Posterior Tibial Nerve Stimulation for Overactive Bladder: Mechanism, Classification, and Management Outlines

Purpose of the Review. Posterior tibial nerve stimulation (PTNS) techniques have dramatically grown after approval to manage overactive bladder (OAB). The present review will focus on the most current data on PTNS types (percutaneous, transcutaneous, and implant) and their mechanism of action, safety, efficacy, advantages, drawbacks, limitation, and clinical applications. Recent Findings. The present review described the recent studies that addressed the tibial nerve stimulation role in OAB management. BlueWind RENOVA system, Bioness StimRouter, and eCoin are examples of emerging technologies that have evolved from interval sessions (percutaneous PTNS and transcutaneous PTNS) to continuous stimulation (implants). These can be efficiently managed at home by patients with minimum burden on the health system and fewer visits, especially in the COVID-19 pandemic. Summary. Our review shows that the tibial nerve stimulation advancements in OAB treatment have been rapidly increasing over the recent years. It is minimally invasive and effective, similar to sacral nerve stimulation (SNM), but less aggressive. Implantable PTNS has been promised in terms of efficacy, safety, and high acceptance rate. However, evidence is still limited to short-term trials, and tolerability, method, and drawbacks remain challenges

Transcriptomic but not genomic variability confers phenotype of breast cancer stem cells

Abstract Background Breast cancer stem cells (BCSCs) are considered responsible for cancer relapse and drug resistance. Understanding the identity of BCSCs may open new avenues in breast cancer therapy. Although several discoveries have been made on BCSC characterization, the factors critical to the origination of BCSCs are largely unclear. This study aimed to determine whether genomic mutations contribute to the acquisition of cancer stem-like phenotype and to investigate the genetic and transcriptional features of BCSCs. Methods We detected potential BCSC phenotype-associated mutation hotspot regions by using whole-genome sequencing on parental cancer cells and derived serial-generation spheres in increasing order of BCSC frequency, and then performed target deep DNA sequencing at bulk-cell and single-cell levels. To identify the transcriptional program associated with BCSCs, bulk-cell and single-cell RNA sequencing was performed. Results By using whole-genome sequencing of bulk cells, potential BCSC phenotype-associated mutation hotspot regions were detected. Validation by target deep DNA sequencing, at both bulk-cell and single-cell levels, revealed no genetic changes specifically associated with BCSC phenotype. Moreover, single-cell RNA sequencing showed profound transcriptomic variability in cancer cells at the single-cell level that predicted BCSC features. Notably, this transcriptomic variability was enriched during the transcription of 74 genes, revealed as BCSC markers. Breast cancer patients with a high risk of relapse exhibited higher expression levels of these BCSC markers than those with a low risk of relapse, thereby highlighting the clinical significance of predicting breast cancer prognosis with these BCSC markers. Conclusions Transcriptomic variability, not genetic mutations, distinguishes BCSCs from non-BCSCs. The identified 74 BCSC markers have the potential of becoming novel targets for breast cancer therapy

A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD)

Abstract Background Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients. Methods Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature. Results A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis. Conclusion Our study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment