# Precision prognosis of colorectal cancer: a multi-tiered model integrating microsatellite instability genes and clinical parameters

## TL;DR

This study creates a more accurate prognosis model for colorectal cancer by combining genetic data, clinical features, and microsatellite instability status.

## Contribution

A novel multi-tiered model integrating MSI-related genes and clinical parameters for improved CRC prognosis.

## Key findings

- An integrated model (Model III) achieved an AUC of 0.825, outperforming individual gene and clinical models.
- Six MSI-related genes were identified to build a prognostic model with an AUC of 0.724.
- The model demonstrated stability in an independent dataset with an AUC of 0.767.

## Abstract

Prognostic assessment for colorectal cancer (CRC) displays substantial heterogeneity, as reliance solely on traditional TNM staging falls short of achieving precise individualized predictions. The integration of diverse biological information sources holds the potential to enhance prognostic accuracy.

To establish a comprehensive multi-tiered precision prognostic evaluation system for CRC by amalgamating gene expression profiles, clinical characteristics, and tumor microsatellite instability (MSI) status in CRC patients.

We integrated genomic data, clinical information, and survival follow-up data from 483 CRC patients obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. MSI-related gene modules were identified using differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Three prognostic models were constructed: MSI-Related Gene Prognostic Model (Model I), Clinical Prognostic Model (Model II), and Integrated Multi-Layered Prognostic Model (Model III) by combining clinical features. Model performance was assessed and compared using Receiver Operating Characteristic (ROC) curves, Kaplan-Meier analysis, and other methods.

Six MSI-related genes were selected for constructing Model I (AUC = 0.724); Model II used two clinical features (AUC = 0.684). Compared to individual models, the integrated Model III exhibited superior performance (AUC = 0.825) and demonstrated good stability in an independent dataset (AUC = 0.767).

This study successfully developed and validated a comprehensive multi-tiered precision prognostic assessment model for CRC, providing an effective tool for personalized medical management of CRC.