TGFβ-Associated Signature Predicts Prognosis and Tumor Microenvironment Infiltration Characterization in Gastric Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35664335", "kind": "science-record", "revision": 4, "content_hash": "sha256:e1988539d1315ff5811cce347f943b130fc333dd254a77672a922dc176de3db9", "created_at": "2026-11-01T18:14:36Z", "updated_at": "2026-11-01T22:06:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "TGFβ-Associated Signature Predicts Prognosis and Tumor Microenvironment Infiltration Characterization in Gastric Carcinoma.", "doi": "10.3389/fgene.2022.818378", "pmid": "35664335", "authors": ["Siyuan Liu", "Zhenghao Li", "Huihuang Li", "Xueyi Wen", "Yu Wang", "Qilin Chen", "Xundi Xu"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Gastric carcinoma (GC) is a carcinoma with a high incidence rate, and it is a deadly carcinoma globally. An effective tool, that is, able to predict different survival outcomes for GC patients receiving individualized treatments is deeply needed. Methods: In total, data from 975 GC patients were collected from TCGA-STAD, GSE15459, and GSE84437. Then, we performed a comprehensive unsupervised clustering analysis based on 54 TGFβ-pathway-related genes and correlated these patterns with tumor microenvironment (TME) cell-infiltrating characteristics. WGCNA was then applied to find the module that had the closest relation with these patterns. The least absolute shrinkage and selection operator (LASSO) algorithm was combined with cross validation to narrow down variables and random survival forest (RSF) was used to create a risk score. Results: We identified two different TGFβ regulation patterns and named them as TGFβ Cluster 1 and Cluster 2. TGFβ Cluster 1 was linked to significantly poorer survival outcomes and represented an inflamed TME subtype of GC. Using WGCNA, a module (magenta) with the closest association with the TGFβ clusters was identified. After narrowing down the gene list by univariate Cox regression analysis, the LASSO algorithm and cross validation, four of the 243 genes in the magenta module were applied to build a risk score. The group with a higher risk score exhibited a considerably poorer survival outcome with high predictive accuracy. The risk score remained an independent risk factor in multivariate Cox analysis. Moreover, we validated this risk score using GSE15459 and GSE84437. Furthermore, we found that the group with a higher risk score represented an inflamed TME according to the evidence that the risk score was remarkably correlated with several steps of cancer immunity cycles and a majority of the infiltrating immune cells. Consistently, the risk score was significantly related to immune checkpoint genes and T cell-inflamed gene expression profiles (GEPs), indicating the value of predicting immunotherapy. Conclusions: We have developed and validated a TGFβ-associated signature, that is, capable of predicting the survival outcome as well as depicting the TME immune characteristics of GC. In summary, this signature may contribute to precision medicine for GC.", "claims": [{"id": "claim-method", "statement": "First, we performed univariate Cox analysis on genes of the module with the closest relationship with the TGFβ clusters to screen genes having prognostic value. Then, the least absolute shrinkage and selector operation (LASSO) algorithm was used for the screened genes. Finally, using the rfsrc function in the “randomForestSRC” R package, a TGFβ-associated risk score was constructed based on the genes filtered by the LASSO algorithm.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35664335", "locator": "sections[8] \"Creation and Validation of the Risk Score\" paragraph 2", "quote": "First, we performed univariate Cox analysis on genes of the module with the closest relationship with the TGFβ clusters to screen genes having prognostic value. Then, the least absolute shrinkage and selector operation (LASSO) algorithm was used for the screened genes. Finally, using the rfsrc function in the “randomForestSRC” R package, a TGFβ-associated risk score was constructed based on the genes filtered by the LASSO algorithm."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35664335", "locator": "sections[18] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "After narrowing down the gene list by univariate Cox regression analysis, the LASSO algorithm and cross validation, four of the 243 genes in the magenta module were applied to build a risk score.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35664335", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "After narrowing down the gene list by univariate Cox regression analysis, the LASSO algorithm and cross validation, four of the 243 genes in the magenta module were applied to build a risk score."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "After narrowing down the gene list by univariate Cox regression analysis, the LASSO algorithm and cross validation, four of the 243 genes in the magenta module were applied to build a risk score.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35664335"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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