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Prostate Cancer Secretome and Membrane Proteome from Pten Conditional Knockout Mice Identify Potential Biomarkers for Disease Progression.

ABSTRACT: Prostate cancer (PCa) is the second most common cause of mortality among men. Tumor secretome is a promising strategy for understanding the biology of tumor cells and providing markers for disease progression and patient outcomes. Here, transcriptomic-based secretome analysis was performed on the PCa tumor transcriptome of Genetically Engineered Mouse Model (GEMM) Pb-Cre4/Pten^f/f mice to identify potentially secreted and membrane proteins-PSPs and PMPs. We combined a selection of transcripts from the GSE 94574 dataset and a list of protein-coding genes of the secretome and membrane proteome datasets using the Human Protein Atlas Secretome. Notably, nine deregulated PMPs and PSPs were identified in PCa (DMPK, PLN, KCNQ5, KCNQ4, MYOC, WIF1, BMP7, F3, and MUC1). We verified the gene expression patterns of Differentially Expressed Genes (DEGs) in normal and tumoral human samples using the GEPIA tool. DMPK, KCNQ4, and WIF1 targets were downregulated in PCa samples and in the GSE dataset. A significant association between shorter survival and KCNQ4, PLN, WIF1, and F3 expression was detected in the MSKCC dataset. We further identified six validated miRNAs (mmu-miR-6962-3p, mmu-miR- 6989-3p, mmu-miR-6998-3p, mmu-miR-5627-5p, mmu-miR-15a-3p, and mmu-miR-6922-3p) interactions that target MYOC, KCNQ5, MUC1, and F3. We have characterized the PCa secretome and membrane proteome and have spotted new dysregulated target candidates in PCa.

SUBMITTER: Santos NJ

PROVIDER: S-EPMC9409251 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Prostate Cancer Secretome and Membrane Proteome from Pten Conditional Knockout Mice Identify Potential Biomarkers for Disease Progression.

Santos Nilton J NJ Camargo Ana Carolina Lima ACL Carvalho Hernandes F HF Justulin Luis Antonio LA Felisbino Sérgio Luis SL

International journal of molecular sciences 20220817 16

Prostate cancer (PCa) is the second most common cause of mortality among men. Tumor secretome is a promising strategy for understanding the biology of tumor cells and providing markers for disease progression and patient outcomes. Here, transcriptomic-based secretome analysis was performed on the PCa tumor transcriptome of Genetically Engineered Mouse Model (GEMM) Pb-Cre4/Ptenf/f mice to identify potentially secreted and membrane proteins-PSPs and PMPs. We combined a selection ...[more]

PMID: 36012492

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Project description:BackgroundMetabolic reprograming is now a recognized hallmark of cancer. The prostate-specific phosphatase and tensin homolog deleted on chromosome 10 (Pten) gene-conditional knockout (KO) mouse carcinogenesis model is highly desirable for studying prostate cancer biology and prevention due to its close resemblance of primary molecular defects and histopathological features of human prostate cancer. We have recently published macromolecular profiling of this model by proteomics and transcriptomics, denoting a preeminence of inflammation and myeloid suppressive immune cell features. Here, we performed metabolomic analyses of Pten-KO prostate versus wild type (WT) counterpart for discernable changes in the aqueous metabolites and contrasted to those in the TRAMP neuroendocrine carcinoma (NECa).MethodsThree matched pairs of tissue-specific conditional Pten-KO mouse prostate and WT prostate of litter/cage-mates at 20-22 weeks of age and three pairs of TRAMP NECa versus WT (28-31 weeks) were profiled for their global aqueous metabolite changes, using hydrophilic interaction liquid chromatography-tandem mass spectrometry.ResultsThe Pten-KO prostate increased purine nucleotide pools, cystathionine, and both reduced and oxidized glutathione (GSH, GSSG), and gluconate/glucuronate species in addition to cholesteryl sulfate and polyamine precursor ornithine. On the contrary, Pten-KO prostate contained diminished pools of glycolytic intermediates and phosphorylcholine derivatives, select amino acids, and their metabolites. Bioinformatic integration revealed a significant shunting of glucose away from glycolysis-citrate cycle and glycerol-lipid genesis to pentose phosphate cycle for NADPH/GSH/GSSG redox and pentose moieties for purine and pyrimidine nucleotides, and glycosylation/glucuronidation. Implicit arginine catabolism to ornithine was consistent with immunosuppression in Pten-KO model. While also increased in cystathionine-GSH/GSSG, purine, and pyrimidine nucleotide pools and glucuronidation at the expense of glycolysis-citrate cycle, the TRAMP NECa increased abundance of many amino acids, methyl donor S-adenosyl-methionine, and intermediates for phospholipids without increasing cholesteryl sulfate or ornithine.ConclusionsThe aqueous metabolomic patterns in Pten-KO prostate and TRAMP NECa shared similarities in the greater pools of cystathionine, GSH/GSSG redox pair, and nucleotides and shunting away from glycolysis-citrate cycle in both models. Remarkable metabolic distinctions between them included metabolisms of many amino acids (protein synthesis; arginine-ornithine/immune suppression) and cholesteryl sulfate and methylation donor for epigenetic regulations.

Project description:Pancreatic ductal adenocarcinoma (PDAC) is extremely aggressive, has an unfavorable prognosis, and there are no biomarkers for early detection of the disease or identification of individuals at high risk for morbidity or mortality. The cellular and molecular complexity of PDAC leads to inconsistences in clinical validations of many proteins that have been evaluated as prognostic biomarkers of the disease. The tumor secretome, a potential source of biomarkers in PDAC, plays a crucial role in cell proliferation and metastasis, as well as in resistance to treatments, which together contribute to a worse clinical outcome. The massive amount of proteomic data from pancreatic cancer that has been generated from previous studies can be integrated and explored to uncover secreted proteins relevant to the diagnosis and prognosis of the disease. The present study aimed to perform an integrated meta-analysis of PDAC proteome and secretome public data to identify potential biomarkers of the disease. Our meta-analysis combined mass spectrometry data obtained from two systematic reviews of the pancreatic cancer literature, which independently selected 20 studies of the secretome and 35 of the proteome. Next, we predicted the secreted proteins using seven in silico tools or databases, which identified 39 secreted proteins shared between the secretome and proteome data. Notably, the expression of 31 genes of these secretome-related proteins was upregulated in PDAC samples from The Cancer Genome Atlas (TCGA) when compared to control samples from TCGA and The Genotype-Tissue Expression (GTEx). The prognostic value of these 39 secreted proteins in predicting survival outcome was confirmed using gene expression data from four PDAC datasets (validation set). The gene expression of these secreted proteins was able to distinguish high- and low-survival patients in nine additional tumor types from TCGA, demonstrating that deregulation of these secreted proteins may also contribute to the prognosis in multiple cancers types. Finally, we compared the prognostic value of the identified secreted proteins in PDAC biomarkers studies from the literature. This analysis revealed that our gene signature performed equally well or better than the signatures from these previous studies. In conclusion, our integrated meta-analysis of PDAC proteome and secretome identified 39 secreted proteins as potential biomarkers, and the tumor gene expression profile of these proteins in patients with PDAC is associated with worse overall survival.

Project description:Previously, we generated a preclinical mouse prostate tumor model based on PSA-Cre driven inactivation of Pten. In this model homogeneous hyperplastic prostates (4-5m) developed at older age (>10m) into tumors. Here, we describe the molecular and histological characterization of the tumors in order to better understand the processes that are associated with prostate tumorigenesis in this targeted mouse Pten knockout model. The morphologies of the tumors that developed were very heterogeneous. Different histopathological growth patterns could be identified, including intraductal carcinoma (IDC), adenocarcinoma and undifferentiated carcinoma, all strongly positive for the epithelial cell marker Cytokeratin (CK), and carcinosarcomas, which were negative for CK. IDC pattern was already detected in prostates of 7-8 month old mice, indicating that it could be a precursor stage. At more than 10 months IDC and carcinosarcoma were most frequently observed. Gene expression profiling discriminated essentially two molecular subtypes, denoted tumor class 1 (TC1) and tumor class 2 (TC2). TC1 tumors were characterized by high expression of epithelial markers like Cytokeratin 8 and E-Cadherin whereas TC2 tumors showed high expression of mesenchyme/stroma markers such as Snail and Fibronectin. These molecular subtypes corresponded with histological growth patterns: where TC1 tumors mainly represented adenocarcinoma/intraductal carcinoma, in TC2 tumors carcinosarcoma was the dominant growth pattern. Further molecular characterization of the prostate tumors revealed an increased expression of genes associated with the inflammatory response. Moreover, functional markers for senescence, proliferation, angiogenesis and apoptosis were higher expressed in tumors compared to hyperplasia. The highest expression of proliferation and angiogenesis markers was detected in TC2 tumors. Our data clearly showed that in the genetically well-defined PSA-Cre;Pten-loxP/loxP prostate tumor model, histopathological, molecular and biological heterogeneity occurred during later stages of tumor development.

Project description:BackgroundProteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals.MethodsWe used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651).ResultsIn the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-β.ConclusionsWe demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

Dataset Information

Prostate Cancer Secretome and Membrane Proteome from Pten Conditional Knockout Mice Identify Potential Biomarkers for Disease Progression.

Publications

Prostate Cancer Secretome and Membrane Proteome from <i>Pten</i> Conditional Knockout Mice Identify Potential Biomarkers for Disease Progression.

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