A three-microRNA signature as a diagnostic and prognostic marker in clear cell renal cancer: An In Silico analysis

Investigating diagnostic potential of long non-coding RNAs in head and neck squamous cell carcinoma using TCGA database and clinical specimens

Head and neck squamous cell carcinoma (HNSCC) is a prevalent and prognostically challenging cancer worldwide. The role of long non-coding RNAs (lncRNAs) in cancer regulation is progressively being understood. This study aims to identify lncRNAs with diagnostic potential as biomarkers for HNSCC. Statistical analysis was performed on expression data from the Cancer Genome Atlas (TCGA) database to identify potential lncRNAs associated with HNSCC. Four selected lncRNAs were validated using real-time quantitative reverse transcription polymerase chain reaction and correlated with clinical factors. Functional roles were further investigated. A total of 488 differentially expressed lncRNAs were identified in TCGA-HNSC. After rigorous evaluation based on p-values, survival analysis, and ROC analysis, 24 lncRNAs were prioritized for additional investigation. LINC00460, LINC00941, CTC-241F20.4, and RP11-357H14.17 were established as candidate diagnostic biomarkers. These lncRNAs exhibited elevated expression in HNSCC tissues and were associated with poor prognosis. Combining them showed high diagnostic accuracy. Notably, LINC00460 and CTC-241F20.4 demonstrated a significant elevation in the advanced stages of HNSCC. We constructed an lncRNA-mRNA regulatory network, and the array of significant regulatory pathways identified included focal adhesion, regulation of epithelial cell migration, and others. Additionally, these lncRNAs were found to influence immune responses by modulating immune cell infiltration in the HNSCC microenvironment. Our research indicates that LINC00460, LINC00941, RP11-357H14.17, and CTC-241F20.4 may have diagnostic and prognostic importance in HNSCC. Furthermore, we have gained insights into their potential functional roles, particularly about immune responses and interactions in the microenvironment.

MicroRNA biomarkers and their use in evaluating the prognosis of lung cancer

OBJECTIVE

We aim to use the microRNA (miRNA, micro-ribonucleic acid) data of lung cancer tissues to establish a miRNA biomarker database for lung cancer that can be used for marker screening and analysis of lung cancer prognosis.

METHODS

We obtained lung cancer-related data from The Cancer Genome Atlas (TCGA) and analyzed the miRNA expression profiles of lung cancer tissues and normal tissues using bioinformatics techniques to develop a new composite miRNA-based model for the prognostic assessment of lung cancer. The predictive power of this model was verified and evaluated based on grouping of data. We also performed RT-qPCR using lung cancer tissues from patients diagnosed with lung cancer.

RESULTS

There was a significant difference between the miRNA expression profiles of lung cancer tissues and normal tissues adjacent to the cancerous lesions. The prognostic survival of patients with lung cancer was closely related to onset age and staging (p = 0.012) but was not related to gender (p = 0.39) and race (p = 0.51). Using three methods of survival model construction, we identified three miRNA composites, namely hsa-mir-21, hsa-mir-141, and has-mir-490, as markers for the prognosis of lung cancer. As confirmed by RT-qPCR, the expressions of hsa-miR-21-5p and hsa-miR-141-5p were upregulated, whereas hsa-miR-490-3p expression was downregulated in lung cancer lesion tissues.

CONCLUSION

The three miRNA composites identified, namely hsa-mir-21, hsa-mir-141, and hsa-mir-490, have the potential to serve as novel prognostic biomarkers and therapeutic targets for lung cancer.

Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion

MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction.

MiR-942-3p as a Potential Prognostic Marker of Gastric Cancer Associated with AR and MAPK/ERK Signaling Pathway

Gastric cancer is a common tumor with high morbidity and mortality. MicroRNA (miRNA) can regulate gene expression at the translation level and various tumorigenesis processes, playing an important role in tumor occurrence and prognosis. This study aims to screen miRNA associated with gastric cancer prognosis as biomarkers and explore the regulatory genes and related signaling pathways. In this work, R language was used for the standardization and differential analysis of miRNA and mRNA expression profiles. Samples were randomly divided into a testing group and a training group. Subsequently, we built the five miRNAs (has-miR-9-3p, has-miR-135b-3p, has-miR-143-5p, has-miR-942-3p, has-miR-196-3p) prognostic modules, verified and evaluated their prediction ability by the Cox regression analysis. They can be used as an independent factor in the prognosis of gastric cancer. By predicting and analyzing potential biological functions of the miRNA target genes, this study found that the AR gene was not only a hub gene in the PPI network, but also associated with excessive survival of patients. In conclusion, this study demonstrated that hsa-miR-942-3p could be a potential prognostic marker of gastric cancer associated with the AR and MAPK/ERK signaling pathways. The results of this study provide insights into the occurrence and development of gastric cancer.

Identification of a Five-miRNA Signature for Diagnosis of Kidney Renal Clear Cell Carcinoma

Motivation: Kidney renal clear cell carcinoma, which is a common type and accounts for 70-80% of renal cell carcinoma, can easily lead to metastasis and even death. A reliable signature for diagnosis of this cancer is in need. Hence, we seek to select miRNAs for identifying kidney renal clear cell carcinoma. Method: A feature selection strategy is used and improved to identify microRNAs for diagnosis of kidney renal clear cell carcinoma. Samples representing kidney renal clear cell carcinoma and normal tissues are split into training and testing groups. Accumulated scores representing the variable importance of each miRNA are derived from an iteration of resampling, training, and scoring. Those miRNAs with higher scores are selected based on the Gaussian mixture model. The sample split is repeated ten times to get more central miRNAs. Results: A total of 611 samples are downloaded from TCGA, each of which contains 1,343 miRNAs. The improved feature selection method is implemented, and five miRNAs are identified as a biomarker for diagnosis of kidney renal clear cell carcinoma. GSE151419 and GSE151423 are selected as the independent testing sets. Experimental results indicate the effectiveness of the selected signature. Both data-driven measurements and knowledge-driven evidence are given to show the effectiveness of our selection results.

miR-1251-5p Overexpression Inhibits Proliferation, Migration, and Immune Escape in Clear Cell Renal Cell Carcinoma by Targeting NPTX2

Background. miR-1251-5p was identified as a tumor suppressor in a variety of malignancies; however, its biological function in clear cell renal cell carcinoma (ccRCC) is unknown. Methods. The Cancer Genome Atlas (TCGA) database was used to download expression information, including miR-1251-5p, in 521 ccRCC tissues and 71 ordinary tissues, and bioinformatics was used to explore possible target mRNAs. The relationship between miR-1251-5p, target mRNA activity, and clinical factors was examined. To estimate the biological activity of miR-1251-5p and target mRNA in ccRCC cells, we used MTT, colony formation, enzyme-linked immunosorbent, and Transwell assays. We employed a dual-luciferase reporter assay and a western blot to examine the molecular mechanisms of miR-1251-5p in ccRCC cells. In addition, the expressions of miR-1251-5p and target mRNA were further verified in the GEO database. Results. Our findings revealed that miR-1251-5p binds with NPTX2’s 3′-UTR. In TCGA and GEO datasets, miR-1251-5p activity is found to be lower in ccRCC tissues than that in nearby conventional tissues, although NPTX2 activity is higher. In ccRCC sufferers, miR-1251-5p and NPTX2 act as biomarkers that indicate a bad prognosis. Meanwhile, in miR-1251-5p tissues, NPTX2 expression and multiple clinical variables (survival status, grade, T staging, N staging, M staging, and clinical stage) had significant differences $\left(p<0.05\right)$ . Structurally, miR-1251-5p inhibited proliferation, migration, and immune escape of ccRCC cells by targeting NPTX2. Conclusion. Our findings indicate that miR-1251-5p constrained ccRCC cell advancement, migration, and immune evasion via targeting NPTX2, providing novel insights into ccRCC target treatment.

Construction of five microRNAs prognostic markers and a prognostic model for clear cell renal cell carcinoma

Background

To determine the role of miRNA in the progression and outcome of renal clear cell carcinoma (ccRCC), establish a model for predicting outcome in patients with ccRCC and verify it using a Cox regression model. The miRNA target genes were predicted to understand their biological functions.

Methods

The microRNAs of 71 normal tissues and 545 tumor tissues were downloaded from TCGA (https://tcga-data.nci.nih.gov/tcga/). We also downloaded 537 clinical materials from this website. The miRNA difference analysis was carried out. A prognostic model was constructed using differential miRNA. The model was verified using Cox survival analysis, receiver operator characteristic (ROC), and independent predictive analysis.

Results

MiR-130b-3p, miR-365b-3p, miR-149-5p, miR-155-5p, and miR-144-5p can be used as independent prognostic indicators. We also analyzed the related functions of the target gene and found that target genes of miRNAs are involved in the signal pathways of some tumors, including cholesterol metabolism, HIF-1 signal pathway, focus adhesion, the Rap1 signal pathway, and hepatitis C.

Conclusions

The prognostic model constructed using five miRNAs is an independent and accurate factor. These miRNAs target genes are involved in regulating a variety of tumorigenesis and signal pathways. Therefore, we have reason to believe that the regulation of signal pathways by miRNA may play a critical role in the occurrence, development, and outcome of ccRCC, provide a new therapeutic target for ccRCC, and improve outcomes.

MIMRDA: A Method Incorporating the miRNA and mRNA Expression Profiles for Predicting miRNA-Disease Associations to Identify Key miRNAs (microRNAs)

Identifying cancer-related miRNAs (or microRNAs) that precisely target mRNAs is important for diagnosis and treatment of cancer. Creating novel methods to identify candidate miRNAs becomes an imminent Frontier of researches in the field. One major obstacle lies in the integration of the state-of-the-art databases. Here, we introduce a novel method, MIMRDA, which incorporates the miRNA and mRNA expression profiles for predicting miRNA-disease associations to identify key miRNAs. As a proof-of-principle study, we use the MIMRDA method to analyze TCGA datasets of 20 types (BLCA, BRCA, CESE, CHOL, COAD, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PRAD, READ, SKCM, STAD, THCA and UCEC) of cancer, which identified hundreds of top-ranked miRNAs. Some (as Category 1) of them are endorsed by public databases including TCGA, miRTarBase, miR2Disease, HMDD, MISIM, ncDR and mTD; others (as Category 2) are supported by literature evidences. miR-21 (representing Category 1) and miR-1258 (representing Category 2) display the excellent characteristics of biomarkers in multi-dimensional assessments focusing on the function similarity analysis, overall survival analysis, and anti-cancer drugs’ sensitivity or resistance analysis. We compare the performance of the MIMRDA method over the Limma and SPIA packages, and estimate the accuracy of the MIMRDA method in classifying top-ranked miRNAs via the Random Forest simulation test. Our results indicate the superiority and effectiveness of the MIMRDA method, and recommend some top-ranked key miRNAs be potential biomarkers that warrant experimental validations.

MicroRNA-196a promotes renal cancer cell migration and invasion by targeting BRAM1 to regulate SMAD and MAPK signaling pathways

Rationale: MicroRNAs (miRNAs) are endogenous ~22nt RNAs that play critical regulatory roles in various biological and pathological processes, including various cancers. Their function in renal cancer has not been fully elucidated. It has been reported that miR-196a can act as oncogenes or as tumor suppressors depending on their target genes. However, the molecular target for miR-196a and the underlying mechanism in miR-196a promoted cell migration and invasion in renal cancer is still not clear.

Methods: The expression, survival and correlation between miR-196a and BRAM1 were investigated using TCGA analysis and validated by RT-PCR and western blot. To visualize the effect of Bram1 on tumor metastasis in vivo, NOD-SCID gamma (NSG) mice were intravenously injected with RCC4 cells (10⁶ cells/mouse) or RCC4 overexpressing Bram1. In addition, cell proliferation assays, migration and invasion assays were performed to examine the role of miR-196a in renal cells in vitro. Furthermore, immunoprecipitation was done to explore the binding targets of Bram1.

Results: TCGA gene expression data from renal clear cell carcinoma patients showed a lower level of Bram1 expression in patients' specimens compared to adjacent normal tissues. Moreover, Kaplan‑Meier survival data clearly show that high expression of Bram1correlates to poor prognosis in renal carcinoma patients. Our mouse metastasis model confirmed that Bram1 overexpression resulted in an inhibition in tumor metastasis. Target-prediction analysis and dual-luciferase reporter assay demonstrated that Bram1 is a direct target of miR-196a in renal cells. Further, our in vitro functional assays revealed that miR-196a promotes renal cell proliferation, migration, and invasion. Rescue of Bram1 expression reversed miR-196a-induced cell migration. MiR-196a promotes renal cancer cell migration by directly targeting Bram1 and inhibits Smad1/5/8 phosphorylation and MAPK pathways through BMPR1A and EGFR.

Conclusions: Our findings thus provide a new mechanism on the oncogenic role of miR-196a and the tumor-suppressive role of Bram1 in renal cancer cells. Dysregulated miR-196a and Bram1 represent potential prognostic biomarkers and may have therapeutic applications in renal cancer.

Circulating microRNAs from the Molecular Mechanisms to Clinical Biomarkers: A Focus on the Clear Cell Renal Cell Carcinoma

microRNAs (miRNAs) are emerging as relevant molecules in cancer development and progression. MiRNAs add a post-transcriptional level of control to the regulation of gene expression. The deregulation of miRNA expression results in changing the molecular circuitry in which miRNAs are involved, leading to alterations of cell fate determination. In this review, we describe the miRNAs that are emerging as innovative molecular biomarkers from liquid biopsies, not only for diagnosis, but also for post-surgery management in cancer. We focus our attention on renal cell carcinoma, in particular highlighting the crucial role of circulating miRNAs in clear cell renal cell carcinoma (ccRCC) management. In addition, the functional deregulation of miRNA expression in ccRCC is also discussed, to underline the contribution of miRNAs to ccRCC development and progression, which may be relevant for the identification and design of innovative clinical strategies against this tumor.

A novel miRNA-based classification model of risks and stages for clear cell renal cell carcinoma patients

Background

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and patients at advanced stage showed poor survival rate. Despite microRNAs (miRNAs) are used as potential biomarkers in many cancers, miRNA biomarkers for predicting the tumor stage of ccRCC are still limitedly identified. Therefore, we proposed a new integrated machine learning (ML) strategy to identify a novel miRNA signature related to tumor stage and prognosis of ccRCC patients using miRNA expression profiles. A multivariate Cox regression model with three hybrid penalties including Least absolute shrinkage and selection operator (Lasso), Adaptive lasso and Elastic net algorithms was used to screen relevant prognostic related miRNAs. The best subset regression (BSR) model was used to identify optimal prognostic model. Five ML algorithms were used to develop stage classification models. The biological significance of the miRNA signature was analyzed by utilizing DIANA-mirPath.

Results

A four-miRNA signature associated with survival was identified and the expression of this signature was strongly correlated with high risk patients. The high risk patients had unfavorable overall survival compared with the low risk group (HR = 4.523, P-value = 2.86e−08). Univariate and multivariate analyses confirmed independent and translational value of this predictive model. A combined ML algorithm identified six miRNA signatures for cancer staging prediction. After using the data balancing algorithm SMOTE, the Support Vector Machine (SVM) algorithm achieved the best classification performance (accuracy = 0.923, sensitivity = 0.927, specificity = 0.919, MCC = 0.843) when compared with other classifiers. Furthermore, enrichment analysis indicated that the identified miRNA signature involved in cancer-associated pathways.

Conclusions

A novel miRNA classification model using the identified prognostic and tumor stage associated miRNA signature will be useful for risk and stage stratification for clinical practice, and the identified miRNA signature can provide promising insight to understand the progression mechanism of ccRCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04189-2.

Review of Prognostic Expression Markers for Clear Cell Renal Cell Carcinoma

Context: The number of prognostic markers for clear cell renal cell carcinoma (ccRCC) has been increasing regularly over the last 15 years, without being integrated and compared. Objective: Our goal was to perform a review of prognostic markers for ccRCC to lay the ground for their use in the clinics. Evidence Acquisition: PubMed database was searched to identify RNA and protein markers whose expression level was reported as associated with survival of ccRCC patients. Relevant studies were selected through cross-reading by two readers. Evidence Synthesis: We selected 249 studies reporting an association with prognostic of either single markers or multiple-marker models. Altogether, these studies were based on a total of 341 distinct markers and 13 multiple-marker models. Twenty percent of these markers were involved in four biological pathways altered in ccRCC: cell cycle, angiogenesis, hypoxia, and immune response. The main genes (VHL, PBRM1, BAP1, and SETD2) involved in ccRCC carcinogenesis are not the most relevant for assessing survival. Conclusion: Among single markers, the most validated markers were KI67, BIRC5, TP53, CXCR4, and CA9. Of the multiple-marker models, the most famous model, ClearCode34, has been highly validated on several independent datasets, but its clinical utility has not yet been investigated. Patient Summary: Over the years, the prognosis studies have evolved from single markers to multiple-marker models. Our review highlights the highly validated prognostic markers and multiple-marker models and discusses their clinical utility for better therapeutic care.

pDriver : A novel method for unravelling personalised coding and miRNA cancer drivers

MOTIVATION

Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalised methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level.

RESULTS

We propose the novel method, pDriver, to discover personalised cancer drivers. pDriver includes two stages: (1) Constructing gene networks for each cancer patient and (2) Discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyse the predicted personalised drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer.

AVAILABILITY AND IMPLEMENTATION

pDriver is available at https://github.com/pvvhoang/pDriver.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Systematic Analysis of microRNA Biomarkers for Diagnosis, Prognosis, and Therapy in Patients With Clear Cell Renal Cell Carcinoma

The ever-increasing morbidity and mortality of clear cell renal cell carcinoma (ccRCC) urgently demands updated biomarkers. MicroRNAs (miRNAs) are involved in diverse biological processes such as cell proliferation, differentiation, apoptosis by regulating their target genes' expression. In kidney cancers, miRNAs have been reported to be involved in tumorigenesis and to be the diagnostic, prognostic, and therapeutic response biomarkers. Here, we performed a systematic analysis for ccRCC-related miRNAs as biomarkers by searching keywords in the NCBI PubMed database and found 118 miRNAs as diagnostic biomarkers, 28 miRNAs as prognostic biomarkers, and 80 miRNAs as therapeutic biomarkers in ccRCC. miRNA-21, miRNA-155, miRNA-141, miRNA-126, and miRNA-221, as significantly differentially expressed miRNAs between cancer and normal tissues, play extensive roles in the cell proliferation, differentiation, apoptosis of ccRCC. GO and KEGG enrichment analysis of these miRNAs' target genes through Metascape showed these target genes are enriched in Protein Domain Specific Binding (GO:0019904). In this paper, we identified highly specific miRNAs in the pathogenesis of ccRCC and explored their potential applications for diagnosis, prognosis, and treatment of ccRCC.

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

OBJECTIVE

This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC).

METHODS

For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis.

RESULTS

We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients' prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat.

CONCLUSIONS

To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

Objective

This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC).

Methods

For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis.

Results

We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients' prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat.

Conclusions

To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

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.

Nomogram Based on microRNA Signature Contributes to Improve Survival Prediction of Clear Cell Renal Cell Carcinoma

Objective

Numerous microRNAs (miRNAs) have been identified in ccRCC and recommended to be used for predicting clear cell renal cell carcinoma (ccRCC) prognosis. However, it is not clear whether a miRNA-based nomogram results in improved survival prediction in patients with ccRCC.

Methods

miRNA profiles from tumors and normal tissues were downloaded from The Cancer Genome Atlas (TCGA) database and analyzed using the "limma" package. The association between differentially expressed miRNAs and patient prognosis was identified using univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Next, all patients were randomly divided into development and validation cohorts at a ratio of 1 : 1. A nomogram was established based on independent prognostic factors in the development cohort. The prognostic performance of the nomogram was validated in both cohorts using the concordance index (C-index) and calibration plots.

Results

Multivariate Cox analysis identified the 13-miRNA signature, as well as AJCC stage and age, as independent prognostic factors after adjusting for other clinical covariates. The nomogram was built based on the independent variables. In the development cohort, the C-index for the constructed nomogram to predict overall survival (OS) was 0.792, which was higher than the C-index (0.731) of the AJCC staging system and C-index (0.778) of the miRNA signature. The nomogram demonstrated good discriminative ability in the validation cohort in predicting OS, with a C-index of 0.762. The calibration plots indicated an excellent agreement between the nomogram predicted survival probability and the actual observed outcomes. Furthermore, decision curve analysis (DCA) indicated that the nomogram was superior to the AJCC staging system in increasing the net clinical benefit.

Conclusions

The novel proposed nomogram based on a miRNA signature is a more reliable and robust tool for predicting the OS of patients with ccRCC compared to AJCC staging system, thus, improving clinical decision-making.

Genome-wide Analysis of the Alternative Splicing Profiles Revealed Novel Prognostic Index for Kidney Renal Cell Clear Cell Carcinoma

Alternative splicing (AS) is a major mechanism that greatly enhanced the diversity of proteome. Mounting evidence demonstrated that aberration of AS are important steps for the initiation and progression of human cancers. Here, we comprehensively investigated the association between whole landscape of AS profiles and the survival outcome of renal cell carcinoma (RCC) patients using RNA-seq data from TCGA SpliceSeq. Because of the limited number size of deaths in kidney chromophobe renal cell carcinoma (KICH) and papillary renal cell carcinoma (KIRP) TCGA cohorts, we only conducted survival analysis in kidney clear renal cell carcinoma (KIRC). We further constructed prognostic index (PI) based on prognosis-related AS events and built correlation network for splicing factors and prognosis-related AS events. According to the results, a total of 5351 AS events in 3522 genes were significantly correlated with the overall survival (OS) of kidney clear cell renal cell carcinoma (KIRC) patients. Seven of the PI models exhibited preferable prognosis-predicting capacity for KIRC with PI-ALL reaching the highest area under curve value of 0.875. The splicing regulatory network between splicing factors and prognosis-related AS events depicted a tangled web of relationships between them. One of the splicing factors: KHDRBS3 was validated by immunohistochemistry to be down-regulated in KIRC tissues. In conclusion, the powerful efficiency of risk stratification of PI models indicated the potential of AS signature as promising prognostic markers for KIRC and the splicing regulation network provided possible genetic mechanism of KIRC.

Combined identification of three miRNAs in serum as effective diagnostic biomarkers for HNSCC

Background: Head and neck squamous cell carcinoma (HNSCC) is a disastrous disease with substantial morbidity and mortality. This study aims to explore the effective diagnostic and prognostic biomarkers for HNSCC.

Methods: MiRNA expression data and corresponding clinical information of HNSCC from The Cancer Genome Atlas (TCGA) database were analyzed comprehensively to identify the miRNAs with diagnostic and prognostic power. The predictive ability of different classifications was analyzed for the three-miRNA combinations. Diagnostic and prognostic value were then evaluated and verified in clinical patients.

Findings: 128 differentially expressed miRNAs in HNSCC tissues were identified in the TCGA dataset, and 10 miRNAs were finally selected for further study. Classification analysis developed a three-miRNA signature of hsa-mir-383, hsa-mir-615, and hsa-mir-877 with the best diagnosis power, which was verified in validation patients. Survival analysis indicated that different expression levels of hsa-mir-383, rather than that of hsa-mir-615 or hsa-mir-877 led to significantly different survival rates in both cohorts. Furthermore, the multivariate Cox hazards analysis suggested that the microRNA signature yielded statistical significance to predict clinical outcome independently from other clinical variables in validation patients.

Interpretation: A three-miRNA signature of hsa-mir-383, hsa-mir-615, and hsa-mir-877 may serve as an excellent diagnostic biomarker for HNSCC, and potential prognostic significance for HNSCC patients.

Funding: This work was supported by the grants of the National Natural Science Foundation of China (81901021), Key Research and Development Program of Shandong (2019GSF108277), China postdoctoral Scinence Foundation Grant (2019M652380), Fundamental Research Funds of Shandong University (2018CJ047).

A Five-microRNA Signature as Prognostic Biomarker in Colorectal Cancer by Bioinformatics Analysis

Mounting evidence has demonstrated that a lot of miRNAs are overexpressed or downregulated in colorectal cancer (CRC) tissues and play a crucial role in tumorigenesis, invasion, and migration. The aim of our study was to screen new biomarkers related to CRC prognosis by bioinformatics analysis. By using the R language edgeR package for the differential analysis and standardization of miRNA expression profiles from The Cancer Genome Atlas (TCGA), 502 differentially expressed miRNAs (343 up-regulated, 159 down-regulated) were screened based on the cut-off criteria of p < 0.05 and |log2FC|>1, then all the patients (421) with differentially expressed miRNAs and complete survival time, status were then randomly divided into train group (212) and the test group (209). Eight miRNAs with p < 0.005 were revealed in univariate cox regression analysis of train group, then stepwise multivariate cox regression was applied for constituting a five-miRNA (hsa-miR-5091, hsa-miR-10b-3p, hsa-miR-9-5p, hsa-miR-187-3p, hsa-miR-32-5p) signature prognostic biomarkers with obviously different overall survival. Test group and entire group shown the same results utilizing the same prescient miRNA signature. The area under curve (AUC) of receiver operating characteristic (ROC) curve for predicting 5 years survival in train group, test group, and whole cohort were 0.79, 0.679, and 0.744, respectively, which demonstrated better predictive power of prognostic model. Furthermore, Univariate cox regression and multivariate cox regression considering other clinical factors displayed that the five-miRNA signature could serve as an independent prognostic factor. In order to predict the potential biological functions of five-miRNA signature, target genes of these five miRNAs were analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway and Gene Ontology (GO) enrichment analysis. The top 10 hub genes (ESR1, ADCY9, MEF2C, NRXN1, ADCY5, FGF2, KITLG, GATA1, GRIA1, KAT2B) of target genes in protein protein interaction (PPI) network were screened by string database and Cytoscape 3.6.1 (plug-in cytoHubba). In addition, 19 of target genes were associated with survival prognosis. Taken together, the current study showed the model of five-miRNA signature could efficiently function as a novel and independent prognosis biomarker and therapeutic target for CRC patients.

A Two-DNA Methylation Signature to Improve Prognosis Prediction of Clear Cell Renal Cell Carcinoma

PURPOSE

Effective biomarkers and models are needed to improve the prognostic prospects of clear cell renal cell carcinoma (ccRCC). The purpose of this work was to identify DNA methylation biomarkers and to evaluate the utility of DNA methylation analysis for ccRCC prognosis.

MATERIALS AND METHODS

An overview of genome-wide methylation of ccRCC tissues derived from The Cancer Genome Atlas (TCGA) database was download for analysis. DNA methylation signatures were identified using Cox regression methods. The potential clinical significance of methylation biomarkers acting as a novel prognostic markers was analyzed using the Kaplan-Meier method and receiver operating characteristic (ROC) curves.

RESULTS

This study analyzed data for 215 patients with information on 23171 DNA methylation sites and identified a two-DNA methylation signature (cg18034859, cg24199834) with the help of a step-wise multivariable Cox regression model. The area under the curve of ROCs for the two-DNA methylation signature was 0.819. The study samples were stratified into low- and high-risk classifications based on an optimal threshold, and the two groups showed markedly different survival rates. Moreover, the two-DNA methylation marker was suitable for patients of varying ages, sex, stages (I and IV), and histologic grade (G2).

CONCLUSION

The two-DNA methylation signature was deemed to be a potential novel prognostic biomarker of use in increasing the accuracy of predicting overall survival of ccRCC patients.

MicroRNA-1251-5p Promotes Carcinogenesis and Autophagy via Targeting the Tumor Suppressor TBCC in Ovarian Cancer Cells

Accounting for more than 70% of ovarian cancer cases, epithelial ovarian malignancy has a low 5-year survival rate. MicroRNAs may be targeted in the clinical treatment of the disease. In this study, we first found that miR-1251-5p was significantly upregulated in human ovarian cancer cell lines and tissues with the cancer progression and stages. Overexpression or inhibition of miR-1251-5p promoted or impeded cell proliferation and cell cycle progression. Subsequently, TBCC, one of the tubulin-binding cofactors (TBCs), was identified as a target of miR-1251-5p to be negatively associated with cell cycle and autophagy. Exogenous overexpression of TBCC inhibited the expressions of CDK4 and LC3BII, but it promoted the expressions of α/β-tubulin and p62 to suppress cell growth and autophagy, particularly under the starving condition; whereas the introduction of miR-1251-5p in TBCC-overexpressing cells rescued the suppressive effects of TBCC on cell cycle and autophagy through the inverse regulation of the above proteins. Finally, miR-1251-5p was proven to enhance xenograft tumor growth through the downregulation of TBCC but upregulation of Ki67 and LC3B in xenograft tumor tissues. Collectively, these results suggest that miR-1251-5p functions as an oncogene to suppress TBCC and α/β-tubulin expression. Thus, the miR-1251-5p/TBCC/α/β-tubulin axis may be targeted for ovarian cancer treatment.

Molecular Mechanisms in Clear Cell Renal Cell Carcinoma: Role of miRNAs and Hypermethylated miRNA Genes in Crucial Oncogenic Pathways and Processes

Clear cell renal cell carcinoma (ccRCC) is the third most common urological cancer, and it has the highest mortality rate. The increasing drug resistance of metastatic ccRCC has resulted in the search for new biomarkers. Epigenetic regulatory mechanisms, such as genome-wide DNA methylation and inhibition of protein translation by interaction of microRNA (miRNA) with its target messenger RNA (mRNA), are deeply involved in the pathogenesis of human cancers, including ccRCC, and may be used in its diagnosis and prognosis. Here, we review oncogenic and oncosuppressive miRNAs, their putative target genes, and the crucial pathways they are involved in. The contradictory behavior of a number of miRNAs, such as suppressive and anti-metastatic miRNAs with oncogenic potential (for example, miR-99a, miR-106a, miR-125b, miR-144, miR-203, miR-378), is examined. miRNAs that contribute mostly to important pathways and processes in ccRCC, for instance, PI3K/AKT/mTOR, Wnt-β, histone modification, and chromatin remodeling, are discussed in detail. We also separately consider their participation in crucial oncogenic processes, such as hypoxia and angiogenesis, metastasis, and epithelial-mesenchymal transition (EMT). The review also considers the interactions of long non-coding RNAs (lncRNAs) and miRNAs of significance in ccRCC. Recent advances in the understanding of the role of hypermethylated miRNA genes in ccRCC and their usefulness as biomarkers are reviewed based on our own data and those available in the literature. Finally, new data and perspectives concerning the clinical applications of miRNAs in the diagnosis, prognosis, and treatment of ccRCC are discussed.

Identification of a three-miRNA signature as a novel potential prognostic biomarker in patients with clear cell renal cell carcinoma

Current studies suggest that some microRNAs (miRNAs) are associated with prognosis in clear cell renal cell carcinoma (ccRCC). In this paper, we aimed to identify a miRNAs signature to improve prognostic prediction for ccRCC patients. Using ccRCC RNA-Seq data of The Cancer Genome Atlas (TCGA) database, we identified 177 differentially expressed miRNAs between ccRCC and paracancerous tissue. Then all the ccRCC tumor samples were divided into training set and validation set randomly. Three-miRNA signature including miR130b, miR-18a, and miR-223 were constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression model in training set. According to optimal cut-off value of three-miRNA signature risk score, all the patients could be classified into high-risk group and low-risk group significantly. Survival of patients was significantly different between two groups (hazard ratio, 5.58, 95% confidence interval, 3.17-9.80; P < 0.0001), and three-miRNA signature performed favorably prognostic and predictive accuracy. The results were further validated in the validation set and total set. Multivariate Cox regression analyses and subgroup analyses showed that three-miRNA signature was an independent prognostic factor. Two nomograms that integrated three-miRNA signature and three clinicopathological risk factors were constructed to predict overall survival and disease-free survival after surgery for ccRCC patients. Functional enrichment analysis showed the possible roles of three-miRNA signature in some cancer-associated biological processes and pathways. In conclusion, we developed a novel three-miRNA signature that performed reliable prognostic for patient survival with ccRCC, it might facilitate ccRCC patients counseling and individualize management.

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.

Comprehensive assessment gene signatures for clear cell renal cell carcinoma prognosis

There are many prognostic gene signature models in clear cell renal cell carcinoma (ccRCC). However, different results from various methods and samples are hard to contribute to clinical practice. It is necessary to develop a robust gene signature for improving clinical practice in ccRCC.A method was proposed to integrate least absolute shrinkage and selection operator and multiple Cox regression to obtain mRNA and microRNA signature from the cancer genomic atlas database for predicting prognosis of ccRCC. The gene signature model consisted by 5 mRNAs and 1 microRNA was identified. Prognosis index (PI) model was constructed from RNA expression and median value of PI is used to classified patients into high- and low-risk groups.The results showed that high-risk patients showed significantly decrease survival comparison with low-risk groups [hazard ratio (HR) =7.13, 95% confidence interval = 3.71-13.70, P < .001]. As the gene signature was mainly consisted by mRNA, the validation data can use transcriptomic data to verify. For comparison of the performance with previous works, other gene signature models and 4 datasets of ccRCC were retrieved from publications and public database. For estimating PI in each model, 3 indicators including HR, concordance index , and the area under the curve of receiver operating characteristic for 3 years were calculated across 4 independent datasets.The comparison results showed that the integrative model from our study was more robust than other models via comprehensive analysis. These findings provide some genes for further study their functions and mechanisms in ccRCC tumorigenesis and malignance, and may be useful for effective clinical decision making of ccRCC patients.

miRNA-21 and miRNA-223 expression signature as a predictor for lymph node metastasis, distant metastasis and survival in kidney renal clear cell carcinoma

Purpose: The aim of this study was to generate a novel miRNA expression signature to effectively assess nodal metastasis, distant metastasis and predict prognosis for patients with kidney renal clear cell carcinoma (KIRC) and explore its potential mechanism of affecting the prognosis. Method: Using expression profiles downloaded from the Cancer Genome Atlas database, we identified multiple miRNAs with differential expression between KIRC and paired normal tissues. The diagnostic values of the differentially expressed miRNAs for nodal metastasis and distant metastasis were evaluated by Receiver Operating Characteristic (ROC) curve analysis. Then, we evaluated the impact of miRNAs on overall survival (OS) by univariate and multivariate COX regression analyzes. This analysis was ultimately used to construct a miRNA signature that effectively assessed nodal metastasis, distant metastasis and predicted prognosis. The functional enrichment analysis of the miRNAs included in the signatures was used to explore its potential molecular mechanism in KIRC. Results: Based on our cutoff criteria (P < 0.05 and |log2FC| > 1.0), we identified 104 differentially expressed microRNAs (miRNAs), including 43 that were up-regulated in KIRC tissues and 61 that were down-regulated. We found 12 miRNAs were potentially diagnostic biomarkers of nodal metastasis and distant metastasis by ROC curve analysis. Two miRNAs (miRNA-21 and miRNA-223) were significant miRNAs independently associated with OS based on Cox univariate and multivariate analysis. We generated a signature index based on expression of these two miRNAs, and the two-miRNA signature is promising as a biomarker for diagnosing nodal metastasis, distant metastasis and predicting 5-year survival rate of KIRC with areas under the curve (AUC)=0.738, 0.659 and 0.731, respectively. Patients were stratified into high-risk and low-risk groups, according to median of the signature prognosis indexes. Patients in the high-risk group had significantly shorter survival times than those in the low-risk group (P = 0.000). The functional enrichment analysis suggested that the target genes of two miRNAs may be involved in various pathways related to cancer, p53 signaling pathway, apoptosis, and MAPK signaling pathway. Conclusion: The two-miRNA signature could assess nodal metastasis, distant metastasis and predict survival of KIRC. As a promising prediction tool, the mechanism of the two miRNAs in KIRC deserves further study.

Upregulated immune checkpoint HHLA2 in clear cell renal cell carcinoma: a novel prognostic biomarker and potential therapeutic target

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a malignant urogenital cancer with high mortality; however, current progress in understanding its molecular mechanism and predicting clinical treatment outcome is limited. Therefore, this study is to evaluate the clinical significance of immune inhibitory molecular human endogenous retrovirus-H long terminal repeat-associating protein 2 (HHLA2) in ccRCC prognosis and transcriptional regulatory network.

METHODS

Expression of HHLA2 in ccRCC was examined by an online database platform ONCOMINE. The ONCOMINE result was independently validated by qRT-PCR and immunohistochemistry. Kaplan-Meier survival was generated using IBM SPSS Statistics V.22. ccRCC tissues cells with high HHLA2 were sorted and subjected to microarray transcriptional profiling and analysis.

RESULTS

It was shown that expression of HHLA2 was statistically significantly increased in ccRCC tissues compared with normal renal tissues at both transcriptional and protein level. Moreover, the expression of HHLA2 was closely correlated with multiple clinicopathological features including tumour size, clinical stage and histological grade. High HHLA2 expression was associated with poor overall survival and clinical outcome. Comprehensive microarray analysis further identified thousands of HHLA2 targets including mRNA, long non-coding RNA and circular RNA that might function in various biological processes, especially, immune response.

CONCLUSION

Increased HHLA2 expression was observed in ccRCC tumour tissue, which leads to a remarkable shorter overall survival and poorer prognosis. Together with other molecular evidence, we have demonstrated that HHLA2 could be a potential prognostic biomarker for ccRCC.

The biological roles and clinical implications of microRNAs in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) accounts for about 3% of tumors in adults as well as 85% of all primary renal carcinoma. And it is the third most predominant urological carcinoma, but it has the maximum mortality rate. Early diagnosis and proper follow-up of ccRCC patients may improve the prognosis of the illness. Thus, it is imperative to understand the new biomarkers of ccRCC and study new method for the modern therapy of this deadly disease. Furthermore, a large number of microRNAs (miRNAs), small non-coding RNAs, have been relevant to tumor type, stage, or survival and miRNAs might be progressed as the markers of diagnosis or prognosis in ccRCC. A growing body of data also advised the rationality of regarding miRNAs as therapeutic targets for ccRCC treatment. In this review, we tried to summarize biogenesis of miRNAs and their expression profiles, biological roles, and clinical implications in ccRCC.