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Background <p>We aimed to develop a predictive model integrating G2M-related genes to enhance the prognostication of colon cancer.</p> Methods <p>Based on the data from TCGA-COAD and GEO (GSE39582, GSE17536, GSE17537), we applied Cox regression with LASSO to create a colon cancer prognostic model and developed a Nomogram for survival prediction.</p> Results <p>Our initial univariate Cox regression analysis identified 15 common prognostic genes. Subsequent LASSO modeling refined this set to nine hub genes: BRIP1, CDCA2, HMGCR, HOXC10, PSMA5, PSMD6, SIX4, SLC12A2, and SPA17. Multi-omics analysis explored their protein expression, miRNA regulation, protein interactions, genomic variations, and drug sensitivity. The G2M prognostic model effectively categorized colon cancer patients into high- and low-risk groups, with the high-risk group showing significantly poorer overall survival. Further analysis of gene expression variances showed enrichment of extracellular matrix-related events. The G2M-based risk stratification correlated with the infiltration levels of immune cells, including Tregs, CD56dim natural killer cells, and M0-type macrophages. To aid clinical decision-making, we developed a Nomogram that developed the risk score with clinical parameters like tumor stage, age, and gender to forecast 1-, 3-, and 5-year survival rates.</p> Conclusions <p>This study presents an innovative prognostic model centered on the G2M, highlighting the indispensable role of G2M-related genes in colon cancer’s clinical progression.</p>

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Development and validation of G2M signature-based prognostic model for stratifying colon cancer prognosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月10日

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  • Xin Song,
  • Yan Dou,
  • Xiaoyong Shen

摘要

Background

We aimed to develop a predictive model integrating G2M-related genes to enhance the prognostication of colon cancer.

Methods

Based on the data from TCGA-COAD and GEO (GSE39582, GSE17536, GSE17537), we applied Cox regression with LASSO to create a colon cancer prognostic model and developed a Nomogram for survival prediction.

Results

Our initial univariate Cox regression analysis identified 15 common prognostic genes. Subsequent LASSO modeling refined this set to nine hub genes: BRIP1, CDCA2, HMGCR, HOXC10, PSMA5, PSMD6, SIX4, SLC12A2, and SPA17. Multi-omics analysis explored their protein expression, miRNA regulation, protein interactions, genomic variations, and drug sensitivity. The G2M prognostic model effectively categorized colon cancer patients into high- and low-risk groups, with the high-risk group showing significantly poorer overall survival. Further analysis of gene expression variances showed enrichment of extracellular matrix-related events. The G2M-based risk stratification correlated with the infiltration levels of immune cells, including Tregs, CD56dim natural killer cells, and M0-type macrophages. To aid clinical decision-making, we developed a Nomogram that developed the risk score with clinical parameters like tumor stage, age, and gender to forecast 1-, 3-, and 5-year survival rates.

Conclusions

This study presents an innovative prognostic model centered on the G2M, highlighting the indispensable role of G2M-related genes in colon cancer’s clinical progression.