Proteomic Biomarkers in Colorectal Cancer

Proteomic biomarkers in colorectal cancer have emerged as a transformative approach to personalise detection, prognosis and therapy. By profiling the full complement of proteins in tumour tissues, biofluids or the tumour microenvironment, high-resolution mass spectrometry and data-driven workflows reveal signatures that reflect underlying molecular subtypes and dynamic changes during disease progression. These signatures can stratify patients according to risk of relapse, chemotherapeutic response or likelihood of metastasis beyond conventional clinicopathological parameters. Furthermore, advances in multiplexed immunohistochemistry and machine-learning algorithms allow candidate proteins to be validated and integrated into classifiers with robust predictive power. Global efforts have highlighted proteins involved in cell adhesion, immune regulation, metabolic reprogramming and extracellular matrix remodelling as promising indicators of early lesions and invasive disease. Despite challenges in standardisation and clinical translation, proteomic biomarkers hold significant promise for guiding individualised care and improving survival outcomes worldwide.

Research from Nature Portfolio

Recent studies have adopted an integrated bioinformatics and experimental strategy to describe cell adhesion-related gene expression across the adenoma–carcinoma sequence. By analysing biopsies of high-risk and low-risk polyp lesions alongside adjacent normal tissue, researchers identified elevated expression patterns of CDC42, TAGLN and GSN that distinguished early premalignant stages and correlated with key clinicopathological features. Receiver operating characteristic analyses yielded area under the curve values approaching 0.80–0.87, underscoring the diagnostic precision of these markers. Subsequent exploration of colorectal cancer cohorts revealed that differential expression of these adhesion molecules also carries prognostic significance, suggesting their potential utility in stratifying patients for surveillance and intervention.

Proteomic Biomarkers in Colorectal Cancer publication trend

The graph below shows the total number of articles in proteomic biomarkers in colorectal cancer across all publications each year (not limited to Nature Index journals).

Technical terms

Proteomic biomarker: A protein or set of proteins whose presence, absence or quantitative change in biological samples indicates a biological state or condition, such as cancer stage or treatment response.

Mass spectrometry: An analytical technique that measures the mass-to-charge ratio of ionised molecules to identify and quantify proteins in complex mixtures.

Classifier: A predictive model, often built using machine-learning algorithms, that assigns samples to predefined categories based on input features such as protein expression levels.

Epithelial–mesenchymal transition (EMT): A cellular programme wherein epithelial cells acquire mesenchymal traits, facilitating invasion and metastasis.

Tumour microenvironment: The complex milieu of cancer cells, stromal cells, immune infiltrates and extracellular matrix that collectively influences tumour growth and therapy response.

References

1. Integrated bioinformatics and wet-lab analysis revealed cell adhesion prominent genes CDC42, TAGLN and GSN as prognostic biomarkers in colonic-polyp lesions. Scientific Reports (2023).
2. High-throughput proteomics profiling-derived signature associated with chemotherapy response and survival for stage II/III colorectal cancer. npj Precision Oncology (2023).
3. LC-MS/MS analysis reveals plasma protein signatures associated with lymph node metastasis in colorectal cancer. Frontiers in Immunology (2024).
4. Combined High—Throughput Proteomics and Random Forest Machine-Learning Approach Differentiates and Classifies Metabolic, Immune, Signaling and ECM Intra-Tumor Heterogeneity of Colorectal Cancer. Cells (2024).