Construction of a Prognostic Model for Lysosome-dependent Cell Death in Gastric Cancer Based on Single-cell RNA-seq and Bulk RNA-seq Data.

Peng NI; Kai Xin GUO; Tian Yi LIANG; Xin Shuang FAN; Yan Qiao HUA; Yang Ye GAO; Shuai Yin CHEN; Guang Cai DUAN; Rong Guang ZHANG

Return

Construction of a Prognostic Model for Lysosome-dependent Cell Death in Gastric Cancer Based on Single-cell RNA-seq and Bulk RNA-seq Data.

Author: Peng NI ¹ ; Kai Xin GUO ¹ ; Tian Yi LIANG ¹ ; Xin Shuang FAN ¹ ; Yan Qiao HUA ¹ ; Yang Ye GAO ¹ ; Shuai Yin CHEN ¹ ; Guang Cai DUAN ¹ ; Rong Guang ZHANG ²
Author Information

1. Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou 450001, Henan, China.
2. Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou 450001, Henan, China;School of Public Health and First Affiliated Hospital, Hainan Medical University, Haikou 570100, Hainan, China.
Publication Type:Journal Article
Keywords: Gastric cancer; Immune infiltration; Lysosomal dependent cell death; Risk model; Single-cell analysis
MeSH: Stomach Neoplasms/pathology*; Humans; Prognosis; Lysosomes/physiology*; RNA-Seq; Cell Death; Single-Cell Analysis; Gene Expression Regulation, Neoplastic; Cell Proliferation; Single-Cell Gene Expression Analysis
From: Biomedical and Environmental Sciences 2025;38(4):416-432
CountryChina
Language:English
Abstract: OBJECTIVE:To identify prognostic genes associated with lysosome-dependent cell death (LDCD) in patients with gastric cancer (GC).
METHODS:Differentially expressed genes (DEGs) were identified using The Cancer Genome Atlas - Stomach Adenocarcinoma. Weighted gene co-expression network analysis was performed to identify the key module genes associated with LDCD score. Candidate genes were identified by DEGs and key module genes. Univariate Cox regression analysis, and least absolute shrinkage and selection operator regression and multivariate Cox regression analyses were performed for the selection of prognostic genes, and risk module was established. Subsequently, key cells were identified in the single-cell dataset (GSE183904), and prognostic gene expression was analyzed. Cell proliferation and migration were assessed using the Cell Counting Kit-8 assay and the wound healing assay.
RESULTS:A total of 4,465 DEGs, 95 candidate genes, and 4 prognostic genes, including C19orf59, BATF2, TNFAIP2, and TNFSF18, were identified in the analysis. Receiver operating characteristic curves indicated the excellent predictive power of the risk model. Three key cell types (B cells, chief cells, and endothelial/pericyte cells) were identified in the GSE183904 dataset. C19orf59 and TNFAIP2 exhibited predominant expression in macrophage species, whereas TNFAIP2 evolved over time in endothelial/pericyte cells and chief cells. Functional experiments confirmed that interfering with C19orf59 inhibited proliferation and migration in GC cells.
CONCLUSION:C19orf59, BATF2, TNFAIP2, and TNFSF18 are prognostic genes associated with LDCD in GC. Furthermore, the risk model established in this study showed robust predictive power.