ArticleBMC cancer2023
Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma.
Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Pyroptosis in cancer therapy: a double-edged sword for immune activation and tumor progression.Molecular cancer · 2025Review
- Single cell analysis and bioinformatics reveal pyroptosis mechanisms in hepatocellular carcinoma.Discover oncology · 2025Article
- The role of hypoxia-senescence co-related molecular subtypes and prognostic characteristics in hepatocellular carcinoma.Scientific reports · 2025Article
- Dual function of Gasdermin E: pyroptosis-mediated pan-cancer suppression versus HCC-specific oncogenic activity.Frontiers in immunology · 2025Review
- ZSCAN16 expedites hepatocellular carcinoma progression via activating TBC1D31.Cell division · 2024Article
- Diagnostic and prognostic value of the gasdermins in gastric cancer.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2024Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We used pyroptosis-related genes to establish a risk-score model for prognostic prediction of liver hepatocellular carcinoma (LIHC) patients. A total of 52 pyroptosis-associated genes were identified. Then, data for 374 LIHC patients and 50 normal individuals were acquired from the TCGA database. Through gene expression analyses, differentially expressed genes (DEGs) were determined. The 13 pyroptosis-related genes (PRGs) confirmed as potential prognostic factors through univariate Cox regression analysis were entered into Lasso and multivariate Cox regression to build a PRGs prognostic signature, containing four PRGs (BAK1, GSDME, NLRP6, and NOD2) determined as independent prognostic factors. mRNA levels were evaluated by qRT-PCR, while overall survival (OS) rates were assessed by the Kaplan-Meier method. Enrichment analyses were done to establish the mechanisms associated with differential survival status of LIHC patients from a tumor immunology perspective. Additionally, a risk score determined by the prognostic model could divide LIHC patients into low- and high-risk groups using median risk score as cut-off. A prognostic nomogram, derived from the prognostic model and integrating clinical characteristics of patients, was constructed. The prognostic function of the model was also validated using GEO, ICGC cohorts, and online databases Kaplan-Meier Plotter. Small interfering RNA-mediated knockdown of GSDME, as well as lentivirus-mediated GSDME knockdown, were performed to validate that knockdown of GSDME markedly suppressed growth of HCC cells both in vivo and in vitro. Collectively, our study demonstrated a PRGs prognostic signature that had great clinical value in prognosis assessment.
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