Generation of Centromere-Associated Protein-E <em>CENP-E<sup>-/-</sup></em> Knockout Cell Lines using the CRISPR/Cas9 System

The CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 system has emerged as a powerful tool for precise and efficient gene editing ...

Fujian Medical University

Fujian Province University

Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.

The roles of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen concentrations in predicting prostate cancer and high-risk prostate cancer.

Prostate cancer (PCA) is an epithelial malignant tumor occurring in the prostate gland. It is the second most common male cancer in the world and one of the top five cancer deaths in men. To combat this disease, it is needed to identify important tumor suppressor genes and elucidate the molecular mechanisms. S100 calcium-binding protein A14 (S100A14), a member of the S100 family, is located on chromosome 1q21.3 and contains an EF-hand motif that binds calcium. S100A14 is involved in a variety of tumor biological processes in several types of cancers. Its expression level and related biological functions are tissue or tumor specific. However, its possible effects on prostate cancer are still unclear. Herein, we found the low expression of S100A14 in human prostate cancer tissues and cell lines. S100A14 suppressed the proliferation of prostate cancer cells and promoted cell apoptosis. Additionally, S100A14 suppressed the motility and EMT processes of prostate cancer cells. We further found S100A14 promoted the expression of FAT1 and activated the Hippo pathway, which, therefore, suppressed the prostate cancer progression. The in vivo assays confirmed that S100A14 suppressed tumor growth of prostate cancer cells through FAT1-mediated Hippo pathway in mice. In conclusion, we clarified the mechanism underlying S100A14 suppressing prostate cancer progression and, therefore, we thought S100A14 could serve as a tumor suppressor protein.

S100A14 inhibits cell growth and epithelial-mesenchymal transition (EMT) in prostate cancer through FAT1-mediated Hippo signaling pathway.

To reduce unnecessary prostate biopsies, we designed a magnetic resonance imaging (MRI)-based nomogram prediction model of prostate maximum sectional area (PA) and investigated its zone area for diagnosing prostate cancer (PCa).

MRI-Based Nomogram of Prostate Maximum Sectional Area and Its Zone Area for Prediction of Prostate Cancer.

During the lifetime of females, mammary epithelial cells undergo cyclical expansion and proliferation depending on the cyclical activation of mammary gland stem/progenitor cells (MaSCs) in response to the change of hormone level. The structural shrink of mammary duct tree and the functional loss of mammary gland occur along with inactivation of MaSCs in old females, even leading to breast cancer occasionally. However, the gene expression signature in MaSCs across the lifespan remains unclear. Herein, we tested the tissue regeneration ability of CD24CD49f MaSCs over six time points from neonatal (4-day-old) to aged mice (360-day-old). Further RNA-seq analyses identified four clusters of gene signatures based on the gene expression patterns. A subset of stemness-related genes was identified, showing the highest level at day 4 of the neonatal age, and the lowest level at the old age. We also identified an aging-related gene signature showing significant change in the old mice, in which an association between aging process and stemness loss was indicated. The aging-related gene signature showed regulation of cancer signaling pathways, as well as aging-related diseases including Huntington disease, Parkinson disease, and Alzheimer disease. Moreover, 425, 1056, 418, and 1107 gene variants were identified at D20, D40, D90, and D180, respectively, which were mostly reported to associated with tumorigenesis and metastasis in cancer. In summary, the current study is the first to demonstrate the gene expression shift in MaSCs from neonatal to aging, which leads to stemness loss, aging, aging-related diseases, and even breast cancer in old mice.

Evolution of gene expression signature in mammary gland stem cells from neonatal to old mice.

Circulating tumor-derived endothelial cell (CTEC) is a new potential tumor biomarker to be associated with cancer development and treatment efficacy. However, few evidences are available for breast cancer.

Circulating Tumor-Derived Endothelial Cells: An Effective Biomarker for Breast Cancer Screening and Prognosis Prediction.

Endocannabinoid anandamide (AEA), progesterone (P4) and β-human chorionic gonadotrophin (β-hCG) are associated with the threatened miscarriage in the early stage. However, no study has investigated whether combing these three hormones could predict threatened miscarriage. Thus, we aim to establish machine learning models utilizing these three hormones to predict threatened miscarriage risk.

Construction of machine learning tools to predict threatened miscarriage in the first trimester based on AEA, progesterone and β-hCG in China: a multicentre, observational, case-control study.

Dyslipidemia is a major cause of arteriosclerotic cardiovascular disease (ASCVD), and low-density lipoprotein cholesterol (LDL-C) is the profile to be reduced to prevent disease progression. Small dense low-density lipoprotein cholesterol (sdLDL-C) has been proven to be a more effective biomarker than LDL-C for ASCVD primary and secondary prevention.  is an important drug metabolism gene. This study aimed to investigate the relationship between sdLDL-C and coronary artery disease (CAD) risk factors and explore the influence of  metabolizer phenotypes on the sdLDL-C lowering efficacy of statins.

 metabolizer phenotypes may affect the efficacy of statins on lowering small dense low-density lipoprotein cholesterol of patients with coronary artery disease.

Cuproptosis is a novel form of cell death in tumours. However, the clinical impact and mechanism of cuproptosis in bladder cancer (BC) remain unclear. This study aimed to explore the functions of long noncoding RNAs (lncRNAs) related to cuproptosis in BC and develop a prognostic predictive model. RNA sequencing and clinicopathological data were derived from The Cancer Genome Atlas and randomly divided into training and validation groups. Cuproptosis-related lncRNAs were identified by Cox regression analysis and least absolute shrinkage and selection operator, and the patients were divided into high- and low-risk groups according to the median value of the signature-based risk score. We established a signature of 17 cuproptosis-associated lncRNAs in the training set. In both sets, patients with higher signature-based risk scores had a notably higher probability of death (P ≤ 0.001) and a shorter survival duration. Cox regression analyses confirmed the risk score as an independent predictor of BC prognosis in the entire set. The area under the curve (AUC) values for 1-, 3-, and 5-year survival were 0.767, 0.734, and 0.764, respectively, confirming that the signature could determine the prognosis of BC. A signature-based nomogram was developed, and its prediction accuracy was validated using calibration curves. Several drugs, including Gemcitabine, Oxaliplatin, Mitoxantrone, Camptothecin, Cytarabine and Irinotecan may benefit low-risk BC patients more. Finally, in vitro experiments confirmed that the cuproptosis-related lncRNAs are highly expressed in bladder cancer cells after cuproptosis induced by exogenous copper ions. In conclusion, a cuproptosis-related lncRNA signature independently predicted prognosis in BC, indicating a possible mechanism and clinical treatment approach.

A prognostic cuproptosis-related lncRNA predictive signature for bladder cancer patients.

Community-acquired pneumonia (CAP) is an extraordinarily heterogeneous illness, both in the range of responsible pathogens and the host response. Metagenomic next-generation sequencing (mNGS) is a promising technology for pathogen detection. However, the clinical application of mNGS for pathogen detection remains challenging.

Microbiological diagnostic performance of metagenomic next-generation sequencing compared with conventional culture for patients with community-acquired pneumonia.

Yue Xu

Department of Cell Biology and Genetics,
The School of Basic Medical Sciences,
Department of Cell Biology and Genetics, The School of Basic Medical Sciences

Fujian Medical University

Generation of Centromere-Associated Protein-E CENP-E^-/- Knockout Cell Lines using the CRISPR/Cas9 System

Yue Xu

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Cell Biology and Genetics,The School of Basic Medical Sciences,Department of Cell Biology and Genetics, The School of Basic Medical Sciences

Fujian Medical University

Generation of Centromere-Associated Protein-E CENP-E-/- Knockout Cell Lines using the CRISPR/Cas9 System

Department of Cell Biology and Genetics,
The School of Basic Medical Sciences,
Department of Cell Biology and Genetics, The School of Basic Medical Sciences

Generation of Centromere-Associated Protein-E CENP-E^-/- Knockout Cell Lines using the CRISPR/Cas9 System