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pmid:34238253
Computational analysis for identification of early diagnostic biomarkers and prognostic biomarkers of liver cancer based on GEO and TCGA databases and studies on pathways and biological functions affecting the survival time of liver cancer.
Shiyong Gao · Jian Gang · Miao Yu · Guosong Xin · Huixin Tan
BMC cancer · 2021 doi:10.1186/s12885-021-08520-1
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34238253", "kind": "science-record", "revision": 7, "content_hash": "sha256:af469121edffc9f1892bf86cb90194cc794b6b351c3875c8a3bb5653f47f91cf", "created_at": "2026-11-01T15:41:42Z", "updated_at": "2026-11-01T22:04:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Computational analysis for identification of early diagnostic biomarkers and prognostic biomarkers of liver cancer based on GEO and TCGA databases and studies on pathways and biological functions affecting the survival time of liver cancer.", "doi": "10.1186/s12885-021-08520-1", "pmid": "34238253", "authors": ["Shiyong Gao", "Jian Gang", "Miao Yu", "Guosong Xin", "Huixin Tan"], "journal": "BMC cancer", "year": 2021, "abstract": "BACKGROUND: Liver cancer is the sixth most commonly diagnosed cancer and the fourth most common cause of cancer death. The purpose of this work is to find new diagnostic biomarkers or prognostic biomarkers and explore the biological functions related to the prognosis of liver cancer.\nMETHODS: GSE25097 datasets were firstly obtained and compared with TCGA LICA datasets and an analysis of the overlapping differentially expressed genes (DEGs) was conducted. Cytoscape was used to screen out the Hub Genes among the DEGs. ROC curve analysis was used to screen the Hub Genes to determine the genes that could be used as diagnostic biomarkers. Kaplan-Meier analysis and Cox proportional hazards model screened genes associated with prognosis biomarkers, and further Gene Set Enrichment Analysis was performed on the prognosis genes to explore the mechanism affecting the survival and prognosis of liver cancer patients.\nRESULTS: 790 DEGs and 2162 DEGs were obtained respectively from the GSE25097 and TCGA LIHC data sets, and 102 Common DEGs were identified by overlapping the two DEGs. Further screening identified 22 Hub Genes from 102 Common DEGs. ROC and survival curves were used to analyze these 22 Hub Genes and it was found that there were 16 genes with a value of AUC > 90%. Among these, the expression levels of ESR1,SPP1 and FOSB genes were closely related to the survival time of liver cancer patients. Three common pathways of ESR1, FOBS and SPP1 genes were identified along with seven common pathways of ESR1 and SPP1 genes and four common pathways of ESR1 and FOSB genes.\nCONCLUSIONS: SPP1, AURKA, NUSAP1, TOP2A, UBE2C, AFP, GMNN, PTTG1, RRM2, SPARCL1, CXCL12, FOS, DCN, SOCS3, FOSB and PCK1 can be used as diagnostic biomarkers for liver cancer, among which FOBS and SPP1 genes can also be used as prognostic biomarkers. Activation of the cell cycle-related pathway, pancreas beta cells pathway, and the estrogen signaling pathway, while on the other hand inhibition of the hallmark heme metabolism pathway, hallmark coagulation pathway, and the fat metabolism pathway may promote prognosis in liver cancer patients.", "claims": [{"id": "claim-method", "statement": "The original microarray data of the GSE25097 and TCGA LIHC datasets were respectively analyzed with R language to screen the differentially expressed genes (DEGs). Adj .p-value < 0.05 and |logFC| > 2 were used as the cut-off criteria. The TCGA LIHC dataset was analyzed with R × 64 3.6.1, using the package DEGseq2, Adj. p value < 0.05 and |logFC| > 2 were regarded as the cut-off criteria. The liver cancer dataset was obtained from TCGA (https://portal.gdc.cancer.gov/). Another gene expression profiling dataset (GSE25097) included information on 243 normal samples and 268 tumor samples, which was downloaded from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/database) and measured in an array (Platform: GPL10687 Rosetta/Merck Human RSTA Affymetrix 1.0 microarray, Custom CDF).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34238253", "locator": "sections[4] \"Data processing\" paragraph 2", "quote": "The original microarray data of the GSE25097 and TCGA LIHC datasets were respectively analyzed with R language to screen the differentially expressed genes (DEGs)."}, {"asset": "source:pmid:34238253", "locator": "sections[4] \"Data processing\" paragraph 2", "quote": "Adj .p-value < 0.05 and |logFC| > 2 were used as the cut-off criteria."}, {"asset": "source:pmid:34238253", "locator": "sections[13] \"Identification of DEGs\" paragraph 2", "quote": "The TCGA LIHC dataset was analyzed with R × 64 3.6.1, using the package DEGseq2, Adj. p value < 0.05 and |logFC| > 2 were regarded as the cut-off criteria."}, {"asset": "source:pmid:34238253", "locator": "sections[3] \"Microarray data\" paragraph 2", "quote": "The liver cancer dataset was obtained from TCGA (https://portal.gdc.cancer.gov/)."}, {"asset": "source:pmid:34238253", "locator": "sections[3] \"Microarray data\" paragraph 2", "quote": "Another gene expression profiling dataset (GSE25097) included information on 243 normal samples and 268 tumor samples, which was downloaded from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/database) and measured in an array (Platform: GPL10687 Rosetta/Merck Human RSTA Affymetrix 1.0 microarray, Custom CDF)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed during the current study are available in the [https://www.ncbi.nlm.nih.gov/geo/database] and https://portal.gdc.cancer.gov/].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34238253", "locator": "sections[29] \"notes\" paragraph 1", "quote": "The datasets generated and/or analyzed during the current study are available in the [https://www.ncbi.nlm.nih.gov/geo/database] and https://portal.gdc.cancer.gov/]."}], "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:34238253"], "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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Adj .p-value < 0.05 and |logFC| > 2 were used as the cut-off criteria. The TCGA LIHC dataset was analyzed with R × 64 3.6.1, using the package DEGseq2, Adj. p value < 0.05 and |logFC| > 2 were regarded as the cut-off criteria. The liver cancer dataset was obtained from TCGA (https://portal.gdc.cancer.gov/). Another gene expression profiling dataset (GSE25097) included information on 243 normal samples and 268 tumor samples, which was downloaded from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/database) and measured in an array (Platform: GPL10687 Rosetta/Merck Human RSTA Affymetrix 1.0 microarray, Custom CDF)."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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