Downregulated ADARB1 Facilitates Cell Proliferation, Invasion and has Effect on the Immune Regulation in Ovarian Cancer

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

Ovarian 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 m6 A, m1 A, and m5 C, 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 m6 A 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.

Transcriptome analysis identification of A-to-I RNA editing in granulosa cells associated with PCOS

Background

Polycystic ovary syndrome (PCOS) is a complex, multifactor disorder in women of reproductive age worldwide. Although RNA editing may contribute to a variety of diseases, its role in PCOS remains unclear.

Methods

A discovery RNA-Seq dataset was obtained from the NCBI Gene Expression Omnibus database of granulosa cells from women with PCOS and women without PCOS (controls). A validation RNA-Seq dataset downloaded from the European Nucleotide Archive Databank was used to validate differential editing. Transcriptome-wide investigation was conducted to analyze adenosine-to-inosine (A-to-I) RNA editing in PCOS and control samples.

Results

A total of 17,395 high-confidence A-to-I RNA editing sites were identified in 3,644 genes in all GC samples. As for differential RNA editing, there were 545 differential RNA editing (DRE) sites in 259 genes with Nucleoporin 43 (NUP43), Retinoblastoma Binding Protein 4 (RBBP4), and leckstrin homology-like domain family A member 1 (PHLDA) showing the most significant three 3'-untranslated region (3'UTR) editing. Furthermore, we identified 20 DRE sites that demonstrated a significant correlation between editing levels and gene expression levels. Notably, MIR193b-365a Host Gene (MIR193BHG) and Hook Microtubule Tethering Protein 3 (HOOK3) exhibited significant differential expression between PCOS and controls. Functional enrichment analysis showed that these 259 differentially edited genes were mainly related to apoptosis and necroptosis pathways. RNA binding protein (RBP) analysis revealed that RNA Binding Motif Protein 45 (RBM45) was predicted as the most frequent RBP binding with RNA editing sites. Additionally, we observed a correlation between editing levels of differential editing sites and the expression level of the RNA editing enzyme Adenosine Deaminase RNA Specific B1 (ADARB1). Moreover, the existence of 55 common differentially edited genes and nine differential editing sites were confirmed in the validation dataset.

Conclusion

Our current study highlighted the potential role of RNA editing in the pathophysiology of PCOS as an epigenetic process. These findings could provide valuable insights into the development of more targeted and effective treatment options for PCOS.

Deregulated Gene Expression Profiles and Regulatory Networks in Adult and Pediatric RUNX1/RUNX1T1-Positive AML Patients

Acute myeloid leukemia (AML) is a heterogeneous and complex disease concerning molecular aberrations and prognosis. RUNX1/RUNX1T1 is a fusion oncogene that results from the chromosomal translocation t(8;21) and plays a crucial role in AML. However, its impact on the transcriptomic profile of different age groups of AML patients is not completely understood. Here, we investigated the deregulated gene expression (DEG) profiles in adult and pediatric RUNX1/RUNX1T1-positive AML patients, and compared their functions and regulatory networks. We retrospectively analyzed gene expression data from two independent Gene Expression Omnibus (GEO) datasets (GSE37642 and GSE75461) and computed their differentially expressed genes and upstream regulators, using limma, GEO2Enrichr, and X2K. For validation purposes, we used the TCGA-LAML (adult) and TARGET-AML (pediatric) patient cohorts. We also analyzed the protein–protein interaction (PPI) networks, as well as those composed of transcription factors (TF), intermediate proteins, and kinases foreseen to regulate the top deregulated genes in each group. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analyses were further performed for the DEGs in each dataset. We found that the top upregulated genes in (both adult and pediatric) RUNX1/RUNX1T1-positive AML patients are enriched in extracellular matrix organization, the cell projection membrane, filopodium membrane, and supramolecular fiber. Our data corroborate that RUNX1/RUNX1T1 reprograms a large transcriptional network to establish and maintain leukemia via intricate PPI interactions and kinase-driven phosphorylation events.

Abnormally Expressed Ferroptosis-Associated FANCD2 in Mediating the Temozolomide Resistance and Immune Response in Glioblastoma

Ferroptosis-related genes (FRGs) have been identified as potential targets involved in oncogenesis and cancer therapeutic response. Nevertheless, the specific roles and underlying mechanisms of FRGs in GBM and temozolomide (TMZ) resistance remain unclear. Through comprehensive bioinformatics, we found that ferroptosis-related Fanconi anemia complementation group D2 (FANCD2) was significantly up-regulated in GBM tissues, and the high expression level of FANCD2 was related to the poor prognosis in primary and recurrent GBM patients. Furthermore, FANCD2 could promote TMZ resistance by attenuating ferroptosis in GBM cells. Knockdown of FANCD2 could increase reactive oxygen species (ROS) levels and inhibit cell survival. The two characteristics were associated with ferroptosis in TMZ-resistant GBM cells T98G-R and U118-R. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that aberrantly expressed FANCD2 was potentially linked with several cancer-associated signaling pathways, including chromosome segregation, DNA replication, and cell cycle transition. In addition, we demonstrated that FANCD2 expression was positively correlated with several tumor-infiltrating lymphocytes (TILs) and multiple immune-associated signatures in GBM. Therefore, up-regulated FANCD2 could protect GBM cells from ferroptosis and promote TMZ resistance. FANCD2 may be a novel therapeutic target in GBM.

Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning

Background: Ovarian cancer (OC) has a high mortality rate and poses a severe threat to women’s health. However, abnormal gene expression underlying the tumorigenesis of OC has not been fully understood. This study aims to identify diagnostic characteristic genes involved in OC by bioinformatics and machine learning.

Methods: We utilized five datasets retrieved from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database, and the Genotype-Tissue Expression (GTEx) Project database. GSE12470 and GSE18520 were combined as the training set, and GSE27651 was used as the validation set A. Also, we combined the TCGA database and GTEx database as validation set B. First, in the training set, differentially expressed genes (DEGs) between OC and non-ovarian cancer tissues (nOC) were identified. Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. Subsequently, the obtained diagnostic-related DEGs were validated in the validation sets. Then, we used the computational approach (CIBERSORT) to analyze the association between immune cell infiltration and DEGs. Finally, we analyzed the prognostic role of several genes on the KM-plotter website and used the human protein atlas (HPA) online database to analyze the expression of these genes at the protein level.

Results: 590 DEGs were identified, including 276 upregulated and 314 downregulated DEGs.The Enrichment analysis results indicated the DEGs were mainly involved in the nuclear division, cell cycle, and IL−17 signaling pathway. Besides, DEGs were also closely related to immune cell infiltration. Finally, we found that BUB1, FOLR1, and PSAT1 have prognostic roles and the protein-level expression of these six genes SFPR1, PSAT1, PDE8B, INAVA and TMEM139 in OC tissue and nOC tissue was consistent with our analysis.

Conclusions: We screened nine diagnostic characteristic genes of OC, including SFRP1, PSAT1, BUB1B, FOLR1, ABCB1, PDE8B, INAVA, BUB1, TMEM139. Combining these genes may be useful for OC diagnosis and evaluating immune cell infiltration.