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Methylation is the main form of RNA modification. N6‐methyladenine (m6A) regulates the splicing and translation of mRNA. Alk B homologue 5 (ALKBH5) participates in the biological regulation of various cancers. However, its role in ovarian carcinogenesis has not been unveiled. In the present study, ALKBH5 showed higher expression in ovarian cancer tissue than in normal ovarian tissue, but lower expression in ovarian cancer cell lines than in normal ovarian cell lines. Interestingly, Toll‐like receptor (TLR4), a molecular functioning in tumour microenvironment (TME), demonstrated the same expression trend. To investigate the effect of abnormal TME on ovarian carcinogenesis, we established an in vitro model in which macrophages and ovarian cancer cells were co‐cultured. In the ovarian cancer cells co‐cultured with M2 macrophages, the expression of ALKBH5 and TLR4 increased. We also verified that TLR4 up‐regulated ALKBH5 expression via activating NF‐κB pathway. Depending on transcriptome sequencing, m6A‐Seq and m6A MeRIP, we found that NANOG served as a target in ALKBH5‐mediated m6A modification. NANOG expression increased after mRNA demethylation, consequently enhancing the aggressiveness of ovarian cancer cells. In conclusion, highly expressed TLR4 activated NF‐κB pathway, up‐regulated ALKBH5 expression and increased m6A level and NANOG expression, all contributing to ovarian carcinogenesis. Our study revealed the role of m6A in ovarian carcinogenesis, providing a clue for inventing new target therapy. [ABSTRACT FROM AUTHOR]

In this paper, we establish global boundedness and Hölder regularity of the weak solution to the following parabolic p(x,t)‐Laplace equation: ut−diva(x,t,u,∇u)=b(x,t,u,∇u)inQT,u(x,t)=0on∂Ω×(0,T),u(x,0)=u0inΩ,under some proper assumptions on a and b. The Hölder continuity given in the present work extends known results to the equation with a general nondivergence term. [ABSTRACT FROM AUTHOR]

Endometrial cancer (EC) is a fatal female reproductive tumor. Bioinformatic tools are increasingly developed to screen out molecular targets related to EC. In this study, GSE17025 and GSE40032 were obtained from Gene Expression Omnibus (GEO). "limma" package and Venn diagram tool were used to identify hub genes. FunRich was used for functional analysis. Retrieval of Interacting Genes Database (STRING) was used to analyze protein‐protein interaction (PPI) complex. Cancer Genome Atlas (TCGA), GEPIA, immunohistochemistry staining, and ROC curve analysis were carried out for validation. Univariate and multivariate regression analyses were performed to predict the risk score. Compound muscle action potential (CMap) was used to find potential drugs. GSEA was also done. We retrieved seven oncogenes which were upregulated and hypomethylated and 12 tumor suppressor genes (TSGs) which were downregulated and hypermethylated. The upregulated and hypomethylated genes were strikingly enriched in term "immune response" while the downregulated and hypermethylated genes were mainly focused on term "aromatic compound catabolic process." TCGA and GEPIA were used to screen out EDNRB, CDO1, NDN, PLCD1, ROR2, ESPL1, PRAME, and PTTG1. Among them, ESPL1 and ROR2 were identified by Cox regression analysis and were used to construct prognostic risk model. The result showed that ESPL1 was a negative independent prognostic factor. Cmap identified aminoglutethimide, luteolin, sulfadimethoxine, and maprotiline had correlation with EC. GSEA results showed that "hedgehog signaling pathway" was enriched. This research inferred potential aberrantly methylated DEGs and dysregulated pathways may participate in EC development and firstly reported eight hub genes, including EDNRB, CDO1, NDN, PLCD1, ROR2, ESPL1, PRAME, and PTTG1 that could be used to predict EC prognosis. Aminoglutethimide and luteolin may be used to fight against EC. [ABSTRACT FROM AUTHOR]

Cervical cancer is the fourth most common malignancy in women worldwide and cervical squamous cell carcinoma (CESC) is the most common histological type of cervical cancer. The dysregulation of genes plays a significant role in cancer. In the present study, we screened out differentially expressed genes (DEGs) of CESC in the GSE63514 data set from the Gene Expression Omnibus database. An integrated bioinformatics analysis was used to select hub genes, as well as to investigate their related prognostic signature, functional annotation, methylation mechanism, and candidate molecular drugs. As a result, a total of 1907 DEGs were identified (944 were upregulated and 963 were downregulated). In the protein–protein interaction network, three hub modules and 30 hub genes were identified. And two hub modules and 116 hub genes were screened out from four CESC‐related modules by the weighted gene coexpression network analysis. The gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to better understand functions and pathways. Genes with a significant prognostic value were found by prognostic signature analysis. And there were five genes (EPHX2, CHAF1B, KIAA1524, CDC45, and RMI2) identified as significant CESC‐associated genes after expression validation and survival analysis. Among them, EPHX2 and RMI2 were noted as two novel key genes for the CESC‐associated methylation and expression. In addition, four candidate small molecule drugs for CESC (camptothecin, resveratrol, vorinostat, and trichostatin A) were defined. Further studies are required to explore these significant CESC‐associated genes for their potentiality in diagnosis, prognosis, and targeted therapy. [ABSTRACT FROM AUTHOR]