Molecular and Metabolic Subtypes Correspondence for Pancreatic Ductal Adenocarcinoma Classification

Espiau-Romera, Pilar; Courtois, Sarah; Parejo-Alonso, Beatriz; Sancho, Patricia

doi:10.3390/jcm9124128

Cited by 23 publications

(16 citation statements)

References 98 publications

(161 reference statements)

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“…Pilar Espiau-Romera's review summarized the correspondence between the molecular subtypes and metabolic subtypes of PDAC, and the metabolic reprogramming involved in PDAC included lipid metabolism, glycolysis, and amino acid consumption. The survival analysis showed that the subtype classification of PDAC based on metabolism was more significant for clinical diagnosis and treatment, because it can not only indicate the prognosis of patients, but also reflect the invasion, drug resistance and other biological characteristics of PDAC (Espiau-Romera et al, 2020 ). In this study, we used the TCGA-PAAD cohort to analyze the effect of glycolysis status on the OS of PDAC patients, suggesting the potential value of glycolysis-related gene signature as prognostic markers.…”

Section: Discussionmentioning

confidence: 99%

Glycolysis-Related Gene Expression Profiling Screen for Prognostic Risk Signature of Pancreatic Ductal Adenocarcinoma

Song

Gong

et al. 2021

Front. Genet.

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Objective: Pancreatic ductal adenocarcinoma (PDAC) is highly lethal. Although progress has been made in the treatment of PDAC, its prognosis remains unsatisfactory. This study aimed to develop novel prognostic genes related to glycolysis in PDAC and to apply these genes to new risk stratification.Methods: In this study, based on the Cancer Genome Atlas (TCGA) PAAD cohort, the expression level of glycolysis-related gene at mRNA level in PAAD and its relationship with prognosis were analyzed. Non-negative matrix decomposition (NMF) clustering was used to cluster PDAC patients according to glycolytic genes. Prognostic glycolytic genes, screened by univariate Cox analysis and LASSO regression analysis were established to calculate risk scores. The differentially expressed genes (DEGs) in the high-risk group and the low-risk group were analyzed, and the signal pathway was further enriched to analyze the correlation between glycolysis genes. In addition, based on RNA-seq data, CIBERSORT was used to evaluate the infiltration degree of immune cells in PDAC samples, and ESTIMATE was used to calculate the immune score of the samples.Results: A total of 319 glycolysis-related genes were retrieved, and all PDAC samples were divided into two clusters by NMF cluster analysis. Survival analysis showed that PDAC patients in cluster 1 had shorter survival time and worse prognosis compared with cluster 2 samples (P < 0.001). A risk prediction model based on 11 glycolysis genes was constructed, according to which patients were divided into two groups, with significantly poorer prognosis in high-risk group than in low-risk group (P < 0.001). Both internal validation and external dataset validation demonstrate good predictive ability of the model (AUC = 0.805, P < 0.001; AUC = 0.763, P < 0.001). Gene aggregation analysis showed that DEGs highly expressed in high-risk group were mainly concentrated in the glycolysis level, immune status, and tumor cell proliferation, etc. In addition, the samples in high-risk group showed immunosuppressed status and infiltrated by relatively more macrophages and less CD8+T cell.Conclusions: These findings suggested that the gene signature based on glycolysis-related genes had potential diagnostic, therapeutic, and prognostic value for PDAC.

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Section: Discussionmentioning

confidence: 99%

Glycolysis-Related Gene Expression Profiling Screen for Prognostic Risk Signature of Pancreatic Ductal Adenocarcinoma

Song

Gong

et al. 2021

Front. Genet.

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“…The extensive fibrosis and matrix deposition prevent proper PDAC tumour vascularisation, generating a hypoxic microenvironment with limited nutrient availability. To thrive in this environment, PDAC cells develop a distinct metabolic programme generally characterised by increased glucose and glutamine uptake and metabolism, lipid and protein scavenging, and autophagy to recycle cellular components ( Espiau-Romera et al, 2020 ). Interestingly, extracellular matrix (ECM) proteins, such as collagen, represent an alternate source of energy for PDAC cells, providing proline that feeds the TCA cycle, thus helping PDAC cells to cope with nutrient deprivation ( Olivares et al, 2017 ).…”

Section: Nutrient Starvation In Pancreatic Ductal Adenocarcinomamentioning

confidence: 99%

“…Globally, PDAC metabolic reprogramming improves survival in presence of low glucose levels ( Bryant et al, 2014 ), by enhancing glycolysis, which then fuels the anabolic branches of PDAC metabolism to provide the cancer cells with building blocks for proliferation ( Ying et al, 2012 ). Accordingly, a glycolytic gene expression signature is related to bad PDAC prognosis, invasiveness and metastatic onset ( Espiau-Romera et al, 2020 ; Tian et al, 2020 ). In fact, samples from metastatic PDAC showed an important enrichment of a glycolysis-related gene signature ( Chaika et al, 2012 ).…”

Section: Nutrient Starvation In Pancreatic Ductal Adenocarcinomamentioning

confidence: 99%

“…Chen et al (2013a),Lin et al (2016) OXPHOS with plasticity towards glycolysisVlashi et al (2011),Shibao et al (2018) Glycolysis related to bad prognosis and metastasisEspiau-Romera et al (2020);Tian et al, 2020) InhibitorsMetformin inhibits self-renewalSancho et al (2015) Metformin inhibits proliferation and invasionFerretti et al (2019) MCT, 2DG inhibit metastasisRoy et al (2015),Kong et al (2016) TABLE 3 | Effects of amino acids in the studied cancer types. and progression Martínez-Chantar et al (2002)↑ Met uptake Palanichamy et al (2016) ↑Stemness Zgheib et al (2019) Tryptophan IDO1 ↑ IDO1 ↑ IDO1 ↑ ↑ Nucleotide synthesis, tumour growth and stemness Newman et al (2021) ↑ distant metastases and immune suppression Opitz et al (2020), Shibata et al (2016) ↑ immune suppression and shorter survival Zhai et al (2017), (Wainwright et al (2012), (2014); Lukas et al (2019) NEAA/ CEAA Arginine ARG2 ↑ ASS1 ↑ tumourigenesis, clonogenicity, anchorage-independent cell growth and invasiveness Wang et al (2016), Zaytouni et al (2017) ↓ ASS1 ↑dependency on exogenous Arg for cell proliferation, growth, and survival Ensor et al (2002) ↓ ASS1 (30% of patients) ↑ dependency on exogenous Arg Syed et al (2013), Mörén et al (2018) to support cell viability and invasion Pavlyk et al (2015) ADI-PEG 20 ↓ stemness Kim et al (2020) Purified ADI ↓ proliferation Takaku et al (1995) ADI-PEG 20 ↑survival and therapeutic synergy with temozolomide in GBM expressing ASS1 Przystal et al (2018) Asparagine ASNS ↓ Dufour et al (2012) ASNS ↓ Zhang et al (2013) ASNS ↑ ↑chemoresistance after ASNS upregulation by glucose deprivation, Cui et al (2007), Pathria et al (2019) L-Asparaginase ↓ proliferation ↑ apoptosis.…”

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confidence: 99%

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Glucose and Amino Acid Metabolic Dependencies Linked to Stemness and Metastasis in Different Aggressive Cancer Types

et al. 2021

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Malignant cells are commonly characterised by being capable of invading tissue, growing self-sufficiently and uncontrollably, being insensitive to apoptosis induction and controlling their environment, for example inducing angiogenesis. Amongst them, a subpopulation of cancer cells, called cancer stem cells (CSCs) shows sustained replicative potential, tumor-initiating properties and chemoresistance. These characteristics make CSCs responsible for therapy resistance, tumor relapse and growth in distant organs, causing metastatic dissemination. For these reasons, eliminating CSCs is necessary in order to achieve long-term survival of cancer patients. New insights in cancer metabolism have revealed that cellular metabolism in tumors is highly heterogeneous and that CSCs show specific metabolic traits supporting their unique functionality. Indeed, CSCs adapt differently to the deprivation of specific nutrients that represent potentially targetable vulnerabilities. This review focuses on three of the most aggressive tumor types: pancreatic ductal adenocarcinoma (PDAC), hepatocellular carcinoma (HCC) and glioblastoma (GBM). The aim is to prove whether CSCs from different tumour types share common metabolic requirements and responses to nutrient starvation, by outlining the diverse roles of glucose and amino acids within tumour cells and in the tumour microenvironment, as well as the consequences of their deprivation. Beyond their role in biosynthesis, they serve as energy sources and help maintain redox balance. In addition, glucose and amino acid derivatives contribute to immune responses linked to tumourigenesis and metastasis. Furthermore, potential metabolic liabilities are identified and discussed as targets for therapeutic intervention.

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“…Nonetheless, genomic profiling has uncovered distinct PDAC molecular subgroups with their own metabolic and prognostic differences that may explain the clinical heterogeneity in PDAC outcomes [20][21][22][23][24][25][26][27][28]. Moffit's classical versus basal subtype classification appears to be the simplest given its binary nature [24].…”

Section: Introductionmentioning

confidence: 99%

PRMT5: An Emerging Target for Pancreatic Adenocarcinoma

Lee

Grimmond

McArthur

et al. 2021

Cancers

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The overall survival of pancreatic ductal adenocarcinoma (PDAC) remains poor and its incidence is rising. Targetable mutations in PDAC are rare, thus novel therapeutic approaches are needed. Protein arginine methyltransferase 5 (PRMT5) overexpression is associated with worse survival and inhibition of PRMT5 results in decreased cancer growth across multiple cancers, including PDAC. Emerging evidence also suggests that altered RNA processing is a driver in PDAC tumorigenesis and creates a partial dependency on this process. PRMT5 inhibition induces altered splicing and this vulnerability can be exploited as a novel therapeutic approach. Three possible biological pathways underpinning the action of PRMT5 inhibitors are discussed; c-Myc regulation appears central to its action in the PDAC setting. Whilst homozygous MTAP deletion and symmetrical dimethylation levels are associated with increased sensitivity to PRMT5 inhibition, neither measure robustly predicts its growth inhibitory response. The immunomodulatory effect of PRMT5 inhibitors on the tumour microenvironment will also be discussed, based on emerging evidence that PDAC stroma has a significant bearing on disease behaviour and response to therapy. Lastly, with the above caveats in mind, current knowledge gaps and the implications and rationales for PRMT5 inhibitor development in PDAC will be explored.

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Molecular and Metabolic Subtypes Correspondence for Pancreatic Ductal Adenocarcinoma Classification

Cited by 23 publications

References 98 publications

Glycolysis-Related Gene Expression Profiling Screen for Prognostic Risk Signature of Pancreatic Ductal Adenocarcinoma

Glycolysis-Related Gene Expression Profiling Screen for Prognostic Risk Signature of Pancreatic Ductal Adenocarcinoma

Glucose and Amino Acid Metabolic Dependencies Linked to Stemness and Metastasis in Different Aggressive Cancer Types

PRMT5: An Emerging Target for Pancreatic Adenocarcinoma

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