Molecular Clustering and Prognostic Features Based on Integrated Databases Predict Survival and Immune Status in Gastric Cancer Patients

Yin Shi¹Jiaying Zhou²Keping Jia³Hao Song⁴Tianlong Zhang⁵Weiwei Yuan⁶Jiahao Ge^7*

• ¹Department of Internal Medicine, Yiwu Maternity and Children Hospital, Yiwu, Zhejiang, China
• ²Department of Gastroenterology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, China
• ³Department of Traditional Chinese Medicine, Yiwu Maternity and Children Hospital, Yiwu, Zhejiang, China
• ⁴Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center, Hefei, China
• ⁵Department of Critical Care Medicine, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, China
• ⁶Department of Thyroid Surgery, Baoshan Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
• ⁷Department of Hepatobiliary and pancreatic surgery, Jinhua Hospital Affiliated to Zhejiang University, Jinhua, Zhejiang, China

Background: Gastric cancer (GC) remains one of the most common malignancies worldwide with high mortality rates despite advances in treatment approaches. Patients frequently develop drug resistance to current therapies, highlighting the critical need for novel prognostic biomarkers that can enhance survival rates and guide immunotherapy decisions in GC patients. Methods: We conducted a comprehensive bioinformatics analyses using integrated clinical data from TCGA and GEO databases. GC cases were categorized into two prognostic-related gene (PRG) clusters, and differentially expressed genes were identified. We established a prognostic model based on 11 key genes, stratified patients into high-risk and low-risk groups, and developed a nomogram model for survival prediction. Expression of selected genes was validated through qRT-PCR and immunohistochemistry in clinical samples. Results: The identified PRGs and gene clusters strongly associated with patient survival, immune system functions, and cancer-related pathways. Risk scores significantly correlated with immune cell abundance, checkpoint expression, and responses to immunotherapy and chemotherapy. For instance, the AUC values of patients at 1-year, 3-year and 5-year survival were all greater than 0.6 in the ROC curves (p < 0.05), which makes our prediction more accurate, and the line graphs predicted a 1-year survival rate exceeding 0.907, a 3-year survival rate exceeding 0.726, and a 5-year survival rate exceeding 0.633 , the calibration curves are almost close to the predicted ones (p < 0.05).Gene Set Enrichment Analysis (GSEA) analysis and Single-cell analysis revealed significant enrichment of multiple biological pathways and variability in expression of these genes across different cell types within the tumor microenvironment. qRT-PCR and immunohistochemistry confirmed significant differences in mRNA and protein expression of CTHRC1, CST6, and AKR1B1 between normal and GC tissues(p < 0.05). Conclusion: Our research establishes a robust molecular signature for predicting GC patient survival and characterizing the tumor immune microenvironment. It aims not only to establish a prognostic model, but also to explore immunobiological functions.The identified prognostic features and key genes (CTHRC1, CST6, and AKR1B1) offer potential as biomarkers and therapeutic targets, potentially guiding more effective personalized treatment strategies for GC patients.