Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32391055", "kind": "science-record", "revision": 7, "content_hash": "sha256:d0a6307c2871c6c049b4b99e944939f1ab03b01f633fcea41244db14cbd518ef", "created_at": "2026-11-01T15:41:01Z", "updated_at": "2026-11-01T22:01:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis.", "doi": "10.3389/fgene.2020.00342", "pmid": "32391055", "authors": ["Dengchuan Wang", "Jun Liu", "Shengshuo Liu", "Wenli Li"], "journal": "Frontiers in genetics", "year": 2020, "abstract": "The dreadful prognosis of hepatocellular carcinoma (HCC) is primarily due to the low early diagnosis rate, rapid progression, and high recurrence rate. Valuable prognostic biomarkers are urgently needed for HCC. In this study, microarray data were downloaded from GSE14520, GSE22058, International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified among GSE14520, GSE22058, and ICGC databases. Weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression modules of DEGs, and genes of key modules were examined to identify hub genes using univariate Cox regression in the ICGC cohort. Expression levels and time-dependent receiver operating characteristic (ROC) and area under the curve (AUC) were determined to estimate the prognostic competence of the hub genes. These hub genes were also validated in the Gene Expression Profiling Interactive Analysis (GEPIA) and TCGA databases. TIMER algorithm and GSCALite database were applied to analyze the association of the hub genes with immunocytotic infiltration and their pathway enrichment. Altogether, 276 DEGs were identified and WGCNA described a unique and significantly DEGs-associated co-expression module containing 148 genes, with 10 hub genes selected by univariate Cox regression in the ICGC cohort (BIRC5, FOXM1, CENPA, KIF4A, DTYMK, PRC1, IGF2BP3, KIF2C, TRIP13, and TPX2). Most of the genes were validated in the GEPIA databases, except IGF2BP3. The results of multivariate Cox regression analysis indicated that the abovementioned hub genes are all independent predictors of HCC. The 10 genes were also confirmed to be associated with immune cell infiltration using the TIMER algorithm. Moreover, four-gene signature was developed, including BIRC5, CENPA, FOXM1, DTYMK. These hub genes and the model demonstrated a strong prognostic capability and are likely to be a therapeutic target for HCC. Moreover, the association of these genes with immune cell infiltration improves our understanding of the occurrence and development of HCC.", "claims": [{"id": "claim-method", "statement": "The genes in the turquoise module of the ICGC cohort were analyzed using univariate Cox regression to identify prognostic markers from among the survival-related candidates.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32391055", "locator": "sections[13] \"Identification of Hub Genes\" paragraph 2", "quote": "The genes in the turquoise module of the ICGC cohort were analyzed using univariate Cox regression to identify prognostic markers from among the survival-related candidates."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GSE14520 and GSE22058 dataset were collected from the GEO with additional datasets obtained from the TCGA (https://portal.gdc.cancer.gov/) and ICGC (https://icgc.org/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32391055", "locator": "sections[20] \"Data Availability Statement\" paragraph 2", "quote": "The GSE14520 and GSE22058 dataset were collected from the GEO with additional datasets obtained from the TCGA (https://portal.gdc.cancer.gov/) and ICGC (https://icgc.org/)."}], "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:32391055"], "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. 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