Genes that are indispensable for survival are essential genes. Many features have been proposed for computational prediction of essential genes. In this paper, the least absolute shrinkage and selection operator method was used to screen key sequence-based features related to gene essentiality. To assess the effects, the selected features were used to predict the essential genes from 31 bacterial species based on a support vector machine classifier. For all 31 bacterial objects (21 Gram-negative objects and ten Gram-positive objects), the features in the three datasets were reduced from 57, 59, and 58, to 40, 37, and 38, respectively, without loss of prediction accuracy. Results showed that some features were redundant for gene essentiality, so could be eliminated from future analyses. The selected features contained more complex (or key) biological information for gene essentiality, and could be of use in related research projects, such as gene prediction, synthetic biology, and drug design.

Ovarian cancer (OC) is the leading cause of cancer-related death among all gynecological tumors. N6-methyladenosine (m6A)-related regulators play essential roles in various tumors, including OC. However, the expression of m6A RNA methylation regulators and the related regulatory network in OC and their correlations with prognosis remain largely unknown. In the current study, we obtained the genome datasets of OC from GDC and GTEx database and analyzed the mRNA levels of 21 key m6A regulators in OC and normal human ovarian tissues. The expression levels of 7 m6A regulators were lower in both the OC tissues and the high-stage group. Notably, the 5-year survival rate of patients with OC presenting low VIRMA expression or high HNRNPA2B1 expression was higher than that of the controls. Next, a risk score model based on the three selected m6A regulators (VIRMA, IGF2BP1, and HNRNPA2B1) was built by performing a LASSO regression analysis, and the moderate accuracy of the risk score model to predict the prognosis of patients with OC was examined by performing ROC curve, nomogram, and univariate and multivariate Cox regression analyses. In addition, a regulatory network of miRNAs-m6A regulators-m6A target genes, including 2 miRNAs, 3 m6A regulators, and 47 mRNAs, was constructed, and one of the pathways, namely, miR-196b-5p-IGF2BP1-PTEN, was initially validated based on bioinformatic analysis and assay verification. These results demonstrated that the risk score model composed of three m6A RNA methylation regulators and the related network of miRNAs-m6A regulators-m6A target genes is valuable for predicting the prognosis of patients with OC, and these molecules may serve as potential biomarkers or therapeutic targets in the future.

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