Predicting miRNA-disease association via graph attention learning and multiplex adaptive modality fusion.

miRNAs are a class of small non-coding RNA molecules that play important roles in gene regulation. They are crucial for maintaining normal cellular functions, and dysregulation or dysfunction of miRNAs which are linked to the onset and advancement of multiple human diseases. Research on miRNAs has unveiled novel avenues in the realm of the diagnosis, treatment, and prevention of human diseases. However, clinical trials pose challenges and drawbacks, such as complexity and time-consuming processes, which create obstacles for many researchers. Graph Attention Network (GAT) has shown excellent performance in handling graph-structured data for tasks such as link prediction. Some studies have successfully applied GAT to miRNA-disease association prediction. However, there are several drawbacks to existing methods. Firstly, most of the previous models rely solely on concatenation operations to merge features of miRNAs and diseases, which results in the deprivation of significant modality-specific information and even the inclusion of redundant information. Secondly, as the number of layers in GAT increases, there is a possibility of excessive smoothing in the feature extraction process, which significantly affects the prediction accuracy. To address these issues and effectively complete miRNA disease prediction tasks, we propose an innovative model called Multiplex Adaptive Modality Fusion Graph Attention Network (MAMFGAT). MAMFGAT utilizes GAT as the main structure for feature aggregation and incorporates a multi-modal adaptive fusion module to extract features from three interconnected networks: the miRNA-disease association network, the miRNA similarity network, and the disease similarity network. It employs adaptive learning and cross-modality contrastive learning to fuse more effective miRNA and disease feature embeddings as well as incorporates multi-modal residual feature fusion to tackle the problem of excessive feature smoothing in GATs. Finally, we employ a Multi-Layer Perceptron (MLP) model that takes the embeddings of miRNA and disease features as input to anticipate the presence of potential miRNA-disease associations. Extensive experimental results provide evidence of the superior performance of MAMFGAT in comparison to other state-of-the-art methods. To validate the significance of various modalities and assess the efficacy of the designed modules, we performed an ablation analysis. Furthermore, MAMFGAT shows outstanding performance in three cancer case studies, indicating that it is a reliable method for studying the association between miRNA and diseases. The implementation of MAMFGAT can be accessed at the following GitHub repository: https://github.com/zixiaojin66/MAMFGAT-master.

Astragaloside IV inhibits pathological functions of gastric cancer-associated fibroblasts through regulation of the HOXA6/ZBTB12 axis.

Cancer-associated fibroblasts (CAFs) play critical roles in the tumor microenvironment and exert tumor-promoting or tumor-retarding effects on cancer development. Astragaloside IV has been suggested to rescue the pathological impact of CAFs in gastric cancer. This study aimed to investigate the potential mechanism of astragaloside IV in the regulation of CAF pathological functions in gastric cancer development. Homeobox A6 (HOXA6), and Zinc Finger and BTB Domain Containing 12 (ZBTB12) are highly expressed in gastric CAFs compared with normal fibroblasts (NFs) based on the GSE62740 dataset. We found that astragaloside IV-stimulated CAFs suppressed cell growth, migration, and invasiveness of gastric cancer cells. HOXA6 and ZBTB12 were downregulated after astragaloside IV treatment in CAFs. Further analysis revealed that HOXA6 or ZBTB12 knockdown in CAFs also exerted inhibitory effects on the malignant phenotypes of gastric cells. Additionally, HOXA6 or ZBTB12 overexpression in CAFs enhanced gastric cancer cell malignancy, which was reversed after astragaloside IV treatment. Moreover, based on the hTFtarget database, ZBTB12 is a target gene that may be transcriptionally regulated by HOXA6. The binding between HOXA6 and ZBTB12 promoter in 293T cells and CAFs was further confirmed. HOXA6 silencing also induced the downregulation of ZBTB12 mRNA and protein in CAFs. Astragaloside IV was demonstrated to regulate the expression of ZBTB12 by mediating the transcriptional activity of HOXA6. Our findings shed light on the therapeutic value of astragaloside IV for gastric cancer.

Case Report: Novel RPGRIP1L Gene Mutations Identified by Whole Exome Sequencing in a Patient With Multiple Primary Tumors.

A 78 years old Chinese woman with five different cancer types and a family history of malignancy was the subject of this study. Pancreatic adenocarcinoma and gingival squamous cell carcinoma tissues were obtained from the patient and sequenced using Whole Exome Sequencing. Whole exome sequencing identified 20 mutation sites in six candidate genes. Sanger Sequencing was used for further validation. The results verified six mutations in three genes, OBSCN, TTN, and RPGRIP1L, in at least one cancer type. Immunohistochemistry was used to verify protein expression. mRNA expression analysis using The Cancer Genome Atlas database revealed that RPGRIP1L was highly expressed in several cancer types, especially in pancreatic adenocarcinoma, and correlated with patient survival and sensitivity to paclitaxel, probably through the TGF-ß signaling pathway. The newly identified somatic mutations in RPGRIP1L might contribute to pathogenesis in the patients. Protein conformation simulation demonstrated that the alterations had caused the binding pocket at position 708 to change from concave to convex, which could restrict contraction and extension, and interfere with the physiological function of the protein. Further studies are required to determine the implication of RPGRIP1L in this family and in multiple primary tumors.

Circular RNA hsa_circ_0007142 Is Upregulated and Targets miR-103a-2-5p in Colorectal Cancer.

Circular RNAs (circRNAs) are a large class of endogenous noncoding RNAs that regulate gene expression and mainly function as microRNA sponges. This study aimed to explore the aberrant expression of circRNAs in colorectal cancer (CRC). Using a circRNA microarray, we identified 892 differentially expressed circRNAs between six pairs of CRC and adjacent paracancerous tissues. Among them, hsa_circ_0007142 was significantly upregulated. Further analysis in 50 CRC clinical samples revealed that hsa_circ_0007142 upregulation was associated with poor differentiation and lymphatic metastasis of CRC. Bioinformatic analysis and luciferase reporter assay showed that hsa_circ_0007142 targeted miR-103a-2-5p in CRC cells. Moreover, the silencing of hsa_circ_0007142 by siRNAs decreased the proliferation, migration, and invasion of HT-29 and HCT-116 cells. Taken together, these findings suggest that hsa_circ_0007142 is upregulated in CRC and targets miR-103a-2-5p to promote CRC.

Dual Laplacian regularized matrix completion for microRNA-disease associations prediction.

Since lots of miRNA-disease associations have been verified, it is meaningful to discover more miRNA-disease associations for serving disease diagnosis and prevention of human complex diseases. However, it is not practical to identify potential associations using traditional biological experimental methods since the process is expensive and time consuming. Therefore, it is necessary to develop efficient computational methods to accomplish this task. In this work, we introduced a matrix completion model with dual Laplacian regularization (DLRMC) to infer unknown miRNA-disease associations in heterogeneous omics data. Specifically, DLRMC transformed the task of miRNA-disease association prediction into a matrix completion problem, in which the potential missing entries of the miRNA-disease association matrix were calculated, the missing association can be obtained based on the prediction scores after the completion procedure. Meanwhile, the miRNA functional similarity and the disease semantic similarity were fully exploited to serve the miRNA-disease association matrix completion by using a dual Laplacian regularization term. In the experiments, we conducted global and local Leave-One-Out Cross Validation (LOOCV) and case studies to evaluate the efficacy of DLRMC on the Human miRNA-disease associations dataset obtained from the HMDDv2.0 database. As a result, the AUCs of DLRMC is 0.9174 and 0.8289 in global LOOCV and local LOOCV, respectively, which significantly outperform a variety of previous methods. In addition, in the case studies on four significant diseases related to human health including Colon Neoplasms, Kidney neoplasms, Lymphoma and Prostate neoplasms, 90%, 92%, 92% and 94% out of the top 50 predicted miRNAs has been confirmed, respectively.