Screening of differential gene expression patterns through survival analysis for diagnosis, prognosis and therapies of clear cell renal cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39348357", "kind": "science-record", "revision": 4, "content_hash": "sha256:69f64ad2c8d4c6e6576218e0b7f8615f958e425b47b12d6246b10539444407bf", "created_at": "2026-11-01T20:29:33Z", "updated_at": "2026-11-01T22:55:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Screening of differential gene expression patterns through survival analysis for diagnosis, prognosis and therapies of clear cell renal cell carcinoma.", "doi": "10.1371/journal.pone.0310843", "pmid": "39348357", "authors": ["Alvira Ajadee", "Sabkat Mahmud", "Md Bayazid Hossain", "Reaz Ahmmed", "Md Ahad Ali", "Md Selim Reza", "Saroje Kumar Sarker", "Md Nurul Haque Mollah"], "journal": "PloS one", "year": 2024, "abstract": "Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of kidney cancer. Although there is increasing evidence linking ccRCC to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. Though drug therapies are the best choice after the metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving it for almost 2 years. In this case, multi-targeted different variants of therapeutic drugs are essential for effective treatment against ccRCC. To understand molecular mechanisms of ccRCC development and progression, and explore multi-targeted different variants of therapeutic drugs, it is essential to identify ccRCC-causing key genes (KGs). In order to obtain ccRCC-causing KGs, at first, we detected 133 common differentially expressed genes (cDEGs) between ccRCC and control samples based on nine (9) microarray gene-expression datasets with NCBI accession IDs GSE16441, GSE53757, GSE66270, GSE66272, GSE16449, GSE76351, GSE66271, GSE71963, and GSE36895. Then, we filtered these cDEGs through survival analysis with the independent TCGA and GTEx database and obtained 54 scDEGs having significant prognostic power. Next, we used protein-protein interaction (PPI) network analysis with the reduced set of 54 scDEGs to identify ccRCC-causing top-ranked eight KGs (PLG, ENO2, ALDOB, UMOD, ALDH6A1, SLC12A3, SLC12A1, SERPINA5). The pan-cancer analysis with KGs based on TCGA database showed the significant association with different subtypes of kidney cancers including ccRCC. The gene regulatory network (GRN) analysis revealed some crucial transcriptional and post-transcriptional regulators of KGs. The scDEGs-set enrichment analysis significantly identified some crucial ccRCC-causing molecular functions, biological processes, cellular components, and pathways that are linked to the KGs. The results of DNA methylation study indicated the hypomethylation and hyper-methylation of KGs, which may lead the development of ccRCC. The immune infiltrating cell types (CD8+ T and CD4+ T cell, B cell, neutrophil, dendritic cell and macrophage) analysis with KGs indicated their significant association in ccRCC, where KGs are positively correlated with CD4+ T cells, but negatively correlated with the majority of other immune cells, which is supported by the literature review also. Then we detected 10 repurposable drug molecules (Irinotecan, Imatinib, Telaglenastat, Olaparib, RG-4733, Sorafenib, Sitravatinib, Cabozantinib, Abemaciclib, and Dovitinib.) by molecular docking with KGs-mediated receptor proteins. Their ADME/T analysis and cross-validation with the independent receptors, also supported their potent against ccRCC. Therefore, these outputs might be useful inputs/resources to the wet-lab researchers and clinicians for considering an effective treatment strategy against ccRCC.", "claims": [{"id": "claim-method", "statement": "The statistical linear models for microarray (LIMMA) data analysis [106] is a popular approach for identification of differentially expressed genes (DEGs) between case and control samples [107,108].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39348357", "locator": "sections[6] \"2.2. Identification of DEGs from microarray gene expression profiles\" paragraph 2", "quote": "The statistical linear models for microarray (LIMMA) data analysis [106] is a popular approach for identification of differentially expressed genes (DEGs) between case and control samples [107,108]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All information about the datasets analyzed in this study is provided in the paper.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39348357", "locator": "sections[30] \"Data Availability\" paragraph 2", "quote": "All information about the datasets analyzed in this study is provided in the paper."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Applying LIMMA to nine microarray datasets comparing ccRCC and control samples identified 133 common differentially expressed genes, including 26 downregulated and 107 upregulated genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39348357", "locator": "sections[17] \"3.1. Identification of DEGs by LIMMA approach\" paragraph 2", "quote": "We have identified the genes that are differentially expressed (DEGs) in each of the nine datasets comparing ccRCC and control samples by applying the statistical LIMMA technique."}, {"asset": "source:pmid:39348357", "locator": "sections[17] \"3.1. Identification of DEGs by LIMMA approach\" paragraph 2", "quote": "The total number of cDEGs was found 133; of these, 26 were downregulated and the remaining 107 were upregulated (S4 Table in S1 File)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Applying LIMMA to nine microarray datasets comparing ccRCC and control samples identified 133 common differentially expressed genes, including 26 downregulated and 107 upregulated 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:39348357"], "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:GSE16441"], "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:GSE16441"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39348357", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1371/journal.pone.0310843", "sha256": 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE16441", "GSE53757", "GSE66270", "GSE66272", "GSE16449", "GSE76351", "GSE66271", "GSE71963 and GSE36895"], "resolved_accessions": ["GSE16441", "GSE53757", "GSE66270", "GSE66272", "GSE16449", "GSE76351", "GSE66271"], "statements": ["All information about the datasets analyzed in this study is provided in the paper."], "basis": "The paper names GSE16441, GSE53757, GSE66270, GSE66272, GSE16449, GSE76351, GSE66271 in Gene Expression Omnibus (GEO) databae plugin in the National Center for Biotechnology Information (NCBI) database and the accession resolves in the repository. 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