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

Journal: Molecular Cancer

Article Title: NAC1 promotes stemness and regulates myeloid-derived cell status in triple-negative breast cancer

doi: 10.1186/s12943-024-02102-y

Figure Lengend Snippet: Effect of NAC1 on proliferation, migration, and invasion of TNBC cells. A Western blot of NAC1 in TNBC cells transfected si-NACC1 or si-NT. B , C Proliferation of TNBC cells with or without depletion of NAC1. D Clonogenic formation of MDA-MB-231 cells with or without depletion of NAC1. E Migration of MDA-MB-231 cells with or without depletion of NAC1. F Wound healing assay for the migratory ability of MDA-MB-231 cells with or without depletion of NAC1. G Matrigel assay for the migratory ability of MDA-MB-231 cells with or without depletion of NAC1

Article Snippet: Analysis of the TNBC single-cell Broad Institute datasets [ ] showed that NACC1 was not only expressed in tumor cells but also in various immune cells (Fig.

Techniques: Migration, Western Blot, Transfection, Wound Healing Assay, Matrigel Assay

Journal: Molecular Cancer

Article Title: NAC1 promotes stemness and regulates myeloid-derived cell status in triple-negative breast cancer

doi: 10.1186/s12943-024-02102-y

Figure Lengend Snippet: Myeloid-derived cells with expression of NAC1 supports CSCs. A Tumor growth rate for knockdown 4T1 allografted cells with or without NK cell depletion. B Expression of NAC1 in MDSCs from 4T1 tumor-bearing or tumor-free BALB/C mice. C Gr1 + /CD11b + cells were isolated from the NACC1 +/+ or NACC1 −/− mice bearing EO771 tumors through negative selection to obtain MDSCs and were further purified using CD45 positive selection assay kit (Stemcell technologies) to eliminate contaminating tumor cells. D EO771 cells co-cultured with NACC1 −/− Gr1 + /CD11b + cells showed reduced CD44 expression compared to the co-culture with NACC1 +/+ Gr1 + /CD11b + cells. E Aldolase activity of EO771 cells co-cultured with NACC1 +/+ or NACC1 −/− mice Gr1 + /CD11b + cells. F Tumor initiation ability of EO771 cells orthotopically inoculated in NACC1 +/+ or NACC1 −/− mice ( n = 5, Tn = 2). G-I EO771 cells were co-cultured with NACC1 +/+ or NACC1 −/− Gr1 + /CD11b + cells, and cell viability was determined using the CellTrace™ CFSE Cell Proliferation Kit ( G ) or luciferase assay ( H )

Article Snippet: Analysis of the TNBC single-cell Broad Institute datasets [ ] showed that NACC1 was not only expressed in tumor cells but also in various immune cells (Fig.

Techniques: Derivative Assay, Expressing, Knockdown, Isolation, Selection, Purification, Cell Culture, Co-Culture Assay, Activity Assay, Luciferase