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Abstract Objective Prediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC. Methods A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing model. Results In multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of LNM. Conclusion The proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans.

Abstract Background More efficient instruments for body constitution identification are needed for clinical practice. We aimed to develop the short-form version of the Constitution in Chinese Medicine Questionnaire (CCMQ) and evaluate for health management. Methods First, the short forms were developed through expert survey, classical test theory (CTT), and modern item response (IRT) based on the CCMQ. A combination of e-mail and manual methods was used in expert survey. Then, five indexes of CTT including criteria value-critical ratio, correlation coefficient, discrete tendency, internal consistency, and factor loading were used. And, IRT method was used through analyzing the discrimination and difficulty parameters of items. Second, the three top-ranked items of each constitution scale were selected for the simplified CCMQ, based on the three combined methods of different conditions and weights. Third, The psychometric properties such as completion time, validity (Construct, criterion, and divergent validity), and reliability (test–retest and internal consistency reliability) were evaluated. Finally, the diagnostic validity of the best short-form used receiver operating characteristic (ROC) curve. Results Three short-form editions were developed, and retained items 27, 23 and 27, which are named as WangQi nine body constitution questionnaire of Traditional Chinese Medicine (short-form) (SF-WQ9CCMQ)- A, B, and C, respectively. SF-WQ9CCMQ- A is showed the best psychometric property on Construct validity, Criterion validity, test–retest reliability and internal consistency reliability. The diagnostic validity indicated that the area under the ROC curve was 0.928 (95%CI: 0.924–0.932) for the Gentleness constitution scale, and were 0.895–0.969 and 0.911–0.981 for unbalance constitution scales using the cut-off value of the original CCMQ as 40 (“yes” standard) and 30 (“tendency” standard), respectively. Conclusions Our study successfully developed a well short-form which has good psychometric property, and excellent diagnostic validity consistent with the original. New and simplified instrument and opportunity are provided for body constitution identification, health management and primary care implementation.

Abstract The widespread application of antiandrogen therapies has aroused a significant increase in the incidence of NEPC, a lethal form of the disease lacking efficient clinical treatments. Here we identified a cell surface receptor neurokinin-1 (NK1R) as a clinically relevant driver of treatment-related NEPC (tNEPC). NK1R expression increased in prostate cancer patients, particularly higher in metastatic prostate cancer and treatment-related NEPC, implying a relation with the progression from primary luminal adenocarcinoma toward NEPC. High NK1R level was clinically correlated with accelerated tumor recurrence and poor survival. Mechanical studies identified a regulatory element in the NK1R gene transcription ending region that was recognized by AR. AR inhibition enhanced the expression of NK1R, which mediated the PKCα-AURKA/N-Myc pathway in prostate cancer cells. Functional assays demonstrated that activation of NK1R promoted the NE transdifferentiation, cell proliferation, invasion, and enzalutamide resistance in prostate cancer cells. Targeting NK1R abrogated the NE transdifferentiation process and tumorigenicity in vitro and in vivo. These findings collectively characterized the role of NK1R in tNEPC progression and suggested NK1R as a potential therapeutic target.

Abstract Despite the great advances in target therapy, lung cancer remains the top cause of cancer-related death worldwide. G protein-coupled receptor neurokinin-1 (NK1R) is shown to play multiple roles in various cancers; however, the pathological roles and clinical implication in lung cancer are unclarified. Here we identified NK1R as a significantly upregulated GPCR in the transcriptome and tissue array of human lung cancer samples, associated with advanced clinical stages and poor prognosis. Notably, NK1R is co-expressed with epidermal growth factor receptor (EGFR) in NSCLC patients’ tissues and co-localized in the tumor cells. NK1R can crosstalk with EGFR by interacting with EGFR, transactivating EGFR phosphorylation and regulating the intracellular signaling of ERK1/2 and Akt. Activation of NK1R promotes the proliferation, colony formation, EMT, MMP2/14 expression, and migration of lung cancer cells. The inhibition of NK1R by selective antagonist aprepitant repressed cell proliferation and migration in vitro. Knockdown of NK1R significantly slowed down the tumor growth in nude mice. The sensitivity of lung cancer cells to gefitinib/osimertinib is highly increased in the presence of the selective NK1R antagonist aprepitant. Our data suggest that NK1R plays an important role in lung cancer development through EGFR signaling and the crosstalk between NK1R and EGFR may provide a potential therapeutic target for lung cancer treatment.