From genetic correlations of Alzheimer's disease to classification with artificial neural network models.

Sporadic Alzheimer's disease (AD) is a complex neurological disorder characterized by many risk loci with potential associations with different traits and diseases. AD, characterized by a progressive loss of neuronal functions, manifests with different symptoms such as decline in memory, movement, coordination, and speech. The mechanisms underlying the onset of AD are not always fully understood, but involve a multiplicity of factors. Early diagnosis of AD plays a central role as it can offer the possibility of early treatment, which can slow disease progression. Currently, the methods of diagnosis are cognitive testing, neuroimaging, or cerebrospinal fluid analysis that can be time-consuming, expensive, invasive, and not always accurate. In the present study, we performed a genetic correlation analysis using genome-wide association statistics from a large study of AD and UK Biobank, to examine the association of AD with other human traits and disorders. In addition, since hippocampus, a part of cerebral cortex could play a central role in several traits that are associated with AD; we analyzed the gene expression profiles of hippocampus of AD patients applying 4 different artificial neural network models. We found 65 traits correlated with AD grouped into 9 clusters: medical conditions, fluid intelligence, education, anthropometric measures, employment status, activity, diet, lifestyle, and sexuality. The comparison of different 4 neural network models along with feature selection methods on 5 Alzheimer's gene expression datasets showed that the simple basic neural network model obtains a better performance (66% of accuracy) than other more complex methods with dropout and weight regularization of the network.

Phenome-wide genetic-correlation analysis and genetically informed causal inference of amyotrophic lateral sclerosis.

Leveraging genome-wide association statistics generated from a large study of amyotrophic lateral sclerosis (ALS; 29,612 cases and 122,656 controls) and UK Biobank (UKB; 4,024 phenotypes, up to 361,194 participants), we conducted a phenome-wide analysis of ALS genetic liability and identified 46 genetically correlated traits, such as fluid intelligence score (rg = - 0.21, p = 1.74 × 10-6), "spending time in pub or social club" (rg = 0.24, p = 2.77 × 10-6), non-work related walking (rg = - 0.25, p = 1.95 × 10-6), college education (rg = - 0.15, p = 7.08 × 10-5), "ever diagnosed with panic attacks (rg = 0.39, p = 4.24 × 10-5), and "self-reported other gastritis including duodenitis" (rg = 0.28, p = 1.4 × 10-3). To assess the putative directionality of these genetic correlations, we conducted a latent causal variable analysis, identifying significant genetic causality proportions (gcp) linking ALS genetic liability to seven traits. While the genetic component of "self-reported other gastritis including duodenitis" showed a causal effect on ALS (gcp = 0.50, p = 1.26 × 10-29), the genetic liability to ALS is potentially causal for multiple traits, also including an effect on "ever being diagnosed with panic attacks" (gcp = 0.79, p = 5.011 × 10-15) and inverse effects on "other leisure/social group activities" (gcp = 0.66, p = 1 × 10-4) and prospective memory result (gcp = 0.35, p = 0.005). Our subsequent Mendelian randomization analysis indicated that some of these associations may be due to bidirectional effects. In conclusion, this phenome-wide investigation of ALS polygenic architecture highlights the widespread pleiotropy linking this disorder with several health domains.

Secreted miR-153 Controls Proliferation and Invasion of Higher Gleason Score Prostate Cancer.

Prostate cancer (PC) is a male common neoplasm and is the second leading cause of cancer death in American men. PC is traditionally diagnosed by the evaluation of prostate secreted antigen (PSA) in the blood. Due to the high levels of false positives, digital rectal examination and transrectal ultrasound guided biopsy are necessary in uncertain cases with elevated PSA levels. Nevertheless, the high mortality rate suggests that new PC biomarkers are urgently needed to help clinical diagnosis. In a previous study, we have identified a network of genes, altered in high Gleason Score (GS) PC (GS ≥ 7), being regulated by miR-153. Until now, no publication has explained the mechanism of action of miR-153 in PC. By in vitro studies, we found that the overexpression of miR-153 in high GS cell lines is required to control cell proliferation, migration and invasion rates, targeting Kruppel-like factor 5 (KLF5). Moreover, miR-153 could be secreted by exosomes and microvesicles in the microenvironment and, once entered into the surrounding tissue, could influence cellular growth. Being upregulated in high GS human PC, miR-153 could be proposed as a circulating biomarker for PC diagnosis.

Identification of microRNA clusters cooperatively acting on epithelial to mesenchymal transition in triple negative breast cancer.

MicroRNAs play important roles in many biological processes. Their aberrant expression can have oncogenic or tumor suppressor function directly participating to carcinogenesis, malignant transformation, invasiveness and metastasis. Indeed, miRNA profiles can distinguish not only between normal and cancerous tissue but they can also successfully classify different subtypes of a particular cancer. Here, we focus on a particular class of transcripts encoding polycistronic miRNA genes that yields multiple miRNA components. We describe 'clustered MiRNA Master Regulator Analysis (ClustMMRA)', a fully redesigned release of the MMRA computational pipeline (MiRNA Master Regulator Analysis), developed to search for clustered miRNAs potentially driving cancer molecular subtyping. Genomically clustered miRNAs are frequently co-expressed to target different components of pro-tumorigenic signaling pathways. By applying ClustMMRA to breast cancer patient data, we identified key miRNA clusters driving the phenotype of different tumor subgroups. The pipeline was applied to two independent breast cancer datasets, providing statistically concordant results between the two analyses. We validated in cell lines the miR-199/miR-214 as a novel cluster of miRNAs promoting the triple negative breast cancer (TNBC) phenotype through its control of proliferation and EMT.

Circulating microRNA: The Potential Novel Diagnostic Biomarkers to Predict Drug Resistance in Temporal Lobe Epilepsy, a Pilot Study.

MicroRNAs (miRNAs) are small noncoding RNAs that have emerged as new potential epigenetic biomarkers. Here, we evaluate the efficacy of six circulating miRNA previously described in the literature as biomarkers for the diagnosis of temporal lobe epilepsy (TLE) and/or as predictive biomarkers to antiepileptic drug response. We measured the differences in serum miRNA levels by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) assays in a cohort of 27 patients (14 women and 13 men; mean ± SD age: 43.65 ± 17.07) with TLE compared to 20 healthy controls (HC) matched for sex, age and ethnicity (11 women and 9 men; mean ± SD age: 47.5 ± 9.1). Additionally, patients were classified according to whether they had drug-responsive (n = 17) or drug-resistant (n = 10) TLE. We have investigated any correlations between miRNAs and several electroclinical parameters. Three miRNAs (miR-142, miR-146a, miR-223) were significantly upregulated in patients (expressed as average expression ± SD). In detail, miR-142 expression was 0.40 ± 0.29 versus 0.16 ± 0.10 in TLE patients compared to HC (t-test, p < 0.01), miR-146a expression was 0.15 ± 0.11 versus 0.07 ± 0.04 (t-test, p < 0.05), and miR-223 expression was 6.21 ± 3.65 versus 1.23 ± 0.84 (t-test, p < 0.001). Moreover, results obtained from a logistic regression model showed the good performance of miR-142 and miR-223 in distinguishing drug-sensitive vs. drug-resistant TLE. The results of this pilot study give evidence that miRNAs are suitable targets in TLE and offer the rationale for further confirmation studies in larger epilepsy cohorts.

Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data.

The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein-protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug-protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.

Identification of Breast Cancer Subtype-Specific Biomarkers by Integrating Copy Number Alterations and Gene Expression Profiles.

Background and Objectives: Breast cancer is a heterogeneous disease categorized into four subtypes. Previous studies have shown that copy number alterations of several genes are implicated with the development and progression of many cancers. This study evaluates the effects of DNA copy number alterations on gene expression levels in different breast cancer subtypes. Materials and Methods: We performed a computational analysis integrating copy number alterations and gene expression profiles in 1024 breast cancer samples grouped into four molecular subtypes: luminal A, luminal B, HER2, and basal. Results: Our analyses identified several genes correlated in all subtypes such as KIAA1967 and MCPH1. In addition, several subtype-specific genes that showed a significant correlation between copy number and gene expression profiles were detected: SMARCB1, AZIN1, MTDH in luminal A, PPP2R5E, APEX1, GCN5 in luminal B, TNFAIP1, PCYT2, DIABLO in HER2, and FAM175B, SENP5, SCAF1 in basal subtype. Conclusions: This study showed that computational analyses integrating copy number and gene expression can contribute to unveil the molecular mechanisms of cancer and identify new subtype-specific biomarkers.

In Silico Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis.

Personalized medicine relies on the integration and consideration of specific characteristics of the patient, such as tumor phenotypic and genotypic profiling. BACKGROUND: Radiogenomics aim to integrate phenotypes from tumor imaging data with genomic data to discover genetic mechanisms underlying tumor development and phenotype. METHODS: We describe a computational approach that correlates phenotype from magnetic resonance imaging (MRI) of breast cancer (BC) lesions with microRNAs (miRNAs), mRNAs, and regulatory networks, developing a radiomiRNomic map. We validated our approach to the relationships between MRI and miRNA expression data derived from BC patients. We obtained 16 radiomic features quantifying the tumor phenotype. We integrated the features with miRNAs regulating a network of pathways specific for a distinct BC subtype. RESULTS: We found six miRNAs correlated with imaging features in Luminal A (miR-1537, -205, -335, -337, -452, and -99a), seven miRNAs (miR-142, -155, -190, -190b, -1910, -3617, and -429) in HER2+, and two miRNAs (miR-135b and -365-2) in Basal subtype. We demonstrate that the combination of correlated miRNAs and imaging features have better classification power of Luminal A versus the different BC subtypes than using miRNAs or imaging alone. CONCLUSION: Our computational approach could be used to identify new radiomiRNomic profiles of multi-omics biomarkers for BC differential diagnosis and prognosis.

Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis.

BACKGROUND: Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network. RESULTS: We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study. CONCLUSIONS: Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.

In silico identification of drug target pathways in breast cancer subtypes using pathway cross-talk inhibition.

BACKGROUND: Despite great development in genome and proteome high-throughput methods, treatment failure is a critical point in the management of most solid cancers, including breast cancer (BC). Multiple alternative mechanisms upon drug treatment are involved to offset therapeutic effects, eventually causing drug resistance or treatment failure. METHODS: Here, we optimized a computational method to discover novel drug target pathways in cancer subtypes using pathway cross-talk inhibition (PCI). The in silico method is based on the detection and quantification of the pathway cross-talk for distinct cancer subtypes. From a BC data set of The Cancer Genome Atlas, we have identified different networks of cross-talking pathways for different BC subtypes, validated using an independent BC dataset from Gene Expression Omnibus. Then, we predicted in silico the effects of new or approved drugs on different BC subtypes by silencing individual or combined subtype-derived pathways with the aim to find new potential drugs or more effective synergistic combinations of drugs. RESULTS: Overall, we identified a set of new potential drug target pathways for distinct BC subtypes on which therapeutic agents could synergically act showing antitumour effects and impacting on cross-talk inhibition. CONCLUSIONS: We believe that in silico methods based on PCI could offer valuable approaches to identifying more tailored and effective treatments in particular in heterogeneous cancer diseases.

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.

In-Silico Integration Approach to Identify a Key miRNA Regulating a Gene Network in Aggressive Prostate Cancer.

Like other cancer diseases, prostate cancer (PC) is caused by the accumulation of genetic alterations in the cells that drives malignant growth. These alterations are revealed by gene profiling and copy number alteration (CNA) analysis. Moreover, recent evidence suggests that also microRNAs have an important role in PC development. Despite efforts to profile PC, the alterations (gene, CNA, and miRNA) and biological processes that correlate with disease development and progression remain partially elusive. Many gene signatures proposed as diagnostic or prognostic tools in cancer poorly overlap. The identification of co-expressed genes, that are functionally related, can identify a core network of genes associated with PC with a better reproducibility. By combining different approaches, including the integration of mRNA expression profiles, CNAs, and miRNA expression levels, we identified a gene signature of four genes overlapping with other published gene signatures and able to distinguish, in silico, high Gleason-scored PC from normal human tissue, which was further enriched to 19 genes by gene co-expression analysis. From the analysis of miRNAs possibly regulating this network, we found that hsa-miR-153 was highly connected to the genes in the network. Our results identify a four-gene signature with diagnostic and prognostic value in PC and suggest an interesting gene network that could play a key regulatory role in PC development and progression. Furthermore, hsa-miR-153, controlling this network, could be a potential biomarker for theranostics in high Gleason-scored PC.

Radiation-Induced Gene Expression Changes in High and Low Grade Breast Cancer Cell Types.

BACKGROUND: There is extensive scientific evidence that radiation therapy (RT) is a crucial treatment, either alone or in combination with other treatment modalities, for many types of cancer, including breast cancer (BC). BC is a heterogeneous disease at both clinical and molecular levels, presenting distinct subtypes linked to the hormone receptor (HR) status and associated with different clinical outcomes. The aim of this study was to assess the molecular changes induced by high doses of ionizing radiation (IR) on immortalized and primary BC cell lines grouped according to Human epidermal growth factor receptor (HER2), estrogen, and progesterone receptors, to study how HR status influences the radiation response. Our genomic approach using in vitro and ex-vivo models (e.g., primary cells) is a necessary first step for a translational study to describe the common driven radio-resistance features associated with HR status. This information will eventually allow clinicians to prescribe more personalized total doses or associated targeted therapies for specific tumor subtypes, thus enhancing cancer radio-sensitivity. METHODS: Nontumorigenic (MCF10A) and BC (MCF7 and MDA-MB-231) immortalized cell lines, as well as healthy (HMEC) and BC (BCpc7 and BCpcEMT) primary cultures, were divided into low grade, high grade, and healthy groups according to their HR status. At 24 h post-treatment, the gene expression profiles induced by two doses of IR treatment with 9 and 23 Gy were analyzed by cDNA microarray technology to select and compare the differential gene and pathway expressions among the experimental groups. RESULTS: We present a descriptive report of the substantial alterations in gene expression levels and pathways after IR treatment in both immortalized and primary cell cultures. Overall, the IR-induced gene expression profiles and pathways appear to be cell-line dependent. The data suggest that some specific gene and pathway signatures seem to be linked to HR status. CONCLUSIONS: Genomic biomarkers and gene-signatures of specific tumor subtypes, selected according to their HR status and molecular features, could facilitate personalized biological-driven RT treatment planning alone and in combination with targeted therapies.

MicroRNA-567 dysregulation contributes to carcinogenesis of breast cancer, targeting tumor cell proliferation, and migration.

PURPOSE: We demonstrated that Hsa-miR-567 expression is significantly downregulated in poor prognosis breast cancer, compared to better prognosis breast cancer, having a role in the control of cell proliferation and migration by regulating KPNA4 gene. METHODS AND RESULTS: In this study, based on our previously published in silico results, we proved both in vitro (cell line studies) and ex vivo (clinical studies), that Hsa-miR-567 expression is significantly downregulated in breast cancer with poor prognosis when compared to breast cancer with better prognosis. More intriguingly, we demonstrated that the ectopic expression of Hsa-miR-567 in poor prognosis breast cancer cell line strongly inhibits in vitro cell proliferation and migration. Furthermore, we showed in vivo that breast cancer cells, stably expressing Hsa-miR-567, xenografted in mouse, reduce tumor growth ability. Consistently, we found that karyopherin 4 (KPNA4), predicted target gene of Hsa-miR-567 as identified by our in silico analysis, is upregulated in highly aggressive MDA-MB-231 breast cancer cell line and patient tissues with poor prognosis with respect to good prognosis. CONCLUSIONS: Our results suggest a potential role of Hsa-miR-567 as a novel prognostic biomarker for BC and as regulator of KPNA4.

SpidermiR: An R/Bioconductor Package for Integrative Analysis with miRNA Data.

Gene Regulatory Networks (GRNs) control many biological systems, but how such network coordination is shaped is still unknown. GRNs can be subdivided into basic connections that describe how the network members interact e.g., co-expression, physical interaction, co-localization, genetic influence, pathways, and shared protein domains. The important regulatory mechanisms of these networks involve miRNAs. We developed an R/Bioconductor package, namely SpidermiR, which offers an easy access to both GRNs and miRNAs to the end user, and integrates this information with differentially expressed genes obtained from The Cancer Genome Atlas. Specifically, SpidermiR allows the users to: (i) query and download GRNs and miRNAs from validated and predicted repositories; (ii) integrate miRNAs with GRNs in order to obtain miRNA-gene-gene and miRNA-protein-protein interactions, and to analyze miRNA GRNs in order to identify miRNA-gene communities; and (iii) graphically visualize the results of the analyses. These analyses can be performed through a single interface and without the need for any downloads. The full data sets are then rapidly integrated and processed locally.

How interacting pathways are regulated by miRNAs in breast cancer subtypes.

BACKGROUND: An important challenge in cancer biology is to understand the complex aspects of the disease. It is increasingly evident that genes are not isolated from each other and the comprehension of how different genes are related to each other could explain biological mechanisms causing diseases. Biological pathways are important tools to reveal gene interaction and reduce the large number of genes to be studied by partitioning it into smaller paths. Furthermore, recent scientific evidence has proven that a combination of pathways, instead than a single element of the pathway or a single pathway, could be responsible for pathological changes in a cell. RESULTS: In this paper we develop a new method that can reveal miRNAs able to regulate, in a coordinated way, networks of gene pathways. We applied the method to subtypes of breast cancer. The basic idea is the identification of pathways significantly enriched with differentially expressed genes among the different breast cancer subtypes and normal tissue. Looking at the pairs of pathways that were found to be functionally related, we created a network of dependent pathways and we focused on identifying miRNAs that could act as miRNA drivers in a coordinated regulation process. CONCLUSIONS: Our approach enables miRNAs identification that could have an important role in the development of breast cancer.

MicroRNAs as Biomarkers for Diagnosis, Prognosis and Theranostics in Prostate Cancer.

Prostate cancer (PC) includes several phenotypes, from indolent to highly aggressive cancer. Actual diagnostic and prognostic tools have several limitations, and there is a need for new biomarkers to stratify patients and assign them optimal therapies by taking into account potential genetic and epigenetic differences. MicroRNAs (miRNAs) are small sequences of non-coding RNA regulating specific genes involved in the onset and development of PC. Stable miRNAs have been found in biofluids, such as serum and plasma; thus, the measurement of PC-associated miRNAs is emerging as a non-invasive tool for PC detection and monitoring. In this study, we conduct an in-depth literature review focusing on miRNAs that may contribute to the diagnosis and prognosis of PC. The role of miRNAs as a potential theranostic tool in PC is discussed. Using a meta-analysis approach, we found a group of 29 miRNAs with diagnostic properties and a group of seven miRNAs with prognostic properties, which were found already expressed in both biofluids and PC tissues. We tested the two miRNA groups on The Cancer Genome Atlas dataset of PC tissue samples with a machine-learning approach. Our results suggest that these 29 miRNAs should be considered as potential panel of biomarkers for the diagnosis of PC, both as in vivo non-invasive test and ex vivo confirmation test.

Roles of RNA-binding proteins in neurological disorders, COVID-19, and cancer.

RNA-binding proteins (RBPs) have emerged as important players in multiple biological processes including transcription regulation, splicing, R-loop homeostasis, DNA rearrangement, miRNA function, biogenesis, and ribosome biogenesis. A large number of RBPs had already been identified by different approaches in various organisms and exhibited regulatory functions on RNAs' fate. RBPs can either directly or indirectly interact with their target RNAs or mRNAs to assume a key biological function whose outcome may trigger disease or normal biological events. They also exert distinct functions related to their canonical and non-canonical forms. This review summarizes the current understanding of a wide range of RBPs' functions and highlights their emerging roles in the regulation of diverse pathways, different physiological processes, and their molecular links with diseases. Various types of diseases, encompassing colorectal carcinoma, non-small cell lung carcinoma, amyotrophic lateral sclerosis, and Severe acute respiratory syndrome coronavirus 2, aberrantly express RBPs. We also highlight some recent advances in the field that could prompt the development of RBPs-based therapeutic interventions.

miRNA-Based Technologies in Cancer Therapy.

The discovery of therapeutic miRNAs is one of the most exciting challenges for pharmaceutical companies. Since the first miRNA was discovered in 1993, our knowledge of miRNA biology has grown considerably. Many studies have demonstrated that miRNA expression is dysregulated in many diseases, making them appealing tools for novel therapeutic approaches. This review aims to discuss miRNA biogenesis and function, as well as highlight strategies for delivering miRNA agents, presenting viral, non-viral, and exosomic delivery as therapeutic approaches for different cancer types. We also consider the therapeutic role of microRNA-mediated drug repurposing in cancer therapy.

Computational analysis of five neurodegenerative diseases reveals shared and specific genetic loci.

Neurodegenerative diseases (ND) are heterogeneous disorders of the central nervous system that share a chronic and selective process of neuronal cell death. A computational approach to investigate shared genetic and specific loci was applied to 5 different ND: Amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), Multiple sclerosis (MS), and Lewy body dementia (LBD). The datasets were analyzed separately, and then we compared the obtained results. For this purpose, we applied a genetic correlation analysis to genome-wide association datasets and revealed different genetic correlations with several human traits and diseases. In addition, a clumping analysis was carried out to identify SNPs genetically associated with each disease. We found 27 SNPs in AD, 6 SNPs in ALS, 10 SNPs in PD, 17 SNPs in MS, and 3 SNPs in LBD. Most of them are located in non-coding regions, with the exception of 5 SNPs on which a protein structure and stability prediction was performed to verify their impact on disease. Furthermore, an analysis of the differentially expressed miRNAs of the 5 examined pathologies was performed to reveal regulatory mechanisms that could involve genes associated with selected SNPs. In conclusion, the results obtained constitute an important step toward the discovery of diagnostic biomarkers and a better understanding of the diseases.