Integrated bioinformatics analysis of SEMA3C in tongue squamous cell carcinoma using machine-learning strategies
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ABSTRACT:
SUBMITTER: Dou H
PROVIDER: S-EPMC10845809 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:Tongue squamous cell carcinoma (TSCC) is a prevalent cancer of the oral cavity. Survival metrics are usually unsatisfactory, even using combined treatment with surgery, radiation, and chemotherapy. Immune checkpoint inhibitors can prolong survival, especially in patients with recurrent or metastatic disease. However, there are few effective biomarkers to provide prognosis and guide immunotherapy. Here, we utilized weighted gene co-expression network analysis to identify the co-expression module and selected the turquoise module for further scrutiny. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed the innate pathways. The findings indicated that cell junction organization, response to topologically incorrect protein, and regulation of cell adhesion pathways may be essential. Eleven crucial predictive genes (PLXNB1, N4BP3, KDELR2, INTS8, PLAU, PPFIBP2, OAF, LMF1, IL34, ZFP3, and MAP7D3) were used to establish a risk model based on Cox and LASSO analyses of The Cancer Genome Atlas and GSE65858 databases (regarding overall survival). Kaplan-Meier analysis and receiver operating characteristic curve suggested that the risk model had better prognostic effectiveness than other clinical traits. Consensus clustering was used to classify TSCC samples into two groups with significantly different survival rates. ESTIMATE and CIBERSORT were used to display the immune landscape of TSCC and indicate the stromal score; specific types of immune cells, including naïve B cells, plasma cells, CD8 T cells, CD4 memory resting and memory activated T cells, follicular helper T cells, and T regulatory cells, may influence the heterogeneous immune microenvironment in TSCC. To further identify hub genes, we downloaded GEO datasets (GSE41613 and GSE31056) and successfully validated the risk model. Two hub genes (PLAU and PPFIBP2) were strongly associated with CD4+ and CD8+ T cells and programmed cell death protein 1 (PD1) and PD-ligand 1.
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