Dataset Information

Lipid metabolism in pancreatic cancer: emerging roles and potential targets.

ABSTRACT: Pancreatic cancer is one of the most serious health issues in developed and developing countries, with a 5-year overall survival rate currently <9%. Patients typically present with advanced disease due to vague symptoms or lack of screening for early cancer detection. Surgical resection represents the only chance for cure, but treatment options are limited for advanced diseases, such as distant metastatic or locally progressive tumors. Although adjuvant chemotherapy has improved long-term outcomes in advanced cancer patients, its response rate is low. So, exploring other new treatments is urgent. In recent years, increasing evidence has shown that lipid metabolism can support tumorigenesis and disease progression as well as treatment resistance through enhanced lipid synthesis, storage, and catabolism. Therefore, a better understanding of lipid metabolism networks may provide novel and promising strategies for early diagnosis, prognosis estimation, and targeted therapy for pancreatic cancer patients. In this review, we first enumerate and discuss current knowledge about the advances made in understanding the regulation of lipid metabolism in pancreatic cancer. In addition, we summarize preclinical studies and clinical trials with drugs targeting lipid metabolic systems in pancreatic cancer. Finally, we highlight the challenges and opportunities for targeting lipid metabolism pathways through precision therapies in pancreatic cancer.

SUBMITTER: Yin X

PROVIDER: S-EPMC9759769 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Lipid metabolism in pancreatic cancer: emerging roles and potential targets.

Yin Xinpeng X Xu Ruiyuan R Song Jianlu J Ruze Rexiati R Chen Yuan Y Wang Chengcheng C Xu Qiang Q

Cancer communications (London, England) 20220915 12

Pancreatic cancer is one of the most serious health issues in developed and developing countries, with a 5-year overall survival rate currently <9%. Patients typically present with advanced disease due to vague symptoms or lack of screening for early cancer detection. Surgical resection represents the only chance for cure, but treatment options are limited for advanced diseases, such as distant metastatic or locally progressive tumors. Although adjuvant chemotherapy has improved long-term outcom ...[more]

PMID: 36107801

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Project description:BackgroundProstate cancer (PCa), the second most prevalent solid tumor among men worldwide, has caused greatly increasing mortality in PCa patients. The effects of lipid metabolism on tumor growth have been explored, but the mechanistic details of the association of lipid metabolism disorders with PCa remain largely elusive.MethodsThe RNA sequencing data of the GSE45604 and The Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD) datasets were extracted from the Gene Expression Omnibus (GEO) and UCSC Xena databases, respectively. The Molecular Signatures Database (MSigDB) was utilized to identify lipid metabolism-related genes. The limma R package was used to identify differentially expressed lipid metabolism-related genes (DE-LMRGs) and differentially expressed microRNAs (DEMs). Moreover, least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), and support vector machine-recursive feature elimination (SVM-RFE) were applied to select signature miRNAs and construct a lipid metabolism-related diagnostic model. The expression levels of selected differentially expressed lipid metabolism-related miRNAs (DE-LMRMs) in PCa and benign prostate hyperplasia (BPH) specimens were verified using quantitative real-time polymerase chain reaction (qRT‒PCR). Furthermore, a transcription factor (TF)-miRNA‒mRNA network was constructed. Eventually, Kaplan‒Meier (KM) curves were plotted to illustrate the associations between signature miRNA-related mRNAs and TFs and overall survival (OS) along with biochemical recurrence-free survival (BCR).ResultsForty-seven LMRMs were screened based on the correlation analysis of 29 DE-LMRGs and 56 DEMs, in which 27 LMRMs were stably expressed in the GSE45604 dataset. Subsequently, receiver operating characteristic (ROC) curves and machine learning methods were employed to develop a lipid metabolism-related diagnostic signature, which may be of diagnostic value for PCa patients. qRT‒PCR results showed that all seven key DE-LMRMs were differentially expressed between PCa and BPH tissues. Eventually, a TF-miRNA‒mRNA network was constructed.ConclusionsThese results suggested that 7 key diagnostic miRNAs were closely related to PCa pathological processes and provided new targets for the diagnosis and treatment of PCa. Moreover, CLIC6 and SCNN1A linked to miR-200c-3p had good prognostic potential and provided valuable insights into the pathogenesis of PCa.

Dataset Information

Lipid metabolism in pancreatic cancer: emerging roles and potential targets.

Publications

Lipid metabolism in pancreatic cancer: emerging roles and potential targets.

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