Identification and verification of key cancer genes associated with prognosis of colorectal cancer based on bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34911835", "kind": "science-record", "revision": 3, "content_hash": "sha256:54d7911508500ff6a9e8c5843fbd0516ec9bf1562b10210ba488e364a729503a", "created_at": "2026-11-01T17:36:37Z", "updated_at": "2026-11-01T21:26:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and verification of key cancer genes associated with prognosis of colorectal cancer based on bioinformatics analysis.", "doi": "10.11817/j.issn.1672-7347.2021.200952", "pmid": "34911835", "authors": ["Yi Qin", "Lu Chen", "Lizhang Chen"], "journal": "Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences", "year": 2021, "abstract": "OBJECTIVES: The biomarkers targeting colorectal cancer (CRC) prognosis are short of high accuracy and sensitivity in clinic. Through bioinformatics analysis, we aim to identify and confirm a series of key genes referred to the diagnosis and prognosis of CRC.\nMETHODS: GSE31905, GSE35279, and GSE41657 were selected as complete RNA sequencing data sets of CRC and colorectal mucosa (CRM) tissues from the NCBI-GEO database, and the differentially expressed genes (DEGs) were analyzed. The common DEGs in these 3 data sets were obtained by Venn map, and enriched by STRING network system and Cytoscape software. The Kaplan-Meier plotter website was used to verify the correlation between the enriched genes and the prognosis of CRC.\nRESULTS: For the whole RNA sequencing data sets of CRC and normal intestinal mucosa samples, the DEGs of CRC and CRM in the 3 data sets (|log2FC|>2 and P<0.05) were screened by GEO2R tool in NCBI-GEO database. By using Venn graph analysis software, the intersection of up-regulated/down-regulated genes in 3 GSE datasets was obtained, and a total 105 up-regulated genes and 140 down-regulated genes were found in the 3 samples. The up-regulated/down-regulated genes were introduced into the STRING network system to obtain the interacting genes. The interacting gene sets were introduced into Cytoscape software, and 61 up-regulated genes were found by Molecular Complex Detection (MCODE) plug-in. Through the Kaplan-Meier plotter website, we found that EPHB2, KLK8, DIAPH3, STC2, OXTR, MMP7, MET, KRT85, KRT6B, KRT23, and KLK10 genes were highly expressed in CRC, and were related to the prognosis.\nCONCLUSIONS: The above 11 genes verified by bioinformatics retrieval and analysis can predict the poor prognosis of CRC to a certain extent, and they provide a possible target for the diagnosis and treatment of CRC.", "claims": [{"id": "claim-method", "statement": "通过NCBI-GEO数据库中自带的GEO2R工具分别筛选出3个数据集中CRC与CRM的DEGs(|log2FC|>2和P<0.05)。", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34911835", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "通过NCBI-GEO数据库中自带的GEO2R工具分别筛选出3个数据集中CRC与CRM的DEGs(|log2FC|>2和P<0.05)。"}], "state": "complete"}, {"id": "claim-data-access", "statement": "从NCBI-GEO数据库中选取关于CRC和正常结直肠黏膜(colorectal mucosa，CRM)组织标本全RNA组测序数据集GSE31905、GSE35279和GSE41657，并分析其中的差异表达基因(differentially expressed genes，DEGs)，先通过Venn图获取这3个数据集的共同DEGs，然后用STRING网络系统和Cytoscape软件进一步富集上述基因，最后在Kaplan-Meier plotter网站上验证富集后的基因与CRC预后的相关性。", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34911835", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "从NCBI-GEO数据库中选取关于CRC和正常结直肠黏膜(colorectal mucosa，CRM)组织标本全RNA组测序数据集GSE31905、GSE35279和GSE41657，并分析其中的差异表达基因(differentially expressed genes，DEGs)，先通过Venn图获取这3个数据集的共同DEGs，然后用STRING网络系统和Cytoscape软件进一步富集上述基因，最后在Kaplan-Meier plotter网站上验证富集后的基因与CRC预后的相关性。"}], "state": "complete"}, {"id": "claim-target-result", "statement": "通过生物信息学分析和验证，上述11个基因可在一定程度上预测CRC的不良预后，为CRC的诊断、治疗和预后提供可能的靶标。", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34911835", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "通过生物信息学分析和验证，上述11个基因可在一定程度上预测CRC的不良预后，为CRC的诊断、治疗和预后提供可能的靶标。"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "通过生物信息学分析和验证，上述11个基因可在一定程度上预测CRC的不良预后，为CRC的诊断、治疗和预后提供可能的靶标。", "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:34911835"], "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:GSE31905"], "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:GSE31905"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34911835", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.11817/j.issn.1672-7347.2021.200952", "sha256": 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