miRNA Expression Data Research Articles

Identification of Functionally Significant Polymorphic Variants in miRNA Genes in Carotid Atherosclerosis

miRNAs are vital molecules of gene expression. They are involved in the pathogenesis of various common diseases, including atherosclerosis, its risk factors, and its complications. A detailed characterization of the spectrum of functionally significant polymorphisms of miRNA genes in patients with advanced carotid atherosclerosis is an important research task. We analyzed miRNA expression and exome sequencing data of carotid atherosclerotic plaques of male patients (n = 8, 66-71 years of age, 67-90% degree of carotid artery stenosis). For further study and analysis of the association between the rs2910164 polymorphism of the MIR146A gene and advanced carotid atherosclerosis, we recruited 112 patients and 72 relatively healthy Slavic residents of Western Siberia. A total of 321 and 97 single nucleotide variants (SNVs) were detected in the nucleotide sequences of pre- and mature miRNAs in carotid atherosclerotic plaques. These variants were located in 206 and 76 miRNA genes, respectively. Integration of the data of exome sequencing and miRNA expression revealed 24 SNVs of 18 miRNA genes that were processed to mature form in carotid atherosclerotic plaques. SNVs with the greatest potential functional significance for miRNA expression predicted in silico were rs2910164:C>G (MIR146A), rs2682818:A>C (MIR618), rs3746444:A>G (MIR499A), rs776722712:C>T (MIR186), rs199822597:G>A (MIR363). The expression of miR-618 was lower in carotid atherosclerotic plaques of patients with the AC rs2682818 genotype of the MIR618 gene compared with the CC genotype (log2FC = 4.8; p = 0.012). We also found an association of rs2910164:C (MIR146A) with the risk of advanced carotid atherosclerosis (OR = 2.35; 95% CI: 1.43-3.85; p = 0.001). Integrative analysis of polymorphisms in miRNA genes and miRNA expression is informative for identifying functionally significant polymorphisms in miRNA genes. The rs2682818:A>C (MIR618) is a candidate for regulating miRNA expression in carotid atherosclerotic plaques. The rs2910164:C (MIR146A) is associated with the risk of advanced carotid atherosclerosis.

MiR-129-2-3p Inhibits Esophageal Carcinoma Cell Proliferation, Migration, and Invasion via Targeting DNMT3B.

The study aims to explore the regulatory mechanism of miR-129-2-3p underlying esophageal carcinoma (EC) cell progression and generate new ideas for targeted treatment of EC. Mature miRNA expression data and total RNA sequencing data of EC in the TCGAESCA dataset were utilized to explore differentially expressed miRNAs (DEmiRNAs). StarBase database was then utilized to predict targets of miRNA. MiR-129-2-3p and DNMT3B expression in EC cell lines was assayed through qRT-PCR and Western blot. CCK-8, scratch healing, and transwell assays were conducted to assess the impact of miR-129-2-3p on EC cell phenotypes. In addition, a dual-luciferase assay was completed to identify the binding relationship between DNMT3B and miR-129-2-3p. MiR-129-2-3p was noticeably less expressed in EC cell lines, while DNMT3B was highly expressed. MiR-129-2-3p could bind to DNMT3B. Furthermore, in vitro functional experiments uncovered that overexpressed miR-129-2-3p repressed EC cell progression while further overexpressing DNMT3B would restore the above inhibitory effect. MiR-129-2-3p is a cancer repressor in EC cells, and it could target DNMT3B, thus hampering the progression of EC cells.

Multi-omics integration method based on attention deep learning network for biomedical data classification

Background and objectiveIntegrating multi-omics data for the comprehensive analysis of the biological processes in human diseases has become one of the most challenging tasks of bioinformatics. Deep learning (DL) algorithms have recently become one of the most promising multi-omics data integration analysis methods. However, existing DL-based studies almost integrate the multi-omics data by concatenation in the input data space or the learned feature space, ignoring the correlations between patients and omics. MethodsWe propose a novel multi-omics integration method, called Multi-omics Attention Deep Learning Network (MOADLN), which is used for biomedical data classification. Firstly, for each type of omics data, we use three fully-connected layers and the self-attention mechanism to reduce dimensionality, and construct the correlations between patients, respectively. Then, we apply the feature vector learned from self-attention to generate the initial category labels. Secondly, for the initial label predicted of each omics data, we use an effective Multi-Omics Correlation Discovery Network (MOCDN) to learn the cross-omic correlations in the label space. Finally, we use the softmax classifier for label prediction. ResultsWe demonstrate that our method outperforms several state-of-the-art methods on two datasets with mRNA expression data, DNA methylation data, and miRNA expression data. In addition, we identified essential biomarkers of relevant diseases by MOADLN, and the generality of MOADLN is also demonstrated in the KIRP and KIRC datasets. ConclusionsMOADLN jointly explores correlations between patients in intra-omics and correlations of cross-omics in label space, which is an effective DL-based classification of biomedical data.

Construction of a serum diagnostic signature based on m5C-related miRNAs for cancer detection.

Currently, no clinically relevant non-invasive biomarkers are available for screening of multiple cancer types. In this study, we developed a serum diagnostic signature based on 5-methylcytosine (m5C)-related miRNAs (m5C-miRNAs) for multiple-cancer detection. Serum miRNA expression data and the corresponding clinical information of patients were collected from the Gene Expression Omnibus database. Serum samples were then randomly assigned to the training or validation cohort at a 1:1 ratio. Using the identified m5C-miRNAs, an m5C-miRNA signature for cancer detection was established using a support vector machine algorithm. The constructed m5C-miRNA signature displayed excellent accuracy, and its areas under the curve were 0.977, 0.934, and 0.965 in the training cohort, validation cohort, and combined training and validation cohort, respectively. Moreover, the diagnostic capability of the m5C-miRNA signature was unaffected by patient age or sex or the presence of noncancerous disease. The m5C-miRNA signature also displayed satisfactory performance for distinguishing tumor types. Importantly, in the detection of early-stage cancers, the diagnostic performance of the m5C-miRNA signature was obviously superior to that of conventional tumor biomarkers. In summary, this work revealed the value of serum m5C-miRNAs in cancer detection and provided a new strategy for developing non-invasive and cost effective tools for large-scale cancer screening.

A tissue-specific profile of miRNAs and their targets related to paeoniaflorin and monoterpenoids biosynthesis in Paeonia lactiflora Pall. by transcriptome, small RNAs and degradome sequencing.

Paeonia lactiflora Pall. (Paeonia) has aroused many concerns due to its extensive medicinal value, in which monoterpene glucoside paeoniflorin and its derivatives are the active chemical components. However, little is known in the molecular mechanism of monoterpenoids biosynthesis, and the regulation network between small RNAs and mRNAs in monoterpenoids biosynthesis has not been investigated yet. Herein, we attempted to reveal the tissue-specific regulation network of miRNAs and their targets related to paeoniaflorin and monoterpenoids biosynthesis in Paeonia by combining mRNA and miRNA expression data with degradome analysis. In all, 289 miRNAs and 30177 unigenes were identified, of which nine miRNAs from seven miRNA families including miR396, miR393, miR835, miR1144, miR3638, miR5794 and miR9555 were verified as monoterpenoids biosynthesis-related miRNAs by degradome sequencing. Moreover, the co-expression network analysis showed that four monoterpenoids-regulating TFs, namely AP2, MYBC1, SPL12 and TCP2, were putatively regulated by five miRNAs including miR172, miR828, miR858, miR156 and miR319, respectively. The present study will improve our knowledge of the molecular mechanisms of the paeoniaflorin and monoterpenoids biosynthesis mediated by miRNA to a new level, and provide a valuable resource for further study on Paeonia.

MicroRNAs combined to radiomic features as a predictor of complete clinical response after neoadjuvant radio-chemotherapy for locally advanced rectal cancer: a preliminary study.

To define a predictive Artificial Intelligence (AI) algorithm based on the integration of a set of biopsy-based microRNAs expression data and radiomic features to understand their potential impact in predicting clinical response (CR) to neoadjuvant radio-chemotherapy (nRCT). The identification of patients who would truly benefit from nRCT for Locally Advanced Rectal Cancer (LARC) could be crucial for an improvement in a tailored therapy. Forty patients with LARC were retrospectively analyzed. An MRI of the pelvis before and after nRCT was performed. In the diagnostic biopsy, the expression levels of 7 miRNAs were measured and correlated with the tumor response rate (TRG), assessed on the surgical sample. The accuracy of complete CR (cCR) prediction was compared for i) clinical predictors; ii) radiomic features; iii) miRNAs levels; and iv) combination of radiomics and miRNAs. Clinical predictors showed the lowest accuracy. The best performing model was based on the integration of radiomic features with miR-145 expression level (AUC-ROC = 0.90). AI algorithm, based on radiomics features and the overexpression of miR-145, showed an association with the TRG class and demonstrated a significant impact on the outcome. The pre-treatment identification of responders/NON-responders to nRCT could address patients to a personalized strategy, such as total neoadjuvant therapy (TNT) for responders and upfront surgery for non-responders. The combination of radiomic features and miRNAs expression data from images and biopsy obtained through standard of care has the potential to accelerate the discovery of a noninvasive multimodal approach to predict the cCR after nRCT for LARC.

MiR-6742-5p regulates the invasion and migration of lung adenocarcinoma cells via mediating FGF8/ERK12/MMP9/MMP2 signaling pathway

microRNAs (miRNAs) are involved in the progression of Lung adenocarcinoma (LUAD), however, the functions of miR-6742-5p in LUAD remains unknown, thereby this study was carried on. The mRNA and miRNA expression data from the LUAD and normal control were obtained from Gene Expression Omnibus (GEO) database, TargetScan and mirDIP were applied to predict the relationship between miR-6742-5p and FGF8.Q-PCR, western blot, dual-luciferase, wound Healing and transwell assays were performed to test the functions of miR-6742-5p in LUAD. Bioinformatics analysis and dual-luciferase identified FGF8 is the target-gene of miR-6742-5p, which is declined in LUAD of human tissues and cell lines, and miR-6742-5P OE suppressed the progression of LUAD in nude mice. MiR-6742-5p OE and KD suppressed or increased the abilities of LUAD' metastasis tested by wound healing and transwell assays H522 and PC-9 cells, these effects about miR-6742-5p OE were reversed by FGF8; miR-6742-5p OE, KD inhibited and increased the expression of FGF8 as its downstream p-ERK1/2, MMP-2/-9, these results were corrected by ERK1/2 inhibitor: Ro 67-7476; the miR-6742-5p KD increased the migrated and invaded cells and suppressed by MMPs inhibitor: S3304. These results identified the negative correlation of miR-6742-5p with FGF8-ERK1/2 signal pathway in LUAD progression. We conclude that miR-6742-5p might be a regulator of LUAD progression by targeting FGF8/ERK1/2/MMPs signaling pathway, which provides a novel therapeutic target for LUAD.

Abstract PR001: Assessing differences in miRNA profiles by race and ethnicity: Implications for creating an equitable ovarian cancer early detection test

Abstract Introduction: Assessing serum micro RNA (miRNA) profiles is an emerging technique for earlier detection of ovarian cancer. A panel of miRNAs for ovarian cancer prediction has been proposed, but it is unknown whether race and ethnicity may influence their expression. The goal of this study was to determine how race affects expression of miRNAs associated with ovarian cancer risk to ensure creation of an equitable test. Methods: Serum samples from 1586 women from the Mass General Brigham BioBank were analyzed for the presence of 179 different miRNAs. The Abcam Fireplex® assay was used to assess miRNA expression determined by flow cytometry, measured in mean fluorescence units (MFI) and log2 normalized prior to analysis. Demographic characteristics and medical histories were abstracted from linked electronic medical records. Patients with missing race/ethnicity were excluded. Differences in miRNA profiles by race/ethnicity (white, non-Hispanic vs non-white) were assessed using dimension reduction. The miRNA expression data was mapped to two-dimensions using constrained least-squares, then the reduced dimension data was used to train a simple neural network to predict race/ethnicity. In addition, pairwise t-test analyses were performed to analyze racial/ethnic differences among eight miRNAs previously reported to be informative for prediction of ovarian cancer. Results: The racial/ethnic composition of the study population was similar to the state population (76.9% white, 13.4% Hispanic, 5.2% black, 2.6% Asian, 1.9% Other). Non-white patients were on average younger (41.9 years ± 13.2 vs 51.3 years ± 15.1, p<0.01) and had fewer comorbid conditions compared to white patients (3.5 comorbidities ± 2.7 vs 4.6 comorbidities ± 2.8, p<0.01). The dimension reduction and classification analysis showed a significant effect of race on miRNA profiles, with miRNAs able to predict race at an AUC of 0.71 (95% CI 0.68-0.74) after 10-fold cross validation. The AUC remained consistent when stratified for age, menopausal status, and comorbidities. Among eight miRNAs highly predictive of ovarian cancer, seven significantly varied by race (hsa-mir-150-5p, hsa-mir-200c-3p, hsa-mir-23b-3p, has-mir-29a-3p, hsa-mir-320c, hsa-mir-320d, and hsa-mir-32-5p, p<0.01). Hsa-mir-200c-3p was the most significantly different by race (7.19 mean log2 MFI among non-white vs 6.94 mean log2 MFI among white patients, p<0.01). Upon analyzing each racial subtype and variation in hsa-mir-200c-3p, black and Hispanic patients had relatively similar mean values which were significantly different from values observed among white patients. Conclusions: miRNA expression is significantly influenced by race and ethnicity, with differences remaining largely consistent even after controlling for confounders. Understanding baseline differences in biomarker test characteristics prior to clinical implementation of novel diagnostic tools is essential to ensuring instruments perform comparably across diverse populations. Citation Format: Stephanie Alimena, James Webber, Laura Wollborn, Marta Williams, Chad B. Sussman, Joyce Y. Wang, Julia Spiegel, Kevin M. Elias. Assessing differences in miRNA profiles by race and ethnicity: Implications for creating an equitable ovarian cancer early detection test [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PR001.

A pan-cancer analysis of the biological function and clinical value of BTLA in tumors

B and T-lymphocyte attenuator (BTLA) plays an immunosuppressive role by inhibiting T- and B-cell functions. BTLA is associated with a variety of diseases, especially cancer immunity. However, the function of BTLA in various cancers and its clinical prognostic value have still not been comprehensively analyzed. This study aimed to identify the relationship between BTLA and cancer from the perspectives of differences in BTLA expression, its clinical value, immune infiltration, and the correlation with immune-related genes in various cancers. Data regarding mRNA expression, miRNA expression, lncRNA expression, and clinical data of patients of 33 existing cancers were collected from the TCGA database. Target miRNA of BTLA and the lncRNA that interacts with the target miRNA were obtained from the StarBase database. Based on bioinformatics analysis methods, the relationship between various types of cancers and BTLA was thoroughly investigated, and a competing endogenous RNA network of BTLA, target miRNA, and interacting lncRNA was constructed. The Kaplan-Meier (KM) prognostic analysis of BTLA and target miRNA (has-miR-137) in various types of cancers was completed using the KM plotter. BTLA expression varied in different cancers, with statistical significance in nine cancer types. KM plotter to analyze the overall survival (OS) and regression-free survival prognosis of cancer patients revealed that the BTLA expression was statistically different in the OS of 11 types of cancers out of 21 types of cancers; the OS of 8 type of cancers was also statistically different. Correlation analysis of tumor immune genes revealed a positive correlation of BTLA expression with immunosuppressive gene (CTLA4 and PDCD1) expression. Gene Set Enrichment Analysis showed that BTLA and its co-expressed genes mainly act through biological processes and pathways, including immune response regulation, cell surface receptor signaling pathway, antigen binding, antigen receptor-mediated signaling pathway, and leukocyte migration. BTLA has the potential as a prognostic marker for CLL, COAD, NSCLC, and OV and a diagnostic marker for CLL, COAD, and KIRC. BTLA has a close and complex relationship with the occurrence and development of tumors, and cancer immunotherapy for BTLA is worthy of further analysis.

A network-based method for identifying cancer driver genes based on node control centrality.

Cancer is one of the major contributors to human mortality and has a serious influence on human survival and health. In biomedical research, the identification of cancer driver genes (cancer drivers for short) is an important task; cancer drivers can promote the progression and generation of cancer. To identify cancer drivers, many methods have been developed. These computational models only identify coding cancer drivers; however, non-coding drivers likewise play significant roles in the progression of cancer. Hence, we propose a Network-based Method for identifying cancer Driver Genes based on node Control Centrality (NMDGCC), which can identify coding and non-coding cancer driver genes. The process of NMDGCC for identifying driver genes mainly includes the following two steps. In the first step, we construct a gene interaction network by using mRNAs and miRNAs expression data in the cancer state. In the second step, the control centrality of the node is used to identify cancer drivers in the constructed network. We use the breast cancer dataset from The Cancer Genome Atlas (TCGA) to verify the effectiveness of NMDGCC. Compared with the existing methods of cancer driver genes identification, NMDGCC has a better performance. NMDGCC also identifies 295 miRNAs as non-coding cancer drivers, of which 158 are related to tumorigenesis of BRCA. We also apply NMDGCC to identify driver genes related to the different breast cancer subtypes. The result shows that NMDGCC detects many cancer drivers of specific cancer subtypes.

MicroRNAs within the Basal-like signature of Quadruple Negative Breast Cancer impact overall survival in African Americans

We previously found that QNBC tumors are more frequent in African Americans compared to TNBC tumors. To characterize this subtype further, we sought to determine the miRNA–mRNA profile in QNBC patients based on race. Both miRNA and mRNA expression data were analyzed from TCGA and validated using datasets from the METABRIC, TCGA proteomic, and survival analysis by KMPLOT. miRNA–mRNAs which include FOXA1 and MYC (mir-17/20a targets); GATA3 and CCNG2 (mir-135b targets); CDKN2A, CDK6, and B7-H3 (mir-29c targets); and RUNX3, KLF5, IL1-β, and CTNNB1 (mir-375 targets) were correlated with basal-like and immune subtypes in QNBC patients and associated with a worse survival. Thus, QNBC tumors have an altered gene signature implicated in racial disparity and poor survival.

The effects of circulating MIR22-3p and MIR16-1-3p levels in different stages of subacute thyroiditis

Subacute thyroiditis (SAT) is an inflammatory thyroid disease with a frequency is 5% among all thyroid diseases. miRNAs are endogenous, non-coding RNAs ranging in length from 19 to 25 nucleotides. They play an important role in the posttranscriptional regulation of gene expression. In this study, we aimed to investigate whether the expression levels of two circulating miRNAs, MIR22 and MIR16-1, can be used as a parameter in the diagnosis and follow-up of SAT disease. Fifty patients diagnosed with SAT and 41 healthy controls were included in this study. Expression levels of miRNAs were determined by real time-PCR method. Expression data of miRNAs were calculated by fold change (2-ΔΔCt) method. The statistical significance of miRNA expression was evaluated by t-test. The expression levels of MIR22-3p and MIR16-1-3p were not found to be statistically different between SAT patients and controls and also between the patients in different stages (hyperthyroid, euthyroid, and hypothyroid) of the disease. According to correlation analyses, we observed a positive strong correlation between erythrocyte sedimentation rate (ESR) and the expression levels of MIR22-3p and MIR16-1-3p (r = 0.960, p = 0.000 and r = 0.865, p = 0.006, respectively), and a positive strong correlation between fT4 and the expression levels of MIR22-3p in SAT patients in euthyroid stage (r = 0.712, p = 0.047). In this study, we showed that the expression levels of MIR22-3p and MIR16-1-3p have correlation with clinical characteristics of SAT disease. Our results suggest that MIR22 and MIR16-1 may be effective in the pathogenesis of SAT.

MicroRNA-592 serves as a novel tumor suppressor in Uveal melanoma: bioinformatics analysis and in vitro cell function verification

ABSTRACT Uveal melanoma (UVM), one common eye tumor in adults, is related with a high risk of metastasis and poor prognosis. Studies have shown that many miRNAs are abnormally expressed in UVM tissues, and play an important regulatory role in the cell proliferation, invasion, and metastasis of UVM. Therefore, it is of great significance to analyze the expression characteristics of microRNAs (miRNAs) in UVM and clarify the role of miRNA in the tumorigenesis and development of UVM. In this study, we firstly downloaded and analyzed miRNA expression data of UVM tissues in TCGA (The Cancer Genome Atlas) database to select the differential expressed miRNAs in different clinical stages (IIA, IIB, IIIA, IIIB, IV). Compared with other stages, microRNA-592 (miR-592) was up-regulated in stage IV UVM patients. Then we used several bioinformatics tools including miRbase, miRDB, RNA22 and TargetScan, and found that it was be conserved in different species. Cell viability was determined by Cell Counting Kit-8. The proliferation and invasion of MUM-2B and C819 cells was measured using Edu assay and Transwell assay. We found that silencing miR-592 enhanced the progression of UVM cells, while miR592 overexpression inhibited the cell growth and invasion. The target genes of miR-592 were predicted by three webservers (miRDB, RNA22, and TargetScan), and verified by Real-Time PCR (qPCR). This is the first study to explore the role of miR-592 in malignant progression of UVM by bioinformatics and cell experiments. Our study suggests that tumor suppressor miR-592 may function as potential therapeutic target and biomarker for UVM.

Identification of potential microRNA diagnostic panels and uncovering regulatory mechanisms in breast cancer pathogenesis

Early diagnosis of breast cancer (BC), as the most common cancer among women, increases the survival rate and effectiveness of treatment. MicroRNAs (miRNAs) control various cell behaviors, and their dysregulation is widely involved in pathophysiological processes such as BC development and progress. In this study, we aimed to identify potential miRNA biomarkers for early diagnosis of BC. We also proposed a consensus-based strategy to analyze the miRNA expression data to gain a deeper insight into the regulatory roles of miRNAs in BC initiation. Two microarray datasets (GSE106817 and GSE113486) were analyzed to explore the differentially expressed miRNAs (DEMs) in serum of BC patients and healthy controls. Utilizing multiple bioinformatics tools, six serum-based miRNA biomarkers (miR-92a-3p, miR-23b-3p, miR-191-5p, miR-141-3p, miR-590-5p and miR-190a-5p) were identified for BC diagnosis. We applied our consensus and integration approach to construct a comprehensive BC-specific miRNA-TF co-regulatory network. Using different combination of these miRNA biomarkers, two novel diagnostic models, consisting of miR-92a-3p, miR-23b-3p, miR-191-5p (model 1) and miR-92a-3p, miR-23b-3p, miR-141-3p, and miR-590-5p (model 2), were obtained from bioinformatics analysis. Validation analysis was carried out for the considered models on two microarray datasets (GSE73002 and GSE41922). The model based on similar network topology features, comprising miR-92a-3p, miR-23b-3p and miR-191-5p was the most promising model in the diagnosis of BC patients from healthy controls with 0.89 sensitivity, 0.96 specificity and area under the curve (AUC) of 0.98. These findings elucidate the regulatory mechanisms underlying BC and represent novel biomarkers for early BC diagnosis.

Manuscript Title: A 4-miRNAs Serum Panel for Obstructive Sleep Apnea Syndrome Screening.

Obstructive sleep apnea syndrome (OSAS) is a common chronic sleep disorder. OSAS is closely related to cardiovascular disease, metabolic disorders, cancer risk, and sudden death. This association has special significance in young people. Although it is known that OSAS has a great impact on physical health, it is estimated that 70-80% of patients with moderate-to-severe OSAS remain undiagnosed. Therefore, a new method for early diagnosis of the disease, the therapeutic effect of OSAS and prevention of complications to important. A total of 110 patients with moderate-to-severe OSAS diagnosed in the Sleep Disorders Diagnosis and Treatment Center of Peking University Shenzhen Hospital were selected. After excluding other diseases, 59 patients were finally selected as the OSAS group. In addition, 60 healthy people were selected as the control group. Serum RNA was then extracted. Eight RNA samples were randomly selected from the two groups for high-throughput miRNA sequencing. The 10 miRNAs with the greatest differences were selected as preselected markers from the results. Then, qRT-PCR was performed on the remaining RNA samples of the two groups to extract and verify the 10 miRNAs, and statistical analysis was performed between groups. A diagnostic panel was constructed by a stepwise logistic regression model combined with the expression data of miRNAs in the validation phase. A four-miRNA panel was identified to better predict OSAS, and the model was calculated using the following formula: Logit (P)= 0.77-1.65 × miR-486-5p - 4.56 × miR-148a-3p + 1.79 × miR-744-5p + 1.13 × let-7d-3p. The AUC for the four-miRNA panel was 0.955 (95% CI: 0.899 to 0.985; sensitivity = 91.38%, specificity = 91.38%). Gene Ontology (GO) annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis was included in bioinformatic analysis. A 4-miRNAs panel as a diagnostic biomarker for OSAS screening is feasible.

SOJNMF: Identifying Multidimensional Molecular Regulatory Modules by Sparse Orthogonality-Regularized Joint Non-Negative Matrix Factorization Algorithm.

Cancer is not only a very aggressive but also a very diverse disease. Recent advances in high-throughput omics technologies of cancer have enabled biomedical researchers to have more opportunities for studying its multi-level biological regulatory mechanism. However, there are few methods to explore the underlying mechanism of cancer by identifying its multidimensional molecular regulatory modules from the multidimensional omics data of cancer. In this paper, we propose a sparse orthogonality-regularized joint non-negative matrix factorization (SOJNMF) algorithm which can integratively analyze multidimensional omics data. This method can not only identify multidimensional molecular regulatory modules, but reduce the overlap rate of features among the multidimensional modules while ensuring the sparsity of the coefficient matrix after decomposition. Gene expression data, miRNA expression data and gene methylation data of liver cancer are integratively analyzed based on SOJNMF algorithm. Then, we obtain 238 multidimensional molecular regulatory modules. The results of permutation test indicate that different omics features within these modules are significantly correlated in statistics. Meanwhile, the results of functional enrichment analysis show that these multidimensional modules are significantly related to the underlying mechanism of the occurrence and development of liver cancer.

MiR-95-3p acts as a prognostic marker and promotes cervical cancer progression by targeting VCAM1.

Cervical cancer patients have a high risk of metastasis and a poor prognosis with shorter disease-free survival. Thus, novel biomarkers and feasible therapies urgently need to be discovered. Previous studies have shown that miR-95-3p plays crucial roles in several cancer types. However, the roles of miR-95-3p in cervical cancer remain unknown. The micro ribonucleic acid (miRNA) expression data and clinical characteristics of cervical cancer samples were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses were conducted to identify the prognostic-related miRNAs. The potential target genes of miR-95-3p were predicted by the TargetScan database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to explore the target gene of miR-95-3p. The effects of miR-95-3p inhibition and overexpression on cell proliferation were inspected by cell counting kit-8 (CCK-8) assays and cell colony formation assays. Wound-healing assays and transwell assays were also used to examine cell migration ability in HeLa and SiHa cells. MiR-95-3p was the only miRNA significantly associated with the poor prognosis of cervical squamous cell carcinoma. A further analysis suggested that vascular cell adhesion molecule 1 (VCAM1) is a target gene of miR-95-3p in cervical cancer, and miR-95-3p promotes the malignant behavior of cervical cancer cells by inhibiting the expression of VCAM1. The CCK-8 and cell colony assays showed that miR-95-3p downregulation significantly suppressed cell proliferation in the HeLa and SiHa cells. The transwell and wound-healing assays showed that miR-95-3p inhibition suppressed cell migration in the HeLa and SiHa cells. Further the Western blot analysis and the quantitative real-time-polymerase chain reaction (qRT-PCR) showed that the knockdown of miR-95-3p in HeLa cells resulted in increased VCAM1 expression. And VCAM1 was highly expressed in the paired adjacent normal cervical epithelium tissue samples, but lowly expressed in the cervical tumor tissue samples. Our study was the first to show that miR-95-3p could serve as a prognostic biomarker of cervical cancer. Mechanistically, we discovered that miR-95-3p inhibited the expression of the cell adhesion molecule VCAM1 and thus promoted further tumor progression.

IsomiRdb: microRNA expression at isoform resolution.

A significant fraction of mature miRNA transcripts carries sequence and/or length variations, termed isomiRs. IsomiRs are differentially abundant in cell types, tissues, body fluids or patients' samples. Not surprisingly, multiple studies describe a physiological and pathophysiological role. Despite their importance, systematically collected and annotated isomiR information available in databases remains limited. We thus developed isomiRdb, a comprehensive resource that compiles miRNA expression data at isomiR resolution from various sources. We processed 42 499 human miRNA-seq datasets (5.9 × 1011 sequencing reads) and consistently analyzed them using miRMaster and sRNAbench. Our database provides online access to the 90 483 most abundant isomiRs (>1 RPM in at least 1% of the samples) from 52 tissues and 188 cell types. Additionally, the full set of over 3 million detected isomiRs is available for download. Our resource can be queried at the sample, miRNA or isomiR level so users can quickly answer common questions about the presence/absence of a particular miRNA/isomiR in tissues of interest. Further, the database facilitates to identify whether a potentially interesting new isoform has been detected before and its frequency. In addition to expression tables, isomiRdb can generate multiple interactive visualisations including violin plots and heatmaps. isomiRdb is free to use and publicly available at: https://www.ccb.uni-saarland.de/isomirdb.

Development of a 4-miRNA prognostic signature for endometrial cancer.

To develop an effective uterine corpus endometrial carcinoma (UCEC) risk assessment tool to monitor treatment outcomes. Limma package was used to analyze differentially expressed microRNAs (miRNAs) between UCEC tissues and normal tissues in the TCGA database. According to univariate Cox risk regression, least absolute shrinkage, and selection operator (LASSO) Cox analysis were performed to screen prognostic miRNAs and construct a risk scoring model. The prognostic performance of signature was evaluated by Kaplan–Meier and receiver operating characteristic. Multivariate Cox regression analysis was used to determine the independent prognostic factors of UCEC. Nomogram was constructed according to age, clinical stage, and risk score. A 4-miRNA signature based on miR-31-5p, miR-34a-5p, miR-26a-1-3p and miR-4772-3p was established. Risk scores of each patient were calculated by the 4-miRNA signature. After z-score, the patients were divided into high- and low-risk groups. The overall survival of high-risk patients was significantly shorter than that of low-risk patients, pointing to the high performance and independence of the 4-miRNA signature in predicting UCEC prognosis. The nomogram showed a high accuracy in predicting overall survival of UCEC patients. We developed a 4-miRNA signature that could effectively predict the prognosis of UCEC.

The Clinical Value of Blood miR-654-5p, miR-126, miR-10b, and miR-144 in the Diagnosis of Colorectal Cancer.

Colorectal cancer (CRC) is the third cause of cancer-related death and the fourth most frequently diagnosed cancer across the globe. The objective of this study is to obtain novel and effective diagnostic markers to enrich CRC diagnosis methods. Herein, exosomal miRNA expression data of CRC and normal blood were subjected to XGBoost algorithm, and 5 miRNAs related to CRC diagnosis were primarily confirmed. Then multilayer perceptron (MLP) classifiers were constructed based on different subsets. Via integrated feature selection (IFS), we noticed that the MLP classifier constructed by the first four miRNAs (miR-654-5p, miR-126, miR-10b, and miR-144) had the highest Matthews correlation coefficient (MCC). Subsequently, principal component analysis (PCA) for dimensionality reduction was performed on samples based on the miR-654-5p, miR-126, miR-10b, and miR-144 expression data. The signature based on these four feature miRNAs, as the analysis indicated, could effectively distinguish CRC samples from normal samples. Further, we extracted the exosomes from clinical blood samples and applied qRT-PCR analysis, which revealed that the expression of these four feature miRNAs was in the trend of that in the test set. Collectively, these four feature miRNAs might be tumor biomarkers in the serum, and our study offers innovative thinking on early-stage CRC diagnosis.