Biomarker identification in clear cell renal cell carcinoma based on miRNA-seq and digital gene expression-seq data

A highly reusable genosensor for late-life depression diagnosis based on microRNA 184 attomolar detection in human plasma

Late-Life Depression (LLD) is one of the most prevalent psychiatric disorders in elderly, causing significant functional impairments. MicroRNAs are small molecules involved in the post-transcriptional regulation of gene expression. Elderly individuals diagnosed with LLD present down regulation of miR-184 (hsa-miR-184) expression compared to healthy patients. Therefore, this miR-184 can be used as a biomarker to diagnose LLD. Current LLD diagnosis depends primarily on clinical subjective identification, based on symptoms and variable scales. This work introduces a novel and facile approach for the LLD diagnosis based on the development of an electrochemical genosensor for miR-184 detection in plasma, using differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS). DPV results presented a 2-Fold increase in current value for healthy patients, compared to individuals with LLD when monitoring ethidium bromide oxidation peak. For EIS, a 1.5-fold increase in charge transfer resistance for healthy elderly subjects was observed in comparison with depressed patients. In addition, the analytical performance of the biosensor was evaluated using DPV, obtaining a linear response ranging from 10^-9 mol L^-1 to 10^-17 mol L^-1 of miR-184 in plasma and a detection limit of 10 atomoles L^-1. The biosensor presented reusability, selectivity and stability, the current response remained 72% up to 50 days of storage. Thus, the genosensor proved to be efficient in the diagnosis of LLD, as well as the accurate quantification of miR-184 in real plasma samples of healthy and depressed patients.

A 9-gene prognostic signature for kidney renal clear cell carcinoma overall survival based on co-expression and regression analyses

This research attempted to screen potential signatures associated with KIRC progression and overall survival by weighted gene co-expression network analysis (WGCNA) and Cox regression. The KIRC-associated mRNA expression and clinical data were accessed from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened by differential analysis. A co-expression network was constructed by "WGCNA". Based on WGCNA module, GO and KEGG analyses were performed. Protein-protein interaction (PPI) network was constructed. Prognostic signatures were screened by Lasso-Cox regression. Prognostic model was evaluated by Receiver Operating Characteristic (ROC) and Kaplan-Meier (K-M) curves. Multivariate Cox and nomogram were introduced to examine whether risk score could be an independent marker. qRT-PCR was introduced to determine expression of 9 hub genes in KIRC clinical tumor tissues and adjacent tissues, respectively. Genes in the green module were highly associated with clinical status, and green module genes were significantly enriched in mitotic nuclear division, cell cycle, and p53 signaling pathway. Twenty-six candidates were subsequently screened out from the green module. Next, a 9-gene prognostic model (DLGAP5, NUF2, TOP2A, RRM2, HJURP, PLK1, AURKB, KIF18A, CCNB2) was constructed. The predicting ability of the model was optimal. Some cancer-related signaling pathways were differently activated between two risk score groups. Additionally, under-expression of some signature genes (AURKB, CCNB2, PLK1, RRM2, TOP2A) was associated with better survival rate for KIRC patients. Meanwhile, all 9 hub genes were substantially overexpressed in KIRC patients. A KIRC prognostic signature was screened in this study, contributing valuable findings to KIRC biomarker development.

Endosulfan induced kidney cell injury by modulating ACE2 through up-regulating miR-429 in HK-2 cells

Endosulfan, a typical organochlorine pesticide, is widely used in agricultural countries and was detected in blood samples from the general population. Studies have shown a positive correlation between chronic kidney disease of unknown aetiology (CKDu) and endosulfan. CKDu has become endemic in agricultural countries, with clinical manifestations of tubulointerstitial fibrosis.The goal of this study was to investigate the effects of endosulfan in kidney cell injury in human renal tubular epithelial cells (HK-2), focusing on apoptosis, inflammatory response, and epithelial-mesenchymal transition (EMT). We found that endosulfan induced apoptosis in HK-2 cells by up-regulating the expression of BAX, APAF-1, Caspase-3 and mitochondrial Cytochrome c was released into the cytosol. Endosulfan caused an inflammatory response, showing the increase in the secretion and mRNA expression levels of IL-6/IL-8. Endosulfan triggered EMT, characterized by downregulation of E-cadherin and upregulation of Vimentin. Western blot results showed that p-Smad3 and Smad3 protein expression were elevated while the expression of Smad7 were decreased in endosulfan-exposed groups. Dual luciferase reporter assay confirmed the potential binding capacity of miR-429 to 3'-UTR of ACE2. Endosulfan causes upregulation of miR-429 and downregulation of ACE2 in HK-2 cells. Overexpression of miR-429 or silencing of ACE2 in HK-2 cells caused apoptosis, inflammation and EMT through TGF signaling pathway. These findings suggest that endosulfan can lead to kidney cell injury by modulating ACE2 through up-regulating miR-429, providing new evidence for the pathogenesis of CKDu.

Identification of CXCL10 as a Prognostic Biomarker for Clear Cell Renal Cell Carcinoma

Background

One of the widespread forms of kidney tumor is clear cell renal cell carcinoma (ccRCC), with poor prognosis and insensitivity to radio chemotherapy as there is limited capacity to understand the disease mechanism. This study aims at identifying potential biomarkers and the underlying processes of ccRCC using bioinformatics analysis.

Methods

Transcriptome data of relevant samples were downloaded from The Cancer Genome Atlas (TCGA) database. R software was used to screen differentially expressed genes (DEGs) using the “edgeR” package. Two types of analysis—Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment—were accomplished by applying Database for Annotation, Visualization, and Integrated Discovery (DAVID) and Search Tool for the Retrieval of Interacting Genes database (STRING) online bioinformatics tools. A protein–protein interaction (PPI) network of the identified DEGs was constructed using Cytoscape software, and hub genes were subsequently selected via the Cytohubba plug-in. The selected genes were input into Oncomine for verification. Finally, selected hub genes were analyzed by doing survival analysis to notice the relationship between survival (OS) rate and the selected genes’ level of expression.

Results

There were 1,855 DEGs found connected to ccRCC, with 1,207 upregulated genes and 648 downregulated genes. G-protein-coupled receptor signaling pathway, integral component of membrane, calcium ion binding, and cytokine–cytokine receptor interaction were among the DEGs discovered. Oncomine confirmed the top six hub genes from the PPI network (C3, CXCR3, CXCL10, CCR5, CCL4, and CCL5). A high level of expression of CXCL10, one of these hub genes, was linked to a poor prognosis in individuals with ccRCC. The results of survival analysis showed that the expression level of CXCL10 was significantly correlated with the prognosis of ccRCC patients (p < 0.05).

Conclusions

From the analysis, the following results were drawn: CXCL10 might be a potential prognostic biomarker and novel therapeutic target for ccRCC.

A methylomics-correlated nomogram predicts the recurrence free survival risk of kidney renal clear cell carcinoma

BACKGROUND

Various studies have suggested that the DNA methylation signatures were promising to identify novel hallmarks for predicting prognosis of cancer. However, few studies have explored the capacity of DNA methylation for prognostic prediction in patients with kidney renal clear cell carcinoma (KIRC). It's very promising to develop a methylomics-related signature for predicting prognosis of KIRC.

METHODS

The 282 patients with complete DNA methylation data and corresponding clinical information were selected to construct the prognostic model. The 282 patients were grouped into a training set (70%, n = 198 samples) to determine a prognostic predictor by univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The internal validation set (30%, n = 84) and an external validation set (E-MTAB-3274) were used to validate the predictive value of the predictor by receiver operating characteristic (ROC) analysis and Kaplan-Meier survival analysis.

RESULTS

We successfully identified a 9-DNA methylation signature for recurrence free survival (RFS) of KIRC patients. We proved the strong robustness of the 9-DNA methylation signature for predicting RFS through ROC analysis (AUC at 1, 3, 5 years in internal dataset (0.859, 0.840, 0.817, respectively), external validation dataset (0.674, 0.739, 0.793, respectively), entire TCGA dataset (0.834, 0.862, 0.842, respectively)). In addition, a nomogram combining methylation risk score with the conventional clinic-related covariates was constructed to improve the prognostic predicted ability for KIRC patients. The result implied a good performance of the nomogram.

CONCLUSIONS

we successfully identified a DNA methylation-associated nomogram, which was helpful in improving the prognostic predictive ability of KIRC patients.

Determination of the key ccRCC-related molecules from monolayer network to three-layer network

Clear cell renal cell carcinoma (ccRCC), with an increasing incidence rate, is one of the ubiquitous cancers. Its pathogenic factors are complicated and the molecular mechanism is not clear. It is essential to analyze the potential key genes related to ccRCC carcinogenesis. In this study, the differentially expressed mRNAs, miRNAs and lncRNAs (DEmRNAs, DEmiRNAs and DElncRNAs) of ccRCC were screened from TCGA database. Then the miRNA-mRNA network, lncRNA-miRNA network and lncRNA-mRNA network were constructed by online database or WGCNA algorithm. Topology attributes of these monolayer networks showed that hsa-mir-155, hsa-mir-200c, hsa-mir-122, hsa-mir-506, hsa-mir-216b, hsa-mir-141, lncRNA AC137723.1 and AC021074.3 are the crucial genes related with the regulatory effects on the proliferation, metastasis and invasion of ccRCC cells. Subsequently, these three monolayer networks were integrated into a lncRNA-miRNA-mRNA multilayer network. Considering node degree, closeness centrality and betweenness centrality, we found hsa-mir-122 is screened out as the only crucial gene in three-layer network. In order to better illustrate the effect of hsa-mir-122 on ccRCC, the lncRNA-hsa-mir-122-mRNA network was constructed with hsa-mir-122 as the center. Pathway analysis of the unique target gene GALNT3 linked to hsa-mir-122 showed that GALNT3 influenced the metabolic process of mucin type O-Glycan biosynthesis. LncRNA AC090377.1 is the unique gene that has target genes among lncRNAs with clinical significance that linked to hsa-mir-122 in the lncRNA-hsa-mir-122-mRNA network. Pathway analysis of AC090377.1 suggested that GUCY2F enriched in phototransduction pathway associated with retina. From monolayer network to three-layer network, hsa-mir-122 is identified as an important molecule in the oncogenesis and progression of ccRCC, offering new strategies to further study of the carcinogenic mechanism of ccRCC.

[MicroRNA-200c-3p inhibits proliferation of nephroblastoma cells by targeting CCNE2]

OBJECTIVE

To predict and verify the target gene of miR-200c-3p and evaluate the inhibitory effect of miR-200c-3p on the proliferation of nephroblastoma cells.

METHODS

The putative target genes of miR-200c-3p were predicted by bioinformatics approach. Nephroblastoma cell models with miR-200c-3p overexpression or knockdown were established in SK-NEP-1 and G401 cells with corresponding control groups. The expressions of CCNE2 in SK-NEP-1 and G401 cells in different groups were detected by RT-PCR and Western blotting. A luciferase reporter assay was used to determine the targeting relationship between miR-200c-3p and CCNE2. The effects of miR-200c-3p overexpression or knockdown on cell proliferation was detected by cell counting kit-8 (CCK-8) assay and soft agarose assay.

RESULTS

CCNE2 was one of the target genes of miR-200c-3p as predicted by bioinformatics methods. Transfection of the two nephroblastoma cell lines with miR-200c-3p mimic resulted in significantly lowered CCNE2 mRNA and protein expressions (P < 0.05). The results of dual-luciferase assay confirmed that miR-200c-3p bound to the 3'UTR of CCNE2. CCK-8 assay and soft agarose assay demonstrated that overexpression of miR-200c-3p significantly inhibited the proliferation of the nephroblastoma cells (P < 0.01), and knocking down miR-200c-3p in the cells produced the opposite effects.

CONCLUSIONS

miR-200c-3p overexpression inhibits the proliferation of nephroblastoma cells by down-regulating its target gene CCNE2.

Co-expression Network Analysis Identifies Fourteen Hub Genes Associated with Prognosis in Clear Cell Renal Cell Carcinoma

Renal cancer is a common genitourinary malignance, of which clear cell renal cell carcinoma (ccRCC) has high aggressiveness and leads to most cancer-related deaths. Identification of sensitive and reliable biomarkers for predicting tumorigenesis and progression has great significance in guiding the diagnosis and treatment of ccRCC. Here, we identified 2397 common differentially expressed genes (DEGs) using paired normal and tumor ccRCC tissues from GSE53757 and The Cancer Genome Atlas (TCGA). Then, we performed weighted gene co-expression network analysis and protein-protein interaction network analysis, 17 candidate hub genes were identified. These candidate hub genes were further validated in GSE36895 and Oncomine database and 14 real hub genes were identified. All the hub genes were up-regulated and significantly positively correlated with pathological stage and histologic grade of ccRCC. Survival analysis showed that the higher expression level of each hub gene tended to predict a worse clinical outcome. ROC analysis showed that all the hub genes can accurately distinguish between tumor and normal samples, and between early stage and advanced stage ccRCC. Moreover, all the hub genes were positively associated with distant metastasis, lymph node infiltration, tumor recurrence and the expression of MKi67, suggesting these genes might promote tumor proliferation, invasion and metastasis. Furthermore, the functional annotation demonstrated that most genes were enriched in cell-cycle related biological function. In summary, our study identified 14 potential biomarkers for predicting tumorigenesis and progression, which might contribute to early diagnosis, prognosis prediction and therapeutic intervention.

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.

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.

Interactions Among Expressed MicroRNAs and mRNAs in the Early Stages of Fowl Adenovirus Aerotype 4-Infected Leghorn Male Hepatocellular Cells

Hydropericardium-hepatitis syndrome (HHS) is caused by some strains of fowl adenovirus serotype 4 (FAdV-4). However, the mechanism of FAdV-4 entry is not well understood. Therefore, to investigate the changes in host cellular response at the early stage of FAdV-4 infection, a conjoint analysis of miRNA-seq and mRNA-seq was utilized with leghorn male hepatocellular (LMH) cells at 30, 60, and 120 min after FAdV-4 infection. In total, we identified 785 differentially expressed (DE) miRNAs and 725 DE mRNAs in FAdV-4-infected LMH cells. Most miRNAs and mRNAs, including gga-miR-148a-3p, gga-miR-148a-5p, gga-miR-15c-3p, CRK, SOCS3, and EGR1, have not previously been reported to be associated with FAdV-4 infection. The conjoint analysis of the obtained data identified 856 miRNA–mRNA pairs at three time points. The interaction network analysis showed that gga-miR-128-2-5p, gga-miR-7475-5p, novel_miR205, and TCF7L1 were located in the core of the network. Furthermore, the relationship between gga-miR-128-2-5p and its target OBSL1 was confirmed using a dual-luciferase reporter system and a real-time quantitative polymerase chain reaction assay. In vitro experiments revealed that both gga-miR-128-2-5p overexpression and OBSL1 loss of function inhibited FAdV-4 entry. These results suggested that gga-miR-128-2-5p plays an important role in FAdV-4 entry by targeting OBSL1. To the best of our knowledge, the present study is the first to analyze host miRNA and mRNA expression at the early stage of FAdV-4 infection; furthermore, the results of this study help to elucidate the molecular mechanisms of FAdV-4 entry.

miR-429-CRKL axis regulates clear cell renal cell carcinoma malignant progression through SOS1/MEK/ERK/MMP2/MMP9 pathway

The pathogenesis and tumorigenesis of clear cell renal cell carcinoma (ccRCC) remain unclear. The deregulations of miR-429, a member of miR-200 family, and v-crk sarcoma virus CT10 oncogene homologue (avian)-like (CRKL), an adaptor protein of CRK family, are involved in the development, metastasis and prognosis of various cancers. Current study aimed to demonstrate the differential expressions of miR-429 and CRKL with their correlationship and molecular regulation mechanism in ccRCC malignancy. miR-429 and CRKL separately showed suppressing and promoting effects in ccRCC. Lower miR-429 expression and higher CRKL expression were negatively correlated in surgical cancerous tissues by promoting the advance of ccRCC. By binding to the 3'-UTR of CRKL, miR-429 reversely regulated CRKL for its functionalities in ccRCC cells. CRKL knockdown and overexpression separately decreased and increased the in vitro migration and invasion of 786-O cells, which were consistent with the influences of miR-429 overexpression and knockdown on 786-O through respectively downregulating and upregulating CRKL via SOS1/MEK/ERK/MMP2/MMP9 pathway. The enhancements of CRKL expression, migration and invasion abilities and SOS1/MEK/ ERK/MMP2/MMP9 activation induced by TGF-β stimulation in 786-O cells could be antagonized by miR-429 overexpression. Exogenous re-expression of CRKL abrogated miR-429 suppression on the migration and invasion of 786-O cells. Collectively, miR-429 deficiency negatively correlated with CRKL overexpression promoted the aggressiveness of cancer cells and advanced the clinical progression of ccRCC patients. miR-429-CRKL axial regulation provides new clues to the fundamental research, diagnosis and treatment of ccRCC.

An effective seven-CpG-based signature to predict survival in renal clear cell carcinoma by integrating DNA methylation and gene expression

AIMS

Currently, using clinicopathological risk factors only is not far from effective to evaluate the risk of disease progression in renal clear cell carcinoma (KIRC) patients. Molecular biomarkers might improve risk stratification of KIRC. DNA methylation occurs the whole process of tumor development and transcriptional disorders are also one of the important characteristics of tumor. Hence, this study aims to develop an effective and independent prognostic signature for KIRC patients by Integrating DNA methylation and gene expression.

MAIN METHODS

Difference analysis was conducted on DNA methylation sites and gene expression data. The Spearman's rank correlation and univariate Cox regression analysis were used to screen out the CpG sites that related with RNAs' expression and KIRC patients' overall survival. Then, a five-CpG-based prognostic classifier was established using LASSO Cox regression method.

KEY FINDINGS

The seven-CpG-based classifier can successfully divide KIRC patients into high-risk from low-risk groups, even after adjustment for standard clinical prognostic factors, such as age, stage, gender and grade. Moreover, the seven-CpG-based signature was more effective as independent prognostic factors than the combined model of these clinical factors. Six differential mRNA genes corresponding to the seven CpG sites are all related to human cancers by functional exploration. The gene functional and pathway enrichment analysis found that genes in immune-related pathways were remarkably different in high and low-risk groups.

SIGNIFICANCE

The new seven-CpG-based signature could helpfully provide insights into the underlying mechanism of KIRC and may be a powerful independent biomarker for predicting of the survival of KIRC patients.

GABRQ expression is a potential prognostic marker for patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer. Novel biomarkers of ccRCC may provide crucial information on tumor features and prognosis. The present study aimed to determine whether the expression of γ-aminobutyric acid (GABA) A receptor subunit θ (GABRQ) could serve as a novel prognostic marker of ccRCC. GABA is the main inhibitory neurotransmitter in the brain that activates the receptor GABA_A, which is comprised of three subunit isoforms: GABRA3, GABRB3 and GABRQ. A recent study reported that GABRQ is involved in the initiation and progression of hepatocellular carcinoma; however, the role of GABRQ in ccRCC remains unknown. In the present study, clinical and transcriptomic data were obtained from cohorts of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Differential GABRQ expression levels among early (TI and II), late (TIII and IV), nonmetastatic (M0) and metastatic (M1, primary tumor) stages of ccRCC samples were then identified. Furthermore, the use of GABRQ as a prognostic gene was analyzed using Uno's C-index based on the time-dependent area under the curve (AUC), the AUC of the receiver operating characteristic curve at 5 years, the Kaplan-Meier survival curve and multivariate analysis. The survival curve analysis revealed that low GABRQ mRNA expression was significantly associated with a poor prognosis of ccRCC (P<0.001 and P=0.0012 for TCGA and ICGC data, respectively). In addition, analyses of the C-index and AUC values further supported this discriminatory power. Furthermore, the prognostic value of GABRQ mRNA expression was confirmed by multivariate Cox regression analysis. Taken together, these results suggested that GABRQ mRNA expression may be considered as a novel prognostic biomarker of ccRCC.

The role of miRNAs as biomarkers in prostate cancer

There is an urged need of non-invasive biomarkers for the implementation of precision medicine. These biomarkers are required to these days for improving prostate cancer (PCa) screening, treatment or stratification in current clinical strategies. There are several commercial kits (Oncotype DX genomic prostate score^®, Prolaris^®, among others) that use genomic changes, rearrangement or even non-coding RNA events. However, none of them are currently used in the routine clinical practice. Many recent studies indicate that miRNAs are relevant molecules (small single-stranded non-coding RNAs that regulate gene expression of more than 30% of human genes) to be implement non-invasive biomarkers. However, contrasting to others tumors, such as breast cancer where miR-21 seems to be consistently upregulated; PCa data are controversial. Here we reported an extended revision about the role of miRNAs in PCa including data of AR signaling, cell cycle, EMT process, CSCs regulation and even the role of miRNAs as PCa diagnostic, prognostic and predictive tool. It is known that current biomedical research uses big-data analysis like Next Generation Sequencing (NGS) analysis. We also conducted an extensive online search, including the main platforms and kits for miRNAs massive analysis (like MiSeq, Nextseq 550, or Ion S5™ systems) indicating their pros, cons and including pre-analytical and analytical issues of miRNA studies.

Prognostic Role of TMED3 in Clear Cell Renal Cell Carcinoma: A Retrospective Multi-Cohort Analysis

Transmembrane p24 trafficking protein 3 (TMED3) is a metastatic suppressor in colon cancer and hepatocellular carcinoma. However, its function in the progression of clear cell renal cell carcinoma (ccRCC) is unknown. Here, we report that TMED3 could be a new prognostic marker for ccRCC. Patient data were extracted from cohorts in the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Differential expression of TMED3 was observed between the low stage (Stage I and II) and high stage (Stage III and IV) patients in the TCGA and ICGC cohorts and between the low grade (Grade I and II) and high grade (Grade III and IV) patients in the TCGA cohort. Further, we evaluated TMED3 expression as a prognostic gene using Kaplan-Meier survival analysis, multivariate analysis, the time-dependent area under the curve (AUC) of Uno’s C-index, and the AUC of the receiver operating characteristics at 5 years. The Kaplan-Meier analysis revealed that TMED3 overexpression was associated with poor prognosis for ccRCC patients. Analysis of the C-indices and area under the receiver operating characteristic curve further supported this. Multivariate analysis confirmed the prognostic significance of TMED3 expression levels (P = 0.005 and 0.006 for TCGA and ICGC, respectively). Taken together, these findings demonstrate that TMED3 is a potential prognostic factor for ccRCC.

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein-protein interaction (PPI) network analysis. After verification of TCGA's ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.