In1-ghrelin splicing variant as a key element in the pathophysiological association between obesity and prostate cancer

2021 ◽  
Author(s):  
Juan M Jiménez-Vacas ◽  
Antonio J Montero-Hidalgo ◽  
Enrique Gómez-Gómez ◽  
Antonio C Fuentes-Fayos ◽  
Francisco Ruiz-Pino ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Specificity ◽  
Metabolic Status ◽  
Grey Zone ◽  
Clinical Value ◽  
Related Factors ◽  
Clinical Model ◽  
Non Invasive ◽  
Splicing Variant ◽  
Specificity And Sensitivity

Abstract Context Recent studies emphasize the importance of considering the metabolic status to develop personalized medicine approaches. This is especially relevant in prostate cancer (PCa), wherein the diagnostic capability of PSA dramatically drops when considering patients with PSA levels ranging 3-10 ng/mL, the so-called “grey-zone”. Hence, additional non-invasive diagnostic and/or prognostic PCa biomarkers are urgently needed, especially in the metabolic-status context. Objective To assess the potential relation of urine In1-ghrelin (a ghrelin splicing variant) levels with metabolic-related/pathological conditions (e.g. obesity/diabetes/BMI/insulin-glucose levels), and to define its potential clinical value in PCa (diagnostic/prognostic capacity) and relationship with PCa-risk in patients with PSA in the grey-zone. Methods Urine In1-ghrelin levels were measured by radioimmunoassay in a clinically/metabolically/pathologically well-characterized cohort of patients without (n=397) or with (n=213) PCa with PSA in the grey-zone. Results Key obesity-related factors associated with PCa-risk (BMI/diabetes/glucose/insulin) were strongly correlated to In1-ghrelin levels. Importantly, In1-ghrelin levels were higher in PCa patients compared to control patients (with suspect of PCa but negative-biopsy). Moreover, high In1-ghrelin levels were associated with increased PCa-risk and linked to PCa-aggressiveness (e.g. tumour-stage/lymphovascular-invasion). In1-ghrelin levels added significant diagnostic value to a clinical model consisting of age, suspicious-DRE, previous-biopsy, and PSA levels. Furthermore, a multivariate model consisting of clinical and metabolic variables, including In1-ghrelin levels, showed high specificity and sensitivity to diagnose PCa (AUC=0.740). Conclusions Urine In1-ghrelin levels are associated with obesity-related factors and PCa risk/aggressiveness, and could represent a novel and valuable non-invasive PCa biomarker, as well as a potential link in the pathophysiological relationship between obesity and PCa.

scholarly journals Assessment of miR-98-5p, miR-152-3p, miR-326 and miR-4289 Expression as Biomarker for Prostate Cancer Diagnosis

2019 ◽  
Vol 20 (5) ◽  
pp. 1154 ◽  
Author(s):  
Leire Moya ◽  
Jonelle Meijer ◽  
Sarah Schubert ◽  
Farhana Matin ◽  
Jyotsna Batra
Keyword(s):  
Prostate Cancer ◽  
Cancer Progression ◽  
Specific Antigen ◽  
Area Under The Curve ◽  
High Specificity ◽  
Overall Survival Rate ◽  
Non Invasive ◽  
Specificity And Sensitivity ◽  
Depth Analysis ◽  
The Us

Prostate cancer (PCa) is one of the most commonly diagnosed cancers worldwide, accounting for almost 1 in 5 new cancer diagnoses in the US alone. The current non-invasive biomarker prostate specific antigen (PSA) has lately been presented with many limitations, such as low specificity and often associated with over-diagnosis. The dysregulation of miRNAs in cancer has been widely reported and it has often been shown to be specific, sensitive and stable, suggesting miRNAs could be a potential specific biomarker for the disease. Previously, we identified four miRNAs that are significantly upregulated in plasma from PCa patients when compared to healthy controls: miR-98-5p, miR-152-3p, miR-326 and miR-4289. This panel showed high specificity and sensitivity in detecting PCa (area under the curve (AUC) = 0.88). To investigate the specificity of these miRNAs as biomarkers for PCa, we undertook an in depth analysis on these miRNAs in cancer from the existing literature and data. Additionally, we explored their prognostic value found in the literature when available. Most studies showed these miRNAs are downregulated in cancer and this is often associated with cancer progression and poorer overall survival rate. These results suggest our four miRNA signatures could potentially become a specific PCa diagnostic tool of which prognostic potential should also be explored.

scholarly journals MicroRNA-183-5p acts as a potential diagnostic biomarker for atherosclerosis and regulates the growth of vascular smooth muscle cell

2019 ◽  
Author(s):  
Bin Sun ◽  
Zhengkun Shan ◽  
Guoyu Sun ◽  
Xiaolong Wang
Keyword(s):  
Smooth Muscle ◽  
Vascular Smooth Muscle ◽  
Roc Analysis ◽  
High Specificity ◽  
Diagnostic Value ◽  
Clinical Value ◽  
Specificity And Sensitivity ◽  
And Migration ◽  
Vsmcs Proliferation ◽  
Proliferation And Migration

Abstract Background Atherosclerosis (AS) is a multifactorial chronic disease, and vascular smooth muscle cells (VSMCs) plays an important role in the pathology of AS. MicroRNAs regulate multiple cellular biological processes. This study aimed to investigate the clinical value of miR-183-5p in AS patients, and further explored the effects of miR-183-5p on the proliferation and migration of VSMCs. Methods qRT-PCR was used to test the level of miR-183-5p. The diagnostic value of miR-183-5p for AS patients was assessed by a receiver operating characteristic (ROC) analysis. Cell proliferation and migration were determined via CCK-8 and Transwell assay. Results MiR-183-5p was highly expressed in AS patients compared with the healthy group. Serum miR-183-5p expression was positively associated with CIMT and CRP in AS patients. The ROC analysis suggested that miR-183-5p had quality to be used as a biomarker with high specificity and sensitivity for AS detection. Overexpression of miR-183-5p promoted the proliferation and migration of VSMCs. Downregulation of miR-183-5p attenuated ox-LDL stimulated VSMCs proliferation and migration. Conclusion MiR-183-5p is highly expressed in AS patients, and downregulation of miR-183-5p attenuated ox-LDL stimulated VSMCs proliferation and migration. MiR-183-5p may be a key molecular for the diagnosis and treatment of AS in the future.

Ghrelin O-acyltransferase (GOAT) enzyme is overexpressed in prostate cancer, and its levels are associated with patient's metabolic status: Potential value as a non-invasive biomarker

2016 ◽  
Vol 383 (1) ◽  
pp. 125-134 ◽  
Author(s):  
Daniel Hormaechea-Agulla ◽  
Enrique Gómez-Gómez ◽  
Alejandro Ibáñez-Costa ◽  
Julia Carrasco-Valiente ◽  
Esther Rivero-Cortés ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Metabolic Status ◽  
Non Invasive ◽  
Potential Value

scholarly journals Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

2021 ◽  
Vol 22 (18) ◽  
pp. 9971
Author(s):  
Matteo Ferro ◽  
Ottavio de Cobelli ◽  
Mihai Dorin Vartolomei ◽  
Giuseppe Lucarelli ◽  
Felice Crocetto ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Imaging Biomarkers ◽  
Clinical Value ◽  
Non Invasive ◽  
Molecular Features ◽  
Specific Organ ◽  
Magnetic Resonance Imaging Mri ◽  
Automated Algorithms

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.

scholarly journals mRBioM: An Algorithm for the Identification of Potential mRNA Biomarkers From Complete Transcriptomic Profiles of Gastric Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Changlong Dong ◽  
Nini Rao ◽  
Wenju Du ◽  
Fenglin Gao ◽  
Xiaoqin Lv ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Gastric Adenocarcinoma ◽  
Colon Adenocarcinoma ◽  
Interaction Network ◽  
High Specificity ◽  
Classification Performance ◽  
Related Factors ◽  
Total Information ◽  
Specificity And Sensitivity ◽  
Information Amount

PurposeIn this work, an algorithm named mRBioM was developed for the identification of potential mRNA biomarkers (PmBs) from complete transcriptomic RNA profiles of gastric adenocarcinoma (GA).MethodsmRBioM initially extracts differentially expressed (DE) RNAs (mRNAs, miRNAs, and lncRNAs). Next, mRBioM calculates the total information amount of each DE mRNA based on the coexpression network, including three types of RNAs and the protein-protein interaction network encoded by DE mRNAs. Finally, PmBs were identified according to the variation trend of total information amount of all DE mRNAs. Four PmB-based classifiers without learning and with learning were designed to discriminate the sample types to confirm the reliability of PmBs identified by mRBioM. PmB-based survival analysis was performed. Finally, three other cancer datasets were used to confirm the generalization ability of mRBioM.ResultsmRBioM identified 55 PmBs (41 upregulated and 14 downregulated) related to GA. The list included thirteen PmBs that have been verified as biomarkers or potential therapeutic targets of gastric cancer, and some PmBs were newly identified. Most PmBs were primarily enriched in the pathways closely related to the occurrence and development of gastric cancer. Cancer-related factors without learning achieved sensitivity, specificity, and accuracy of 0.90, 1, and 0.90, respectively, in the classification of the GA and control samples. Average accuracy, sensitivity, and specificity of the three classifiers with machine learning ranged within 0.94–0.98, 0.94–0.97, and 0.97–1, respectively. The prognostic risk score model constructed by 4 PmBs was able to correctly and significantly (∗∗∗p < 0.001) classify 269 GA patients into the high-risk (n = 134) and low-risk (n = 135) groups. GA equivalent classification performance was achieved using the complete transcriptomic RNA profiles of colon adenocarcinoma, lung adenocarcinoma, and hepatocellular carcinoma using PmBs identified by mRBioM.ConclusionsGA-related PmBs have high specificity and sensitivity and strong prognostic risk prediction. MRBioM has also good generalization. These PmBs may have good application prospects for early diagnosis of GA and may help to elucidate the mechanism governing the occurrence and development of GA. Additionally, mRBioM is expected to be applied for the identification of other cancer-related biomarkers.

Intragenic expression profile in tissue and plasma for the detection of TMPRSS2 rearrangements associated with prostate cancer

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 5162-5162
Author(s):  
H. R. Sanders ◽  
H. Li ◽  
K. Z. Qu ◽  
Z. J. Zhang ◽  
A. D. Sferruzza ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Specificity ◽  
Ffpe Tissue ◽  
Rt Pcr ◽  
Multiple Partner ◽  
Prostate Hyperplasia ◽  
Non Invasive ◽  
Normal Individuals ◽  
Plasma Rna ◽  
Benign Prostate

5162 Background: TMPRSS2 gene rearrangements have been reported in 40%-85% of prostate cancer (PCa) patients and have not been found in normal individuals or those with benign prostate hyperplasia (BPH). However, multiple partner genes, including ETS transcription genes, and breakpoints have been reported. We developed an assay based on TMPRSS2 5′ and 3′ intragenic differential expression (IDE) to potentially serve as a diagnostic or prognostic marker for PCa. Methods: We analyzed TMPRSS2 in FFPE tissue from 20 patients (9 PCa and 11 BPH) and plasma from 42 patients (32 PCa and 10 BPH). IDE was expressed as a ratio of 3′:5′ transcript levels which were determined by real-time RT-PCR using distinct primer/probe sets. A normal 3′:5′ ratio (≥30) was established by comparing nonmalignant cells to tumor cells from FFPE tissue. This cutoff was subsequently used to identify abnormal ratios in plasma specimens. Results: In FFPE tissue, 100% of PCa samples had a 3′:5′ratio <30 and 91% of BPH samples were ≥30 ( Table ). RNA in 48% of plasma samples passed our QC criteria for acceptability. The 3′:5′ ratios were <30 in 47% and ≥30 in 6.7% PCa plasma. Conclusions: By measuring IDE, we are not limited to screening for known TMPRSS2/ETS gene translocations. In tissue, this approach enabled us to identify patients with PCa vs. BPH with high specificity. Although work is needed to improve plasma RNA quality, IDE of plasma TMPRSS2 may be a useful non-invasive diagnostic or prognostic tool. [Table: see text] No significant financial relationships to disclose.

scholarly journals Evaluation of MicroRNAs as Non-Invasive Diagnostic Markers in Urinary Cells from Patients with Suspected Prostate Cancer

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 578
Author(s):  
Angelika Borkowetz ◽  
Andrea Lohse-Fischer ◽  
Jana Scholze ◽  
Ulrike Lotzkat ◽  
Christian Thomas ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Molecular Markers ◽  
Diagnostic Performance ◽  
Operating Characteristic ◽  
Clinical Performance ◽  
Diagnostic Value ◽  
Non Invasive ◽  
Diagnostic Potential ◽  
Specificity And Sensitivity ◽  
Diagnostic Power

Currently used tumor markers for early diagnosis of prostate cancer (PCa) are often lacking sufficient specificity and sensitivity. Therefore, the diagnostic potential of selected microRNAs in comparison to serum PSA levels and PSA density (PSAD) was explored. A panel of 12 PCa-associated microRNAs was quantified by qPCR in urinary sediments from 50 patients with suspected PCa undergoing prostate biopsy, whereupon PCa was detected in 26 patients. Receiver operating characteristic (ROC) curve analyses revealed a potential for non-invasive urine-based PCa detection for miR-16 (AUC = 0.744, p = 0.012; accuracy = 76%) and miR-195 (AUC = 0.729, p = 0.017; accuracy = 70%). While serum PSA showed an insufficient diagnostic value (AUC = 0.564, p = 0.656; accuracy = 50%) in the present cohort, PSAD displayed an adequate diagnostic performance (AUC = 0.708, p = 0.031; accuracy = 70%). Noteworthy, the combination of PSAD with the best candidates miR-16 and miR-195 either individually or simultaneously improved the diagnostic power (AUC = 0.801–0.849, p < 0.05; accuracy = 76–90%). In the sub-group of patients with PSA ≤ 10 ng/mL (n = 34), an inadequate diagnostic power of PSAD alone (AUC = 0.595, p = 0.524; accuracy = 68%) was markedly surpassed by miR-16 and miR-195 individually as well as by their combination with PSAD (AUC = 0.772–0.882, p < 0.05; accuracy = 74–85%). These findings further highlight the potential of urinary microRNAs as molecular markers with high clinical performance. Overall, these results need to be validated in a larger patient cohort.

scholarly journals Multiplex real-time PCR assay combined with rolling circle amplification (MPRP) using universal primers for non-invasive detection of tumor-related mutations

RSC Advances ◽  
2018 ◽  
Vol 8 (48) ◽  
pp. 27375-27381 ◽  
Author(s):  
Jian Gong ◽  
Yishuai Li ◽  
Ting Lin ◽  
Xiaoyan Feng ◽  
Li Chu
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
Rolling Circle Amplification ◽  
High Specificity ◽  
Universal Primers ◽  
Multiplex Detection ◽  
Pcr Assay ◽  
Rolling Circle ◽  
Non Invasive ◽  
Specificity And Sensitivity

The MPRP system for SNP discrimination was developed, which showed high specificity and sensitivity for multiplex detection of tumor-related mutations.

scholarly journals Transmembrane and Tetratricopeptide Repeat Containing 4 Is a Novel Diagnostic Marker for Prostate Cancer with High Specificity and Sensitivity

Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1029
Author(s):  
Rania Makboul ◽  
Islam F. Abdelkawi ◽  
Dalia M. Badary ◽  
Mahmoud R. A. Hussein ◽  
Johng S. Rhim ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Sensitivity And Specificity ◽  
High Sensitivity ◽  
High Specificity ◽  
Normal Prostate ◽  
Protein Levels ◽  
Prostate Epithelial Cells ◽  
Specificity And Sensitivity ◽  
Histopathologic Diagnosis ◽  
Tissue Specimens

The histopathologic diagnosis of prostate cancer (PCa) from biopsies is a current challenge if double or triple staining is needed. Therefore, there is an urgent need for development of a new reliable biomarker to diagnose PCa patients. We aimed to explore and compare the expression of TMTC4 in PCa cells and tissue specimens and evaluate its sensitivity and specificity. The expression of TMTC4 in PCa and normal prostate epithelial cells was determined by real-time PCR and Western blot analyses. Immunohistochemical (IHC) staining of TMTC4 was performed on tissues collected from PCa and benign prostatic hyperplasia (BPH). Our results show a high expression of TMTC4 on mRNA and protein levels in PCa versus BPH1 and normal cells (p < 0.05). IHC results show strong cytoplasmic expressions in PCa cases (p < 0.001) as compared to BPH cases. The overall accuracy as measured by the AUC was 1.0 (p < 0.001). The sensitivity and specificity of the protein were 100% and 96.6%, respectively. Taken together, we report a high TMTC4 expression in PCa cells and tissues and its ability to differentiate between PCa and BPH with high sensitivity and specificity. This finding can be carried over to clinical practice after its confirmation by further studies.

scholarly journals Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI

2022 ◽  
Vol 11 ◽  
Author(s):  
Elena Bertelli ◽  
Laura Mercatelli ◽  
Chiara Marzi ◽  
Eva Pachetti ◽  
Michela Baccini ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Deep Learning ◽  
Receiver Operating Curve ◽  
Clinical Workflow ◽  
Test Set ◽  
Prostate Imaging ◽  
Non Invasive ◽  
Specificity And Sensitivity ◽  
Cancer Aggressiveness

Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.

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