Molecular Subtypes and Prognostic Signature of Pyroptosis-Related lncRNAs in Glioma Patients

Tanzhu, Guilong; Li, Na; Li, Zhanzhan; Zhou, Rongrong; Shen, Liangfang

doi:10.3389/fonc.2022.779168

Cited by 21 publications

(17 citation statements)

References 53 publications

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“…Meanwhile, Tanzhu et al also developed a pyroptosis-related lncRNA signature for glioma patients; the ROC curves were adopted for evaluating the output of their signature, in the TCGA cohort, the respective values of 1-, 3-, and 5-year OS were 0.869, 0.886, and 0.899, respectively. It was similar to our results that are shown in Figure 2(h) , but the predictive capacity of model decreased in the CGGA cohort [ 25 ]; in our signature, it still showed outstanding performance for predicting 1-, 3-, and 5-year OS. The respective AUC areas of ROC analysis were 0.791, 0.856, and 0.868, while compared to the ROC results of another published pyroptosis-related lncRNAs model in GBM patients, our signature also showed analogously excellent power of prediction [ 26 ].…”

Section: Discussionsupporting

confidence: 91%

Construction and Verification of a Novel Pyroptosis-Related lncRNA Signature Associated with Immune Landscape in Gliomas

Feng

Chen

Liu

et al. 2022

Journal of Oncology

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Gliomas are the most common tumor in the central nervous system with limited prognostic markers making it difficult to research progression. Induction of cellular immunogenic death is a promising treatment for glioma. Pyroptosis is one of the recently discovered programmed immuogenic cell death modes which remains unclear in glioma. We obtained glioma datasets from the CGGA and TCGA websites. Pearson correlation analysis was used to find pyroptosis-related lncRNAs. Subsequently, the univariate, LASSO, and multivariate Cox regression were applied to construct a prognostic signature based on pyroptosis-related lncRNAs. Kaplan-Meier plots, ROC curves, and PCA were utilized for testing the prognostic performance of the signature. We conducted the univariate and multivariate Cox regressions to ascertain if the signature worked as an independent factor for predicting overall survival (OS) for individuals with glioma from other characteristics. For evaluating the immune landscape differences between the subgroups, ESTIMATE, CIBERTSORT, and ssGSEA were adopted. Additionally, biological functions and pathways of DEGs were identified by KEGG and GO. We also screened potential drugs and measured sensitivities of chemotherapeutics between the subgroups by CellMiner and pRRophetic package. Finally, shRNA was conducted to knockdown of COX10-AS1 in U87 cells to determine its relationship with pyroptosis. We successfully created an effective pyroptosis-related lncRNA signature that divided individuals into groups of low- and high-risk, and individuals in the high-risk group were with poor prognosis in comparison to the individuals in the other group. A nomogram including clinical factors and risk scores to predict the OS was built. Furthermore, the two groups appeared to have different immune landscapes; the high-risk group showed greater levels of ESTIMATE scores, immune cell infiltration, and immune checkpoints. Additionally, immune-related pathways and functions were shown to be enriched according to KEGG and GO findings. Knockdown of COX10-AS1 inhibited U87 cell growth, upregulated CASP1 and NLRP3, and released more IL1-β and IL-18 than the negative control. In summary, our study developed an lncRNA signature related to pyroptosis for OS prediction of gliomas and demonstrated its relationship with immune infiltration and drug sensitivity.

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Section: Discussionsupporting

confidence: 91%

Construction and Verification of a Novel Pyroptosis-Related lncRNA Signature Associated with Immune Landscape in Gliomas

Feng

Chen

Liu

et al. 2022

Journal of Oncology

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“…To construct accurate and stable prognostic models for glioma, many researchers have focused their attention on cell‐death‐related genes. Genes associated with ferroptosis, pyroptosis or autophagy have previously been included in the construction of glioma prognostic models, and these models showed good prediction power 22,34–36 . We reasonably speculated that cuproptosis, a recently defined form of cell death, is correlated with glioma prognosis.…”

Section: Discussionmentioning

confidence: 79%

“…Many studies have highlighted the power of lncRNA‐based computational models to predict glioma prognosis and tumour response to therapy. The pathways involved in these lncRNAs include immune, autophagy, ferroptosis and pyroptosis 19–22 . However, a prognosis predictive model based on cuproptosis‐related lncRNAs for glioma has not been reported.…”

Section: Introductionmentioning

confidence: 99%

Identification of cuproptosis‐related lncRNAs for prognosis and immunotherapy in glioma

Wang

et al. 2022

J Cellular Molecular Medi

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Glioma is a highly invasive primary brain tumour, making it challenging to accurately predict prognosis for glioma patients. Cuproptosis is a recently discovered cell death attracting significant attention in the tumour field. Whether cuproptosis-related genes have prognostic predictive value has not been clarified. In this study, uni-/multivariate Cox and Lasso regression analyses were applied to construct a risk model based on cuproptosis-related lncRNAs using TCGA and CGGA cohorts. A nomogram was constructed to quantify individual risk, including clinical and genic characteristics and risk. GO and KEGG analyses were used to define functional enrichment of DEGs. Tumour mutation burden (TMB) and immune checkpoint analyses were performed to evaluate potential responses to ICI therapy. Ten prognostic lncRNAs were obtained from Cox regression. Based on the median risk score, patients were divided into high-and low-risk groups. Either for grade 2-3 or for grade 4, glioma patients with high-risk exhibited significant poorer prognoses. The risk was an independent risk factor associated with overall survival. The high-risk group was functionally associated with immune responses and cancer-related pathways. The high-risk group was associated with higher TMB scores. The expression levels of many immune checkpoints in the high-risk group were significantly higher than those in the low-risk group. Differentiated immune pathways were primarily enriched in the IFN response, immune checkpoint and T-cell co-stimulation pathways. In conclusion, we established a risk model based on cuproptosis-related lncRNAs showing excellent prognostic prediction ability but also indicating the immuno-microenvironment status of glioma.

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“…Moreover, we selected some relevant literature to compare the predictive power of the prognostic models. Compared with other models, our prediction model has better predictive power ( Figure 7B ) ( 31 – 35 ).…”

Section: Resultsmentioning

confidence: 89%

Recurrence- and Malignant Progression-Associated Biomarkers in Low-Grade Gliomas and Their Roles in Immunotherapy

Teng

Zhu

et al. 2022

Front. Immunol.

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Despite a generally better prognosis than high-grade glioma (HGG), recurrence and malignant progression are the main causes for the poor prognosis and difficulties in the treatment of low-grade glioma (LGG). It is of great importance to learn about the risk factors and underlying mechanisms of LGG recurrence and progression. In this study, the transcriptome characteristics of four groups, namely, normal brain tissue and recurrent LGG (rLGG), normal brain tissue and secondary glioblastoma (sGBM), primary LGG (pLGG) and rLGG, and pLGG and sGBM, were compared using Chinese Glioma Genome Atlas (CGGA) and Genotype-Tissue Expression Project (GTEx) databases. In this study, 296 downregulated and 396 upregulated differentially expressed genes (DEGs) with high consensus were screened out. Univariate Cox regression analysis of data from The Cancer Genome Atlas (TCGA) yielded 86 prognostically relevant DEGs; a prognostic prediction model based on five key genes (HOXA1, KIF18A, FAM133A, HGF, and MN1) was established using the least absolute shrinkage and selection operator (LASSO) regression dimensionality reduction and multivariate Cox regression analysis. LGG was divided into high- and low-risk groups using this prediction model. Gene Set Enrichment Analysis (GSEA) revealed that signaling pathway differences in the high- and low-risk groups were mainly seen in tumor immune regulation and DNA damage-related cell cycle checkpoints. Furthermore, the infiltration of immune cells in the high- and low-risk groups was analyzed, which indicated a stronger infiltration of immune cells in the high-risk group than that in the low-risk group, suggesting that an immune microenvironment more conducive to tumor growth emerged due to the interaction between tumor and immune cells. The tumor mutational burden and tumor methylation burden in the high- and low-risk groups were also analyzed, which indicated higher gene mutation burden and lower DNA methylation level in the high-risk group, suggesting that with the accumulation of genomic mutations and epigenetic changes, tumor cells continued to evolve and led to the progression of LGG to HGG. Finally, the value of potential therapeutic targets for the five key genes was analyzed, and findings demonstrated that KIF18A was the gene most likely to be a potential therapeutic target. In conclusion, the prediction model based on these five key genes can better identify the high- and low-risk groups of LGG and lay a solid foundation for evaluating the risk of LGG recurrence and malignant progression.

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Molecular Subtypes and Prognostic Signature of Pyroptosis-Related lncRNAs in Glioma Patients

Cited by 21 publications

References 53 publications

Construction and Verification of a Novel Pyroptosis-Related lncRNA Signature Associated with Immune Landscape in Gliomas

Construction and Verification of a Novel Pyroptosis-Related lncRNA Signature Associated with Immune Landscape in Gliomas

Identification of cuproptosis‐related lncRNAs for prognosis and immunotherapy in glioma

Recurrence- and Malignant Progression-Associated Biomarkers in Low-Grade Gliomas and Their Roles in Immunotherapy

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