Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis

Front Oncol. 2024 Jun 19:14:1413273. doi: 10.3389/fonc.2024.1413273. eCollection 2024.

Abstract

Background: Angiogenesis plays a pivotal role in colorectal cancer (CRC), yet its underlying mechanisms demand further exploration. This study aimed to elucidate the significance of angiogenesis-related genes (ARGs) in CRC through comprehensive multi-omics analysis.

Methods: CRC patients were categorized according to ARGs expression to form angiogenesis-related clusters (ARCs). We investigated the correlation between ARCs and patient survival, clinical features, consensus molecular subtypes (CMS), cancer stem cell (CSC) index, tumor microenvironment (TME), gene mutations, and response to immunotherapy. Utilizing three machine learning algorithms (LASSO, Xgboost, and Decision Tree), we screen key ARGs associated with ARCs, further validated in independent cohorts. A prognostic signature based on key ARGs was developed and analyzed at the scRNA-seq level. Validation of gene expression in external cohorts, clinical tissues, and blood samples was conducted via RT-PCR assay.

Results: Two distinct ARC subtypes were identified and were significantly associated with patient survival, clinical features, CMS, CSC index, and TME, but not with gene mutations. Four genes (S100A4, COL3A1, TIMP1, and APP) were identified as key ARCs, capable of distinguishing ARC subtypes. The prognostic signature based on these genes effectively stratified patients into high- or low-risk categories. scRNA-seq analysis showed that these genes were predominantly expressed in immune cells rather than in cancer cells. Validation in two external cohorts and through clinical samples confirmed significant expression differences between CRC and controls.

Conclusion: This study identified two ARG subtypes in CRC and highlighted four key genes associated with these subtypes, offering new insights into personalized CRC treatment strategies.

Keywords: angiogenesis; colorectal cancer; machine learning algorithms; multi-omics analysis; signature.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was partially supported by research funding from the National Natural Science Foundation of China (No. 82160446), Guangxi Medical Scientific Research Project (Z-A20221197), and Guangxi Key Laboratory for Basic Research in Fast Track Surgery for Digestive Tract Cancers (GXEKL202305).