Abstract Background Pinin (PNN), a desmosome associated protein, was demonstrated to be over-expressed and act as a tumor-promoting factor in ovarian cancer, hepatocellular carcinoma and colorectal cancer. However, the precise role of PNN in prostate cancer is still unknown. Methods The expression levels of PNN were assessed in prostate cancer by qRT-PCR, western blotting and immunohistochemical staining. The other proteins were quantified by western blotting. PNN-depleted cells were produced by infecting with lentivirus bearing short hairpin RNAs against PNN. PNN over-expression was performed by transfecting PNN expression vector. The proliferation of each cell line was assessed by MTS and colony formation assays. Tumors were induced on nude mice by injecting tumor cells subcutaneously. Apoptosis and cell cycle were evaluated by flow cytometry. Transwell and wound healing assay were performed to determined the ability of cell invasion and migration. The TCGA data were analyzed with GEPIA (Gene Expression Profiling Interactive Analysis) and GraphPad Prism. Results Here, we reported that PNN was upregulated in prostate cancer tissues and PNN expression was positively associated with Gleason score, tumor stage and tumor metastasis. PNN promoted cell growth and tumorigenicity in vitro and in vivo, and might modulate cell growth through driving G1/S transition via CDK6, CDK2, and Cyclin D1 in prostate cancer cells. Furthermore, PNN accelerated cell invasion, migration and EMT processes of prostate cancer cells accompanied with the up-regulation of MMP-2, MMP-9, N-cadherin, Vimentin and down-regulation of E-cadherin. Mechanism study demonstrated that the proliferation- and motility-promoting effects of PNN on prostate cancer cells dependent on the activation of PI3K/AKT/CREB signaling, which was reversed by AKT and CREB inhibitors. Conclusions Collectively, these findings indicated that PNN plays important roles in prostate cancer tumorigenesis and progression and it may be a potential therapeutic target for prostate cancer treatment.

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Purpose Lymphovascular invasion (LVI) and perineural invasion (PNI) are independent prognostic factors in patients with colorectal cancer (CRC). In this study, we aimed to develop and validate a preoperative predictive model based on high‐throughput radiomic features and clinical factors for accurate prediction of LVI/PNI in these patients. Methods Two hundred and sixty‐three patients who underwent colorectal resection for histologically confirmed CRC between 1 February 2011 and 30 June 2020 were retrospectively enrolled. Between 1 February 2011 and 30 September 2018, 213 patients were randomly divided into a training cohort (n = 149) and a validation cohort (n = 64) by a ratio of 7:3. We used a 10000‐iteration bootstrap analysis to estimate the prediction error and confidence interval for two cohorts. The independent test cohort consisted of 50 patients between 1 October 2018 and 30 June 2020. Regions of interest (ROIs) were manually delineated in high‐resolution T2‐weighted and diffusion‐weighted images using ITK‐SNAP software on each CRC tumor slice. In total, 3356 radiomic features were extracted from each ROI. Next, we used the maximum relevance minimum redundancy and least absolute shrinkage and selection operator algorithms to select the strongest of these features to establish a clinical‐radiomics model for predicting LVI/PNI. Receiver‐operating characteristic and calibration curves were then plotted to evaluate the predictive performance of the model in the training, validation, and independent test cohorts. Results A multiparametric clinical‐radiomics model combining MRI‐reported extramural vascular invasion (EMVI) status and a Radiomics score for the LVI/PNI estimation was established. This model had significant predictive power in the training cohort (area under the curve [AUC] 0.91; 95% confidence interval [CI]: 0.85–0.97), validation cohort (AUC: 0.88; 95% CI: 0.79–89), and independent test cohorts (AUC 0.83, 95% CI 0.72–0.95). The model performed well in the independent test cohort with sensitivity of 0.818, specificity of 0.714, and accuracy of 0.760. Calibration curve and decision curve analysis demonstrated clinical benefits. Conclusion Multiparametric clinical‐radiomics models can accurately predict LVI/PNI in patients with CRC. Our model has predictive ability that should improve preoperative diagnostic performance and allow more individualized treatment decisions.