Comprehensive analysis identifies DNA damage repair-related gene HCLS1 associated with good prognosis in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37969376", "kind": "science-record", "revision": 2, "content_hash": "sha256:e7c2334f5c2771f45987060dc4ba3daedde713f51b6db321ca620fb3b63b8518", "created_at": "2026-11-01T19:02:52Z", "updated_at": "2026-11-01T21:31:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis identifies DNA damage repair-related gene HCLS1 associated with good prognosis in lung adenocarcinoma.", "doi": "10.21037/tcr-23-921", "pmid": "37969376", "authors": ["Tingjun Liu", "Ankang Hu", "Hao Chen", "Yan Li", "Yonghui Wang", "Yao Guo", "Tingya Liu", "Jie Zhou", "Debao Li", "Quangang Chen"], "journal": "Translational cancer research", "year": 2023, "abstract": "Background: Lung cancer is the leading cause of cancer-associated mortality. Lung adenocarcinoma (LUAD) amounts to more than 40% of all lung malignancies. Therefore, developing clinically useful biomarkers for this disease is critical. DNA damage repair (DDR) is a complicated signal transduction process that ensures genomic stability. DDR should be comprehensively analyzed to elucidate their clinical significance and tumor immune microenvironment interactions.\nMethods: In this study, DDR-related genes (DRGs) were selected to investigate their prognostic impact on LUAD. A regression-based prognostic model was established based on The Cancer Genome Atlas (TCGA)-LUAD cohort and three external Gene Expression Omnibus (GEO) validation cohorts (GSE31210, GSE68465, and GSE72094). The robust, established model could independently predict the clinical outcomes in patients. Then, the prognostic performance of risk profiles was assessed using a time-dependent receiver operating characteristic (ROC) curve, Cox regression, nomogram, and Kaplan-Meier analyses. Furthermore, the potential biological functions and infiltration status of DRGs in LUAD were investigated with ESTIMATE and CIBERSORT. Finally, the effects of HCLS1 on the clinical features, prognosis, biological function, immune infiltration, and treatment response in LUAD were systematically analyzed.\nResults: Eleven DRGs were constructed to categorize patients into high- and low-risk groups. The risk score was an independent predictor of overall survival (OS). HCLS1 expression was downregulated in LUAD samples and linked with clinicopathological features. Multivariate Cox regression analysis using the Kaplan-Meier plotter revealed that low HCLS1 expression was independently associated with poor OS. Moreover, the HCLS1 high-expression group had higher immune-related gene expression and ESTIMATE scores. It was positively correlated with the infiltration of M1 macrophages, activated memory CD4 T cells, CD8 T cells, memory B cells, resting dendritic cells, and memory CD4 T cells, Tregs, and neutrophils.\nConclusions: A new classification system was developed for LUAD according to DDR characteristics. This stratification has important clinical values, reliable prognosis, and immunotherapy in patients with LUAD. Moreover, HCLS1 is a potential prognostic biomarker of LUAD that correlates with the extent of immune cell infiltration in the tumor microenvironment (TME).", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) regression model was used to avoid overfitting, and the glmnet R package was utilized to select the best candidate genes to enter the predictive model (19).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37969376", "locator": "sections[7] \"Establishment and validation of the DRG prognostic model\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) regression model was used to avoid overfitting, and the glmnet R package was utilized to select the best candidate genes to enter the predictive model (19)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The normalized RNA-sequencing datasets (N=502) and clinically relevant information of LUAD samples (N=522) were retrieved from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37969376", "locator": "sections[4] \"Data retrieval\" paragraph 2", "quote": "The normalized RNA-sequencing datasets (N=502) and clinically relevant information of LUAD samples (N=522) were retrieved from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The risk score was an independent predictor of overall survival (OS).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37969376", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The risk score was an independent predictor of overall survival (OS)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The risk score was an independent predictor of overall survival (OS).", "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:37969376"], "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:GSE31210"], "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:GSE31210"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37969376", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-23-921", "sha256": 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