A spliceosome-associated gene signature aids in predicting prognosis and tumor microenvironment of hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37301543", "kind": "science-record", "revision": 3, "content_hash": "sha256:34491f4802493a02e7cff5f269d2f67183a94dd416615db8e9c2f2e98e4c37d5", "created_at": "2026-11-01T19:02:01Z", "updated_at": "2026-11-01T22:09:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A spliceosome-associated gene signature aids in predicting prognosis and tumor microenvironment of hepatocellular carcinoma.", "doi": "10.18632/aging.204765", "pmid": "37301543", "authors": ["Huaxiang Wang", "Ruling Wang", "Jian Fang"], "journal": "Aging", "year": 2023, "abstract": "Splicing alterations have been shown to be key tumorigenesis drivers. In this study, we identified a novel spliceosome-related genes (SRGs) signature to predict the overall survival (OS) of patients with hepatocellular carcinoma (HCC). A total of 25 SRGs were identified from the GSE14520 dataset (training set). Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were utilized to construct the signature using genes with predictive significance. We then constructed a risk model using six SRGs (BUB3, IGF2BP3, RBM3, ILF3, ZC3H13, and CCT3). The reliability and predictive power of the gene signature were validated in two validation sets (TCGA and GSE76427 dataset). Patients in training and validation sets were divided into high and low-risk groups based on the gene signature. Patients in high-risk groups exhibited a poorer OS than in low-risk groups both in the training set and two validation sets. Next, risk score, BCLC staging, TNM staging, and multinodular were combined in a nomogram for OS prediction, and the decision curve analysis (DCA) curve exhibited the excellent prediction performance of the nomogram. The functional enrichment analyses demonstrated high-risk score patients were closely related to multiple oncology characteristics and invasive-related pathways, such as Cell cycle, DNA replication, and Spliceosome. Different compositions of the tumor microenvironment and immunocyte infiltration ratio might contribute to the prognostic difference between high and low-risk score groups. In conclusion, a spliceosome-related six-gene signature exhibited good performance for predicting the OS of patients with HCC, which may aid in clinical decision-making for individual treatment.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression and LASSO regression model analysis were applied to recognize the SRGs that impact prognosis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37301543", "locator": "sections[4] \"Construction of the prognostic signature\" paragraph 2", "quote": "Univariate Cox regression and LASSO regression model analysis were applied to recognize the SRGs that impact prognosis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Transcription profiling data and corresponding clinical data of GSE14520 and GSE76427, and LIHC liver cancer dataset can obtain from the GEO database (GSE14520: (https://ftp.ncbi.nlm.nih.gov/geo/series/GSE14nnn/GSE14520/matrix/), GSE76427: (https://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76427/matrix/)) and the TCGA database (https://xenabrowser.net/datapages/?dataset=TCGA-LIHC.htseq_counts.tsv&host=https%3A%2F%2Fgdc.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37301543", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "Transcription profiling data and corresponding clinical data of GSE14520 and GSE76427, and LIHC liver cancer dataset can obtain from the GEO database (GSE14520: (https://ftp.ncbi.nlm.nih.gov/geo/series/GSE14nnn/GSE14520/matrix/), GSE76427: (https://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76427/matrix/)) and the TCGA database (https://xenabrowser.net/datapages/?dataset=TCGA-LIHC.htseq_counts.tsv&host=https%3A%2F%2Fgdc.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In conclusion, a spliceosome-related six-gene signature exhibited good performance for predicting the OS of patients with HCC, which may aid in clinical decision-making for individual treatment.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37301543", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In conclusion, a spliceosome-related six-gene signature exhibited good performance for predicting the OS of patients with HCC, which may aid in clinical decision-making for individual treatment."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In conclusion, a spliceosome-related six-gene signature exhibited good performance for predicting the OS of patients with HCC, which may aid in clinical decision-making for individual treatment.", "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:37301543"], "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:37301543", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.204765", "sha256": 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can obtain from the GEO database (GSE14520: (https://ftp.ncbi.nlm.nih.gov/geo/series/GSE14nnn/GSE14520/matrix/), GSE76427: (https://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76427/matrix/)) and the TCGA database (https://xenabrowser.net/datapages/?dataset=TCGA-LIHC.htseq_counts.tsv&host=https%3A%2F%2Fgdc.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Transcription profiling data and corresponding clinical data of GSE14520 and GSE76427, and LIHC liver cancer dataset can obtain from the GEO database (GSE14520: (https://ftp.ncbi.nlm.nih.gov/geo/series/GSE14nnn/GSE14520/matrix/), GSE76427: (https://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76427/matrix/)) and the TCGA database (https://xenabrowser.net/datapages/?dataset=TCGA-LIHC.htseq_counts.tsv&host=https%3A%2F%2Fgdc.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443).", 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