Accurate Molecular Classification of Kidney Cancer Subtypes Using MicroRNA Signature

Use of Droplet Digital Polymerase Chain Reaction to Identify Biomarkers for Differentiation of Benign and Malignant Renal Masses

Several microRNAs (miRNAs) have been identified as cell-free biomarkers for detecting renal cell carcinoma (RCC). Droplet digital polymerase chain reaction (ddPCR) is a unique technology for nucleic acid quantification. It has the potential for superior precision, reproducibility, and diagnostic performance in identifying circulating miRNA biomarkers compared to conventional quantitative real-time PCR (qRT-PCR). This study aims to evaluate the performance of ddPCR compared to qRT- PCR in identifying miRNA biomarkers that differentiate malignant from benign renal masses. Potential biomarkers of RCC were identified from a literature review. RNA was extracted from the plasma of 56 patients. All the samples underwent analysis via ddPCR as well as qRT-PCR, and expression levels were recorded for the following miRNAs: miR-93, -144, -210, -221, and -222. Tumors were grouped into low-grade ccRCC, high-grade ccRCC, papillary RCC, and benign masses (primarily angiomyolipoma). The miRNA miR-210 (p = 0.034) and the combination of miRs-210 and miR-222 (p = 0.003) were expressed at significantly higher rates among those with RCC than those with benign masses, as measured by ddPCR. Using the combination of miR-210 and miR-222, ddPCR identified significant differences between the subgroups: papillary RCC versus benign (p = 0.03), low-grade ccRCC versus benign (p = 0.026), and high-grade ccRCC versus benign (p = 0.002). The only significant difference between these subgroups using qRT-PCR was between high-grade ccRCC and benign (p = 0.045). All the AUCs were significant when comparing each RCC subgroup with benign for both PCR technologies. Using a combination of miR-210 and miR-222, ddPCR identified significant differences between benign and malignant renal masses that were not identified as significant by conventional qRT-PCR.

Decoding the Possible Molecular Mechanisms in Pediatric Wilms Tumor and Rhabdoid Tumor of the Kidney through Machine Learning Approaches

Objective: Wilms tumor (WT) and Rhabdoid tumor (RT) are pediatric renal tumors and their differentiation is based on histopathological and molecular analysis. The present study aimed to introduce the panels of mRNAs and microRNAs involved in the pathogenesis of these cancers using deep learning algorithms. Methods: Filter, graph, and association rule mining algorithms were applied to the mRNAs/microRNAs data. Results: Candidate miRNAs and mRNAs with high accuracy (AUC: 97%/93% and 94%/97%, respectively) could differentiate the WT and RT classes in training and test data. Let-7a-2 and C19orf24 were identified in the WT, while miR-199b and RP1-3E10.2 were detected in the RT by analysis of Association Rule Mining. Conclusion: The application of the machine learning methods could identify mRNA/miRNA patterns to discriminate WT from RT. The identified miRNAs/mRNAs panels could offer novel insights into the underlying molecular mechanisms that are responsible for the initiation and development of these cancers. They may provide further insight into the pathogenesis, prognosis, diagnosis, and molecular-targeted therapy in pediatric renal tumors.

MicroRNAs as Targets for Cancer Diagnosis: Interests and Limitations

MicroRNAs are small RNAs with ability to attach to the large number of RNA that regulate gene expression on post-transcriptional level via inhibition or degradation of specific mRNAs. MiRNAs in cells are the primary regulators of functions such as cell growth, differentiation, and apoptosis and considerably influence cell function. The expression levels of microRNAs change in human diseases, including cancer. These changes highlight their essential role in cancer pathogenesis. Ubiquitous irregular expression profiles of miRNAs have been detected in various human cancers using genome-wide identification techniques, which are emerging as novel diagnostic and prognostic cancer biomarkers of high specificity and sensitivity. The measurable miRNAs with enhanced stability in blood, tissues, and other body fluids provide a comprehensive source of miRNA-dependent biomarkers for human cancers. The leading role of miRNAs as potential biomarkers in human cancers is discussed in this article. In addition, the interests and difficulties of miRNAs as biomarkers have been explored.

SUPREME: multiomics data integration using graph convolutional networks

To pave the road towards precision medicine in cancer, patients with similar biology ought to be grouped into same cancer subtypes. Utilizing high-dimensional multiomics datasets, integrative approaches have been developed to uncover cancer subtypes. Recently, Graph Neural Networks have been discovered to learn node embeddings utilizing node features and associations on graph-structured data. Some integrative prediction tools have been developed leveraging these advances on multiple networks with some limitations. Addressing these limitations, we developed SUPREME, a node classification framework, which integrates multiple data modalities on graph-structured data. On breast cancer subtyping, unlike existing tools, SUPREME generates patient embeddings from multiple similarity networks utilizing multiomics features and integrates them with raw features to capture complementary signals. On breast cancer subtype prediction tasks from three datasets, SUPREME outperformed other tools. SUPREME-inferred subtypes had significant survival differences, mostly having more significance than ground truth, and outperformed nine other approaches. These results suggest that with proper multiomics data utilization, SUPREME could demystify undiscovered characteristics in cancer subtypes that cause significant survival differences and could improve ground truth label, which depends mainly on one datatype. In addition, to show model-agnostic property of SUPREME, we applied it to two additional datasets and had a clear outperformance.

LncRNA NNT-AS1/hsa-miR-485-5p/HSP90 axis in-silico and clinical prospect correlated-to histologic grades-based CRC stratification: A step toward ncRNA Precision

The oncogenic effects of long non-coding RNA (lncRNA) Nicotinamide Nucleotide Transhydrogenase-antisense RNA1 (NNT-AS1) role in colorectal cancer (CRC) hasn't been sufficiently inspected in relation to the Homo sapiens (hsa)-microRNA (miR)- 485-5p/ heat shock protein 90 (HSP90) axis, clinically. qRT-PCR was performed to detect lncRNA NNT-AS1 and hsa-miR-485-5p expression levels in 60 Egyptian patients' sera. HSP90 serum level was quantified using Enzyme-linked immunosorbent assay (ELISA). The relative expression level of the studied non-coding RNAs as well as the HSP90 ELISA concentration were correlated with patients clinicopathological characteristics and correlated to each other. The axis diagnostic utility in comparison with carbohydrate antigen 19-9 (CA19-9) and carcinoembryonic antigen (CEA) tumor markers (TMs) was studied by receiver operating characteristic (ROC) curve analysis. The relative lncRNA NNT-AS1 expression level fold change 56.7 (13.5-112) and HSP90 protein ELISA level 6.68 (5.14-8.77) (ng/mL) were elevated, while, for hsa-miR-485-5p 0.0474 (0.0236-0.135) expression fold change was repressed in CRC Egyptian patients' cohort sera, being compared to 28 apparently healthy control subjects. LncRNA NNT-AS1 specificity is 96.4% and a sensitivity of 91.7%, hsa-miR-485-5p showed 96.4% specificity, 90% sensitivity, and for HSP90 89.3%, 70% specificity and sensitivity, respectively. Those specificities and sensitivities were superior to the classical CRC TMs. A significant negative correlation was found between hsa-miR-485-5p with lncRNA NNT-AS1 (r = -0.933) expression fold change or with HSP90 protein blood level (r = -0.997), but, significant positive correlation was there between lncRNA NNT-AS1 and HSP90 (r = 0.927). LncRNA NNT-AS1/hsa-miR-485-5p/HSP90 axis could be a prospect for CRC development as well as diagnosis. Being correlated and related to CRC histologic grades 1-3, therefore, lncRNA NNT-AS1/hsa-miR-485-5p/HSP90 axis (not individually) expression approved clinically and in silico, could aid treatment precision.

Inflammation- and cancer-related microRNAs in rat renal cortex after subchronic exposure to fluoride

The proximal tubule is a target of subchronic exposure to fluoride (F) in the kidney. Early markers are used to classify kidney damage, stage, and prognosis. MicroRNAs (miRNAs) are small sequences of non-coding single-stranded RNA that regulate gene expression and play an essential role in developing many pathologies, including renal diseases. This study aimed to evaluate the expression of Cytokine-Chemokine molecules (IL-1α/1β/4/6/10, INF-γ, MIP-1α, MCP-1, RANTES, and TGF β1/2/3) and inflammation-related miRNAs to evidence the possible renal mechanisms involved in subchronic exposure to F. Total protein and miRNAs were obtained from the renal cortex of male Wistar rats exposed to 0, 15 and 50 mg NaF/L through drinking water during 40 and 80 days. In addition, cytokines-chemokines were analyzed by multiplexing assay, and a panel of 77 sequences of inflammatory-related miRNAs was analyzed by qPCR. The results show that cytokines-chemokines expression was concentration- and time-dependent with F, where the 50 mg NaF/L were the main altered groups. The miRNAs expression resulted in statistically significant differences in thirty-four miRNAs in the 50 mg NaF/L groups at 40 and 80 days. Furthermore, a molecular interaction network analysis was performed. The relevant pathways modified by subchronic exposure to fluoride were related to extracellular matrix-receptor interaction, Mucin type O-glycan biosynthesis, Gap junction, and miRNAs involved with renal cell carcinoma. Thus, F-induced cytokines-chemokines suggest subchronic inflammation; detecting miRNAs related to cancer and proliferation indicates a transition from renal epithelium to pathologic tissue after fluoride exposure.

Personalized immune subtypes based on machine learning predict response to checkpoint blockade in gastric cancer

Immune checkpoint inhibitors (ICI) show high efficiency in a small fraction of advanced gastric cancer (GC). However, personalized immune subtypes have not been developed for the prediction of ICI efficiency in GC. Herein, we identified Pan-Immune Activation Module (PIAM), a curated gene expression profile (GEP) representing the co-infiltration of multiple immune cell types in tumor microenvironment of GC, which was associated with high expression of immunosuppressive molecules such as PD-1 and CTLA-4. We also identified Pan-Immune Dysfunction Genes (PIDG), a conservative PIAM-derivated GEP indicating the dysfunction of immune cell cooperation, which was associated with upregulation of metastatic programs (extracellular matrix receptor interaction, TGF-β signaling, epithelial-mesenchymal transition and calcium signaling) but downregulation of proliferative signalings (MYC targets, E2F targets, mTORC1 signaling, and DNA replication and repair). Moreover, we developed 'GSClassifier', an ensemble toolkit based on top scoring pairs and extreme gradient boosting, for population-based modeling and personalized identification of GEP subtypes. With PIAM and PIDG, we developed four Pan-immune Activation and Dysfunction (PAD) subtypes and a GSClassifier model 'PAD for individual' with high accuracy in predicting response to pembrolizumab (anti-PD-1) in advance GC (AUC = 0.833). Intriguingly, PAD-II (PIAMhighPIDGlow) displayed the highest objective response rate (60.0%) compared with other subtypes (PAD-I, PIAMhighPIDGhigh, 0%; PAD-III, PIAMlowPIDGhigh, 0%; PAD-IV, PIAMlowPIDGlow, 17.6%; P = 0.003), which was further validated in the metastatic urothelial cancer cohort treated with atezolizumab (anti-PD-L1) (P = 0.018). In all, we provided 'GSClassifier' as a refined computational framework for GEP-based stratification and PAD subtypes as a promising strategy for exploring ICI responders in GC. Metastatic pathways could be potential targets for GC patients with high immune infiltration but resistance to ICI therapy.

Identification of RNU44 as an Endogenous Reference Gene for Normalizing Cell-Free RNA in Tuberculosis

Background

Normalization of cell-free RNA (cf-RNA) concentration can be affected by variable experimental conditions and thus impact the performance of their diagnostic potential. Our study aimed to identify appropriate endogenous reference genes for cf-RNA biomarker evaluation in the diagnosis of tuberculosis (TB).

Methods

Subjects consisting of patients with TB with and without malignancy, patients with pneumonia, and healthy controls were recruited. Candidate reference genes were screened and identified by literature reviewing and RNA-Seq analysis. Expression levels of the candidate genes were determined by reverse-transcription real-time quantitative polymerase chain reaction in plasma from patients with TB and healthy controls. The stability of gene expression was assessed by geNorm, NormFinder, BestKeeper, the Comparative Delta Ct method, and RefFinder. Differential expression of 2 small RNAs (sRNAs) encoding by genome of Mycobacterium tuberculosis in plasma of patients with TB were determined by both absolute quantification and relative quantification with candidate reference genes.

Results

According to the stability ranking analyzed with the 5 computational programs, the top 4 candidates—miR-93, RNU44, miR-16, and glyceraldehyde 3-phosphate dehydrogenase—were used to normalize the transcript levels of 2 mycobacterial sRNAs, MTS2823 and MTS1338, which were observed to have higher copy numbers in the plasma of patients with TB. Normalization with RNU44 displayed significantly higher levels of the 2 M tuberculosis sRNAs in the patients’ plasma than those of healthy controls.

Conclusions

RNU44 was demonstrated as a proper endogenous gene for cf-RNA normalization in TB diagnosis.

Panels of mRNAs and miRNAs for decoding molecular mechanisms of Renal Cell Carcinoma (RCC) subtypes utilizing Artificial Intelligence approaches

Renal Cell Carcinoma (RCC) encompasses three histological subtypes, including clear cell RCC (KIRC), papillary RCC (KIRP), and chromophobe RCC (KICH) each of which has different clinical courses, genetic/epigenetic drivers, and therapeutic responses. This study aimed to identify the significant mRNAs and microRNA panels involved in the pathogenesis of RCC subtypes. The mRNA and microRNA transcripts profile were obtained from The Cancer Genome Atlas (TCGA), which were included 611 ccRCC patients, 321 pRCC patients, and 89 chRCC patients for mRNA data and 616 patients in the ccRCC subtype, 326 patients in the pRCC subtype, and 91 patients in the chRCC for miRNA data, respectively. To identify mRNAs and miRNAs, feature selection based on filter and graph algorithms was applied. Then, a deep model was used to classify the subtypes of the RCC. Finally, an association rule mining algorithm was used to disclose features with significant roles to trigger molecular mechanisms to cause RCC subtypes. Panels of 77 mRNAs and 73 miRNAs could discriminate the KIRC, KIRP, and KICH subtypes from each other with 92% (F1-score ≥ 0.9, AUC ≥ 0.89) and 95% accuracy (F1-score ≥ 0.93, AUC ≥ 0.95), respectively. The Association Rule Mining analysis could identify miR-28 (repeat count = 2642) and CSN7A (repeat count = 5794) along with the miR-125a (repeat count = 2591) and NMD3 (repeat count = 2306) with the highest repeat counts, in the KIRC and KIRP rules, respectively. This study found new panels of mRNAs and miRNAs to distinguish among RCC subtypes, which were able to provide new insights into the underlying responsible mechanisms for the initiation and progression of KIRC and KIRP. The proposed mRNA and miRNA panels have a high potential to be as biomarkers of RCC subtypes and should be examined in future clinical studies.

Noncoding RNome as Enabling Biomarkers for Precision Health

Noncoding RNAs (ncRNAs), in the form of structural, catalytic or regulatory RNAs, have emerged to be critical effectors of many biological processes. With the advent of new technologies, we have begun to appreciate how intracellular and circulatory ncRNAs elegantly choreograph the regulation of gene expression and protein function(s) in the cell. Armed with this knowledge, the clinical utility of ncRNAs as biomarkers has been recently tested in a wide range of human diseases. In this review, we examine how critical factors govern the success of interrogating ncRNA biomarker expression in liquid biopsies and tissues to enhance our current clinical management of human diseases, particularly in the context of cancer. We also discuss strategies to overcome key challenges that preclude ncRNAs from becoming standard-of-care clinical biomarkers, including sample pre-analytics standardization, data cross-validation with closer attention to discordant findings, as well as correlation with clinical outcomes. Although harnessing multi-modal information from disease-associated noncoding RNome (ncRNome) in biofluids or in tissues using artificial intelligence or machine learning is at the nascent stage, it will undoubtedly fuel the community adoption of precision population health.

The Role, Significance, and Association of MicroRNA-10a/b in Physiology of Cancer

MicroRNAs (miRNAs) are small non-coding RNAs that regulate the translation of mRNA and protein, mainly at the posttranscriptional level. Global expression profiling of miRNAs has demonstrated a broad spectrum of aberrations that correlated with several diseases, and miRNA- 10a and miRNA-10b were the first examined miRNAs to be involved in abnormal activities upon dysregulation, including many types of cancers and progressive diseases. It is expected that the same miRNAs behave inconsistently within different types of cancer. This review aims to provide a set of information about our updated understanding of miRNA-10a and miRNA-10b and their clinical significance, molecular targets, current research gaps, and possible future applications of such potent regulators.

Novel Diagnostic Value of Driver Gene Transcription Signatures to Characterise Clear Cell Renal Cell Carcinoma, ccRCC

Routine molecular tumour diagnostics are augmented by DNA-based qualitative and quantitative molecular techniques detecting mutations of DNA. However, in the past decade, it has been unravelled that the phenotype of cancer, as it’s an extremely complex disease, cannot be fully described and explained by single or multiple genetic variants affecting only the coding regions of the genes. Moreover, studying the manifestation of these somatic mutations and the altered transcription programming—driven by genomic rearrangements, dysregulation of DNA methylation and epigenetic landscape—standing behind the tumorigenesis and detecting these changes could provide a more detailed characterisation of the tumour phenotype. Consequently, novel comparative cancer diagnostic pipelines, including DNA- and RNA-based approaches, are needed for a global assessment of cancer patients. Here we report, that by monitoring the expression patterns of key tumour driver genes by qPCR, the normal and the tumorous samples can be separated into distinct categories. Furthermore, we also prove that by examining the transcription signatures of frequently affected genes at 3p25, 3p21 and 9p21.3 genomic regions, the ccRCC (clear cell renal cell carcinoma) and non-tumorous kidney tissues can be distinguished based on the mRNA level of the selected genes. Our results open new diagnostics possibilities where the mRNA signatures of tumour drivers can supplement the DNA-based approaches providing a more precise diagnostics opportunity leading to determine more precise therapeutic protocols.

Epigenetic Regulation in Exposome-Induced Tumorigenesis: Emerging Roles of ncRNAs

Environmental factors, including pollutants and lifestyle, constitute a significant role in severe, chronic pathologies with an essential societal, economic burden. The measurement of all environmental exposures and assessing their correlation with effects on individual health is defined as the exposome, which interacts with our unique characteristics such as genetics, physiology, and epigenetics. Epigenetics investigates modifications in the expression of genes that do not depend on the underlying DNA sequence. Some studies have confirmed that environmental factors may promote disease in individuals or subsequent progeny through epigenetic alterations. Variations in the epigenetic machinery cause a spectrum of different disorders since these mechanisms are more sensitive to the environment than the genome, due to the inherent reversible nature of the epigenetic landscape. Several epigenetic mechanisms, including modifications in DNA (e.g., methylation), histones, and noncoding RNAs can change genome expression under the exogenous influence. Notably, the role of long noncoding RNAs in epigenetic processes has not been well explored in the context of exposome-induced tumorigenesis. In the present review, our scope is to provide relevant evidence indicating that epigenetic alterations mediate those detrimental effects caused by exposure to environmental toxicants, focusing mainly on a multi-step regulation by diverse noncoding RNAs subtypes.

Validation in an Independent Cohort of MiR-122, MiR-1271, and MiR-15b as Urinary Biomarkers for the Potential Early Diagnosis of Clear Cell Renal Cell Carcinoma

Simple Summary

The survival of patients with the most common type of kidney cancer (called Clear cell renal cell carcinoma—ccRCC) would dramatically improve if it was diagnosed earlier. Early diagnosis can be achieved using imaging techniques, but they are too expensive and therefore cannot be used to screen the population at risk for ccRCC. A few months ago, we published a study that evaluated the amount of certain small RNAs present in urine and showed that they are present at different levels in the urine of ccRCC patients vs. healthy subjects, and based on this discrepancy, we developed an algorithm that can anticipate the presence of kidney cancer. Such studies, however, can suffer from a technical bias called overfitting, such that the method may seem predictive even when it is not. In the present study, we sought to address this possibility and evaluate the amount of the same small RNAs in the urine of an independent cohort. As a result, we demonstrate that the previously developed algorithm has a sensitivity of 96% and specificity of 65%, thus validating this technique for potential application in the early diagnosis of ccRCC with a noninvasive assay.

Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma, and the absence of symptoms in the early stages makes metastasis more likely and reduces survival. To aid in the early diagnosis of ccRCC, we recently developed a method based on urinary miR-122-5p, miR-1271-5p, and miR-15b-5p levels and three controls. The study here presented aimed to validate the previously published method through its application on an independent cohort. The expression of miRNAs in urine specimens from 28 ccRCC patients and 28 healthy subjects (HSs) of the same sex and age was evaluated by RT-qPCR. Statistical analyses were performed, including the preparation of receiver operating characteristic (ROC) curves. The mean ccRCC diameter in ccRCC patients was 4.2 ± 2.4 mm. Urinary miRNA levels were higher in patients than in HSs. The data were processed using the previously developed algorithm (7p-urinary score), and the area under the curve (AUC) of the algorithm’s ROC curve was 0.81 (p-value = 0.0003), with a sensitivity of 96% and specificity of 65%. Therefore, the 7p-urinary score is a potential tool for the early diagnosis of ccRCC.

The Impact of Modifying Sunitinib Treatment Scheduling on Renal Cancer Tumor Biology and Resistance

With sunitinib treatment of metastatic renal cell carcinoma, most patients end up developing resistance over time. Recent clinical trials have shown that individualizing treatment protocols could delay resistance and result in better outcomes. We developed an in vivo xenograft tumor model and compared tumor growth rate, morphological, and transcriptomic differences between alternative and traditional treatment schedules. Our results show that the alternative treatment regime could delay/postpone cancer progression. Additionally, we identified distinct morphological changes in the tumor with alternative and traditional treatments, likely due to the significantly dysregulated signaling pathways between the protocols. Further investigation of the signaling pathways underlying these morphological changes may lead potential therapeutic targets to be used in a combined treatment with sunitinib, which offers promise in postponing/reversing the resistance of sunitinib.

MicroRNAs in Body Fluids: A More Promising Biomarker for Clear Cell Renal Cell Carcinoma

Renal cell carcinoma (RCC) is the second most common cancer of the urinary system, accounting for approximately 10–15% of kidney cancers in the world. Clear cell renal cell carcinoma (ccRCC) is the most common RCC subtype with the highest mortality. Surgical resection or puncture of tumor tissue is still an important clinical treatment and diagnosis of ccRCC, but its high recurrence rate and poor prognosis often lead to the short survival period of patients. Hence, the development of novel molecular biomarkers is of great clinical importance. miRNAs are endogenous non-coding small RNAs with a length of 19–24 nt. A growing number of studies have reported that miRNAs, as proto-oncogenes or tumor suppressor genes, play a key role in the development of ccRCC and might be effective diagnostic and prognostic biomarkers. In addition, miRNAs can also predict the efficacy of treatment drug, thus improving the accuracy of clinical medication. Furthermore, non-invasive detection of miRNAs or extracellular vesicles (EV) in body fluids has better convenience and repeatability, which shows remarkable advantages compared with tissue detection. In this review, we summarized the typical miRNAs reported in recent years and place emphasis on evaluating miRNAs in different body fluids to provide reference for the clinical diagnosis and prognosis of ccRCC in the future.

[Biomarkers for renal cell tumours]

During the last three decades, renal tumours have become increasingly well differentiated on the basis of their histopathological and molecular features. This subtyping has increasingly impacted clinical practice because more therapeutic options are available in organ-confined and metastatic renal cell tumours. The knowledge of the underlying molecular alterations is essential to develop molecular targeted therapies and to select the most effective systemic therapy for each patient. This manuscript gives an overview of the molecular differentiation on the one hand, and on diagnostic, prognostic and predictive biomarkers on the other hand.

Identification of miR-20a-5p as Robust Normalizer for Urine microRNA Studies in Renal Cell Carcinoma and a Profile of Dysregulated microRNAs

Renal cell carcinoma (RCC) is the third most frequent urinary malignancy and one of the most lethal. Current diagnostic and follow-up techniques are harmful and unspecific in low-grade tumors. Novel minimally invasive markers such as urine microRNAs (miRNAs) are under study. However, discrepancies arise among studies in part due to lack of consent regarding normalization. We aimed to identify the best miRNA normalizer for RCC studies performed in urine samples together with a miRNA profile with diagnostic value and another for follow-up. We evaluated the performance of 120 candidate miRNAs in the urine of 16 RCC patients and 16 healthy controls by RT-qPCR followed by a stability analysis with RefFinder. In this screening stage, miR-20a-5p arose as the most stably expressed miRNA in RCC and controls, with a good expression level. Its stability was validated in an independent cohort of 51 RCC patients and 32 controls. Using miR-20a-5p as normalizer, we adjusted and validated a diagnostic model for RCC with three miRNAs (miR-200a-3p, miR-34a-5p and miR-365a-3p) (AUC = 0.65; Confidence Interval 95% [0.51, 0.79], p = 0.043). let-7d-5p and miR-205-5p were also upregulated in patients compared to controls. Comparing RCC samples before surgery and fourteen weeks after, we identified let-7d-5p, miR-152-3p, miR-30c-5p, miR-362-3p and miR-30e-3p as potential follow-up profile for RCC. We identified validated targets of most miRNAs in the renal cell carcinoma pathway. This is the first study that identifies a robust normalizer for urine RCC miRNA studies, miR-20a-5p, which may allow the comparison of future studies among laboratories. Once confirmed in a larger independent cohort, the miRNAs profiles identified may improve the non-invasive diagnosis and follow-up of RCC.

Identification of Distinct Molecular Subtypes of Endometrioid Adenocarcinoma

Endometrial carcinoma (EC) is one of the most common gynecological cancers worldwide. Endometrioid adenocarcinoma (EAC) is the major form of EC, accounting for 75–80% of cases. Currently, there is no molecular classification system for EAC, so there are no corresponding targeted treatments. In this study, we identified two distinct molecular subtypes of EAC with different gene expression patterns and clinicopathologic characteristics. Subtype I EAC cases, accounting for the majority of cases (56%), were associated with an earlier stage, a more well-differentiated grade, a lower tumor invasion rate, and a more favorable prognosis, and the median tumor necrosis percent (15%) was also significantly higher in subtype I EAC. In contrast, subtype II EAC represents high-grade EAC, with a higher tumor invasion rate and tumor weight. The up-regulated genes in subtype I EAC were associated with the immune response, defense response, cell motion, and cell motility pathway, whereas the up-regulated genes in subtype II EAC were associated with the cell cycle, DNA replication, and RNA processing pathways. Additionally, we identified three potential subtype-specific biomarkers, comprising MDM2 (MDM2 proto-oncogene) for subtype I, and MSH2 (mutS homolog 2) and MSH6 (mutS homolog 6) for subtype II.

An Integrative Analysis of The Micro-RNAs Contributing in Stemness, Metastasis and B-Raf Pathways in Malignant Melanoma and Melanoma Stem Cell

Objective

Epithelial-mesenchymal transition (EMT) and the stemness potency in association with BRAF mutation are in dispensable to the progression of melanoma. Recently, microRNAs (miRNAs) have been introduced as the regulator of a multitude of oncogenic functions in most of tumors. Therefore identifying and interpreting the expression patterns of these miRNAs is essential. The present study sought to find common miRNAs regulating all three important pathways in melanoma development.

Materials and Methods

In this experimental study, 18 miRNAs that importantly contribute to EMT and have a role in regulating self-renewal and the BRAF pathway were selected based on current literature and cross-analysis with available databases. Subsequently, their expression patterns were evaluated in 20 melanoma patients, normal tissues, serum from patients and control subjects, and melanospheres. Pattern discovery and integrative regulatory network analysis were used to find the most important miRNAs in melanoma progression.

Results

Among 18 selected miRNAs, miR-205, -141, -203, -15b, and -9 were differentially expressed in tumor samples than normal tissues. Among them, miR-205, -15b, and -9 significantly expressed in serum samples and healthy donors. Attribute Weighting and decision trees (DT) analysis presented evidence that the combination of miR-205, -203, -9, and -15b can regulate self-renewal and EMT process, by affecting CDH1, CCND1, and VEGF expression.

Conclusion

We suggested here that miR-205, -15b, -203, -9 pattern as the key miRNAs linked to melanoma status, the pluripotency, proliferation, and motility of malignant cells. However, further investigations are required to find the mechanisms underlying the combinatory effects of the above mentioned miRNAs.

A self-organizing deep neuro-fuzzy system approach for classification of kidney cancer subtypes using miRNA genomics data

Kidney cancer is a dangerous disease affecting many patients all over the world. Early-stage diagnosis and correct identification of kidney cancer subtypes play an essential role in the patient's survival; therefore, its subtypes diagnosis and classification are the main challenges in kidney cancer treatment. Medical studies have proved that miRNA dysregulation can increase the risk of cancer. Thus, in this paper, we propose a new machine learning approach for significant miRNAs identification and kidney cancer subtype classification to design an automatic diagnostic tool. The proposed method contains two main steps: feature selection and classification. First, we apply the feature selection algorithm to choose the candidate miRNAs for each subtype. The feature selection algorithm utilizes the AMGM measure to select significant miRNAs with high discriminant power. Next, the candidate miRNAs are fed to a classifier to evaluate the candidate features. In the classification step, the proposed self-organizing deep neuro-fuzzy system is employed to classify kidney cancer subgroups. The new deep neuro-fuzzy system consists of a deep structure in the rule layer and novel architecture in the fuzzifier layer. The proposed self-organizing deep neuro-fuzzy system can help us to overcome the main obstacles in the field of neuro-fuzzy system applications, such as the curse of dimensionality. The goal of this paper is to illustrate that the neuro-fuzzy system can very useful in high dimensional data, such as genomics data, using the proposed deep neuro-fuzzy system. The obtained results illustrated that our proposed method has succeeded in classifying kidney cancer subtypes with high accuracy based on the selected miRNAs.

Identification of the miRNA signature associated with survival in patients with ovarian cancer

Ovarian cancer is a major gynaecological malignant tumor associated with a high mortality rate. Identifying survival-related variants may improve treatment and survival in patients with ovarian cancer. In this work, we proposed a support vector regression (SVR)-based method called OV-SURV, which is incorporated with an inheritable bi-objective combinatorial genetic algorithm for feature selection to identify a miRNA signature associated with survival in patients with ovarian cancer. There were 209 patients with miRNA expression profiles and survival information of ovarian cancer retrieved from The Cancer Genome Atlas database. OV-SURV achieved a mean correlation coefficient of 0.77±0.01and a mean absolute error of 0.69±0.02 years using 10-fold cross-validation. Analysis of the top ranked miRNAs revealed that the miRNAs, hsa-let-7f, hsa-miR-1237, hsa-miR-98, hsa-miR-933, and hsa-miR-889, were significantly associated with the survival in patients with ovarian cancer. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that four of these miRNAs, hsa-miR-182, hsa-miR-34a, hsa-miR-342, and hsa-miR-1304, were highly enriched in fatty acid biosynthesis, and the five miRNAs, hsa-let-7f, hsa-miR-34a, hsa-miR-342, hsa-miR-1304, and hsa-miR-24, were highly enriched in fatty acid metabolism. The prediction model with the identified miRNA signature consisting of prognostic biomarkers can benefit therapeutic decision making of ovarian cancer.

An Accurate Tool for Uncovering Cancer Subtypes by Fast Kernel Learning Method to Integrate Multiple Profile Data

In recent years, cancer has become a severe threat to human health. If we can accurately identify the subtypes of cancer, it will be of great significance to the research of anti-cancer drugs, the development of personalized treatment methods, and finally conquer cancer. In this paper, we obtain three feature representation datasets (gene expression profile, isoform expression and DNA methylation data) on lung cancer and renal cancer from the Broad GDAC, which collects the standardized data extracted from The Cancer Genome Atlas (TCGA). Since the feature dimension is too large, Principal Component Analysis (PCA) is used to reduce the feature vector, thus eliminating the redundant features and speeding up the operation speed of the classification model. By multiple kernel learning (MKL), we use Kernel target alignment (KTA), fast kernel learning (FKL), Hilbert-Schmidt Independence Criterion (HSIC), Mean to calculate the weight of kernel fusion. Finally, we put the combined kernel function into the support vector machine (SVM) and get excellent results. Among them, in the classification of renal cell carcinoma subtypes, the maximum accuracy can reach 0.978 by using the method of MKL (HSIC calculation weight), while in the classification of lung cancer subtypes, the accuracy can even reach 0.990 with the same method (FKL calculation weight).

Integrated Analysis Revealed the MicroRNA-Based Prognostic Predicting Signature for Papillary Renal Cell Carcinoma

Renal cell carcinoma (RCC) is one of the most frequently occurring tumors worldwide. Herein, we established a microRNA (miRNA) predicting signature to assess the prognosis of papillary-type RCC (PRCC) patients. miR-1293, miR-34a, miR-551b, miR-937, miR-299, and miR-3199-2 were used in building the overall survival (OS)-related signature, whereas miR-7156, miR-211, and miR-301b were used to construct the formula of recurrence-free survival (RFS) with the help of LASSO Cox regression analysis. The Kaplan-Meier and receiver operating characteristic curves indicated good discrimination and efficiency of the two signatures. Functional annotation for the downstream genes of the OS/RFS-related miRNAs exposed the potential mechanisms of PRCC. Notably, the multivariate analyses suggested that the two signatures were independent risk factors for PRCC patients and had better prognostic capacity than any other classifier. In addition, the nomogram indicated synthesis effects and showed better predictive performance than clinicopathologic features and our signatures. We validated the OS and RFS prediction formulas in clinical samples and met our expectations. Finally, we established two novel miRNA-based OS and RFS predicting signatures for PRCC, which are reliable tools for assessing the prognosis of PRCC patients.

Extracellular heat shock proteins and cancer: New perspectives

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High expression of extracellular heat shock proteins (HSPs) indicates highly aggressive tumors.

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HSP profiling of extracellular vesicles (EVs) derived from various biological fluids and released by immune cells may open new perspectives for an identification of diagnostic, prognostic and predictive biomarkers of cancer.

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Identification of specific microRNAs targeting HSPs in EVs may be a promising strategy for the discovery of novel biomarkers of cancer.

Heat shock proteins (HSPs) are a large family of molecular chaperones aberrantly expressed in cancer. The expression of HSPs in tumor cells has been shown to be implicated in the regulation of apoptosis, immune responses, angiogenesis and metastasis. Given that extracellular vesicles (EVs) can serve as potential source for the discovery of clinically useful biomarkers and therapeutic targets, it is of particular interest to study proteomic profiling of HSPs in EVs derived from various biological fluids of cancer patients. Furthermore, a divergent expression of circulating microRNAs (miRNAs) in patient samples has opened new opportunities in exploiting miRNAs as diagnostic tools. Herein, we address the current literature on the expression of extracellular HSPs with particular interest in HSPs in EVs derived from various biological fluids of cancer patients and different types of immune cells as promising targets for identification of clinical biomarkers of cancer. We also discuss the emerging role of miRNAs in HSP regulation for the discovery of blood-based biomarkers of cancer. We outline the importance of understanding relationships between various HSP networks and co-chaperones and propose the model for identification of HSP signatures in cancer. Elucidating the role of HSPs in EVs from the proteomic and miRNAs perspectives may provide new opportunities for the discovery of novel biomarkers of cancer.

Detection of urinary miRNAs for diagnosis of clear cell renal cell carcinoma

The lack of symptoms at the early stages of clear cell renal cell carcinoma (ccRCC) allows the tumour to metastasize, leading to a dramatic reduction in patient survival. Therefore, we studied and set up a method based on urinary microRNAs (miRNAs) for the diagnosis of ccRCC. First, miRNA expression in ccRCC specimens and kidney tissues from healthy subjects (HSs) was investigated through analysis of data banks and validated by comparing expression of miRNAs in ccRCC and adjacent non-cancerous kidney tissue specimens by RT-qPCR. Subsequently, we developed an algorithm to establish which miRNAs are more likely to be found in the urine of ccRCC patients that indicated miR-122, miR-1271, and miR-15b as potential interesting markers. The evaluation of their levels and three internal controls in the urine of 13 patients and 14 HSs resulted in the development of a score (7p-urinary score) to evaluate the presence of ccRCC in patients. The resulting area under the Receiver Operating Characteristic (ROC) curve, sensitivity, and specificity were equal to 0.96, 100% (95% CI 75–100%), and 86% (95% CI 57–98%), respectively. In conclusion, our study provides a proof of concept that combining the expression values of some urinary miRNAs might be useful in the diagnosis of ccRCC.

Long intergenic non-coding RNA 1939 eliminates proliferation and migration of human renal cell carcinoma (RCC) cells by down-regulation of miR-154

Renal carcinoma (RCC) is widely accepted as a malignant tumour of urinary system. Long intergenic non-coding RNA 1939 (LINC01939) is a novel lncRNA which was found to be down-regulated in RCC. Thus, we set out to explore the effect and regulation mechanism of LINC01939 in RCC. LINC01939 and miR-154 in RCC tissues and cell lines were detected using qRT-PCR assay. To examine cellular viability of ACHN and CAKI-1 cells, cell counting kit-8 (CCK-8) assay was exploited here. Flow cytometric analysis was conducted to examine apoptosis. Cell mobility was valued through wound healing assays. Western blotting was applied for examination of proteins related to proliferation, apoptosis, migration and Wnt/β-catenin/Notch. LINC01939 was down-regulated in RCC tissues. LINC01939 overexpression impeded proliferation and migration, and induced apoptosis. Further study found that the overexpression of LINC01939 strongly suppressed miR-154 expression. Then, the inhibiting effect of overexpressed LINC01939 on proliferation and mobility and the promoting role of LINC01939 in apoptosis were abolished by the combination of miR-154 mimic. Finally, we found that overexpressed LINC01939 inactivated Wnt/β-catenin and Notch through suppressing miR-154. Up-regulation of LINC01939 inhibited proliferation and migration of RCC cells by down-regulating miR-154.

MicroRNA Signature in Renal Cell Carcinoma

Renal cell carcinoma (RCC) includes 2.2% of all diagnosed cancers and 1.8% of cancer-related mortalities. The available biomarkers or screening methods for RCC suffer from lack of sensitivity or high cost, necessitating identification of novel biomarkers that facilitate early diagnosis of this cancer especially in the susceptible individuals. MicroRNAs (miRNAs) have several advantageous properties that potentiate them as biomarkers for cancer detection. Expression profile of miRNAs has been assessed in biological samples from RCC patients. Circulatory or urinary levels of certain miRNAs have been proposed as markers for RCC diagnosis or follow-up. Moreover, expression profile of some miRNAs has been correlated with response to chemotherapy, immunotherapy or targeted therapeutic options such as sunitinib. In the current study, we summarize the results of studies that assessed the application of miRNAs as biomarkers, therapeutic targets or modulators of response to treatment modalities in RCC patients.

The panoramic picture of pepsinogen gene family with pan-cancer

Background

It is well known that pepsinogen (PGs), as an important precursor of pepsin performing digestive function, has a good correlation with the occurrence and development of gastric cancer and it is also known that ectopic PGs expression is related to the prognosis of some cancers. However, the panoramic picture of pepsinogen gene family in human cancer is not clear. This study focused on elucidating the expression profile, activated pathway, immune cells infiltration, mutation, and copy number variation of PGs and their potential role in human cancer.

Method

Based on the next generation sequence data from TCGA, Oncomine, and CCLE, the molecular changes and clinical correlation of PGs in 33 tumor types were analyzed systematically by R language, including the expression, mutation, and copy number variation of PGs and their correlation with cancer‐related signal transduction pathway, immune cell infiltration, and prognostic potential in different cancers.

Results

PGs expression profiles appear different in 33 tumors. The transcriptional expression of PGs was detected in 16 of all 33 tumors. PGC was highly expressed in cholangiocarcinoma, colon adenocarcinoma, rectum adenocarcinoma, uterine corpus endometrial carcinoma, bladder urothelial carcinoma and breast cancer, while decreased in stomach adenocarcinoma, kidney renal clear cell carcinoma, prostate adenocarcinoma, lung squamous cell carcinoma, and esophageal carcinoma. PGA3, PGA4, and PGA5 were expressed in most normal tissues, but decreased in cancer tissues. PGs expression was significantly related to the activation or inhibition of many signal transduction pathways, in which PGC and PGA5 are more likely to be associated with cancer‐related pathways. PGC participated in 33 regulatory network pathways in pan‐cancer, mainly distributed in stomach adenocarcinoma, esophageal carcinoma, and lung squamous cell carcinoma, respectively. PGC and PGA3 expression were significantly correlated with immune cell infiltration. The results of survival analysis showed that different PGs expression play significantly different prognostic roles in different cancers. PGC was correlated with poor survival in brain lower grade glioma, skin cutaneous melanoma, and higher survival in kidney renal clear cell carcinoma, acute myeloid leukemia, mesothelioma, and uveal melanoma. PGA4 was only associated with higher survival in kidney renal clear cell carcinoma. Genetic variation analysis showed that PGC gene often mutated in uterine corpus endometrial carcinoma and stomach adenocarcinoma had extensive copy number amplification in various tumor types. PGC expression was upregulated with the increase of copy number in cholangiocarcinoma, esophageal carcinoma, and kidney renal papillary cell carcinoma, while in stomach adenocarcinoma, PGC was upregulated regardless of whether the copy number was increased or decreased.

Conclusions

PGs was expressed unevenly in a variety of cancer tissues and was related to many carcinogenic pathways and involved in the immune regulation. PGC participated in 33 regulatory pathways in human cancer. Different PGs expression play significantly different prognostic roles in different cancers. The variation of copy number of PGC gene could affect the PGC expression. These findings suggested that PGs, especially PGC have characteristic of broad‐spectrum expression in multiple cancers rather than being confined to the gastric mucosa, which may made PGs be useful biomarkers for prediction/diagnosis/prognosis and effective targets for treatment in human cancer.

Deficiency of NEAT1 prevented MPP+-induced inflammatory response, oxidative stress and apoptosis in dopaminergic SK-N-SH neuroblastoma cells via miR-1277-5p/ARHGAP26 axis

Noncoding RNAs including long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) have been documented to play prominent role in neurodegenerative diseases including Parkinson's disease (PD). This study intended to investigate the role of lncRNA nuclear enriched assembly transcript 1 (NEAT1) in MPP⁺-induced PD model in dopaminergic neuronblastoma SK-N-SH cells, as well as its mechanism through sponging miRNA (miR)-1277-5p. Real-time PCR and western blotting revealed that NEAT1 and ARHGAP26 were upregulated, and miR-1277-5p was downregulated in MPP⁺-treated SK-N-SH cells in a certain of concentration- and time- dependent manner. MPP⁺ induced apoptosis in SK-N-SH cells, as evidenced by decreased cell viability and Bcl-2 expression, and elevated apoptosis rate and levels of Bax and cleaved caspase-3, which were examined by MTT assay, flow cytometry and western blotting. Moreover, commercial assay kits indicated that inflammatory response and oxidative stress were provoked in response to MPP⁺, due to promoted contents of interleukin (IL)-6, IL-1β, tumor necrosis factor-α, malondialdehyde, and lactate dehydrogenase, accompanied with suppressed superoxide dismutase and glutathione peroxidase levels. Notably, MPP⁺-induced apoptosis, inflammatory response and oxidative stress in SK-N-SH cells were mitigated by NEAT1 knockdown and/or miR-1277-5p overexpression. Moreover, silencing of miR-1277-5p could abrogate the suppression of NEAT1 deficiency on MPP⁺-induced cell injury. Similarly, upregulating miR-1277-5p-elicited neuroprotection in MPP⁺-induced SK-N-SH cells was reversed by ARHGAP26 restoration. Dual-luciferase reporter assay demonstrated a direct interaction between miR-1277-5p and NEAT1 or ARHGAP26. Collectively, NEAT1 upregulation might contribute to MPP⁺-induced neuron injury via NEAT1-miR-1277-5p-ARHGAP26 competing endogenous RNAs (ceRNAs) pathway.

Expressions of miR-302a, miR-105, and miR-888 Play Critical Roles in Pathogenesis, Radiotherapy, and Prognosis on Rectal Cancer Patients: A Study From Rectal Cancer Patients in a Swedish Rectal Cancer Trial of Preoperative Radiotherapy to Big Database Analyses

Differential expressions and functions of various micoRNAs (miRNAs) have been intensively studied in both colon and rectal cancers. However, the importance of miRNAs on radiotherapy (RT) response and clinical outcome in rectal cancer patients remains unclear. In this study, we used real-time polymerase chain reaction to examine the expressions of miR-302a, miR-105, and miR-888 in normal mucosa and cancer tissue from rectal cancer patients with and without preoperative RT. The biological function of miR-302a, miR-105, and miR-888 expression was further analyzed and identified through the public databases: TCGA (The Cancer Genome Atlas) and GEPIA (Gene Expression Profiling Interactive Analysis). The results showed that the expression of miR-105 in rectal cancer was higher than that in normal mucosa in RT (P = 0.042) and non-RT patients (P = 0.003) and was associated with mucinous histological type (P = 0.004), COX-2 (P = 0.042), and p73 expression (P = 0.030). The expression of miR-302a was shown more frequently in cancers with necrosis (P = 0.033) and with WRAP53 expression (P = 0.015), whereas miR-888 expression occurred more frequently in tumors with protein the expression of survivin (P = 0.015), AEG-1 (astrocyte elevated gene-1) (P = 0.003), and SATB1 (special AT-rich sequence binding protein 1) (P = 0.036). Moreover, TargetScan also predicted AEG-1 and SATB1 as putative targets for miR-888. The miRNA-gene network analysis showed that ABI2 was associated with all the three miRNAs, with lower expression and good diagnostic value in rectal cancers. The TCGA database demonstrated the association of miR-105 expression with high carcinoembryonic antigen level (P = 0.048). RT reduced the expressions of miR-302a, miR-105, and miR-888. Prognostic analysis showed that miR-888 expression was independently associated with worse survival of patients without RT [overall survival, P = 0.001; disease-free survival, P = 0.009]. Analysis of biological function revealed that the protein serine/threonine kinase activity and PI3K-AKT signaling pathway were the most significantly enriched functions and pathways, respectively. Our findings suggest that miR-105 is involved in rectal cancer pathogenesis and miR-888 is associated with prognosis. MiR-302a, miR-105, and miR-888 have potential influence on the pathogenesis, RT, and prognosis of rectal cancer.

Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis

Background

Tumorigenesis is highly heterogeneous, and using clinicopathological signatures only is not enough to effectively distinguish clear cell renal cell carcinoma (ccRCC) and improve risk stratification of patients. DNA methylation (DNAm) with the stability and reversibility often occurs in the early stage of tumorigenesis. Disorders of transcription and metabolism are also an important molecular mechanisms of tumorigenesis. Therefore, it is necessary to identify effective biomarkers involved in tumorigenesis through multi-omics analysis, and these biomarkers also provide new potential therapeutic targets.

Method

The discovery stage involved 160 pairs of ccRCC and matched normal tissues for investigation of DNAm and biomarkers as well as 318 cases of ccRCC including clinical signatures. Correlation analysis of epigenetic, transcriptomic and metabolomic data revealed the connection and discordance among multi-omics and the deregulated functional modules. Diagnostic or prognostic biomarkers were obtained by the correlation analysis, the Least Absolute Shrinkage and Selection Operator (LASSO) and the LASSO-Cox methods. Two classifiers were established based on random forest (RF) and LASSO-Cox algorithms in training datasets. Seven independent datasets were used to evaluate robustness and universality. The molecular biological function of biomarkers were investigated using DAVID and GeneMANIA.

Results

Based on multi-omics analysis, the epigenetic measurements uniquely identified DNAm dysregulation of cellular mechanisms resulting in transcriptomic alterations, including cell proliferation, immune response and inflammation. Combination of the gene co-expression network and metabolic network identified 134 CpG sites (CpGs) as potential biomarkers. Based on the LASSO and RF algorithms, five CpGs were obtained to build a diagnostic classifierwith better classification performance (AUC > 99%). A eight-CpG-based prognostic classifier was obtained to improve risk stratification (hazard ratio (HR) > 4; log-rank test, p-value < 0.01). Based on independent datasets and seven additional cancers, the diagnostic and prognostic classifiers also had better robustness and stability. The molecular biological function of genes with abnormal methylation were significantly associated with glycolysis/gluconeogenesis and signal transduction.

Conclusion

The present study provides a comprehensive analysis of ccRCC using multi-omics data. These findings indicated that multi-omics analysis could identify some novel epigenetic factors, which were the most important causes of advanced cancer and poor clinical prognosis. Diagnostic and prognostic biomarkers were identified, which provided a promising avenue to develop effective therapies for ccRCC.

Increased Complement 3 With Suppression of miR-145 Induces the Synthetic Phenotype in Vascular Smooth Muscle Cells From Spontaneously Hypertensive Rats

Background

We previously reported that vascular smooth muscle cells (VSMCs) from spontaneously hypertensive rats (SHRs) show the increased expression of complement 3 (C3) and the synthetic phenotype. We targeted the SHR C3 gene (C3 knockout [C3KO] SHRs) by the zinc finger gene editing method. In the current study, we investigated the mechanisms underlying the increased expression of C3 and the role of endogenous C3 in the synthetic phenotype of SHR VSMCs in comparison to cells from Wistar‐Kyoto (WKY) rats and C3KO SHRs.

Methods and Results

Nonmuscle myosin heavy chain staining of aortas from SHRs at 1 day after birth was stronger in comparison to WKY rats and C3KO SHRs. DNA synthesis in VSMCs from SHRs was significantly higher in comparison to WKY rats and C3KO SHRs. Immunohistochemical staining of renin and liver X receptor α in VSMCs from SHRs was stronger in comparison to WKY rats and C3KO SHRs. The expression of renin, Krüppel‐like factor 5, and liver X receptor α proteins in VSMCs from SHRs was significantly higher in comparison to WKY rats and C3KO SHRs. The expression of synthetic phenotype markers osteopontin, matrix gla, and l‐caldesmon, growth factors transforming growth factor‐β1 and platelet‐derived growth factor‐A, transcription factors Krüppel‐like factor 5 and liver X receptor α, and angiotensinogen mRNAs in VSMCs from SHRs was significantly higher in comparison to WKY rats and C3KO SHRs. The expression of miR‐145 mRNA in VSMCs from SHRs was suppressed in comparison to cells from WKY rats. miR‐145 inhibitor significantly increased the expression of C3 in VSMCs from WKY rats, but not in cells from SHRs.

Conclusions

These findings indicate that the increased C3 with the suppression of miR‐145 induces the synthetic phenotype through Krüppel‐like factor 5 and the activation of the renin‐angiotensin system through liver X receptor α in VSMCs from SHRs.

MicroRNA profile in stage I clear cell renal cell carcinoma predicts progression to metastatic disease

OBJECTIVE

This study sought to identify microRNA (miRNA) profiles of small, pathologically confirmed stage 1 clear cell renal cell carcinoma (ccRCC) tumors that are associated with progression to metachronous metastatic disease.

MATERIALS AND METHODS

Fifty-five pathologic stage 1 ccRCC tumors ≤5cm, from 2 institutions, were examined in a miRNA screening, followed by a validation study. For the screening phase 752 miRNA were evaluated on each sample to identify those with differential expression between tumors that subsequently did (n = 10) or did not (n = 10) progress to metastatic disease. For the validation, 35 additional samples (20 nonprogressors and 15 with distant progression) were utilized to investigate 20 miRNA to determine if a miRNA panel could differentiate aggressive tumors: associations of miRNA expression with cancer specific survival was also investigated.

RESULTS

In the screening analysis, 35 miRNA were differentially expressed (P < 0.05, FDR < 0.1) between the groups. In the validation, 11 miRNA were confirmed to have differential expression. The miRNA -10a-5p, -23b-3p, and -26a-5p differentiated nonprogressive and distant progressive disease with a sensitivity of 73.3% and a specificity of 85% (AUC=0.893). In addition, levels of miR-30a-3p and -145-5p were identified as independent prognostic factors of cancer specific survival.

CONCLUSIONS

This investigation identified miRNA biomarkers that may differentiate between non-progressive ccRCC tumors and those that progress to metastatic disease in this group of stage I tumors. The miRNA profiles determined in this study have the potential to identify patients with small renal masses who are likely to have progressive ccRCC. Such information may be valuable to incorporate into predictive models.

Identification of miR-21-5p and miR-210-3p serum levels as biomarkers for patients with papillary renal cell carcinoma: a multicenter analysis

Background

Expression of circulating serum microRNAs has not been studied in a cohort of patients with papillary renal cell carcinoma (pRCC) so far. We hypothesized that miRNA deregulation in malignant tissue is reflected in serum and could be used for non-invasive diagnosis of pRCC as well as differentiation between type 1 and type 2 pRCC.

Methods

We selected 11 differentially regulated miRNAs from the Cancer Genome Atlas (TCGA) pRCC data set as potential serum validation candidates. Serum miRNA expression was determined by qRT-PCR in a total of 34 pRCC type 1, 33 pRCC type 2 and 33 control subjects of three german high-volume medical centers.

Results

Heatmap and principal component analysis showed that miRNA expression did not cluster the samples into distinct sample groups and that miRNA levels did not significantly discriminate healthy individuals from patients with pRCC, nor between patients with type 1 and type 2 pRCC. However, miR-21-5p levels were significantly increased in patients with advanced pRCC (>pT3, and/or pN+ and/or pM+) in comparison to localized pRCC. Moreover, adding the expression of miR-210-3p, which was significantly down-regulated in localized pRCC sera in comparison to healthy sera, additionally increased diagnostic accuracy in our study cohort.

Conclusions

In our multicenter cohort, we were not able to identify a single miRNA serum marker for pRCC including its subclasses. However, our study revealed that miR-21-5p levels were elevated in advanced disease (with added diagnostic accuracy via addition of miR-210-3p expression), proposing these two miRs as potential biomarkers in pRCC.

Anti-angiogenesis and Immunotherapy: Novel Paradigms to Envision Tailored Approaches in Renal Cell-Carcinoma

Although decision making strategy based on clinico-histopathological criteria is well established, renal cell carcinoma (RCC) represents a spectrum of biological ecosystems characterized by distinct genetic and molecular alterations, diverse clinical courses and potential specific therapeutic vulnerabilities. Given the plethora of drugs available, the subtype-tailored treatment to RCC subtype holds the potential to improve patient outcome, shrinking treatment-related morbidity and cost. The emerging knowledge of the molecular taxonomy of RCC is evolving, whilst the antiangiogenic and immunotherapy landscape maintains and reinforces their potential. Although several prognostic factors of survival in patients with RCC have been described, no reliable predictive biomarkers of treatment individual sensitivity or resistance have been identified. In this review, we summarize the available evidence able to prompt more precise and individualized patient selection in well-designed clinical trials, covering the unmet need of medical choices in the era of next-generation anti-angiogenesis and immunotherapy.

A miRNA- and mRNA-seq-Based Feature Selection Approach for Kidney Cancer Biomakers

Microarray data sets have been used for predicting cancer biomarkers. Yet, replication of the prediction has not been fully satisfied. Recently, new data sets called deep sequencing data sets have been generated, with an advantage of less noise in computational analysis. In this study, we analyzed the kidney miRNA and mRNA sequence data sets for predicting cancer markers using 5 different statistical feature selection methods. In the results, we obtained 3 mRNA- and 27 miRNA-based cancer biomarkers to compare with the normal samples. In addition, we clustered the kidney cancer subtypes using a nonnegative matrix factorization method and obtained significant results of survival analysis from the 2 separate groups including miRNA-342 and its target eukaryotic translation initiation factor 5A (EIF5A).

miRNA profiling in renal carcinoma suggest the existence of a group of pro-angionenic tumors in localized clear cell renal carcinoma

Renal cell carcinoma comprises a variety of entities, the most common being the clear-cell, papillary and chromophobe subtypes. These subtypes are related to different clinical evolution; however, most therapies have been developed for clear-cell carcinoma and there is not a specific treatment based on different subtypes. In this study, one hundred and sixty-four paraffin samples from primary nephrectomies for localized tumors were analyzed. MiRNAs were isolated and measured by microRNA arrays. Significance Analysis of Microarrays and Consensus Cluster algorithm were used to characterize different renal subtypes. The analyses showed that chromophobe renal tumors are a homogeneous group characterized by an overexpression of miR 1229, miR 10a, miR 182, miR 1208, miR 222, miR 221, miR 891b, miR 629-5p and miR 221-5p. On the other hand, clear cell renal carcinomas presented two different groups inside this histological subtype, with differences in miRNAs that regulate focal adhesion, transcription, apoptosis and angiogenesis processes. Specifically, one of the defined groups had an overexpression of proangiogenic microRNAs miR185, miR126 and miR130a. In conclusion, differences in miRNA expression profiles between histological renal subtypes were established. In addition, clear cell renal carcinomas had different expression of proangiogenic miRNAs. With the emergence of antiangiogenic drugs, these differences could be used as therapeutic targets in the future or as a selection method for tailoring personalized treatments.

TGF-β and microRNA Interplay in Genitourinary Cancers

Genitourinary cancers (GCs) include a large group of different types of tumors localizing to the kidney, bladder, prostate, testis, and penis. Despite highly divergent molecular patterns, most GCs share commonly disturbed signaling pathways that involve the activity of TGF-β (transforming growth factor beta). TGF-β is a pleiotropic cytokine that regulates key cancer-related molecular and cellular processes, including proliferation, migration, invasion, apoptosis, and chemoresistance. The understanding of the mechanisms of TGF-β actions in cancer is hindered by the "TGF-β paradox" in which early stages of cancerogenic process are suppressed by TGF-β while advanced stages are stimulated by its activity. A growing body of evidence suggests that these paradoxical TGF-β actions could result from the interplay with microRNAs: Short, non-coding RNAs that regulate gene expression by binding to target transcripts and inducing mRNA degradation or inhibition of translation. Here, we discuss the current knowledge of TGF-β signaling in GCs. Importantly, TGF-β signaling and microRNA-mediated regulation of gene expression often act in complicated feedback circuits that involve other crucial regulators of cancer progression (e.g., androgen receptor). Furthermore, recently published in vitro and in vivo studies clearly indicate that the interplay between microRNAs and the TGF-β signaling pathway offers new potential treatment options for GC patients.

Transcription Factor and miRNA Interplays Can Manifest the Survival of ccRCC Patients

Clear cell renal cell carcinoma (ccRCC) still remains a higher mortality rate in worldwide. Obtaining promising biomakers is very crucial for improving the diagnosis and prognosis of ccRCC patients. Herein, we firstly identified eight potentially prognostic miRNAs (hsa-miR-144-5p, hsa-miR-223-3p, hsa-miR-365b-3p, hsa-miR-3613-5p, hsa-miR-9-5p, hsa-miR-183-5p, hsa-miR-335-3p, hsa-miR-1269a). Secondly, we found that a signature containing these eight miRNAs showed obviously superior to a single miRNA in the prognostic effect and credibility for predicting the survival of ccRCC patients. Thirdly, we discovered that twenty-two transcription factors (TFs) interact with these eight miRNAs, and a signature combining nine TFs (TFAP2A, KLF5, IRF1, RUNX1, RARA, GATA3, IKZF1, POU2F2, and FOXM1) could promote the prognosis of ccRCC patients. Finally, we further identified eleven genes (hsa-miR-365b-3p, hsa-miR-223-3p, hsa-miR-1269a, hsa-miR-144-5p, hsa-miR-183-5p, hsa-miR-335-3p, TFAP2A, KLF5, IRF1, MYC, IKZF1) that could combine as a signature to improve the prognosis effect of ccRCC patients, which distinctly outperformed the eight-miRNA signature and the nine-TF signature. Overall, we identified several new prognosis factors for ccRCC, and revealed a potential mechanism that TFs and miRNAs interplay cooperatively or oppositely regulate a certain number of tumor suppressors, driver genes, and oncogenes to facilitate the survival of ccRCC patients.

Current Concepts of Non-Coding RNAs in the Pathogenesis of Non-Clear Cell Renal Cell Carcinoma

Renal cell carcinoma (RCC) is a relatively rare malignancy of the urinary tract system. RCC is a heterogenous disease in terms of underlying histology and its associated underlying pathobiology, prognosis and treatment schedule. The most prevalent histological RCC subtype is clear-cell renal cell carcinoma (ccRCC), accounting for about 70-80% of all RCCs. Though the pathobiology and treatment schedule for ccRCC are well-established, non-ccRCC subtypes account for 20%-30% of RCC altogether, and their underlying molecular biology and treatment options are poorly defined. The class of non-coding RNAs-molecules that are generally not translated into proteins-are new cancer drivers and suppressors in all types of cancer. Of these, small non-coding microRNAs (miRNAs) contribute to carcinogenesis by regulating posttranscriptional gene silencing. Additionally, a growing body of evidence supports the role of long non-coding RNAs (lncRNAs) in cancer development and progression. Most studies on non-coding RNAs in RCC focus on clear-cell histology, and there is a relatively limited number of studies on non-ccRCC subtypes. The aim of this review is to give an overview of the current knowledge regarding the role of non-coding RNAs (including short and long non-coding RNAs) in non-ccRCC and to highlight possible implications as diagnostic, prognostic and predictive biomarkers.

The potential role of miR-126, miR-21 and miR-10b as prognostic biomarkers in renal cell carcinoma

Renal cell carcinoma (RCC) is the most commonly diagnosed renal tumor, consisting of ~3% of all malignancies worldwide. The prognosis of RCC can vary widely, and detecting patients at risk of recurrence at an early stage of disease may improve patient outcome. The factors presently used in a clinical setting cannot reliably predict the natural history of the disease. Therefore, there is a requirement to identify novel biomarkers that can aid in predicting patient outcome. Previous studies have indicated that microRNAs (miRNAs/miRs) are potential candidates as prognostic biomarkers for patients suffering from RCC. Consequently, the aims of the present study were to validate the potential of 3 of these miRNAs to predict the prognosis of patients with RCC, and to investigate the stability of endogenous control genes for miRNA studies in RCC tissues. The expression of 7 endogenous controls was measured using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in formalin-fixed paraffin-embedded tumor and benign tissues from patients suffering from clear cell RCC (ccRCC). The analyses identified RNU48 and U47 as the most stable endogenous controls. The expression of miR-126, miR-21 and miR-10b was analyzed using RT-qPCR in renal tissues from 116 patients diagnosed with ccRCC. All three investigated miRNAs were differentially expressed between malignant and benign tissues. miR-126 and miR-10b were also differentially expressed between grades and stages of ccRCC. In a univariate, but not in a multivariate model, low expression of miR-126 was associated with shorter time to recurrence of the disease. The results of the present study indicate that of the 3 miRNAs investigated, the expression of miR-126 has the strongest potential as a prognostic biomarker for patients suffering from ccRCC.

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

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.

MicroRNA‑222‑3p promotes tumor cell migration and invasion and inhibits apoptosis, and is correlated with an unfavorable prognosis of patients with renal cell carcinoma

The aim of the present study was to investigate the role of microRNA (miR)‑222‑3p in renal cell carcinoma (RCC). The expression level of miR‑222‑3p was detected in RCC tissues and cell lines (ACHN, 786‑O, Caki‑1 and 769‑P) and was identified to be significantly upregulated compared with the level in adjacent normal renal tissues and HK‑2 cells. Further in vitro experiments demonstrated that the over-expression of miR‑222‑3p promoted the migration and invasion, and attenuated the apoptosis of 786‑O cells, whereas the knockdown of miR‑222‑3p suppressed the migration and invasion and induced the apoptosis of 786‑O cells. Similar results were observed in the ACHN cell line in terms of migration, invasion and apoptosis. Furthermore, the expression level of miR‑222‑3p was measured in 42 RCC formaldehyde‑fixed paraffin‑embedded samples, and the association between the expression of miR‑222‑3p and the pathological characteristics and overall survival rate of patients with RCC was analyzed. The results demonstrated that patients with a higher expression of miR‑222‑3p had a significantly lower overall survival rate, compared with those with a lower expression of miR‑222‑3p [hazard ratio (HR)=5.120; P=0.036]. Multivariate analysis identified that patients with a higher expression of miR‑222‑3p retained the statistically significant decrease in overall survival rate compared with patients with a lower expression of miR‑222‑3p (HR=5.636; P=0.030). Furthermore, Kaplan‑Meier survival curves indicated that patients with higher miR‑222‑3p had significantly lower overall survival rates compared with patients with lower miR‑222‑3p (P=0.020). Taken together, these results suggested that miR‑222‑3p serves as an onco‑miR in RCC and may be a potential prognostic biomarker and therapeutic target in patients with RCC.

A Machine Learning Approach for the Classification of Kidney Cancer Subtypes Using miRNA Genome Data

Kidney cancer is one of the deadliest diseases and its diagnosis and subtype classification are crucial for patients’ survival. Thus, developing automated tools that can accurately determine kidney cancer subtypes is an urgent challenge. It has been confirmed by researchers in the biomedical field that miRNA dysregulation can cause cancer. In this paper, we propose a machine learning approach for the classification of kidney cancer subtypes using miRNA genome data. Through empirical studies we found 35 miRNAs that possess distinct key features that aid in kidney cancer subtype diagnosis. In the proposed method, Neighbourhood Component Analysis (NCA) is employed to extract discriminative features from miRNAs and Long Short Term Memory (LSTM), a type of Recurrent Neural Network, is adopted to classify a given miRNA sample into kidney cancer subtypes. In the literature, only a couple of kidney subtypes have been considered for classification. In the experimental study, we used the miRNA quantitative read counts data, which was provided by The Cancer Genome Atlas data repository (TCGA). The NCA procedure selected 35 of the most discriminative miRNAs. With this subset of miRNAs, the LSTM algorithm was able to group kidney cancer miRNAs into five subtypes with average accuracy around 95% and Matthews Correlation Coefficient value around 0.92 under 10 runs of randomly grouped 5-fold cross-validation, which were very close to the average performance of using all miRNAs for classification.

Identification and validation of a four-miRNA (miRNA-21-5p, miRNA-9-5p, miR-149-5p, and miRNA-30b-5p) prognosis signature in clear cell renal cell carcinoma

Purpose

Clear cell renal cell carcinoma (ccRCC) is one of the most common cancers with high mortality worldwide. However, biomarkers for predicting prognosis in ccRCC are limited. In this study, we attempted to identify potential prognostic biomarkers of ccRCC.

Methods

Clinical information and the preprocessed ccRCC mature miRNA expression profiles in The Cancer Genome Atlas database were downloaded from UCSC Xena. The miRNAs differentially expressed between ccRCCs and matched normal tissues were analyzed using the “limma” package. A miRNA-based signature was constructed using the multivariate Cox regression model with prognosis index (PI) formula. Patients with ccRCC were divided into low-risk and high-risk subgroups according to median PI. The survival times were compared between the two groups using Kaplan–Meier analysis with log-rank test. The training set was used to construct a miRNA-based signature for predicting prognosis. The test set was used to verify the signature. Target gene prediction and functional enrichment analysis of the four miRNAs were performed using miRNet.

Results

We identified four miRNAs, miRNA-21-5p, miRNA-9-5p, miR-149-5p, and miRNA-30b-5p, as independent prognostic indicators. Next, we used these four miRNAs to construct a four-miRNA PI for each patient. Results revealed that patients in the high-risk group (n=119) had significantly shorter survival time than those in the low-risk group (n=118) (high-risk/low-risk group log-rank P=0.000). This four-miRNA signature is an independent prognostic factor compared with routine clinicopathological features in the test set. These miRNAs targeted 1,634 genes, and a miRNA-target gene network was constructed using miRNet. The target genes of these four miRNAs were involved in various pathways related to cancer.

Conclusion

Our observations suggest that the four-miRNA signature correlated with the survival of patients with ccRCC and can be used as a prognostic biomarker of ccRCC.

MicroRNA‑320a suppresses tumour cell proliferation and invasion of renal cancer cells by targeting FoxM1

An increasing body of evidence has indicated that microRNAs (miRNAs/miRs) may play an important role in tumourigenesis and tumour progression. Recent studies have demonstrated that miR‑320a is aberrantly expressed in a variety of different types of human cancer. The results of the present study confirmed that the expression of miR‑320a was decreased in clinical specimens and cell lines. Expression of miR‑320a inhibited the growth and invasive ability of ACHN and Caki‑1 cells. Bioinformatics analysis and a luciferase reporter assay demonstrated that forkhead box protein M1 (FoxM1) was directly regulated by miR‑320a. Rescue experiments in vitro revealed that the upregulation of FoxM1 antagonized the miR‑320a‑mediated malignant phenotype in renal cancer. Furthermore, experiments employing a xenograft mouse model revealed that the upregulation of miR‑320a inhibited the proliferation of renal cancer cells in nude mice when FoxM1 protein expression was reduced. Collectively, the present study demonstrated a novel molecular interaction regulated by miR‑320a, which may provide a novel insight into the treatments for renal cancer.