Edge-relational window-attentional graph neural network for gene expression prediction in spatial transcriptomics analysis.

Spatial transcriptomics (ST), containing gene expression with fine-grained (i.e., different windows) spatial location within tissue samples, has become vital in developing innovative treatments. Traditional ST technology, however, rely on costly specialized commercial equipment. Addressing this, our article aims to creates a cost-effective, virtual ST approach using standard tissue images for gene expression prediction, eliminating the need for expensive equipment. Conventional approaches in this field often overlook the long-distance spatial dependencies between different sample windows or need prior gene expression data. To overcome these limitations, we propose the Edge-Relational Window-Attentional Network (ErwaNet), enhancing gene prediction by capturing both local interactions and global structural information from tissue images, without prior gene expression data. ErwaNet innovatively constructs heterogeneous graphs to model local window interactions and incorporates an attention mechanism for global information analysis. This dual framework not only provides a cost-effective solution for gene expression predictions but also obviates the necessity of prior knowledge gene expression information, a significant advantage in the field of cancer research where it enables a more efficient and accessible analytical paradigm. ErwaNet stands out as a prior-free and easy-to-implement Graph Convolution Network (GCN) method for predicting gene expression from tissue images. Evaluation of the two public breast cancer datasets shows that ErwaNet, without additional information, outperforms the state-of-the-art (SOTA) methods. Code is available at https://github.com/biyecc/ErwaNet.

ScRNA-seq reveals novel immune-suppressive T cells and investigates CMV-TCR-T cells cytotoxicity against GBM.

BACKGROUND: Glioblastoma (GBM) is a fatal primary brain malignancy in adults. Previous studies have shown that cytomegalovirus (CMV) is a risk factor for tumorigenesis and aggressiveness for glioblastoma. However, little is known about how CMV infection affects immune cells in the tumor microenvironment of GBM. Furthermore, there has been almost no engineered T-cell receptor (TCR)-T targeting CMV for GBM research to date. METHODS: We evaluated the CMV infection status of patients with GBM's tumor tissue by immune electron microscopy, immunofluorescence, and droplet digital PCR. We performed single-cell RNA sequencing for CMV-infected GBM to investigate the effects of CMV on the GBM immune microenvironment. CellChat was applied to analyze the interaction between cells in the GBM tumor microenvironment. Additionally, we conducted single-cell TCR/B cell receptor (BCR) sequencing and Grouping of Lymphocyte Interactions with Paratope Hotspots 2 algorithms to acquire specific CMV-TCR sequences. Genetic engineering was used to introduce CMV-TCR into primary T cells derived from patients with CMV-infected GBM. Flow cytometry was used to measure the proportion and cytotoxicity status of T cells in vitro. RESULTS: We identified two novel immune cell subpopulations in CMV-infected GBM, which were bipositive CD68+SOX2+ tumor-associated macrophages and FXYD6+ T cells. We highlighted that the interaction between bipositive TAMs or cancer cells and T cells was predominantly focused on FXYD6+ T cells rather than regulatory T cells (Tregs), whereas, FXYD6+ T cells were further identified as a group of novel immunosuppressive T cells. CMV-TCR-T cells showed significant therapeutic effects on the human-derived orthotopic GBM mice model. CONCLUSIONS: These findings provided an insight into the underlying mechanism of CMV infection promoting the GBM immunosuppression, and provided a novel potential immunotherapy strategy for patients with GBM.

LncRNA PELATON, a Ferroptosis Suppressor and Prognositic Signature for GBM.

PELATON is a long noncoding RNA also known as long intergenic nonprotein coding RNA 1272 (LINC01272). The known reports showed that PELATON functions as an onco-lncRNA or a suppressor lncRNA by suppressing miRNA in colorectal cancer, gastric cancer and lung cancer. In this study, we first found that PELATON, as an onco-lncRNA, alleviates the ferroptosis driven by mutant p53 and promotes mutant p53-mediated GBM proliferation. We also first confirmed that PELATON is a new ferroptosis suppressor lncRNA that functions as a ferroptosis inhibitor mainly by mutant P53 mediating the ROS ferroptosis pathway, which inhibits the production of ROS, reduces the levels of divalent iron ions, promotes the expression of SLC7A11, and inhibits the expression of ACSL4 and COX2.PELATON can inhibit the expression of p53 in p53 wild-type GBM cells and regulate the expression of BACH1 and CD44, but it has no effect on p53, BACH1 and CD44 in p53 mutant GBM cells. PELATON and p53 can form a complex through the RNA binding protein EIF4A3. Knockdown of PELATON resulted in smaller mitochondria, increased mitochondrial membrane density, and enhanced sensitivity to ferroptosis inducers to inhibit GBM cell proliferation and invasion. In addition, we established a favourite prognostic model with NCOA4 and PELATON. PELATON is a promising target for the prognosis and treatment of GBM.

In silico drug repositioning based on integrated drug targets and canonical correlation analysis.

BACKGROUND: Besides binding to proteins, the most recent advances in pharmacogenomics indicate drugs can regulate the expression of non-coding RNAs (ncRNAs). The polypharmacological feature in drugs enables us to find new uses for existing drugs (namely drug repositioning). However, current computational methods for drug repositioning mainly consider proteins as drug targets. Meanwhile, these methods identify only statistical relationships between drugs and diseases. They provide little information about how drug-disease associations are formed at the molecular target level. METHODS: Herein, we first comprehensively collect proteins and two categories of ncRNAs as drug targets from public databases to construct drug-target interactions. Experimentally confirmed drug-disease associations are downloaded from an established database. A canonical correlation analysis (CCA) based method is then applied to the two datasets to extract correlated sets of targets and diseases. The correlated sets are regarded as canonical components, and they are used to investigate drug's mechanism of actions. We finally develop a strategy to predict novel drug-disease associations for drug repositioning by combining all the extracted correlated sets. RESULTS: We receive 400 canonical components which correlate targets with diseases in our study. We select 4 components for analysis and find some top-ranking diseases in an extracted set might be treated by drugs interfacing with the top-ranking targets in the same set. Experimental results from 10-fold cross-validations show integrating different categories of target information results in better prediction performance than only using proteins or ncRNAs as targets. When compared with 3 state-of-the-art approaches, our method receives the highest AUC value 0.8576. We use our method to predict new indications for 789 drugs and confirm 24 predictions in the top 1 predictions. CONCLUSIONS: To the best of our knowledge, this is the first computational effort which combines both proteins and ncRNAs as drug targets for drug repositioning. Our study provides a biologically relevant interpretation regarding the forming of drug-disease associations, which is useful for guiding future biomedical tests.

CircCDK14 Promotes Tumor Progression and Resists Ferroptosis in Glioma by Regulating PDGFRA.

CircRNAs have garnered significant interest in recent years due to their regulation in human tumorigenesis, yet, the function of most glioma-related circRNAs remains unclear. In this study, using RNA-Seq, we screened differentially regulated circRNAs in glioma, in comparison to non-tumor brain tissue. Loss- and gain-of-function strategies were used to assess the effect of circCDK14 on tumor progression both in vitro and in vivo. Luciferase reporter, RNA pull-down and fluorescence in situ hybridization assays were carried out to validate interactions between circCDK14 and miR-3938 as well as miR-3938 and PDGFRA. Transmission electron microscopic observation of mitochondria, iron and reactive oxygen species assays were employed for the detection of circCDK14 effect on glioma cells' sensitivity to erastin-induced ferroptosis (Fp). Our findings indicated that circCDK14 was overexpressed in glioma tissues and cell lines, and elevated levels of circCDK14 induced poor prognosis of glioma patients. CircCDK14 promotes the migration, invasion and proliferation of glioma cells in vitro as well as tumorigenesis in vivo. An evaluation of the underlying mechanism revealed that circCDK14 sponged miR-3938 to upregulate oncogenic gene PDGFRA expression. Moreover, we also found that circCDK14 reduced glioma cells' sensitivity to Fp by regulating PDGFRA expression. In conclusion, circCDK14 induces tumor in glioma and increases malignant tumor behavior via the miR-3938/PDGFRA axis. Hence, the miR-3938/PDGFRA axis may be an excellent candidate of anti-glioma therapy.

Giant Pharyngeal Recess Cyst: A Case Report.

A pharyngeal recess cyst is a benign lesion, located at the nasopharyngeal recess with limited development. Pharyngeal recess cysts rarely occur. This case report describes a young male patient presenting with a foreign body sensation in the pharynx. Electronic nasopharyngoscope examination revealed a large nasopharyngeal cyst, whose root was located in the left pharyngeal recess. Complete surgical resection was performed, and the patient successfully recovered.Pharyngeal recess cysts are rare lesions that can be diagnosed based on imaging and endoscopy findings. It is treated surgically and has a favorable prognosis.

Pharyngeal Ulcer as the First Sign of Pemphigus.

Pemphigus is a rare autoimmune mucocutaneous bullous disease that can be life-threatening. We report a case of pemphigus vulgaris with pharyngeal ulcer as the initial presentation that was treated with glucocorticoid therapy.

Corrigendum: Insights Into Exosomal Non-Coding RNAs Sorting Mechanism and Clinical Application.

[This corrects the article DOI: 10.3389/fonc.2021.664904.].

Insight for Immunotherapy of HCMV Infection.

Human cytomegalovirus (HCMV), a ubiquitous in humans, has a high prevalence rate. Young people are susceptible to HCMV infection in developing countries, while older individuals are more susceptible in developed countries. Most patients have no obvious symptoms from the primary infection. Studies have indicated that the virus has gradually adapted to the host immune system. Therefore, the control of HCMV infection requires strong immune modulation. With the recent advances in immunotherapy, its application to HCMV infections is receiving increasing attention. Here, we discuss the immune response to HCMV infection, the immune escape mechanism, and the different roles that HCMV plays in various types of immunotherapy, including vaccines, adoptive cell therapy, checkpoint blockade therapy, and targeted antibodies.

CircRNA NALCN acts as an miR-493-3p sponge to regulate PTEN expression and inhibit glioma progression.

BACKGROUND: An increasing number of studies have shown that circular RNAs (circRNAs) play important roles in the regulation of tumor progression. Therefore, we explored the expression characteristics, function, and related mechanism of the newly identified circNALCN in glioma. METHODS: RNA sequencing was used to analyze the expression profiles of circRNAs in brain tissue from five glioma cases and four normal controls. Quantitative real-time polymerase chain reaction was implemented to examine the levels of circNALCN, miR-493-3p, and phosphatase and tensin homolog (PTEN). Cell counting kit 8 assays were performed to analyze cell proliferation, and cell migration was assessed by the wound healing test and Transwell assay. Dual-luciferase reporter, fluorescence in situ hybridization, and RNA pulldown assays were performed to confirm the role of circNALCN as an miR-493-3p sponge, weakening the inhibitory effect of miR-493-3p on target PTEN expression. RESULTS: The downregulated expression of circNALCN was observed in both glioma tissues and cell lines. CircNALCN expression was negatively correlated with World Health Organization grade and overall survival in patients with glioma. Functionally, the overexpression of circNALCN significantly inhibited the proliferation and migration of glioma cells, whereas miR-493-3p mimics counteracted these effects. The mechanistic analysis demonstrated that circNALCN acted as a competing endogenous RNA for miR-493-3p to relieve the repressive effects of miR-493-3p on its target, PTEN, suppressing glioma tumorigenesis. CONCLUSIONS: CircNALCN inhibits the progression of glioma through the miR-493-3p/PTEN axis, providing a developable biomarker and therapeutic target for glioma patients.

Insights Into Exosomal Non-Coding RNAs Sorting Mechanism and Clinical Application.

Exosomes are natural nanoscale bilayer phospholipid vesicles that can be secreted by almost all types of cells and are detected in almost all types of body fluids. Exosomes are effective mediators of cell-cell signaling communication because of their ability to carry and transfer a variety of bioactive molecules, including non-coding RNAs. Non-coding RNAs have also been found to exert strong effects on a variety of biological processes, including tumorigenesis. Many researchers have established that exosomes encapsulate bioactive non-coding RNAs that alter the biological phenotype of specific target cells in an autocrine or a paracrine manner. However, the mechanism by which the producer cells package non-coding RNAs into exosomes is not well understood. This review focuses on the current research on exosomal non-coding RNAs, including the biogenesis of exosomes, the possible mechanism of sorting non-coding RNAs, their biological functions, and their potential for clinical application in the future.

Schistosoma japonicum Infection in Treg-Specific USP21 Knockout Mice.

The E3 deubiquitinating enzyme ubiquitin-specific proteolytic enzyme 21 (USP21) plays vital roles in physiological activities and is required for Treg-cell-mediated immune tolerance. Using a murine model infected with Schistosoma japonicum, we observed that there were more cercariae developed into adults and more eggs deposited in the livers of the USP21fl/flFOXP3Cre (KO) mice. However, immunohistochemistry showed that the degree of egg granuloma formation and liver fibrosis was reduced. In USP21fl/flFOXP3Cre mice, levels of IFN-gamma, IL-4, anti-soluble egg antigen (SEA) IgG and anti-soluble worm antigen preparation (SWAP) IgG increased in blood, as determined using ELISAs and multiplex fluorescent microsphere immunoassays, while the levels of IL-10, lL-17A, IL-23, IL-9, and anti-SEA IgM decreased. In addition, the levels of the USP21 protein and mRNA in the liver and spleen of KO mice decreased. We further observed increased Th1 responses amplified by Tregs (regulatory T cells) and compromised Th17 responses, which alleviated the liver immunopathology. We speculated that these changes were related to polarization of Th1-like Tregs. Our results revealed the roles of USP21 in Treg-cell-mediated regulation of immune interactions between Schistosoma and its host. USP21 may have potential for regulating hepatic fibrosis in patients with schistosomiasis.

In silico drug repositioning based on the integration of chemical, genomic and pharmacological spaces.

BACKGROUND: Drug repositioning refers to the identification of new indications for existing drugs. Drug-based inference methods for drug repositioning apply some unique features of drugs for new indication prediction. Complementary information is provided by these different features. It is therefore necessary to integrate these features for more accurate in silico drug repositioning. RESULTS: In this study, we collect 3 different types of drug features (i.e., chemical, genomic and pharmacological spaces) from public databases. Similarities between drugs are separately calculated based on each of the features. We further develop a fusion method to combine the 3 similarity measurements. We test the inference abilities of the 4 similarity datasets in drug repositioning under the guilt-by-association principle. Leave-one-out cross-validations show the integrated similarity measurement IntegratedSim receives the best prediction performance, with the highest AUC value of 0.8451 and the highest AUPR value of 0.2201. Case studies demonstrate IntegratedSim produces the largest numbers of confirmed predictions in most cases. Moreover, we compare our integration method with 3 other similarity-fusion methods using the datasets in our study. Cross-validation results suggest our method improves the prediction accuracy in terms of AUC and AUPR values. CONCLUSIONS: Our study suggests that the 3 drug features used in our manuscript are valuable information for drug repositioning. The comparative results indicate that integration of the 3 drug features would improve drug-disease association prediction. Our study provides a strategy for the fusion of different drug features for in silico drug repositioning.

Prediction of the Age at Onset of Spinocerebellar Ataxia Type 3 with Machine Learning.

BACKGROUND: In polyglutamine (polyQ) disease, the investigation of the prediction of a patient's age at onset (AAO) facilitates the development of disease-modifying intervention and underpins the delay of disease onset and progression. Few polyQ disease studies have evaluated AAO predicted by machine-learning algorithms and linear regression methods. OBJECTIVE: The objective of this study was to develop a machine-learning model for AAO prediction in the largest spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) population from mainland China. METHODS: In this observational study, we introduced an innovative approach by systematically comparing the performance of 7 machine-learning algorithms with linear regression to explore AAO prediction in SCA3/MJD using CAG expansions of 10 polyQ-related genes, sex, and parental origin. RESULTS: Similar prediction performance of testing set and training set in each models were identified and few overfitting of training data was observed. Overall, the machine-learning-based XGBoost model exhibited the most favorable performance in AAO prediction over the traditional linear regression method and other 6 machine-learning algorithms for the training set and testing set. The optimal XGBoost model achieved mean absolute error, root mean square error, and median absolute error of 5.56, 7.13, 4.15 years, respectively, in testing set 1, with mean absolute error (4.78 years), root mean square error (6.31 years), and median absolute error (3.59 years) in testing set 2. CONCLUSION: Machine-learning algorithms can be used to predict AAO in patients with SCA3/MJD. The optimal XGBoost algorithm can provide a good reference for the establishment and optimization of prediction models for SCA3/MJD or other polyQ diseases. © 2020 International Parkinson and Movement Disorder Society.

Ethyl pyruvate inhibits glioblastoma cells migration and invasion through modulation of NF-κB and ERK-mediated EMT.

BACKGROUND: Glioblastoma is a grade IV glioma with the highest degree of malignancy and extremely high incidence. Because of the poor therapeutic effect of surgery and radiochemotherapy, glioblastoma has a high recurrence rate and lethality, and is one of the most challenging tumors in the field of oncology. Ethyl pyruvate (EP), a stable lipophilic pyruvic acid derivative, has anti-inflammatory, antioxidant, immunomodulatory and other cellular protective effects. It has been reported that EP has potent anti-tumor effects on many types of tumors, including pancreatic cancer, prostate cancer, liver cancer, gastric cancer. However, whether EP has anti-tumor effect on glioblastoma or not is still unclear. METHODS: Glioblastoma U87 and U251 cells were treated with different concentrations of EP for 24 h or 48 h. CCK8 assay and Colony-Formation assay were performed to test the viability and proliferation. Wound-healing assay and Transwell assay were carried out to measure cell invasion and migration. Western blot was not only used to detect the protein expression of epithelial-mesenchymal transition (EMT)-related molecules, but also to detect the expression and activation levels of NF-κB (p65) and Extracellular Signal Regulated Kinase (ERK). RESULTS: In glioblastoma U87 and U251 cells treated with EP, the viability, proliferation, migration, invasion abilities were inhibited in a dose-dependent manner. EP inhibited EMT and the activation of NF-κB (p65) and ERK. With NF-κB (p65) and ERK activated, EMT, migration and invasion of U87 and U251 cells were promoted. However the activation of NF-κB (p65) and ERK were decreased, EMT, migration and invasion abilities were inhibited in U87 and U251 cells treated with EP. CONCLUSION: EP inhibits glioblastoma cells migration and invasion by blocking NF-κB and ERK-mediated EMT.

Comparative analysis of similarity measurements in miRNAs with applications to miRNA-disease association predictions.

BACKGROUND: As regulators of gene expression, microRNAs (miRNAs) are increasingly recognized as critical biomarkers of human diseases. Till now, a series of computational methods have been proposed to predict new miRNA-disease associations based on similarity measurements. Different categories of features in miRNAs are applied in these methods for miRNA-miRNA similarity calculation. Benchmarking tests on these miRNA similarity measures are warranted to assess their effectiveness and robustness. RESULTS: In this study, 5 categories of features, i.e. miRNA sequences, miRNA expression profiles in cell-lines, miRNA expression profiles in tissues, gene ontology (GO) annotations of miRNA target genes and Medical Subject Heading (MeSH) terms of miRNA-associated diseases, are collected and similarity values between miRNAs are quantified based on these feature spaces, respectively. We systematically compare the 5 similarities from multi-statistical views. Furthermore, we adopt a rule-based inference method to test their performance on miRNA-disease association predictions with the similarity measurements. Comprehensive comparison is made based on leave-one-out cross-validations and a case study. Experimental results demonstrate that the similarity measurement using MeSH terms performs best among the 5 measurements. It should be noted that the other 4 measurements can also achieve reliable prediction performance. The best-performed similarity measurement is used for new miRNA-disease association predictions and the inferred results are released for further biomedical screening. CONCLUSIONS: Our study suggests that all the 5 features, even though some are restricted by data availability, are useful information for inferring novel miRNA-disease associations. However, biased prediction results might be produced in GO- and MeSH-based similarity measurements due to incomplete feature spaces. Similarity fusion may help produce more reliable prediction results. We expect that future studies will provide more detailed information into the 5 feature spaces and widen our understanding about disease pathogenesis.

Multi-View Based Multi-Model Learning for MCI Diagnosis.

Mild cognitive impairment (MCI) is the early stage of Alzheimer's disease (AD). Automatic diagnosis of MCI by magnetic resonance imaging (MRI) images has been the focus of research in recent years. Furthermore, deep learning models based on 2D view and 3D view have been widely used in the diagnosis of MCI. The deep learning architecture can capture anatomical changes in the brain from MRI scans to extract the underlying features of brain disease. In this paper, we propose a multi-view based multi-model (MVMM) learning framework, which effectively combines the local information of 2D images with the global information of 3D images. First, we select some 2D slices from MRI images and extract the features representing 2D local information. Then, we combine them with the features representing 3D global information learned from 3D images to train the MVMM learning framework. We evaluate our model on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed model can effectively recognize MCI through MRI images (accuracy of 87.50% for MCI/HC and accuracy of 83.18% for MCI/AD).

miR-632 Promotes Laryngeal Carcinoma Cell Proliferation, Migration, and Invasion Through Negative Regulation of GSK3ß.

Laryngeal cancer, one of the most common head and neck malignancies, is an aggressive neoplasm. Increasing evidence has demonstrated that microRNAs (miRNAs) exert important roles in oncogenesis and progression of diverse types of human cancers. miR-632, a tumor-related miRNA, has been reported to be dysregulated and implicated in human malignancies; however, its biological role in laryngeal carcinoma remains to be elucidated. The present study aimed at exploring the role of miR-632 in laryngeal cancer and clarifying the potential molecular mechanisms involved. In the current study, miR-632 was found to be significantly upregulated both in laryngeal cancer tissues and laryngeal cancer cell lines. Functional studies demonstrated that miR-632 accelerated cell proliferation and colony formation, facilitated cell migration and invasion, and enhanced the expression of cell proliferation-associated proteins, cyclin D1 and c-myc. Notably, miR-632 could directly bind to the 3'-untranslated region (3'-UTR) of glycogen synthase kinase 3ß (GSK3ß) to suppress its expression in laryngeal cancer cells. Mechanical studies revealed that miR-632 promoted laryngeal cancer cell proliferation, migration, and invasion through negative modulation of GSK3ß. Pearson's correlation analysis revealed that miR-632 expression was inversely correlated with GSK3ß mRNA expression in laryngeal cancer tissues. Taken together, our findings suggest that miR-632 functions as an oncogene in laryngeal cancer and may be used as a novel therapeutic target for laryngeal cancer.

High Notch1 expression affects chemosensitivity of head and neck squamous cell carcinoma to paclitaxel and cisplatin treatment.

BACKGROUND: Notch1 expression has been reported to be associated with chemotherapy resistance and poor prognosis, but the role of Notch1 in head and neck squamous cell carcinoma (HNSCC) sensitivity to anticancer drugs remains unclear. The aim of this study was to evaluate HNSCC sensitivity to paclitaxel and cisplatin in vitro and the chemotherapeutic response of HNSCC to these two drugs in vivo. METHODS: We used immunohistochemistry to assess Notch1 expression in fresh HNSCC samples treated by PF (cisplatin+5- fluorouracil) and TPF (paclitaxel + cisplatin+5- fluorouracil). We also assessed the sensitivity of two HNSCC cell lines to the Notch1 inhibitor of N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT). The overall and progression-free survival were assessed. RESULTS: High Notch1 expression was significantly associated with paclitaxel resistance (P < 0.01). DAPT increased sensitivity to paclitaxel and cisplatin in vitro (P < 0.05). Compared with patients with Notch1 expression, patients without Notch1 expression were more likely to have a response to neoadjuvant chemotherapy with PF (P < 0.01) or TPF (P < 0.01) and had significantly better overall survival (P < 0.05) and progression-free survival (P < 0.05). Among patients without Notch1 expression (but not among patients with Notch1 expression), those who received TPF had significantly better survival than those who received PF (P < 0.05). CONCLUSION: Taken together, our findings may provide some evidence to partially support the predictive value of Notch1 expression in the therapeutic response to paclitaxel and cisplatin.

Prediction and interpretation of miRNA-disease associations based on miRNA target genes using canonical correlation analysis.

BACKGROUND: It has been shown that the deregulation of miRNAs is associated with the development and progression of many human diseases. To reduce time and cost of biological experiments, a number of algorithms have been proposed for predicting miRNA-disease associations. However, the existing methods rarely investigated the cause-and-effect mechanism behind these associations, which hindered further biomedical follow-ups. RESULTS: In this study, we presented a CCA-based model in which the possible molecular causes of miRNA-disease associations were comprehensively revealed by extracting correlated sets of genes and diseases based on the co-occurrence of miRNAs in target gene profiles and disease profiles. Our method directly suggested the underlying genes involved, which could be used for experimental tests and confirmation. The inference of associated diseases of a new miRNA was made by taking into account the weight vectors of the extracted sets. We extracted 60 pairs of correlated sets from 404 miRNAs with two profiles for 2796 target genes and 362 diseases. The extracted diseases could be considered as possible outcomes of miRNAs regulating the target genes which appeared in the same set, some of which were supported by independent source of information. Furthermore, we tested our method on the 404 miRNAs under the condition of 5-fold cross validations and received an AUC value of 0.84606. Finally, we extensively inferred miRNA-disease associations for 100 new miRNAs and some interesting prediction results were validated by established databases. CONCLUSIONS: The encouraging results demonstrated that our method could provide a biologically relevant prediction and interpretation of associations between miRNAs and diseases, which were of great usefulness when guiding biological experiments for scientific research.