Research Paper Volume 12, Issue 22 pp 22626—22655

Colon cancer-specific diagnostic and prognostic biomarkers based on genome-wide abnormal DNA methylation

Yilin Wang1,2, , Ming Zhang1,2, , Xiaoyun Hu1,2, , Wenyan Qin1,2, , Huizhe Wu1,2, , Minjie Wei1,2, ,

  • 1 Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang 110122, Liaoning Province, P. R. China
  • 2 Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, Liaoning Cancer Immune Peptide Drug Engineering Technology Research Center, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, China Medical University, Shenyang 110122, Liaoning Province, P. R. China

Received: December 20, 2019       Accepted: July 25, 2020       Published: November 17, 2020      

https://doi.org/10.18632/aging.103874
How to Cite

Copyright: © 2020 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Abnormal DNA methylation is a major early contributor to colon cancer (COAD) development. We conducted a cohort-based systematic investigation of genome-wide DNA methylation using 299 COAD and 38 normal tissue samples from TCGA. Through conditional screening and machine learning with a training cohort, we identified one hypomethylated and nine hypermethylated differentially methylated CpG sites as potential diagnostic biomarkers, and used them to construct a COAD-specific diagnostic model. Unlike previous models, our model precisely distinguished COAD from nine other cancer types (e.g., breast cancer and liver cancer; error rate ≤ 0.05) and from normal tissues in the training cohort (AUC = 1). The diagnostic model was verified using a validation cohort from The Cancer Genome Atlas (AUC = 1) and five independent cohorts from the Gene Expression Omnibus (AUC ≥ 0.951). Using Cox regression analyses, we established a prognostic model based on six CpG sites in the training cohort, and verified the model in the validation cohort. The prognostic model sensitively predicted patients’ survival (p ≤ 0.00011, AUC ≥ 0.792) independently of important clinicopathological characteristics of COAD (e.g., gender and age). Thus, our DNA methylation analysis provided precise biomarkers and models for the early diagnosis and prognostic evaluation of COAD.

Abbreviations

AUC: area under the curve; BLCA: bladder cancer; BRCA: breast cancer; CESC: cervical cancer; COAD: colon cancer; DMP: differentially methylated CpG site; GBM: glioblastoma; GEO: Gene Expression Omnibus; GO: Gene Ontology; GSEA: Gene Set Enrichment Analysis; HNSC: head and neck cancer; Hyper-: hypermethylated; Hypo-: hypomethylated; IINIP: intestinal immune network for IgA production; KEGG: Kyoto Encyclopedia of Genes and Genomes; LIHC: liver cancer; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; OS: overall survival; ROC: receiver operating characteristic; TCGA: The Cancer Genome Atlas; UCEC: endometrial cancer.