., Mangham C., Whynes D., and Hardcastle J., 2011, Nottingham trial of faecal occult blood testing for colorectal cancer: a 20-year follow-up, Gut, 61(7): 1036-1040.

https://doi.org/10.1136/gutjnl-2011-300774

Seligson D., Horvath S., McBrian M., Mah V., Yu H., Tze S., Wang Q., Chia D., Goodglick L., and Kurdistani S., 2009, Global levels of histone modifications predict prognosis in different cancers, The American Journal of Pathology, 174(5): 1619-1628.

https://doi.org/10.2353/ajpath.2009.080874

Siegel R.L., Giaquinto A.N., and Jemal A., 2024, Cancer statistics, 2024, CA: A Cancer Journal for Clinicians, 74(1): 12-49.

https://doi.org/10.3322/caac.21820

Siegel R., Miller K., Fedewa S., Ahnen D., Meester R., Barzi A., and Jemal A., 2017, Colorectal cancer statistics, 2017, CA: A Cancer Journal for Clinicians, 67(3): 177-193.

https://doi.org/10.3322/caac.21395

Silverbush D., Cristea S., Yanovich-Arad G., Geiger T., Beerenwinkel N., and Sharan R., 2019, Simultaneous integration of multi-omics data improves the identification of cancer driver modules, Cell Systems, 8(5): 456-466.

https://doi.org/10.1016/j.cels.2019.04.005

Shaukat A., Mongin S., Geisser M., Lederle F., Bond J., Mandel J., and Church T., 2013, Long-term mortality after screening for colorectal cancer, The New England Journal of Medicine, 369(12): 1106-1114.

https://doi.org/10.1056/NEJMoa1300720

Sokic-Milutinovic A., 2019, Appropriate management of attenuated familial adenomatous polyposis: report of a case and review of the literature, Dig Dis, 37(5): 400-405.

https://doi.org/10.1159/000497207

Smith R., Andrews K., Brooks D., Fedewa S., Manassaram-Baptiste D., Saslow D., and Wender R., 2019, Cancer screening in the united states, 2019: a review of current american cancer society guidelines and current issues in cancer screening, CA: A Cancer Journal for Clinicians, 69(3): 184-210.

https://doi.org/10.3322/caac.21557

Stoffel E.M., and Murphy C.C., 2020, Epidemiology and mechanisms of the increasing incidence of colon and rectal cancers in young adults, Gastroenterology, 158(2): 341-353.

https://doi.org/10.1053/j.gastro.2019.07.055

Sudmant P., Rausch T., Gardner E., Handsaker R., Abyzov A., Huddleston J., Zhang Y., Ye K., Jun G., Fritz M., Konkel M., Malhotra A., Stütz A., Shi X., Casale F., Chen J., Hormozdiari F., Dayama G., Chen K., Malig M., Chaisson M., Walter K., Meiers S., Kashin S., Garrison E., Auton A., Lam H., Mu X., Alkan C., Antaki D., Bae T., Cerveira E., Chines P., Chong Z., Clarke L., Dal E., Ding L., Emery S., Fan X., Gujral M., Kahveci F., Kidd J., Kong Y., Lameijer E., McCarthy S., Flicek P., Gibbs R., Marth G., Mason C., Menelaou A., Muzny D., Nelson B., Noor A., Parrish N., Pendleton M., Quitadamo A., Raeder B., Schadt E., Romanovitch M., Schlattl A., Sebra R., Shabalin A., Untergasser A., Walker J., Wang M., Yu F., Zhang C., Zhang J., Zheng-Bradley X., Zhou W., Zichner T., Sebat J., Batzer M., Mccarroll S., Mills R., Gerstein M., Bashir A., Stegle O., Devine S., Lee C., Eichler E., and Korbel J., 2015, An integrated map of structural variation in 2,504 human genomes, Nature, 526(7571): 75-81.

https://doi.org/10.1038/nature15394

Subramanian I., Verma S., Kumar S., Jere A., and Anamika K., 2020, Multi-omics data integration, interpretation, and its application, Bioinformatics and Biology Insights, 14: 1177932219899051.

https://doi.org/10.1177/1177932219899051

Sun X., Guo Y., Zhang Y., Zhao P., Wang Z., Wei Z., and Qiao H., 2021, Colon cancer-related genes identification and function study based on single-cell multi-omics integration, Frontiers in Cell and Developmental Biology, 9: 789587.

https://doi.org/10.3389/fcell.2021.789587

Tamborero D., Lopez-Bigas N., and Gonzalez-Perez A., 2013, Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression, PLoS One, 8(2): e55489.

https://doi.org/10.1371/journal.pone.0055489

Telenti A., Pierce L., Biggs W., Iulio J., Wong E., Fabani M., Kirkness E., Moustafa A., Shah N., Xie C., Brewerton S., Bulsara N., Garner C., Metzker G., Sandoval E., Perkins B., Och F., Turpaz Y., and Venter J., 2016, Deep sequencing of 10 000 human genomes, Proceedings of the National Academy of Sciences, 113(42): 11901-11906.

https://doi.org/10.1073/pnas.1613365113

Tomczak K., Czerwińska P., and Wiznerowicz M., 2015, The cancer genome atlas (TCGA): an immeasurable source of knowledge, Contemp Oncol (Pozn), 19(1a): A68-A77.

https://doi.org/10.5114/wo.2014.47136

Tong D., Tian Y., Zhou T., Ye Q., Li J., Ding K., and Li J., 2020a, Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data, BMC Medical Informatics and Decision Making, 20(1): 22.

https://doi.org/10.1186/s12911-020-1043-1

Tong L., Mitchel J., Chatlin K., and Wang M., 2020b, Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis, BMC Medical Informatics and Decision Making, 20(1): 225.

https://doi.org/10.1186/s12911-020-01225-8

Tong L., Wu H., and Wang M.D., 2021, Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer, Methods, 189: 74-85.

https://doi.org/10.1016/j.ymeth.2020.07.008

Triff K., McLean M., Konganti K., Pang J., Callaway E., Zhou B., Ivanov I., and Chapkin R., 2017, Assessment of histone tail modifications and transcriptional profiling during colon cancer progression reveals a global decrease in H3K4me3 activity, Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, 1863(6): 1392-1402.

https://doi.org/10.1016/j.bbadis.2017.03.009

Vlachavas E., Bohn J., Ückert F., and Nürnberg S., 2021, A detailed catalogue of multi-omics methodologies for identification of putative biomarkers and causal molecular networks in translational cancer research, International Journal of Molecular Sciences, 22(6): 2822.

https://doi.org/10.3390/ijms22062822

Wang B., Mezlini A., Demir F., Fiume M., Tu Z., Brudno M., Haibe-Kains B., and Goldenberg A., 2014, Similarity network fusion for aggregating data types on a genomic scale, Nature Methods, 11(3): 333-337.

https://doi.org/10.1038/nmeth.2810

Wang H., Zheng H., Wang J., Wang C., and Wu F., 2016, Integrating omic data with a multiplex network-based approach for the identification of cancer subtypes, IEEE Transactions on Nanobioscience, 15(4): 335-342.

https://doi.org/10.1109/TNB.2016.2556640

Xiao J., Leng A., Zhang Y., Wen Z., He J., and Ye G., 2019, CUEDC2: multifunctional roles in carcinogenesis, Frontiers in Bioscience (Landmark Ed), 24(5): 935-946.

https://doi.org/10.2741/4759

Xu A., Chen J., Peng H., Han G., and Cai H., 2019, Simultaneous interrogation of cancer omics to identify subtypes with significant clinical differences, Frontiers in Genetics, 10: 236.

https://doi.org/10.3389/fgene.2019.00236

Xu Z., Omar M., Benedetti E., Rosenthal J., Umeton R., Krumsiek J., Pomerantz M., Imada E., Loda M., and Marchionni L., 2022, Multi-omics biomarkers aid prostate cancer prognostication, bioRxiv, 2022-09.

https://doi.org/10.1101/2022.09.20.508244

Yang H., Jin W., Liu H., Wang X., Wu J., Gan D., Cui C., Han Y., Han C., and Wang Z., 2020, A novel prognostic model based on multi-omics features predicts the prognosis of colon cancer patients, Molecular Genetics & Genomic Medicine, 8(7): e1255.

https://doi.org/10.1002/mgg3.1255

Yang Y., Tian S., Qiu Y., Zhao P., and Zou Q., 2022, MDICC: novel method for multi-omics data integration and cancer subtype identification, Briefings in Bioinformatics, 23(3): bbac132.

https://doi.org/10.1093/bib/bbac132

Yin Z., Yan X., Wang Q., Deng Z., Tang K., Cao Z., and Qiu T., 2020, Detecting prognosis risk biomarkers for colon cancer through multi-omics-based prognostic analysis and target regulation simulation modeling, Frontiers in Genetics, 11: 524.

https://doi.org/10.3389/fgene.2020.00524

Zhao N., Guo M., Wang K., Zhang C., and Liu X., 2020, Identification of pan-cancer prognostic biomarkers through integration of multi-omics data, Frontiers in Bioengineering and Biotechnology, 8: 268.

https://doi.org/10.3389/fbioe.2020.00268

Zhu B., Song N., Shen R., Arora A., Machiela M., Song L., Landi M., Ghosh D., Chatterjee N., Baladandayuthapani V., and Zhao H., 2017, Integrating clinical and multiple omics data for prognostic assessment across human cancers, Scientific Reports, 7(1): 16954.

https://doi.org/10.1038/s41598-017-17031-8