SH3RF2 contributes to cisplatin resistance in ovarian cancer cells by promoting RBPMS degradation

Novel factors of cisplatin resistance in epithelial ovarian tumours

Ovarian tumours are these days one of the biggest oncogynecological problems. In addition to surgery, the treatment of ovarian cancer includes also chemotherapy in which platinum preparations are one of the most used chemotherapeutic drugs. The principle of antineoplastic effects of cisplatin (cis-diamminedichloroplatinum(II), CDDP) is its binding to the DNA and the formation of adducts. While DNA adducts induce the process of apoptosis, or inhibit the process of DNA replication, which prevents further division of tumour cells, various molecular mechanisms can reverse this process. On the other hand, with increasing scientific knowledge, it is becoming clearer that chemotherapy resistance is a very complex process. In this regard, factors and the amount of their expression may regulate the effect of resistance to chemotherapy. This review focuses on new molecular mechanisms and factors such as mitochondrial dynamics, epithelial-mesenchymal transition (EMT), cluster of differentiation, exosomes and others, that could be involved in the emergence of CDDP resistance.

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

Background

Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis.

Methods

Next-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes.

Results

A total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network.

Conclusions

This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.

Identification of a 9-gene signature to enhance biochemical recurrence prediction in primary prostate cancer: A benchmarking study using ten machine learning methods and twelve patient cohorts

Prostate cancer (PCa) is a prevalent malignancy among men worldwide, and biochemical recurrence (BCR) after radical prostatectomy (RP) is a critical turning point commonly used to guide the development of treatment strategies for primary PCa. However, the clinical parameters currently in use are inadequate for precise risk stratification and informing treatment choice. To address this issue, we conducted a study that collected transcriptomic data and clinical information from 1662 primary PCa patients across 12 multicenter cohorts globally. We leveraged 101 algorithm combinations that consisted of 10 machine learning methods to develop and validate a 9-gene signature, named BCR SCR, for predicting the risk of BCR after RP. Our results demonstrated that BCR SCR generally outperformed 102 published prognostic signatures. We further established the clinical significance of these nine genes in PCa progression at the protein level through immunohistochemistry on Tissue Microarray (TMA). Moreover, our data showed that patients with higher BCR SCR tended to have higher rates of BCR and distant metastasis after radical radiotherapy. Through drug target prediction analysis, we identified nine potential therapeutic agents for patients with high BCR SCR. In conclusion, the newly developed BCR SCR has significant translational potential in accurately stratifying the risk of patients who undergo RP, monitoring treatment courses, and developing new therapies for the disease.