Background:The cyclin-dependent kinase inhibitor 3 (CDKN3) has been perceived as a tumour suppressor. Paradoxically, CDKN3 is often overexpressed in human cancer. It was unclear if CDKN3 overexpression is linked to alternative splicing variants or mutations that produce dominant-negative CDKN3.Methods:We analysed CDKN3 expression and its association with patient survival in three cohorts of lung adenocarcinoma. We also examined CDKN3 mutations in the Cancer Genome Atlas (TCGA) and the Moffitt Cancer Center's Total Cancer Care (TCC) projects. CDKN3 transcripts were further analysed in a panel of cell lines and lung adenocarcinoma tissues. CDKN3 mRNA and protein levels in different cell cycle phases were examined.Results:CDKN3 is overexpressed in non small cell lung cancer. High CDKN3 expression is associated with poor overall survival in lung adenocarcinoma. Two CDKN3 transcripts were detected in all samples. These CDKN3 transcripts represent the full length CDKN3 mRNA and a normal transcript lacking exon 2, which encodes an out of frame 23-amino acid peptide with little homology to CDKN3. CDKN3 mutations were found to be very rare. CDKN3 mRNA and protein were elevated during the mitosis phase of cell cycle.Conclusions:CDKN3 overexpression is prognostic of poor overall survival in lung adenocarcinoma. CDKN3 overexpression in lung adenocarcinoma is not attributed to alternative splicing or mutation but is likely due to increased mitotic activity, arguing against CDKN3 as a tumour suppressor.

This work demonstrates that differentially expressed proteins can be identified by proteomics technology combined with immunohistochemistry and western blot analyses. We have identified one such protein, transgelin, as a novel biomarker for GA.

Parkinson's disease (PD) is a progressive, chronic, and neurodegenerative disorder that is primarily diagnosed by clinical examinations and magnetic resonance imaging (MRI). In this study, we proposed a machine learning based radiomics method to predict PD. Fifty healthy controls (HC) along with 70 PD patients underwent restingstate magnetic resonance imaging (rs-fMRI). For all subjects, we extracted five types of 6664 features, including mean amplitude of low-frequency fluctuation (mALFF), mean regional homogeneity (mReHo), resting-state functional connectivity (RSFC), voxelmirrored homotopic connectivity (VMHC) and gray matter (GM) volume. After conducting dimension reduction utilizing Least absolute shrinkage and selection operator (LASSO), fifty-three radiomic features including 46 RSFCs, 1 mALFF, 3 mReHos, 1 VMHC, 2 GM volumes and 1 clinical factor were retained. The selected features also indicated the most discriminative regions for PD. We further conducted model fitting procedure for classifying subjects in the training set employing random forest and support volume machine (SVM) to evaluate the performance of the two methods. After cross-validation, both methods achieved 100% accuracy and area under curve (AUC) for distinguishing between PD and HC in the training set. In the testing set, SVM performed better than random forest with the accuracy, true positive rate (TPR) and AUC being 85%, 1 and 0.97, respectively. These findings demonstrate the radiomics technique has the potential to support radiological diagnosis and to achieve high classification accuracy for clinical diagnostic systems for patients with PD.