Identification of LGR4 as a prognostic biomarker in KRAS-mutant lung adenocarcinoma: Evidence from integrated bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37986325", "kind": "science-record", "revision": 3, "content_hash": "sha256:5e6d649aeb37ed5d1e680a34a32f640c1599c18d2863d21bcce94868e0f76698", "created_at": "2026-11-01T19:02:54Z", "updated_at": "2026-11-01T22:10:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of LGR4 as a prognostic biomarker in KRAS-mutant lung adenocarcinoma: Evidence from integrated bioinformatics analysis.", "doi": "10.1097/MD.0000000000036084", "pmid": "37986325", "authors": ["Yasmeen Dodin"], "journal": "Medicine", "year": 2023, "abstract": "Globally, lung cancer is the leading cause of cancer-related deaths, primarily non-small cell lung cancer. Kirsten Rat Sarcoma Oncogene Homolog (KRAS) mutations are common in non-small cell lung cancer and linked to a poor prognosis. Covalent inhibitors targeting KRAS-G12C mutation have improved treatment for some patients, but most KRAS-mutant lung adenocarcinoma (KRAS-MT LUAD) cases lack targeted therapies. This gap in treatment options underscores a significant challenge in the field. Our study aimed to identify hub/key genes specifically associated with KRAS-MT LUAD. These hub genes hold the potential to serve as therapeutic targets or biomarkers, providing insights into the pathogenesis and prognosis of lung cancer. We performed a comprehensive analysis on KRAS-MT LUAD samples using diverse data sources. This included TCGA project data for RNA-seq, clinical information, and somatic mutations, along with RNA-seq data for adjacent normal tissues. DESeq2 identified differentially expressed genes (DEGs), while weighted gene co-expression network analysis revealed co-expression modules. Overlapping genes between DEGs and co-expression module with the highest significance were analyzed using gene set enrichment analysis and protein-protein interaction network analysis. Hub genes were identified with the Maximal Clique Centrality algorithm in Cytoscape. Prognostic significance was assessed through survival analysis and validated using the GSE72094 dataset from Gene Expression Omnibus (GEO) database. In KRAS-MT LUAD, 3122 DEGs were found (2131 up-regulated, 985 down-regulated). The blue module, among 25 co-expression modules from weighted gene co-expression network analysis, had the strongest correlation. 804 genes overlapped between DEGs and the blue module. Among 20 hub genes in the blue module, leucine-rich repeats containing G protein-coupled receptor 4 (LGR4) overexpression correlated with worse overall survival. The prognostic significance of LGR4 was confirmed using GSE72094, but surprisingly, the direction of the association was opposite to what was expected. LGR4 stands as a promising biomarker in KRAS-MT LUAD prognosis. Contrasting associations in TCGA and GSE72094 datasets reveal the intricate nature of KRAS-MT LUAD. Additional explorations are imperative to grasp the precise involvement of LGR4 in lung adenocarcinoma prognosis, particularly concerning KRAS mutations. These insights could potentially pave the way for targeted therapeutic interventions, addressing the existing unmet demands in this specific subgroup.", "claims": [{"id": "claim-method", "statement": "Kaplan–Meier estimates were used to analyze the survival probabilities, and the log-rank test was employed to determine the statistical significance.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37986325", "locator": "sections[8] \"2.6. Hub genes survival analysis\" paragraph 2", "quote": "Kaplan–Meier estimates were used to analyze the survival probabilities, and the log-rank test was employed to determine the statistical significance."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analyzed during the current study are publicly available.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37986325", "locator": "sections[23] \"fn-group\" paragraph 1", "quote": "The datasets generated during and/or analyzed during the current study are publicly available."}], "state": "complete"}, {"id": "claim-target-result", "statement": "LGR4 was associated with overall survival in KRAS-MT LUAD, but the direction of the association was opposite between the TCGA and GSE72094 datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37986325", "locator": "sections[16] \"3.6. Hub genes survival analysis\" paragraph 2", "quote": "The analysis of the 20 hub genes within the blue module revealed a significant correlation between the increased expression of 1 gene, LGR4, and a lower OS rate in KRAS-MT LUAD patients (P = .012)."}, {"asset": "source:pmid:37986325", "locator": "sections[17] \"3.7. External validation of the survival-related hub genes\" paragraph 2", "quote": "The results obtained from this external dataset are presented in Figure 10, further supporting the survival significance of LGR4 as observed in our initial analysis, but with the opposite direction (low expression levels of LGR4 were associated with poor survival)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "LGR4 was associated with overall survival in KRAS-MT LUAD, but the direction of the association was opposite between the TCGA and GSE72094 datasets.", "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:37986325"], "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:GSE72094"], "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:GSE72094"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37986325", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1097/MD.0000000000036084", "sha256": 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