Wen F, Guan X, Qu HX, Jiang XJ. Integrated analysis of single-cell and bulk RNA-seq establishes a novel signature for prediction in gastric cancer. World J Gastrointest Oncol 2023; 15(7): 1215-1226 [PMID: 37546563 DOI: 10.4251/wjgo.v15.i7.1215]
Corresponding Author of This Article
Xiang-Jun Jiang, PhD, Doctor, Department of Gastroenterology, Qingdao Municipal Hospital, No. 1 Jiaozhou Road, Qingdao 266071, Shandong Province, China. drjxj@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Clinical and Translational Research
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastrointest Oncol. Jul 15, 2023; 15(7): 1215-1226 Published online Jul 15, 2023. doi: 10.4251/wjgo.v15.i7.1215
Integrated analysis of single-cell and bulk RNA-seq establishes a novel signature for prediction in gastric cancer
Fei Wen, Xin Guan, Hai-Xia Qu, Xiang-Jun Jiang
Fei Wen, Qingdao University, Medical College, Qingdao 266000, Shandong Province, China
Xin Guan, Hai-Xia Qu, Xiang-Jun Jiang, Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao 266071, Shandong Province, China
Author contributions: Jiang XJ designed and coordinated the study; Wen F, Qu HX, and Guan X performed data collection and analysis; Wen F interpreted the data and wrote the manuscript; All authors approved the final version of the article.
Institutional review board statement: Given that our article is based on a study of sequencing data in the public database, GEO, there are no ethical issues involved, so the institutional review board approval form or document and institutional animal care and use committee approval form or document are not applicable.
Conflict-of-interest statement: All the authors report having no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xiang-Jun Jiang, PhD, Doctor, Department of Gastroenterology, Qingdao Municipal Hospital, No. 1 Jiaozhou Road, Qingdao 266071, Shandong Province, China. drjxj@163.com
Received: February 1, 2023 Peer-review started: February 1, 2023 First decision: March 21, 2023 Revised: March 31, 2023 Accepted: May 8, 2023 Article in press: May 8, 2023 Published online: July 15, 2023 Processing time: 161 Days and 2.5 Hours
Core Tip
Core Tip: In this study, we integrated and analyzed three single-cell RNA sequencing datasets and 10 bulk RNA sequencing datasets of gastric cancer (GC) from the Gene Expression Omnibus database. We conducted a differential expression analysis of epithelial cell subpopulations from GC tissue and normal gastric mucosa tissue and constructed GC prediction classifiers using the Least absolute shrinkage and selection operator (LASSO) method and random forest method. The LASSO prediction model was further validated in the Cancer Genome Atlas stomach adenocarcinoma dataset. TIMP1, PLOD3, CKS2, TYMP, TNFRSF10B, CPNE1, GDF15, BCAP31, and CLDN7 were selected as the predictive genes for GC. This study provides a new approach for constructing prediction models based on single-cell sequencing data and offers new reference targets for the clinical diagnosis and treatment of GC.
