No abstract

CHCHD2 and CHCHD10 are homolog mitochondrial proteins that play key roles in the neurological, cardiovascular, and reproductive systems. They are also involved in the mitochondrial metabolic process. Although previous research has concentrated on their functions within mitochondria, their functions within apoptosis, synaptic plasticity, cell migration as well as lipid metabolism remain to be concluded. The review highlights the different roles played by CHCHD2 and/or CHCHD10 binding to various target proteins (such as OPA-1, OMA-1, PINK, and TDP43) and reveals their non-negligible effects in cognitive impairments and motor neuron diseases. This review focuses on the functions of CHCHD2 and/or CHCHD10. This review reveals protective effects and mechanisms of CHCHD2 and CHCHD10 in neurodegenerative diseases characterized by cognitive and motor deficits, such as frontotemporal dementia (FTD), Lewy body dementia (LBD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). However, there are numerous specific mechanisms that have yet to be elucidated, and additional research into these mechanisms is required.

Prohibitins (PHBs) are conserved proteins in eukaryotic cells, which are mainly located in the inner mitochondrial membrane (IMM), cell nucleus, and cell membrane. PHBs play crucial roles in various cellular functions, including the cell cycle regulation, tumor suppression, immunoglobulin M receptor binding, and aging. In addition, recent in vitro and in vivo studies have revealed that PHBs are important in nervous system diseases. PHBs can prevent apoptosis, inflammation, mitochondrial dysfunction, and autophagy in neurological disorders through different molecules and pathways, such as OPA-1, PINK1/Parkin, IL6/STAT3, Tau, NO, LC3, and TDP43. Therefore, PHBs show great promise in the protection of neurological disorders. This review summarizes the relevant studies on the relationship between PHBs and neurological disorders and provides an update on the molecular mechanisms of PHBs in nervous system diseases.

This study was designed to analyze the characteristics of bladder cancer-related genes and establish a prognostic model of bladder cancer. The model passed an independent external validation set test. Differentially expressed genes (DEGs) related to bladder cancer were obtained from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx) databases. WGCNA was used to fit the GSE188715, TCGA, and GTEx RNA-Seq data. Fusing the module genes with the high significance in tumor development extracted from WGCNA and DEGs screened from multiple databases. 709 common prognostic-related genes were obtained. The 709 genes were enriched in the Gene Ontology database. Univariate Cox and LASSO regression analyses were used to screen out 21 prognostic-related genes and further multivariate Cox regression established a bladder cancer prognostic model consisting of 8 genes. After the eight-gene prognostic model was established, the Human Protein Atlas (HPA) database, GEPIA 2, and quantitative real-time PCR (qRT-PCR) verified the differential expression of these genes. Gene Set Enrichment Analysis and immune infiltration analysis found biologically enrichment pathways and cellular immune infiltration related to this bladder cancer prognostic model. Then, we selected bladder cancer patients in the TCGA database to evaluate the predictive ability of the model on the training set and validation set. The overall survival status of the two TCGA patient groups in the training and the test sets was obtained by Kaplan–Meier survival analysis. Three-year survival rates in the training and test sets were 37.163% and 25.009% for the low-risk groups and 70.000% and 62.235% for the high-risk groups, respectively. Receiver operating characteristic curve (ROC) analysis showed that the areas under the curve (AUCs) for the training and test sets were above 0.7. In an external independent validation database GSE13507, Kaplan–Meier survival analysis showed that the three-year survival rates of the high-risk and the low-risk groups in this database were 56.719% and 76.734%, respectively. The AUCs of the ROC drawn in the external validation set were both above 0.65. Here, we constructed a prognostic model of bladder cancer based on data from the GEO, TCGA, and GTEx databases. This model has potential prognostic and clinical auxiliary diagnostic value.