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Expressional and Prognostic Value of S100A16 in Pancreatic Cancer Via Integrated Bioinformatics Analyses.

ABSTRACT: Studies have shown that the calcium-binding protein family S100 may play a role in the development of pancreatic cancer (PC), but the role of S100A16 in PC is still unknown. In this study, Oncomine was first used to detect the expression level and prognosis of S100A16 in PC and other tumors. The results showed that S100A16 was highly expressed in PC tissues compared with a normal pancreas, and the increased expression level may be related to poor prognosis in PC patients. The TCGA and ICGC RNA-seq data of PC patients were downloaded, and the S100A16-related differentially expressed genome (DEGs) was defined by taking the intersection of two gene sets. The GO and KEGG pathways were then analyzed. For clinical analysis, boxplots were depicted for the correlation between clinical characteristics and S100A16 expression. Then Cox regression was applied for exploring the prognostic value of S100A16 for PDAC patients. Based on the Cox regression model, we further estabished a S100A16-related risk score system to strengthen the ability to predict patients' prognosis. After integrating the risk score model and multiple clinicopathological factors, we finally established a nomogram that could predict the survival time of patients. Moreover, Gene set enrichment the effect of S100A16 expression differences on downstream biological processes. At last, using TIMER, ImmuneCellAI and GSEA we analyzed the correlation between S100A16 and pancreatic cancer immune infiltration and predicted the response of patients to checkpoint Blocker (ICB). In summary, S100A16 is involved in the occurrence and development of PC, affecting the prognosis of patients, and may have potential reference values for the immunotherapy of PC.

SUBMITTER: Tu G

PROVIDER: S-EPMC8072221 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundGastric cancer is one of the most common malignancies worldwide. Although the diagnosis and treatment of this disease have substantially improved in recent years, the five-year survival rate of gastric cancer is still low due to local recurrence and distant metastasis. An in-depth study of the molecular pathogenesis of gastric cancer and related prognostic markers will help improve the quality of life and prognosis of patients with this disease. The purpose of this study was to identify and verify key SNPs in genes with prognostic value for gastric cancer.MethodsSNP-related data from gastric cancer patients were obtained from The Cancer Genome Atlas (TCGA) database, and the functions and pathways of the mutated genes were analyzed using DAVID software. A protein-protein interaction (PPI) network was constructed using the STRING database and visualized by Cytoscape software, and molecular complex detection (MCODE) was used to screen the PPI network to extract important mutated genes. Ten hub genes were identified using cytoHubba, and the expression levels and the prognostic value of the central genes were determined by UALCAN and Kaplan-Meier Plotter. Finally, quantitative PCR and Western blotting were used to verify the expression of the hub genes in gastric cancer cells.ResultsFrom the database, 945 genes with mutations in more than 25 samples were identified. The PPI network had 360 nodes and 1616 edges. Finally, cytoHubba identified six key genes (TP53, HRAS, BRCA1, PIK3CA, AKT1, and SMARCA4), and their expression levels were closely related to the survival rate of gastric cancer patients.ConclusionOur results indicate that TP53, HRAS, BRCA1, PIK3CA, AKT1, and SMARCA4 may be key genes for the development and prognosis of gastric cancer. Our research provides an important bioinformatics foundation and related theoretical foundation for further exploring the molecular pathogenesis of gastric cancer and evaluating the prognosis of patients.

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Expressional and Prognostic Value of S100A16 in Pancreatic Cancer Via Integrated Bioinformatics Analyses.

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