Bioinformatics Study Identified EGF as a Crucial Gene in Papillary Renal Cell Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35655917", "kind": "science-record", "revision": 5, "content_hash": "sha256:c36f1c39f774130cc0e8a404ee5aed82d17f0f9ce522647f3589b7858ed0e251", "created_at": "2026-11-01T18:14:35Z", "updated_at": "2026-11-01T22:49:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics Study Identified EGF as a Crucial Gene in Papillary Renal Cell Cancer.", "doi": "10.1155/2022/4761803", "pmid": "35655917", "authors": ["GenYi Qu", "Hao Wang", "Cheng Tang", "Guang Yang", "Yong Xu"], "journal": "Disease markers", "year": 2022, "abstract": "Background: Due to a lack of knowledge of the disease process, papillary renal cell carcinoma (PRCC) has a dismal outlook. This research was aimed at uncovering the possible biomarkers and the underlying principles in PRCC using a bioinformatics method.\nMethods: We searched the Gene Expression Omnibus (GEO) datasets to obtain the GSE11151 and GSE15641 gene expression profiles of PRCC. We used the R package limma to identify the differentially expressed genes (DEGs). The online tool DAVID and ClusterProfiler package in R software were used to analyze Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway dominance, respectively. The STRING database was utilized to construct the PPI network of DEGs. Using the Cytoscape technology, a protein-protein interaction (PPI) network that associated with DEGs was created, and the hub genes were identified using the Cytoscape plug-in CytoHubba. The hub genes were subjected to a Kaplan-Meier analysis to identify their correlations with survival rates.\nResults: From the selected datasets, a total of 240 common DEGs were identified in the PRCC, including 50 upregulated genes and 190 downregulated regulated genes. Renal growth, external exosome, binding of heparin, and metabolic processes were all substantially associated with DEGs. The CytoHubba plug-in-based analysis identified the 10 hub genes (ALB, KNG1, C3, CXCL12, EGF, TIMP1, VCAN, PLG, LAMC1, and CASR) from the original PPI network. The higher expression group of EGF was associated with poor outcome in patients with PRCC.\nConclusions: We revealed important genes and proposed biological pathways that may be implicated in the formation of PRCC. EGF might be a predictive biomarker for PRCC and therefore should be investigated as a novel treatment strategy.", "claims": [{"id": "claim-method", "statement": "DEGs between PRCC and normal samples were detected using the “limma” program (http://www.bioconductor.org/packages/release/bioc/html/limma.html).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35655917", "locator": "sections[4] \"2.2. Data Preprocessing and Identification of DEGs\" paragraph 2", "quote": "DEGs between PRCC and normal samples were detected using the “limma” program (http://www.bioconductor.org/packages/release/bioc/html/limma.html)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The two gene expression profiling datasets (GSE11151 and GSE15641) were downloaded from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). GSE11151 was contributed by Yusenko et al.; and GSE15641 was contributed by Jones et al.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35655917", "locator": "sections[17] \"Data Availability\" paragraph 2", "quote": "The two gene expression profiling datasets (GSE11151 and GSE15641) were downloaded from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). GSE11151 was contributed by Yusenko et al.; and GSE15641 was contributed by Jones et al."}], "state": "complete"}, {"id": "claim-target-result", "statement": "From the selected datasets, a total of 240 common DEGs were identified in the PRCC, including 50 upregulated genes and 190 downregulated regulated genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35655917", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "From the selected datasets, a total of 240 common DEGs were identified in the PRCC, including 50 upregulated genes and 190 downregulated regulated genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "From the selected datasets, a total of 240 common DEGs were identified in the PRCC, including 50 upregulated genes and 190 downregulated regulated 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:35655917"], "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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