CT radiomics model for predicting the Ki-67 proliferation index of pure-solid non-small cell lung cancer: a multicenter study.

Purpose: This study aimed to explore the efficacy of the computed tomography (CT) radiomics model for predicting the Ki-67 proliferation index (PI) of pure-solid non-small cell lung cancer (NSCLC). Materials and methods: This retrospective study included pure-solid NSCLC patients from five centers. The radiomics features were extracted from thin-slice, non-enhanced CT images of the chest. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were used to reduce and select radiomics features. Logistic regression analysis was employed to build predictive models to determine Ki-67-high and Ki-67-low expression levels. Three prediction models were established: the clinical model, the radiomics model, and the nomogram model combining the radiomics signature and clinical features. The prediction efficiency of different models was evaluated using the area under the curve (AUC). Results: A total of 211 NSCLC patients with pure-solid nodules or masses were included in the study (N=117 for the training cohort, N=49 for the internal validation cohort, and N=45 for the external validation cohort). The AUC values for the clinical models in the training, internal validation, and external validation cohorts were 0.73 (95% CI: 0.64-0.82), 0.75 (95% CI:0.62-0.89), and 0.72 (95% CI: 0.57-0.86), respectively. The radiomics models showed good predictive ability in diagnosing Ki-67 expression levels in the training cohort (AUC, 0.81 [95% CI: 0.73-0.89]), internal validation cohort (AUC, 0.81 [95% CI: 0.69-0.93]) and external validation cohort (AUC, 0.78 [95% CI: 0.64-0.91]). Compared to the clinical and radiomics models, the nomogram combining both radiomics signatures and clinical features had relatively better diagnostic performance in all three cohorts, with the AUC of 0.83 (95% CI: 0.76-0.90), 0.83 (95% CI: 0.71-0.94), and 0.81 (95% CI: 0.68-0.93), respectively. Conclusion: The nomogram combining the radiomics signature and clinical features may be a potential non-invasive method for predicting Ki-67 expression levels in patients with pure-solid NSCLC.

Bioinformatic Analysis of the Possible Regulative Network of miR-30a/e in Cardiomyocytes 2 Days Post Myocardial Infarction.

BACKGROUND: Both miR-30a and miR-30e are significantly downregulated in cardiomyocytes (CMs) 2 days (d) post myocardial infarction (MI). This study aimed to identify their possible regulative network in CMs 2d post-MI. METHODS: The dysregulated mRNAs in left ventricle tissues 2d post-MI in mice model were retrieved from one previous publication. The verified target genes of miR-30a/e and the predicted targets (upregulated 2d post-MI) were subjected to analysis of the involvement in biological processes according to their enrichment in gene ontology (GO) terms. RESULTS: The known targets of miR-30a/e can regulate cellular responses to glucose starvation via targeting TP53, BECH1 and HSPA5, and also control cardiac epithelial to mesenchymal transition via targeting ETS-related gene (ERG), SNAI1 and NOTCH1. Bioinformatic prediction further showed that miR-30a might regulate some biological processes related to CM responses to MI via some other potential targets, such as platelet aggregation (possibly via ITGB3 and STXBP1), regulation of intrinsic apoptotic signaling pathway in response to deoxyribonucleic acid damage (possibly via SNAI1) and positive regulation of tyrosine phosphorylation of Stat3 protein (possibly via LYN, SOCS3 and SLCF1). CONCLUSIONS: Considering the importance of these genes in cellular responses to MI, it is meaningful to further investigate the regulative effect of miR-30a/e on their expression, as well as their regulative network in CMs.