Dataset Information

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.

ABSTRACT: BACKGROUND:The AMP-activated protein kinase (AMPK) is an evolutionarily conserved regulator of cellular energy homeostasis. As a nexus for transducing metabolic signals, AMPK cooperates with other energy-sensing pathways to modulate cellular responses to metabolic stressors. With metabolic reprogramming being a hallmark of cancer, the utility of agents targeting AMPK has received continued scrutiny and results have demonstrated conflicting effects of AMPK activation in tumorigenesis. Harnessing multi-omics datasets from human tumors, we seek to evaluate the seemingly pleiotropic, tissue-specific dependencies of AMPK signaling dysregulation. METHODS:We interrogated copy number variation and differential transcript expression of 92 AMPK pathway genes across 21 diverse cancers involving over 18,000 patients. Cox proportional hazards regression and receiver operating characteristic analyses were used to evaluate the prognostic significance of AMPK dysregulation on patient outcomes. RESULTS:A total of 24 and seven AMPK pathway genes were identified as having loss- or gain-of-function features. These genes exhibited tissue-type dependencies, where survival outcomes in glioma patients were most influenced by AMPK inactivation. Cox regression and log-rank tests revealed that the 24-AMPK-gene set could successfully stratify patients into high- and low-risk groups in glioma, sarcoma, breast and stomach cancers. The 24-AMPK-gene set could not only discriminate tumor from non-tumor samples, as confirmed by multidimensional scaling analyses, but is also independent of tumor, node and metastasis staging. AMPK inactivation is accompanied by the activation of multiple oncogenic pathways associated with cell adhesion, calcium signaling and extracellular matrix organization. Anomalous AMPK signaling converged on similar groups of transcriptional targets where a common set of transcription factors were identified to regulate these targets. We also demonstrated crosstalk between pro-catabolic AMPK signaling and two pro-anabolic pathways, mammalian target of rapamycin and peroxisome proliferator-activated receptors, where they act synergistically to influence tumor progression significantly. CONCLUSION:Genetic and transcriptional aberrations in AMPK signaling have tissue-dependent pro- or anti-tumor impacts. Pan-cancer investigations on molecular changes of this pathway could uncover novel therapeutic targets and support risk stratification of patients in prospective trials.

SUBMITTER: Chang WH

PROVIDER: S-EPMC7433212 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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.

Chang Wai Hoong WH Lai Alvina G AG

BMC cancer 20200817 1

<h4>Background</h4>The AMP-activated protein kinase (AMPK) is an evolutionarily conserved regulator of cellular energy homeostasis. As a nexus for transducing metabolic signals, AMPK cooperates with other energy-sensing pathways to modulate cellular responses to metabolic stressors. With metabolic reprogramming being a hallmark of cancer, the utility of agents targeting AMPK has received continued scrutiny and results have demonstrated conflicting effects of AMPK activation in tumorigenesis. Har ...[more]

PMID: 32807122

Similar Datasets

Project description:BackgroundThe advent of high throughput sequencing has enabled researchers to systematically evaluate the genetic variations in cancer, identifying many cancer-associated genes. Although cancers in the same tissue are widely categorized in the same group, they demonstrate many differences concerning their mutational profiles. Hence, there is no definitive treatment for most cancer types. This reveals the importance of developing new pipelines to identify cancer-associated genes accurately and re-classify patients with similar mutational profiles. Classification of cancer patients with similar mutational profiles may help discover subtypes of cancer patients who might benefit from specific treatment types.ResultsIn this study, we propose a new machine learning pipeline to identify protein-coding genes mutated in many samples to identify cancer subtypes. We apply our pipeline to 12,270 samples collected from the international cancer genome consortium, covering 19 cancer types. As a result, we identify 17 different cancer subtypes. Comprehensive phenotypic and genotypic analysis indicates distinguishable properties, including unique cancer-related signaling pathways.ConclusionsThis new subtyping approach offers a novel opportunity for cancer drug development based on the mutational profile of patients. Additionally, we analyze the mutational signatures for samples in each subtype, which provides important insight into their active molecular mechanisms. Some of the pathways we identified in most subtypes, including the cell cycle and the Axon guidance pathways, are frequently observed in cancer disease. Interestingly, we also identified several mutated genes and different rates of mutation in multiple cancer subtypes. In addition, our study on "gene-motif" suggests the importance of considering both the context of the mutations and mutational processes in identifying cancer-associated genes. The source codes for our proposed clustering pipeline and analysis are publicly available at: https://github.com/bcb-sut/Pan-Cancer .

Project description:Background: PDZ binding kinase (PBK) is a serine/threonine kinase, which belongs to the mitogen-activated protein kinase kinase (MAPKK) family. It has been shown to be a critical gene in the regulation of mitosis and tumorigenesis, but the role of PBK in various cancers remains unclear. In this study, we systematically explored the prognostic and predictive value of PBK expression in 33 cancer types. Methods: Public databases including the cBioPortal database, GDSC database, GTEx database, CCLE database, and TCGA database were used to detect the PBK expression and its association with the prognosis, clinicopathologic stage, TMB, MSI, immune microenvironment, immune checkpoints, immune cell infiltration, enrichment pathways, and IC50 across pan-cancer. The statistical analyses and visualization were conducted using R software. Results: PBK expression is relatively high in most cancers compared to their normal counterparts, and this gene is barely expressed in normal tissues. High expression of PBK is significantly associated with poor prognosis and clinicopathologic stages I, II, and III in different cancers. Furthermore, PBK expression is strongly associated with TMB in 23 cancer types and associated with MSI in nine cancer types. Moreover, the correlation analysis of the microenvironment and immune cells indicated that PBK is negatively correlated with the immune infiltration levels but positively correlated with the infiltration levels of M0 and M1 macrophages, T cells CD4 memory activated, and T cells follicular helper. GSEA analysis revealed that the biological function or pathways relevant to the cell cycle and mitosis were frequently enriched at the level of high expression of PBK. Conclusion: These results revealed the oncogenic role of PBK, which is significantly upregulated in various cancers and indicated poor prognosis and immune infiltration in multiple cancers. It also suggested that PBK may serve as a biomarker in multiple tumor progress and patient survival.

Dataset Information

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.

Publications

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.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets