Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug-gene interaction networks analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35676421", "kind": "science-record", "revision": 3, "content_hash": "sha256:5de0e546d9cfd63f1fa80e3b11235c4f2f8457198d0da15c2948a9fd0eb30450", "created_at": "2026-11-01T18:14:37Z", "updated_at": "2026-11-01T21:28:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug-gene interaction networks analysis.", "doi": "10.1038/s41598-022-13719-8", "pmid": "35676421", "authors": ["Habib MotieGhader", "Parinaz Tabrizi-Nezhadi", "Mahshid Deldar Abad Paskeh", "Behzad Baradaran", "Ahad Mokhtarzadeh", "Mehrdad Hashemi", "Hossein Lanjanian", "Seyed Mehdi Jazayeri", "Masoud Maleki", "Ehsan Khodadadi", "Sajjad Nematzadeh", "Farzad Kiani", "Mazaher Maghsoudloo", "Ali Masoudi-Nejad"], "journal": "Scientific reports", "year": 2022, "abstract": "Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF-TG (transcription factors-target genes) network was drawn. Afterward, a list of drugs targeting TF-TG genes was obtained from the DGIdb V4.0 database, and two drug-gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF-TG, and drug-gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF-TG, and drug-gene interaction networks.", "claims": [{"id": "claim-method", "statement": "First of all, differentially expressed genes (GEGs) were calculated between the normal and NSCLC groups applying the adjusted p-value and Benjamini & Hochberg's method based on the GEO2R tool.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35676421", "locator": "sections[11] \"Gene co-expression network and gene modules\" paragraph 2", "quote": "First of all, differentially expressed genes (GEGs) were calculated between the normal and NSCLC groups applying the adjusted p-value and Benjamini & Hochberg's method based on the GEO2R tool."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The corresponding author can provide the datasets utilized in this study on a reasonable request. 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The raw dataset is available on Information Gene expression Omnibus (GEO) with GSE21933 accession number (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21933)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35676421", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks.", "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:35676421"], "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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