DNM3OS Facilitates Ovarian Cancer Progression by Regulating miR-193a-3p/MAP3K3 Axis

DNM3OS Enhances the Apoptosis and Senescence of Spermatogonia Associated with Nonobstructive Azoospermia by Providing miR-214-5p and Decreasing E2F2 Expression

Background

Nonobstructive azoospermia (NOA) is a complex disease characterized by the spermatogenic dysfunction of testicular tissues. The roles played by long noncoding RNAs (lncRNAs) in NOA pathogenesis have not been extensively studied.

Methods

Microarray assays were performed on samples of testicular biopsy tissue obtained from patients with NOA for the purpose of identifying differentially expressed lncRNAs and messenger RNA (mRNA) transcripts, and the results were verified by quantitative real-time polymerase chain reaction. Mouse-derived GC-1 spermatogonia (spg) cells undergoing treatment with Adriamycin (ADR) were used to investigate the biological functions of the selected lncRNAs in vitro. The target microRNAs (miRNAs) of lncRNAs and the target mRNAs of miRNAs were predicted by a bioinformatics analysis. Functional studies performed using the CCK-8 assay, EdU incorporation assay, apoptosis detection, and senescence-associated β-galactosidase (SA-β-Gal) staining were conducted using GC-1 spg cells.

Results

Totals of 2,652 lncRNAs and 2,625 mRNAs were found to be differentially expressed in the testicular tissue of NOA patients when compared with patients in a control group. Dynamin 3 opposite strand (DNM3OS) was a provider of pe-miR-214-5p that positively regulates miR-214-5p expression in GC-1 spg cells. The E2 factor (E2F) family of transcription factor 2 (E2F2) was initially predicted and subsequently verified to be a downstream gene of miR-214-5p. E2F2 expression was upregulated after DNM3OS knockdown in ADR-treated GC-1 spg cells. Moreover, knockdown of either DNM3OS or miR-214-5p significantly alleviated ADR-induced decreases in cellular activity and proliferation, as well as increases in apoptosis and senescence of mouse spermatogonial GC-1 spg cells.

Conclusions

DNM3OS was found to regulate the apoptosis and senescence of spermatogonia by providing miR-214-5p and decreasing E2F2 expression, suggesting it as a novel target for gene therapy of male infertility.

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis

Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.

Hypoxia-Related lncRNA Prognostic Model of Ovarian Cancer Based on Big Data Analysis

Background

Hypoxia is regarded as a key factor in promoting the occurrence and development of ovarian cancer. In ovarian cancer, hypoxia promotes cell proliferation, epithelial to mesenchymal transformation, invasion, and metastasis. Long non-coding RNAs (lncRNAs) are extensively involved in the regulation of many cellular mechanisms, i.e., gene expression, cell growth, and cell cycle.

Materials and Methods

In our study, a hypoxia-related lncRNA prediction model was established by applying LASSO-penalized Cox regression analysis in public databases. Patients with ovarian cancer were divided into two groups based on the median risk score. The survival rate was analyzed in the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets, and the mechanisms were investigated.

Results

Through the prognostic analysis of DElncRNAs (differentially expressed long non-coding RNAs), a total of 5 lncRNAs were found to be closely associated with OS (overall survival) in ovarian cancer patients. It was evaluated through Kaplan-Meier analysis that low-risk patients can live longer than high-risk patients (TCGA: p = 1.302e - 04; ICGC: 1.501e - 03). The distribution of risk scores and OS status revealed that higher risk score will lead to lower OS. It was evaluated that low-risk group had higher immune score (p = 0.0064) and lower stromal score (p = 0.00023).

Conclusion

It was concluded that a hypoxia-related lncRNA model can be used to predict the prognosis of ovarian cancer. Our designed model is more accurate in terms of age, grade, and stage when predicting the overall survival of the patients of ovarian cancer.

The m6A-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Tumor Immune Infiltration in Ovarian Cancer

Background: OV is the most lethal gynecological malignancy. M6A and lncRNAs have a great impact on OV development and patient immunotherapy response. In this paper, we decided to establish a reliable signature of mRLs. Method: The lncRNAs associated with m6A in OV were analyzed and obtained by co-expression analysis of the TCGA-OV database. Univariate, LASSO and multivariate Cox regression analyses were employed to establish the model of mRLs. K-M analysis, PCA, GSEA and nomogram based on the TCGA-OV and GEO database were conducted to prove the predictive value and independence of the model. The underlying relationship between the model and TME and cancer stemness properties were further investigated through immune feature comparison, consensus clustering analysis and pan-cancer analysis. Results: A prognostic signature comprising four mRLs, WAC-AS1, LINC00997, DNM3OS and FOXN3-AS1, was constructed and verified for OV according to the TCGA and GEO database. The expressions of the four mRLs were confirmed by qRT-PCR in clinical samples. Applying this signature, one can identify patients more effectively. The samples were divided into two clusters, and the clusters had different overall survival rates, clinical features and tumor microenvironments. Finally, pan-cancer analysis further demonstrated that the four mRLs were significantly related to immune infiltration, TME and cancer stemness properties in various cancer types. Conclusions: This study provided an accurate prognostic signature for patients with OV and elucidated the potential mechanism of the mRLs in immune modulation and treatment response, giving new insights into identifying new therapeutic targets.