Identification of biomarkers in colon cancer based on bioinformatic analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35117850", "kind": "science-record", "revision": 5, "content_hash": "sha256:51839a711f439f898bf34e305d149d732681846d1522cdc0fd8b0c5b285fb211", "created_at": "2026-11-01T17:36:52Z", "updated_at": "2026-11-01T22:05:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of biomarkers in colon cancer based on bioinformatic analysis.", "doi": "10.21037/tcr-20-845", "pmid": "35117850", "authors": ["Ying Zhu", "Leitao Sun", "Jieru Yu", "Yuying Xiang", "Minhe Shen", "Harpreet S Wasan", "Shanming Ruan", "Shengliang Qiu"], "journal": "Translational cancer research", "year": 2020, "abstract": "BACKGROUND: Colon cancer is one of the most common cancers in the world. Targeting biomarkers is helpful for the diagnosis and treatment of colon cancer. This study aimed to identify biomarkers in colon cancer, in addition to those that have already been reported, using microarray datasets and bioinformatics analysis.\nMETHODS: We downloaded two mRNA microarray datasets (GSE44076 and GSE47074) for colon cancer from the Gene Expression Omnibus (GEO) database and the most recent colon cancer data (COAD) from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) between colon cancer and adjacent normal tissues were determined based on these three datasets. Additionally, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and protein-protein interaction (PPI) network analysis. The hub genes in the PPI network were then selected and analysed.\nRESULTS: We identified 150 DEGs and the GO enrichment analysis revealed that these DEGs were enriched in functions related to accelerating the cell cycle, promoting tumour cell accumulation, promoting cell division, positively regulating cell division, and negatively regulating apoptosis. The KEGG pathway analysis indicated that the DEGs were also involved in the cell cycle pathway. In the PPI network, 34 hub genes were found to be enriched in cell division. Prognostic analysis of the 34 hub genes revealed that eight genes (CCNB1, CHEK1, DEPDC1, ECT2, GINS2, HMMR, KIF14, and KIF18A) were associated with the prognosis of colon cancer. And our qRT-PCR results confirmed that DEPDC1, ECT2, GINS2, HMMR and KIF18A were highly expressed in colon cancer cells.\nCONCLUSIONS: The genes DEPDC1, ECT2, GINS2, HMMR, and KIF18A could serve as novel diagnostic biomarkers of colon cancer.", "claims": [{"id": "claim-data-access", "statement": "The mRNA expression profile data in GEO and TCGA databases are publicly available for downloading.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35117850", "locator": "sections[4] \"Data screening from the TCGA database\" paragraph 3", "quote": "The mRNA expression profile data in GEO and TCGA databases are publicly available for downloading."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We obtained 150 genes that were common among the three datasets, which are presented as a Venn diagram (Figure 1).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35117850", "locator": "sections[11] \"Identification of DEGs\" paragraph 2", "quote": "We obtained 150 genes that were common among the three datasets, which are presented as a Venn diagram (Figure 1)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We obtained 150 genes that were common among the three datasets, which are presented as a Venn diagram (Figure 1).", "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:35117850"], "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:GSE44076"], "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:GSE44076"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35117850", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-20-845", "sha256": 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