• biomarker
• gastric cancer
• gastrointestinal disorders
• miRNA
• pancreatic cancer
• salivary

• Cancer therapy
• Delivery system
• Esophageal squamous cell carcinoma
• MicroRNAs

• Esophageal Neoplasms
• MicroRNA
• Plasma
• Squamous Cell Carcinoma

• Biomarkers, Tumor/genetics
• Esophageal Neoplasms/diagnosis
• Esophageal Neoplasms/genetics
• Esophageal Squamous Cell Carcinoma/diagnosis
• Esophageal Squamous Cell Carcinoma/genetics
• Gene Expression Regulation, Neoplastic
• Humans
• MicroRNAs/genetics
• Prognosis

• KCHUGR-202002503 Korean College of Helicobacter and Upper Gastrointestinal Research

Predicting possible associations between miRNAs and diseases would provide new perspectives on disease diagnosis, pathogenesis, and gene therapy. In this work, considering the limited accessibility, high time consumption and high cost in traditional biological researches, we presented a novel computational method called SMMDA by incorporating multiple similarity profiles and a novel disease rep-resentation to accelerate the identification of potential miRNA-disease associations. SMMDA was intended to be useful for the prediction of associations between miRNAs and diseases, and to be effective for prevention, diagnosis, treatment and prognosis of Human diseases.

Increasing evidence has suggested that microRNAs (miRNAs) are significant in research on human diseases. Predicting possible associations between miRNAs and diseases would provide new perspectives on disease diagnosis, pathogenesis, and gene therapy. However, considering the intrinsic time-consuming and expensive cost of traditional Vitro studies, there is an urgent need for a computational approach that would allow researchers to identify potential associations between miRNAs and diseases for further research. In this paper, we presented a novel computational method called SMMDA to predict potential miRNA-disease associations. In particular, SMMDA first utilized a new disease representation method (MeSHHeading2vec) based on the network embedding algorithm and then fused it with Gaussian interaction profile kernel similarity information of miRNAs and diseases, disease semantic similarity, and miRNA functional similarity. Secondly, SMMDA utilized a deep auto-coder network to transform the original features further to achieve a better feature representation. Finally, the ensemble learning model, XGBoost, was used as the underlying training and prediction method for SMMDA. In the results, SMMDA acquired a mean accuracy of 86.68% with a standard deviation of 0.42% and a mean AUC of 94.07% with a standard deviation of 0.23%, outperforming many previous works. Moreover, we also compared the predictive ability of SMMDA with different classifiers and different feature descriptors. In the case studies of three common Human diseases, the top 50 candidate miRNAs have 47 (esophageal neoplasms), 48 (breast neoplasms), and 48 (colon neoplasms) are successfully verified by two other databases. The experimental results proved that SMMDA has a reliable prediction ability in predicting potential miRNA-disease associations. Therefore, it is anticipated that SMMDA could be an effective tool for biomedical researchers.

Esophageal squamous cell carcinoma (ESCC) is the major subtype of esophageal cancer that is prevalent in Eastern Asia. Despite recent advances in therapy, the outcome of ESCC patients is still dismal. MicroRNAs (miRNAs) are non-coding RNAs which can negatively modulate gene expression at the post-transcriptional level. The involvement and roles of miRNAs have become one of the hot topics of cancer research in recent years. In ESCC, genetic variations within miRNA coding genes were found to have distinct epidemiological significance in different populations. Dysregulated expression of several miRNAs was reported to be associated with therapeutic response. Functionally, miRNAs can act either in an oncogenic or a tumor-suppressive manner during tumorigenesis of ESCC by interrupting signaling pathways associated with cell proliferation, metabolism, cancer stemness, and resistance to chemo- or radiotherapy. Moreover, miRNAs modulate metastasis of ESCC by targeting genes that regulate cytoskeleton dynamics, extracellular matrix remodeling, epithelial-mesenchymal transition, and tumor microenvironment. Most importantly, mounting evidence suggests that inhibiting oncogenic miRNAs or restoring the loss of tumor-suppressive miRNAs has therapeutic potential in the treatment of ESCC. Here, we review and discuss recent studies on the significance, biological functions, and therapeutic potential of miRNAs in tumorigenesis and metastasis of ESCC.

• MicroRNAs
• Dysregulation
• Tumorigenesis
• Metastasis
• Therapeutic potential
• Esophageal squamous cell carcinoma

Esophageal cancer (EC) is a common malignancy of the digestive tract, with high incidence. The objective of this study was to investigate the effect of miR‐630 expression on esophageal cancer (EC) cell invasion and migration.

The study group comprised 58 EC patients admitted to our hospital from April 2014 to 2016, and the control group comprised 60 healthy people visiting the hospital during the same period. miR‐630 levels in the peripheral blood of the two groups were compared, and the diagnostic value of miR‐630 for EC was analyzed. EC cell lines were used to evaluate the influence of miR‐630 expression on EC cell invasion and migration.

miR‐630 expression was low in EC (p < 0.050). A receiver operating characteristic curve analysis showed that miR‐630 expression had a good diagnostic value for EC (p < 0.050) and was associated with disease course, pathological stage, differentiation degree, tumor metastasis, and patient prognosis and survival (p < 0.05). The ROC curve analysis showed that when cutoff value was 5.38, the diagnostic sensitivity and specificity of miR‐630 for EC were 73.33% and 76.67%, respectively; area under the ROC curve was 0.778 (95%CI 0.695–0.861). Transfection of miR‐630 into EC cells indicated that miR‐630 overexpression can reduce EC cell invasion and migration (p < 0.05). miR‐630 expression is low in EC and has good diagnostic value for EC.

miR‐630 overexpression can reduce EC cell invasion and migration, showing a possible key role of miR‐630 in EC diagnosis and treatment in the future.

• diagnostic value
• esophageal cancer
• invasion
• miR-630
• migration

In recent studies, microRNAs have been demonstrated as stable detectable biomarkers in blood for cancer. In addition, computer-aided biomarker discovery has now become an attractive paradigm for precision diagnosis.

In this study, we identified and evaluated miR-139-3p as a biomarker for screening of esophageal squamous cell carcinoma using the Cancer Genome Atlas and Gene Expression Omnibus database analyses. We identified possible miR-139-3p target genes through the predicted database and esophageal squamous cell carcinoma upregulated genes from the Cancer Genome Atlas and Gene. Bioinformatics analysis was performed to determine key miR-139-3p targets and pathways associated with esophageal carcinoma. Finally, the expression and expected significance of hub genes were evaluated via the Genotype-Tissue Expression project.

MiR-139-3p was significantly downregulated in patients with esophageal squamous cell carcinoma/esophageal carcinoma. In GSE 122497, the area under the curve-receiver operating characteristic value, sensitivity, and specificity for serum miR-139-3p were 0.754, 67.49%, and 80.00%, respectively. The pattern specification process, skeletal system development, and regionalization process were the most enriched interactions in esophageal carcinoma. In addition, Epstein-Barr virus infection, human T-cell leukemia virus 1 infection, and human cytomegalovirus infection were identified as crucial pathways. Six hub genes (CD1A, FCGR2A, ANPEP, CD1B, membrane metalloendopeptidase, and TWIST1) were found, and FCGR2A and membrane metalloendopeptidase were further confirmed by genotype-tissue expression. High expression of membrane metalloendopeptidase correlated with a better overall survival but not with disease-free survival of patients with esophageal carcinoma.

MiR-139-3p was identified as a candidate biomarker for predicting esophageal squamous cell carcinoma based on network analysis. MiR-139-3p acted as a tumor suppressor by targeting membrane metalloendopeptidase in esophageal carcinoma, and low expression of membrane metalloendopeptidase may indicate a better prognosis of patients with esophageal carcinoma.

• TCGA
• bioinformatics
• biomarkers
• esophageal squamous cell carcinoma
• miR-139-3p
• microRNA

To profile the landscape of methylation N⁶ adenosine (m⁶A) RNA regulators in colonic adenocarcinoma (COAD) and to explore potential diagnostic and prognostic biomarkers, we assessed the differential expression patterns of m⁶A RNA methylation regulators between 418 COAD patients and 41 controls based on profiling from The Cancer Genome Atlas (TCGA) database. We plotted the receiver operating characteristic (ROC) curves and calculated the area under the curve (AUC) values to estimate the discrimination ability. The relationship between the expression of m⁶A RNA methylation regulators and clinicopathological characteristics was explored. Kaplan-Meier plotter, log-rank test, and Cox regression were used and a nomogram was created to explore the prognostic significance of m⁶A-related genes in overall survival at the mRNA level. Pathway analysis was performed by gene set enrichment analysis (GSEA) using TCGA dataset, and a coexpression network was built based on the STRING database. We observed that YTHDF1, METTL3, and KIAA1429 were significantly upregulated, while YTHDF3, YTHDC2, METTL14, and ALKBH5 were significantly downregulated in COAD samples compared to normal samples. YTHDF1 had the highest diagnostic value. Low expression of YTHDF3 predicted a poor survival rate in COAD patients. YTHDC2 was related to sex and showed a downward trend as clinical stage increased. Our results indicate that the YT521-B homology (YTH) domain family (“readers”), especially YTHDF1, YTHDF3, and YTHDC2, might play a significant role in the detection, progression, and prognosis of COAD, indicating that they are promising cancer biomarkers.

Recent studies have indicated that microRNAs (miRNAs) are closely related to sundry human sophisticated diseases. According to the surmise that functionally similar miRNAs are more likely associated with phenotypically similar diseases, researchers have proposed a variety of valid computational models through integrating known miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity to discover the potential miRNA-disease relationships in biomedical researches. Taking account of the limitations of previous computational models, a new computational model based on biased heat conduction for MiRNA-Disease Association prediction (BHCMDA) was proposed in this paper, which can achieve the AUC of 0.8890 in LOOCV (Leave-One-Out Cross Validation) and the mean AUC of 0.9060, 0.8931 under the framework of twofold cross validation, fivefold cross validation, respectively. In addition, BHCMDA was further implemented to the case studies of three vital human cancers, and simulation results illustrated that there were 88% (Esophageal Neoplasms), 92% (Colonic Neoplasms) and 92% (Lymphoma) out of top 50 predicted miRNAs having been confirmed by experimental literatures, separately, which demonstrated the good performance of BHCMDA as well. Thence, BHCMDA would be a useful calculative resource for potential miRNA-disease association prediction.

• biased heat conduction
• bipartite graph network
• clustering algorithm
• integrated similarity
• miRNA-disease association

• Autoantibodies
• Biomarker
• Blood-based
• Diagnosis
• Esophageal squamous cell carcinoma
• MicroRNA

• Biomarkers, Tumor/blood
• Early Detection of Cancer/methods
• Esophageal Neoplasms/blood
• Esophageal Neoplasms/diagnosis
• Esophageal Neoplasms/mortality
• Esophageal Squamous Cell Carcinoma/blood
• Esophageal Squamous Cell Carcinoma/diagnosis
• Esophageal Squamous Cell Carcinoma/mortality
• Humans
• Prognosis
• ROC Curve

We analyzed data from two non-coding RNA profiling arrays made available by the Gene Expression Omnibus (GEO) and found 17 miRNAs with remarkable differential expression between malignant and normal esophageal tissue. Correlation analysis between expression of these 17 miRNAs and patients’ clinicopathological characteristics showed that miR-203 was down-regulated in esophageal carcinoma (EC) tissues and was significantly associated with lymph node metastasis and poor overall survival. Overexpression of miR-203 significantly attenuated cellular proliferation, migration and invasion by EC cells in culture. Additionally, gene expression profiles and bioinformatics analysis revealed KIF5C to be a direct target of miR-203, and KIF5C overexpression partially counteracted the tumor inhibitory effects of miR-203 on EC cells. We also observed that miR-203, reduced KIFC5 protein levels, promoted cytoplasmic accumulation of Axin2, and reversed the invasive phenotype of EC cells. Taken together, these data demonstrate that miR-203 is a tumor suppressor in EC cells and its expression level could potentially be used as a prognostic indicator for EC patient outcomes.

• KIF5C
• esophageal carcinoma
• miR-203

• disease
• kernel fusion
• miRNA
• miRNA-disease association
• regularized least squares

• esophageal cancer
• microrna
• clinical features
• overall survival

• Adult
• Aged
• Biomarkers, Tumor/genetics
• Computational Biology/methods
• Esophageal Neoplasms/genetics
• Esophageal Neoplasms/mortality
• Esophageal Neoplasms/pathology
• Esophageal Squamous Cell Carcinoma/genetics
• Esophageal Squamous Cell Carcinoma/mortality
• Esophageal Squamous Cell Carcinoma/pathology
• Female
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic/genetics
• Humans
• Kaplan-Meier Estimate
• Lymphatic Metastasis/genetics
• Male
• MicroRNAs/genetics
• Middle Aged
• Oligonucleotide Array Sequence Analysis
• Prognosis
• Real-Time Polymerase Chain Reaction

In the last few decades, cumulative experimental researches have witnessed and verified the important roles of microRNAs (miRNAs) in the development of human complex diseases. Benefitting from the rapid growth both in the availability of miRNA-related data and the development of various analysis methodologies, up until recently, some computational models have been developed to predict human disease related miRNAs, efficiently and quickly.

In this work, we proposed a computational model of Random Walk and Binary Regression-based MiRNA-Disease Association prediction (RWBRMDA). RWBRMDA extracted features for each miRNA from random walk with restart on the integrated miRNA similarity network for binary logistic regression to predict potential miRNA-disease associations. RWBRMDA obtained AUC of 0.8076 in the leave-one-out cross validation. Additionally, we carried out three different patterns of case studies on four human complex diseases. Specifically, Esophageal cancer and Prostate cancer were conducted as one kind of case study based on known miRNA-disease associations in HMDD v2.0 database. Out of the top 50 predicted miRNAs, 94 and 90% were respectively confirmed by recent experimental reports. To simulate new disease without known related miRNAs, the information of known Breast cancer related miRNAs was removed. As a result, 98% of the top 50 predicted miRNAs for Breast cancer were confirmed. Lymphoma, the verified ratio of which was 88%, was used to assess the prediction robustness of RWBRMDA based on the association records in HMDD v1.0 database.

We anticipated that RWBRMDA could benefit the future experimental investigations about the relation between human disease and miRNAs by generating promising and testable top-ranked miRNAs, and significantly reducing the effort and cost of identification works.

The online version of this article (10.1186/s12859-019-2640-9) contains supplementary material, which is available to authorized users.

• Binary regression
• Disease
• Random walk
• miRNA-disease association
• microRNA

• Elemental determinations
• Esophageal squamous cell carcinoma
• ICP-MS
• Multivariate analysis

• Aged
• Diagnosis, Differential
• Discriminant Analysis
• Esophageal Neoplasms/chemistry
• Esophageal Neoplasms/diagnosis
• Esophageal Squamous Cell Carcinoma/chemistry
• Esophageal Squamous Cell Carcinoma/diagnosis
• Female
• Humans
• Least-Squares Analysis
• Male
• Metals/analysis
• Middle Aged
• Principal Component Analysis
• Selenium/analysis

• Disease
• Heterogenous network
• Maximal entropy random walk
• miRNA-disease association
• microRNA

• Esophageal neoplasms
• diagnosis
• meta-analysis
• microRNA
• prognosis
• systematic review

• biomarker
• diagnosis
• esophageal squamous cell carcinoma
• meta-analysis
• microRNA
• plasma

• biomarker
• esophageal cancer
• lncRNAs
• miRNAs
• therapeutic target

Esophageal carcinoma (ESCA) is one of the most common malignancies worldwide, and its pathogenesis is complex. In this study, we identified differentially expressed miRNAs (DEMs) and genes (DEGs) of ESCA from The Cancer Genome Atlas (TCGA) database. The diagnostic values of DEMs were determined by receiver operating characteristic (ROC) analyses and validated based on data from Gene Expression Omnibus (GEO). The top five DEMs with the best diagnostic values were selected, and their potential targets were predicted by various in silico methods. These target genes were then identified among the DEGs from TCGA. Furthermore, the overlapping genes were subjected to protein-protein interaction (PPI) analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The miRNA-transcription factor (TF) regulatory relations were determined using CircuitsDB and TransmiR. Finally, the regulatory networks of miRNA-TF and miRNA-gene were constructed and analyzed. A total of 136 DEMs and 3541 DEGs were identified in ESCA. The top five DEMs with the highest area under the receiver operating characteristic curve (AUC) values were miRNA-93 (0.953), miRNA-21 (0.928), miRNA-4746 (0.915), miRNA-196a-1 (0.906) and miRNA-196a-2 (0.906). The combined AUC of these five DEMs was 0.985. The KEGG analysis with 349 overlapping genes showed that the calcium signaling pathway and the neuroactive ligand-receptor interaction were the most relevant pathways. The regulatory networks of miRNA-TF and miRNA-gene, including 38 miRNA-TF and 560 miRNA-gene pairs, were successfully established. Our findings may provide new insights into the molecular mechanisms of ESCA pathogenesis. Future research will aim to explore the role of novel miRNAs in the pathogenesis and improve the early diagnosis of ESCA.

• DEGs
• DEMs
• TCGA
• diagnosis
• esophageal carcinoma

The differential expression of microRNAs (miRNAs) in plasma of esophageal squamous cell carcinoma (ESCC) patients may serve as a diagnostic biomarker. A four-stage study was conducted to identify plasma miRNAs with potential in detecting ESCC. Exiqon panels (2 ESCC pools vs. 1 normal control (NC) pool) were applied in the screening phase to obtain miRNA profiles. The identified miRNAs were further evaluated through training (36 ESCC VS. 42 NCs) and testing stages (101 ESCC VS. 113 NCs) with qRT-PCR assays. A six-miRNA signature including up-regulated miR-106a, miR-18a, miR-20b, miR-486-5p, miR-584 and down-regulated miR-223-3p in ESCC was identified. The signature could accurately discriminate ESCC patients from NCs with areas under the receiver operating characteristic curve of 0.935, 0.959 and 0.966 for the training, testing and the additional validation stage (41 ESCC VS. 50 NCs), respectively. MiR-106a and miR-584 were significantly up-regulated in tumor tissues with qRT-PCR assays. And miR-584 was also up-regulated in ESCC tissues from TCGA database. In addition, exosomal miR-223-3p and miR-584 were consistently dysregulated with those in plasma and could also act as biomarkers in diagnosis of ESCC. In conclusion, we identified a six-miRNA signature in plasma which could act as a non-invasive biomarker in detection of ESCC.

• TCGA
• diagnosis
• esophageal squamous cell carcinoma
• exosomes
• miRNA

Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneous biological datasets to predict potential associations between miRNAs and diseases is an important topic in the field of biology, medicine, and bioinformatics. In this study, considering the limitations in the previous computational methods, we developed the computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNA-disease associations into a heterogeneous graph. HGIMDA obtained AUCs of 0.8781 and 0.8077 based on global and local leave-one-out cross validation, respectively. Furthermore, HGIMDA was applied to three important human cancers for performance evaluation. As a result, 90% (Colon Neoplasms), 88% (Esophageal Neoplasms) and 88% (Kidney Neoplasms) of top 50 predicted miRNAs are confirmed by recent experiment reports. Furthermore, HGIMDA could be effectively applied to new diseases and new miRNAs without any known associations, which overcome the important limitations of many previous computational models.

• disease
• heterogeneous network
• microRNA
• microRNA-disease association
• similarity

• Area Under Curve
• Disease
• Humans
• Machine Learning
• MicroRNAs/metabolism
• Models, Biological
• Neural Networks, Computer
• ROC Curve

• biomarker
• long non-coding RNAs
• microRNAs
• ovarian cancer
• protein-coding genes