Calcyphosine promotes the proliferation of glioma cells and serves as a potential therapeutic target

The lysophosphatidic acid-regulated signal transduction network in ovarian cancer cells and its role in actomyosin dynamics, cell migration and entosis

Lysophosphatidic acid (LPA) species accumulate in the ascites of ovarian high-grade serous cancer (HGSC) and are associated with short relapse-free survival. LPA is known to support metastatic spread of cancer cells by activating a multitude of signaling pathways via G-protein-coupled receptors of the LPAR family. Systematic unbiased analyses of the LPA-regulated signal transduction network in ovarian cancer cells have, however, not been reported to date. Methods: LPA-induced signaling pathways were identified by phosphoproteomics of both patient-derived and OVCAR8 cells, RNA sequencing, measurements of intracellular Ca²⁺ and cAMP as well as cell imaging. The function of LPARs and downstream signaling components in migration and entosis were analyzed by selective pharmacological inhibitors and RNA interference. Results: Phosphoproteomic analyses identified > 1100 LPA-regulated sites in > 800 proteins and revealed interconnected LPAR1, ROCK/RAC, PKC/D and ERK pathways to play a prominent role within a comprehensive signaling network. These pathways regulate essential processes, including transcriptional responses, actomyosin dynamics, cell migration and entosis. A critical component of this signaling network is MYPT1, a stimulatory subunit of protein phosphatase 1 (PP1), which in turn is a negative regulator of myosin light chain 2 (MLC2). LPA induces phosphorylation of MYPT1 through ROCK (T853) and PKC/ERK (S507), which is majorly driven by LPAR1. Inhibition of MYPT1, PKC or ERK impedes both LPA-induced cell migration and entosis, while interference with ROCK activity and MLC2 phosphorylation selectively blocks entosis, suggesting that MYPT1 figures in both ROCK/MLC2-dependent and -independent pathways. We finally show a novel pathway governed by LPAR2 and the RAC-GEF DOCK7 to be indispensable for the induction of entosis. Conclusion: We have identified a comprehensive LPA-induced signal transduction network controlling LPA-triggered cytoskeletal changes, cell migration and entosis in HGSC cells. Due to its pivotal role in this network, MYPT1 may represent a promising target for interfering with specific functions of PP1 essential for HGSC progression.

Single-Cell RNA Sequencing Reveals the Role of Epithelial Cell Marker Genes in Predicting the Prognosis of Colorectal Cancer Patients

Single-cell RNA sequencing (scRNA-seq) is increasingly used in studies on gastrointestinal cancers. This study investigated the prognostic value of epithelial cell-associated biomarkers in colorectal cancer (CRC) using scRNA-seq data. We downloaded and analysed scRNA-seq data from four CRC samples from the Gene Expression Omnibus (GEO), and we identified marker genes of malignant epithelial cells (MECs) using CRC transcriptome and clinical data downloaded from The Cancer Genome Atlas (TCGA) and GEO as training and validation cohorts, respectively. In the TCGA training cohort, weighted gene correlation network analysis, univariate Cox proportional hazard model (Cox) analysis, and least absolute shrinkage and selection operator regression analysis were performed on the marker genes of MEC subsets to identify a signature of nine prognostic MEC-related genes (MECRGs) and calculate a risk score based on the signature. CRC patients were divided into high- and low-risk groups according to the median risk score. We found that the MECRG risk score was significantly correlated with the clinical features and overall survival of CRC patients, and that CRC patients in the high-risk group showed a significantly shorter survival time. The univariate and multivariate Cox regression analyses showed that the MECRG risk score can serve as an independent prognostic factor for CRC patients. Gene set enrichment analysis revealed that the MECRG signature genes are involved in fatty acid metabolism, p53 signalling, and other pathways. To increase the clinical application value, we constructed a MECRG nomogram by combining the MECRG risk score with other independent prognostic factors. The validity of the nomogram is based on receiver operating characteristics and calibration curves. The MECRG signature and nomogram models were well validated in the GEO dataset. In conclusion, we established an epithelial cell marker gene-based risk assessment model based on scRNA-seq analysis of CRC samples for predicting the prognosis of CRC patients.

Label-Free Liquid Chromatography-Mass Spectrometry Proteomic Analysis of the Urinary Proteome for Measuring the Escitalopram Treatment Response From Major Depressive Disorder

Major depressive disorder (MDD) is a common mental disorder that can cause substantial impairments in quality of life. Clinical treatment is usually built on a trial-and-error method, which lasts ~12 weeks to evaluate whether the treatment is efficient, thereby leading to some inefficient treatment measures. Therefore, we intended to identify early candidate urine biomarkers to predict efficient treatment response in MDD patients. In this study, urine samples were collected twice from 19 respondent and 10 non-respondent MDD patients receiving 0-, 2-, and 12-week treatments with escitalopram. Differential urinary proteins were subsequently analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Our two pilot tests suggested that the urine proteome reflects changes associated with major depressive disorder at the early stage of treatment measures. On week 2, 20 differential proteins were identified in the response group compared with week 0, with 14 of these proteins being associated with the mechanisms of MDD. In the non-response group, 60 differential proteins were identified at week 2, with 28 of these proteins being associated with the mechanisms of MDD. In addition, differential urinary proteins at week 2 between the response and non-response groups can be clearly distinguished by using orthogonal projection on latent structure-discriminant analysis (OPLS-DA). Our small pilot tests indicated that the urine proteome can reflect early effects of escitalopram therapy between the response and non-response groups since at week 2, which may provide potential early candidate urine biomarkers to predict efficient treatment measures in MDD patients.