Ovarian cancer subtypes based on the regulatory genes of RNA modifications: Novel prediction model of prognosis

Bioinformatics analysis of the role of RNA modification regulators in polycystic ovary syndrome

Purpose

Polycystic ovary syndrome (PCOS) is the most common metabolic and endocrine disorder affecting women of reproductive age. The pathogenesis of PCOS is influenced by factors such as race, genetics, environment, hyperandrogenemia, hyperinsulinemia, and obesity. However, the molecular mechanisms linking RNA modification and PCOS remain underexplored. This study aims to investigate the potential genetic and molecular pathways connecting RNA modification with PCOS through bioinformatics analyses.

Methods

The GSE34526, GSE5850, and GSE98421 datasets were obtained from the National Center for Biotechnology Information Gene Expression Omnibus database. We identified intersecting differentially expressed genes (DEGs) and RNA modification-related genes within the GSE34526 dataset and visualized the overlaps using a Venn diagram. Subsequent analyses included Gene Ontology (GO), pathway enrichment (Kyoto Encyclopedia of Genes and Genomes), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis. Additionally, we constructed a protein-protein interaction network as well as mRNA-miRNA, mRNA-RNA binding protein, and mRNA-transcription factor (TF) regulatory networks. The expression and receiver operating characteristic curves of hub genes were also identified.

Results

The expression of several RNA modification-related DEGs (RMRDEGs) (ALYREF, NUDT1, AGO2, TET2, YTHDF2, and TRMT61B) showed significant differences in PCOS patients. GSEA and GSVA indicated that RMRDEGs were enriched in the hedgehog, MAPK, JAK STAT, and Notch pathways. Key transcription factors, including SP7, KLF8, HCFC1, IRF1, and MLLT1, were identified in the TF regulatory networks.

Conclusions

These findings suggest that there are gene and miRNA profile alterations exist in PCOS patients and highlight immune-related differences. This knowledge could pave the way for new research directions in the diagnosis and treatment of PCOS.

Identification and validation of immunity- and disulfidptosis-related genes signature for predicting prognosis in ovarian cancer

Background

Ovarian cancer (OC) ranks as the fifth most prevalent neoplasm in women and exhibits an unfavorable prognosis. To improve the OC patient's prognosis, a pioneering risk signature was formulated by amalgamating disulfidptosis-related genes.

Methods

A comparative analysis of OC tissues and normal tissues was carried out, and differentially expressed disulfidptosis-related genes (DRGs) were found using the criteria of |log2 (fold change) | > 0.585 and adjusted P-value <0.05. Subsequently, the TCGA training set was utilized to create a prognostic risk signature, which was validated by employing both the TCGA testing set and the GEO dataset. Moreover, the immune cell infiltration, mutational load, response to chemotherapy, and response to immunotherapy were analyzed. To further validate these findings, QRT-PCR analysis was conducted on ovarian tumor cell lines.

Results

A risk signature was created using fourteen differentially expressed genes (DEGs) associated with disulfidptosis, enabling the classification of ovarian cancer (OC) patients into high-risk group (HRG) and low-risk group (LRG). The HRG exhibited a lower overall survival (OS) compared to the LRG. In addition, the risk score remained an independent predictor even after incorporating clinical factors. Furthermore, the LRG displayed lower stromal, immune, and estimated scores compared to the HRG, suggesting a possible connection between the risk signature, immune cell infiltration, and mutational load. Finally, the QRT-PCR experiments revealed that eight genes were upregulated in the human OC cell line SKOV3 compared with the human normal OC line IOSE80, while six genes were down-regulated.

Conclusions

A fourteen-biomarker signature composed of disulfidptosis-related genes could serve as a valuable risk stratification tool in OC, facilitating the identification of patients who may benefit from individualized treatment and follow-up management.

A novel nomogram model for lung adenocarcinoma subtypes based on RNA-modification regulatory genes

Background

In non-small cell lung cancer (NSCLC), lung adenocarcinoma (LUAD) is the most common subtype. RNA modification has become the frontier and hotspot of current tumor research.

Results

In this study, 109 genes that regulate RNA modifications were identified according to The Cancer Genome Atlas (TCGA). A differential gene expression analysis identified 46 differentially expressed RNA modification regulatory genes (DERRGs). LUAD samples were stratified into two distinct clusters based on the expression of these DERRGs. A significant correlation was observed between these clusters and patient survival rates, as well as clinical features. Furthermore, a four-DERRG signature (EIF3B, HNRNPC, IGF2BP1, and METTL3) developed using LASSO regression. According to the calculated risk scores from this signature, LUAD patients were categorized into high-risk and low-risk groups. Patients in the low-risk group exhibited a more favorable prognosis. A prognostic nomogram was crafted, integrating the four-DERRGs signature with clinical parameters. The nomogram was revealed that OS, age, clinical stage, immune cell infiltration, and immune checkpoint molecule expression were significantly linked to the OS of LUAD. GSEA analysis found that the DERRGs were primarily regulated immune pathways.

Conclusions

This study developed four DERRGs signatures and formulated a nomogram model for precise prognosis estimation in LUAD patients. The study's insights are instrumental for advancing diagnosis, prognosis, and therapeutic strategies for LUAD.

The role of RNA methylation in tumor immunity and its potential in immunotherapy

RNA methylation, a prevalent post-transcriptional modification, has garnered considerable attention in research circles. It exerts regulatory control over diverse biological functions by modulating RNA splicing, translation, transport, and stability. Notably, studies have illuminated the substantial impact of RNA methylation on tumor immunity. The primary types of RNA methylation encompass N6-methyladenosine (m6A), 5-methylcytosine (m5C), N1-methyladenosine (m1A), and N7-methylguanosine (m7G), and 3-methylcytidine (m3C). Compelling evidence underscores the involvement of RNA methylation in regulating the tumor microenvironment (TME). By affecting RNA translation and stability through the "writers", "erasers" and "readers", RNA methylation exerts influence over the dysregulation of immune cells and immune factors. Consequently, RNA methylation plays a pivotal role in modulating tumor immunity and mediating various biological behaviors, encompassing proliferation, invasion, metastasis, etc. In this review, we discussed the mechanisms and functions of several RNA methylations, providing a comprehensive overview of their biological roles and underlying mechanisms within the tumor microenvironment and among immunocytes. By exploring how these RNA modifications mediate tumor immune evasion, we also examine their potential applications in immunotherapy. This review aims to provide novel insights and strategies for identifying novel targets in RNA methylation and advancing cancer immunotherapy efficacy.

Identification of ALYREF in pan cancer as a novel cancer prognostic biomarker and potential regulatory mechanism in gastric cancer

ALYREF is considered as a specific mRNA m5C-binding protein which recognizes m5C sites in RNA and facilitates the export of RNA from the nucleus to the cytoplasm. Expressed in various tissues and highly involved in the transcriptional regulation, ALYREF has the potential to become a novel diagnostic marker and therapeutic target for cancer patients. However, few studies focused on its function during carcinogenesis and progress. In order to explore the role of ALYREF on tumorigenesis, TCGA and GTEx databases were used to investigate the relationship of ALYREF to pan-cancer. We found that ALYREF was highly expressed in majority of cancer types and that elevated expression level was positively associated with poor prognosis in many cancers. GO and KEGG analysis showed that ALYREF to be essential in regulating the cell cycle and gene mismatch repair in tumor progression. The correlation analysis of tumor heterogeneity indicated that ALYREF could be specially correlated to the tumor stemness in stomach adenocarcinoma (STAD). Furthermore, we investigate the potential function of ALYREF on gastric carcinogenesis. Prognostic analysis of different molecular subtypes of gastric cancer (GC) unfolded that high ALYREF expression leads to poor prognosis in certain subtypes of GC. Finally, enrichment analysis revealed that ALYREF-related genes possess the function of regulating cell cycle and apoptosis that cause further influences in GC tumor progression. For further verification, we knocked down the expression of ALYREF by siRNA in GC cell line AGS. Knockdown of ALYREF distinctly contributed to inhibition of GC cell proliferation. Moreover, it is observed that knocked-down of ALYREF induced AGS cells arrested in G1 phase and increased cell apoptosis. Our findings highlighted the essential function of ALYREF in tumorigenesis and revealed the specific contribution of ALYREF to gastric carcinogenesis through pan-cancer analysis and biological experiments.

ALYREF (Aly/REF export factor): A potential biomarker for predicting cancer occurrence and therapeutic efficacy

5-Methylcytosine (m5C) methylation is present in almost all types of RNA as an essential epigenetic modification. It is dynamically modulated by its associated enzymes, including m5C methyltransferases (NSUN, DNMT and TRDMT family members), demethylases (TET family and ALKBH1) and binding proteins (YTHDF2, ALYREF and YBX1). Among them, aberrant expression of the RNA-binding protein ALYREF can facilitate a variety of malignant phenotypes such as maintenance of proliferation, malignant heterogeneity, metastasis, and drug resistance to cell death through different regulatory mechanisms, including pre-mRNA processing, mRNA stability, and nuclear-cytoplasmic shuttling. The induction of these cellular processes by ALYREF results in treatment resistance and poor outcomes for patients. However, there are currently few reports of clinical applications or drug trials related to ALYREF. In addition, the looming observations on the role of ALYREF in the mechanisms of carcinogenesis and disease prognosis have triggered considerable interest, but critical evidence is not available. For example, animal experiments and ALYREF small molecule inhibitor trials. In this review, we, therefore, revisit the literature on ALYREF and highlight its importance as a prognostic biomarker for early prevention and as a therapeutic target.

Recent advances in the potential role of RNA N4-acetylcytidine in cancer progression

N4-acetylcytidine (ac4C) is a highly conserved chemical modification widely found in eukaryotic and prokaryotic RNA, such as tRNA, rRNA, and mRNA. This modification is significantly associated with various human diseases, especially cancer, and its formation depends on the catalytic activity of N-acetyltransferase 10 (NAT10), the only known protein that produces ac4C. This review discusses the detection techniques and regulatory mechanisms of ac4C and summarizes ac4C correlation with tumor occurrence, development, prognosis, and drug therapy. It also comments on a new biomarker for early tumor diagnosis and prognosis prediction and a new target for tumor therapy. Video Abstract.

TGF-β-regulated different iron metabolism processes in the development and cisplatin resistance of ovarian cancer

The impact of different iron metabolism processes (DIMP) on ovarian cancer remains unclear. In this study, we employed various gene chips and databases to investigate the role of DIMP in the initiation and development of ovarian cancer. cBioPortal was used to determine mutations in DIMP-associated genes in ovarian cancer. Kaplan-Meier plotter was used to examine the influence of DIMP on the prognosis of ovarian cancer. By analyzing 1669 serous ovarian cancer cases, we identified a range of mutations in iron metabolism genes, notably in those coding for the transferrin receptor (19%), melanotransferrin (19%), and ceruloplasmin (10%) in the iron import process, and glucose-6-phosphate isomerase (9%), hepcidin antimicrobial peptide (9%), metal regulatory transcription factor 1 (8%), and bone morphogenetic protein 6 (8%) in the iron regulation process. Compared to the unaltered group, the group with gene alterations exhibited a higher tumor mutation burden count (43 vs. 54) and more advanced histologic grade (78.19% vs. 87.90%). Compared to the normal ovarian counterparts, a reduction in expression was observed in 9 out of the 14 genes involved in iron utilization and 4 out of the 5 genes involved in iron export in ovarian cancer; in contrast, an increase in expression was observed in 2 out of the 3 genes involved in iron storage in ovarian cancer. Furthermore, in cisplatin-resistant cells compared to cisplatin-sensitive ones, the expression of all genes in iron storage and 13 out of 14 genes in iron import was decreased, while that of 8 out of the 10 genes in iron utilization was increased. In addition, survival curve analysis indicated that a higher expression in the majority of genes in the iron import process (12/21), or a reduced expression in most genes in the iron export process (4/5) correlated with poor progression-free survival. Additionally, TGF-β could regulate the expression of most iron metabolism-associated genes; particularly, expression of genes involved in the iron storage process (2/2) was inhibited after TGF-β1 or TGF-β2 treatment. In conclusion, DIMP plays multifaceted roles in the initiation, chemo-resistance, and prognosis of ovarian cancer. Therapeutically targeting DIMP may pave the way for more tailored treatment approaches for ovarian cancer.

Contribution of m5C RNA Modification-Related Genes to Prognosis and Immunotherapy Prediction in Patients with Ovarian Cancer

Background

5-Methylcytosine (m5C) RNA modification is closely implicated in the occurrence of a variety of cancers. Here, we established a novel prognostic signature for ovarian cancer (OC) patients based on m5C RNA modification-related genes and explored the correlation between these genes with the tumor immune microenvironment.

Methods

Methylated-RNA immunoprecipitation sequencing helped us to identify candidate genes related to m5C RNA modification at first. Based on TCGA database, we screened the differentially expressed candidate genes related to the prognosis and constructed a prognostic model using LASSO Cox regression analyses. Notably, the accuracy of the model was evaluated by Kaplan-Meier analysis and receiver operator characteristic curves. Independent prognostic risk factors were investigated by Cox proportional hazard model. Furthermore, we also analyzed the biological functions and pathways involved in the signature. Finally, the immune response of the model was visualized in great detail.

Results

Totally, 2,493 candidate genes proved to be involved in m5C modification of RNA for OC. We developed a signature with prognostic value consisting of six m5C RNA modification-related genes. Specially, samples have been split into two cohorts with low- and high-risk scores according to the model, in which the low-risk OC patients exhibited dramatically better overall survival time than those with high-risk scores. Besides, not only was this model a prognostic factor independent of other clinical characteristics but it predicted the intensity of the immune response in OC. Significantly, the accuracy and availability of the signature were verified by ICGC database.

Conclusions

Our study bridged the gap between m5C RNA modification and the prognosis of OC and was expected to provide an effective breakthrough for immunotherapy in OC patients.

The roles and mechanism of m6A RNA methylation regulators in cancer immunity

N⁶-methyladenosine (m⁶A), the most common internal modification in RNA, can be regulated by three types of regulators, including methyltransferases (writers), demethylases (erasers), and m⁶A binding proteins (readers). Recently, immunotherapy represented by immune checkpoint blocking has increasingly become an effective cancer treatment, and increasing shreds of evidence show that m⁶A RNA methylation affects cancer immunity in various cancers. Until now, there have been few reviews about the role and mechanism of m⁶A modification in cancer immunity. Here, we first summarized the regulation of m⁶A regulators on the expression of target messenger RNAs (mRNA) and their corresponding roles in inflammation, immunity response, immune process and immunotherapy in various cancer cells. Meanwhile, we described the roles and mechanisms of m⁶A RNA modification in tumor microenvironment and immune response by affecting the stability of non-coding RNA (ncRNA). Moreover, we also discussed the m⁶A regulators or its target RNAs which might be used as predictor of cancer diagnosis and prognosis, and shed light on the potentiality of m⁶A methylation regulators as therapeutic targets in cancer immunity.

Identification and verification of m7G-Related genes as biomarkers for prognosis of sarcoma

Background: Increasing evidence indicates a crucial role for N7-methylguanosine (m7G) methylation modification in human disease development, particularly cancer, and aberrant m7G levels are closely associated with tumorigenesis and progression via regulation of the expression of multiple oncogenes and tumor suppressor genes. However, the role of m7G in sarcomas (SARC) has not been adequately evaluated. Materials and methods: Transcriptome and clinical data were gathered from the TCGA database for this study. Normal and SARC groups were compared for the expression of m7G-related genes (m7GRGs). The expression of m7GRGs was verified using real-time quantitative PCR (RT-qPCR) in SARC cell lines. Then, differentially expressed genes (DEGs) were identified between high and low m7GRGs expression groups in SARC samples, and GO enrichment and KEGG pathways were evaluated. Next, prognostic values of m7GRGs were evaluated by Cox regression analysis. Subsequently, a prognostic model was constructed using m7GRGs with good prognostic values by Lasso regression analysis. Besides, the relationships between prognostic m7GRGs and immune infiltration, clinical features, cuproptosis-related genes, and antitumor drugs were investigated in patients with SARC. Finally, a ceRNA regulatory network based on m7GRGs was constructed. Results: The expression of ten m7GRGs was higher in the SARC group than in the control group. DEGs across groups with high and low m7GRGs expression were enriched for adhesion sites and cGMP-PKG. Besides, we constructed a prognostic model that consists of EIF4A1, EIF4G3, NCBP1, and WDR4 m7GRGs for predicting the survival likelihood of sarcoma patients. And the elevated expression of these four prognostic m7GRGs was substantially associated with poor prognosis and elevated expression in SARC cell lines. Moreover, we discovered that these four m7GRGs expressions were negatively correlated with CD4⁺ T cell levels, dendritic cell level and tumor purity, and positively correlated with tumor mutational burden, microsatellite instability, drug sensitivity and cuproptosis-related genes in patients with sarcomas. Then, a triple regulatory network of mRNA, miRNA, and lncRNA was established. Conclusion: The current study identified EIF4A1, EIF4G3, NCBP1, and WDR4 as prognostic genes for SARC that are associated with m7G.These findings extend our knowledge of m7G methylation in SARC and may guide the development of innovative treatment options.