Experimental prognostic model integrating N6-methyladenosine-related programmed cell death genes in colorectal cancer

Qihui Wu; Xiaodan Fu; Xiaoyun He; Jiaxin Liu; Yimin Li; Chunlin Ou

doi:10.1016/j.isci.2023.108720

Experimental prognostic model integrating N6-methyladenosine-related programmed cell death genes in colorectal cancer

iScience. 2023 Dec 13;27(1):108720. doi: 10.1016/j.isci.2023.108720. eCollection 2024 Jan 19.

Authors

Qihui Wu¹, Xiaodan Fu², Xiaoyun He³, Jiaxin Liu⁴, Yimin Li⁵, Chunlin Ou^{2

6}

Affiliations

¹ Department of Gynecology, Xiangya Hospital, Central South University, Changsha 410008, China.
² Department of Pathology, Xiangya Hospital, Central South University, Changsha 410008, China.
³ Departments of Ultrasound Imaging, Xiangya Hospital, Central South University, Changsha 410008, China.
⁴ Department of Pathology, School of Basic Medical Sciences, Central South University, Changsha 410078, China.
⁵ Department of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China.
⁶ National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008, China.

Abstract

Colorectal cancer (CRC) intricacies, involving dysregulated cellular processes and programmed cell death (PCD), are explored in the context of N6-methyladenosine (m6A) RNA modification. Utilizing the TCGA-COADREAD/CRC cohort, 854 m6A-related PCD genes are identified, forming the basis for a robust 10-gene risk model (CDRS) established through LASSO Cox regression. qPCR experiments using CRC cell lines and fresh tissues was performed for validation. The CDRS served as an independent risk factor for CRC and showed significant associations with clinical features, molecular subtypes, and overall survival in multiple datasets. Moreover, CDRS surpasses other predictors, unveiling distinct genomic profiles, pathway activations, and associations with the tumor microenvironment. Notably, CDRS exhibits predictive potential for drug sensitivity, presenting a novel paradigm for CRC risk stratification and personalized treatment avenues.

Keywords: Diagnostic technique in health technology; Health informatics; Health sciences; Health technology; Medical informatics; Medicine; Oncology; Screening in health technology.