BackgroundBreast 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.MethodsWe 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.ResultsBy 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.ConclusionsTranscriptomic 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.Electronic supplementary materialThe online version of this article (10.1186/s40880-018-0326-8) contains supplementary material, which is available to authorized users.

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.

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.

Background and aims: microRNAs (miRNAs) have been reported to regulate proliferation and migration by downregulating the expression of target genes. The aims of this study were to investigate whether miR-4316 inhibited proliferation and migration by downregulating vascular endothelial growth factor A (VEGF-A) and its clinical significance in gastric cancer (GC). Methods: The clinical tissues of the GC patients for miR-4316 and VEGF-A were detected by qRT-PCR. The protein levels of VEGF-A and c-Met were determined by western blotting. Cell Proliferation, migration, and colony forming assays were conducted to show whether miR-4316 affects proliferation by CCK-8, migration by transwell, wound healing and colony formation assays. The bioinformatic methods and luciferase reporter assay were applied to detect the relationship between miRNA and VEGF-A on its targeting 3-untranslated regions (3-UTRs). CCK-8, colony formation, wound healing, and transwell assay were performed to explore the function of miR-4316. Results: The results of qRT-PCR indicated that miR-4316 expression level was significantly downregulated in human GC tissues and GC cell lines compared with their control. miR-4316 inhibited proliferation, migration and colony formation in GC cell lines by reducing VEGF-A. And western blot results indicated that miR-4316 significantly inhibited GC through repressing VEGF-A and c-Met. The investigation of Luciferase assay indicated that VEGF-A is a direct target gene of miR-4316. Conclusions: miR-4316 suppressed proliferation and migration of GC through the VEGF-A gene. MiR-4316 acts as a tumor suppressor by targeting VEGF-A and this indicated that MiR-4316 might be a potential therapeutic target for GC.

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Background Lung adenocarcinoma (LUAD) remains one of the world’s most known aggressive malignancies with a high mortality rate. Molecular biological analysis and bioinformatics are of great importance as they have recently occupied a large area in the studies related to the identification of various biomarkers to predict survival for LUAD patients. In our study, we attempted to identify a new prognostic model by developing a new algorithm to calculate the allele frequency deviation (AFD), which in turn may assist in the early diagnosis and prediction of clinical outcomes in LUAD. Method First, a new algorithm was developed to calculate AFD using the whole-exome sequencing (WES) dataset. Then, AFD was measured for 102 patients, and the predictive power of AFD was assessed using Kaplan–Meier analysis, receiver operating characteristic (ROC) curves, and area under the curve (AUC). Finally, multivariable cox regression analyses were conducted to evaluate the independence of AFD as an independent prognostic tool. Result The Kaplan–Meier analysis showed that AFD effectively segregated patients with LUAD into high-AFD-value and low-AFD-value risk groups (hazard ratio HR = 1.125, 95% confidence interval CI 1.001–1.26, p = 0.04) in the training group. Moreover, the overall survival (OS) of patients who belong to the high-AFD-value group was significantly shorter than that of patients who belong to the low-AFD-value group with 42.8% higher risk and 10% lower risk of death for both groups respectively (HR for death = 1.10; 95% CI 1.01–1.2, p = 0.03) in the training group. Similar results were obtained in the validation group (HR = 4.62, 95% CI 1.22–17.4, p = 0.02) with 41.6%, and 5.5% risk of death for patients who belong to the high and low-AFD-value groups respectively. Univariate and multivariable cox regression analyses demonstrated that AFD is an independent prognostic model for patients with LUAD. The AUC for 5-year survival were 0.712 and 0.86 in the training and validation groups, respectively. Conclusion AFD was identified as a new independent prognostic model that could provide a prognostic tool for physicians and contribute to treatment decisions.

Background Due to the late and poor prognosis of non-small lung cancer(NSCLC), the mortality of patients is high, underlines the need to identify a credible prognostic marker for NSCLC patients. The aim of our study is to examine the association of allele frequency deviation (AFD) with the patient's survival, as well as identification and validation of a new prognostic signature to predict NSCLC overall survival(OS).Methods First, we developed a new algorithm to calculate AFD from whole-exome sequencing(WES) data, then we compared the predictability of the patient's survival between AFD, tumor mutation burden (TMB) and change of variants allele frequency (dVAF). Second, we overlapped the differentially expressed genes (DEGs) from our data with the genes associated with the survival of The Cancer Genome Atlas (TCGA) database to confirm all genes significantly related to the survival of lung cancer. We identified 149 genes, 31 of which are new genes and have not been reported for lung cancer, that was used to develop a new prognostic model. Lung cancer adenocarcinoma (LUAD) data from the TCGA database was used to validate the gene-signature model. The prognostic model relating to the genes was established and validated in training and LUAD validation groups.Results There was a significant association found between the high AFD value and poor survival among non-small cell lung cancer (NSCLC) patients. A novel seven genes (UCN2, RIMS2, CAVIN2, GRIA1, PKHD1L1, PGM5, CLIC6) were obtained through multivariate Cox regression analysis and significantly associated with NSCLC patients survival. Cox regression analysis confirmed that AFD and 7-gene signature are an independent prognostic marker in NSCLC patients. The AUC for 5-year survival in AFD and the AUC for 3-year survival in both training and validation groups were greater than 0.7.Conclusion As a result, AFD and 7-gene signatures were identified as new independent predictive factors used for predicting the survival among NSCLC patients.

Background: Due to the late and poor prognosis of non-small lung cancer(NSCLC), the mortality of patients is high, underlines the need to identify a credible prognostic marker for NSCLC patients. The aim of our study is to examine the association of allele frequency deviation (AFD) with the patient's survival, as well as identification and validation of a new prognostic signature to predict NSCLC overall survival(OS).Methods: First, we developed a new algorithm to calculate AFD from whole-exome sequencing(WES) data, then we compared the predictability of the patient's survival between AFD, tumor mutation burden (TMB) and change of variants allele frequency (dVAF). Second, we overlapped the differentially expressed genes (DEGs) from our data with the genes associated with the survival of The Cancer Genome Atlas (TCGA) database to confirm all genes significantly related to the survival of lung cancer. We identified 149 genes, 31 of which are new genes and have not been reported for lung cancer, that was used to develop a new prognostic model. Lung cancer adenocarcinoma (LUAD) data from the TCGA database was used to validate the gene-signature model. The prognostic model relating to the genes was established and validated in training and LUAD validation groups. Results: There was a significant association found between the high AFD value and poor survival among non-small cell lung cancer (NSCLC) patients. A novel seven genes (UCN2, RIMS2, CAVIN2, GRIA1, PKHD1L1, PGM5, CLIC6) were obtained through multivariate Cox regression analysis and significantly associated with NSCLC patients survival. Cox regression analysis confirmed that AFD and 7-gene signature are an independent prognostic marker in NSCLC patients. The AUC for 5-year survival in AFD and the AUC for 3-year survival in both training and validation groups were greater than 0.7.Conclusion: As a result, AFD and 7-gene signatures were identified as new independent predictive factors used for predicting the survival among NSCLC patients.