Cancer-associated fibroblast related gene signature in Helicobacter pylori-based subtypes of gastric carcinoma for prognosis and tumor microenvironment estimation in silico analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36744128", "kind": "science-record", "revision": 3, "content_hash": "sha256:8a2547afdf74fef3c65a319514d767ad74443fab3fe6386b5fec7ae38f3b6819", "created_at": "2026-11-01T19:01:19Z", "updated_at": "2026-11-01T22:08:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Cancer-associated fibroblast related gene signature in Helicobacter pylori-based subtypes of gastric carcinoma for prognosis and tumor microenvironment estimation in silico analysis.", "doi": "10.3389/fmed.2023.1079470", "pmid": "36744128", "authors": ["Ruofan Xu", "Le Yang", "Zhewen Zhang", "Yuxuan Liao", "Yao Yu", "Dawei Zhou", "Jiahao Li", "Haoyu Guan", "Wei Xiao"], "journal": "Frontiers in medicine", "year": 2023, "abstract": "Introduction: Gastric cancer (GC) remains the major constituent of cancer-related deaths and a global public health challenge with a high incidence rate. Helicobacter pylori (HP) plays an essential role in promoting the occurrence and progression of GC. Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the tumor microenvironment (TME), which is related to the metastasis of GC. However, the regulation mechanisms of CAFs in HP-related GC are not elucidated thoroughly.\nMethods: HP-related genes (HRGs) were downloaded from the GSE84437 and TCGA-GC databases. The two databases were combined into one cohort for training. Furthermore, the consensus unsupervised clustering analysis was obtained to sort the training cohort into different groups for the identification of differential expression genes (DEGs). Weighted correlation network analysis (WGCNA) was performed to verify the correlation between the DEGs and cancer-associated fibroblasts which were key components in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) was executed to find cancer-associated fibroblast-related differential expression genes (CDEGs) for the further establishment of a prognostic model.\nResults and discussion: In this study, 52 HP-related genes (HRGs) were screened out based on the GSE84437 and TCGA-GC databases. A total of 804 GC samples were analyzed, respectively, and clustered into two HP-related subtypes. The DEGs identified from the two subtypes were proved to have a relationship with TME. After WGCNA and LASSO, the CAFs-related module was identified, from which 21 gene signatures were confirmed. Then, a CDEGs-Score was constructed and its prediction efficiency in GC patients was conducted for validation. Overall, a highly precise nomogram was established for enhancing the adaptability of the CDEGs-Score. Furthermore, our findings revealed the applicability of CDEGs-Score in the sensitivity of chemotherapeutic drugs. In general, our research provided brand-new possibilities for comprehending HP-related GC, evaluating survival, and more efficient therapeutic strategies.", "claims": [{"id": "claim-method", "statement": "Cancer-associated fibroblasts (CAFs) related module was applied to the “survival” R package for univariate Cox regression on the basis of that *p < 0.05 was considered as the cut-off value. Then, performing the R package “glmnet,” these genes were included in least absolute shrinkage and selection operator (LASSO) regression to avoid overfitting.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36744128", "locator": "sections[8] \"Construction and validation of the CDEGs prognostic risk score\" paragraph 2", "quote": "Cancer-associated fibroblasts (CAFs) related module was applied to the “survival” R package for univariate Cox regression on the basis of that *p < 0.05 was considered as the cut-off value. Then, performing the R package “glmnet,” these genes were included in least absolute shrinkage and selection operator (LASSO) regression to avoid overfitting."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36744128", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The CDEGs-Score showed prognostic predictive performance with 1-, 3-, and 5-year overall-survival AUC values of 0.661, 0.691, and 0.680, respectively, in the all-cohort validation.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36744128", "locator": "sections[19] \"Validation of the CDEGs-related model\" paragraph 2", "quote": "On the basis of the risk score, the AUC values of 1-, 3-, and 5-year overall survival (OS) for all cohorts were 0.661, 0.691, and 0.680, respectively (Figure 8D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The CDEGs-Score showed prognostic predictive performance with 1-, 3-, and 5-year overall-survival AUC values of 0.661, 0.691, and 0.680, respectively, in the all-cohort validation.", "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:36744128"], "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": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE84437"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE84437"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36744128", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fmed.2023.1079470", "sha256": "sha256:ac9a8cbfee17088efd9324581da6d01f6b62f57c791b28914605988174c89537", "bytes": 54771, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fmed.2023.1079470", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9889637", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE84437", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84437", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE84437", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. 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