Pyroptosis-related genes features on prediction of the prognosis in liver cancer: An integrated analysis of bulk and single-cell RNA sequencing.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39416843", "kind": "science-record", "revision": 4, "content_hash": "sha256:defe5eb3044a78ab9371469d8066089dbf0f3373b46391885330cb073631eb3d", "created_at": "2026-11-01T20:29:39Z", "updated_at": "2026-11-01T22:55:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Pyroptosis-related genes features on prediction of the prognosis in liver cancer: An integrated analysis of bulk and single-cell RNA sequencing.", "doi": "10.1016/j.heliyon.2024.e38438", "pmid": "39416843", "authors": ["Zhihao Zhang", "Feng Liu", "Xin Lan", "Fuhai Wang", "Jiahao Sun", "Honglong Wei"], "journal": "Heliyon", "year": 2024, "abstract": "Objective: This study explores the impact of pyroptosis-related genes (PRG) on the prognosis of liver cancer (LC).\nMethods: 421 samples (371 tumor samples and 50 normal samples) from the Cancer Genome Atlas (TCGA) were included in this study. GSE14520 dataset (data of RNA expression and relevant clinicopathological features), GSE125449 dataset (single-cell data in LC) and HCCDB18 dataset (validation on the reliability of the model) were downloaded as appropriate. Download the PRG and its corresponding pathway information from the gene set enrichment analysis (GSEA) website. The consensus clustering was performed by ConsensusClusterPlus package. Differentially expressed genes (DEGs) were identified using limma package, and prognostic features were constructed using un/multivariate and Lasso Cox regression. Pathway enrichment analysis was conducted by ssGSEA method. Receiver Operating Characteristic and the survival analysis were conducted by timeROC and Survminer packages. The Seurat package was used for single-cell RNA sequencing (scRNA-seq) analysis. For cellular validation, following the quantification on the key genes via reverse-transcription quantitative PCR, the Transwell and scratch assays were applied to evaluate the in-vitro invasion and migration of LC cells Huh-7.\nResults: 12 prognosis-related genes were identified to be related to the progression of LC. Three subtypes including C1, C2 and C3 were categorized using the 12 prognosis-related genes and PRGs significantly related to the prognosis of LC patients. The worst and best prognosis was seen in C3 subtype and C2 subtype, respectively. Hallmark pathway enrichment analysis has shown the concurrent immunoactivation and immune escape in C3 subtype. A RiskScore model was constructed using 8 key genes (KPNA2, UCK2, FTCD, CBX2, RAB32, HMMR, S100A9 and ANXA10) from the DEGs of three subtypes. The RiskScore system as an independent prognostic factor dividing the patients into high and low risk groups, and patients of the high-risk group had poor prognosis in both test set and validation set. A nomogram model combining the risk score had the extreme higher benefit. Further, 6 subclusters were identified from scRNA-seq analysis, where the highest PYROPTOSIS score was seen in Monocytic-Macrophages. The quantification on the key genes has suggested the high expressions of KPNA2, UCK2, CBX2, RAB32, HMMR and S100A9 and the low expressions of FTCD and ANXA10 in LC cells Huh-7. Particularly, UCK2 knockdown evidently diminished the number of invaded and migrated LC cells in vitro.\nConclusion: The risk model associated with pyproptosis is crucial for the tumor immunity of LC and may serve as a prognostic indicator for patients suffering from LC. Our findings will offer new perspectives for immunotherapies targeting LC.", "claims": [{"id": "claim-method", "statement": "Hereafter, the univariate and LASSO Cox and stepwise regression analysis of DEGs was performed to identify the candidate model genes (Fig. 4B and C). Following this, a stepwise multivariable regression analysis was employed to further narrow down the genes and calculated the regression coefficient.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39416843", "locator": "sections[18] \"Construction of prognostic models\" paragraph 2", "quote": "Hereafter, the univariate and LASSO Cox and stepwise regression analysis of DEGs was performed to identify the candidate model genes (Fig. 4B and C). Following this, a stepwise multivariable regression analysis was employed to further narrow down the genes and calculated the regression coefficient."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed during the current study are available in the [GSE14520] repository [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE14520], and [GSE125449] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE125449].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39416843", "locator": "sections[26] \"Data availability statement\" paragraph 2", "quote": "The datasets generated and/or analyzed during the current study are available in the [GSE14520] repository [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE14520], and [GSE125449] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE125449]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "An 8-gene pyroptosis-related RiskScore model was constructed as an independent prognostic factor that separated liver cancer patients into high- and low-risk groups, with the high-risk group having poorer prognosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39416843", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A RiskScore model was constructed using 8 key genes (KPNA2, UCK2, FTCD, CBX2, RAB32, HMMR, S100A9 and ANXA10) from the DEGs of three subtypes."}, {"asset": "source:pmid:39416843", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The RiskScore system as an independent prognostic factor dividing the patients into high and low risk groups, and patients of the high-risk group had poor prognosis in both test set and validation set."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "An 8-gene pyroptosis-related RiskScore model was constructed as an independent prognostic factor that separated liver cancer patients into high- and low-risk groups, with the high-risk group having poorer prognosis.", "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:39416843"], "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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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:39416843", "data:geo:GSE125449"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE125449"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:35:29Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 14504, "completion_tokens": 1645, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:39416843", "type": "paper", "label": "pmid:39416843"}, {"id": "source:pmid:39416843", "type": "manuscript", "label": "PMID 39416843 full text"}, {"id": "data:geo:GSE125449", "type": "dataset", "label": "GEO GSE125449"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39416843", "type": "paper", "label": "pmid:39416843"}, {"id": "source:pmid:39416843", "type": "manuscript", "label": "PMID 39416843 full text"}, {"id": "data:geo:GSE125449", "type": "dataset", "label": "GEO GSE125449"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39416843", "type": "extraction", "used": ["source:pmid:39416843"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE125449", "type": "accession_resolution", "used": ["data:geo:GSE125449"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:39416843", "parent_hash": "sha256:a8f9ebdce7c1d4ebe772a6a56fb9f4aa9db11c2fc798511044c93744de6c2add", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE125449 licence not read", "asset:data:geo:GSE125449 not downloaded", "asset:data:geo:GSE14520 licence not read", "asset:data:geo:GSE14520 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["r cancer: An integrated analysis of bulk and single-cell RNA sequencing.", "er packages. The Seurat package was used for single-cell RNA sequencing (scRNA-seq) analysis. For cellular validatio", "age was used for single-cell RNA sequencing (scRNA-seq) analysis. For cellular validation, followin", "Further, 6 subclusters were identified from scRNA-seq analysis, where the highest PYROPTOSIS score"], "order_basis": "paper_text_order:72", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["integrated analysis of bulk and single-cell RNA sequencing.", "The Seurat package was used for single-cell RNA sequencing (scRNA-seq) analysis. For cellular validatio"], "order_basis": "paper_text_order:85", "id": "assay:2"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ANXA10 in LC cells Huh-7. Particularly, UCK2 knockdown evidently diminished the number of invaded a", "Methods Cell culture and transfection In this study, the following cell lines, inc", "at the conditions of 37 °C, 5 % CO2. For the transfection via liposome, the corresponding small interf", "enePharma (Shanghai, China), which were then transfected into Huh7 cells with the help of lipofectami"], "order_basis": "paper_text_order:2589", "id": "assay:3"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Hereafter, the univariate and LASSO Cox and stepwise regression analysis of DEGs was performed to identify the candidate model genes (Fig. 4B and C). Following this, a stepwise multivariable regression analysis was employed to further narrow down the genes and calculated the regression coefficient."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}