USP39 interacts with SIRT7 to promote cervical squamous cell carcinoma by modulating autophagy and oxidative stress via FOXM1.

BACKGROUND: Sirtuin 7 (SIRT7) is an oncogene that promotes tumor progression in various malignancies, however, its role and regulatory mechanism in cervical squamous cell carcinoma (CSCC) is unknown. Herein, we attempted to investigate the functional role and molecular mechanism of SIRT7 underlying CSCC progression. METHODS: SIRT7 expression was evaluated in CSCC cells using various assays. We then used a series of function gain-and-loss experiments to determine the role of SIRT7 in CSCC progression. Furthermore, mechanism experiments were conducted to assess the interaction between SIRT7/USP39/FOXM1 in CSCC cells. Additionally, rescue assays were conducted to explore the regulatory function of USP39/FOXM1 in CSCC cellular processes. RESULTS: SIRT7 was highly expressed in CSCC patient tissues and cell lines. SIRT7 deficiency showed significant repression on the proliferation, and autophagy of CSCC cells in vitro and tumorigenesis in vivo. Similarly, apoptosis and ROS production in CSCC cells were accelerated after the SIRT7 knockdown. Moreover, SIRT7 and USP39 were found colocalized in the cell nucleus. Interestingly, SIRT7 was revealed to deacetylate USP39 to promote its protein stability in CSCC cells. USP39 protein was also verified to be upregulated in CSCC tissues and cells. USP39 silencing showed suppressive effects on CSCC cell growth. Mechanistically, USP39 was revealed to upregulate SIRT7 by promoting the transcriptional activity of FOXM1. Rescue assays also indicated that SIRT7 promoted autophagy and inhibited ROS production in CSCC cells by regulating USP39/FOXM1. CONCLUSION: The SIRT7/USP39/FOXM1 positive feedback network regulates autophagy and oxidative stress in CSCC, thus providing a new direction for CSCC-targeted therapy.

Identification of metastasis and prognosis-associated genes for serous ovarian cancer.

Serous ovarian cancer is one of the most fatal gynecological tumors with an extremely low 5-year survival rate. Most patients are diagnosed at an advanced stage with wide metastasis. The dysregulation of genes serves an important role in the metastasis progression of ovarian cancer. Differentially expressed genes (DEGs) between primary tumors and metastases of serous ovarian cancer were screened out in the gene expression profile of GSE73168 from Gene Expression Omnibus (GEO). Cytoscape plugin cytoHubba and weighted gene co-expression network analysis (WGCNA) were utilized to select hub genes. Univariate and multivariate Cox regression analyses were used to screen out prognosis-associated genes. Furthermore, the Oncomine validation, prognostic analysis, methylation mechanism, gene set enrichment analysis (GSEA), TIMER database analysis and administration of candidate molecular drugs were conducted for hub genes. Nine hundred and fifty-seven DEGs were identified in the gene expression profile of GSE73168. After using Cytoscape plugin cytoHubba, 83 genes were verified. In co-expression network, the blue module was most closely related to tumor metastasis. Furthermore, the genes in Cytoscape were analyzed, showing that the blue module and screened 17 genes were closely associated with tumor metastasis. Univariate and multivariate Cox regression revealed that the age, stage and STMN2 were independent prognostic factors. The Cancer Genome Atlas (TCGA) suggested that the up-regulated expression of STMN2 was related to poor prognosis of ovarian cancer. Thus, STMN2 was considered as a new key gene after expression validation, survival analysis and TIMER database validation. GSEA confirmed that STMN2 was probably involved in ECM receptor interaction, focal adhesion, TGF beta signaling pathway and MAPK signaling pathway. Furthermore, three candidate small molecule drugs for tumor metastasis (diprophylline, valinomycin and anisomycin) were screened out. The quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and western blot showed that STMN2 was highly expressed in ovarian cancer tissue and ovarian cancer cell lines. Further studies are needed to investigate these prognosis-associated genes for new therapy target.

MiR-200a-3p promoted the malignant behaviors of ovarian cancer cells through regulating PCDH9.

BACKGROUND: Increasing evidence has revealed that the aberrant expression of microRNAs (miRNAs) plays vital roles in the development and progression of ovarian cancer. MiR-200a-3p was found to act as an oncogene in a variety of cancers, however, the expression and function of miR-200a-3p in ovarian cancer has not been characterized. MATERIALS AND METHODS: The expression of miR-200a-3p in ovarian cancer tissues and cell lines was detected by the RT-qPCR. The influence of miR-200a-3p on the growth of ovarian cancer cells was determined with the Cell Counting Kit-8 assay, colony formation and cell invasion assay. The binding of miR-200a-3p with the 3'-untranslated region (UTR) of PDCH9 was detected by luciferase reporter assay. The expression of PCDH9 was investigated by RT-qPCR and Western blot analysis. RESULTS: miR-200a-3p was up-regulated in ovarian cancer tissues and cell lines. Highly expressed miR-200a-3p was significantly associated with the tumor size, tumor metastasis and TNM stage. Overexpression of miR-200a-3p markedly promoted the proliferation, colony formation and invasion of ovarian cancer cells. Functional study uncovered that miR-200a-3p bound the 3'-untranslated region (UTR) of PCDH9 and decreased the expression of PCDH9 in ovarian cancer cells. The expression of miR-200a-3p in ovarian cancer tissues was significantly negatively correlated with that of PCDH9. Restored PCDH9 inhibited the promoting effect of miR-200a-3p on the proliferation of ovarian cancer cells. CONCLUSION: Our results suggested the potential oncogenic function of miR-200a-3p via modulating PCDH9 in ovarian cancer.

Comparison of efficacy of robotic surgery, laparoscopy, and laparotomy in the treatment of ovarian cancer: a meta-analysis.

BACKGROUND: We intended to compare the clinical effect of robotic surgery with laparoscopy and laparotomy in ovarian cancer treatment. METHODS: The included studies were retrieved from PubMed, Embase, and the Cochrane Library databases. The Methodological Index for Nonrandomized Studies (MINORS) was used to evaluate the study quality. Effect measures were presented with weighted mean difference (WMD)/odds ratio (OR) and 95% confidence interval (CI), and heterogeneity test was assessed using Q test and I2 statistics to determine the use of the random effects model or fixed effects model. Egger's test was used to assess the publication bias. RESULTS: A total of eight studies was included in this meta-analysis with a MINORS score of 16-18. In the random effects model, estimated blood loss (EBL) of robotic surgery was significantly less compared with laparotomy (WMD = - 521.7027, 95% CI - 809.7816; - 233.6238). In the fixed effects model, length of hospital stay (LHS) (WMD = - 5.2225, 95% CI - 6.1485; - 4.2965) and postoperative complication (PC) (OR = 0.4710, 95% CI 0.2537; 0.8747) of robotic surgery were significantly less, and overall survival (OS) rate (OR = 6.4355, 95% CI 1.6722; 24.7678, P = 0.0070) of robotic surgery was significantly higher compared with laparotomy. There was no difference in the effect size of all variables between robotic surgery and laparoscopy. Meanwhile, a publication bias (t = 6.8290, P = 0.002405) was only identified for PC in robotic surgery and laparotomy groups; no publication bias was identified for the other variables. CONCLUSIONS: Despite the above results, it failed to show oncological safety and recurrence by pathological stages or histologic types in this meta-analysis, and those confounding factors might affect the clinical outcome. Future meta-analyses with a larger number of eligible randomized controlled trial studies were needed to determine the most suitable treatment method for patients with different stages and types of ovarian cancer.

Optimal subset of signature miRNAs consisting of 7 miRNAs that can serve as a novel diagnostic and prognostic predictor for the progression of cervical cancer.

Cervical cancer is the second most commonly diagnosed cancer in women. Novel prognostic biomarkers are required to predict the progression of cervical cancer. Cervical cancer expression data were obtained from The Cancer Genome Atlas (TCGA) database. MicroRNAs (miRNAs) significantly differentially expressed between early­ and advanced­stage samples were identified by expression analysis. An optimal subset of signature miRNAs for pathologic stage prediction was delineated using the random forest algorithm and was used for the construction of a cervical cancer­specific support vector machine (SVM) classifier. The roles of signature miRNAs in cervical cancer were analyzed by functional annotation. In total, 44 significantly differentially expressed miRNAs were identified. An optimal subset of 7 signature miRNAs was identified, including hsa­miR­144, hsa­miR­147b, hsa­miR­218­2, hsa­miR­425, hsa­miR­451, hsa­miR­483 and hsa­miR­486. The signature miRNAs were used to construct an SVM classifier and exhibited a good performance in predicting pathologic stages of samples. SVM classification was found to be an independent prognostic factor. Functional enrichment analysis indicated that these signature miRNAs are involved in tumorigenesis. In conclusion, the subset of signature miRNAs could potentially serve as a novel diagnostic and prognostic predictor for cervical cancer.

Identifying Molecular Markers of Cervical Cancer Based on Competing Endogenous RNA Network Analysis.

AIM: Recurrence being a major challenge for the treatment of cervical cancer, we aimed at identifying novel molecular markers of cervical cancer to improve recurrence prediction. METHODS: Cervical cancer samples were obtained from the Cancer Genome Atlas. Prognosis-associated long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs between recurrent and nonrecurrent samples were acquired using expression analysis. Regulatory relationships among these prognosis-associated RNAs were predicted and used to construct a competing endogenous RNA (ceRNA) network. Key prognostic lncRNAs, miRNAs, and mRNAs were identified using the ceRNA network, followed by the Kaplan-Meier survival analysis to reveal the influence of these key prognostic RNAs on prognosis. RESULTS: In total, 15 lncRNAs, 10 miRNAs, and 348 mRNAs were identified as significant prognosis-associated molecules. The cervical cancer-related ceRNA network contained 13 prognosis-associated lncRNAs, 5 prognosis-associated miRNAs, and 120 prognosis-associated mRNAs. Key prognostic lncRNAs, miRNAs, and mRNAs were further identified using the ceRNA network. The key prognostic lncRNAs included H19 and HOTAIR, those for miRNAs included hsa-miR-133b, hsa-miR-138, and hsa-miR-301b, and those for mRNAs included Wnt family member 2, fibroblast growth factor 7, fibronectin 1, synaptic vesicle glycoprotein 2A, and bone morphogenetic protein 7. CONCLUSION: The key prognostic lncRNAs, miRNAs, and mRNAs may serve as potential molecular markers for recurrence prediction and may contribute to the treatment of cervical cancer.

The long noncoding RNA LINC00473, a target of microRNA 34a, promotes tumorigenesis by inhibiting ILF2 degradation in cervical cancer.

Cervical cancer is one of the most common gynecological malignancies in women worldwide. The long non-coding RNA (lncRNA) LINC00473 is increased in some human cancer tissues and it plays important roles in tumorigenesis. However, neither the expression pattern nor the biological functions of LINC00473 have been elucidated in cervical cancer so far. In the present study, gain- and loss-of-function assays showed that LINC00473 promoted cell proliferation and inhibited cell apoptosis in cervical cancer cells in vitro. Moreover, we found that LINC00473 enhanced the growth of cervical cancer cells in vivo. Mechanistic investigation showed that LINC00473 directly interacted with ILF2 and suppressed its degradation. Finally, we demonstrated that miR-34a reduced the stability of LINC00473. These findings may have important implications for developing novel therapeutic strategies for cervical cancer.