Evidence map›Paper›PMID 40333895›Full record

ArticleAnnals of clinical and translational neurology2025

A PANoptosis-Based Signature for Survival and Immune Predication in Glioblastoma Multiforme.

Jun Yang, Da Lin, Dongyuan Liu, Dongxu Zhang, Hao Wang

Abstract read
In one paragraph

Article in Annals of clinical and translational neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Jun YangDepartment of Neurosurgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Da LinDepartment of Neurosurgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Dongyuan LiuDepartment of Neurosurgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Dongxu ZhangDepartment of Neurosurgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Hao WangDepartment of Neurosurgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China.ORCID 0009-0002-8320-289X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivePANoptosis is a concept of total cell death characterized by pyroptosis, apoptosis, and necroptosis. We aimed to explore the clinical significance of PANoptosis-related genes (PARGs) in glioblastoma multiforme (GBM).

methodsExpression profiles of GBM were downloaded from the XENA database as a training dataset to construct a differentially expressed PARGs (DE-PARGs)-based risk score (RS) model, and the prognostic prediction role was validated in the CGGA database and GSE108474 using Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) curve. Meanwhile, independent prognostic clinical factors were screened, and their prognosis predictive activity was evaluated by a nomogram model. Furthermore, the relationships between key DE-PARGs and immune cell infiltration, as well as chemotherapy drug sensitivity were analyzed.

resultsThe RS model consisting of five DE-PARGs was constructed, including NOD2, NLRP2, NLRP7, GATA3, and TERT. ROC and KM curves confirmed the good potency of the RS prognostic model both in XENA database and GSE108474. Three clinical prognostic factors, including chemotherapy, pharmaceutical therapy, and RS model, were selected as individual prognostic factors. The nomogram model showed RS contributed most to survival probability, followed by chemotherapy and pharmaceutical therapy. In high- and low-risk groups, B cell memory, NK cell resting, and macrophage M1 had significant differences. As compared with the immune checkpoint therapy non-responder group, the responder involved a higher ratio of patients sub-grouped into the low-risk group. Three drugs between high- and low-risk groups had significant differences, including Cisplatin, Gefitinib, and Vorinostat.

interpretationOur data exhibit the prognostic value of PARGs in GBM and offer new insights for GBM pathogenesis and immune treatment.

Indexed as

Brain NeoplasmsGlioblastomaPyroptosisBiomarkers, TumorFemaleHumansMaleNomogramsPrognosisBiomarkers, Tumorglioblastoma multiformePANoptosis‐related geneprognostic valuerisk score

Identifiers

PMID40333895
PMCPMC12257131

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.