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Identification of candidate diagnostic and prognostic biomarkers for pancreatic carcinoma.

ABSTRACT: BACKGROUND:Pancreatic carcinoma (PC) is one of the most aggressive cancers affecting human health. It is essential to identify candidate biomarkers for the diagnosis and prognosis of PC. The present study aimed to investigate the diagnosis and prognosis biomarkers of PC. METHODS:Differentially expressed genes (DEGs) were identified from the mRNA expression profiles of GSE62452, GSE28735 and GSE16515. Functional analysis and the protein-protein interaction network analysis was performed to explore the biological function of the identified DEGs. Diagnosis markers for PC were identified using ROC curve analysis. Prognosis markers were identified via survival analysis of TCGA data. The protein expression pattern of the identified genes was verified in clinical tissue samples. A retrospective clinical study was performed to evaluate the correlation between the expression of candidate proteins and survival time of patients. Moreover, comprehensive analysis of the combination of multiple genes/proteins for the prognosis prediction of PC was performed using both TCGA data and clinical data. In vitro studies were undertaken to elaborate the potential roles of these biomarkers in clonability and invasion of PC cells. FINDINGS:In total, 389 DEGs were identified. These genes were mainly associated with pancreatic secretion, protein digestion and absorption, cytochrome P450 drug metabolism, and energy metabolism pathway. The top 10 genes were filtered out following Fisher's exact test. ROC curve analysis demonstrated that TMPRSS4, SERPINB5, SLC6A14, SCEL, and TNS4 could be used as biomarkers for the diagnosis of PC. Survival analysis of TCGA data and clinical data suggested that TMC7, TMPRSS4, SCEL, SLC2A1, CENPF, SERPINB5 and SLC6A14 can be potential biomarkers for the prognosis of PC. Comprehensive analysis show that a combination of identified genes/proteins can predict the prognosis of PC. Mechanistically, the identified genes attributes to clonability and invasiveness of PC cells. INTERPRETATION:We synthesized several sets of public data and preliminarily clarified pathways and functions of PC. Candidate molecular markers were identified for diagnosis and prognosis prediction of PC including a novel gene, TMC7. Moreover, we found that the combination of TMC7, TMPRSS4, SCEL, SLC2A1, CENPF, SERPINB5 and SLC6A14 can serve as a promising indicator of the prognosis of PC patients. The candidate proteins may attribute to clonability and invasiveness of PC cells. This research provides a novel insight into molecular mechanisms as well as diagnostic and prognostic markers of PC. FUND: National Natural Science Foundation of China [No. 81602646 &81802339], Natural Science Foundation of Guangdong Province [No. 2016A030310254] and China Postdoctoral Science Foundation [No. 2016M600648].

SUBMITTER: Cheng Y

PROVIDER: S-EPMC6412825 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Identification of candidate diagnostic and prognostic biomarkers for pancreatic carcinoma.

Cheng Yang Y Wang Kunyuan K Geng Lanlan L Sun Jingjing J Xu Wanfu W Liu Dingli D Gong Sitang S Zhu Yun Y

EBioMedicine 20190111

<h4>Background</h4>Pancreatic carcinoma (PC) is one of the most aggressive cancers affecting human health. It is essential to identify candidate biomarkers for the diagnosis and prognosis of PC. The present study aimed to investigate the diagnosis and prognosis biomarkers of PC.<h4>Methods</h4>Differentially expressed genes (DEGs) were identified from the mRNA expression profiles of GSE62452, GSE28735 and GSE16515. Functional analysis and the protein-protein interaction network analysis was perf ...[more]

PMID: 30639415

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Project description:Primary liver cancer is a rapidly progressing neoplasm with high morbidity and mortality rates. The present study aimed to identify potential diagnostic and prognostic biomarkers, and candidate targeted agents for hepatitis B virus (HBV)-associated early stage hepatocellular carcinoma (HCC). The gene expression profiles were extracted from the Gene Expression Omnibus database. Differentially expressed genes (DEGs), hub genes and the enrichment of signaling pathways were filtered out via a high-throughput sequencing method. The association between hub genes and the effects of the abnormal expression of hub genes on the rate of genetic variation, overall survival (OS), relapse-free survival (RFS), progression-free survival (PFS) and disease-free survival (DSS) of patients with HCC, as well as pathological stage and grade, were analyzed using different databases. A total of 1,582 DEGs were identified. Gene Ontology analysis revealed that the DEGs were mainly involved in the 'oxidation-reduction process', 'steroid metabolic process', 'metabolic process' and 'fatty acid beta-oxidation'. Enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathways revealed that the DEGs were mainly associated with 'metabolic pathways', 'PPAR signaling pathway', 'fatty acid degradation' and the 'cell cycle'. A total of 8 hub genes were extracted. Additionally, the abnormal expression levels of hub genes were closely associated with the OS, RFS, PFS and DSS of patients, the pathological stage and the grade. Furthermore, abnormal expression levels of the 8 hub genes were found in >30% of all samples. Several small molecular compounds that may reverse the altered DEGs were identified based on Connectivity Map analysis, including phenoxybenzamine, GW-8510, resveratrol, 0175029-0000 and daunorubicin. In conclusion, the dysfunction of fat metabolic pathways, the cell cycle, oxidation-reduction processes and viral carcinogenesis may serve critical roles in the occurrence of HBV-associated early stage HCC. The identified 8 hub genes may act as robust biomarkers for diagnosis and prognosis. Some small molecular compounds may be promising targeted agents against HBV-associated early stage HCC.

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Project description:BackgroundGastric cancer (GC) is one of the most common cancer worldwide. It is essential to identify non-invasive diagnostic and prognostic biomarkers of GC. The aim of the present study was to screen candidate biomarkers associated with the pathogenesis and prognosis of GC by a novel strategy.MethodsThe expression level of gene higher in cancer than in adjacent non-cancer tissue was defined as "positive", and the top 5% genes with "positive rate" were filtered out as candidate diagnostic biomarkers in three Gene Expression Omnibus (GEO) datasets. Further, a prognostic risk model was constructed by multivariate Cox regression analysis in GEO dataset and validated in The Cancer Genome Atlas (TCGA). The expression level of candidate biomarkers was determined in serum and serum-derived exosomes of GC patients. Moreover, the effect of biomarkers in exosomes on migration of GC cells was analyzed by transwell assay.ResultsTen candidate biomarkers (AGT, SERPINH1, WNT2, LIPG, PLAU, COL1A1, MMP7, MXRA5, CXCL1 and COL11A1) were identified with efficient diagnostic value in GC. A prognostic gene signature consisted of AGT, SERPINH1 and MMP7 was constructed and showed a good performance in predicting overall survivals in TCGA. Consistently, serum levels of the three biomarkers also showed high sensitivity and specificity in distinguishing GC patients from controls. In addition, the expression level of the three biomarkers were associated with malignant degree and decreased after surgery in GC patients. Moreover, the expression level of AGT and MMP7 in exosomes correlated positively with serum level. The exosomes derived from serum of GC patients can promote migration of SGC-7901 cells. After neutralized the expression level of three proteins in exosomes with antibodies, the migration of GC cells was obviously suppressed.ConclusionsOur findings provided a novel strategy to identify diagnostic biomarkers based on public datasets, and suggested that the three-gene signature was a candidate diagnostic and prognostic biomarker for patients with GC.

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Identification of candidate diagnostic and prognostic biomarkers for pancreatic carcinoma.

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Identification of candidate diagnostic and prognostic biomarkers for pancreatic carcinoma.

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