Screening of possible biomarkers and therapeutic targets in kidney renal clear cell carcinoma: Evidence from bioinformatic analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36313709", "kind": "science-record", "revision": 4, "content_hash": "sha256:0ebc90978cf9a547d2ad4fe52fdf248581db9bc7752b136df2550cc871f409f7", "created_at": "2026-11-01T18:15:44Z", "updated_at": "2026-11-01T22:07:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Screening of possible biomarkers and therapeutic targets in kidney renal clear cell carcinoma: Evidence from bioinformatic analysis.", "doi": "10.3389/fonc.2022.963483", "pmid": "36313709", "authors": ["Longfei Peng", "Zhangjun Cao", "Qi Wang", "Lu Fang", "Songbai Yan", "Dian Xia", "Jinyou Wang", "Liangkuan Bi"], "journal": "Frontiers in oncology", "year": 2022, "abstract": "Renal cell carcinoma (RCC), as one of the most common urological malignancies, has many histologic and molecular subtypes, among which clear cell renal cell carcinoma (ccRCC) is one of the most common causes of tumor-related deaths. However, the molecular mechanism of ccRCC remains unclear. In order to identify the candidate genes that may exist in the occurrence and development of ccRCC, microarray datasets GSE6344, GSE16441, GSE36895, GSE53757 and GSE76351 had been downloaded from Gene Expression Omnibus (GEO) database. Apart from that, the differentially expressed genes (DEGs) were screened through Bioinformatics & Evolutionary Genomics. In addition, the protein-protein interaction network (PPI) was constructed, and the module analysis was performed using STRING and Cytoscape. By virtue of DAVID online database, GO/KEGG enrichment analysis of DEGs was performed. Consequently, a total of 118 DEGs were screened, including 24 up-regulated genes and 94 down-regulated genes. The plug-in MCODE of Cytoscape was adopted to analyze the most significant modules of DEGs. What's more, the genes with degree greater than 10 in DEGs were selected as the hub genes. The overall survival (OS) and disease progression free survival (DFS) of 9 hub genes were analyzed through GEPIA2 online platform. As shown by the survival analysis, SLC34A1, SLC12A3, SLC12A1, PLG, and ENO2 were closely related to the OS of ccRCC, whereas SLC34A1 and LOX were closely related to DFS. Among 11 SLC members, 6 SLC members were highly expressed in non-cancerous tissues (SLC5A2, SLC12A1, SLC12A3, SLC34A1, SLC34A2, SLC34A3). Besides, SLC12A5 and SLC12A7 were highly expressed in ccRCC. Furthermore, SLC12A1-A7, SLC34A1 and SLC34A3 were closely related to OS, whereas SLC12A2/A4/A6/A7 and SLC34A1/A3 were closely related to DFS. In addition, 5 algorithms were used to analyze hub genes, the overlapping genes were AQP2 and KCNJ1. To sum up, hub gene can help us understand the molecular mechanism of the occurrence and development of ccRCC, thereby providing a theoretical basis for the diagnosis and targeted therapy of ccRCC.", "claims": [{"id": "claim-method", "statement": "The GEO accession was searched through GEO2R (www.ncbi.nlm.nih.gov/geo/geo2r/), and cancer tissues were compared with non-cancer tissues.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36313709", "locator": "sections[4] \"Screening of DEGs\" paragraph 2", "quote": "The GEO accession was searched through GEO2R (www.ncbi.nlm.nih.gov/geo/geo2r/), and cancer tissues were compared with non-cancer tissues."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36313709", "locator": "sections[14] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Consequently, a total of 118 DEGs were screened, including 24 up-regulated genes and 94 down-regulated genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36313709", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Consequently, a total of 118 DEGs were screened, including 24 up-regulated genes and 94 down-regulated genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Consequently, a total of 118 DEGs were screened, including 24 up-regulated genes and 94 down-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:36313709"], "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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