Identification of key genes and functions of circulating tumor cells in multiple cancers through bioinformatic analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32972417", "kind": "science-record", "revision": 7, "content_hash": "sha256:cf8d13ab5bb7c66ab5092226095f8812e1712b450d16f11f12da0611e981b538", "created_at": "2026-11-01T15:41:11Z", "updated_at": "2026-11-01T22:02:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of key genes and functions of circulating tumor cells in multiple cancers through bioinformatic analysis.", "doi": "10.1186/s12920-020-00795-w", "pmid": "32972417", "authors": ["Yibing Guan", "Fangshi Xu", "Yiyuan Wang", "Juanhua Tian", "Ziyan Wan", "Zhenlong Wang", "Tie Chong"], "journal": "BMC medical genomics", "year": 2020, "abstract": "BACKGROUND: Circulating tumor cells (CTCs) play a key role in cancer progression, especially metastasis, due to the rarity and heterogeneity of CTCs, fewer researches have been conducted on them at the molecular level. However, through the Gene Expression Omnibus (GEO) database, this kind of minority researches can be well integrated, the gene expression differences between CTCs and primary tumors can be identified, and molecular targets for CTCs can be found.\nMETHODS: We analyzed 7 sets of gene chips (GSE82198, GSE99394, GSE31023, GSE65505, GSE67982, GSE76250, GSE50746) obtained by GEO. Analysis of differentially expressed genes (DEGs) between CTCs and corresponding primary tumors by NetworkAnalyst. Metascape tool for Gene Ontology (GO) / Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differential genes and visual display. Cytoscape performs protein-protein interaction (PPI) analysis and obtains the hub genes. Renal cancer patients' clinical specimens to verify the correctness of enrichment results. Prognostic analysis of hub genes in kidney cancer patients using the Kaplan-Meier plotter survival analysis tool.\nRESULTS: We obtained a total of 589 DEGs. The GO / KEGG enrichment results indicate that the DEGs are mainly concentrated in cell adhesion, epithelial-mesenchymal transition (EMT), and apoptosis. Renal cancer clinical specimens suggest that CTCs have epithelial and mesenchymal types. At the same time, PSMC2 can be used as a poor prognostic indicator for renal cancer patients.\nCONCLUSIONS: In summary, our study suggests that compared with primary tumors, CTCs mainly change cell adhesion, EMT, and apoptosis. PSMC2 can be used as a poor prognostic factor.", "claims": [{"id": "claim-method", "statement": "At the same time, to determine the prognostic value of the top 10 hub genes, Kaplan–Meier plotter bioinformatics analysis was used to analyze overall survival and relapse-free survival of patients with renal clear cell carcinoma.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32972417", "locator": "sections[13] \"Phenotypic identification of CTCs and prognostic value of hub gene\" paragraph 2", "quote": "At the same time, to determine the prognostic value of the top 10 hub genes, Kaplan–Meier plotter bioinformatics analysis was used to analyze overall survival and relapse-free survival of patients with renal clear cell carcinoma."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed during the current study are available in the GEO database through GEO accession numbers GSE82198, GSE99394, GSE31023, GSE65505, GSE67982, GSE76250, GSE50746.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32972417", "locator": "sections[25] \"notes\" paragraph 1", "quote": "The datasets analyzed during the current study are available in the GEO database through GEO accession numbers GSE82198, GSE99394, GSE31023, GSE65505, GSE67982, GSE76250, GSE50746."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:32972417"], "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:GSE31023"], "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:GSE31023"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32972417", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12920-020-00795-w", "sha256": 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"read_table", "reader_status": "available", "identifier": "section[12]/table-wrap[1]#Tab2", "version": null, "note": "Top ten hub genes with higher score of connectivity", "structure": {"pmcid": "PMC7513313", "section_index": 12, "section_title": "PPI network construction and hub gene identification", "row_count": 11, "column_count": 14}, "table": {"label": "Table 2", "caption": "Top ten hub genes with higher score of connectivity", "rows": [["node_name", "MCC", "DMNC", "MNC", "Degree", "EPC", "BottleNeck", "EcCentricity", "Closeness", "Radiality", "Betweenness", "Stress", "ClusteringCoefficient", "Total score"], ["CDC5L", "37", "0.45", "5", "16", "82.59", "77", "0.08", "70.57", "9.12", "15,833", "47,326", "0.07", "63,457.23"], ["ITGAV", "251", "0.41", "8", "14", "100.09", "39", "0.10", "72.76", "9.30", "9450", "37,160", "0.26", "47,105.29"], ["CD59", "847", "0.71", "6", "15", "97.09", "32", "0.10", "64.82", "8.82", "8631", "33,426", "0.27", "43,128.32"], ["EDN1", "29", "0.45", "5", 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