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Objectives: Chemoresistance induced by cisplatin has become the major impediment to lung cancer chemotherapy. This study explored the potential chemoresistant genes and underlying mechanisms of chemoresistance in NSCLC. Materials and methods: Gene expression profile was integrated with DNA methylation profile to screen the candidate chemoresistant genes. Bioinformatic analysis and immunohistochemistry were used to analyse the association of a candidate gene with the characteristics of NSCLC patients. Recombinant lentivirus vectors were utilized to overexpress or silence candidate gene. Microarrays and immunoblotting were applied to explore the downstream targets of candidate gene. Xenograft models were established to validate the findings in vitro. Results: An increased ZNF300 expression was detected in three chemoresistant cell lines of NSCLC, and the higher expression of ZNF300 was associated with poor OS of NSCLC patients. Cells with upregulated ZNF300 presented chemoresistance and enhanced aggressive growth compared to cells with downregulated ZNF300. ZNF300 inhibited MAPK/ERK pathways and activated CDK1 through inhibiting WEE1 and MYT1 and modulating MYC/AURKA/BORA/PLK1 axis. ICA and ATRA improved the anti‐tumour effect of cisplatin on chemoresistant cells by inducing differentiation. Conclusions: ZNF300 promotes chemoresistance and aggressive behaviour of NSCLC through regulation of proliferation and differentiation by downregulating MAPK/ERK pathways and regulation of slow‐cycling phenotype via activating CDK1 by inhibiting WEE1/MYT1 and modulating MYC/AURKA/BORA/PLK1 axis. Cisplatin, combined with ATRA and ICA, might be beneficial in chemoresistant cases of NSCLC. [ABSTRACT FROM AUTHOR]

Currently, decision-making regarding biochemical recurrence (BCR) following prostatectomy relies solely on clinical parameters. We therefore attempted to develop an integrated prediction model based on a molecular signature and clinicopathological features, in order to forecast the risk for BCR and guide clinical decision-making for postoperative therapy. Using high-throughput screening and least absolute shrinkage and selection operator (LASSO) in the training set, a novel gene signature for biochemical recurrence-free survival (BCRFS) was established. Validation of the prognostic value was performed in five other independent datasets, including our patient cohort. Multivariate Cox regression analysis was performed to evaluate the importance of risk for BCR. Time-dependent receiver operating characteristic (tROC) was used to evaluate the predictive power. In combination with relevant clinicopathological features, a decision tree was built to improve the risk stratification. The gene signature exhibited a strong capacity for identifying high-risk BCR patients, and multivariate Cox regression analysis demonstrated that the gene signature consistently acted as a risk factor for BCR. The decision tree was successfully able to identify the high-risk subgroup. Overall, the gene signature established in the present study is a powerful predictor and risk factor for BCR after radical prostatectomy. [ABSTRACT FROM AUTHOR]