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An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma.

ABSTRACT: 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.

SUBMITTER: de Oliveira G

PROVIDER: S-EPMC7140071 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma.

de Oliveira Grasieli G Freire Paula Paccielli PP Cury Sarah Santiloni SS de Moraes Diogo D Oliveira Jakeline Santos JS Dal-Pai-Silva Maeli M Reis Patrícia Pintor PP Carvalho Robson Francisco RF

Cancers 20200318 3

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 ...[more]

PMID: 32197468

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Project description:A comprehensive meta-analysis of publicly available gene expression microarray data obtained from human-derived pancreatic ductal adenocarcinoma (PDAC) tissues and their histologically matched adjacent tissue samples was performed to provide diagnostic and prognostic biomarkers, and molecular targets for PDAC. An integrative meta-analysis of four submissions (GSE62452, GSE15471, GSE62165, and GSE56560) containing 105 eligible tumor-adjacent tissue pairs revealed 344 differentially over-expressed and 168 repressed genes in PDAC compared to the adjacent-to-tumor samples. The validation analysis using TCGA combined GTEx data confirmed 98.24% of the identified up-regulated and 73.88% of the down-regulated protein-coding genes in PDAC. Pathway enrichment analysis showed that "ECM-receptor interaction", "PI3K-Akt signaling pathway", and "focal adhesion" are the most enriched KEGG pathways in PDAC. Protein-protein interaction analysis identified FN1, TIMP1, and MSLN as the most highly ranked hub genes among the DEGs. Transcription factor enrichment analysis revealed that TCF7, CTNNB1, SMAD3, and JUN are significantly activated in PDAC, while SMAD7 is inhibited. The prognostic significance of the identified and validated differentially expressed genes in PDAC was evaluated via survival analysis of TCGA Pan-Cancer pancreatic ductal adenocarcinoma data. The identified candidate prognostic biomarkers were then validated in four external validation datasets (GSE21501, GSE50827, GSE57495, and GSE71729) to further improve reliability. A total of 28 up-regulated genes were found to be significantly correlated with worse overall survival in patients with PDAC. Twenty-one of the identified prognostic genes (ITGB6, LAMC2, KRT7, SERPINB5, IGF2BP3, IL1RN, MPZL2, SFTA2, MET, LAMA3, ARNTL2, SLC2A1, LAMB3, COL17A1, EPSTI1, IL1RAP, AK4, ANXA2, S100A16, KRT19, and GPRC5A) were also found to be significantly correlated with the pathological stages of the disease. The results of this study provided promising prognostic biomarkers that have the potential to differentiate PDAC from both healthy and adjacent-to-tumor pancreatic tissues. Several novel dysregulated genes merit further study as potentially promising candidates for the development of more effective treatment strategies for PDAC.

Project description:Pancreatic ductal adenocarcinoma (PDAC) accounts for approximately 90-95% exocrine malignant tumors of the pancreas. The high prevalence of metastasis and the difficulty of early diagnosis lead to a dismal prognosis. MicroRNAs (miRNAs) play a critical role in extensive biological processes. The purpose of this study was to evaluate the feasibility of stool miRNAs as novel biomarker for PDAC screening. MiRNAs were extracted from clinical specimens which included cancer and matched adjacent benign pancreatic tissues of 30 PDAC patients, pancreatic juice of 20 from the 30 PDAC patients and 10 chronic pancreatitis (CP) patients, stool samples of the 30 PDAC patients, the 10 CP patients and 15 healthy volunteers. Relative expression of a panel of 5 dysregulated miRNAs (miR-21, miR-155, miR-196a, miR-216 and miR-217) was analyzed with qRT-PCR. Receiver operating characteristic curve (ROC) analysis was performed to assess the diagnosing value of stool miRNAs in PDAC patients. The study showed that our methods of extracting and detecting miRNAs from pancreatic juice and stool specimens had high reproducibility. Compared to matched adjacent benign pancreatic tissues and pancreatic juice of CP patients, the expression of miR-21 (P = 0.0021 and P = 0.0027) as well as miR-155 (P = 0.0087 and P = 0.0067) was significantly higher and the expression of miR-216 (P < 0.0001 and P = 0.0044) was significantly lower in primary tumor tissues and pancreatic juice of PDAC patients. PDAC patients had a significantly higher stool miR-21 and miR-155 (P = 0.0049 and P = 0.0112) and lower miR-216 level (P = 0.0002) compared to normal controls. The same results were obtained in the expression levels of stool miR-21, miR-155 and miR-216 between PDAC and CP patients (P = 0.0337, P = 0.0388 and P = 0.0117, respectively). Receiver operating characteristic (ROC) analysis by using stool miRNAs expression indicated that combination of miR-21 and miR-155 had best sensitivity of 93.33% while the combination of miR-21, miR-155 and miR-216 would be best for detecting and screening PDAC with area under the curve (AUC) of 0.8667 (95% CI: 0.7722-0.9612) and a better balance of sensitivity and specificity (83.33% vs. 83.33%). Our data indicate that miRNAs could be extracted and detected from pancreatic juice and stool efficiently and reproducibly. MiR-21, miR-155 and miR-216 in stool have the potential of becoming biomarkers for screening PDAC.

Dataset Information

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma.

Publications

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma.

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