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Docetaxel is the preferred chemotherapeutic agent in patients with castrate‐resistant prostate cancer (CRPC). However, patients eventually develop docetaxel resistance and in the absence of effective treatment options. Consequently, it is essential to investigate the mechanisms generating docetaxel resistance and develop novel alternative therapeutic targets. RNA sequencing was undertaken on docetaxel‐sensitive and docetaxel‐resistant prostate cancer (PCa) cells. Subsequently, chemoresistance, cancer stemness, and lipid metabolism were investigated. To obtain insight into the precise activities and action mechanisms of NOTCH3 in docetaxel‐resistant PCa, immunoprecipitation, mass spectrometry, ChIP, luciferase reporter assay, cell metabolism, and animal experiments were performed. Through RNA sequencing analysis, we found that NOTCH3 expression was markedly higher in docetaxel‐resistant cells relative to parental cells, and that this trend was continued in docetaxel‐resistant PCa tissues. Experiments in vitro and in vivo revealed that NOTCH3 enhanced stemness, lipid metabolism, and docetaxel resistance in PCa. Mechanistically, NOTCH3 is bound to TUBB3 and activates the MAPK signaling pathway. Moreover, NOTCH3 was directly regulated by MEF2A in docetaxel‐resistant cells. Notably, targeting NOTCH3 and the MEF2A/TUBB3 signaling axis was related to docetaxel chemoresistance in PCa. Overall, these results demonstrated that NOTCH3 fostered stemness, lipid metabolism, and docetaxel resistance in PCa via the TUBB3 and MAPK signaling pathways. Therefore, NOTCH3 may be employed as a prognostic biomarker in PCa patients. NOTCH3 could be a therapeutic target for PCa patients, particularly those who have developed docetaxel resistance. [ABSTRACT FROM AUTHOR]

Primary Sjögren's syndrome (pSS) is a chronic inflammatory autoimmune disease, which mainly damages patients' exocrine glands. Sensitive early diagnostic indicators and effective treatments for pSS are lacking. Using machine learning methods to find diagnostic markers and effective therapeutic ways for pSS is of great significance. In our study, first, 1643 differentially expressed genes (DEGs; 737 were upregulated and 906 were downregulated) were ultimately screened out and analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes based on the datasets from the Gene Expression Omnibus. Then, support vector machine, least absolute shrinkage and selection operator regression, random forest, and weighted correlation network analysis were used to screen out feature genes from DEGs. Subsequently, the intersection of the feature genes was taken to screen 10 genes as hub genes. Meanwhile, the analysis of the diagnostic efficiency of 10 hub genes showed their good diagnostic value for pSS, which was validated through immunohistochemistry on the paraffin sections of the labial gland. Subsequently, a multi-factor regulatory network and correlation analysis of hub genes were performed, and the results showed that ELAVL1 and IGF1R were positively correlated with each other but both negatively correlated with the other seven hub genes. Moreover, several meaningful results were detected through the immune infiltration landscape. Finally, we used molecular docking to screen potential therapeutic compounds of pSS based on the hub genes. We found that the small molecules DB08006, DB08036, and DB15308 had good docking scores with ELAVL1 and IGF1R simultaneously. Our study might provide effective diagnostic biomarkers and new therapeutic ideas for pSS. Primary Sjögren's syndrome(pSS) is a chronic inflammatory autoimmune disease, sensitive early diagnostic indicators and effective treatments for pSS are lacking. The goal of our study was to screen out the effective markers and related potential therapeutic agents for pSS by analyzing the experimental dataset from multiple data platforms and validating through the validation dataset combined with experiments. This might provide effective diagnostic biomarkers and new therapeutic ideas for treating pSS. [ABSTRACT FROM AUTHOR]

Background. Despite the constant iteration of small-molecule inhibitors and immune checkpoint inhibitors, PRAD (prostate adenocarcinoma) patients with distant metastases and biochemical recurrence maintain a poor survival outcome along with an increasing morbidity in recent years. N7-Methylguanine, a new-found type of RNA modification, has demonstrated an essential role in tumor progression but has hardly been studied for its effect on prostate carcinoma. The current study aimed to seek m7G (N7-methylguanosine) related prognostic biomarkers and potential targets for PRAD treatment. Methods. 42 genes related to m7G were collected from former literatures and GSEA (Gene Set Enrichment Analysis) website. Then, RNA-seq (RNA sequencing) and clinical data from TCGA-PRAD (The Cancer Genome Atlas-Prostate) cohort were retrieved to screen the differentially expressed m7G genes to further construct a multivariate Cox prognostic model for PRAD. Next, GSE116918, a prostate cancer cohort acquired from GEO (Gene Expression Omnibus) database, was analyzed for the external validation group to assess the ability to predict BFFS (biochemical failure-free survival) of our m7G prognostic signature. Kaplan-Meier, ROC (receiver operator characteristic), AUC (areas under ROC curve), and calibration curves were adopted to display the performance of this prognostic signature. In addition, immune infiltration analysis was implemented to evaluate the effect of these m7G genes on immunoinfiltrating cells. Correlation with drug susceptibility of the m7G signature was also analyzed by matching drug information in CellMiner database. Results. The m7G-related prognostic signature, including three genes (EIF3D, EIF4A1, LARP1) illustrated superior prognostic ability for PRAD in both training and validation cohorts. The 5-year AUC were 0.768 for TCGA-PRAD and 0.608 for GSE116918. It can well distinguish patients into different risk groups of biochemical recurrence (p =1e-04 for TCGA-PRAD and p =0.0186 for GSE116918). Immune infiltration analysis suggested potential regulation of m7G genes on neutrophils and dendritic cells in PRAD. Conclusions. A m7G-related prognostic signature was constructed and validated in the current study, giving new sights of m7G methylation in predicting the prognostic and improving the treatment of PRAD. [ABSTRACT FROM AUTHOR]

Background: Despite the constant iteration of small-molecule inhibitors and immune checkpoint inhibitors, PRAD (prostate adenocarcinoma) patients with distant metastases and biochemical recurrence maintain a poor survival outcome along with an increasing morbidity in recent years. N 7 -Methylguanine, a new-found type of RNA modification, has demonstrated an essential role in tumor progression but has hardly been studied for its effect on prostate carcinoma. The current study aimed to seek m 7 G (N7-methylguanosine) related prognostic biomarkers and potential targets for PRAD treatment., Methods: 42 genes related to m 7 G were collected from former literatures and GSEA (Gene Set Enrichment Analysis) website. Then, RNA-seq (RNA sequencing) and clinical data from TCGA-PRAD (The Cancer Genome Atlas-Prostate) cohort were retrieved to screen the differentially expressed m 7 G genes to further construct a multivariate Cox prognostic model for PRAD. Next, GSE116918, a prostate cancer cohort acquired from GEO (Gene Expression Omnibus) database, was analyzed for the external validation group to assess the ability to predict BFFS (biochemical failure-free survival) of our m 7 G prognostic signature. Kaplan-Meier, ROC (receiver operator characteristic), AUC (areas under ROC curve), and calibration curves were adopted to display the performance of this prognostic signature. In addition, immune infiltration analysis was implemented to evaluate the effect of these m 7 G genes on immunoinfiltrating cells. Correlation with drug susceptibility of the m 7 G signature was also analyzed by matching drug information in CellMiner database., Results: The m 7 G-related prognostic signature, including three genes (EIF3D, EIF4A1, LARP1) illustrated superior prognostic ability for PRAD in both training and validation cohorts. The 5-year AUC were 0.768 for TCGA-PRAD and 0.608 for GSE116918. It can well distinguish patients into different risk groups of biochemical recurrence ( p  =1e-04 for TCGA-PRAD and p  =0.0186 for GSE116918). Immune infiltration analysis suggested potential regulation of m 7 G genes on neutrophils and dendritic cells in PRAD., Conclusions: A m 7 G-related prognostic signature was constructed and validated in the current study, giving new sights of m 7 G methylation in predicting the prognostic and improving the treatment of PRAD., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Wangli Mei et al.)