scholarly journals Accelerated Protein Biomarker Discovery from FFPE tissue samples using Single-shot, Short Gradient Microflow SWATH MS

2019 ◽  
Author(s):  
Rui Sun ◽  
Christie Hunter ◽  
Chen Chen ◽  
Weigang Ge ◽  
Nick Morrice ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Biomarker Discovery ◽  
Single Shot ◽  
Protein Biomarkers ◽  
Differential Abundance ◽  
Tissue Samples ◽  
Peptide Precursors ◽  
High Consistency ◽  
Method R ◽  
Acquisition Method

ABSTRACTWe report and evaluated a microflow, single-shot, short gradient SWATH MS method intended to accelerate the discovery and verification of protein biomarkers in clinical specimens. The method uses 15-min gradient microflow-LC peptide separation, an optimized SWATH MS window configuration and OpenSWATH software for data analysis.We applied the method to a cohort 204 of FFPE prostate tissue samples from 58 prostate cancer patients and 10 prostatic hyperplasia patients. Altogether we identified 27,976 proteotypic peptides and 4,043 SwissProt proteins from these 204 samples. Compared to a reference SWATH method with 2-hour gradient the accelerated method consumed only 27% instrument time, quantified 80% proteins and showed reduced batch effects. 3,800 proteins were quantified by both methods in two different instruments with relatively high consistency (r = 0.77). 75 proteins detected by the accelerated method with differential abundance between clinical groups were selected for further validation. A shortlist of 134 selected peptide precursors from the 75 proteins were analyzed using MRM-HR, exhibiting high quantitative consistency with the 15-min SWATH method (r = 0.89) in the same sample set. We further verified the capacity of these 75 proteins in separating benign and malignant tissues (AUC = 0.99) in an independent prostate cancer cohort (n=154).Overall our data show that the single-shot short gradient microflow-LC SWATH MS method achieved about 4-fold acceleration of data acquisition with reduced batch effect and a moderate level of protein attrition compared to a standard SWATH acquisition method. Finally, the results showed comparable ability to separate clinical groups.

scholarly journals Accelerated Protein Biomarker Discovery from FFPE Tissue Samples Using Single-Shot, Short Gradient Microflow SWATH MS

2020 ◽  
Vol 19 (7) ◽  
pp. 2732-2741 ◽  
Author(s):  
Rui Sun ◽  
Christie Hunter ◽  
Chen Chen ◽  
Weigang Ge ◽  
Nick Morrice ◽  
...  
Keyword(s):  
Biomarker Discovery ◽  
Ffpe Tissue ◽  
Single Shot ◽  
Protein Biomarker ◽  
Tissue Samples

scholarly journals HERV-K Gag RNA and Protein Levels Are Elevated in Malignant Regions of the Prostate in Males with Prostate Cancer

Viruses ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 449
Author(s):  
Simin D. Rezaei ◽  
Joshua A. Hayward ◽  
Sam Norden ◽  
John Pedersen ◽  
John Mills ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cell Lines ◽  
Endogenous Retrovirus ◽  
Human Endogenous Retrovirus ◽  
Normal Prostate ◽  
Tissue Samples ◽  
Gag Protein ◽  
Protein Levels ◽  
Prostate Epithelial Cells ◽  
Prostate Cancers

Heightened expression of human endogenous retrovirus (HERV) sequences has been associated with a range of malignancies, including prostate cancer, suggesting that they may serve as useful diagnostic or prognostic cancer biomarkers. We analysed the expression of HERV-K (Gag and Env/Np9 regions), HERV-E 4.1 (Pol and Env regions), HERV-H (Pol) and HERV-W (Gag) sequences in prostate cancer cells lines and normal prostate epithelial cells using qRT-PCR. HERV expression was also analysed in matched malignant and benign prostate tissue samples from men with prostate cancer (n = 27, median age 65.2 years (range 47–70)) and compared to prostate cancer-free male controls (n = 11). Prostate cancer epithelial cell lines exhibited a signature of HERV RNA overexpression, with all HERVs analysed, except HERV-E Pol, showing heightened expression in at least two, but more commonly all, cell lines analysed. Analysis of primary prostate material indicated increased expression of HERV-E Pol but decreased expression of HERV-E Env in both malignant and benign regions of the prostate in men with prostate cancer as compared to those without. Expression of HERV-K Gag was significantly higher in malignant regions of the prostate in men with prostate cancer as compared to matched benign regions and prostate cancer-free men (p < 0.001 for both), with 85.2% of prostate cancers donors showing malignancy-associated upregulation of HERV-K Gag RNA. HERV-K Gag protein was detected in 12/18 (66.7%) malignant tissues using immunohistochemistry, but only 1/18 (5.6%) benign tissue sections. Heightened expression of HERV-K Gag RNA and protein appears to be a sensitive and specific biomarker of prostate malignancy in this cohort of men with prostate carcinoma, supporting its potential utility as a non-invasive, adjunct clinical biomarker.

scholarly journals Expression profiling of RNA based markers of prostate cancer in urine and tissue samples

2012 ◽  
Vol 10 (8) ◽  
pp. S101
Author(s):  
Eva Bolton ◽  
Diarmaid Moran ◽  
Armelle Meunier ◽  
Laure Marignol ◽  
Donal Hollywood ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Expression Profiling ◽  
Tissue Samples

scholarly journals Downregulation and Prognostic Performance of MicroRNA 224 Expression in Prostate Cancer

2013 ◽  
Vol 59 (1) ◽  
pp. 261-269 ◽  
Author(s):  
Konstantinos Mavridis ◽  
Konstantinos Stravodimos ◽  
Andreas Scorilas
Keyword(s):  
Prostate Cancer ◽  
Cox Regression ◽  
Specific Antigen ◽  
Progression Free Survival ◽  
Prognostic Indicator ◽  
Disease Stage ◽  
Clinical Value ◽  
Prostate Tumors ◽  
Tissue Samples ◽  
Total Rna

INTRODUCTION The extensive use of prostate-specific antigen as a general prostate cancer biomarker has introduced the hazards of overdiagnosis and overtreatment. Recent studies have revealed the immense biomarker capacity of microRNAs (miRNAs) in prostate cancer. The aim of this study was to analyze the expression pattern of miR-224, a cancer-related miRNA, in prostate tumors and investigate its clinical utility. METHODS Total RNA was isolated from 139 prostate tissue samples. After the polyadenylation of total RNA by poly(A) polymerase, cDNA was synthesized with a suitable poly(T) adapter. miR-224 expression was assessed by quantitative real-time PCR and analyzed with the comparative quantification cycle method, Cq(2−ΔΔCq). We performed comprehensive biostatistical analyses to explore the clinical value of miR-224 in prostate cancer. RESULTS miR-224 expression was significantly downregulated in malignant samples compared with benign samples (P &lt; 0.001). Higher miR-224 expression levels were found in prostate tumors that were less aggressive (P = 0.017) and in an earlier disease stage (P = 0.018). Patients with prostate cancer who were positive for miR-224 had significantly enhanced progression-free survival intervals compared with miR-224–negative patients (P = 0.021). Univariate bootstrap Cox regression confirmed that miR-224 was associated with favorable prognosis (hazard ratio 0.314, P = 0.013); nonetheless, multivariate analysis, adjusted for conventional markers, did not identify miR-224 as an independent prognostic indicator. CONCLUSIONS miR-224 is aberrantly expressed in prostate cancer. Its assessment by cost-effective quantitative molecular methodologies could provide a useful biomarker for prostate cancer.

scholarly journals Proteomics in Diagnosis of Prostate Cancer/ Протеомика Во Дијагноза На Простатниот Карцином

PRILOZI ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 5-36 ◽  
Author(s):  
Katarina Davalieva ◽  
Momir Polenakovic
Keyword(s):  
Prostate Cancer ◽  
Tumor Tissue ◽  
Biomarker Discovery ◽  
Molecular Level ◽  
Specific Antigen ◽  
Shotgun Proteomics ◽  
Comparative Proteomics ◽  
Label Free ◽  
The Past ◽  
Proteomics Research

Abstract Prostate cancer (PCa) is the second most frequently diagnosed malignancy in men worldwide. The introduction of prostate specific antigen (PSA) has greatly increased the number of men diagnosed with PCa but at the same time, as a result of the low specificity, led to overdiagnosis, resulting to unnecessary biopsies and high medical cost treatments. The primary goal in PCa research today is to find a biomarker or biomarker set for clear and effecttive diagnosis of PCa as well as for distinction between aggressive and indolent cancers. Different proteomic technologies such as 2-D PAGE, 2-D DIGE, MALDI MS profiling, shotgun proteomics with label-based (ICAT, iTRAQ) and label-free (SWATH) quantification, MudPIT, CE-MS have been applied to the study of PCa in the past 15 years. Various biological samples, including tumor tissue, serum, plasma, urine, seminal plasma, prostatic secretions and prostatic-derived exosomes were analyzed with the aim of identifying diagnostic and prognostic biomarkers and developing a deeper understanding of the disease at the molecular level. This review is focused on the overall analysis of expression proteomics studies in the PCa field investigating all types of human samples in the search for diagnostics biomarkers. Emphasis is given on proteomics platforms used in biomarker discovery and characterization, explored sources for PCa biomarkers, proposed candidate biomarkers by comparative proteomics studies and the possible future clinical application of those candidate biomarkers in PCa screening and diagnosis. In addition, we review the specificity of the putative markers and existing challenges in the proteomics research of PCa.

scholarly journals KLF2 Inhibits the Migration and Invasion of Prostate Cancer Cells by Downregulating MMP2

2018 ◽  
Vol 13 (1) ◽  
pp. 155798831881690 ◽  
Author(s):  
Binshuai Wang ◽  
Mingyuan Liu ◽  
Yimeng Song ◽  
Changying Li ◽  
Shudong Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Tumor Suppressor ◽  
Cell Function ◽  
Malignant Tumors ◽  
Suppressor Gene ◽  
Migration And Invasion ◽  
Tissue Samples ◽  
Tumor Tissues ◽  
Luciferase Activity Assay

KLF2, a member of the Kruppel-like factor (KLF) family, is thought to be a tumor suppressor in many kinds of malignant tumors. Its functions in prostate cancer (PCa) are unknown. This study aimed to explore the role of KLF2 in the migration and invasion of PCa cells. The expression of KLF2 was measured by immunohistochemistry in PCa tissues and in paired non-tumor tissues. KLF2 and MMP2 expression in cells was measured by Western blot and RT-qPCR. Adenoviruses and siRNAs were used in cell function tests to investigate the role of KLF2 in regulating MMP2. Interactions between KLF2 and MMP2 were analyzed by a luciferase activity assay. The present study, for the first time, identified that KLF2 was downregulated both in PCa clinical tissue samples and in cancer cell lines. The overexpression of KLF2 inhibited the migration and invasion of PCa cells via the suppression of MMP2.This study demonstrates that KLF2 might act as a tumor suppressor gene in PCa and that the pharmaceutical upregulation of KLF2 may be a potential approach for treatment.

scholarly journals Systematical assessment of cancer stemness traits via one-class logistic regression (OCLR) algorithm and associations with tumor immune infiltrates in prostate cancer based on seven cohorts

2020 ◽  
Author(s):  
Hongxi Chen ◽  
Jinliang Xie ◽  
Peng Jin
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Progression Free Survival ◽  
Consensus Clustering ◽  
Connectivity Map ◽  
Cancer Stemness ◽  
Differential Abundance ◽  
Free Survival ◽  
Cell Cycle Inhibitor

Abstract Background: Prostate cancer stemness (PCS) cells have been reported to drive tumor progression, recurrence and drug resistance. However, there is lacking systematical assessment of stemness traits and associations with immunological properties in prostate adenocarcinoma (PRAD). Methods We collected 7 PRAD cohorts with 1465 men and calculated the stemness indices for each sample using the innovative one-class logistic regression (OCLR) machine learning algorithm. We selected the mRNAsi to quantify the stemness traits that correlated significantly with prognosis and accordingly identified 21 PCS-related CpG loci and 13 pivotal signature. Meanwhile, we conducted consensus clustering and classified the total cohorts into 5 PCS clusters with distinct outcomes based on the 13-gene panel. Additionally, we implemented the CIBERSORT algorithm to infer the differential abundance across 5 PCS clusters. Lastly, we used the Connectivity Map (CMap) resource to screen potential compounds for targeting PRAD stemness. Results: The 13-gene based PCS model possessed high predictive significance for progression-free survival (PFS) that was trained and validated in 7 independent cohorts. We found that PCScluster5 possessed the highest stemness fractions and suffered from the worst prognosis. Immune infiltration analysis shows that the activated immune cells (CD8+ T cell and dendritic cells) infiltrated significantly less in PCScluster5 than other clusters, especially PCScluster1, supporting the negative regulations between stemness and anticancer immunity. High mRNAsi was also found to be associated with up-regulation of immunosuppressive checkpoints, like PDL1. Finally, several potential compounds, including the top hits of cell cycle inhibitor and FOXM1 inhibitor, were identified for targeting PRAD stemness. Conclusion: Our study comprehensively evaluated the PRAD stemness traits based on large cohorts and established a 13-gene based classifier for predicting prognosis or potential strategies for stemness treatment.

scholarly journals Functional Proteomic Profiling of Phosphodiesterases Using SeraFILE Separations Platform

2012 ◽  
Vol 2012 ◽  
pp. 1-8
Author(s):  
Amita R. Oka ◽  
Matthew P. Kuruc ◽  
Ketan M. Gujarathi ◽  
Swapan Roy
Keyword(s):  
Biomarker Discovery ◽  
Brain Homogenate ◽  
Disease Diagnosis ◽  
Bovine Brain ◽  
Tissue Cell ◽  
Kinetic Properties ◽  
Proteomic Profiling ◽  
Tissue Samples ◽  
Camp Hydrolysis ◽  
Few Data

Functional proteomic profiling can help identify targets for disease diagnosis and therapy. Available methods are limited by the inability to profile many functional properties measured by enzymes kinetics. The functional proteomic profiling approach proposed here seeks to overcome such limitations. It begins with surface-based proteome separations of tissue/cell-line extracts, using SeraFILE, a proprietary protein separations platform. Enzyme kinetic properties of resulting subproteomes are then characterized, and the data integrated into proteomic profiles. As a model, SeraFILE-derived subproteomes of cyclic nucleotide-hydrolyzing phosphodiesterases (PDEs) from bovine brain homogenate (BBH) and rat brain homogenate (RBH) were characterized for cAMP hydrolysis activity in the presence (challenge condition) and absence of cGMP. Functional profiles of RBH and BBH were compiled from the enzyme activity response to the challenge condition in each of the respective subproteomes. Intersample analysis showed that comparable profiles differed in only a few data points, and that distinctive subproteomes can be generated from comparable tissue samples from different animals. These results demonstrate that the proposed methods provide a means to simplify intersample differences, and to localize proteins attributable to sample-specific responses. It can be potentially applied for disease and nondisease sample comparison in biomarker discovery and drug discovery profiling.

Sign in / Sign up

Export Citation Format

Share Document