Journal: Data in Brief

Article Title: Data on microRNAs and microRNA-targeted mRNAs in Xenopus ectoderm

doi: 10.1016/j.dib.2016.09.054

Figure Lengend Snippet:

Article Snippet: The reference index was prepared from Xenopus laevis transcriptome dataset (courtesy of Taejoon Kwan and Ed Marcotte, UT Austin).

Techniques: Next-Generation Sequencing, Generated, Sequencing

Journal: Scientific Reports

Article Title: Aging and putative frailty biomarkers are altered by spaceflight

doi: 10.1038/s41598-024-57948-5

Figure Lengend Snippet: Genelab datasets analyzed in the manuscript.

Article Snippet: OSD-101 , Rodent Research-1 (RR1) NASA Validation Flight: Mouse left gastrocnemius muscle transcriptomic, proteomic, and epigenomic data , Galazka J, Globus R "Rodent Research 1", GeneLab, Version 5, http://doi.org/10.26030/sdmt-ae51 , Gastrocnemius muscle.

Techniques: Gene Expression, Expressing, Biomarker Discovery, Irradiation

Journal: NAR Cancer

Article Title: Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology

doi: 10.1093/narcan/zcac033

Figure Lengend 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.

Article Snippet: Single cell transcriptomics datasets for LGG and SKCM cancer types were downloaded from the Broad Institute Single Cell Portal under accession number SCP271 ( ) and GEO Accession viewer under accession ID GSE72056 , respectively.

Techniques: Gene Expression, Labeling

Journal: NAR Cancer

Article Title: Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology

doi: 10.1093/narcan/zcac033

Figure Lengend Snippet: TCGA cancer abbreviations. Cancers of interest are highlighted in colors

Article Snippet: Single cell transcriptomics datasets for LGG and SKCM cancer types were downloaded from the Broad Institute Single Cell Portal under accession number SCP271 ( ) and GEO Accession viewer under accession ID GSE72056 , respectively.

Techniques:

Journal: NAR Cancer

Article Title: Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology

doi: 10.1093/narcan/zcac033

Figure Lengend 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.

Article Snippet: Single cell transcriptomics datasets for LGG and SKCM cancer types were downloaded from the Broad Institute Single Cell Portal under accession number SCP271 ( ) and GEO Accession viewer under accession ID GSE72056 , respectively.

Techniques: Expressing

Journal: NAR Cancer

Article Title: Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology

doi: 10.1093/narcan/zcac033

Figure Lengend 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.

Article Snippet: Single cell transcriptomics datasets for LGG and SKCM cancer types were downloaded from the Broad Institute Single Cell Portal under accession number SCP271 ( ) and GEO Accession viewer under accession ID GSE72056 , respectively.

Techniques: Comparison

Journal: NAR Cancer

Article Title: Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology

doi: 10.1093/narcan/zcac033

Figure Lengend 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.

Article Snippet: Single cell transcriptomics datasets for LGG and SKCM cancer types were downloaded from the Broad Institute Single Cell Portal under accession number SCP271 ( ) and GEO Accession viewer under accession ID GSE72056 , respectively.

Techniques: Single-cell Transcriptomics, Expressing, Activity Assay