Identification of key candidate biomarkers for severe influenza infection by integrated bioinformatical analysis and initial clinical validation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33448094", "kind": "science-record", "revision": 8, "content_hash": "sha256:60f66b2e21d58c1224dde42c1bee5238fd19651dab724413ab39c1beacf100db", "created_at": "2026-11-01T15:41:22Z", "updated_at": "2026-11-01T22:45:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of key candidate biomarkers for severe influenza infection by integrated bioinformatical analysis and initial clinical validation.", "doi": "10.1111/jcmm.16275", "pmid": "33448094", "authors": ["Shuai Liu", "Zhisheng Huang", "Xiaoyan Deng", "Xiaohui Zou", "Hui Li", "Shengrui Mu", "Bin Cao"], "journal": "Journal of cellular and molecular medicine", "year": 2021, "abstract": "One of the key barriers for early identification and intervention of severe influenza cases is a lack of reliable immunologic indicators. In this study, we utilized differentially expressed genes screening incorporating weighted gene co-expression network analysis in one eligible influenza GEO data set (GSE111368) to identify hub genes associated with clinical severity. A total of 10 genes (PBI, MMP8, TCN1, RETN, OLFM4, ELANE, LTF, LCN2, DEFA4 and HP) were identified. Gene set enrichment analysis (GSEA) for single hub gene revealed that these genes had a close association with antimicrobial response and neutrophils activity. To further evaluate these genes' ability for diagnosis/prognosis of disease developments, we adopted double validation with (a) another new independent data set (GSE101702); and (b) plasma samples collected from hospitalized influenza patients. We found that 10 hub genes presented highly correlation with disease severity. In particular, BPI and MMP8 encoding proteins in plasma achieved higher expression in severe and dead cases, which indicated an adverse disease development and suggested a frustrating prognosis. These findings provide new insight into severe influenza pathogenesis and identify two significant candidate genes that were superior to the conventional clinical indicators. These candidate genes or encoding proteins could be biomarker for clinical diagnosis and therapeutic targets for severe influenza infection.", "claims": [{"id": "claim-method", "statement": "We downloaded the matrix files of two data sets from GEO. The R package ‘limma’ was performed for DEGs identifying between influenza samples and healthy samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33448094", "locator": "sections[6] \"Identification of DEGs and functional enrichment analyses\" paragraph 2", "quote": "We downloaded the matrix files of two data sets from GEO. The R package ‘limma’ was performed for DEGs identifying between influenza samples and healthy samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available data sets were analysed in this study. These data can be found in GSE111368 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111368) and GSE101702 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111368).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33448094", "locator": "sections[30] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "Publicly available data sets were analysed in this study. These data can be found in GSE111368 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111368) and GSE101702 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111368)."}], "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:33448094"], "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:GSE101702"], "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:GSE101702"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33448094", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1111/jcmm.16275", "sha256": "sha256:6a827ee62137655f76f52db9ef719b153834b5b410d33e86753c3ef0ec38e2d5", "bytes": 51500, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1111/jcmm.16275", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7875920", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE101702", "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=GSE101702", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE101702", "version": null, "note": "Publicly available data sets were analysed in this study. 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"access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[4]/table-wrap[1]#jcmm16275-tbl-0001", "version": null, "note": "Demographics and clinical characteristics of influenza patients", "structure": {"pmcid": "PMC7875920", "section_index": 4, "section_title": "Clinical samples", "row_count": 29, "column_count": 5}, "table": {"label": "TABLE 1", "caption": "Demographics and clinical characteristics of influenza patients", "rows": [["", "Total (N = 63)", "Moderate (N = 34)", "Severe (N = 29)", "P value"], ["Gender", "", "", "", "0.5074"], ["Female", "30 (48%)", "18 (53%)", "12 (41%)"], ["Male", "33 (52%)", "16 (47%)", "17 (59%)"], ["Age/y (mean (SD))", "55.88 (14.52)", "59.00 (11.23)", "52.24 (17.11)", "0.0753"], ["Flu virus strains", "", "", "", "0.0654"], ["Influenza type A", "41 (65%)", "25 (74%)", "16 (55%)"], ["Influenza type B", "20(32%)", "7(20%)", "13(45%)"], ["Influenza type A and B", "2 (3%)", "2 (6%)", "0 (0%)"], ["Comorbidity"], ["Pneumonia disease", "52 (82%)", "23 (67%)", "29 (100%)", "0.0023"], ["Cancer", "3 (4%)", "3 (8%)", "0 (0%)", "0.2427"], ["Hypertension", "20 (31%)", "12 (35%)", "8 (27%)", "0.7012"], ["Diabetes", "15 (23%)", "6 (17%)", "9 (31%)", "0.3437"], ["Laboratory tests (mean (SD))"], ["Total leucocytes (×109/L)", "9.68 (6.34)", "8.80 (5.80)", "10.70 (6.88)", "0.1949"], ["Neutrophil (×109/L)", "8.01 (5.88)", "6.73 (5.01)", "9.51 (6.53)", "0.0557"], ["Proportion of neutrophil (%)", "80.25 (13.56)", "74.71 (15.63)", "86.76 (6.16)", "0.0005"], ["Lymphocytes (×109/L)", "0.92 (0.57)", "1.03 (0.66)", "0.80 (0.41)", "0.2639"], ["Proportion of lymphocytes (%)", "13.36 (12.09)", "15.55 (11.12)", "10.78 (12.85)", "0.0252"], ["Respiratory support"], ["Extracorporeal membrane oxygenation", "12 (19%)", "0 (0%)", "12 (41%)", "0.0001"], ["Invasive mechanical ventilation", "29 (46%)", "0 (0%)", "29 (100%)", "0.0000"], ["Non‐invasive mechanical ventilatory support", "31 (49%)", "16 (47%)", "15 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