We conclude that miR-31 is a crucial regulator in hepatocellular carcinoma, miR-31 and its target gene NDRG3 may be potential therapeutic targets for HCC treatment in the future.

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One of the most promising approaches of handling constrained optimization problems (COPs) is to adopt multi-objective methods, which can provide a trade-off between the objective and constraints. However, the multi-objective-based constraint-handling techniques take preference over infeasible solutions, some promising feasible solutions cannot survive during the course of search because they are dominated ones. Furthermore, some nondominated infeasible solutions with worse objective values should not be reserved in that they are far from the feasible optimal solution. To address these two problems, this paper proposes a Pareto front transformation model which transforms a part of potential feasible solutions into nondominated ones. Meanwhile, combined with the dynamic multi-objective technique, certain nondominated infeasible solutions with worse objective values will be rejected. In this way, the search can towards the global optimum from both the feasible and infeasible sides of the search space. The proposed Pareto front transformation model is integrated into a multi-objective-based constrained evolutionary algorithm (CEA). The new designed algorithm is named PT-MOEA, and it is compared with seven peer multi-objective-based CEAs and five state-of-the-art CEAs on solving IEEE CEC 2006 and IEEE CEC 2010 test suites, respectively. Experimental results demonstrate the competitiveness of the proposed method in comparison with its competitors for solving COPs.

Background: The incidence of clear cell renal cell carcinoma (ccRCC) is high and has increased gradually in recent years. At present, due to the lack of effective prognostic indicators, the prognosis of ccRCC patients is greatly affected.Necroptosis is a type of cell death, and along with cell necrosis is considered a new cancer treatment strategy. The aim of this study was to construct a new marker for predicting the prognosis of ccRCC patients based on long non-coding RNA (nrlncRNAs) associated with necroptosis.Methods: RNA sequence data and clinical information of ccRCC patients from the Cancer Genome Atlas database (TCGA) were downloaded. NrlncRNA was identified by Pearson correlation study. The differentially expressed nrlncRNA and nrlncRNA pairs were identified by univariate Cox regression and Lasso-Cox regression. Finally, a Kaplan-Meier survival study, Cox regression, clinicopathological features correlation study, and receiver operating characteristic (ROC) spectrum were used to evaluate the prediction ability of 25-nrlncrnas for markers. In addition, correlations between the risk values and sensitivity to tumor-infiltrating immune cells, immune checkpoint inhibitors, and targeted drugs were also investigated.Results: In the current research, a novel marker of 25-nrlncRNAs pairs was developed to improve prognostic prediction in patients with ccRCC. Compared with clinicopathological features, nrlncRNAs had a higher diagnostic validity for markers, with the 1-year, 3-years, and 5-years operating characteristic regions being 0.902, 0.835, and 0.856, respectively, and compared with the stage of 0.868, an increase of 0.034. Cox regression and stratified survival studies showed that this marker could be an independent predictor of ccRCC patients. In addition, patients with different risk scores had significant differences in tumor-infiltrating immune cells, immune checkpoint, and semi-inhibitory concentration of targeted drugs. The feature could be used to evaluate the clinical efficacy of immunotherapy and targeted drug therapy.Conclusion: 25-nrlncRNAs pair markers may help to evaluate the prognosis and molecular characteristics of ccRCC patients, which improve treatment methods and can be more used in clinical practice.

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