The Non-Coding Transcriptome of Prostate Cancer: Implications for Clinical Practice

Multi-Omics Study Reveals Nc886/vtRNA2-1 as a Positive Regulator of Prostate Cancer Cell Immunity

Noncoding RNA 886 has emerged as a pivotal regulatory RNA with distinct functions across tissues, acting as a regulator of protein activity by directly binding to protein partners. While it is well recognized as a tumor suppressor in prostate cancer, the underlying molecular mechanisms remain elusive. To discover the principal pathways regulated by nc886 in prostate cancer, we used a transcriptomic and proteomic approach analyzing malignant DU145, LNCaP, PC3, and benign RWPE-1 prostate cell line models transiently transfected with in vitro transcribed nc886 or antisense oligonucleotides. Multiomics revelead a significant enrichment of immune system-related pathways across the cell lines, including cytokines and interferon signaling. The interferon response provoked by nc886 was validated by functional assays. The invariability of PKR phosphorylation and NF-κB activity in the gain/loss of nc886 function experiments and the positive regulation of innate immunity suggest a PKR-independent mechanism of nc886 action. Accordingly, the GSEA of the PRAD-TCGA data set revealed immune stimulation as the nc886 most associated node also in the clinical setting. Our study showed that the reduction of nc886 leads to a blunted immune response in prostate cancer.

Integrating single-cell RNA-sequencing and bulk RNA-sequencing data to explore the role of mitophagy-related genes in prostate cancer

Prostate cancer (PCa) is the most common malignancy of the male urinary system. Mitophagy, as a type of autophagy, can remove damaged mitochondria in cells. Mitophagy-related genes (MRGs) have been shown to play critical roles in the development of PCa. To this end, based on the comprehensive analysis of RNA-seq and scRNA-seq data of PCa samples and their controls, this paper identified PCa subtypes and constructed a prognostic model. In this paper, we downloaded scRNA-seq and RNA-seq data from Gene Expression Omnibus (GEO) and TCGA database. Based on the R package "Seurat" to process the scRNA-seq data, a total of five cell types were identified. Each cell population was scored based on the R package "AUCell" and using the intersection genes between MRGs and each cell population. The B cell population was then identified as a high-scoring cell population. Differentially expressed genes in RNA-seq data were identified based on the R package "limma" and intersected with previously intersected genes. Then, based on univariate Cox regression analysis and Lasso-Cox regression analysis, the prognostic genes were screened, and the risk model was constructed (composed of ADH5, CAT, BCAT2, DCXR, OGT, and FUS). The model is validated on internal and external test sets. Independent prognostic analysis identified age, N stage, and risk score as independent prognostic factors. This paper's risk models and prognostic genes can provide a reference for developing novel therapeutic targets for PCa.

Vault RNAs (vtRNAs): Rediscovered non-coding RNAs with diverse physiological and pathological activities

The physicochemical characteristics of RNA admit non-coding RNAs to perform a different range of biological acts through various mechanisms and are involved in regulating a diversity of fundamental processes. Notably, some reports of pathological conditions have proved abnormal expression of many non-coding RNAs guides the ailment. Vault RNAs are a class of non-coding RNAs containing stem regions or loops with well-conserved sequence patterns that play a fundamental role in the function of vault particles through RNA-ligand, RNA-RNA, or RNA-protein interactions. Taken together, vault RNAs have been proposed to be involved in a variety of functions such as cell proliferation, nucleocytoplasmic transport, intracellular detoxification processes, multidrug resistance, apoptosis, and autophagy, and serve as microRNA precursors and signaling pathways. Despite decades of investigations devoted, the biological function of the vault particle or the vault RNAs is not yet completely cleared. In this review, the current scientific assertions of the vital vault RNAs functions were discussed.

Integrating single-cell and bulk RNA sequencing to predict prognosis and immunotherapy response in prostate cancer

Prostate cancer (PCa) stands as a prominent contributor to morbidity and mortality among males on a global scale. Cancer-associated fibroblasts (CAFs) are considered to be closely connected to tumour growth, invasion, and metastasis. We explored the role and characteristics of CAFs in PCa through bioinformatics analysis and built a CAFs-based risk model to predict prognostic treatment and treatment response in PCa patients. First, we downloaded the scRNA-seq data for PCa from the GEO. We extracted bulk RNA-seq data for PCa from the TCGA and GEO and adopted "ComBat" to remove batch effects. Then, we created a Seurat object for the scRNA-seq data using the package "Seurat" in R and identified CAF clusters based on the CAF-related genes (CAFRGs). Based on CAFRGs, a prognostic model was constructed by univariate Cox, LASSO, and multivariate Cox analyses. And the model was validated internally and externally by Kaplan-Meier analysis, respectively. We further performed GO and KEGG analyses of DEGs between risk groups. Besides, we investigated differences in somatic mutations between different risk groups. We explored differences in the immune microenvironment landscape and ICG expression levels in the different groups. Finally, we predicted the response to immunotherapy and the sensitivity of antitumour drugs between the different groups. We screened 4 CAF clusters and identified 463 CAFRGs in PCa scRNA-seq. We constructed a model containing 10 prognostic CAFRGs by univariate Cox, LASSO, and multivariate Cox analysis. Somatic mutation analysis revealed that TTN and TP53 were significantly more mutated in the high-risk group. Finally, we screened 31 chemotherapeutic drugs and targeted therapeutic drugs for PCa. In conclusion, we identified four clusters based on CAFs and constructed a new CAFs-based prognostic signature that could predict PCa patient prognosis and response to immunotherapy and might suggest meaningful clinical options for the treatment of PCa.

Non-coding RNAs in metabolic reprogramming of bone and soft tissue sarcoma: Fundamental mechanism and clinical implication

Sarcomas, comprising approximately 1% of human malignancies, show a poor response to treatment and easy recurrence. Metabolic reprogramming play an important role in tumor development in sarcomas. Accumulating evidence shows that non-coding RNAs (ncRNAs) participate in regulating the cellular metabolism of sarcomas, which improves the understanding of the development of therapy-resistant tumors. This review addresses the regulatory roles of metabolism-related ncRNAs and their implications for sarcoma initiation and progression. Dysregulation of metabolism-related ncRNAs is common in sarcomas and is associated with poor survival. Emerging studies show that abnormal expression of metabolism-related ncRNAs affects cellular metabolism, including glucose, lipid, and mitochondrial metabolism, and leads to the development of aggressive sarcomas. This review summarizes recent advances in the roles of dysregulated metabolism-related ncRNAs in sarcoma development and stemness and describes their potential to serve as biological biomarkers for disease diagnosis and prognosis prediction, as well as therapeutic targets for treating refractory sarcomas.

RNA expression profiling from the liquid fraction of synovial fluid in knee joint osteoarthritis patients

OBJECTIVE

To investigate the RNA profile of synovial fluid (SF) from osteoarthritis (OA) patients and carry out cluster analysis of OA-related genes.

METHODS

RNA of SF from OA patients was isolated using RNA-specific Trizol. A cDNA library was built and subjected to the second-generation sequencing using HisSeq4000 with a data size of 8G. The sequencing reads were aligned to the UCSC human reference genome (hg38) using Tophat with default parameters. Gene function enrichment was generated using DAVID.

RESULTS

The minimum weight 0.096 µg RNA of SF sample was used for sequencing analysis, which produced 66,154,562 clean reads, 91.28% of which were matched to the reference with 2,682 genes identified. Some of the unmatchable reads matched RNAs of bacteria, mainly Pseudomonas. The detected human RNAs in samples fell into different categories of genes, including protein-coding ones, processed and unprocessed pseudogenes, and long noncoding, antisense and miscellaneous RNAs that mediate various biological functions. Interestingly, 80% of the expressed genes belonged to the mitochondrial genome.

CONCLUSION

These results suggest that less than 0.1 µg RNA is sufficient for establishing a cDNA library and deep sequencing, and that the liquid fraction of SF contains a whole RNA repertoire that may reflect a history of previous microorganism infections.

Non-coding RNAs rewire cancer metabolism networks

Non-coding RNAs (ncRNAs) are functional RNAs with limited or no protein-coding ability. These interact with their target molecules and participate in the precise regulation of disease development. Metabolic reprogramming is a hallmark in cancer, and is considered essential in meeting increased macromolecular biosynthesis and energy generation of tumors. Recent studies have revealed the involvement of ncRNAs in several metabolic regulations of cancer through direct modulation of metabolic enzyme activities or participation of metabolism-related signaling pathways. Elucidation of how ncRNAs regulate metabolic reprogramming of cancers has opened up a novel intention to understand the mechanism of metabolic rewiring and also the opportunities of utilizing ncRNA-based therapeutics for targeting the metabolism in cancer treatment.

vtRNA2-1/nc886 Produces a Small RNA That Contributes to Its Tumor Suppression Action through the microRNA Pathway in Prostate Cancer

vtRNA2-1 is a vault RNA initially classified as microRNA precursor hsa-mir-886 and recently proposed as "nc886", a new type of non-coding RNA involved in cancer progression acting as an oncogene and tumor suppressor gene in different tissues. We have shown that vtRNA2-1/nc886 is epigenetically repressed in neoplastic cells, increasing cell proliferation and invasion in prostate tissue. Here we investigate the ability of vtRNA2-1/nc886 to produce small-RNAs and their biological effect in prostate cells. The interrogation of public small-RNA transcriptomes of prostate and other tissues uncovered two small RNAs, snc886-3p and snc886-5p, derived from vtRNA2-1/nc886 (previously hsa-miR-886-3p and hsa-miR-886-5p). Re-analysis of PAR-CLIP and knockout of microRNA biogenesis enzymes data showed that these small RNAs are products of DICER, independent of DROSHA, and associate with Argonaute proteins, satisfying microRNA attributes. In addition, the overexpression of snc886-3p provokes the downregulation of mRNAs bearing sequences complementary to its "seed" in their 3'-UTRs. Microarray and in vitro functional assays in DU145, LNCaP and PC3 cell lines revealed that snc886-3p reduced cell cycle progression and increases apoptosis, like its precursor vtRNA2-1/nc886. Finally, we found a list of direct candidate targets genes of snc886-3p upregulated and associated with disease condition and progression in PRAD-TCGA data. Overall, our findings suggest that vtRNA2-1/nc886 and its processed product snc886-3p are synthesized in prostate cells, exerting a tumor suppressor action.

Noncoding RNAs as Molecular Targets of Resveratrol Underlying Its Anticancer Effects

Cancer is a significant disease burden worldwide. Chemotherapy is the mainstay of cancer treatment. Clinically used chemotherapeutic agents may elicit severe side effects. Remarkably, most of cancer cells develop chemoresistance after a period of treatment. Therefore, it is imperative to seek more effective agents without side effects. In recent years, increasing research efforts have attempted to identify natural agents that may be used alone or in combination with traditional therapeutics for cancer management. Resveratrol is a natural polyphenolic phytoalexin that can be found in various foods including blueberries, peanuts, and red wine. As a natural food ingredient, resveratrol possesses antioxidant, anti-inflammatory, and cardioprotective properties. Moreover, resveratrol exhibited promising effects in suppressing the initiation and progression of cancers. Noncoding RNAs (ncRNAs) have been universally accepted as vital regulators in cancer pathogenesis. The modulation of miRNAs and lncRNAs by resveratrol has been described. Thus, the mechanism involving the domination of ncRNA function is one of the keys to understand the anticancer effects of resveratrol. In this review, we focus on the antagonistic effects of resveratrol on cancer progression through regulation of miRNAs and lncRNAs. We also discuss the potential application of resveratrol in cancer management.

Known epigenetic biomarkers for prostate cancer detection and management: exploring the potential of blood-based liquid biopsies

INTRODUCTION

Although prostate cancer (PCa) stands as an important cause of cancer-related deaths, a sizeable proportion of diagnosed cases are clinically insignificant. Hence, novel and more specific biomarkers to identify clinically significant PCa are needed. Liquid biopsies offer the potential to accurately identify cancer markers, including PCa. Epigenetic biomarkers such as cell-free DNA and circulating RNAs have emerged as minimally invasive cancer markers. Areas covered: Herein, we provide an overview of epigenetic biomarkers current state based on a comprehensive review of the relevant literature in blood-based liquid biopsies and challenges/limitations of this new and growing field of cancer biomarkers. Expert opinion: The epigenetic-based biomarkers characteristics make them attractive to the clinics and their minimally invasive assessment are a promising opportunity for PCa detection/management. The main limitations are the lack of robust validation studies and integrated approaches. Future studies would benefit from a change in focus to a 'selected PCa detection'.

LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer

Although modern methods of whole genome DNA methylation analysis have a wide range of applications, they are not suitable for clinical diagnostics due to their high cost and complexity and due to the large amount of sample DNA required for the analysis. Therefore, it is crucial to be able to identify a relatively small number of methylation sites that provide high precision and sensitivity for the diagnosis of pathological states. We propose an algorithm for constructing limited subsamples from high-dimensional data to form diagnostic panels. We have developed a tool that utilizes different methods of selection to find an optimal, minimum necessary combination of factors using cross-entropy loss metrics (LogLoss) to identify a subset of methylation sites. We show that the algorithm can work effectively with different genome methylation patterns using ensemble-based machine learning methods. Algorithm efficiency, precision and robustness were evaluated using five genome-wide DNA methylation datasets (totaling 626 samples), and each dataset was classified into tumor and non-tumor samples. The algorithm produced an AUC of 0.97 (95% CI: 0.94-0.99, 9 sites) for prostate adenocarcinoma and an AUC of 1.0 (from 2 to 6 sites) for urothelial bladder carcinoma, two types of kidney carcinoma and colorectal carcinoma. For prostate adenocarcinoma we showed that identified differential variability methylation patterns distinguish cluster of samples with higher recurrence rate (hazard ratio for recurrence = 0.48, 95% CI: 0.05-0.92; log-rank test, p-value < 0.03). We also identified several clusters of correlated interchangeable methylation sites that can be used for the elaboration of biological interpretation of the resulting models and for further selection of the sites most suitable for designing diagnostic panels. LogLoss-BERAF is implemented as a standalone python code and open-source code is freely available from https://github.com/bioinformatics-IBCH/logloss-beraf along with the models described in this article.

Long Non-coding RNA NEAT1: A Novel Target for Diagnosis and Therapy in Human Tumors

The nuclear paraspeckle assembly transcript 1 (NEAT1, a long non-coding RNA) is frequently overexpressed in human tumors, and higher NEAT1 expression is correlated with worse survival in cancer patients. NEAT1 drives tumor initiation and progression by modulating the expression of genes involved in the regulation of tumor cell growth, migration, invasion, metastasis, epithelial-to-mesenchymal transition, stem cell-like phenotype, chemoresistance and radioresistance, indicating the potential for NEAT1 to be a novel diagnostic biomarker and therapeutic target. Mechanistically, NEAT1 functions as a scaffold RNA molecule by interacting with EZH2 (a subunit of the polycomb repressive complex) to influence the expression of downstream effectors of EZH2, it also acts as a microRNA (miRNA) sponge to suppress the interactions between miRNAs and target mRNAs, and affects the expression of miR-129 by promoting the DNA methylation of the miR-129 promoter region. Knockdown of NEAT1 via small interfering RNA or short hairpin RNA inhibits the malignant behavior of tumor cells. In this review, we highlight the latest insights into the expression pattern, biological roles and mechanisms underlying the function and regulation of NEAT1 in tumors, and especially focus on its clinical implication as a new diagnostic biomarker and an attractive therapeutic target for cancers.