Integrated bioinformatics analysis of potential biomarkers for pancreatic cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35403252", "kind": "science-record", "revision": 4, "content_hash": "sha256:4689477f13427cf4b4068780885ff604fdd2f3fc276d96836bac7523de3f8220", "created_at": "2026-11-01T18:14:13Z", "updated_at": "2026-11-01T22:06:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated bioinformatics analysis of potential biomarkers for pancreatic cancer.", "doi": "10.1002/jcla.24381", "pmid": "35403252", "authors": ["Huaqing Shi", "Hao Xu", "Changpeng Chai", "Zishun Qin", "Wence Zhou"], "journal": "Journal of clinical laboratory analysis", "year": 2022, "abstract": "BACKGROUND: Pancreatic cancer, particularly pancreatic ductal adenocarcinoma (PDA), is an aggressive malignancy associated with a low 5-year survival rate. Poor outcomes associated with PDA are attributable to late detection and inoperability. Most patients with PDA are diagnosed with locally advanced and metastatic disease. Such cases are primarily treated with chemotherapy and radiotherapy. Because of the lack of effective molecular targets, early diagnosis and successful therapies are limited. The purpose of this study was to screen key candidate genes for PDA using a bioinformatic approach and to research their potential functional, pathway mechanisms associated with PDA progression. It may help to understand the role of associated genes in the development and progression of PDA and identify relevant molecular markers with value for early diagnosis and targeted therapy.\nMATERIALS AND METHODS: To identify novel genes associated with carcinogenesis and progression of PDA, we analyzed the microarray datasets GSE62165, GSE125158, and GSE71989 from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A protein-protein interaction (PPI) network was constructed using STRING, and module analysis was performed using Cytoscape. Gene Expression Profiling Interactive Analysis (GEPIA) was used to evaluate the differential expression of hub genes in patients with PDA. In addition, we verified the expression of these genes in PDA cell lines and normal pancreatic epithelial cells.\nRESULTS: A total of 202 DEGs were identified and these were found to be enriched for various functions and pathways, including cell adhesion, leukocyte migration, extracellular matrix organization, extracellular region, collagen trimer, membrane raft, fibronectin-binding, integrin binding, protein digestion, and absorption, and focal adhesion. Among these DEGs, 12 hub genes with high degrees of connectivity were selected. Survival analysis showed that the hub genes (HMMR, CEP55, CDK1, UHRF1, ASPM, RAD51AP1, DLGAP5, KIF11, SHCBP1, PBK, and HMGB2) may be involved in the tumorigenesis and development of PDA, highlighting their potential as diagnostic and therapeutic factors in PDA.\nCONCLUSIONS: In summary, the DEGs and hub genes identified in the present study not only contribute to a better understanding of the molecular mechanisms underlying the carcinogenesis and progression of PDA but may also serve as potential new biomarkers and targets for PDA.", "claims": [{"id": "claim-method", "statement": "GEO2R is an interactive web tool used to identify DEGs between different datasets by comparing them in a GEO series. This tool was also used to calculate the p‐value and |logFC|, and the cutoff threshold for identifying DEGs was set at p < 0.05, and |logFC|≥1.0).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35403252", "locator": "sections[5] \"Identification of DEGs\" paragraph 2", "quote": "GEO2R is an interactive web tool used to identify DEGs between different datasets by comparing them in a GEO series. This tool was also used to calculate the p‐value and |logFC|, and the cutoff threshold for identifying DEGs was set at p < 0.05, and |logFC|≥1.0)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35403252", "locator": "sections[21] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 202 DEGs were identified in datasets GSE62165, GSE125158, and GSE71989, respectively, with 202 overlapping DEGs between the three datasets (Figure 1).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35403252", "locator": "sections[12] \"Identification of DEGs\" paragraph 2", "quote": "Using GEO2R, 6887, 1770, and 4367 DEGs were identified in datasets GSE62165, GSE125158, and GSE71989, respectively, with 202 overlapping DEGs between the three datasets (Figure 1)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 202 DEGs were identified in datasets GSE62165, GSE125158, and GSE71989, respectively, with 202 overlapping DEGs between the three datasets (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:35403252"], "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:GSE125158"], "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:GSE125158"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35403252", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1002/jcla.24381", "sha256": 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