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

Abstract

BackgroundOvarian cancer (OC) is a female reproductive system tumor. RNA modifications play key roles in gene expression regulation. The growing evidence demonstrates that RNA methylation is critical for various biological functions, and that its dysregulation is related to the progression of cancer in human.MethodOC samples were classified into different subtypes (Clusters 1 and 2) based on various RNA-modification regulatory genes (RRGs) in the process of RNA modifications (m1A, m6A, m6Am, m5C, m7G, ac4C, m3C, and Ψ) by nonnegative matrix factorization method (NMF). Based on differently expressed RRGs (DERRGs) between clusters, a pathologically specific RNA-modification regulatory gene signature was constructed with Lasso regression. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic ability of the identified model. The correlations of clinicopathological features, immune subtypes, immune scores, immune cells, and tumor mutation burden (TMB) were also estimated between different NMF clusters and riskscore groups.ResultsIn this study, 59 RRGs in the process of RNA modifications (m1A, m6A, m6Am, m5C, m7G, ac4C, m3C, and Ψ) were obtained from TCGA database. These RRGs were interactional, and sample clusters based on these regulators were significantly correlated with survival rate, clinical characteristics (involving survival status and pathologic stage), drug sensibility, and immune microenvironment. Furthermore, Lasso regression based on these 21 DERRGs between clusters 1 and 2 constructed a four-DERRG signature (ALYREF, ZC3H13, WTAP, and METTL1). Based on this signature, 307 OC patients were classified into high- and low-risk groups based on median value of riskscores from lasso regression. This identified signature was significantly associated with overall survival, radiation therapy, age, clinical stage, cancer status, and immune cells (involving CD4+ memory resting T cells, plasma cells, and Macrophages M1) of ovarian cancer patients. Further, GSEA revealed that multiple biological behaviors were significantly enriched in different groups.ConclusionsOC patients were classified into two subtypes per these RRGs. This study identified four-DERRG signature (ALYREF, ZC3H13, WTAP, and METTL1) in OC, which was an independent prognostic model for patient stratification, prognostic evaluation, and prediction of response to immunotherapy in ovarian cancer by classifying OC patients into high- and low-risk groups.

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

“…Previous studies found m7GRGs expression in ovarian cancer (OC) is significantly correlated with immune cells (involving CD4 + memory resting T cells, plasma cells, and Macrophages M1) (Zheng et al, 2022). In Hepatocellular carcinoma high m7G risk led to a decreased infiltration level of CD8 + T cells, whereas it increased the infiltration levels of Tregs and macrophages (Zhou et al, 2022).…”

Emerging role of m6A modification in ovarian cancer: progression, drug resistance, and therapeutic prospects

“…The four differentially expressed RNA-modification regulatory genes (DERRG) signature, comprising the Aly/REF export factor (ALYREF), ZC3H13, WTAP, and methyltransferase like 1 (METTL1), were recognized as a self-sufficient prognostic model in OC. This model is valuable for categorizing patients, assessing patient prognosis, and predicting patient response to immunotherapy in patients with OC ( 70 ). CACNA1G-AS1, ACAP2-IT1, AC010894.3, and UBA6-AS1 were discovered as prognostic signatures in OC, and each of these genes was associated with methyltransferase-like 5 (METTL5), RBM15, IGF2BP1, and YTH N6-methyladenosine RNA-binding protein C2 (YTHDC1), respectively, suggesting that they regulate the m6A regulatory gene ( 71 ).…”

Critical roles and clinical perspectives of RNA methylation in cancer

“…The different expression patterns of RNA modification regulators between cancers and para‐cancerous tissues enable researchers to accurately make a differential diagnosis of benign and malignant diseases 434,435 . The relationship between RNA modification levels and patient prognosis presents great opportunities for patients with more precise prognosis evaluation and personalized therapies 436–439 . Additionally, in the era of precision medicine, the clinical significance of metabolism‐focused RNA methylation extends to the potential development of personalized treatment strategies.…”

“… 434 , 435 The relationship between RNA modification levels and patient prognosis presents great opportunities for patients with more precise prognosis evaluation and personalized therapies. 436 , 437 , 438 , 439 Additionally, in the era of precision medicine, the clinical significance of metabolism‐focused RNA methylation extends to the potential development of personalized treatment strategies. Although current research is still in early stages and primarily focuses on basic investigations, numerous in vitro and in vivo assays have shown exhibited results for therapeutic approaches targeting RNA methylation in overcoming drug resistance in several cancer treatments.…”