Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30428899", "kind": "science-record", "revision": 7, "content_hash": "sha256:c68b8711ab309a50a5b6b61d379a68c33c0ccd629ccfcdfbae41bd628ac9dfdb", "created_at": "2026-11-01T15:40:38Z", "updated_at": "2026-11-01T22:00:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods.", "doi": "10.1186/s12957-018-1519-y", "pmid": "30428899", "authors": ["Chunyang Li", "Xiaoxi Zeng", "Haopeng Yu", "Yonghong Gu", "Wei Zhang"], "journal": "World journal of surgical oncology", "year": 2018, "abstract": "BACKGROUND: Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future.\nMETHODS: Combination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms.\nRESULTS: After quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively.\nCONCLUSIONS: All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical.", "claims": [{"id": "claim-method", "statement": "Candidate genes were further screened by survival analysis and cox regression analysis in TCGA database with “survival” package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30428899", "locator": "sections[6] \"Hub gene screening by survival and cox regression analyses in TCGA\" paragraph 2", "quote": "Candidate genes were further screened by survival analysis and cox regression analysis in TCGA database with “survival” package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study are downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo/), and the datasets used in this study were GSE16515, GSE22780, and GSE15471.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30428899", "locator": "sections[19] \"ack\" paragraph 1", "quote": "The datasets used and/or analyzed during the current study are downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo/), and the datasets used in this study were GSE16515, GSE22780, and GSE15471."}], "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:30428899"], "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:GSE15471"], "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:GSE15471"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:30428899", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12957-018-1519-y", "sha256": 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DGEs-181 candidate genes-genes bearing top 10 degrees in PPI-2 hub genes by survival analysis and cox analysis", "MMP7, ITGA2", "2", "78.21%", "81.31%"], ["5", "84.62%"], ["10", "87.18%"], ["23", "92.31%"], ["27", "93.59%"], ["Method 2: 724 DGEs-181 candidate genes-genes bearing top 10 degrees in PPI", "ALB, EGF, FN1, ITGA2, COL1A2, SPARC, COL3A1, TIMP1, COL5A1, COL11A1, MMP7", "2", "79.49%", "83.54%"], ["4", "70.51%"], ["6", "76.92%"], ["9", "78.20%"], ["13", "80.77%"], ["15", "88.46%"], ["18", "83.33%"], ["Method 3: 724 DGEs-genes bearing top 10 degrees in PPI-2 hub genes by survival analysis and cox analysis", "TOP2A, MAD2L1", "2", "65.38%", "69.82%"], ["5", "69.23%"], ["8", "65.38%"], ["12", "66.67%"], ["23", "67.95%"], ["Method 4: 724 DGEs-genes bearing top 10 degrees in PPI", "CCNB1, CCNA2, MAD2L1, TOP2A, UBE2C, CDC20, TTK, MELK, BUB1B, NDC80", "2", "70.51%", "74.81%"], ["5", "71.80%"], ["8", "76.92%"], ["13", "75.64%"], ["23", "74.36%"], ["0.400", "Method 5: 724 DGEs-181 candidate genes-genes bearing top 10 degrees in PPI-1 hub genes by survival analysis and cox analysis", "ITGA2", "2", "74.36%", "69.23%"], ["5", "80.77%"], ["10", "80.77%"], ["14", "80.77%"], ["18", "82.05%"], ["22", "85.90%"], ["Method 6: 724 DGEs-181 candidate genes-genes bearing top 10 degrees in PPI", "ALB, EGF, ITGA2, FN1, COL1A2, TIMP1, MMP1, COL3A1, PTGS2, CEL", "2", "82.05%", "83.72%"], ["4", "71.80%"], ["6", "79.49%"], ["10", "75.64%"], ["13", "74.36%"], ["18", "73.08%"], ["Method7:724 DGEs-genes bearing top10 degrees in PPI-2 hub genes by survival analysis and cox analysis", "TOP2A, MAD2L1", "2", "65.38%", "69.82%"], ["5", "69.23%"], ["8", "65.38%"], ["12", "66.67%"], ["23", "67.95%"], ["Method8:724 DGEs-genes bearing top 10 degrees in PPI", "ALB, GAPDH, EGF, TOP2A, CCNB1, NDC80, CCNA2, CDC20, UBE2C, BUB1B, MAD2L1, TTK, OIP5, KIF11", "2", "71.79%", "73.05%"], ["6", "73.08%"], ["11", "69.23%"], ["15", "70.51%"], ["22", "73.08%"]], "row_count": 45, "column_count": 6, 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