A Pyroptosis-Related Gene Prognostic Index Correlated with Survival and Immune Microenvironment in Glioma

Zheng, Jianglin; Zhou, Zhongqiang; Qiu, Yue; Wang, Minjie; Yu, Hao; Wu, Zhipeng; Wang, Xuan; Jiang, Xiao Bing

doi:10.2147/jir.s341774

Cited by 18 publications

(17 citation statements)

References 43 publications

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“…The specific role of cuproptosis in LGG remains unclear. Furthermore, although many prognostic signatures have been established based on the patterns of pseudogenes ( 43 ), N6-methylandenosine ( 44 ), immunity ( 45 ), ferroptosis ( 46 ), and pyroptosis ( 47 ) their predictive ability needs to be further improved, and there is still a lack of a prognostic signature based on the characteristics of cuproptosis. In the present study, we systematically investigated the global alterations in 13 CRGs at genetic and transcriptional levels in LGG, established a scoring system consisting of five signature core genes, and eventually constructed a quantitative nomogram by integrating the CRG score and clinicopathological features including grades, IDH status, and age.…”

Section: Discussionmentioning

confidence: 99%

Identification of a novel cuproptosis-related gene signature and integrative analyses in patients with lower-grade gliomas

Bao

Duan

et al. 2022

Front. Immunol.

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BackgroundCuproptosis is a newly discovered unique non-apoptotic programmed cell death distinguished from known death mechanisms like ferroptosis, pyroptosis, and necroptosis. However, the prognostic value of cuproptosis and the correlation between cuproptosis and the tumor microenvironment (TME) in lower-grade gliomas (LGGs) remain unknown.MethodsIn this study, we systematically investigated the genetic and transcriptional variation, prognostic value, and expression patterns of cuproptosis-related genes (CRGs). The CRG score was applied to quantify the cuproptosis subtypes. We then evaluated their values in the TME, prognostic prediction, and therapeutic responses in LGG. Lastly, we collected five paired LGG and matched normal adjacent tissue samples from Sun Yat-sen University Cancer Center (SYSUCC) to verify the expression of signature genes by quantitative real-time PCR (qRT-PCR) and Western blotting (WB).ResultsTwo distinct cuproptosis-related clusters were identified using consensus unsupervised clustering analysis. The correlation between multilayer CRG alterations with clinical characteristics, prognosis, and TME cell infiltration were observed. Then, a well-performed cuproptosis-related risk model (CRG score) was developed to predict LGG patients’ prognosis, which was evaluated and validated in two external cohorts. We classified patients into high- and low-risk groups according to the CRG score and found that patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P<0.001). A high CRG score implies higher TME scores, more significant TME cell infiltration, and increased mutation burden. Meanwhile, the CRG score was significantly correlated with the cancer stem cell index, chemoradiotherapy sensitivity–related genes and immune checkpoint genes, and chemotherapeutic sensitivity, indicating the association with CRGs and treatment responses. Univariate and multivariate Cox regression analyses revealed that the CRG score was an independent prognostic predictor for LGG patients. Subsequently, a highly accurate predictive model was established for facilitating the clinical application of the CRG score, showing good predictive ability and calibration. Additionally, crucial CRGs were further validated by qRT-PCR and WB.ConclusionCollectively, we demonstrated a comprehensive overview of CRG profiles in LGG and established a novel risk model for LGG patients’ therapy status and prognosis. Our findings highlight the potential clinical implications of CRGs, suggesting that cuproptosis may be the potential therapeutic target for patients with LGG.

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Section: Discussionmentioning

confidence: 99%

Identification of a novel cuproptosis-related gene signature and integrative analyses in patients with lower-grade gliomas

Bao

Duan

et al. 2022

Front. Immunol.

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“…In gliomas, DPP4-like enzyme activity increases with tumor more malignancy, mainly due to DPP4 and FAP ( Stremenova et al, 2007 ). Non-malignant brain tissue contains DPP4-like enzymatic activity primarily attributable to DPP8/9 ( Stremenova et al, 2007 ), it has also been shown that DPP9 is upregulated in glioma ( Zheng et al, 2022 ). In contrast, more abundant expression of DPP8/9 was currently detected in meningiomas.…”

Section: Dpp8/9 and Diseasesmentioning

confidence: 99%

New insights into the role of dipeptidyl peptidase 8 and dipeptidyl peptidase 9 and their inhibitors

et al. 2022

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Dipeptidyl peptidase 8 (DPP8) and 9 (DPP9) are widely expressed in mammals including humans, mainly locate in the cytoplasm. The DPP8 and DPP9 (DPP8/9) belong to serine proteolytic enzymes, they can recognize and cleave N-terminal dipeptides of specific substrates if proline is at the penultimate position. Because the localization of DPP8/9 is different from that of DPP4 and the substrates for DPP8/9 are not yet completely clear, their physiological and pathological roles are still being further explored. In this article, we will review the recent research advances focusing on the expression, regulation, and functions of DPP8/9 in physiology and pathology status. Emerging research results have shown that DPP8/9 is involved in various biological processes such as cell behavior, energy metabolism, and immune regulation, which plays an essential role in maintaining normal development and physiological functions of the body. DPP8/9 is also involved in pathological processes such as tumorigenesis, inflammation, and organ fibrosis. In recent years, related research on immune cell pyroptosis has made DPP8/9 a new potential target for the treatment of hematological diseases. In addition, DPP8/9 inhibitors also have great potential in the treatment of tumors and chronic kidney disease.

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“…Forty-four pyroptosis genes (AIM2, APIP, CASP1, CASP3, CASP4, CASP5, CASP6, CASP8, CASP9, DHX9, DDX58, ELANE, GSDMA, GSDMB, GSDMC, GSDMD, GSDME, IFI16, IL18, IL1B, MAPK8, MAPK9, NAIP, NFKB1, NFKB2, NLRC3, NLRC4, NLRP1, NLRP12, NLRP2, NLRP3, NLRP6, NLRP7, NLRP9, NOD1, NOD2, PJVK, PLCG1, PRKACA, SCAF11, TIRAP, TNF, GPX4, and IL6) were obtained from the literature (30)(31)(32)(33)(34)(35)(36)(37). The names and abbreviations of the 44 pyroptosis-related genes are shown in Table 1.…”

Section: Construction Of a Machine Learning Prognostic Model For Pyro...mentioning

confidence: 99%

Identification of pyroptosis-related lncRNA signature and AC005253.1 as a pyroptosis-related oncogene in prostate cancer

Tang

2022

Front. Oncol.

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BackgroundPyroptosis and prostate cancer (PCa) are closely related. The role of pyroptosis-related long non-coding RNAs (lncRNAs) (PRLs) in PCa remains elusive. This study aimed to explore the relationship between PRL and PCa prognosis.MethodsGene expression and clinical signatures were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. A PRL risk prediction model was established by survival random forest analysis and least absolute shrinkage and selection operator regression. Functional enrichment, immune status, immune checkpoints, genetic mutations, and drug susceptibility analyses related to risk scores were performed by the single-sample gene set enrichment analysis, gene set variation analysis, and copy number variation analysis. PRL expression was verified in PCa cells. Cell Counting Kit-8, 5-ethynyl-2′-deoxyuridine, wound healing, transwell, and Western blotting assay were used to detect the proliferation, migration, invasion, and pyroptosis of PCa cells, respectively.ResultsPrognostic features based on six PRL (AC129507.1, AC005253.1, AC127502.2, AC068580.3, LIMD1-AS1, and LINC01852) were constructed, and patients in the high-score group had a worse prognosis than those in the low-score group. This feature was determined to be independent by Cox regression analysis, and the area under the curve of the 1-, 3-, and 5-year receiver operating characteristic curves in the testing cohort was 1, 0.93, and 0.92, respectively. Moreover, the external cohort validation confirmed the robustness of the PRL risk prediction model. There was a clear distinction between the immune status of the two groups. The expression of multiple immune checkpoints was also reduced in the high-score group. Gene mutation proportion in the high-score group increased, and the sensitivity to drugs increased significantly. Six PRLs were upregulated in PCa cells. Silencing of AC005253.1 inhibited cell proliferation, migration, and invasion in DU145 and PC-3 cells. Moreover, silencing of AC005253.1 promoted pyroptosis and inflammasome AIM2 expression.ConclusionsOverall, we constructed a prognostic model of PCa with six PRLs and identified their expression in PCa cells. The experimental verification showed that AC005253.1 could affect the proliferation, migration, and invasion abilities of PCa cells. Meanwhile, AC005253.1 may play an important role in PCa by affecting pyroptosis through the AIM2 inflammasome. This result requires further research for verification.

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A Pyroptosis-Related Gene Prognostic Index Correlated with Survival and Immune Microenvironment in Glioma

Cited by 18 publications

References 43 publications

Identification of a novel cuproptosis-related gene signature and integrative analyses in patients with lower-grade gliomas

Identification of a novel cuproptosis-related gene signature and integrative analyses in patients with lower-grade gliomas

New insights into the role of dipeptidyl peptidase 8 and dipeptidyl peptidase 9 and their inhibitors

Identification of pyroptosis-related lncRNA signature and AC005253.1 as a pyroptosis-related oncogene in prostate cancer

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