FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs

Explainable ensemble learning method for OCT detection with transfer learning

The accuracy and interpretability of artificial intelligence (AI) are crucial for the advancement of optical coherence tomography (OCT) image detection, as it can greatly reduce the manual labor required by clinicians. By prioritizing these aspects during development and application, we can make significant progress towards streamlining the clinical workflow. In this paper, we propose an explainable ensemble approach that utilizes transfer learning to detect fundus lesion diseases through OCT imaging. Our study utilized a publicly available OCT dataset consisting of normal subjects, patients with dry age-related macular degeneration (AMD), and patients with diabetic macular edema (DME), each with 15 samples. The impact of pre-trained weights on the performance of individual networks was first compared, and then these networks were ensemble using majority soft polling. Finally, the features learned by the networks were visualized using Grad-CAM and CAM. The use of pre-trained ImageNet weights improved the performance from 68.17% to 92.89%. The ensemble model consisting of the three CNN models with pre-trained parameters loaded performed best, correctly distinguishing between AMD patients, DME patients and normal subjects 100% of the time. Visualization results showed that Grad-CAM could display the lesion area more accurately. It is demonstrated that the proposed approach could have good performance of both accuracy and interpretability in retinal OCT image detection.

A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations

Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.

Predicting metabolite-disease associations based on auto-encoder and non-negative matrix factorization

Metabolism refers to a series of orderly chemical reactions used to maintain life activities in organisms. In healthy individuals, metabolism remains within a normal range. However, specific diseases can lead to abnormalities in the levels of certain metabolites, causing them to either increase or decrease. Detecting these deviations in metabolite levels can aid in diagnosing a disease. Traditional biological experiments often rely on a lot of manpower to do repeated experiments, which is time consuming and labor intensive. To address this issue, we develop a deep learning model based on the auto-encoder and non-negative matrix factorization named as MDA-AENMF to predict the potential associations between metabolites and diseases. We integrate a variety of similarity networks and then acquire the characteristics of both metabolites and diseases through three specific modules. First, we get the disease characteristics from the five-layer auto-encoder module. Later, in the non-negative matrix factorization module, we extract both the metabolite and disease characteristics. Furthermore, the graph attention auto-encoder module helps us obtain metabolite characteristics. After obtaining the features from three modules, these characteristics are merged into a single, comprehensive feature vector for each metabolite-disease pair. Finally, we send the corresponding feature vector and label to the multi-layer perceptron for training. The experiment demonstrates our area under the receiver operating characteristic curve of 0.975 and area under the precision-recall curve of 0.973 in 5-fold cross-validation, which are superior to those of existing state-of-the-art predictive methods. Through case studies, most of the new associations obtained by MDA-AENMF have been verified, further highlighting the reliability of MDA-AENMF in predicting the potential relationships between metabolites and diseases.

miRNA-1260b Promotes Breast Cancer Cell Migration and Invasion by Downregulating CCDC134

BACKGROUND

Breast cancer (BRCA) is the most common type of cancer among women worldwide. MiR-1260b has been widely demonstrated to participate in multiple crucial biological functions of cancer tumorigenesis, but its functional effect and mechanism in human breast cancer have not been fully understood.

METHODS

qRT-PCR was used to detect miR-1260b expression in 29 pairs of breast cancer tissues and normal adjacent tissues. Besides, the expression level of miR-1260b in BRCA cells was also further validated by qRT-PCR. miR-1260b played its role in the prognostic process by using Kaplan-Meier curves. In addition, miR-1260b knockdown and target gene CCDC134 overexpression model was constructed in cell line MDA-MB-231. Transwell migration and invasion assay was performed to analyze the effect of miR-1260b and CCDC134 on the biological function of BRCA cells. TargetScan and miRNAWalk were used to find possible target mRNAs. The relationship between CCDC134 and immune cell surface markers was analyzed using TIMER and database and the XIANTAO platform. GSEA analysis was used to identify possible CCDC134-associated molecular mechanisms and pathways.

RESULTS

In the present study, miR-1260b expression was significantly upregulated in human breast cancer tissue and a panel of human breast cancer cell lines, while the secretory protein coiled-coil domain containing 134 (CCDC134) exhibited lower mRNA expression. High expression of miR-1260b was associated with poor overall survival among the patients by KM plot. Knockdown of miR-1260b significantly suppressed breast cancer cell migration and invasion and yielded the opposite result. In addition, overexpression of CCDC134 could inhibit breast cancer migration and invasion, and knockdown yielded the opposite result. There were significant positive correlations of CCDC134 with CD25 (IL2RA), CD80 and CD86. GSEA showed that miR-1260b could function through the MAPK pathway by downregulating CCDC134.

CONCLUSION

Collectively, these results suggested that miR-1260b might be an oncogene of breast cancer and might promote the migration and invasion of BRCA cells by down-regulating its target gene CCDC134 and activating MAPK signaling pathway as well as inhibiting immune function and causing immune escape in human breast cancer.

CmirC: an integrated database of clustered miRNAs co-localized with copy number variations in cancer

Genomic rearrangements and copy number variations (CNVs) are the major regulators of clustered microRNAs (miRNAs) expression. Several clustered miRNAs are harbored in and around chromosome fragile sites (CFSs) and cancer-associated genomic hotspots. Aberrant expression of such clusters can lead to oncogenic or tumor suppressor activities. Here, we developed CmirC (Clustered miRNAs co-localized with CNVs), a comprehensive database of clustered miRNAs co-localized with CNV regions. The database consists of 481 clustered miRNAs co-localized with CNVs and their expression patterns in 35 cancer types of the TCGA. The portal also provides information on CFSs, miRNA cluster candidates, genomic coordinates, target gene networks, and gene functionality. The web portal is integrated with advanced tools such as JBrowse, NCBI-BLAST, GeneSCF, visNetwork, and NetworkD3 to help the researchers in data analysis, visualization, and browsing. This portal provides a promising avenue for integrated data analytics and offers additional evidence for the complex regulation of clustered miRNAs in cancer. The web portal is freely accessible at http://slsdb.manipal.edu/cmirclust to explore clinically significant miRNAs.

Research progress of miRNA-disease association prediction and comparison of related algorithms

With an in-depth understanding of noncoding ribonucleic acid (RNA), many studies have shown that microRNA (miRNA) plays an important role in human diseases. Because traditional biological experiments are time-consuming and laborious, new calculation methods have recently been developed to predict associations between miRNA and diseases. In this review, we collected various miRNA-disease association prediction models proposed in recent years and used two common data sets to evaluate the performance of the prediction models. First, we systematically summarized the commonly used databases and similarity data for predicting miRNA-disease associations, and then divided the various calculation models into four categories for summary and detailed introduction. In this study, two independent datasets (D5430 and D6088) were compiled to systematically evaluate 11 publicly available prediction tools for miRNA-disease associations. The experimental results indicate that the methods based on information dissemination and the method based on scoring function require shorter running time. The method based on matrix transformation often requires a longer running time, but the overall prediction result is better than the previous two methods. We hope that the summary of work related to miRNA and disease will provide comprehensive knowledge for predicting the relationship between miRNA and disease and contribute to advanced computation tools in the future.

Inferring Metabolite-Disease Association Using Graph Convolutional Networks

As is well known, biological experiments are time-consuming and laborious, so there is absolutely no doubt that developing an effective computational model will help solve these problems. Most of computational models rely on the biological similarity and network-based methods that cannot consider the topological structures of metabolite-disease association graphs. We proposed a novel method based on graph convolutional networks to infer potential metabolite-disease association, named MDAGCN. We first calculated three kinds of metabolite similarities and three kinds of disease similarities. The final similarity of disease and metabolite will be obtained by integrating three kinds' similarities of each and filtering out the noise similarity values. Then metabolite similarity network, disease similarity network and known metabolite-disease association network were used to construct a heterogenous network. Finally, heterogeneous network with rich information is fed into the graph convolutional networks to obtain new features of a node through aggregation of node information so as to infer the potential associations between metabolites and diseases. Experimental results show that MDAGCN achieves more reliable results in cross validation and case studies when compared with other existing methods.

RUNX3 Expression Level Is Correlated with the Clinical and Pathological Characteristics in Endometrial Cancer: A Systematic Review and Meta-analysis

Human Runt-associated transcription factor 3 (RUNX3) plays an important role in the development and progression of endometrial cancer (EC). However, the clinical and pathological significance of RUNX3 in EC needs to be further studied. In order to clarify the clinical and pathological significance of RUNX3, a systematic review and meta-analysis was conducted in EC patients. Keywords RUNX3, endometrial cancer, and uterine cancer were searched in Cochrane Library, Web of Knowledge, PubMed, CBM, MEDLINE, and Chinese CNKI database for data up to Dec 31, 2018. References, abstracts, and meeting proceedings were manually searched in supplementary. Outcomes were various clinical and pathological features. The two reviewers performed the literature searching, data extracting, and method assessing independently. Meta-analysis was performed by RevMan5.3.0. A total of 563 EC patients were enrolled from eight studies. Meta-analysis results showed that the expression of RUNX3 has significant differences in these comparisons: lymph node (LN) metastasis vs. non-LN metastasis (P = 0.26), EC tissues vs. normal tissues (P < 0.00001), clinical stages I/II vs. II/IV (P < 0.00001), muscular infiltration < 1/2 vs. muscular infiltration ≥ 1/2 (P < 0.00001), and G1 vs. G2/G3 (P < 0.00001). The decreasing expression of RUNX3 is associated with poor TNM stage and muscular infiltration. It is indicated that RUNX3 was involved in the suppression effect of EC. However, further multicenter randomized controlled trials are needed considering the small sample size of the included trials.

MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph

Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.

Epigenetic Regulation of MicroRNA Clusters and Families during Tumor Development

MicroRNAs are small non-coding single-stranded RNA molecules regulating gene expression on a post-transcriptional level based on the seed sequence similarity. They are frequently clustered; thus, they are either simultaneously transcribed into a single polycistronic transcript or they may be transcribed independently. Importantly, microRNA families that contain the same seed region and thus target related signaling proteins, may be localized in one or more clusters, which are in a close relationship. MicroRNAs are involved in basic physiological processes, and their deregulation is associated with the origin of various pathologies, including solid tumors or hematologic malignancies. Recently, the interplay between the expression of microRNA clusters and families and epigenetic machinery was described, indicating aberrant DNA methylation or histone modifications as major mechanisms responsible for microRNA deregulation during cancerogenesis. In this review, the most studied microRNA clusters and families affected by hyper- or hypomethylation as well as by histone modifications are presented with the focus on particular mechanisms. Finally, the diagnostic and prognostic potential of microRNA clusters and families is discussed together with technologies currently used for epigenetic-based cancer therapies.

A comprehensive survey on computational methods of non-coding RNA and disease association prediction

The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.