Background: Ovarian cancer (OV) is the leading cause of death in gynecological cancer. The dysregulation of N6-methyladenosine (m6A) modification is commonly found in cancers. However, there is a lack of research into m6A RNA methylation regulators in OV. Methods:The RNA-Seq of 379 OV tissues and 88 healthy ovarian tissues was downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, respectively. A Gene Ontology (GO) functional analysis was performed to verify the function of m6A RNA methylation regulators. Kaplan-Meier (K-M) curves and the log-rank (Mantel-Cox) test were used for the survival analysis. A Cox regression analysis was used to identify the genes related to overall survival (OS) and build the prediction model.Results: m6A RNA methylation regulators were dysregulated in OV tissues compared with normal tissues (P<0.05), and patients with a high expression of KIAA1429 and YTHDC2 had a poor prognosis (P<0.05).A prognostic model was constructed based on the m6A RNA methylation regulators. Based on the risk signature, the patients were classified into high-and low-risk groups. The low-risk group's OS rate was significantly better than that of the high-risk group. The validity and accuracy of the prognostic model were verified by using TCGA and Gene Expression Omnibus (GEO) datasets, and the risk score from the prognostic model acted as an independent prognostic indicator in predicting the survival of OV patients.Conclusions: m6A RNA methylation regulators were dysregulated in OV tissues. More importantly, the prognostic model comprising the five selected m6A RNA methylation regulators could be a valuable tool for predicting the prognosis of OV patients.

Background Increasing evidence has been confirmed that small nucleolar RNAs (SnoRNAs) play critical roles in tumorigenesis and exhibit prognostic value in clinical practice. However, there is short of systematic research on SnoRNAs in ovarian cancer (OV). Material/Methods 379 OV patients with RNA‐Seq and clinical parameters from TCGA database and 5 paired clinical OV tissues were embedded in our study. Cox regression analysis was used to identify prognostic SnoRNAs and construct prediction model. SNORic database was adopted to examine the copy number variation of SnoRNAs. ROC curves and KM plot curves were applied to validate the prognostic model. Besides, the model was validated in 5 paired clinical tissues by real‐time PCR, H&E staining and immunohistochemistry. Results A prognostic model was constructed on the basis of SnoRNAs in OV patients. Patients with higher RiskScore had poor clinicopathological parameters, including higher age, larger tumor size, advanced stage and with tumor status. KM plot analysis confirmed that patients with higher RiskScore had poorer prognosis in subgroup of age, tumor size, and stage. 7 of 9 SnoRNAs in the prognostic model had positive correlation with their host genes. Moreover, 5 of 9 SnoRNAs in the prognostic model correlated with their CNVs, and SNORD105B had the strongest correction with its CNVs. ROC curve showed that the RiskScore had excellent specificity and accuracy. Further, results of H&E staining and immunohistochemistry of Ki67, P53 and P16 confirmed that patients with higher RiskScore are more malignant. Conclusions In summary, we identified a nine‐SnoRNAs signature as an independent indicator to predict prognosis of OV, providing a prospective prognostic biomarker and potential therapeutic targets for ovarian cancer.

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Background Heart development protein with EGF‐like domains 1 (HEG1), generally related to angiogenesis and embryonic development, was reported to participate in the occurrence and progression of some tumors recently. However, the role of HEG1 in lung adenocarcinoma (LUAD) is unclear. Patients and Methods To explore the effect of HEG1 on LUAD, GEPIA platform and UALCAN database, as well as Kaplan–Meier plotter were adopted to analyze the association of HEG1 with clinicopathological characteristics and survival outcomes for LUAD firstly. And then the HEG1 in LUAD tissues, blood and cell lines were detected by qRT‐PCR, western blot, immunofluorescence, immunohistochemistry, and ELISA. Gene set enrichment analysis (GSEA) was conducted to identify pathways that might be affected by HEG1 in LUAD. Results In this study, HEG1 in lung tissues and cell lines of LUAD were significantly downregulated compared to benign pulmonary disease tissues and alveolar epithelial cells ( p < 0.05). Moreover, compared with other groups, patients with advanced tumor stage had lower HEG1 mRNA expression levels ( p = 0.025), which were negatively correlated with Ki67 index in tumor tissues ( r = −0.427, p = 0.033). On the other hand, the LUAD patients with lower HEG1 had shorter overall survival (OS) (HR = 0.51, 95% CI: 0.40–0.65, p < 0.001) according to Kaplan–Meier plotter. In addition, HEG1 in serum of LUAD patients was negatively associated with CEA ( r = −0.636, p < 0.001). GSEA showed that HEG1 was enriched in various metabolic‐related pathways, including glucose metabolism, lipid metabolism, and nucleotide metabolism signaling. Conclusions HEG1 was downregulated in LUAD patients and associated with poor prognosis, which indicating HEG1 may serve as a potential biomarker for diagnosis and prognosis of LUAD.