single cell transcriptomics datasets for lgg cancer type (Broad Institute Inc)
90
Structured Review
Broad Institute Inc
single cell transcriptomics datasets for lgg cancer type
![PANoptosis has a prognostic impact in cancers. ( A ) Consensus Clustering showing three distinct clusters (PANoptosis low, PANoptosis medium and PANoptosis high) based on PANoptosis gene expression <t>for</t> <t>SKCM.</t> ( B ) Heatmap depicting gene expression profiles of 27 PANoptosis markers including sensors and upstream regulators, adaptors and effectors of PANoptosis as scaled Z-scores for SKCM tumor samples. For brevity, 13 out of the 27 genes are labeled, but 27 distinct rows are shown. ( C ) Boxplot showing the distribution of PANoptosis scores in the three PANoptosis clusters for cancer subtypes of interest: <t>LGG,</t> KIRC and SKCM. ( D ) Forest plot showing N1 = number of samples in PANoptosis high cluster, N2 = number of samples in PANoptosis low cluster, P -value and hazard ratio (HR) with 95% CI for overall survival (OS) when comparing PANoptosis high versus low for each cancer type where there is significant prognostic impact ( P -value < 0.05). ( E–G ) Kaplan–Meier curves showing OS across the PANoptosis high and PANoptosis low groups in the three cancer types with significant differences in survival (PANoptosis high beneficial [HR < 1] or detrimental [HR > 1]). *** P -value < 0.001.](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_3737/pmc09623737/pmc09623737__zcac033fig1.jpg)
Single Cell Transcriptomics Datasets For Lgg Cancer Type, supplied by Broad Institute Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/single+cell+transcriptomics/pmc09623737-91-5-14?v=Broad+Institute+Inc
Average 90 stars, based on 1 article reviews
Single Cell Transcriptomics Datasets For Lgg Cancer Type, supplied by Broad Institute Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/single+cell+transcriptomics/pmc09623737-91-5-14?v=Broad+Institute+Inc
Average 90 stars, based on 1 article reviews
single cell transcriptomics datasets for lgg cancer type - by Bioz Stars,
2026-08
90/100 stars
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1) Product Images from "Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology"
Article Title: Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology
Journal: NAR Cancer
doi: 10.1093/narcan/zcac033
Figure Legend Snippet: PANoptosis has a prognostic impact in cancers. ( A ) Consensus Clustering showing three distinct clusters (PANoptosis low, PANoptosis medium and PANoptosis high) based on PANoptosis gene expression for SKCM. ( B ) Heatmap depicting gene expression profiles of 27 PANoptosis markers including sensors and upstream regulators, adaptors and effectors of PANoptosis as scaled Z-scores for SKCM tumor samples. For brevity, 13 out of the 27 genes are labeled, but 27 distinct rows are shown. ( C ) Boxplot showing the distribution of PANoptosis scores in the three PANoptosis clusters for cancer subtypes of interest: LGG, KIRC and SKCM. ( D ) Forest plot showing N1 = number of samples in PANoptosis high cluster, N2 = number of samples in PANoptosis low cluster, P -value and hazard ratio (HR) with 95% CI for overall survival (OS) when comparing PANoptosis high versus low for each cancer type where there is significant prognostic impact ( P -value < 0.05). ( E–G ) Kaplan–Meier curves showing OS across the PANoptosis high and PANoptosis low groups in the three cancer types with significant differences in survival (PANoptosis high beneficial [HR < 1] or detrimental [HR > 1]). *** P -value < 0.001.
Techniques Used: Gene Expression, Labeling
Figure Legend Snippet: TCGA cancer abbreviations. Cancers of interest are highlighted in colors
Techniques Used:
Figure Legend Snippet: Multiple survival models identify key prognostic PANoptosis markers for LGG, KIRC and SKCM. ( A ) Forest plot for key PANoptosis genes whose high expression leads to a poor prognosis for LGG identified through univariate survival models. ( B ) PANoptosis genes with non-zero coefficients and the fraction of times they appeared during the 100 random runs of the GLMnet model for LGG. ( C ) Top 10 PANoptosis genes with highest prognostic relevance determined by the optimal RFS model for LGG. ( D ) Forest plot for key PANoptosis genes whose high expression leads to a poor prognosis for KIRC identified through univariate survival models. ( E ) PANoptosis genes with non-zero coefficients and the fraction of times they appeared during the 100 random runs of the GLMnet model for KIRC. ( F ) Top 10 PANoptosis genes with highest prognostic relevance determined by the optimal RFS model for KIRC. ( G ) Forest plot for key PANoptosis genes whose high expression leads to better prognosis for SKCM identified by univariate survival models. ( H ) PANoptosis genes with non-zero coefficients and the fraction of times they appeared during the 100 random runs of the GLMnet model for SKCM. ( I ) Top 10 PANoptosis genes with highest prognostic relevance determined by the optimal RFS model for SKCM. (A–I) Blue bars represent a negative coefficient (higher expression is beneficial for survival), and red bars represent a positive coefficient (higher expression is detrimental for survival). The orange boxes highlight the genes which are prognostic across the univariate, GLMNet and RFS survival models and were considered as the ‘Top’ PANoptosis markers. (B, C, E, F, H, I) The boxplots correspond to variable importance estimated using a subsampling approach.
Techniques Used: Expressing
Figure Legend Snippet: Survival models built using key PANoptosis markers predict survival on independent test sets. ( A ) Comparison of AUC metric at t ∈ {2,4,5} years between Coxnet, GLMnet and RFS survival models for LGG. ( B ) Comparison of AUC metric at t ∈ {2,3,5} years between Coxnet, GLMnet and RFS survival models for KIRC. ( C ) Comparison of AUC metric at t ∈ {1,2,3} years between Coxnet, GLMnet and RFS models for SKCM.
Techniques Used: Comparison
Figure Legend Snippet: Single cell transcriptomics provides evidence for PANoptosis in individual cells in LGG and SKCM datasets. ( A ) Expression profiles of PANoptosis genes across different cell types in the LGG dataset. ( B ) PANoptosis activity across different cell types in the LGG dataset estimated using ssGSEA. ( C ) Expression profiles of PANoptosis genes across different cell types for the SKCM dataset. ( D ) PANoptosis activity across different cell types in the SKCM dataset estimated using ssGSEA.
Techniques Used: Single-cell Transcriptomics, Expressing, Activity Assay