Identification of colon cancer subtypes based on multi-omics data-construction of methylation markers for immunotherapy

Benjie Xu; Jie Lian; Xiangyi Pang; Yue Gu; Jiahao Zhu; Yan Zhang; Haibo Lu

doi:10.3389/fonc.2024.1335670

Identification of colon cancer subtypes based on multi-omics data-construction of methylation markers for immunotherapy

Front Oncol. 2024 Jan 22:14:1335670. doi: 10.3389/fonc.2024.1335670. eCollection 2024.

Authors

Benjie Xu^#¹, Jie Lian^#¹, Xiangyi Pang¹, Yue Gu², Jiahao Zhu¹, Yan Zhang^{2

3}, Haibo Lu¹

Affiliations

¹ Department of Outpatient Chemotherapy, Harbin Medical University Cancer Hospital, Harbin, China.
² School of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, China.
³ College of Pathology, Qiqihar Medical University, Qiqihar, China.

^# Contributed equally.

Abstract

Background: Being the most widely used biomarker for immunotherapy, the microsatellite status has limitations in identifying all patients who benefit in clinical practice. It is essential to identify additional biomarkers to guide immunotherapy. Aberrant DNA methylation is consistently associated with changes in the anti-tumor immune response, which can promote tumor progression. This study aims to explore immunotherapy biomarkers for colon cancers from the perspective of DNA methylation.

Methods: The related data (RNA sequencing data and DNA methylation data) were obtained from The Cancer Genome Atlas (TCGA) and UCSC XENA database. Methylation-driven genes (MDGs) were identified through the Pearson correlation analysis. Unsupervised consensus clustering was conducted using these MDGs to identify distinct clusters of colon cancers. Subsequently, we evaluated the immune status and predicted the efficacy of immunotherapy by tumor immune dysfunction and exclusion (Tide) score. Finally, The Quantitative Differentially Methylated Regions (QDMR) software was used to identify the specific DNA methylation markers within particular clusters.

Results: A total of 282 MDGs were identified by integrating the DNA methylation and RNA-seq data. Consensus clustering using the K-means algorithm revealed that the optimal number of clusters was 4. It was revealed that the composition of the tumor immune microenvironment (TIME) in Cluster 1 was significantly different from others, and it exhibited a higher level of tumor mutation burdens (TMB) and stronger anti-tumor immune activity. Furthermore, we identified three specific hypermethylation genes that defined Cluster 1 (PCDH20, APCDD1, COCH). Receiver operating characteristic (ROC) curves demonstrated that these specific markers could effectively distinguish Cluster 1 from other clusters, with an AUC of 0.947 (95% CI 0.903-0.990). Finally, we selected clinical samples for immunohistochemical validation.

Conclusion: In conclusion, through the analysis of DNA methylation, consensus clustering of colon cancer could effectively identify the cluster that benefit from immunotherapy along with specific methylation biomarkers.

Keywords: DNA methylation; colon cancer; immunotherapy; microsatellite status; specific DNA methylation markers.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the National Natural Science Foundation of China (grant nos. U20A20376 and 61972116), Beijing Award Foundation (YXJL-2020-0818-0478), Wu Jieping Medical Foundation (320.6750.2020-19-20), Heilongjiang Province Postdoctoral Funding Project (LBH-Z21189), Harbin Medical University Innovative Science Research Funded Project (grant no.31041220028) and China Postdoctoral Science Foundation (grant no.2022MD713747).