The pan‐cancer mutational landscape of the PPAR pathway reveals universal patterns of dysregulated metabolism and interactions with tumor immunity and hypoxia

Abstract

Peroxisome proliferator‐activated receptors (PPARs) are a family of nuclear receptors that regulate lipid metabolism and bioenergetic demands within living systems. Consequently, aberrant expression of PPAR genes could predispose individuals to diseases, including cancer. PPAR signaling exerts pleiotropic functions in cancer, yet little is known about the interactions between genetic and transcriptional events of pathway genes in a pan‐cancer context. Employing multidimensional datasets of over 18,000 patients involving 21 cancers, we performed systematic characterization on copy number alteration and differential transcript expression of 74 PPAR pathway genes. We identified 18 genes demonstrating mutually exclusive patterns of loss‐ and gain‐of‐function phenotypes. These genes successfully predicted patient survival rates in bladder, renal, glioma, liver, and stomach/esophageal cancers. Dysregulated PPAR signaling in these cancers converged on common downstream pathways associated with multiple metabolic processes. Moreover, clinically relevant relationships between PPARs and hypoxia were observed, where hypoxia further aggravates disease phenotypes in tumor subtypes with aberrant PPAR signaling. In glioma samples, including astrocytoma and oligoastrocytoma, PPAR hyperactivation is associated with immunosuppression through increased regulatory T cell expression. Our analysis reveals underappreciated levels of diversity and conservation in PPAR genes that could lay the groundwork for therapeutic strategies targeting tumor metabolism, immunity, and hypoxia.

An integrative pan-cancer investigation reveals common genetic and transcriptional alterations of AMPK pathway genes as important predictors of clinical outcomes across major cancer types

“…Copy number variation, differential expression, multidimensional scaling and survival analyses: Detailed methods of the above analyses were previously published and thus will not be repeated here as per the journal guidelines (19,20). To summarize, discrete amplification and deletion indicators for copy number variation analyses were obtained from GISTIC gene-level tables (21).…”

“…Calculating the 24-AMPK-gene score, peroxisome proliferator-activated receptors (PPAR) score and mammalian target of rapamycin (mTOR) score: AMPK scores were calculated from the mean expression of the following genes : SLC2A4, FOXO3, PPP2CB, PIK3CD, CAB39L, CCNA1, FBP1, FBP2, FOXO1, HMGCR, IRS2, PIK3R1, SIRT1, TBC1D1, PPARGC1A, PPP2R2C, MLYCD, PFKFB3, PPP2R2B, PRKAA2, LEPR, CAB39, IRS1 and PFKFB1. PPAR scores for each patient were calculated by taking the mean expression of PPAR signature genes: PLIN5, PPARG, ACADM, GK, CPT2, SCP2, ACAA1, APOA1, PPARA, ACOX2, ANGPTL4, FABP3, PLIN2, AQP7, ACSL1, FABP5, ACADL, and PCK2 (20). mTOR/PI3K/AKT scores for each patient were calculated using the following equation: mTOR/PI3K/AKT score = AKT + mTOR + GSK3 + S6K + S6 -PTEN (25).…”

An integrative pan-cancer investigation reveals common genetic and transcriptional alterations of AMPK pathway genes as important predictors of clinical outcomes across major cancer types

“…Detailed methods of the above analyses were previously published and thus will not be repeated here as per the journal guidelines [19][20][21][22][23][24][25][26]. To summarize, discrete amplification and deletion indicators for copy number variation analyses were obtained from GISTIC gene-level tables [27].…”

“…Calculating the 24-AMPK-gene score, peroxisome proliferator-activated receptors (PPAR) score and mammalian target of rapamycin (mTOR) score AMPK scores were calculated from the mean expression of the following genes: SLC2A4, FOXO3, PPP2CB, PIK3CD, CAB39L, CCNA1, FBP1, FBP2, FOXO1, HMGC R, IRS2, PIK3R1, SIRT1, TBC1D1, PPARGC1A, PPP2R2C, MLYCD, PFKFB3, PPP2R2B, PRKAA2, LEPR, CAB39, IRS1 and PFKFB1. PPAR scores for each patient were calculated by taking the mean expression of PPAR signature genes: PLIN5, PPARG, ACADM, GK, CPT2, SCP2, ACAA1, APOA1, PPARA, ACOX2, ANGPTL4, FABP3, PLIN2, AQP7, ACSL1, FABP5, ACADL, and PCK2 [19]. mTOR/PI3K/AKT scores for each patient were calculated using the following equation: mTOR/ PI3K/AKT score = AKT + mTOR + GSK3 + S6K + S6 -PTEN [31].…”

DNA methylation regulates glioma cell cycle through down-regulating MiR-133a expression

“…PPARγ is a peroxisome proliferator-activated receptor that belongs to type II nuclear hormone receptor family, and elicits numerous biological effects upon ligand binding and activation [13]. Recent studies show that activated PPARγ in tumor cells leads to the constitutive activation of various signaling pathways [14]. In pancreatic cancer cells for example, activated PPARγ can induce differentiation, regulate cell cycle and modulate the expression levels of apoptotic and anti-apoptotic genes, thus driving tumor growth [15].…”