Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis

CTRR-ncRNA: A Knowledgebase for Cancer Therapy Resistance and Recurrence Associated Non-coding RNAs

Cancer therapy resistance and recurrence (CTRR) are the dominant causes of death in cancer patients. Recent studies have indicated that non-coding RNAs (ncRNAs) can not only reverse the resistance to cancer therapy but also are crucial biomarkers for the evaluation and prediction of CTRR. Herein, we developed CTRR-ncRNA, a knowledgebase of CTRR-associated ncRNAs, aiming to provide an accurate and comprehensive resource for research involving the association between CTRR and ncRNAs. Compared to most of the existing cancer databases, CTRR-ncRNA is focused on the clinical characterization of cancers, including cancer subtypes, as well as survival outcomes and responses to personalized therapy of cancer patients. Information pertaining to biomarker ncRNAs has also been documented for the development of personalized CTRR prediction. A user-friendly interface and several functional modules have been incorporated into the database. Based on the preliminary analysis of genotype-phenotype relationships, universal ncRNAs have been found to be potential biomarkers for CTRR. The CTRR-ncRNA is a translation-oriented knowledgebase and it provides a valuable resource for mechanistic investigations and explainable artificial intelligence-based modeling. CTRR-ncRNA is freely available to the public at http://ctrr.bioinf.org.cn/.

Identification of stage-associated exosome miRNAs in colorectal cancer by improved robust and corroborative approach embedded miRNA-target network

Background

Colorectal cancer (CRC) is a common gastrointestinal tumor with high morbidity and mortality. At the molecular level, patients at different stages present considerable heterogeneity. Although the miRNA in exosome is an effective biomarker to reveal tumor progression, studies based on stage-associated exosome miRNA regulatory network analysis still lacking. This study aims to identify CRC stage-associated exosome miRNAs and reveal their potential function in tumor progression.

Methods

In this study, serum and cellular exosome miRNA expression microarrays associated with CRC were downloaded from GEO database. Stage-common (SC) and stage-specific (SS) differentially expressed miRNAs were extracted and their targets were identified based on 11 databases. Furthermore, miRNA SC and SS regulatory function networks were built based on the CRC phenotypic relevance of miRNA targets, and the corresponding transcription factors were identified. Concurrently, the potential stage-associated miRNAs were identified by receiver-operating characteristic (ROC) curve analysis, survival analysis, drug response analysis, ceRNA analysis, pathway analysis and a comprehensive investigation of 159 publications.

Results

Ten candidate stage-associated miRNAs were identified, with three SC (miR-146a-5p, miR-22-3p, miR-23b-3p) and seven SS (I: miR-301a-3p, miR-548i; IIIA: miR-23a-3p; IV: miR-194-3p, miR-33a-3p, miR-485-3p, miR-194-5p) miRNAs. Additionally, their targets were enriched in several vital cancer-associated pathways such as TGF-beta, p53, and hippo signaling pathways. Moreover, five key hotspot target genes (CCNA2, MAPK1, PTPRD, MET, and CDKN1A) were demonstrated to associated with better overall survival in CRC patients. Finally, miR-23b-3p, miR-301a-3p and miR-194-3p were validated being the most stably expressed stage-associated miRNAs in CRC serum exosomes, cell exosomes and tissues.

Conclusions

These CRC stage-associated exosome miRNAs aid to further mechanism research of tumor progression and provide support for better clinical management in patients with different stages.

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.

Comparison of miRNA expressions among benign, premalignant and malignant lesions of the larynx: could they be transformation biomarkers?

Background

The malignancy potential of the laryngeal lesions are one of the major concerns of the surgeons about choosing the treatment options, forming surgical margins, deciding the follow-up periods. Finding a biomarker to overcome these concerns are ongoing challenges and recently microRNAs (miRNAs) are attributed as possible candidates since they can regulate gene expressions in the human genome. The objective of our study was to investigate their capability as a transformation biomarker for malignant laryngeal lesions.

Materials and methods

We investigated mature miRNA expressions in paraffin-embedded surgical specimens of human laryngeal tissues grouped as benign, premalignant or malignant (n = 10 in each). miRNA profiling was carried out by quantitative Real-Time polymerase chain reaction (RT-qPCR) and data were analyzed according to fold regulation.

Results

Our results demonstrated that 9 miRNAs were upregulated as the lesions become more malignant. Among them Hs_miR-183_5p, Hs_miR-155_5p, and Hs_miR-106b_3p expressions were significantly 4.16 (p = 0.032), 2.72 (p = 0.028) and 3.01 (p = 0.022) fold upregulated respectively in premalignant lesions compared to the benign lesions. Moreover, their expressions were approximately 2.76 fold higher in the malignant group than in the premalignant group compared to the benign group. Besides them, significant 7.57 (p = 0.036), 4.45 (p = 0.045) and 5.98 (p = 0.023) fold upregulations of Hs_miR-21_5p, Hs_miR-218_3p, and Hs_miR-210_3p were noticed in the malignant group but not in the premalignant group when compared to the benign group, respectively.

Conclusion

MiRNAs might have important value to help the clinicians for their concerns about the malignancy potentials of the laryngeal lesions. Hs_miR-183_5p, Hs_miR-155_5p, and Hs_miR-106b_3p might be followed as transformation marker, whereas Hs_miR-21_5p, Hs_miR-218_3p, and Hs_miR-210_3p might be a biomarker prone to malignancy.

MiR-378a-3p as a putative biomarker for hepatocellular carcinoma diagnosis and prognosis: Computational screening with experimental validation

BACKGROUND

Hepatocellular carcinoma (HCC) is a malignant disease with high morbidity and mortality, and the molecular mechanism for the genesis and progression is complex and heterogeneous. Biomarker discovery is crucial for the personalized and precision treatment of HCC. The accumulation of reported microRNA biomarkers makes it possible to combine computational identification with experimental validation to accelerate the discovery of novel biomarker.

RESULTS

In the present work, we applied a rational computer-aided biomarker discovery model to screen for the HCC diagnosis biomarker. Two HCC-associated networks were constructed based on the microRNA and mRNA expression profiles, and the potential microRNA biomarkers were identified based on their unique regulatory and influential power in the network. These putative biomarkers were then experimentally validated. One prominent example among these identified biomarkers is MiR-378a-3p: It was shown to independently regulate several important transcription factors such as PLAGL2 and β-catenin, affecting the β-catenin signaling. Such mechanism may indicate a potential tumor suppressor role of MiR-378a-3p and the impact of its abnormal expression on the cell growth and invasion of HCC.

CONCLUSIONS

A bioinformatics model with network topological and functional characterization was successfully applied to the identification of HCC biomarkers. The predicted microRNA biomarkers were than validated with experiments using human HCC cell lines, model animal, and clinical specimens. The results confirmed the prediction by our proposed model that miR-378a-3p was a putative biomarker for diagnosis and prognosis of HCC.

Identification of a novel immune-related microRNA prognostic model in clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is a type of kidney cancer, and one of the most common malignant tumors. Many studies have shown that certain microRNAs (miRNAs) play an important role in the occurrence and development of ccRCC. Nevertheless, the prognosis of ccRCC patients is very rarely based on these “immuno-miRs”. Our aim was thus to determine the relationship between immune-related miRNA signatures and ccRCC.

Methods

We downloaded the miRNA expression data from 521 KIRC and 71 normal tissues in The Cancer Genome Atlas (TCGA). We used “limma” package and univariate Cox regression analysis to identify differentially expressed miRNAs (DEMs) that related to overall survival (OS). We applied lasso and multivariate Cox regression analyses to construct a prognostic model based on immuno-miRs. We evaluated the performance of model by using the Kaplan-Meier method. Furthermore, Cox regression analysis was used to determine independent prognostic signatures in ccRCC.

Results

A total of 59 significant immuno-miRs were identified. We use univariate Cox regression analysis to acquire 18 immune-related miRNAs which were markedly related to OS of ccRCC patients in the training set. We then constructed the 9-immune-related-miRNA prognostic model (miR-21, miR-342, miR-149, miR-130b, miR-223, miR-365a, miR-9-1, and miR-146b) by using lasso and multivariate Cox regression. Further analysis suggested that the immune-related prognostic model could be an independent prognostic indicator for patients with ccRCC. The prognostic performance of the 9-immune-related-miRNA prognostic model was further validated successfully in the testing set.

Conclusions

We established a novel immune-based prognostic model of ccRCC based on potential prognostic immune-related miRNAs. Our results indicated that the 9-miRNA signature could be a practical and reliable prognostic tool for ccRCC.

Heterogeneous miRNA-mRNA Regulatory Networks of Visceral and Subcutaneous Adipose Tissue in the Relationship Between Obesity and Renal Clear Cell Carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is one of the most lethal urologic cancer. Associations of both visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) with ccRCC have been reported, and underlying mechanisms of VAT perhaps distinguished from SAT, considering their different structures and functions. We performed this study to disclose different miRNA-mRNA networks of obesity-related ccRCC in VAT and SAT using datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA); and find out different RNAs correlated with the prognosis of ccRCC in VAT and SAT.

METHODS

We screened out different expressed (DE) mRNAs and miRNAs of obesity, in both VAT and SAT from GEO datasets, and constructed miRNA-mRNA networks of obesity-related ccRCC. To evaluate the sensitivity and specificity of RNAs in networks of obesity-related ccRCC in both VAT and SAT, Receiver Operating Characteristic (ROC) analyses were conducted using TCGA datasets. Spearman correlation analyses were then performed to find out RNA pairs with inverse correlations. We also performed Cox regression analyses to estimate the association of all DE RNAs of obesity with the overall survival.

RESULTS

136 and 185 DE mRNAs of obesity in VAT and SAT were found out. Combined with selected DE miRNAs, miRNA-mRNA networks of obesity-related ccRCC were constructed. By performing ROC analyses, RNAs with same trend as shown in networks and statistically significant ORs were selected to be paired. Three pairs were finally remained in Spearman correlation analyses, including hsa-miR-182&ATP2B2, hsa-miR-532&CDH2 in VAT, and hsa-miR-425&TFAP2B in SAT. Multivariable Cox regression analyses showed that several RNAs with statistically significant adjusted HRs remained consistent trends as shown in DE analyses of obesity. Risk score analyses using selected RNAs showed that the overall survival time of patients in the low-risk group was significantly longer than that in the high-risk group regardless of risk score models.

CONCLUSIONS

We found out different miRNA-mRNA regulatory networks of obesity-related ccRCC for both VAT and SAT; and several DE RNAs of obesity-related ccRCC were found to remain consistent performance in terms of ccRCC prognosis. Our findings could provide valuable evidence on the targeted therapy of obesity-related ccRCC.

Phenotype-genotype network construction and characterization: a case study of cardiovascular diseases and associated non-coding RNAs

The phenotype–genotype relationship is a key for personalized and precision medicine for complex diseases. To unravel the complexity of the clinical phenotype–genotype network, we used cardiovascular diseases (CVDs) and associated non-coding RNAs (ncRNAs) (i.e. miRNAs, long ncRNAs, etc.) as the case for the study of CVDs at a systems or network level. We first integrated a database of CVDs and ncRNAs (CVDncR, http://sysbio.org.cn/cvdncr/) to construct CVD–ncRNA networks and annotate their clinical associations. To characterize the networks, we then separated the miRNAs into two groups, i.e. universal miRNAs associated with at least two types of CVDs and specific miRNAs related only to one type of CVD. Our analyses indicated two interesting patterns in these CVD–ncRNA networks. First, scale-free features were present within both CVD–miRNA and CVD–lncRNA networks; second, universal miRNAs were more likely to be CVDs biomarkers. These results were confirmed by computational functional analyses. The findings offer theoretical guidance for decoding CVD–ncRNA associations and will facilitate the screening of CVD ncRNA biomarkers.

Database URL: http://sysbio.org.cn/cvdncr/

Biomarker Discovery for the Carcinogenic Heterogeneity Between Colon and Rectal Cancers Based on lncRNA-Associated ceRNA Network Analysis

Background

Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Emerging evidence has revealed that risk factors and metastatic patterns differ greatly between colon and rectal cancers. However, the molecular mechanism underlying their pathogenic differences remains unclear. Therefore, we here aimed to identify non-coding RNA biomarkers based on lncRNA-associated ceRNA network (LceNET) to elucidate the carcinogenic heterogeneity between colon and rectal cancers.

Methods

A global LceNET in human was constructed by employing experimental evidence-based miRNA-mRNA and miRNA-lncRNA interactions. Then, four context-specific ceRNA networks related to cancer initiation and metastasis were extracted by mapping differentially expressed lncRNAs, miRNAs and mRNAs to the global LceNET. Notably, a novel network-based bioinformatics model was proposed and applied to identify lncRNA/miRNA biomarkers and critical ceRNA triplets for understanding the carcinogenic heterogeneity between colon and rectal cancers. Moreover, the identified biomarkers were further validated by their diagnostic/prognostic performance, expression pattern and correlation analysis.

Results

Based on network modeling, lncRNA KCNQ1OT1 (AUC>0.85) and SNHG1 (AUC>0.94) were unveiled as common diagnostic biomarkers for the initiation and metastasis of colon and rectal cancers. qRT-PCR analysis uncovered that these lncRNAs had significantly higher expression level in CRC cell lines with high metastatic potential. In particular, KCNQ1OT1 and SNHG1 function in colon and rectal cancers via different ceRNA mechanisms. For example, KCNQ1OT1/miR-484/ANKRD36 axis was involved in the initiation of colon cancer, while KCNQ1OT1/miR-181a-5p/PCGF2 axis was implicated in the metastasis of rectal cancer; the SNHG1/miR-484/ORC6 axis played a role in colon cancer, while SNHG1/miR-423-5p/EZH2 and SNHG1/let-7b-5p/ATP6V1F axes participated in the initiation and metastasis of rectal cancer, respectively. In these ceRNA triplets, miR-484, miR-181a-5p, miR-423-5p and let-7b-5p were identified as miRNA biomarkers with excellent distinguishing ability between normal and tumor tissues, and ANKRD36, PCGF2, EZH2 and ATP6V1F were closely related to the prognosis of corresponding cancer.

Conclusion

The landscape of lncRNA-associated ceRNA network not only facilitates the exploration of non-coding RNA biomarkers, but also provides deep insights into the oncogenetic heterogeneity between colon and rectal cancers, thereby contributing to the optimization of diagnostic and therapeutic strategies of CRC.

Computer-aided biomarker discovery for precision medicine: data resources, models and applications

Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.

The prognostic value of miRNA-18a-5p in clear cell renal cell carcinoma and its function via the miRNA-18a-5p/HIF1A/PVT1 pathway

Purpose Clear cell renal cell carcinoma(ccRCC) is the most common type of renal cell carcinoma. While it is curable when detected at an early stage, some patients presented with advanced disease have poor prognosis. We aimed to identify key genes and miRNAs associated with clinical prognosis in ccRCC.

Methods The microarray datasets were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were analyzed by using GEO2R. Then, Functional enrichment analysis was performed using the DAVID. A retrospective series of 254 ccRCC patients with complete clinical information was included in this study. Kaplan-Meier analysis and multivariate cox regression analysis were used for prognostic analysis. Wound healing assay and transwell assay were designed to evaluate the migration and invasion ability of ccRCC cell lines.

Results miRNA-18a was identified to be related with prognosis of ccRCC by using Kaplan-Meier analysis and multivariate cox regression analysis demonstrated that the prognostic value of miRNA-18a was independent of clinical features. Further studies showed that up-regulation of miRNA-18a had a positive effect on migration and invasion of ccRCC cells. The target gene (HIF1A) of the miRNA-18a was predicted by using the miRPathDB database. The transcription factors of DEGs were identified by using the i-cisTarget. Luckily, HIF1A was found to be one of the transcription factors of DEGs. Among these DEGs, PVT1 may be regulated by HIF1A and be related with prognosis of ccRCC. Finally, validation of miRNA18a/HIF1A/PVT1 pathway was checked via reverse transcription-polymerase chain reaction (RT-PCR) assay in both cell lines and clinical tumor samples.

Conclusion Our research revealed that miRNA18a/HIF1A/PVT1 pathway might play a crucial role in ccRCC progression, providing novel insights into understanding of ccRCC molecular mechanisms. Importantly, miRNA-18a could serve as a potential diagnostic biomarker and therapeutic targets for ccRCC patients.

Integrative Analysis of Multi-Genomic Data for Kidney Renal Cell Carcinoma

Accounting for nine out of ten kidney cancers, kidney renal cell carcinoma (KIRC) is by far the most common type of kidney cancer. In view of limited and ineffective available therapies, understanding the genetic basis of disease becomes important for better diagnosis and treatment. The present studies are based on a single type of genomic data. These studies do not consider interactions between genomic data types and their underlying biological relationships in the disease. However, the current availability of multiple genomic data and the possibility of combining it have facilitated a better understanding of the cancer's characterization. But high dimensionality and the existence of complex interactions (within and between genomic data types) are the two main challenges of integrative methods to analyze cancer effectively. In this paper, we propose a method to build an integrative model based on Bayesian model averaging procedure for improved prediction of clinical outcome in cancer survival. The proposed method initially uses dimensionality reduction techniques to generate low-dimensional latent features for the predictive models and then incorporates interactions between them. It defines the latent features using principal components and their sparse version. It compares the predictive performance of models based on these two latent features on real data. These models also validate several ccRCC-specific cancer biomarkers previously reported in the literature. Applied on kidney renal cell carcinoma (KIRC) dataset of The Cancer Genome Atlas (TCGA), the method achieves better prediction with sparse principal components model by including latent feature interactions as compared to without including them.

Elevated Plasma microRNA-105-5p Level in Patients With Idiopathic Parkinson's Disease: A Potential Disease Biomarker

Parkinson's disease (PD) is the second most common neurodegenerative disease, which still lacks a biomarker to aid in diagnosis and to differentiate diagnosis at the early stage of the disease. microRNAs (miRNAs) are small and evolutionary conserved non-coding RNAs that are involved in post-transcriptional gene regulation. Several miRNAs have been proposed as potential biomarkers in several diseases. In the present study, we screened miRNAs using a network vulnerability analysis, to evaluate their potential as PD biomarkers. We first extracted miRNAs that were differentially expressed between PD and healthy controls (HC) samples. Then we constructed the PD-specific miRNA-mRNA network and screened miRNA biomarkers using a new bioinformatics model. With this model, we identified miR-105-5p as a putative biomarker for PD. Moreover, we measured miR-105-5p levels in the plasma of patients with idiopathic PD (IPD) (n = 319), neurological disease controls (NDC, n = 305) and HC (n = 273) using reverse transcription real-time quantitative PCR (RT-qPCR). Our data clearly demonstrated that the plasma miR-105-5p level in IPD patients was significantly higher than those of HC (251%, p < 0.001) and NDC (347%, p < 0.001). There was no significant difference in miR-105-5p expression between IPD patients with or without anti-PD medications. Interestingly, we found that the plasma miR-105-5p expression level may be able to differentiate IPD from parkinsonian syndrome, essential tremor and other neurodegenerative diseases. We believe that a change in the plasma miR-105-5p level is a potential biomarker for IPD.

MicroRNA-497-5p down-regulation increases PD-L1 expression in clear cell renal cell carcinoma

Recent advances in immunotherapy are raising hope to treat clear cell renal cell carcinoma (ccRCC) with PD-L1 inhibitors, but only a small portion of patients are PD-L1 positive. The heterogeneous expression pattern of PD-L1 in patient population suggests that PD-L1 expression is under the control of diverse regulatory mechanisms. Although recent studies have identified numerous novel PD-L1 regulators, reports on microRNAs which modulate PD-L1 expression are much scarce. In this study, we confirmed that PD-L1 expression was up-regulated in ccRCC compared to paired normal tissues. Using miRDB and miRTarBase, 11 microRNAs were predicted to target PD-L1. After measuring the microRNA panel with TaqMan assays, we found that microRNA-497-5p down-regulation was associated with PD-L1 up-regulation. In TCGA-KIRC dataset, microRNA-497-5p down-regulation was also associated with PD-L1 up-regulation as well as shorter survival. We further validated that PD-L1 was a direct target of microRNA-497-5p in two RCC cell lines. In addition, microRNA-497-5p inhibited cell proliferation, clone formation and migration, while promoted apoptosis in in-vitro assays. Our study reveals a novel regulatory mechanism of PD-L1 expression and the potential of miR-497-5p as therapeutic target and biomarker deserves further investigation.

Synaptotagmin7 Is Overexpressed In Colorectal Cancer And Regulates Colorectal Cancer Cell Proliferation

Purpose: Synaptotagmin7 (SYT7) belongs to the synaptotagmin gene family and plays an important role in synaptic transmission. However, the function of this gene in most human cancer especially in colorectal cancer (CRC) remains unknown. In this research, we examined SYT7's role in CRC and tried to reveal its underlying mechanism.

Methods: We examined SYT7's expression levels in normal colorectal tissue and CRC tissues from 83 patients and analyzed the possible correlation between the expression level of SYT7 and pathological characteristics. The influences of SYT7 knockdown on cell growth were detected by Celigo image cytometer, colony formation assay, cell cycle analysis and apoptosis assay in vitro. The possible molecular mechanism was assessed using a microarray and Ingenuity Pathway Analysis.

Results: Our results show that the expression of SYT7 is upregulated in colorectal cancer tissues in comparison with normal tissues and positively correlated with the pathological stage of colorectal cancer. (P=0.015). We examined SYT7's role in human colorectal cancer cell line RKO by using SYT7-shRNA and revealed that SYT7 knockdown inhibit cell proliferation (P=8.6E-5), clonogenic ability (P=4.5E-6) and promoted G2/M Phase arrest and apoptosis (P=4.6E-7). Multiple cancer-associated pathways regulated by SYT7 were unraveled by microarray and Ingenuity Pathway Analysis.

Conclusions: Our study suggests that SYT7 plays an important role in the development of CRC and SYT7 may become a new therapeutic target in CRC.

Ankyrin repeat and single KH domain 1 (ANKHD1) drives renal cancer cell proliferation via binding to and altering a subset of miRNAs

Clear cell renal cell carcinoma (ccRCC) represents the most common kidney cancer worldwide. Increased cell proliferation associated with abnormal microRNA (miRNA) regulation are hallmarks of carcinogenesis. Ankyrin repeat and single KH domain 1 (ANKHD1) is a highly conserved protein found to interact with core cancer pathways in Drosophila; however, its involvement in RCC is completely unexplored. Quantitative PCR studies coupled with large-scale genomics data sets demonstrated that ANKHD1 is significantly up-regulated in kidneys of RCC patients when compared with healthy controls. Cell cycle analysis revealed that ANKHD1 is an essential factor for RCC cell division. To understand the molecular mechanism(s) utilized by ANKHD1 to drive proliferation, we performed bioinformatics analyses that revealed that ANKHD1 contains a putative miRNA-binding motif. We screened 48 miRNAs with tumor-enhancing or -suppressing activities and found that ANKHD1 binds to and regulates three tumor-suppressing miRNAs (i.e. miR-29a, miR-205, and miR-196a). RNA-immunoprecipitation assays demonstrated that ANKHD1 physically interacts with its target miRNAs via a single K-homology domain, located in the C terminus of the protein. Functionally, we discovered that ANKHD1 positively drives ccRCC cell mitosis via binding to and suppressing mainly miR-29a and to a lesser degree via miR-196a/205, leading to up-regulation in proliferative genes such as CCDN1. Collectively, these data identify ANKHD1 as a new regulator of ccRCC proliferation via specific miRNA interactions.

Identification of biomarker microRNAs for predicting the response of colorectal cancer to neoadjuvant chemoradiotherapy based on microRNA regulatory network

Preoperative radiotherapy or chemoradiotherapy has become a standard procedure for treatment of patients with locally advanced colorectal cancer (CRC). However, patients’ responses to treatment are different and personalized. MicroRNAs (miRNAs) are promising biomarkers for predicting personalized responses. In this study, we collected 30 publicly reported miRNAs associated with chemoradiotherapy of CRC. We extracted 46 differentially expressed miRNAs from samples of responders and non-responders to preoperative radiotherapy from the Gene Expression Omnibus dataset (Student's t test, p-value < 0.05 and |fold-change| > 2). We performed a systematic and integrative bioinformatics analysis to identify biomarker miRNAs for prediction of CRC responses to chemoradiotherapy. Using the bioinformatics model, miR-198, miR-765, miR-671-5p, miR-630, miR-371-5p, miR-575, miR-202, miR-483-5p and miR-513a-5p were screened as putative biomarkers for treatment response. Literature validation and functional enrichment analysis were exploited to confirm the reliability of the predicted miRNAs. Quantitative polymerase chain reaction showed that seven of the candidates were significantly differentially expressed between radiosensitive and insensitive CRC cell lines. The unique target genes of miR-198 and miR-765 were altered significantly upon transfection of specific miRNA mimics in the radiosensitive cell line. These results demonstrated the predictive power of our model and suggested that miR-198, miR-765, miR-630, miR-371-5p, miR-575, miR-202 and miR-513a-5p could be used for predicting the response of CRC to preoperative chemoradiotherapy.

MicroRNAs as possible biomarkers for screening of aortic aneurysms: a systematic review and validation study

CONTEXT

There is an urgent need to identify non-invasive biomarkers for the early detection of aortic aneurysms, preceding a fatal event. The potential role for MicroRNAs (miRNAs) as diagnostic markers for aortic aneurysms was investigated through the present systematic review.

OBJECTIVE

To perform a comprehensive review on published studies examining the association of miRNAs with aortic aneurysms and further validate these results with plasma samples collected from thoracic aortic aneurysm (TAA) patients.

METHODS

The literature search was performed via numerous databases and articles were only included if they fulfilled the predefined eligibility criteria. The miRNAs reported three times or more with expression consistency were validated using plasma samples from TAA patients collected before and following surgery.

RESULTS

Twenty-four articles were selected from the literature search and 11 miRNAs were chosen for validation using our samples. The miRNAs which were further validated were found to follow the trend in the regulation pattern as with the majority of the published data. MiRNA hsa-miR-193a-5p was found to be significantly down-regulated in the plasma samples collected before the aneurysmal removal when compared with postsurgical serum samples.

CONCLUSIONS

Numerous miRNAs have been associated with aortic aneurysms, and specifically hsa-miR-193a-5p and hsa-miR-30b-5p; therefore they warrant further investigation as potential biomarkers. Registration: The protocol of the review was registered in Prospero Databases (ID: CRD42016039953).

Biomarker MicroRNAs for Diagnosis of Oral Squamous Cell Carcinoma Identified Based on Gene Expression Data and MicroRNA-mRNA Network Analysis

Oral squamous cell carcinoma is one of the most malignant tumors with high mortality rate worldwide. Biomarker discovery is critical for early diagnosis and precision treatment of this disease. MicroRNAs are small noncoding RNA molecules which often regulate essential biological processes and are good candidates for biomarkers. By integrative analysis of both the cancer-associated gene expression data and microRNA-mRNA network, miR-148b-3p, miR-629-3p, miR-27a-3p, and miR-142-3p were screened as novel diagnostic biomarkers for oral squamous cell carcinoma based on their unique regulatory abilities in the network structure of the conditional microRNA-mRNA network and their important functions. These findings were confirmed by literature verification and functional enrichment analysis. Future experimental validation is expected for the further investigation of their molecular mechanisms.

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

Accumulating evidence has demonstrated that some specific miRNAs were aberrantly expressed in renal clear cell carcinoma and participated in many biological processes. The aim of this study was to investigate a panel of miRNA signature for diagnosis and prognosis of renal clear cell carcinoma (KIRC). Here, we performed a comprehensive analysis for miRNA expression profiles and corresponding clinical information of 516 KIRC patients from The Cancer Genome Atlas (TCGA). In the study, a total of 63 differentially expressed miRNAs were identified, of which 34 were up-regulated and 29 were down-regulated. We constructed a panel of three-miRNA that were significantly associated with KIRC diagnosis and KIRC patients' prognosis. The three-miRNA signature reached a sensitivity of 98.3% and a specificity of 97.2% in the diagnosis of KIRC. Using the three-miRNA signature, we classified the KIRC patients into high-risk group and low-risk group. The Kaplan- Meier curves showed that KIRC patients with high risk scores had significantly worsen overall survival (OS) and disease free survival (DFS) than KIRC patients with low risk scores. In the univariate and multivariate Cox regression analysis, three-miRNA signature was an independent prognostic factor in OS. In conclusion, the three-miRNA signature could be used as a diagnostic and prognostic biomarker in KIRC, and therefore, may help to provide significant clinical implication for the treatment of KIRC.

Diagnostic MicroRNA Biomarker Discovery for Non-Small-Cell Lung Cancer Adenocarcinoma by Integrative Bioinformatics Analysis

Lung cancer is the leading cause of cancer death and its incidence is ranked high in men and women worldwide. Non-small-cell lung cancer (NSCLC) adenocarcinoma is one of the most frequent histological subtypes of lung cancer. The aberration profile and the molecular mechanism driving its progression are the key for precision therapy of lung cancer, while the screening of biomarkers is essential to the precision early diagnosis and treatment of the cancer. In this work, we applied a bioinformatics method to analyze the dysregulated interaction network of microRNA-mRNA in NSCLC, based on both the gene expression data and the microRNA-gene regulation network. Considering the properties of the substructure and their biological functions, we identified the putative diagnostic biomarker microRNAs, some of which have been reported on the PubMed citations while the rest, that is, miR-204-5p, miR-567, miR-454-3p, miR-338-3p, and miR-139-5p, were predicted as the putative novel microRNA biomarker for the diagnosis of NSCLC adenocarcinoma. They were further validated by functional enrichment analysis of their target genes. These findings deserve further experimental validations for future clinical application.

MYC and hsa‑miRNA‑423‑5p as biomarkers in nasopharyngeal carcinoma revealed by miRNA‑mRNA‑pathway network integrated analysis

The present study was performed to identify the dysregulated microRNAs (miRNAs/miRs) and mRNAs, and enriched pathways involved in nasopharyngeal carcinoma (NPC) through the establishment of an miRNA-mRNA-pathways network. mRNA and miRNA expression profiles were collected from the European Molecular Biology Laboratory-European Bioinformatics Institute. Differentially expressed genes and differentially expressed miRNA were selectively screened using the metaDE package. Following prediction of the risk genes and pathway pairs involved in NPC, an miRNA-mRNA-pathway network was constructed by merging the miRNA-mRNA pairs, the mRNA-pathway pairs and the mRNA-mRNA pairs. The miRNA and mRNA biomarkers, as well as the functional pathway pairs, were identified in the network analysis, based on the topological properties of nodes in the network. Additionally, 10-fold cross-validation was performed to evaluate the performance of the selected risk genes and their corresponding miRNA in NPC by calculating the area under the curve (AUC). In total, 99 upregulated and 841 downregulated genes, and 192 upregulated and 26 downregulated miRNAs were identified. The miRNA-mRNA-pathway network was established using 403 miRNA-mRNA pairs, including 40 miRNAs and 302 risk genes, as well as 22 prominent pathway pairs. Network analysis demonstrated that v-myc avian myelocytomatosis viral oncogene homolog (MYC) and hsa-miR-423-5p were the mRNA and miRNA signatures for NPC, respectively. The AUC of these biomarkers for NPC was 0.7568 and 0.7798, respectively. Additionally, the focal adhesion pair pathway in cancer was identified to be associated with NPC. MYC and hsa-miR-423-5p have been identified to be critical biomarkers in NPC as revealed by miRNA-mRNA-pathway network integrated analysis, suggesting a direction for further research into the diagnosis and treatment of NPC.

High miR-122 expression promotes malignant phenotypes in ccRCC by targeting occludin

Renal cell carcinoma (RCC) is the most common neoplasm of the adult kidney, and clear cell RCC (ccRCC) represents its most common histological subtype. Although several studies have reported high expression of miR-122 in ccRCC, its physiological role remains unclear. To clarify the role of miR-122 in ccRCC, we compared miR-122 expression levels in non-cancerous tissue and ccRCC. Significant upregulation of miR-122 was observed in ccRCC specimens. Moreover, ccRCC patients with high miR-122 expression showed poor progression-free survival compared to those with low miR-122 expression. Overexpression of miR-122 using an miRNA mimic promoted proliferation, migration, and invasion activities of ccRCC cells. miR-122 directly targets occludin, a known component of tight junctions. Occludin knockdown promoted the cell migration activity but not proliferation or invasion activities of ccRCC cells. In human clinical specimens, miR-122 expression inversely correlated with occludin protein expression. These findings show that miR-122 is an oncomiR in ccRCC.

Interactions Between Genetics, Lifestyle, and Environmental Factors for Healthcare

The occurrence and progression of diseases are strongly associated with a combination of genetic, lifestyle, and environmental factors. Understanding the interplay between genetic and nongenetic components provides deep insights into disease pathogenesis and promotes personalized strategies for people healthcare. Recently, the paradigm of systems medicine, which integrates biomedical data and knowledge at multidimensional levels, is considered to be an optimal way for disease management and clinical decision-making in the era of precision medicine. In this chapter, epigenetic-mediated genetics-lifestyle-environment interactions within specific diseases and different ethnic groups are systematically discussed, and data sources, computational models, and translational platforms for systems medicine research are sequentially presented. Moreover, feasible suggestions on precision healthcare and healthy longevity are kindly proposed based on the comprehensive review of current studies.

Knowledge-Guided Bioinformatics Model for Identifying Autism Spectrum Disorder Diagnostic MicroRNA Biomarkers

Autism spectrum disorder (ASD) is a severe neurodevelopmental disease with a high incidence and effective biomarkers are urgently needed for its diagnosis. A few previous studies have reported the detection of miRNA biomarkers for autism diagnosis, especially those based on bioinformatics approaches. In this study, we developed a knowledge-guided bioinformatics model for identifying autism miRNA biomarkers. We downloaded gene expression microarray data from the GEO Database and extracted genes with expression levels that differed in ASD and the controls. We then constructed an autism-specific miRNA-mRNA network and inferred candidate autism biomarker miRNAs based on their regulatory modes and functions. We defined a novel parameter called the autism gene percentage as autism-specific knowledge to further facilitate the identification of autism-specific biomarker miRNAs. Finally, 11 miRNAs were screened as putative autism biomarkers, where eight miRNAs (72.7%) were significantly dysregulated in ASD samples according to previous reports. Functional enrichment results indicated that the targets of the identified miRNAs were enriched in autism-associated pathways, such as Wnt signaling (in KEGG and IPA), cell cycle (in KEGG), and glioblastoma multiforme signaling (in IPA), thereby supporting the predictive power of our model.

ZYG11A serves as an oncogene in non-small cell lung cancer and influences CCNE1 expression

By analyzing The Cancer Genome Atlas (TCGA) database, we identified ZYG11A as a potential oncogene. We determined the expression of ZYG11A in NSCLC tissues and explored its clinical significance. And also evaluated the effects of ZYG11A on NSCLC cell proliferation, migration, and invasion both in vitro and in vivo. Our results show that ZYG11A is hyper-expressed in NSCLC tissues compared to adjacent normal tissues, and increased expression of ZYG11A is associated with a poor prognosis (HR: 2.489, 95%CI: 1.248-4.963, p = 0.010). ZYG11A knockdown induces cell cycle arrest and inhibits proliferation, migration, and invasion of NSCLC cells. ZYG11A knockdown also results in decreased expression of CCNE1. Over-expression of CCNE1 in cells with ZYG11A knockdown restores their oncogenic activities. Our data suggest that ZYG11A may serve as a novel oncogene promoting tumorigenicity of NSCLC cells by inducing cell cycle alterations and increasing CCNE1 expression.

Novel Biomarker MicroRNAs for Subtyping of Acute Coronary Syndrome: A Bioinformatics Approach

Acute coronary syndrome (ACS) is a life-threatening disease that affects more than half a million people in United States. We currently lack molecular biomarkers to distinguish the unstable angina (UA) and acute myocardial infarction (AMI), which are the two subtypes of ACS. MicroRNAs play significant roles in biological processes and serve as good candidates for biomarkers. In this work, we collected microRNA datasets from the Gene Expression Omnibus database and identified specific microRNAs in different subtypes and universal microRNAs in all subtypes based on our novel network-based bioinformatics approach. These microRNAs were studied for ACS association by pathway enrichment analysis of their target genes. AMI and UA were associated with 27 and 26 microRNAs, respectively, nine of them were detected for both AMI and UA, and five from each subtype had been reported previously. The remaining 22 and 21 microRNAs are novel microRNA biomarkers for AMI and UA, respectively. The findings are then supported by pathway enrichment analysis of the targets of these microRNAs. These novel microRNAs deserve further validation and will be helpful for personalized ACS diagnosis.

Renal Carcinogenesis, Tumor Heterogeneity, and Reactive Oxygen Species: Tactics Evolved

SIGNIFICANCE

The number of kidney cancers is growing 3-5% each year due to unknown etiologies. Intra- and inter-tumor mediators increase oxidative stress and drive tumor heterogeneity. Recent Advances: Technology advancement in state-of-the-art instrumentation and methodologies allows researchers to detect and characterize global landscaping modifications in genes, proteins, and pathophysiology patterns at the single-cell level.

CRITICAL ISSUES

We postulate that the sources of reactive oxygen species (ROS) and their activation within subcellular compartments will change over a timeline of tumor evolvement and contribute to tumor heterogeneity. Therefore, the complexity of intracellular changes within a tumor and ROS-induced tumor heterogeneity coupled to the advancement of detecting these events globally are limited at the level of data collection, organization, and interpretation using software algorithms and bioinformatics.

FUTURE DIRECTIONS

Integrative and collaborative research, combining the power of numbers with careful experimental design, protocol development, and data interpretation, will translate cancer biology and therapeutics to a heightened level or leave the abundant raw data as stagnant and underutilized. Antioxid. Redox Signal. 25, 685-701.

Screening key microRNAs for castration-resistant prostate cancer based on miRNA/mRNA functional synergistic network

High-throughput methods have been used to explore the mechanisms by which androgen-sensitive prostate cancer (ASPC) develops into castration-resistant prostate cancer (CRPC). However, it is difficult to interpret cryptic results by routine experimental methods. In this study, we performed systematic and integrative analysis to detect key miRNAs that contribute to CRPC development. From three DNA microarray datasets, we retrieved 11 outlier microRNAs (miRNAs) that had expression discrepancies between ASPC and CRPC using a specific algorithm. Two of the miRNAs (miR-125b and miR-124) have previously been shown to be related to CRPC. Seven out of the other nine miRNAs were confirmed by quantitative PCR (Q-PCR) analysis. MiR-210, miR-218, miR-346, miR-197, and miR-149 were found to be over-expressed, while miR-122, miR-145, and let-7b were under-expressed in CRPC cell lines. GO and KEGG pathway analyses revealed that miR-218, miR-197, miR-145, miR-122, and let-7b, along with their target genes, were found to be involved in the PI3K and AKT3 signaling network, which is known to contribute to CRPC development. We then chose five miRNAs to verify the accuracy of the analysis. The target genes of each miRNA were altered significantly upon transfection of specific miRNA mimics in the C4–2 CRPC cell line, which was consistent with our pathway analysis results. Finally, we hypothesized that miR-218, miR-145, miR-197, miR-149, miR-122, and let-7b may contribute to the development of CRPC through the influence of Ras, Rho proteins, and the SCF complex. Further investigation is needed to verify the functions of the identified novel pathways in CRPC development.

Biological Networks for Cancer Candidate Biomarkers Discovery

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.

MicroRNA biomarker identification for pediatric acute myeloid leukemia based on a novel bioinformatics model

Acute myeloid leukemia (AML) in children is a complex and heterogeneous disease. The identification of reliable and stable molecular biomarkers for diagnosis, especially early diagnosis, remains a significant therapeutic challenge. Aberrant microRNA expression could be used for cancer diagnosis and treatment selection. Here, we describe a novel bioinformatics model for the prediction of microRNA biomarkers for the diagnosis of paediatric AML based on computational functional analysis of the microRNA regulatory network substructure. microRNA-196b, microRNA-155 and microRNA-25 were identified as putative diagnostic biomarkers for pediatric AML. Further systematic analysis confirmed the association of the predicted microRNAs with the leukemogenesis of AML. In vitro q-PCR experiments showed that microRNA-155 is significantly overexpressed in children with AML and microRNA-196b is significantly overexpressed in subgroups M4–M5 of the French-American-British classification system. These results suggest that microRNA-155 is a potential diagnostic biomarker for all subgroups of paediatric AML, whereas microRNA-196b is specific for subgroups M4–M5.

miR-29b and miR-198 overexpression in CD8+ T cells of renal cell carcinoma patients down-modulates JAK3 and MCL-1 leading to immune dysfunction

Background

Mammalian microRNAs (miR) regulate the expression of genes relevant for the development of adaptive and innate immunity against cancer. Since T cell dysfunction has previously been reported in patients with renal cell carcinoma (RCC; clear cell type), we aimed to analyze these immune cells for genetic and protein differences when compared to normal donor T cells freshly after isolation and 35 days after in vitro stimulation (IVS) with HLA-matched RCC tumor cells.

Methods

We investigated gene expression profiles of tumor-reactive CD8⁺ T cells obtained from RCC patient and compared with their HLA-matched healthy sibling donors using a microarray approach. In addition, miRNAs analysis was performed in a validation cohort of peripheral blood CD8⁺ T cells from 25 RCC patients compared to 15 healthy volunteers.

Results

We observed that CD8⁺ T cells from RCC patients expressed reduced levels of anti-apoptotic and proliferation-associated gene products when compared with normal donor T cells both pre- and post-IVS. In particular, JAK3 and MCL-1 were down-regulated in patient CD8⁺ T cells versus their normal counterparts, likely due to defective suppressor activity of miR-29b and miR-198 in RCC CD8⁺ T cells. Indeed, specific inhibition of miR-29b or miR-198 in peripheral blood mononuclear cells (PBMCs) isolated from RCC patients, resulted in the up-regulation of JAK3 and MCL-1 proteins and significant improvement of cell survival in vitro.

Conclusions

Our results suggest that miR-29b and miR-198 dysregulation in RCC patient CD8⁺ T cells is associated with dysfunctional immunity and foreshadow the development of miR-targeted therapeutics to correct such T cell defects in vivo.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-016-0841-9) contains supplementary material, which is available to authorized users.

Comparison of Prognostic MicroRNA Biomarkers in Blood and Tissues for Gastric Cancer

Gastric cancer (GC) still keeps up high mortality worldwide with poor prognosis. Efficient and non-invasive prognostic biomarkers are urgently needed. MicroRNAs are non-coding RNAs playing roles in post-transcriptional gene regulation, which contribute to various biological processes such as development, differentiation and carcinogenesis. MicroRNA expression profiles have been associated with the prognosis and outcome in GC. MicroRNA prognostic biomarkers have been identified from blood or tissues samples, but with different prognostic features. Understanding the various roles of microRNAs in different sample sources of GC will provide deep insights into GC progression. In this review, we highlight the distinct prognostic roles of microRNAs biomarkers in blood and tissue according to their relationships with prognostic parameters, survival rates and target pathways. This will be useful for non-invasive biomarker development and selection in prognosis of GC.

Prognostic value of meta-signature miRNAs in renal cell carcinoma: an integrated miRNA expression profiling analysis

To identify a robust panel of microRNA (miRNA) signatures that can distinguish renal cell carcinoma (RCC) from normal kidney using miRNA expression levels. We performed a comprehensive meta-analysis of 29 published studies that compared the miRNA expression profiles of RCC tissues and adjacent normal tissues (NT) to determine candidate miRNAs as prognostic biomarkers for RCC. Using vote-counting strategy and robust rank aggregation method, we identified a statistically significant miRNA meta-signature of two upregulated (miR-21, miR-210) and three downregulated (miR-141, miR-200c and miR-429) miRNAs. X-tile plot was used to generate the optimum cut-off point for the 15 different deregulated miRNAs and Kaplan-Meier method was used to calculate CSS. In a cohort of 45 patients, the high expression of miR-21 (HR: 5.46, 95%CI: 2.02-53.39) and miR-210 (HR: 6.85, 95%CI: 2.13-43.36), the low expression of miR-141 (HR: 0.16, 95%CI: 0.004-0.18), miR-200c (HR: 0.08, 95%CI: 0.01-0.43) and miR-429 (HR: 0.18, 95%CI: 0.02-0.50) were associated with poor cancer-specific survival (CSS) following RCC resection. We also constructed a five-miRNAs-based classifier as a reliable prognostic and predictive tool for CSS in patients with RCC, especially in clear cell RCC (ccRCC) (HR: 5.46, 95% CI: 1.51-19.66). This method might facilitate patient counselling and individualise management of RCC.

Integrated genomic analysis identifies subclasses and prognosis signatures of kidney cancer

PURPOSE

To define robust miRNA-based molecular classifiers for human clear cell renal cell carcinoma (ccRCC) subgrouping and prognostication.

EXPERIMENTAL DESIGN

Multidimensional data of over 500 clear cell renal cell carcinoma (ccRCC) patients were retrieved from The Cancer Genome Atlas (TCGA) archive. Data analysis was based on a novel computational approach that selectively considers patients with extreme expression values of miRNAs to detect survival-associated molecular signatures.

RESULTS

Our in silico analysis unveiled a novel ccRCC-specific 5-miRNA (miR-10b, miR-21, miR-143, miR-183, and miR-192) signature able, when combined with information from conventional TNM staging and the age of the patient, to prognosticate ccRCC outcome more accurately than known ccRCC miRNA signatures or TNM staging alone. Furthermore, our approach revealed the existence of 6 distinct subgroups of ccRCC characterized by discrete differences in overall survival, tumor stage, and mutational spectra in key ccRCC tumor suppressor genes. It also demonstrated that BAP1 mutations correlate with tumor progression rather than overall survival.

CONCLUSION

Integrated analysis of multidimensional data from the TCGA archive allowed to draw a portrait of distinct molecular subclasses of human ccRCC and to define signatures for prognosticating disease outcome. Together, these results offer new prospects for more accurate stratification and prognostication of ccRCC.

Approaches to uncovering cancer diagnostic and prognostic molecular signatures

The recent rapid development of high-throughput technology enables the study of molecular signatures for cancer diagnosis and prognosis at multiple levels, from genomic and epigenomic to transcriptomic. These unbiased large-scale scans provide important insights into the detection of cancer-related signatures. In addition to single-layer signatures, such as gene expression and somatic mutations, integrating data from multiple heterogeneous platforms using a systematic approach has been proven to be particularly effective for the identification of classification markers. This approach not only helps to uncover essential driver genes and pathways in the cancer network that are responsible for the mechanisms of cancer development, but will also lead us closer to the ultimate goal of personalized cancer therapy.

Integrative bioinformatics analysis reveals new prognostic biomarkers of clear cell renal cell carcinoma

BACKGROUND

The outcome of clear cell renal cell carcinoma (ccRCC) is still unpredictable. Even with new targeted therapies, the average progression-free survival is dismal. Markers for early detection and progression could improve disease outcome.

METHODS

To identify efficient and hitherto unrecognized pathogenic factors of the disease, we performed a uniquely comprehensive pathway analysis and built a gene interaction network based on large publicly available data sets assembled from 28 publications, comprising a 3-prong approach with high-throughput mRNA, microRNA, and protein expression profiles of 593 ccRCC and 389 normal kidney samples. We validated our results on 2 different data sets of 882 ccRCC and 152 normal tissues. Functional analyses were done by proliferation, migration, and invasion assays following siRNA (small interfering RNA) knockdown.

RESULTS

After integration of multilevel data, we identified aryl-hydrocarbon receptor (AHR), grainyhead-like-2 (GRHL2), and KIAA0101 as new pathogenic factors. GRHL2 expression was associated with higher chances for disease relapse and retained prognostic utility after controlling for grade and stage [hazard ratio (HR), 3.47, P = 0.012]. Patients with KIAA0101-positive expression suffered worse disease-free survival (HR, 3.64, P < 0.001), and in multivariate analysis KIAA0101 retained its independent prognostic significance. Survival analysis showed that GRHL2- and KIAA0101-positive patients had significantly lower disease-free survival (P = 0.002 and P < 0.001). We also found that KIAA0101 silencing decreased kidney cancer cell migration and invasion in vitro.

CONCLUSIONS

Using an integrative system biology approach, we identified 3 novel factors as potential biomarkers (AHR, GRHL2 and KIAA0101) involved in ccRCC pathogenesis and not linked to kidney cancer before.

Identification of microRNAs as potential biomarker for gastric cancer by system biological analysis

Gastric cancers (GC) have the high morbidity and mortality rates worldwide and there is a need to identify sufficiently sensitive biomarkers for GC. MicroRNAs (miRNAs) could be promising potential biomarkers for GC diagnosis. We employed a systematic and integrative bioinformatics framework to identify GC-related microRNAs from the public microRNA and mRNA expression dataset generated by RNA-seq technology. The performance of the 17 candidate miRNAs was evaluated by hierarchal clustering, ROC analysis, and literature mining. Fourteen have been found to be associated with GC and three microRNAs (miR-211, let-7b, and miR-708) were for the first time reported to associate with GC and may be used for diagnostic biomarkers for GC.

Exploring the miRNA-mRNA regulatory network in clear cell renal cell carcinomas by next-generation sequencing expression profiles

Altered microRNA (miRNA) expression is a hallmark of many cancer types. The combined analysis of miRNA and messenger RNA (mRNA) expression profiles is crucial to identifying links between deregulated miRNAs and oncogenic pathways. Therefore, we investigated the small non-coding (snc) transcriptomes of nine clear cell renal cell carcinomas (ccRCCs) and adjacent normal tissues for alterations in miRNA expression using a publicly available small RNA-Sequencing (sRNA-Seq) raw-dataset. We constructed a network of deregulated miRNAs and a set of differentially expressed genes publicly available from an independent study to in silico determine miRNAs that contribute to clear cell renal cell carcinogenesis. From a total of 1,672 sncRNAs, 61 were differentially expressed across all ccRCC tissue samples. Several with known implications in ccRCC development, like the upregulated miR-21-5p, miR-142-5p, as well as the downregulated miR-106a-5p, miR-135a-5p, or miR-206. Additionally, novel promising candidates like miR-3065, which i.a. targets NRP2 and FLT1, were detected in this study. Interaction network analysis revealed pivotal roles for miR-106a-5p, whose loss might contribute to the upregulation of 49 target mRNAs, miR-135a-5p (32 targets), miR-206 (28 targets), miR-363-3p (22 targets), and miR-216b (13 targets). Among these targets are the angiogenesis, metastasis, and motility promoting oncogenes c-MET, VEGFA, NRP2, and FLT1, the latter two coding for VEGFA receptors.

Identification of microRNA as sepsis biomarker based on miRNAs regulatory network analysis

Sepsis is regarded as arising from an unusual systemic response to infection but the physiopathology of sepsis remains elusive. At present, sepsis is still a fatal condition with delayed diagnosis and a poor outcome. Many biomarkers have been reported in clinical application for patients with sepsis, and claimed to improve the diagnosis and treatment. Because of the difficulty in the interpreting of clinical features of sepsis, some biomarkers do not show high sensitivity and specificity. MicroRNAs (miRNAs) are small noncoding RNAs which pair the sites in mRNAs to regulate gene expression in eukaryotes. They play a key role in inflammatory response, and have been validated to be potential sepsis biomarker recently. In the present work, we apply a miRNA regulatory network based method to identify novel microRNA biomarkers associated with the early diagnosis of sepsis. By analyzing the miRNA expression profiles and the miRNA regulatory network, we obtained novel miRNAs associated with sepsis. Pathways analysis, disease ontology analysis, and protein-protein interaction network (PIN) analysis, as well as ROC curve, were exploited to testify the reliability of the predicted miRNAs. We finally identified 8 novel miRNAs which have the potential to be sepsis biomarkers.

The construction of an amino acid network for understanding protein structure and function

Amino acid networks (AANs) are undirected networks consisting of amino acid residues and their interactions in three-dimensional protein structures. The analysis of AANs provides novel insight into protein science, and several common amino acid network properties have revealed diverse classes of proteins. In this review, we first summarize methods for the construction and characterization of AANs. We then compare software tools for the construction and analysis of AANs. Finally, we review the application of AANs for understanding protein structure and function, including the identification of functional residues, the prediction of protein folding, analyzing protein stability and protein-protein interactions, and for understanding communication within and between proteins.

Identification of candidate miRNA biomarkers from miRNA regulatory network with application to prostate cancer

Background

MicroRNAs (miRNAs) are a class of non-coding regulatory RNAs approximately 22 nucleotides in length that play a role in a wide range of biological processes. Abnormal miRNA function has been implicated in various human cancers including prostate cancer (PCa). Altered miRNA expression may serve as a biomarker for cancer diagnosis and treatment. However, limited data are available on the role of cancer-specific miRNAs. Integrative computational bioinformatics approaches are effective for the detection of potential outlier miRNAs in cancer.

Methods

The human miRNA-mRNA target network was reconstructed by integrating multiple miRNA-mRNA interaction datasets. Paired miRNA and mRNA expression profiling data in PCa versus benign prostate tissue samples were used as another source of information. These datasets were analyzed with an integrated bioinformatics framework to identify potential PCa miRNA signatures. In vitro q-PCR experiments and further systematic analysis were used to validate these prediction results.

Results

Using this bioinformatics framework, we identified 39 miRNAs as potential PCa miRNA signatures. Among these miRNAs, 20 had previously been identified as PCa aberrant miRNAs by low-throughput methods, and 16 were shown to be deregulated in other cancers. In vitro q-PCR experiments verified the accuracy of these predictions. miR-648 was identified as a novel candidate PCa miRNA biomarker. Further functional and pathway enrichment analysis confirmed the association of the identified miRNAs with PCa progression.

Conclusions

Our analysis revealed the scale-free features of the human miRNA-mRNA interaction network and showed the distinctive topological features of existing cancer miRNA biomarkers from previously published studies. A novel cancer miRNA biomarker prediction framework was designed based on these observations and applied to prostate cancer study. This method could be applied for miRNA biomarker prediction in other cancers.

Post genome-wide association studies functional characterization of prostate cancer risk loci

Background

Over the last decade, genome-wide association studies (GWAS) have discovered many risk associated single nucleotide polymorphisms (SNPs) of prostate cancer (PCa). However, the majority of the associated PCa SNPs, including those in linkage disequilibrium (LD) blocks, are generally not located in protein coding regions. The systematical investigation of the functional roles of these SNPs, especially the non-coding SNPs, becomes very necessary and helpful to the understanding of the molecular mechanism of PCa.

Results

In this work, we proposed a comprehensive framework at network level to integrate the SNP annotation, target gene assignment, gene ontology (GO) classification, pathway enrichment analysis and regulatory network reconstruction to illustrate the molecular functions of PCa associated SNPs. By LD expansion, we first identified 1828 LD SNPs using 49 reported GWAS SNPs as a start. We carefully annotated these 1828 LD SNPs via either UCSC known genes, UCSC regulation elements, or expression Quantitative Trait Loci (eQTL) data. As a result, we found 1154 SNPs were functionally annotated and obtained 205 unique PCa genes for further enrichment analysis. The enriched GO biological processes and pathways were found mainly related to regulation of cell death, apoptosis, cell proliferation, and metabolic process, which have been proved essential to cancer development. We constructed PCa genes specific transcription regulatory networks, finding several important genetic regulators for PCa, such as IGF-1/IGF-2 receptors, SP1, CREB1, and androgen receptor (AR).

Conclusions

A comprehensive framework was proposed for integrative and systematic analysis of PCa SNPs, the analysis can provide essential information for the understanding of the regulatory function of GWAS SNPs in PCa, and will facilitate the discovery of novel candidate biomarkers for diagnosis and prognosis of PCa.