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Abstract LncRNAs are highly implicated in oxidative stress (OS) during the growth of mammalian follicles. TAK1 binding protein 2 gene (TAB2) has been suggested to involve in the normal apoptosis and proliferation of granulosa cells (GCs), the main supporting cells in ovarian follicles. In this study, we found that TAB2 increased the expressions of SOD1, P50, and P65 to suppress the OS, thereby inhibiting the apoptosis and promoting the proliferation in GCs. Notably, DNMTs appeared to mediate the expression of TAB2 without the changes of DNA methylation at TAB2’s promoter. We identified an antisense lncRNA of TAB2, discovered that DNA methylation regulated the transcription of TAB2-AS in GCs, and found TAB2-AS medicated the follicular growth of ovaries in vivo. Mechanistically, the hypomethylation of the CpG site (−1759/−1760) activated the transcription of TAB2-AS, and the 1–155 nt and 156-241 nt of TAB2-AS were respectively complementary to 4368–4534 nt and 4215–4300 nt of TAB2’s mRNA to increase the expression of TAB2. Moreover, TAB2-AS inhibited the OS and apoptosis of GCs, while promoted the proliferation of GCs to expedite the follicular growth, which was in line with that of TAB2. Collectively, these findings revealed the antisense lncRNA mechanism mediated by DNA methylation, and TAB2-AS might be the target to control OS during follicular growth in mammals.

An amendment to this paper has been published and can be accessed via the original article.

Abstract Background Ovarian cancer (OV) is one of the most common malignant tumors of gynecology oncology. The lack of effective early diagnosis methods and treatment strategies result in a low five-year survival rate. Also, immunotherapy plays an important auxiliary role in the treatment of advanced OV patient, so it is of great significance to find out effective immune-related tumor markers for the diagnosis and treatment of OV. Methods Based on the consensus clustering analysis of single-sample gene set enrichment analysis (ssGSEA) score transformed via The Cancer Genome Atlas (TCGA) mRNA profile, we obtained two groups with high and low levels of immune infiltration. Multiple machine learning methods were conducted to explore prognostic genes associated with immune infiltration. Simultaneously, the correlation between the expression of mark genes and immune cells components was explored. Results A prognostic classifier including 5 genes (CXCL11, S1PR4, TNFRSF17, FPR1 and DHRS95) was established and its robust efficacy for predicting overall survival was validated via 1129 OV samples. Some significant variations of copy number on gene loci were found between two risk groups and it showed that patients with fine chemosensitivity has lower risk score than patient with poor chemosensitivity (P = 0.013). The high and low-risk groups showed significantly different distribution (P