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pmid:35397536
A new nomogram model for prognosis of hepatocellular carcinoma based on novel gene signature that regulates cross-talk between immune and tumor cells.
Youpeng Wang · Yeni Yang · Ziyin Zhao · Hongfa Sun · Dingan Luo · Lakshmi Huttad · Bingyuan Zhang · Bing Han
BMC cancer · 2022 doi:10.1186/s12885-022-09465-9
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Complete file
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The purpose of this study is to construct a new prognostic model based on immune-related genes that regulate cross-talk between immune and tumor cells to assess the prognosis and explore possible mechanisms.\nMETHOD: The immune cell abundance ratio of 424 cases in the TCGA-LIHC database is obtained through the CIBERSORT algorithm. The differential gene analysis and cox regression analysis is used to screen IRGs. In addition, the function of IRGs was preliminarily explored through the co-culture of M2 macrophages and HCC cell lines. The clinical validation, nomogram establishment and performing tumor microenvironment score were validated.\nRESULTS: We identified 4 immune cells and 9 hub genes related to the prognosis. Further, we identified S100A9, CD79B, TNFRSF11B as an IRGs signature, which is verified in the ICGC and GSE76427 database. Importantly, IRGs signature is closely related to the prognosis, tumor microenvironment score, clinical characteristics and immunotherapy, and nomogram combined with clinical characteristics is more conducive to clinical promotion. In addition, after co-culture with M2 macrophages, the migration capacity and cell pseudopod of MHCC97H increased significantly. And CD79B and TNFRSF11B were significantly down-regulated in MHCC97H, Huh7 and LM3, while S100A9 was up-regulated.\nCONCLUSION: We constructed an IRGs signature and discussed possible mechanisms. The nomogram established based on IRGs can accurately predict the prognosis of HCC patients. These findings may provide a suitable therapeutic target for HCC.", "claims": [{"id": "claim-method", "statement": "The RNA-Seq (FPKM format) of 424 specimens were analyzed using the CIBERSORT algorithm and obtained a ratio matrix of 22 immune cells (perm = 100) [14, 15].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35397536", "locator": "sections[4] \"Identifying survival-related immune cells\" paragraph 2", "quote": "The RNA-Seq (FPKM format) of 424 specimens were analyzed using the CIBERSORT algorithm and obtained a ratio matrix of 22 immune cells (perm = 100) [14, 15]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analysed during the current study are available in TCGA, ICGC and GSE76427. 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