RNA modifications in gynecological cancer: current status and future directions.

Currently, more than 170 modifications have been identified on RNA. RNA modification mainly regulates RNA splicing, intracellular transport, degradation, translation, and stability. Gynecologic cancer (GC) mainly includes cervical cancer (CCA), ovarian cancer (OC), Endometrial cancer (EMC), among others, is the leading cause of cancer-related death. At present, there is still a lack of effective means to eradicate such diseases, so it is important to conduct more in-depth research on gynecological cancers. Numerous studies have shown that a series of epigenetic changes occur during the development of gynecologic cancer. This article reviews the latest findings on the functional significance of RNA modification in gynecologic cancer and discusses the therapeutic potential of RNA modification-related inhibitors in the treatment of gynecologic cancer.

High-throughput virtual screening to identify potential small molecule inhibitors of the Zα domain of the adenosine deaminases acting on RNA 1(ADAR1).

Changes in RNA editing are closely associated with diseases such as cancer, viral infections, and autoimmune disorders. Adenosine deaminase (ADAR1), which acts on RNA 1, plays a key role in adenosine to inosine editing and is a potential therapeutic target for these various diseases. The p150 subtype of ADAR1 is the only one that contains a Zα domain that binds to both Z-DNA and Z-RNA. The Zα domain modulates immune responses and may be suitable targets for antiviral therapy and cancer immunotherapy. In this study, we attempted to utilize molecular docking to identify potential inhibitors that bind to the ADAR1 Zα domain. The virtual docking method screened the potential activity of more than 100,000 compounds on the Zα domain of ADAR1 and filtered to obtain the highest scoring results.We identified 71 compounds promising to bind to ADAR1 and confirmed that two of them, lithospermic acid and Regaloside B, interacts with the ADAR1 Zα domain by surface plasmonic resonance technique. The molecular dynamics calculation of the complex of lithospermic acid and ADAR1 also showed that the binding effect of lithospermic acid to ADAR1 was stable.This study provides a new perspective for the search of ADAR1 inhibitors, and further studies on the anti-ADAR11 activity of these compounds have broad prospects.

The construction and analysis of tricarboxylic acid cycle related prognostic model for cervical cancer.

Introduction: Cervical cancer (CC) is the fourth most common malignant tumor in term of in incidence and mortality among women worldwide. The tricarboxylic acid (TCA) cycle is an important hub of energy metabolism, networking one-carbon metabolism, fatty acyl metabolism and glycolysis. It can be seen that the reprogramming of cell metabolism including TCA cycle plays an indispensable role in tumorigenesis and development. We aimed to identify genes related to the TCA cycle as prognostic markers in CC. Methods: Firstly, we performed the differential expressed analysis the gene expression profiles associated with TCA cycle obtained from The Cancer Genome Atlas (TCGA) database. Differential gene list was generated and cluster analysis was performed using genes with detected fold changes >1.5. Based on the subclusters of CC, we analysed the relationship between different clusters and clinical information. Next, Cox univariate and multivariate regression analysis were used to screen genes with prognostic characteristics, and risk scores were calculated according to the genes with prognostic characteristics. Additionally, we analyzed the correlation between the predictive signature and the treatment response of CC patients. Finally, we detected the expression of ench prognostic gene in clinical CC samples by quantitative polymerase chain reaction (RT-qPCR). Results: We constructed a prognostic model consist of seven TCA cycle associated gene (ACSL1, ALDOA, FOXK2, GPI, MDH1B, MDH2, and MTHFD1). Patients with CC were separated into two groups according to median risk score, and high-risk group had a worse prognosis compared to the low-risk group. High risk group had lower level of sensitivity to the conventional chemotherapy drugs including cisplatin, paclitaxel, sunitinib and docetaxel. The expression of ench prognostic signature in clinical CC samples was verified by qRT-PCR. Conclusion: There are several differentially expressed genes (DEGs) related to TCA cycle in CC. The risk score model based on these genes can effectively predict the prognosis of patients and provide tumor markers for predicting the prognosis of CC.