Comprehensive genomic signature of pyroptosis-related genes and relevant characterization in hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36650832", "kind": "science-record", "revision": 3, "content_hash": "sha256:ad2dca4a2b1005e5f24d9a1e23831a65559a8289fb8cbfc1b8838439a2e52a74", "created_at": "2026-11-01T19:01:12Z", "updated_at": "2026-11-01T22:08:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive genomic signature of pyroptosis-related genes and relevant characterization in hepatocellular carcinoma.", "doi": "10.7717/peerj.14691", "pmid": "36650832", "authors": ["Sheng Wang", "Songsen Gao", "Liang Shan", "Xueyi Qian", "Jiajie Luan", "Xiongwen Lv"], "journal": "PeerJ", "year": 2023, "abstract": "Background: Currently, the most predominant type of liver cancer is hepatocellular carcinoma (HCC), which is also the fourth leading cause of cancer-related death in the global population. Pyroptosis is an emerging form of cell death that affects the prognosis of cancer patients by modulating tumor cell migration, proliferation and invasion. However, the evaluation of pyroptosis in the prognosis of HCC is still insufficient.\nMethods: A total of 365 HCC patients from the TCGA-LIHC cohort were classified into two distinct subtypes using consensus clustering of pyroptosis-related genes (PRGs). Following univariate Cox analysis of differentially expressed genes between subtypes, we established a prognostic model (PRGs-score, PRGS) by LASSO Cox analysis. We further tested the predictive power of the prognostic model in the ICGC (LIRI-JP) and GEO (GSE14520) cohorts. The tumor microenvironment (TME) was studied using the CIBERSORT. The enrichment scores for immune cells and immune functions in low- and high-PRGS groups were assessed using ssGSEA. The IMvigor210 cohort was used to investigate the immunotherapy efficacy. Furthermore, we validated the expression of prognostic genes in PRGS by RT-qPCR in vitro.\nResults: The subtyping of HCC based on PRGs exhibited distinct clinical characteristics. We developed a prognostic model PRGS by differentially expressed genes between different subtypes. The results showed that PRGS could well forecast the survival of HCC patients in different cohorts and was associated with the immune microenvironment. Moreover, PRGS was considered to be an independent prognostic risk factor and superior to other pyroptosis-related signatures. Low-PRGS implied greater immune cell infiltration and better overall survival with immunotherapy. The results of RT-qPCR also showed that prognostic genes were significantly dysregulated in HCC.\nConclusions: PRGS has promising application in forecasting the prognosis of HCC patients, and its relationship with the immune microenvironment provides a basis for the subsequent treatment and research of HCC.", "claims": [{"id": "claim-method", "statement": "We utilized univariate Cox regression analysis to determine the pyroptosis-related DEGs correlated with OS in the training cohort (TCGA-LIHC, n = 365), screened by the criterion of p-value < 0.0001. Next, the LASSO Cox algorithm eliminated overfitting between prognostic genes to perform penalty parameter tuning by ten-fold cross validation according to the “glmnet” R package in an attempt to narrow the range of prognostic genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36650832", "locator": "sections[6] \"Construction of the pyroptosis-related prognostic PRGS\" paragraph 2", "quote": "We utilized univariate Cox regression analysis to determine the pyroptosis-related DEGs correlated with OS in the training cohort (TCGA-LIHC, n = 365), screened by the criterion of p-value < 0.0001. Next, the LASSO Cox algorithm eliminated overfitting between prognostic genes to perform penalty parameter tuning by ten-fold cross validation according to the “glmnet” R package in an attempt to narrow the range of prognostic genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data is available at figshare: Wang, Sheng (2022): Raw_data.zip. figshare. Dataset. https://doi.org/10.6084/m9.figshare.21732488.v1.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36650832", "locator": "sections[27] \"Additional Information and Declarations\" paragraph 4", "quote": "The raw data is available at figshare: Wang, Sheng (2022): Raw_data.zip. figshare. Dataset. https://doi.org/10.6084/m9.figshare.21732488.v1."}], "state": "complete"}, {"id": "claim-target-result", "statement": "PRGS forecast survival in HCC patients across independent cohorts and was associated with the tumor immune microenvironment.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36650832", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The results showed that PRGS could well forecast the survival of HCC patients in different cohorts and was associated with the immune microenvironment."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "PRGS forecast survival in HCC patients across independent cohorts and was associated with the tumor immune microenvironment.", "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:36650832"], "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:36650832", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.14691", "sha256": "sha256:dce16163c6c687c597256547f15c24755c796e1b0c7848b5527d12916f404a01", "bytes": 59563, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.14691", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9840857", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "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": "The raw data is available at figshare: Wang, Sheng (2022): Raw_data.zip. figshare. Dataset. https://doi.org/10.6084/m9.figshare.21732488.v1.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The raw data is available at figshare: Wang, Sheng (2022): Raw_data.zip. figshare. 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Next, the LASSO Cox algorithm eliminated overfitting between prognostic genes to perform penalty parameter tuning by ten-fold cross validation according to the “glmnet” R package in an attempt to narrow the range of prognostic genes."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}, {"id": "method-4", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {"fold_change": "h the criteria of |log2(FC)| > log2(1"}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "limma"}}, {"id": "method-5", "family": "clustering", "specific": "kmeans", "label": "Clustering", "dsl_operations": ["kmeans_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "k-means"}}, {"id": "method-6", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.8, "band": "high", "quote": "Chi-square"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "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": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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