Abstract Cardiorenal syndromes constellate primary dysfunction of either heart or kidney whereby one organ dysfunction leads to the dysfunction of another. The role of several microRNAs (miRNAs) has been implicated in number of diseases, including hypertension, heart failure, and kidney diseases. Wide range of miRNAs has been identified as ideal candidate biomarkers due to their stable expression. Current study was aimed to identify crucial miRNAs and their target genes associated with cardiorenal syndrome and to explore their interaction analysis. Three differentially expressed microRNAs (DEMs), namely, hsa-miR-4476, hsa-miR-345-3p, and hsa-miR-371a-5p, were obtained from [45]GSE89699 and [46]GSE87885 microRNA data sets, using R/GEO2R tools. Furthermore, literature mining resulted in the retrieval of 15 miRNAs from scientific research and review articles. The miRNAs-gene networks were constructed using miRNet (a Web platform of miRNA-centric network visual analytics). CytoHubba (Cytoscape plugin) was adopted to identify the modules and the top-ranked nodes in the network based on Degree centrality, Closeness centrality, Betweenness centrality, and Stress centrality. The overlapped miRNAs were further used in pathway enrichment analysis. We found that hsa-miR-21-5p was common in 8 pathways out of the top 10. Based on the degree, 5 miRNAs, namely, hsa-mir-122-5p, hsa-mir-222-3p, hsa-mir-21-5p, hsa-mir-146a-5p, and hsa-mir-29b-3p, are considered as key influencing nodes in a network. We suggest that the identified miRNAs and their target genes may have pathological relevance in cardiorenal syndrome (CRS) and may emerge as potential diagnostic biomarkers. Keywords: CRS, DEMs, literature mining, miRNA-mRNA network, module analysis, pathways Introduction The performance of the heart and the renal function is strongly interconnected with each other. It is well known that acute or chronic dysfunction in one organ may induce acute or chronic dysfunction in the other organ.^ [47]1 The presence of impaired renal function during heart disease and vice versa is collectively known as cardiorenal syndrome (CRS).^ [48]1 The CRS is categorized in 5 different subcategories (types 1-5) on the basis of potential underlying pathophysiological mechanisms.^ [49]2 Renal and cardiovascular complications cover a wide range of diseases such as chronic kidney disease (CKD), coronary artery disease (CAD), congestive heart failure, and arrhythmias.^ [50]3 MicroRNAs (miRNAs), an endogenous small (18-24 nucleotides) noncoding RNA molecules, have been found associated with several pathological conditions.^ [51]4 It is well known that miRNAs regulate expression of genes at posttranscriptional level by degrading or blocking the translation mechanism of target gene/messenger RNA (mRNA).^ [52]5 The miRNAs are involved in regulation of many cellular processes, including proliferation, differentiation, and programmed cell death.^ [53]6 Stable forms of miRNAs have been traced in human body fluids like blood and urine.^ [54]7 The miRNAs have been recently discovered with a wider role in renal cancer, diabetic nephropathy, and hypertensive renal injury.^ [55]8 Moreover, it has been found that a single miRNA may be responsible for altering complex genetic networks by affecting different genes simultaneously.^ [56]9 The therapeutic management of CRS is considered as a huge challenge. Drugs supposed to treat the cardiovascular diseases may lead to impairment in the functioning of kidneys and vice versa.^ [57]10 The miRNAs are involved in the development and progression of different diseases and therefore may be used as biomarkers. Several miRNAs with various expression degrees have been reported in both acute and chronic state of primary organ failure in CRS.^[58]11,[59]12 The miR-21 has been found common in all CRS types.^ [60]13 Interestingly, miR-21 has a higher level of expression in both heart and kidneys and its elevated levels have been found associated with poor outcome in most of the primary organ failures.^ [61]12 These findings provide a notion that suppression of miR-21 may show a ray of hope for therapeutics and treatment of CRS.^ [62]12 This study was aimed to identify crucial miRNAs and their target genes associated with CRS. [63]GSE89699 and [64]GSE87885 data sets were chosen for the analysis of differentially expressed microRNAs (DEMs). Interaction analysis of miRNA and their target genes may pave for the finding of novel pathological mechanisms and biomarkers for CRS. Materials and Methods Data retrieval and preprocessing of data The [65]GSE89699 and [66]GSE87885 miRNA expression profiles were retrieved from the functional genomics data repository, Gene Expression Omnibus, and NCBI ([67]www.ncbi.nlm.nih.gov/geo/). The miRNA data set [68]GSE89699 consists of data from 8 samples, 7 diseased and 1 normal sample, whereas [69]GSE87885 consists of data from 5 samples, 2 diseased and 3 normal samples.^ [70]14 The miRNA expression profile in [71]GSE89699 was detected using the [72]GPL18402 platform (Unrestricted_Human_miRNA_V19.0_Microarray, Design Id: 046064; Agilent, Karnataka, India), whereas miRNA expression profile in [73]GSE87885 was detected using the [74]GPL22555 platform (μParaflo human miRNA array; LC Sciences, Qingdao, China). The [75]GSE89699 and [76]GSE87885 were normalized and preprocessed through R/GEO2R ([77]http://www.ncbi.nlm.nih.gov/geo/geo2r/) tool. R/GEO2R is a Web-based analytical tool with in-built Linear Models for Microarray Data (Limma) R package and GEO query.^ [78]15 Literature mining The PubMed search engine was used for literature mining of key miRNAs related to CRS. The keywords for literature mining included microRNA expression, miRNA expression, cardiorenal syndrome, renocardio syndrome, cardiovascular and chronic/acute kidney disease, heart and kidney disease, renal and myocardial/heart/congestive failure, acute kidney injury and coronary artery disease, CRS biomarkers, CRS Type 1-5, CKD and CVD, AKI and MI, CAD and CRF, diagnosis and prognosis. In addition, published research and review articles were manually searched to add more data on miRNAs related to CRS. A detailed description of workflow is given in [79]Figure 1. Figure 1. Figure 1. [80]Open in a new tab Methodology which has been adopted to retrieve miRNAs from different sources. miRNA indicates microRNA. MiRNA gene network construction Generated list of miRNAs was submitted to miRNet tool (an miRNA-centric network visual analytics platform) for construction of miRNAs-mRNA network.^ [81]16 The miRNet is a Google Cloud Computing Engine with 64G RAM and 8 CPU cores (n2-highmem-8). Four well-annotated databases (miRTarBase v8.0, TarBase v8.0, miRanda, and miRecords) were used to retrieve the data of target genes searched against CRS-related miRNAs. Visualization and analysis of miRNAs-mRNA network were carried out by using the Cytoscape software (version 3.7.1).^ [82]17 Module detection and pathways enrichment analysis Cytoscape with cytoHubba plugin was used to identify highly interconnected regions in the miRNAs-mRNA network. It is a facilitated platform for the analysis and visualization of molecular interaction networks.^ [83]18 The cytoHubba (version 0.1) was used to identify the important functional modules and the higher ranked genes/proteins in the network based on Degree centrality, Closeness centrality, Betweenness centrality, and Stress centrality. Degree centrality assigns an importance score based simply on the number of links held by each node. Degree tells us that how many direct, “one hop” connections each node has to other nodes in the network. It is the simplest measure of node connectivity. Sometimes it is useful to look at in-degree (number of inbound links) and out-degree (number of outbound links) as distinct measures, eg, when looking at transactional data or account activity. Closeness centrality scores each node based on their “closeness” to all other nodes in the network. Closeness measure calculates the shortest paths between all nodes and then assigns each node a score based on its sum of shortest paths. It is used for finding the factors that are best placed to influence the entire network most quickly. Betweenness centrality measures the number of times a node lies on the shortest path between other nodes. Betweenness tells us which nodes are “bridges” between nodes in a network. It does this by identifying all the shortest paths and then counting how many times each node falls on one. It is useful for analyzing communication dynamics. The stress of a node in a biological network, for instance, a protein-signaling network, can indicate the relevance of a protein as functionally capable of holding together communicating nodes. The higher is the value, the higher is the relevance of the protein in connecting regulatory molecules. Due to the nature of this stress centrality, it is possible that the stress simply indicates a molecule heavily involved in cellular processes but not relevant to maintain the communication between other proteins. The cytoHubba provides a user interface in a very simple way to analyze a network with 11 scoring methods. Top-ranked nodes of a particular scoring method (Degree centrality, Closeness centrality, Betweenness centrality, and Stress centrality) were retrieved from the cytoHubba tab in the Cytoscape control panel. Furthermore, DIANA-mirPath, a Web-based server, was used for the analysis of overlapped miRNAs and for generating heatmaps.^ [84]19 Results Identification of DEMs The [85]GSE89699 and [86]GSE87885 data sets were first preprocessed and normalized, and then differentially expressed microRNAs (DEMs) were extracted. The P value < .05 and |log fold change| > 0.5 were taken as cutoff values for statistically significant differentially expressed genes (DEGs) or miRNAs. The data sets obtained before and after normalization are depicted in boxplots ([87]Figure 2). The [88]GSE87885 revealed 171 total DEMs in which 93 were upregulated and 78 were downregulated miRNAs, whereas [89]GSE89699 revealed 81 down-regulated miRNAs. Overlapped DEMs among these 2 data sets were explored by Venny 2.1.0 ([90]http://bioinfogp.cnb.csic.es/tools/venny/). Results showed that 3 miRNAs were common in both GSE data sets. Finally, we have 18 miRNAs listed in [91]Table 1. Fifteen miRNAs among these were retrieved from literature. Figure 2. [92]Figure 2. [93]Open in a new tab Boxplot indicating the expression values in the GSE series data sets. If the data sets are not normalized, it shows false results (the value of log fold change varies) of the probe expression due to noise, duplicity, and redundancy so that normalization of data sets is required. (A) Prenormalization of [94]GSE87885 where green color indicates normal samples ([95]GSM2342245, [96]GSM2342246 and [97]GSM2342247) and violet color for disease samples ([98]GSM2342248 and [99]GSM2342249). (B) Normalized data sets. Table 1. List of different miRNAs, their target gene counts, and mature sequences which have been retrieved by literature mining and microarray data sets. miRBase ID Retrieval method Target genes count Mature sequence References