A methylation-related signature for predicting prognosis and sensitivity to first-line therapies in gastric cancer

Chenlin Cao; Zhiyong Luo; Hong Zhang; Shuo Yao; Hui Lu; Kun Zheng; Yali Wang; Man Zou; Wan Qin; Huihua Xiong; Xianglin Yuan; Yihua Wang; Rodrigo Nascimento Pinheiro; Renata D'Alpino Peixoto; Yanmei Zou; Hua Xiong

doi:10.21037/jgo-23-770

A methylation-related signature for predicting prognosis and sensitivity to first-line therapies in gastric cancer

J Gastrointest Oncol. 2023 Dec 31;14(6):2354-2372. doi: 10.21037/jgo-23-770. Epub 2023 Dec 27.

Authors

Chenlin Cao^#^{1

2}, Zhiyong Luo^#³, Hong Zhang^#¹, Shuo Yao¹, Hui Lu⁴, Kun Zheng^{1

5}, Yali Wang¹, Man Zou¹, Wan Qin¹, Huihua Xiong¹, Xianglin Yuan¹, Yihua Wang^{5

6}, Rodrigo Nascimento Pinheiro⁷, Renata D'Alpino Peixoto⁸, Yanmei Zou¹, Hua Xiong¹

Affiliations

¹ Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
² Department of the Second Clinical College, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
³ Division of Breast and Thyroid Surgery, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
⁴ Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
⁵ Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK.
⁶ Institute for Life Sciences, University of Southampton, Southampton, UK.
⁷ Surgical Oncology, Base Hospital of Federal District, Brasília, Brazil.
⁸ Department of Gastrointestinal Medical Oncology, Oncoclinicas, Av. Brigadeiro Faria Lima, São Paulo, Brazil.

^# Contributed equally.

Abstract

Background: Methylation modification patterns play a crucial role in human cancer progression, especially in gastrointestinal cancers. We aimed to use methylation regulators to classify patients with gastric adenocarcinoma and build a model to predict prognosis, promoting the application of precision medicine.

Methods: We obtained RNA sequencing data and clinical data from The Cancer Genome Atlas (TCGA) database (n=335) and Gene Expression Omnibus (GEO) database (n=865). Unsupervised consensus clustering was used to identify subtypes of gastric adenocarcinoma. We performed functional enrichment analysis, immune infiltration analysis, drug sensitivity analysis, and molecular feature analysis to determine the clinical application for different subtypes. The univariate Cox regression analysis and the LASSO regression analysis were subsequently used to identify prognosis-related methylation regulators and construct a risk model.

Results: Through unsupervised consensus clustering, patients were divided into two subtypes (cluster A and cluster B) with different clinical outcomes. Cluster B included patients with a better prognosis outcome and who were more likely to respond to immunotherapy. We then successfully built a predictive model and found five methylation-related genes (CHAF1A, CPNE8, PHLDA3, SPARC, and EHF) potentially significant to the prognosis of patients. The 1-, 3-, and 5-year areas under the curve of the risk model were 0.712, 0.696, and 0.759, respectively. The risk score was an independent prognostic factor and had the highest concordance index among common clinical indicators. Meanwhile, the tumor microenvironment, sensitivity of chemotherapeutic drugs, molecular features, and oncogenic dedifferentiation differed significantly across the risk groups and subtypes.

Conclusions: We classified patients with gastric adenocarcinoma based on methylation regulators, which has positive implications for first-line clinical treatment. The prognostic model could predict the prognosis of patients and help to promote the development of precision medicine.

Keywords: Gastric cancer (GC); immunotherapy; methylation modification; precision medicine; prognostic model.