Discovery of Potent, Selective, and Orally Bioavailable DYRK2 Inhibitors for the Treatment of Prostate Cancer.

Prostate cancer (PCa) seriously threatens male health, and targeting dual-specificity tyrosine phosphorylation-regulated kinase 2 (DYRK2) has been verified to reduce PCa burden, while the research progress on the DYRK2 inhibitors was relatively slow. In this work, we discovered DYRK2 inhibitor 12 (IC50 = 9681 nM) through virtual screening. Subsequently, we performed systematic structural optimization to obtain 54 (IC50 = 14 nM). Compound 54 exhibited high selectivity among 215 kinases and significantly suppressed the proliferation and metastasis of PCa cells in vitro. Moreover, compound 54 displayed high safety, favorable bioavailability, and potent tumor growth inhibitory activity in vivo, which could be used as a potential candidate in the discovery of novel anti-PCa drugs.

Machine-learning-based classification of diffuse large B-cell lymphoma patients by a 7-mRNA signature enriched with immune infiltration and cell cycle.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) exhibits remarkable heterogeneity but still remains undiagnosed in identifying the subpopulation of DLBCL to predict the prognosis and guide clinical treatment. METHODS: Molecular subgroups were identified in gene expression data from GSE10846 by a consensus clustering algorithm. And gene set enrichment analysis, immune infiltration, and the proposed cell cycle algorithm were applied to explore the biological functions of different subtypes. Meanwhile, univariate and multivariate Cox regression analyses were used to evaluate independent prognostic factors of DLBCL. Finally, the prognostic model, including some key genes screened by Lasso regression, Random Forest algorithm, and point-biserial correlation, was constructed by an optimal classifier from seven machine learning algorithms and validated by another three external datasets (GSE34171, GSE87371, GSE31312). RESULTS: Comprehensive genomic analysis of 1,143 DLBCL samples identify 2 molecularly, prognostically relevant subtypes: immune-enriched (IME) and cell-cycle-enriched (CCE). Then a new predictive model including seven key genes (SERPING1, TIMP2, NME1, DCTPP1, RFC4, POLE2, and SNRPD1) was developed with high prediction accuracy (88.6%) and strong predictive power (AUC = 0.973) based on the Support Vector Machine (SVM) algorithm in 414 patients from GSE10846. The predictive power was similar in another three testing sets (HR > 1.400, p < 0.05). CONCLUSION: This model could evaluate survival independently with strong predictive power compared with other clinical risk factors. Our study constructed a reliable model to predict two new subtypes of DLBCL patients, which could guide the implementation of individualized treatment.

Integrative analysis identifies two molecular and clinical subsets in Luminal B breast cancer.

Comprehensive multiplatform analysis of Luminal B breast cancer (LBBC) specimens identifies two molecularly distinct, clinically relevant subtypes: Cluster A associated with cell cycle and metabolic signaling and Cluster B with predominant epithelial mesenchymal transition (EMT) and immune response pathways. Whole-exome sequencing identified significantly mutated genes including TP53, PIK3CA, ERBB2, and GATA3 with recurrent somatic mutations. Alterations in DNA methylation or transcriptomic regulation in genes (FN1, ESR1, CCND1, and YAP1) result in tumor microenvironment reprogramming. Integrated analysis revealed enriched biological pathways and unexplored druggable targets (cancer-testis antigens, metabolic enzymes, kinases, and transcription regulators). A systematic comparison between mRNA and protein displayed emerging expression patterns of key therapeutic targets (CD274, YAP1, AKT1, and CDH1). A potential ceRNA network was developed with a significantly different prognosis between the two subtypes. This integrated analysis reveals a complex molecular landscape of LBBC and provides the utility of targets and signaling pathways for precision medicine.

Discovery of Potent DYRK2 Inhibitors with High Selectivity, Great Solubility, and Excellent Safety Properties for the Treatment of Prostate Cancer.

Prostate cancer (PCa) is a common male cancer with high incidence and mortality, and hormonal therapy as the major treatment for PCa patients is troubled by the inevitable resistance that makes us identify novel targets for PCa. Dual-specificity tyrosine phosphorylation-regulated kinase 2 (DYRK2) was found to be an effective target for the treatment of PCa, but the research on its inhibitors is rather little. In this work, a potent DYRK2 inhibitor 43 (IC50 = 0.6 nM) was acquired through virtual screening and structural optimization, which displayed high selectivity among 205 kinases; meanwhile, detailed interactions of 43 with DYRK2 were illustrated by the cocrystal. Furthermore, 43 possessed great water solubility (29.5 mg/mL), favorable safety properties (LD50 > 10,000 mg/kg), and potent anti-PCa activities, which could be used as a potential candidate in further preclinical studies.

A 5-Pathway Signature Predicts Prognosis Based on Immune-Derived lncRNAs in Patients with Breast Cancer.

Background: Currently, predictive models were not developed based on the signaling pathway signatures of immune-related lncRNAs in breast cancer (BRCA) patients. Methods: We selected unsupervised hierarchical clustering algorithm to classify patients with BRCA based on the significant immune-derived lncRNAs from the TCGA dataset. And different methods including ESTIMATE, ImmuneCellAI, and CIBERSORT were performed to evaluate the immune infiltration of tumor microenvironment. Using Lasso regression algorithm, we filtered the significant signaling pathways enriched by GSEA, GSVA, or PPI analysis to develop a prognostic model. And a nomogram integrated with clinical factors and significant pathways was constructed to predict the precise probability of overall survival (OS) of BRCA patients in the TCGA dataset (n = 1,098) and another two testing sets (n = 415). Results: BRCA patients were stratified into the PC (n = 571) and GC (n = 527) subgroup with significantly different prognosis with 550 immune-related lncRNAs in the TCGA dataset. Integrated analysis revealed different immune response, oncogenic signaling, and metabolic reprograming pathways between these two subgroups. And a 5-pathway signature could predict the prognosis of BRCA patients between these two subgroups independently in the TCGA dataset, which was confirmed in another two cohorts from the GEO dataset. In the TCGA dataset, 5-year OS rate was 78% (95% CI: 73-84) vs. 82% (95% CI: 77-87) for the PC and GC group (HR = 1.63 (95% CI: 1.17-2.28), p = 0.004). The predictive power was similar in another two testing sets (HR > 1.20, p < 0.01). Finally, a nomogram is developed for clinical application, which integrated this signature and age to accurately predict the survival probability in BRCA patients. Conclusion: This 5-pathway signature correlated with immune-derived lncRNAs was able to precisely predict the prognosis for patients with BRCA and provided a rich source characterizing immune-related lncRNAs and further informed strategies to target BRCA vulnerabilities.