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Integrative Analysis of Steroid Metabolism-Based Molecular Subtypes Reveals Prognostic Subtypes and Therapeutic
Linen Li1, Huiling Zhu2, Guang Gao2,3
1Department of Gastroenterology, Shangrao People's Hospital, Shangrao, Jiangxi, China.
Objective:
This study aimed to identify steroid metabolism-related molecular subtypes, investigate the gene expression patterns of these subtypes, and construct a prognostic risk model as well as predict therapeutic response in gastric cancer.
Methods:
We analyzed 410 TCGA-STAD and 483 GSE84437 gastric cancer samples. Unsupervised consensus clustering based on steroid metabolism-related genes identified molecular subtypes. Differential expression and functional enrichment analyses (GO, KEGG, GSEA) were performed. Prognostic genes were intersected with survival-associated genes, and a Lasso-Cox regression model was used to build a seven-gene risk score. Immune infiltration, tumor mutational burden (TMB), immune checkpoint expression, and drug sensitivity were evaluated.
Results:
Two steroid metabolism-related gastric cancer subtypes were identified, with steroid-metabolism-poor prognosis subtype showing poorer overall survival. Differential expression analysis revealed 1,709 genes enriched in immune regulation, calcium signaling, cell adhesion, and extracellular matrix remodeling. Seven key genes (PRICKLE1, SERPINE1, APOD, RIMS1, GLP2R, CDH19, GRP) were used to construct a risk score, which correlated with advanced stage, steroid-metabolism-poor prognosis subtype subtype, and worse survival, and was an independent prognostic factor. High-risk and steroid-metabolism-poor prognosis subtype tumors displayed higher immune infiltration and immune scores, lower TMB, and upregulated immune checkpoint genes, indicating an immunosuppressive microenvironment. Drug sensitivity differed across subtypes and risk groups, suggesting potential implications for personalized therapy.
Conclusions:
Steroid metabolism defines molecular heterogeneity, immune features, and prognosis in gastric cancer. The seven-gene risk model provides a reliable tool for survival prediction and may guide personalized therapeutic strategies.
