Prognostic value of cancer-associated fibroblasts-related genes in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37701101", "kind": "science-record", "revision": 3, "content_hash": "sha256:c9ab821853f69f53290ebc6b43b8953d528b2bdded7f162236a9e91b7cd166ca", "created_at": "2026-11-01T19:02:31Z", "updated_at": "2026-11-01T22:10:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognostic value of cancer-associated fibroblasts-related genes in lung adenocarcinoma.", "doi": "10.21037/tcr-23-199", "pmid": "37701101", "authors": ["Wenchao Li", "Shengnan Shi"], "journal": "Translational cancer research", "year": 2023, "abstract": "Background: The incidence of lung adenocarcinoma is in the forefront of malignant tumors in the world. The purpose of this study was to investigate the role of cancer-associated fibroblast-related genes (CAFRGs) in the occurrence, diagnosis and development of lung adenocarcinoma.\nMethods: RNA data and corresponding clinical information of lung adenocarcinoma patients were acquired from The Cancer Genome Atlas (TCGA) database. Consensus clustering was performed to identify different molecular subgroups. The tumor immune states of different subgroups were determined by Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE; https://bioinformatics.mdanderson.org/estimate/index.html), microenvironment cell populations (MCP)-counter (which can reliably quantify the abundance of eight immune cell populations and two stromal cell populations), and single sample gene set enrichment analysis (ssGSEA) analyses. In order to elucidate the potential mechanism of CAFRGs, functional enrichment analysis including gene ontology (GO), Kyoto Encyclopedia of Genes and Genome (KEGG), and GSEA analysis were performed on the differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis were used to construct the prognostic risk model, which was verified by lung adenocarcinoma data from Gene Expression Omnibus (GEO) dataset GSE37745.\nResults: This study identified two molecular subgroups with significant differences in survival. High immunoscore and immune cell infiltration were more common in the subgroup with better prognosis. GO and KEGG analysis showed that DEGs between the two different subgroups were mainly concentrated in the mitotic cell cycle, cell proliferation, vascular development, and humoral immune response, adaptive immune-related pathways. GSEA analysis indicated that RNA degradation and P53 signaling pathway might be related to the increased invasiveness of lung adenocarcinoma. Risk models based on CAFRGs have demonstrated potent potential for predicting lung adenocarcinoma survival and have been validated in validation cohorts. The nomogram combined with risk model and clinical characteristics can predict the prognosis of patients with lung adenocarcinoma.\nConclusions: The expression of CAFRGs is related to tumor immune microenvironment (TIME) of lung adenocarcinoma patients, and can predict the prognosis of lung adenocarcinoma patients.", "claims": [{"id": "claim-method", "statement": "The survival state, survival time and gene expression data were integrated using R software package “glmnet” for LASSO regression analysis, and the survival curve was drawn using R software package “survival”.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37701101", "locator": "sections[11] \"Statistical method\" paragraph 2", "quote": "The survival state, survival time and gene expression data were integrated using R software package “glmnet” for LASSO regression analysis, and the survival curve was drawn using R software package “survival”."}], "state": "complete"}, {"id": "claim-data-access", "statement": "RNA data and corresponding clinical information of lung adenocarcinoma patients were acquired from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database, and missing completion and standardization processing were performed on sequencing data.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37701101", "locator": "sections[4] \"Data collection\" paragraph 2", "quote": "RNA data and corresponding clinical information of lung adenocarcinoma patients were acquired from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database, and missing completion and standardization processing were performed on sequencing data."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Four prognostic genes were identified by LASSO analysis, and the resulting risk model had a C-index of 0.6794 in the training cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37701101", "locator": "sections[17] \"Construction and verification of risk model\" paragraph 2", "quote": "Four prognostic gene tags were identified from 43 CAFRGs by LASSO analysis (Figure 6A,6B) and the model equation for four gene construction was: RiskScore = −0.0418 × PRKCD + 0.0227 × VEGFC + 0.0972 × DKK1 + 0.0179 × IGFBP1. Multivariate Cox regression was used to assess the prognostic significance of these genes in the sample, and the concordance index (C-index) was 0.6794 (Figure 6C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Four prognostic genes were identified by LASSO analysis, and the resulting risk model had a C-index of 0.6794 in the training cohort.", "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:37701101"], "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. 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