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pmid:36778205

A Novel Prognostic Pyroptosis-Related Gene Signature Correlates to Oxidative Stress and Immune-Related Features in Gliomas.

Shulian Zeng · Wenjun Li · Hao Ouyang · Ying Xie · Xiaoqiang Feng · Li Huang

Oxidative medicine and cellular longevity · 2023 doi:10.1155/2023/4256116

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

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title
A Novel Prognostic Pyroptosis-Related Gene Signature Correlates to Oxidative Stress and Immune-Related Features in Gliomas.
doi
10.1155/2023/4256116
pmid
36778205
authors array · 6 items
[0]
Shulian Zeng
[1]
Wenjun Li
[2]
Hao Ouyang
[3]
Ying Xie
[4]
Xiaoqiang Feng
[5]
Li Huang
journal
Oxidative medicine and cellular longevity
year
2023
abstract
Gliomas are highly invasive and aggressive tumors having the highest incidence rate of brain cancer. Identifying effective prognostic and potential therapeutic targets is necessitated. The relationship of pyroptosis, a form of programmed cellular death, with gliomas remains elusive. We constructed and validated a prognostic model for gliomas using pyroptosis-related genes. Differentially expressed pyroptosis-related genes were screened using the "limma" package. Based on LASSO-Cox regression, nine significant genes including CASP1, CASP3, CASP6, IL32, MKI67, MYD88, PRTN3, NOS1, and VIM were employed to construct a prognostic model in the TCGA cohort; the results were validated in the CGGA cohort. According to the median risk score, the patients were classified into two risk groups, namely, high- and low-risk groups. Patients at high risk had worse prognoses relative to those at low risk evidenced by the Kaplan-Meier curve analysis. The two groups exhibited differences in immune cell infiltration and TMB scores, with high immune checkpoint levels, TMB scores, and immune cell infiltration levels in the high-risk group. KEGG and GO analyses suggested enrichment in immune-related pathways. Furthermore, we found that the genes in our signature strongly correlated with oxidative stress-related pathways and the subgroups exhibited different ssGSEA scores. Some small molecules targeted the genes in the model, and we verified their drug sensitivities between the risk groups. The scRNA-seq dataset, GSE138794, was processed using the "Seurat" package to assess the level of risk gene expression in specific cell types. Finally, the MYD88 level was lowered in the U87 glioma cell line using si-RNA constructs. Cellular proliferation was impaired, and fewer pyroptosis-related cytokines were released upon exposure to LPS. In summary, we built a pyroptosis-related gene model that accurately classified glioma patients into high- and low-risk groups. The findings suggest that the signature may be an effective prognostic predictive tool for gliomas.
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Using the “glmnet” package, a least absolute shrinkage and selection operator- (LASSO-) COX regression for the overlapping genes was performed to minimize overfitting.
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Using the “glmnet” package, a least absolute shrinkage and selection operator- (LASSO-) COX regression for the overlapping genes was performed to minimize overfitting.
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The raw data in this study were sourced from public datasets. TCGA-LGG and TCGA-GBM datasets were from The Cancer Genome Atlas (https://portal.gdc.cancer.gov/), and CGGA-693 and CGGA-325 datasets were from the Chinese Glioma Genome Atlas (http://www.cgga.org.cn/). Pyroptosis-related genes were obtained from the GeneCards database (https://www.genecards.org/).
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sections[29] "Data Availability" paragraph 2
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The raw data in this study were sourced from public datasets. TCGA-LGG and TCGA-GBM datasets were from The Cancer Genome Atlas (https://portal.gdc.cancer.gov/), and CGGA-693 and CGGA-325 datasets were from the Chinese Glioma Genome Atlas (http://www.cgga.org.cn/). Pyroptosis-related genes were obtained from the GeneCards database (https://www.genecards.org/).
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The risk score remained an independent predictive factor for OS after correcting for confounders, as evidenced by multivariate COX regression analysis results (CGGA cohort: HR = 1.267, 95% CI = 1.164-1.378, p < 0.001; TCGA cohort: HR = 2.054, 95% CI = 1.654-2.550, p < 0.001) (Figures 4(b) and 4(d)).
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sections[20] "3.4. Validation of Clinical Independence of the Pyroptosis-Related Prognostic Signature in the CGGA and TCGA Cohorts and Construction of a Predictive Nomogram for the Latter" paragraph 2
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The risk score remained an independent predictive factor for OS after correcting for confounders, as evidenced by multivariate COX regression analysis results (CGGA cohort: HR = 1.267, 95% CI = 1.164-1.378, p < 0.001; TCGA cohort: HR = 2.054, 95% CI = 1.654-2.550, p < 0.001) (Figures 4(b) and 4(d)).
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The risk score remained an independent predictive factor for OS after correcting for confounders, as evidenced by multivariate COX regression analysis results (CGGA cohort: HR = 1.267, 95% CI = 1.164-1.378, p < 0.001; TCGA cohort: HR = 2.054, 95% CI = 1.654-2.550, p < 0.001) (Figures 4(b) and 4(d)).
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36778205", "kind": "science-record", "revision": 3, "content_hash": "sha256:540cb88d093066d491b1271b869757c10fb7363f18696c149b385121937431ab", "created_at": "2026-11-01T19:01:22Z", "updated_at": "2026-11-01T22:08:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Novel Prognostic Pyroptosis-Related Gene Signature Correlates to Oxidative Stress and Immune-Related Features in Gliomas.", "doi": "10.1155/2023/4256116", "pmid": "36778205", "authors": ["Shulian Zeng", "Wenjun Li", "Hao Ouyang", "Ying Xie", "Xiaoqiang Feng", "Li Huang"], "journal": "Oxidative medicine and cellular longevity", "year": 2023, "abstract": "Gliomas are highly invasive and aggressive tumors having the highest incidence rate of brain cancer. Identifying effective prognostic and potential therapeutic targets is necessitated. The relationship of pyroptosis, a form of programmed cellular death, with gliomas remains elusive. We constructed and validated a prognostic model for gliomas using pyroptosis-related genes. Differentially expressed pyroptosis-related genes were screened using the \"limma\" package. Based on LASSO-Cox regression, nine significant genes including CASP1, CASP3, CASP6, IL32, MKI67, MYD88, PRTN3, NOS1, and VIM were employed to construct a prognostic model in the TCGA cohort; the results were validated in the CGGA cohort. According to the median risk score, the patients were classified into two risk groups, namely, high- and low-risk groups. Patients at high risk had worse prognoses relative to those at low risk evidenced by the Kaplan-Meier curve analysis. The two groups exhibited differences in immune cell infiltration and TMB scores, with high immune checkpoint levels, TMB scores, and immune cell infiltration levels in the high-risk group. KEGG and GO analyses suggested enrichment in immune-related pathways. Furthermore, we found that the genes in our signature strongly correlated with oxidative stress-related pathways and the subgroups exhibited different ssGSEA scores. Some small molecules targeted the genes in the model, and we verified their drug sensitivities between the risk groups. The scRNA-seq dataset, GSE138794, was processed using the \"Seurat\" package to assess the level of risk gene expression in specific cell types. Finally, the MYD88 level was lowered in the U87 glioma cell line using si-RNA constructs. Cellular proliferation was impaired, and fewer pyroptosis-related cytokines were released upon exposure to LPS. In summary, we built a pyroptosis-related gene model that accurately classified glioma patients into high- and low-risk groups. The findings suggest that the signature may be an effective prognostic predictive tool for gliomas.", "claims": [{"id": "claim-method", "statement": "Using the “glmnet” package, a least absolute shrinkage and selection operator- (LASSO-) COX regression for the overlapping genes was performed to minimize overfitting.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36778205", "locator": "sections[4] \"2.2. Construction of the Pyroptosis-Related Gene Signature\" paragraph 2", "quote": "Using the “glmnet” package, a least absolute shrinkage and selection operator- (LASSO-) COX regression for the overlapping genes was performed to minimize overfitting."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data in this study were sourced from public datasets. TCGA-LGG and TCGA-GBM datasets were from The Cancer Genome Atlas (https://portal.gdc.cancer.gov/), and CGGA-693 and CGGA-325 datasets were from the Chinese Glioma Genome Atlas (http://www.cgga.org.cn/). Pyroptosis-related genes were obtained from the GeneCards database (https://www.genecards.org/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36778205", "locator": "sections[29] \"Data Availability\" paragraph 2", "quote": "The raw data in this study were sourced from public datasets. TCGA-LGG and TCGA-GBM datasets were from The Cancer Genome Atlas (https://portal.gdc.cancer.gov/), and CGGA-693 and CGGA-325 datasets were from the Chinese Glioma Genome Atlas (http://www.cgga.org.cn/). Pyroptosis-related genes were obtained from the GeneCards database (https://www.genecards.org/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The risk score remained an independent predictive factor for OS after correcting for confounders, as evidenced by multivariate COX regression analysis results (CGGA cohort: HR = 1.267, 95% CI = 1.164-1.378, p < 0.001; TCGA cohort: HR = 2.054, 95% CI = 1.654-2.550, p < 0.001) (Figures 4(b) and 4(d)).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The risk score remained an independent predictive factor for OS after correcting for confounders, as evidenced by multivariate COX regression analysis results (CGGA cohort: HR = 1.267, 95% CI = 1.164-1.378, p < 0.001; TCGA cohort: HR = 2.054, 95% CI = 1.654-2.550, p < 0.001) (Figures 4(b) and 4(d))."}, "evidence": [{"asset": "source:pmid:36778205", "locator": "sections[20] \"3.4. Validation of Clinical Independence of the Pyroptosis-Related Prognostic Signature in the CGGA and TCGA Cohorts and Construction of a Predictive Nomogram for the Latter\" paragraph 2", "quote": "The risk score remained an independent predictive factor for OS after correcting for confounders, as evidenced by multivariate COX regression analysis results (CGGA cohort: HR = 1.267, 95% CI = 1.164-1.378, p < 0.001; TCGA cohort: HR = 2.054, 95% CI = 1.654-2.550, p < 0.001) (Figures 4(b) and 4(d))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The risk score remained an independent predictive factor for OS after correcting for confounders, as evidenced by multivariate COX regression analysis results (CGGA cohort: HR = 1.267, 95% CI = 1.164-1.378, p < 0.001; TCGA cohort: HR = 2.054, 95% CI = 1.654-2.550, p < 0.001) (Figures 4(b) and 4(d)).", "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:36778205"], "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:GSE138794"], "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:GSE138794"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36778205", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2023/4256116", "sha256": "sha256:57918d67f4de2b8634976150da43e2933dedc397a891bc4a25f8321a6b8336d7", "bytes": 49643, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2023/4256116", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9909087", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE138794", "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=GSE138794", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE138794", "version": null, "note": "The raw data in this study were sourced from public datasets. 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