Correlation analysis of tumor mutation burden of hepatocellular carcinoma based on data mining.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34295561", "kind": "science-record", "revision": 3, "content_hash": "sha256:a21d31285c2a2660faab059330b4c7a5ad9d5bcd9ce19540107134009e89f213", "created_at": "2026-11-01T17:35:43Z", "updated_at": "2026-11-01T21:25:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Correlation analysis of tumor mutation burden of hepatocellular carcinoma based on data mining.", "doi": "10.21037/jgo-21-259", "pmid": "34295561", "authors": ["Weijie Zhou", "Dalang Fang", "Yongfei He", "Jie Wei"], "journal": "Journal of gastrointestinal oncology", "year": 2021, "abstract": "BACKGROUND: The aim of this study was to determine the relationship between tumor mutation burden (TMB) and prognosis of patients with hepatocellular carcinoma (HCC), and to explore the differential expression of genes in HCC by TMB and the relationship between immune cells, TMB, and HCC.\nMETHODS: Somatic variation data, gene transcriptional expression data and clinical information of patients with HCC were obtained from cancer genome map (TCGA) database. Analyze the characteristics of the gene mutation data of the sample, divide the high and low TMB groups and draw the survival curve at the same time, carry on the difference analysis to the gene of TMB, further carry on the univariate Cox regression analysis and Lasso regression analysis and construct the clinical model. Download the dataset GSE14520, from the Gene Expression Omnibus (GEO) database to verify the genes of the prognostic model. The differential genes were analyzed by gene ontology (GO) enrichment analysis and Kyoto encyclopedia of genes and genomes by (KEGG) enrichment analysis. Then the relative abundance of 22 immune cell types in HCC and normal control samples was calculated. Finally, the correlation between the scores of immune cells and Risk model was analyzed.\nRESULTS: Tumor protein p53 (TP53), catenin1 (CTNNB1), titin (TTN), mucin 16 (MUC16), and albumin (ALB) are the most common top 5 mutations in HCC. The prognosis of high level TMB group is worse than that of low TMB group. A total of 122 differentially expressed genes were screened by differential analysis of TMB genes. SQSTM1, ME1, BAMBI and PTTG1 are independent risk factors for poor prognosis of HCC. GO and KEGG analysis showed that the differential genes were mainly in extracellular matrix and immune response. There were significant differences in the distribution of Macrophages M0 and T cells CD4 native cells between HCC and normal tissues, which were correlated with the differential genes of TMB and correlated with prognosis.\nCONCLUSIONS: There is a negative correlation between TMB and the prognosis of patients with HCC. TMB has an effect on the differential expression of genes in HCC cells and the distribution of immune cells in tumor tissues.", "claims": [{"id": "claim-method", "statement": "Analyze the characteristics of the gene mutation data of the sample, divide the high and low TMB groups and draw the survival curve at the same time, carry on the difference analysis to the gene of TMB, further carry on the univariate Cox regression analysis and Lasso regression analysis and construct the clinical model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34295561", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Analyze the characteristics of the gene mutation data of the sample, divide the high and low TMB groups and draw the survival curve at the same time, carry on the difference analysis to the gene of TMB, further carry on the univariate Cox regression analysis and Lasso regression analysis and construct the clinical model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Somatic variation data, gene transcriptional expression data and clinical information of patients with HCC were obtained from cancer genome map (TCGA) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34295561", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Somatic variation data, gene transcriptional expression data and clinical information of patients with HCC were obtained from cancer genome map (TCGA) database."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:34295561"], "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:GSE14520"], "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:GSE14520"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34295561", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/jgo-21-259", "sha256": "sha256:924436f06211bd1a02d9c92797b251fecf0ba9f2d70b3a81dc4f87a309ea42af", "bytes": 2185, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/jgo-21-259", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8261312", "source": "ncbi_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE14520", "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=GSE14520", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE14520", "version": null, "note": "Somatic variation data, gene transcriptional expression data and clinical information of patients with HCC were obtained from cancer genome map 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