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pmid:37092360
Identification of immune-related genes contributing to head and neck squamous cell carcinoma development using weighted gene co-expression network analysis.
Qiaojuan Guo · Tianzhu Lu · Hanchuan Xu · Qingfeng Luo · Zhiliang Liu · Sicong Jiang · Jianji Pan · Shaojun Lin · Mengyao Lin · Fang Guo
Cancer reports (Hoboken, N.J.) · 2023 doi:10.1002/cnr2.1808
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Experiment
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Analysis
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Complete file
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Differential gene expression, immune cell infiltration, and survival analyses were performed to screen key genes. The expression of these key genes was validated in Oncomine and gene expression omnibus (GEO) datasets and by immunohistochemistry (IHC).\nRESULTS: 1869 and 1578 genes were significantly upregulated and downregulated in HNSCC. WGCNA showed that the brown module was associated with the most significant number of immune-related genes. PPI network analysis demonstrated that PPL, SCEL, KRT4, KRT24, KRT78, KRT13, SPRR3, TGM3, CRCT1, and CRNN were key components in the brown module. Furthermore, the expression levels of KRT4, KRT78, KRT13, and SPRR3 in HNSCC correlated with infiltration levels of CD8+ T cells and macrophages. Survival analyses revealed that the expression of KRT78, KRT13, and SPRR3 in HNSCC correlated with overall survival (OS). The IHC assay indicated that KRT13 (p = .042), KRT78 (p < .001), and SPRR3 (p = .022) protein expression levels in HNSCC were significantly lower than in normal tissues. Analysis of GSE65858 and GSE41613 datasets showed that a worse OS was associated with low expression of KRT78 (p = .0086, and p = .005) and SPRR3 (p = .017, and p = .02).\nCONCLUSIONS: Our findings suggest that KRT4, KRT78, KRT13, and SPRR3 are related to the occurrence and development of HNSCC. Importantly, KRT78 and SPRR3 might serve as diagnostic and prognostic biomarkers of HNSCC.", "claims": [{"id": "claim-method", "statement": "We used DESeq2 13 to analyze the differential expression of genes in the transcriptomic data of TCGA HNSCC database. WGCNA 16 was used to identify co‐expressed gene modules in DEGs and convert the adjacency matrix into a topological overlap matrix (TOM). 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METHODS: Transcriptomic data from The Cancer Genome Atlas (TCGA) HNS", "ucted a comprehensive integrated analysis of transcriptomic and clinical data of HNSCC patients from TCG", "rrence of HNSCC, we performed WGCNA based on transcriptomic data. Using a soft‐threshold of 4 (Figure 4A"], "order_basis": "paper_text_order:369", "id": "assay:1"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ene expression omnibus (GEO) datasets and by immunohistochemistry (IHC). RESULTS: 1869 and 1578 genes were sig", "(GEO) datasets and by immunohistochemistry (IHC). RESULTS: 1869 and 1578 genes were signific", "C correlated with overall survival (OS). The IHC assay indicated that KRT13 (p = .042), KRT78"], "order_basis": "paper_text_order:842", "id": "assay:2"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["logrank_test"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We used DESeq2 13 to analyze the differential expression of genes in the transcriptomic data of TCGA HNSCC database. WGCNA 16 was used to identify co‐expressed gene modules in DEGs and convert the adjacency matrix into a topological overlap matrix (TOM). Survival curves were drawn using the Kaplan–Meier method, and differences between groups were assessed using the log‐rank test."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}