The DDR-related gene signature with cell cycle checkpoint function predicts prognosis, immune activity, and chemoradiotherapy response in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35840978", "kind": "science-record", "revision": 4, "content_hash": "sha256:7ab5fd61031bb1435a0c925efaf1cd95a53eb5e5b78fefb78c3533da05591ad5", "created_at": "2026-11-01T18:14:54Z", "updated_at": "2026-11-01T22:07:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The DDR-related gene signature with cell cycle checkpoint function predicts prognosis, immune activity, and chemoradiotherapy response in lung adenocarcinoma.", "doi": "10.1186/s12931-022-02110-w", "pmid": "35840978", "authors": ["Quan Li", "Pan Zhang", "Huixiao Hu", "Hang Huang", "Dong Pan", "Guangyun Mao", "Burong Hu"], "journal": "Respiratory research", "year": 2022, "abstract": "BACKGROUND: As a DNA surveillance mechanism, cell cycle checkpoint has recently been discovered to be closely associated with lung adenocarcinoma (LUAD) prognosis. It is also an essential link in the process of DNA damage repair (DDR) that confers resistance to radiotherapy. Whether genes that have both functions play a more crucial role in LUAD prognosis remains unclear.\nMETHODS: In this study, DDR-related genes with cell cycle checkpoint function (DCGs) were selected to investigate their effects on the prognosis of LUAD. The TCGA-LUAD cohort and two GEO external validation cohorts (GSE31210 and GSE42171) were performed to construct a prognosis model based on the least absolute shrinkage and selection operator (LASSO) regression. Patients were divided into high-risk and low-risk groups based on the model. Subsequently, the multivariate COX regression was used to construct a prognostic nomogram. The ssGSEA, CIBERSORT algorithm, TIMER tool, CMap database, and IC50 of chemotherapeutic agents were used to analyze immune activity and responsiveness to chemoradiotherapy.\nRESULTS: 4 DCGs were selected as prognostic signatures, and patients in the high-risk group had a lower overall survival (OS). The lower infiltration levels of immune cells and the higher expression levels of immune checkpoints appeared in the high-risk group. The damage repair pathways were upregulated, and chemotherapeutic agent sensitivity was poor in the high-risk group.\nCONCLUSIONS: The 4-DCGs signature prognosis model we constructed could predict the survival rate, immune activity, and chemoradiotherapy responsiveness of LUAD patients.", "claims": [{"id": "claim-method", "statement": "Subsequently, the least absolute shrink-age and selection operator (LASSO) regression model was used to prevent overfitting, selecting the best candidate genes into the prognosis model by using the R package “glmnet”.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35840978", "locator": "sections[8] \"Establishment and validation of the DCGs prognostic model\" paragraph 4", "quote": "Subsequently, the least absolute shrink-age and selection operator (LASSO) regression model was used to prevent overfitting, selecting the best candidate genes into the prognosis model by using the R package “glmnet”."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Public datasets this study used can be found in The Cancer Genome Atlas (https://portal.gdc.cancer.gov/) and the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). Additional data named “Additional file 1” and “Additional file 2” have been uploaded.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35840978", "locator": "sections[32] \"Availability of data and materials\" paragraph 2", "quote": "Public datasets this study used can be found in The Cancer Genome Atlas (https://portal.gdc.cancer.gov/) and the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). Additional data named “Additional file 1” and “Additional file 2” have been uploaded."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The TCGA-LUAD cohort and two GEO external validation cohorts (GSE31210 and GSE42171) were performed to construct a prognosis model based on the least absolute shrinkage and selection operator (LASSO) regression.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35840978", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The TCGA-LUAD cohort and two GEO external validation cohorts (GSE31210 and GSE42171) were performed to construct a prognosis model based on the least absolute shrinkage and selection operator (LASSO) regression."}, {"asset": "source:pmid:35840978", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "4 DCGs were selected as prognostic signatures, and patients in the high-risk group had a lower overall survival (OS)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The TCGA-LUAD cohort and two GEO external validation cohorts (GSE31210 and GSE42171) were performed to construct a prognosis model based on the least absolute shrinkage and selection operator (LASSO) regression.", "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:35840978"], "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:35840978", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12931-022-02110-w", "sha256": "sha256:4039fae55bbe07b4d77c23b60841cd914961d9964a01aece0416ea5c20d91252", "bytes": 54546, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12931-022-02110-w", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9288070", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE31210", "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=GSE31210", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE31210", "version": null, "note": "Public datasets this study used can be found in The Cancer Genome Atlas (https://portal.gdc.cancer.gov/) and the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). 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