Genetic expression and mutational profile analysis in different pathologic stages of hepatocellular carcinoma patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34238242", "kind": "science-record", "revision": 7, "content_hash": "sha256:42cf3c6661897182fa7706228d4601e37c1aeac833337e49c5021c294ab17820", "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": "Genetic expression and mutational profile analysis in different pathologic stages of hepatocellular carcinoma patients.", "doi": "10.1186/s12885-021-08442-y", "pmid": "34238242", "authors": ["Xingjie Gao", "Chunyan Zhao", "Nan Zhang", "Xiaoteng Cui", "Yuanyuan Ren", "Chao Su", "Shaoyuan Wu", "Zhi Yao", "Jie Yang"], "journal": "BMC cancer", "year": 2021, "abstract": "BACKGROUND: The clinical pathologic stages (stage I, II, III-IV) of hepatocellular carcinoma (HCC) are closely linked to the clinical prognosis of patients. This study aims at investigating the gene expression and mutational profile in different clinical pathologic stages of HCC.\nMETHODS: Based on the TCGA-LIHC cohort, we utilized a series of analytical approaches, such as statistical analysis, random forest, decision tree, principal component analysis (PCA), to identify the differential gene expression and mutational profiles. The expression patterns of several targeting genes were also verified by analyzing the Chinese HLivH060PG02 HCC cohort, several GEO datasets, HPA database, and diethylnitrosamine-induced HCC mouse model.\nRESULTS: We identified a series of targeting genes with copy number variation, which is statistically associated with gene expression. Non-synonymous mutations mainly existed in some genes (e.g.,TTN, TP53, CTNNB1). Nevertheless, no association between gene mutation frequency and pathologic stage distribution was detected. The random forest and decision tree modeling analysis data showed a group of genes related to different HCC pathologic stages, including GAS2L3 and SEMA3F. Additionally, our PCA data indicated several genes associated with different pathologic stages, including SNRPA and SNRPD2. Compared with adjacent normal tissues, we observed a highly expressed level of GAS2L3, SNRPA, and SNRPD2 (P = 0.002) genes in HCC tissues of our HLivH060PG02 cohort. We also detected the high expression pattern of GAS2L3, SEMA3F, SNRPA, and SNRPD2 in the datasets of GSE102079, GSE76427, GSE64041, GSE121248, GSE84005, and the qPCR assay using diethylnitrosamine-induced HCC mouse model. Moreover, SEMA3F and SNRPD2 protein were highly stained in the HCC tissues of the HPA database. The high expression level of these four genes was associated with the poor survival prognosis of HCC cases.\nCONCLUSIONS: Our study provides evidence regarding the gene expression and mutational profile in different clinical pathologic stages of TCGA HCC cases. Identifying four targeting genes, including GAS2L3, SNRPA, SNRPD2, and SEMA3F, offers insight into the molecular mechanisms associated with different prognoses of HCC.", "claims": [{"id": "claim-method", "statement": "The plots with 95% confidence interval, P value of logrank test, HR (hazards ratio), and P value of Mantel-Cox test were generated.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34238242", "locator": "sections[12] \"Survival curve analysis\" paragraph 2", "quote": "The plots with 95% confidence interval, P value of logrank test, HR (hazards ratio), and P value of Mantel-Cox test were generated."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed in our study are partly available in the TCGA (http://tcga-data.nci.nih.gov/tcga/), GEO (https://www.ncbi.nlm.nih.gov/geo/), and HPA (https://www.proteinatlas.org/about) databases. Others are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34238242", "locator": "sections[32] \"notes\" paragraph 1", "quote": "The datasets generated and/or analyzed in our study are partly available in the TCGA (http://tcga-data.nci.nih.gov/tcga/), GEO (https://www.ncbi.nlm.nih.gov/geo/), and HPA (https://www.proteinatlas.org/about) databases. Others are available from the corresponding author on reasonable request."}], "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:34238242"], "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:GSE102079"], "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:GSE102079"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34238242", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-021-08442-y", "sha256": "sha256:8aa1babd5c6c64bafad5a99fe6f7fd544e1f554f317f1e1952a85bea35331b32", "bytes": 48544, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12885-021-08442-y", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8268469", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE102079", "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=GSE102079", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE102079", "version": null, "note": "The datasets generated and/or analyzed in our study are partly available in the TCGA (http://tcga-data.nci.nih.gov/tcga/), GEO (https://www.ncbi.nlm.nih.gov/geo/), and HPA (https://www.proteinatlas.org/about) databases. 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