# Itaconate‐Related Gene Signatures as Prognostic Markers in Colon Cancer: Insights From Transcriptomic and Spatial Analysis

## TL;DR

This study identifies itaconate-related gene signatures that predict colon cancer prognosis and treatment response using advanced genomic and spatial analysis.

## Contribution

The first comprehensive analysis of itaconate and Hallmark pathway-related genes in colon cancer using multi-omics data and spatial transcriptomics.

## Key findings

- 10 prognosis-related genes were identified and validated across multiple datasets.
- The optimal model (Enet) outperformed existing clinical features and models in predicting survival.
- High-risk patients showed higher TMB and immune checkpoint gene expression, suggesting better immunotherapy response.

## Abstract

Colon cancer is one of the most prevalent malignant tumors. Accurate evaluation of patient prognosis and optimization of treatment strategies continue to be major research focuses in colon cancer. Based on The Cancer Genome Atlas (TCGA) database, this study is the first to comprehensively analyze the expression, biological roles, and prognosis of itaconate and Hallmark pathway–related genes in colon cancer using bulk transcriptomics, single‐cell transcriptomics, and spatial transcriptomics data. Through strict screening in 448 colon cancer patients from TCGA database (training set) and 7 colon cancer prognostic models from the Gene Expression Omnibus (GEO) database (including 1473 cases in the validation set), 10 prognosis‐related genes (TIMP1, FJX1, CD36, CXCL1, ETS2, CDKN2A, INHBB, PLEC, TUBB2, and P4HA1) were selected. The optimal prognostic prediction model (Enet [alpha = 0.2]) was constructed and validated, which showed good prognostic predictive value in both the training and validation sets (average C‐index > 0.7) and was superior to previous conventional clinical features and 22 prognostic models developed by researchers in the past 4 years. ScRNAseq (GSE225857) and spatial transcriptomics analyses clarified the cell‐specific expression and spatial distribution characteristics of these genes in the tumor microenvironment (TME), with high functional scores mainly enriched in epithelial and stromal cells. Tissue microarray (TMA) showed that the high‐risk group had higher tumor mutation burden (TMB) and higher expression of immune checkpoint genes, suggesting higher sensitivity to immunotherapy. Drug sensitivity analysis identified four potentially effective drugs, such as sepantronium bromide, which had better effects on high‐risk patients. This study provides a theoretical basis and new targets for precise prognosis and stratified treatment of colon cancer.