IFI35 and IFIT3 are potentially important biomarkers for early diagnosis and treatment of esophageal squamous cell carcinoma: based on WGCNA and machine learning analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40463715", "kind": "science-record", "revision": 3, "content_hash": "sha256:eda79844e8dfc951bf2d74eefcb5e4ba6a7e9aceb7023ed7ac21835fd9830bcd", "created_at": "2026-11-01T20:30:35Z", "updated_at": "2026-11-01T22:14:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "IFI35 and IFIT3 are potentially important biomarkers for early diagnosis and treatment of esophageal squamous cell carcinoma: based on WGCNA and machine learning analysis.", "doi": "10.3389/fgene.2025.1583202", "pmid": "40463715", "authors": ["Hao Wu", "Liang Yang", "Xiaokun Weng"], "journal": "Frontiers in genetics", "year": 2025, "abstract": "Background: Esophageal squamous cell carcinoma (ESCC) does not have distinct and highly sensitive biomarkers, making its diagnosis difficult. Consequently, identifying dependable biomarkers is critical, as these indicators can facilitate accurate ESCC diagnosis and enable effective prognostic evaluation.\nMethods: ESCC datasets (GSE29001, GSE20347, GSE45670, and GSE161533) were sourced from the GEO, and the Limma package identified differentially expressed genes (DEGs). To characterize co-expression network, weighted gene co-expression network analysis (WGCNA) was performed, allowing for the identification of relevant co-expression modules. To assess the biological pathways of intersecting genes, we performed pathway enrichment analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The Support Vector Machine Recursive Feature Elimination (SVM), along with Least Absolute Shrinkage and Selection Operator (LASSO) regression, was applied to identify clinical biomarkers. Finally, the differences of immune cell infiltration were also detected.\nResults: 1,019 genes were derived by integrating DEGs with co-expressed module genes. KEGG and GO revealed a strong association between these genes and processes such as chemotaxis and IL-17 signaling pathways. Two hub genes (IFIT3 and IFI35) were selected through LASSO regression and SVM. Additionally, ROC curve analysis confirmed their potential for reliable diagnostic performance. Furthermore, differences in immune cell infiltration were observed.\nConclusion: Collectively, IFIT3 and IFI35 emerged as promising candidate biomarkers, offering novel insights to enhance early detection and guide targeted treatment strategies for ESCC.", "claims": [{"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40463715", "locator": "sections[19] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Collectively, IFIT3 and IFI35 emerged as promising candidate biomarkers, offering novel insights to enhance early detection and guide targeted treatment strategies for ESCC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40463715", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Collectively, IFIT3 and IFI35 emerged as promising candidate biomarkers, offering novel insights to enhance early detection and guide targeted treatment strategies for ESCC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Collectively, IFIT3 and IFI35 emerged as promising candidate biomarkers, offering novel insights to enhance early detection and guide targeted treatment strategies for ESCC.", "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:40463715"], "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:GSE161533"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE161533"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40463715", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2025.1583202", "sha256": "sha256:3b9f7b478befeab2f9956dfb722e6614e8765b368587c801fb8cdaa000c14d94", "bytes": 27441, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2025.1583202", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12129983", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE161533", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE161533", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE161533", "version": null, "note": "Publicly available datasets were analyzed in this study. 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