No abstract

Membrane excitability is a fundamentally important feature for all excitable cells including both neurons and muscle cells. However, the background depolarizing conductances in excitable cells, especially in muscle cells, are not well characterized. Although mutations in transmembrane channel-like (TMC) proteins TMC1 and TMC2 cause deafness and vestibular defects in mammals, their precise action modes are elusive. Here, we discover that both TMC-1 and TMC-2 are required for normal egg laying in C. elegans. Mutations in these TMC proteins cause membrane hyperpolarization and disrupt the rhythmic calcium activities in both neurons and muscles involved in egg laying. Mechanistically, TMC proteins enhance membrane depolarization through background leak currents and ectopic expression of both C. elegans and mammalian TMC proteins results in membrane depolarization. Therefore, we have identified an unexpected role of TMC proteins in modulating membrane excitability. Our results may provide mechanistic insights into the functions of TMC proteins in hearing loss and other diseases.

ObjectiveTo investigate the preoperative predictive value of non-invasive imaging biomarkers for programmed cell death protein 1/programmed cell death protein ligand 1 (PD-1/PD-L1) expression and outcome in intrahepatic cholangiocarcinoma (ICC) using machine learning.MethodsPD-1/PD-L1 expression in 98 ICC patients was assessed by immunohistochemistry, and their prognostic effects were analysed using Cox regression and Kaplan-Meier analysis. Radiomic features were extracted from MRI in the arterial and portal vein phases, and three sets of Radiomics score (Radscore) with good performance were derived respectively as biomarkers for predicting PD-1, PD-L1 expression and overall survival (OS). PD-1 and PD-L1 expression models were developed using the Radscore (arterial phase), clinico-radiological factors and clinical factors, individually and in combination. The imaging-based OS predictive model was constructed by combining independent predictors among clinico-radiological, clinical factors and OS Radscore. Pathology-based OS model using pathological and clinical factors was also constructed and compared with imaging-based OS model.ResultsThe highest area under the curves of the models predicting PD-1 and PD-L1 expression was 0.897 and 0.890, respectively. PD-1+ and PD-L1+ cases had worse outcomes than negative cases. The 5-year survival rates of PD-1+ and PD-1− cases were 12.5% and 48.3%, respectively (p<0.05), whereas the 5-year survival was 21.9% and 39.4% for PD-L1+ and PD-L1− cases, respectively (p<0.05). The imaging-based OS model involved predictors of clinico-radiological ‘imaging classification’, radiomics ‘Radscore’ from arterial phase and carcinoembryonic antigen (CEA) level (C-index:0.721). It performed better than pathology-based model (C-index: 0.698) constructed by PD-1/PD-L1 expression status and CEA level. The imaging-based OS model is potential for practice when the pathology assay is unavailable and could divide ICC patients into high-risk and low-risk groups, with 1-year, 3-year and 5-year survival rates of 57.1%, 14.3% and 12.4%, and 87.8%, 63.3% and 55.3%, respectively (p<0.001).ConclusionsMRI radiomics could derive promising and non-invasive biomarker in evaluating PD-1/PD-L1 expression and prognosis of ICC patients.

Background: Microsatellite instability (MSI) is a predictive biomarker for response to chemotherapy and a prognostic biomarker for clinical outcomes of rectal cancer. The purpose of this study was to develop and validate radiomics models for preoperative prediction of the MSI status of rectal cancer based on magnetic resonance (MR) images.Methods: This study retrospectively recruited 491 rectal cancer patients with pathologically confirmed MSI status. Patients were randomly divided into a training cohort (n=327) and a validation cohort (n=164).The most predictive radiomics features were selected using intraclass correlation coefficient (ICC) analysis, the two-sample t test, and the least absolute shrinkage and selection operator (LASSO) method. XGBoost models were constructed in the training cohort to discriminate the MSI status using clinical factors, radiomics features, or a combined model incorporating both the radiomics signature and independent clinical characteristics. The diagnostic performance of these three models was evaluated in the validation cohort based on their area under the curve (AUC), sensitivity, specificity, and accuracy.Results: Among the 491 rectal cancer patients, the prevalence of MSI was 10.39% (51/491). Following ICC analysis, two-sample t test, and LASSO regression, six radiomics features were selected for subsequent analysis. The combined model, which incorporated both the clinical factors and radiomics features achieved an AUC of 0.895 [95% confidence interval (CI), 0.838-0.938] in the validation cohort, and showed better performance in predicting MSI status than the other two models using either clinical factors (P=0.015) or radiomics features (P=0.204) alone.Conclusions: Radiomics features based on preoperative T2-weighted MR imaging (MRI) are associated with the MSI status of rectal cancer. Combinational analysis of clinical factors and radiomics features may improve predictive performance and potentially contribute to noninvasive personalized therapy selection.