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. 2022 Jun 21;14(12):5034-5058.
doi: 10.18632/aging.204124. Epub 2022 Jun 21.

Multi-omics analysis reveals the panoramic picture of necroptosis-related regulators in pan-cancer

Affiliations

Multi-omics analysis reveals the panoramic picture of necroptosis-related regulators in pan-cancer

Guanghao Li et al. Aging (Albany NY). .

Abstract

Background: Unlike apoptosis, necroptosis is a tightly regulated form of programmed cell death (PCD) that occurs in a caspase-independent manner and is mainly triggered by receptor-interacting serine/threonine-protein kinases RIPK1 and RIPK3 and the RIPK3 substrate mixed-lineage kinase domain-like protein (MLKL). A growing body of evidence has documented that necroptosis, as a novel therapeutic strategy to overcome apoptosis resistance, has potential pro- or anti-tumoral effects in tumorigenesis, metastasis, and immunosurveillance. However, comprehensive multi-omics studies on regulators of necroptosis from a pan-cancer perspective are lacking.

Methods: In the present study, a pan-cancer multi-omics analysis of necroptosis-related regulators was performed by integrating over 10,000 multi-dimensional cancer genomic data across 33 cancer types from TCGA, 481 small-molecule drug response data from CTRP, and normal tissue data from GTEx. Pan-cancer pathway-level analyses of necroptosis were conducted by gene set variation analysis (GSVA), including differential expression, clinical relevance, immune cell infiltration, and regulation of cancer-related pathways.

Results: Genomic alterations and abnormal epigenetic modifications were associated with dysregulated gene expression levels of necroptosis-related regulators. Changes in the gene expression levels of necroptosis-related regulators significantly influenced cancer progression, intratumoral heterogeneity, alterations in the immunological condition, and regulation of cancer marker-related pathways. These changes, in turn, caused differences in potential drug sensitivity and the prognosis of patients.

Conclusion: Necroptosis-related regulators are expected to become novel biomarkers of prognosis and provide a fresh perspective on cancer diagnosis and treatment.

Keywords: anti-tumor immunity; genomics; methylation; necroptosis; pan-cancer.

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Conflict of interest statement

CONFLICTS OF INTEREST: The authors declare no conflicts of interest related to this study.

Figures

Figure 1
Figure 1
mRNA expression and survival analysis of necroptosis-related regulators. (A) mRNA expression of necroptosis-related regulators in the GTEx normal tissues. (B) Differential mRNA expression of necroptosis-related regulators between paired tumor and normal tissue. The size of dots is positively correlated with the FDR significance. The color of the bubble represents the fold change between tumor vs. normal. The bubble was filtered by the fold change (FC>2) and significance (FDR ≤ 0.05). (C) Subtype-related changes in gene expression of necroptosis-related regulators. The bubble color from white to red represents the FDR significance, and the bubble size is positively correlated with the FDR significance. The black outline border of bubble indicates FDR ≤ 0.05. (D) The trend of the gene expression of necroptosis-related regulators from stage I to stage IV in different cancers. The blue trend line and red trend line represent fall and rise tendency, respectively. (E) Survival analysis of necroptosis-related regulators in different cancers. The bubble color from blue to red represents the hazard ratio from low to high, bubble size is positively correlated with the Cox P-value significance.
Figure 2
Figure 2
Methylation analysis of necroptosis-related regulators. (A) Differential methylation status of necroptosis-related regulators between normal and tumor tissues in different cancers. The red bubble and blue bubble represent hypermethylation and hypomethylation in tumors, respectively. The bubble size is positively correlated with the FDR significance, and the bubble was filtered by FDR significance (FDR ≤ 0.05). (B) Correlation between methylation level and mRNA expression. The blue bubble and red bubble represent negative and positive correlations, respectively. The bubble size is positively correlated with the significance of FDR. (C) Methylation survival analysis of necroptosis-related regulators in different cancers. The bubble color from blue to red represents the hazard ratio from low to high, and the bubble size is positively correlated with the Cox P-value significance. (D) Kaplan-Meier curves between high and low methylation groups of MLKL in SKCM and LGG.
Figure 3
Figure 3
Single nucleotide variation (SNV) analysis of necroptosis-related regulators. (A) Oncoplot showing the SNV frequency distribution of necroptosis-related regulators in pan-cancer. Side barplot and top barplot show the number of variants in each gene and each sample, respectively. (B) The percentage heatmap showed the SNV frequency of necroptosis-related regulators in specific cancer type. The color depth is positively correlated with mutate frequency. The number in each cell represents the number of mutated samples in specific cancer. The 0 and blank in the cell indicate there is no mutation in specific gene coding region and all regions of a specific gene, respectively. (C) Lollipop plot showing the mutation site, type and frequency of TLR3 in UCEC. (D) Lollipop plot showing the mutation site, type and frequency of TLR3 in SKCM. (E) Kaplan-Meier curve between WT and Mutant groups of TLR3 in UCEC. (F) Kaplan-Meier curve between WT and Mutant groups of MLKL in SKCM.
Figure 4
Figure 4
Copy number variation (CNV) analysis of necroptosis-related regulators. (A) CNV pie plot showing the constitute of Heterozygous/Homozygous CNV of necroptosis-related regulators in different cancers. Hete Amp, heterozygous amplification; Homo Amp, homozygous amplification; Hete Del, heterozygous deletion; Homo Del, homozygous deletion; None, no CNV. (B) Heterozygous CNV plot showing the percentage of heterozygous amplification (red bubble) and deletion (blue bubble) of necroptosis-related regulators in different cancers. The bubble size is positively correlated with percentage. (C) The association between CNV level and mRNA expression of necroptosis-related regulators in different cancers. Blue bubble and red bubble represent negative and positive correlations, respectively. The deeper the color, the stronger the correlation. Bubble size is positively correlated with the FDR significance. (D) Scatter plot showing the correlation between TLR3 CNV and its mRNA expression in LIHC. (E) CNV survival analysis of necroptosis-related regulators in different cancers. (F) Kaplan-Meier curve showing the survival difference between different CNV types and wild type of TLR3 in LIHC.
Figure 5
Figure 5
The miRNA regulation network of necroptosis-related regulators. The connection between miRNA and gene suggests the miRNA can regulate the gene. The node size is positively correlated with the node's degree, and the width of the line is decided by the absolute value of the correlation coefficient.
Figure 6
Figure 6
Pathway activity analysis of necroptosis-related regulators. (A) Gene-pathway network showed the regulatory relationship between necroptosis-related regulators and cancer pathways in pan-cancer. Different colors represent different cancer types. (B) The heatmap showed the percentage of cancer types in which the specific necroptosis-related regulator has an effect (FDR ≤ 0.05) on the specific pathway in pan-cancer.
Figure 7
Figure 7
Immune subtype and drug sensitivity analysis of necroptosis-related regulators. (A) Expression differences of necroptosis-related regulators between six pan-cancer immune subtypes. (B) Bubble plot showing the correlation between drug sensitivity (IC50) and gene expression level of necroptosis-related regulators in CTRP database. Positive correlation (red bubble) indicates one gene with high expression was resistant to a drug, while negative correlation (blue bubble) indicates one gene with high expression was sensitive to a drug. The color depth and size of bubble are positively correlated with the correlation coefficient and the FDR significance, respectively. Black outline border indicates FDR ≤ 0.05.
Figure 8
Figure 8
Gene Set Enrichment Analysis (GSVA) analysis of necroptosis-related regulators. (A) The differences of necroptosis score between tumor and normal samples in pan-cancer. The necroptosis score represents the integrated level of the expression of necroptosis-related regulators, which is positively correlated with gene expression. (B) The trend of the necroptosis score from stage I to stage IV in different cancers. The blue trend line and red trend line represent fall and rise tendency, respectively. (C) Box plot showing the differences of necroptosis score between different cancer subtypes. (D) Survival analysis of necroptosis score in different cancer types, including overall survival (OS), progression-free survival (PFS), disease-specific survival (DSS), and disease-free survival (DFI). (E) Heatmap showing the correlation between the necroptosis score and immune cell infiltration in different cancer types. *P ≤ 0.05; #FDR ≤ 0.05. (F) Heatmap showing the correlation between the necroptosis score and pathway activity in different cancer types. *P ≤ 0.05; #FDR ≤ 0.05.

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