Genome Wide-scale CRISPR-Cas9 Knockout Screens Identify a Fitness Score for Optimized Risk Stratification in Colorectal Cancer

Overview

Journal J Transl Med

Publisher Biomed Central

Specialties Biomedical Engineering
General Medicine

Date 2024 Jun 10

PMID 38858785

Authors

Xiangchou Yang

Jieyu Liu

Shuaibin Wang

Wail Hussein Ahmed Al-Ameer

Jingting Ji

Jiaqi Cao

Hassan Mansour S Dhaen

Ying Lin

Yangyang Zhou

Chenguo Zheng

Affiliations

Soon will be listed here.

Abstract

Background: The molecular complexity of colorectal cancer poses a significant challenge to the clinical implementation of accurate risk stratification. There is still an urgent need to find better biomarkers to enhance established risk stratification and guide risk-adapted treatment decisions.

Methods: we systematically analyzed cancer dependencies of 17 colorectal cancer cells and 513 other cancer cells based on genome-scale CRISPR-Cas9 knockout screens to identify colorectal cancer-specific fitness genes. A regression model was built using colorectal cancer-specific fitness genes, which was validated in other three independent cohorts. 30 published gene expression signatures were also retrieved.

Findings: We defined a total of 1828 genes that were colorectal cancer-specific fitness genes and identified a 22 colorectal cancer-specific fitness gene (CFG22) score. A high CFG22 score represented unfavorable recurrence and mortality rates, which was validated in three independent cohorts. Combined with age, and TNM stage, the CFG22 model can provide guidance for the prognosis of colorectal cancer patients. Analysis of genomic abnormalities and infiltrating immune cells in the CFG22 risk stratification revealed molecular pathological difference between the subgroups. Besides, drug analysis found that CFG22 high patients were more sensitive to clofibrate.

Interpretation: The CFG22 model provided a powerful auxiliary prediction tool for identifying colorectal cancer patients with high recurrence risk and poor prognosis, optimizing precise treatment and improving clinical efficacy.

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PMID: 39886382 PMC: 11775580. DOI: 10.1039/d4cb00212a.

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