Exploring the methylation status of CFTR and PKIA genes as potential biomarkers for lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37644544", "kind": "science-record", "revision": 4, "content_hash": "sha256:bae1f244d17a14bfbc62df8485440a02a3ce65a33e039d358c42047f9c6cdd92", "created_at": "2026-11-01T19:02:26Z", "updated_at": "2026-11-01T22:52:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring the methylation status of CFTR and PKIA genes as potential biomarkers for lung adenocarcinoma.", "doi": "10.1186/s13023-023-02807-1", "pmid": "37644544", "authors": ["Bowen Xu", "Jingang Zhang", "Weigang Chen", "Wei Cai"], "journal": "Orphanet journal of rare diseases", "year": 2023, "abstract": "BACKGROUND: One of the most prevalent cancers in the world is lung cancer, with adenocarcinoma (LUAD) making up a significant portion of cases. According to the National Cancer Institute (NCI), there are new cases and fatality rates per 100,000 individuals as follows: New instances of lung and bronchial cancer occur annually at a rate of 50.0 per 100,000 persons. The yearly death rate for men and women is 35.0 per 100,000. DNA methylation is one of the earliest discovered and widely studied epigenetic regulatory mechanisms, and its abnormality is closely related to the occurrence and development of cancer. However, the prognostic value of DNA methylation and LUAD needs to be further explored to improve the survival prediction of LUAD patients.\nMETHODS: The transcriptome data and clinical data of LUAD were downloaded from TCGA and GEO databases, and the Illumina Human Methylation450 array (450k array) data were downloaded from the TCGA database. Firstly, the intersection of the expressed genes of the two databases is corrected, the differential analysis is performed, and the methylation data is evaluated by the MethylMix package to obtain differentially methylated genes. Independent prognostic genes were screened out using univariate and multivariate Cox regression analysis, and a methylation prognostic model was developed using univariate Cox analysis and validated with the GSE30219 dataset in the GEO database. Survival analysis between methylation high-risk and low-risk groups was performed and a methylation-based gene prognostic model was constructed. Finally, the prediction of potential drugs associated with the LUAD gene signature using Drug Sensitivity Genomics in Cancer (GDSC).\nRESULTS: In this study, a total of 555 samples from the TCGA database and 307 samples from GSE30219 were included, and a total of 24 differential methylation driver genes were identified. Univariate and multivariate Cox regression analyzes were used to screen out independent prognostic genes, involving 2 genes: CFTR, PKIA. Survival analysis was different between the methylation high-risk group and the low-risk group, the CFTR high methylation group and the low methylation group were poor, and the opposite was true for PKIA.\nCONCLUSIONS: Our study revealed that the methylation status of CFTR and PKIA can serve as potential prognostic biomarkers and therapeutic targets in lung cancer.", "claims": [{"id": "claim-method", "statement": "Use the “MethylMix” package of R software to screen methylation-driven genes, Cox regression analysis was used to study the correlation between gene methylation and expression, and 24 genes were obtained.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37644544", "locator": "sections[5] \"Differential methylation analysis\" paragraph 2", "quote": "Use the “MethylMix” package of R software to screen methylation-driven genes, Cox regression analysis was used to study the correlation between gene methylation and expression, and 24 genes were obtained."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data and R scripts in this study are available from the corresponding author upon reasonable request. Publicly available datasets were analyzed in this study, these can be found in The Cancer Genome Atlas (https://portal.gdc.cancer.gov/), and Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/ GSE30219).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37644544", "locator": "sections[26] \"Data Availability\" paragraph 2", "quote": "All data and R scripts in this study are available from the corresponding author upon reasonable request. Publicly available datasets were analyzed in this study, these can be found in The Cancer Genome Atlas (https://portal.gdc.cancer.gov/), and Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/ GSE30219)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A methylation-based prognostic model incorporating CFTR and PKIA was constructed and validated in 555 TCGA and GSE30219 samples, with 24 differential methylation driver genes identified and CFTR and PKIA selected as independent prognostic genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37644544", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In this study, a total of 555 samples from the TCGA database and 307 samples from GSE30219 were included, and a total of 24 differential methylation driver genes were identified."}, {"asset": "source:pmid:37644544", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Univariate and multivariate Cox regression analyzes were used to screen out independent prognostic genes, involving 2 genes: CFTR, PKIA."}, {"asset": "source:pmid:37644544", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Our study revealed that the methylation status of CFTR and PKIA can serve as potential prognostic biomarkers and therapeutic targets in lung cancer."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A methylation-based prognostic model incorporating CFTR and PKIA was constructed and validated in 555 TCGA and GSE30219 samples, with 24 differential methylation driver genes identified and CFTR and PKIA selected as independent prognostic genes.", "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:37644544"], "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:GSE30219"], "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:GSE30219"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37644544", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s13023-023-02807-1", "sha256": "sha256:44b1d295212294060bae9e8bd54c139d449ce9ad7a66a7899368d3f741e2c07e", "bytes": 33230, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s13023-023-02807-1", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10466921", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE30219", "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=GSE30219", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE30219", "version": null, "note": "All data and R scripts in this study are available from the corresponding author upon reasonable request. Publicly available datasets were analyzed in this study, these can be found in The Cancer Genome Atlas (https://portal.gdc.cancer.gov/), and Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/ GSE30219).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All data and R scripts in this study are available from the corresponding author upon reasonable request. 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