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Background: Cervical cancer is a prevalent malignancy of the female reproductive system. Cervical intraepithelial neoplasia (CIN) is a precursor lesion for CC. Various studies have examined circulating microRNAs (miRNAs) as potential early diagnostic markers for CC and CIN. However, the findings have been inconclusive. Therefore, it is necessary to evaluate the diagnostic accuracy and identify potential sources of variability among these studies. Methods: The PubMed, Cochrane Library, Embase, and Web of Science databases were searched to identify relevant literature. Then, Stata 14.0 was utilized to calculate summary estimates for diagnostic parameters, including sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic (ROC). To scrutinize the heterogeneity, the Cochran-Q test and I2 statistic were utilized. As significant heterogeneity was observed, the random effects model was chosen. To explore potential sources of the heterogeneity, subgroup and regression analyses were conducted. Results: We analysed 12 articles reporting on 24 studies involving 1817 patients and 1731 healthy controls. The pooled sensitivity was 0.77 (95% CI 0.73–0.81), the specificity was 0.81 (95% CI 0.73–0.86), the PLR was 3.99 (95% CI 2.81–5.65), the NLR was 0.28 (95% CI 0.23–0.35), the DOR was 14.18 (95% CI 8.47–23.73), and the area under the curve (AUC) was 0.85 (95% CI 0.81–0.87). Subgroup analysis revealed that multiple miRNAs can improve diagnostic performance; the pooled sensitivity of multiple miRNAs was 0.78 (95% CI 0.68–0.86), the specificity was 0.85 (95% CI 0.78–0.90), and the AUC was 0.89 (95% CI 0.86–0.91). Conclusion: This study suggested that circulating microRNAs may be biomarkers for early CC diagnosis. [ABSTRACT FROM AUTHOR]

Drought stress limits agricultural productivity worldwide. Identifying and characterizing genetic components of drought stress-tolerance networks may improve crop resistance to drought stress. We show that the regulatory module formed by miR166 and its target gene, ATHB14-LIKE, functions in the regulation of drought tolerance in soybean (Glycine max). Drought stress represses the accumulation of miR166, leading to upregulation of its target genes. Optimal knockdown of miR166 in the stable transgenic line GmSTTM166 conferred drought tolerance without affecting yield. Expression of ABA signaling pathway genes was regulated by the miR166-mediated regulatory pathway, and ATHB14-LIKE directly activates some of these genes. There is a feedback regulation between ATHB14-LIKE and MIR166 genes, and ATHB14-LIKE inhibits MIR166 expression. These findings reveal that drought-triggered regulation of the miR166-mediated regulatory pathway increases plants drought resistance, providing new insights into drought stress regulatory network in soybean. [ABSTRACT FROM AUTHOR]

More and more studies have shown that microRNAs (miRNAs) play an indispensable role in the study of complex diseases in humans. Traditional biological experiments to detect miRNA-disease associations are expensive and time-consuming. Therefore, it is necessary to propose efficient and meaningful computational models to predict miRNA-disease associations. In this study, we aim to propose a miRNA-disease association prediction model based on sparse learning and multilayer random walks (SLMRWMDA). The miRNA-disease association matrix is decomposed and reconstructed by the sparse learning method to obtain richer association information, and at the same time, the initial probability matrix for the random walk with restart algorithm is obtained. The disease similarity network, miRNA similarity network, and miRNA-disease association network are used to construct heterogeneous networks, and the stable probability is obtained based on the topological structure features of diseases and miRNAs through a multilayer random walk algorithm to predict miRNA-disease potential association. The experimental results show that the prediction accuracy of this model is significantly improved compared with the previous related models. We evaluated the model using global leave-one-out cross-validation (global LOOCV) and fivefold cross-validation (5-fold CV). The area under the curve (AUC) value for the LOOCV is 0.9368. The mean AUC value for 5-fold CV is 0.9335 and the variance is 0.0004. In the case study, the results show that SLMRWMDA is effective in inferring the potential association of miRNA-disease. [ABSTRACT FROM AUTHOR]