Gangqiang Guo

RNA epigenetic modifications in ovarian cancer: The changes, chances, and challenges

AbstractOvarian cancer (OC) is the most common female cancer worldwide. Patients with OC have high mortality because of its complex and poorly understood pathogenesis. RNA epigenetic modifications, such as m6A, m1A, and m5C, are closely associated with the occurrence and development of OC. RNA modifications can affect the stability of mRNA transcripts, nuclear export of RNAs, translation efficiency, and decoding accuracy. However, there are few overviews that summarize the link between m6A RNA modification and OC. Here, we discuss the molecular and cellular functions of different RNA modifications and how their regulation contributes to the pathogenesis of OC. By improving our understanding of the role of RNA modifications in the etiology of OC, we provide new perspectives for their use in OC diagnosis and treatment.This article is categorized under: RNA Processing > RNA Editing and Modification RNA in Disease and Development > RNA in Disease

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study

Abstract Background Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)–based model using PCD-related genes. Methods We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. Results The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients. Conclusions The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Fatty Acid Metabolism-Related lncRNA Prognostic Signature for Serous Ovarian Carcinoma