scholarly journals Reference-free transcriptome exploration reveals novel RNAs for prostate cancer diagnosis

2019 ◽  
Vol 2 (6) ◽  
pp. e201900449 ◽  
Author(s):  
Marina Pinskaya ◽  
Zohra Saci ◽  
Mélina Gallopin ◽  
Marc Gabriel ◽  
Ha TN Nguyen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Rna Sequencing ◽  
The Cancer Genome Atlas ◽  
Diagnostic Tools ◽  
Bench Test ◽  
Specific Expression ◽  
Sequencing Technologies ◽  
Cancer Genome Atlas ◽  
Limited Power ◽  
Tissue Specimens

The use of RNA-sequencing technologies held a promise of improved diagnostic tools based on comprehensive transcript sets. However, mining human transcriptome data for disease biomarkers in clinical specimens are restricted by the limited power of conventional reference-based protocols relying on unique and annotated transcripts. Here, we implemented a blind reference-free computational protocol, DE-kupl, to infer yet unreferenced RNA variations from total stranded RNA-sequencing datasets of tissue origin. As a bench test, this protocol was powered for detection of RNA subsequences embedded into putative long noncoding (lnc)RNAs expressed in prostate cancer. Through filtering of 1,179 candidates, we defined 21 lncRNAs that were further validated by NanoString for robust tumor-specific expression in 144 tissue specimens. Predictive modeling yielded a restricted probe panel enabling more than 90% of true-positive detections of cancer in an independent The Cancer Genome Atlas cohort. Remarkably, this clinical signature made of only nine unannotated lncRNAs largely outperformed PCA3, the only used prostate cancer lncRNA biomarker, in detection of high-risk tumors. This modular workflow is highly sensitive and can be applied to any pathology or clinical application.

scholarly journals Blind exploration of the unreferenced transcriptome reveals novel RNAs for prostate cancer diagnosis

2019 ◽  
Author(s):  
M. Pinskaya ◽  
Z. Saci ◽  
M. Gallopin ◽  
N. H. Nguyen ◽  
M. Gabriel ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Rna Sequencing ◽  
Single Molecule ◽  
The Cancer Genome Atlas ◽  
Diagnostic Tools ◽  
Bench Test ◽  
Specific Expression ◽  
High Coverage ◽  
Sequencing Technologies ◽  
Tissue Specimens

AbstractThe broad use of RNA-sequencing technologies held a promise of improved diagnostic tools based on comprehensive transcript sets. However, mining human transcriptome data for disease biomarkers in clinical specimens is restricted by the limited power of conventional reference-based protocols relying on uniquely mapped reads and transcript annotations. Here, we implemented a blind reference-free computational protocol, DE-kupl, to directly infer RNA variations of any origin, including yet unreferenced RNAs, from high coverage total stranded RNA-sequencing datasets of tissue origin. As a bench test, this protocol was powered for detection of RNA subsequences embedded into unannotated putative long noncoding (lnc)RNAs expressed in prostate cancer tissues. Through filtering and visual inspection of 1,179 candidates, we defined 21 lncRNA probes that were further validated for robust tumor-specific expression by NanoString single molecule-based RNA measurements in 144 tissue specimens. Predictive modeling yielded a restricted probe panel enabling over 90% of true positive detection of cancer in an independent dataset from The Cancer Genome Atlas. Remarkably, this clinical signature made of only 9 unannotated lncRNAs largely outperformed PCA3, the only RNA biomarker approved by the Food and Drug Administration agency, specifically, in detection of high-risk prostate tumors. The proposed reference-free computational workflow is modular, highly sensitive and robust and can be applied to any pathology and any clinical application.

scholarly journals Upgrading the Repertoire of miRNAs in Gastric Adenocarcinoma to Provide a New Resource for Biomarker Discovery

2019 ◽  
Vol 20 (22) ◽  
pp. 5697 ◽  
Author(s):  
Michelle E. Pewarchuk ◽  
Mateus C. Barros-Filho ◽  
Brenda C. Minatel ◽  
David E. Cohn ◽  
Florian Guisier ◽  
...  
Keyword(s):  
Gastric Adenocarcinoma ◽  
Biomarker Discovery ◽  
The Cancer Genome Atlas ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Specific Expression ◽  
Novel Mirna ◽  
Cancer Genome Atlas ◽  
Context Specific ◽  
Independent Cohort

Recent studies have uncovered microRNAs (miRNAs) that have been overlooked in early genomic explorations, which show remarkable tissue- and context-specific expression. Here, we aim to identify and characterize previously unannotated miRNAs expressed in gastric adenocarcinoma (GA). Raw small RNA-sequencing data were analyzed using the miRMaster platform to predict and quantify previously unannotated miRNAs. A discovery cohort of 475 gastric samples (434 GA and 41 adjacent nonmalignant samples), collected by The Cancer Genome Atlas (TCGA), were evaluated. Candidate miRNAs were similarly assessed in an independent cohort of 25 gastric samples. We discovered 170 previously unannotated miRNA candidates expressed in gastric tissues. The expression of these novel miRNAs was highly specific to the gastric samples, 143 of which were significantly deregulated between tumor and nonmalignant contexts (p-adjusted < 0.05; fold change > 1.5). Multivariate survival analyses showed that the combined expression of one previously annotated miRNA and two novel miRNA candidates was significantly predictive of patient outcome. Further, the expression of these three miRNAs was able to stratify patients into three distinct prognostic groups (p = 0.00003). These novel miRNAs were also present in the independent cohort (43 sequences detected in both cohorts). Our findings uncover novel miRNA transcripts in gastric tissues that may have implications in the biology and management of gastric adenocarcinoma.

scholarly journals Promoter Methylation of PRKCB, ADAMTS12, and NAALAD2 Is Specific to Prostate Cancer and Predicts Biochemical Disease Recurrence

2021 ◽  
Vol 22 (11) ◽  
pp. 6091
Author(s):  
Kristina Daniunaite ◽  
Arnas Bakavicius ◽  
Kristina Zukauskaite ◽  
Ieva Rauluseviciute ◽  
Juozas Rimantas Lazutka ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Clinical Practice ◽  
Promoter Methylation ◽  
Disease Recurrence ◽  
The Cancer Genome Atlas ◽  
Cancer Dataset ◽  
Protein Coding ◽  
Diagnosis And Prognosis ◽  
Genome Wide ◽  
Cancer Genome Atlas

The molecular diversity of prostate cancer (PCa) has been demonstrated by recent genome-wide studies, proposing a significant number of different molecular markers. However, only a few of them have been transferred into clinical practice so far. The present study aimed to identify and validate novel DNA methylation biomarkers for PCa diagnosis and prognosis. Microarray-based methylome data of well-characterized cancerous and noncancerous prostate tissue (NPT) pairs was used for the initial screening. Ten protein-coding genes were selected for validation in a set of 151 PCa, 51 NPT, as well as 17 benign prostatic hyperplasia samples. The Prostate Cancer Dataset (PRAD) of The Cancer Genome Atlas (TCGA) was utilized for independent validation of our findings. Methylation frequencies of ADAMTS12, CCDC181, FILIP1L, NAALAD2, PRKCB, and ZMIZ1 were up to 91% in our study. PCa specific methylation of ADAMTS12, CCDC181, NAALAD2, and PRKCB was demonstrated by qualitative and quantitative means (all p < 0.05). In agreement with PRAD, promoter methylation of these four genes was associated with the transcript down-regulation in the Lithuanian cohort (all p < 0.05). Methylation of ADAMTS12, NAALAD2, and PRKCB was independently predictive for biochemical disease recurrence, while NAALAD2 and PRKCB increased the prognostic power of multivariate models (all p < 0.01). The present study identified methylation of ADAMTS12, NAALAD2, and PRKCB as novel diagnostic and prognostic PCa biomarkers that might guide treatment decisions in clinical practice.

scholarly journals Interfocal heterogeneity challenges the clinical usefulness of molecular classification of primary prostate cancer

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kristina Totland Carm ◽  
Andreas M. Hoff ◽  
Anne Cathrine Bakken ◽  
Ulrika Axcrona ◽  
Karol Axcrona ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Molecular Classification ◽  
Primary Diagnosis ◽  
The Cancer Genome Atlas ◽  
Clinical Usefulness ◽  
Whole Exome ◽  
Primary Prostate Cancer ◽  
Cancer Genome Atlas ◽  
Prostate Cancers

Abstract Prostate cancer is a highly heterogeneous disease and typically multiple distinct cancer foci are present at primary diagnosis. Molecular classification of prostate cancer can potentially aid the precision of diagnosis and treatment. A promising genomic classifier was published by The Cancer Genome Atlas (TCGA), successfully classifying 74% of primary prostate cancers into seven groups based on one cancer sample per patient. Here, we explore the clinical usefulness of this classification by testing the classifier’s performance in a multifocal context. We analyzed 106 cancer samples from 85 distinct cancer foci within 39 patients. By somatic mutation data from whole-exome sequencing and targeted qualitative and quantitative gene expression assays, 31% of the patients were uniquely classified into one of the seven TCGA classes. Further, different samples from the same focus had conflicting classification in 12% of the foci. In conclusion, the level of both intra- and interfocal heterogeneity is extensive and must be taken into consideration in the development of clinically useful molecular classification of primary prostate cancer.

scholarly journals Identification of a Ubiquitin Related Genes Signature for Predicting Prognosis of Prostate Cancer

2022 ◽  
Vol 12 ◽  
Author(s):  
Guoda Song ◽  
Yucong Zhang ◽  
Hao Li ◽  
Zhuo Liu ◽  
Wen Song ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Roc Curve ◽  
Cox Regression ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Post Translational Modifications ◽  
Training Cohort ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

Background: Ubiquitin and ubiquitin-like (UB/UBL) conjugations are one of the most important post-translational modifications and involve in the occurrence of cancers. However, the biological function and clinical significance of ubiquitin related genes (URGs) in prostate cancer (PCa) are still unclear.Methods: The transcriptome data and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA), which was served as training cohort. The GSE21034 dataset was used to validate. The two datasets were removed batch effects and normalized using the “sva” R package. Univariate Cox, LASSO Cox, and multivariate Cox regression were performed to identify a URGs prognostic signature. Then Kaplan-Meier curve and receiver operating characteristic (ROC) curve analyses were used to evaluate the performance of the URGs signature. Thereafter, a nomogram was constructed and evaluated.Results: A six-URGs signature was established to predict biochemical recurrence (BCR) of PCa, which included ARIH2, FBXO6, GNB4, HECW2, LZTR1 and RNF185. Kaplan-Meier curve and ROC curve analyses revealed good performance of the prognostic signature in both training cohort and validation cohort. Univariate and multivariate Cox analyses showed the signature was an independent prognostic factor for BCR of PCa in training cohort. Then a nomogram based on the URGs signature and clinicopathological factors was established and showed an accurate prediction for prognosis in PCa.Conclusion: Our study established a URGs prognostic signature and constructed a nomogram to predict the BCR of PCa. This study could help with individualized treatment and identify PCa patients with high BCR risks.

scholarly journals Biological Validation of RNA Sequencing Data From Formalin-Fixed Paraffin-Embedded Primary Melanomas

2018 ◽  
pp. 1-19 ◽  
Author(s):  
Lawrence N. Kwong ◽  
Mariana Petaccia De Macedo ◽  
Lauren Haydu ◽  
Aron Y. Joon ◽  
Michael T. Tetzlaff ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Formalin Fixed Paraffin ◽  
Ffpe Samples ◽  
Formalin Fixed Paraffin Embedded ◽  
Cancer Genome Atlas ◽  
Formalin Fixed ◽  
Genome Atlas

Purpose Initiatives such as The Cancer Genome Atlas and International Cancer Genome Consortium have generated high-quality, multiplatform molecular data from thousands of frozen tumor samples. Although these initiatives have provided invaluable insight into cancer biology, a tremendous potential resource remains largely untapped in formalin-fixed, paraffin-embedded (FFPE) samples that are more readily available but which can present technical challenges because of crosslinking of fragile molecules such as RNA. Materials and Methods We extracted RNA from FFPE primary melanomas and assessed two gene expression platforms—genome-wide RNA sequencing and targeted NanoString—for their ability to generate coherent biologic signals. To do so, we generated an improved approach to quantifying gene expression pathways. We refined pathway scores through correlation-guided gene subsetting. We also make comparisons to The Cancer Genome Atlas and other publicly available melanoma datasets. Results The comparison of the gene expression patterns to each other, to established biologic modules, and to clinical and immunohistochemical data confirmed the fidelity of biologic signals from both platforms using FFPE samples to known biology. Moreover, correlations with patient outcome data were consistent with previous frozen-tissue–based studies. Conclusion FFPE samples from previously difficult-to-access cancer types, such as small primary melanomas, represent a valuable and previously unexploited source of analyte for RNA sequencing and NanoString platforms. This work provides an important step toward the use of such platforms to unlock novel molecular underpinnings and inform future biologically driven clinical decisions.

scholarly journals A risk prediction model of DNA methylation improves prognosis evaluation and indicates gene targets in prostate cancer

Epigenomics ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 333-352 ◽  
Author(s):  
Enchong Zhang ◽  
Xueying Hou ◽  
Baoxian Hou ◽  
Mo Zhang ◽  
Yongsheng Song
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
Training Group ◽  
The Cancer Genome Atlas ◽  
Cell Counting ◽  
Migration And Invasion ◽  
Transwell Invasion Assay ◽  
Cancer Genome Atlas ◽  
Selection Operator ◽  
Therapy Target

Aim: Prostate cancer (PCa) is the most common malignancy found in males worldwide. Although it is mostly indolent, PCa still poses a serious threat to long-term health. Materials & methods: The Cancer Genome Atlas data were randomly divided into training and validation groups. Least absolute shrinkage and selection operator regression on DNA methylation data in the training group was conducted to build the model, which was validated in the validation group. Weighted correlation network analysis was conducted on RNA-seq data to identify the therapy target. Functional validation (western blot, quantitative real-time PCR, cell transfection, Cell Counting Kit-8 assay, colony formation assay, wound healing assay and transwell invasion assay) for the target was conducted. Results: The model is an independent predictor of prognosis. The knockdown of FOXD1 inhibits cell proliferation, migration and invasion of PCa. Conclusion: The risk of patients could be evaluated by the model, which revealed that FOXD1 might promote poor prognosis.

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