Dataset Information

Plasma Messenger RNAs Identified Through Bioinformatics Analysis are Novel, Non-Invasive Prostate Cancer Biomarkers.

ABSTRACT: Aim:To identify new biomarkers of prostate cancer (PCa) for the diagnosis and prediction of clinical outcomes. Materials and Methods:Existing microarray data of PCa tissues in the Oncomine database were analyzed and candidate differentially expressed genes (DEGs) that may be novel and noninvasive biomarkers were obtained. On this basis, plasma mRNA was extracted from PCa patients and healthy donors. Furthermore, plasma mRNA expression of DEGs was evaluated by qRT-PCR. Finally, the diagnostic power of the biomarkers was evaluated in comparison to the clinical characteristics of the patients. Results:In this study, the top five significantly overexpressed mRNA (AMACR, PPP1R14b, PCA3, DLX1, and RPL22L1) and the top five significantly underexpressed mRNA (DUOX1, EFS, GSTP1, S100A16, and NCRNA00087) were selected for further validation in PCa patients and healthy donors by qRT-PCR. The results showed that AMACR, DLX1, PCA3, DUOX1, and GSTP1 mRNA were stably amplified in plasma. Additionally, DLX1, PCA3, DUOX1, and GSTP1 mRNA expression was significantly different between PCa circulating free mRNA samples and healthy donors. These mRNAs may be useful biomarkers for PCa diagnosis. Conclusion:Analysis of the expression of genes in the Oncomine database showed that DLX1, PCA3, and DUOX1 expressions have a cancer specific pattern in PCa. Collectively, DLX1, PCA3, and DUOX1 may be useful candidate biomarkers for PCa diagnosis.

SUBMITTER: Wang R

PROVIDER: S-EPMC6974148 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Plasma Messenger RNAs Identified Through Bioinformatics Analysis are Novel, Non-Invasive Prostate Cancer Biomarkers.

Wang Rui R Wu Yingzi Y Yu Jin J Yang Guizhu G Yi Hao H Xu Bin B

OncoTargets and therapy 20200117

<h4>Aim</h4>To identify new biomarkers of prostate cancer (PCa) for the diagnosis and prediction of clinical outcomes.<h4>Materials and methods</h4>Existing microarray data of PCa tissues in the Oncomine database were analyzed and candidate differentially expressed genes (DEGs) that may be novel and noninvasive biomarkers were obtained. On this basis, plasma mRNA was extracted from PCa patients and healthy donors. Furthermore, plasma mRNA expression of DEGs was evaluated by qRT-PCR. Finally, the ...[more]

PMID: 32021296

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Project description:BackgroundLipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer.Methodology/principal findingsUsing lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient's age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer.Conclusions/significanceUsing lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy.

Project description:Gallbladder carcinoma (GBC) is a common malignancy and is usually diagnosed in the late stages of the disease. The identification of new effective early diagnostic biomarkers could represent an effective approach in reducing mortality in GBC. Altered expression of long non-coding RNAs (lncRNAs) is believed to be associated with the emergence and development of GBC. Our study aims to identify the expression of a range of circulating lncRNAs, including HOTAIR, ANRIL, H19, CCAT1 and MEG3, in matched serum and tissues of GBC for diagnosis and its association with clinicopathological features. The case and control study included matched serum and tissues from 63 GBC, 19 cholecystitis (CC), and 46 normal controls (NC). RNA extraction and cDNA synthesis from serum and fresh tissue match were performed using commercially available kits. Relative expression was assessed using SYBR Green real-time quantitative polymerase chain reaction. Circulating lncRNA levels including HOTAIR, ANRIL and H19 were upregulated in serum samples, while MEG3 and CCAT1 were downregulated in GBC compared to controls. The trend towards upregulation and downregulation was comparable in the tissue. HOTAIR and MEG3 levels were significantly different between serum CC and early-stage GBC (p = 0.0373, 0.0020), while H19 was significantly upregulated comparing early-stage GBC to advanced-stage GBC (p = 0.018). The expression of ANRIL was significant with M stage (p = 0.0488), H19 with stage (p = 0.009), M stage (p=<0.0001) & stage (0.009) and CCAT1 with M stage (0.044). When distinguishing GBC and NC, AUC for HOTAIR was 0.75, ANRIL 0.78, H19 0.74, CCAT1 0.80 and 0.96 for MEG3. The combination sensitivity for lncRNAs ranged from 84.13% (CI: 72.74-92.12%) to 100.0% (CI: 94.31-100.0%). Significant diagnostic value in discriminating pathologic stage was observed for ANRIL and MEG3 (p = 0.022, p = 0.0005). LncRNA show a significant change in expression in GBC and in discrimination of early stage from late-stage disease. The detection of 2 lncRNAs in panels, in coordination with radiology, could represent a potential serum-based biomarker for early-stage GBC diagnosis.

Dataset Information

Plasma Messenger RNAs Identified Through Bioinformatics Analysis are Novel, Non-Invasive Prostate Cancer Biomarkers.

Publications

Plasma Messenger RNAs Identified Through Bioinformatics Analysis are Novel, Non-Invasive Prostate Cancer Biomarkers.

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