Gene Expression Profiling in Colorectal Cancer Prognosis
Summary
Gene expression profiling has emerged as a pivotal approach to stratify colorectal cancer patients according to risk of recurrence and overall survival. By quantifying RNA transcripts across the tumour genome, distinct molecular signatures can be generated that reflect underlying biology, including cell cycle deregulation, hypoxia response and epithelial-mesenchymal transition. These signatures not only complement traditional clinicopathological staging but also uncover novel prognostic subgroups, guide adjuvant therapy decisions and inform the development of targeted interventions. Integration of high-throughput transcriptomic data with bioinformatics methods has enabled robust risk scores and nomograms that outperform conventional staging systems, paving the way for more personalised management of colorectal cancer. Emerging technologies such as single-cell analyses and spatial transcriptomics are further refining our understanding of intratumoural heterogeneity and its impact on patient outcome.
Research from Nature Portfolio
Recent studies have constructed predictive models that integrate hypoxia-related gene expression with clinical variables to enhance prognostic accuracy. A four-gene hypoxia signature was combined with age and TNM stage into a nomogram that demonstrated superior concordance with overall survival across discovery and validation cohorts, surpassing the prognostic value of staging alone. This approach emphasises the clinical utility of gene expression-based risk scores in identifying patients at high risk of poor outcome, thereby informing decisions on adjuvant treatment strategies.
Gene Expression Profiling in Colorectal Cancer Prognosis publication trend
The graph below shows the total number of articles in gene expression profiling in colorectal cancer prognosis across all publications each year (not limited to Nature Index journals).
Technical terms
Gene expression profiling: Quantitative measurement of RNA transcript levels across the genome in a given sample.
Nomogram: A graphical tool that integrates multiple variables to predict clinical outcomes for individual patients.
Transcriptomics: The comprehensive study of all RNA transcripts expressed by a genome under specific conditions.
Single-cell RNA sequencing: A method for profiling gene expression at the resolution of individual cells to reveal cellular heterogeneity.
Liquid biopsy: Non-invasive collection and analysis of circulating tumour components (such as RNA or DNA) from body fluids.
Epithelial-mesenchymal transition (EMT): A biological process whereby epithelial cells acquire mesenchymal traits, facilitating invasion and metastasis.
References
- Transcriptomic correlates of cell cycle checkpoints with distinct prognosis, molecular characteristics, immunological regulation, and therapeutic response in colorectal adenocarcinoma. Frontiers in Immunology (2023).
- PLA2G12A as a Novel Biomarker for Colorectal Cancer with Prognostic Relevance. International Journal of Molecular Sciences (2023).
- Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine. Cancers (2024).
- Prognostic nomogram of hypoxia-related genes predicting overall survival of colorectal cancer–Analysis of TCGA database. Scientific Reports (2019).
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Nature Index 2026 Cancer
Cancer incidence is on the rise. Countries are meeting the challenge in different ways, such as through nationwide action plans and broadening early detection programmes. However, coordinating these efforts to make them accessible, targeted and effective is a balancing act. In this supplement, we explore the factors that are shaping these first lines of defence and the research efforts that are informing them.
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