Proteomic Biomarkers for the Detection of Endometrial Cancer

Long non‑coding RNA SNHG12 regulates cell proliferation, invasion and migration in endometrial cancer by targeting miR‑4429

Long non-coding RNA small nucleolar RNA host gene 12 (SNHG12) has been demonstrated to be oncogenic. The aim of the present study was to examine the effects of SNHG12 on the progression of endometrial cancer (EC). The expression levels of SNHG12 and microRNA (miR)-4429 were assessed in EC cell lines by reverse transcription-quantitative PCR. Plasmids, including SNHG12 short hairpin RNAs (shRNAs), shRNA negative control (NC), SNHG12 overexpression (OV), OV-NC, miR-4429 mimic and mimic-NC, were transfected into RL95-2 cells. Post-transfection, Cell Counting Kit-8, Transwell Matrigel and wound-healing assays were performed to assess cell proliferation, invasion and migration, respectively. Cell cycle phase distribution was assessed by flow cytometry. The protein expression levels of matrix metalloproteinase (MMP)2 and MMP9 were detected by western blotting. miR-4429 target genes were predicted by bioinformatics analysis using target prediction online tools; the findings of this analysis were verified using a dual-luciferase reporter system. Identified as a target of miR-4429, SNHG12 was overexpressed in EC cell lines with decreased expression of miR-4429. Further experiments demonstrated that SNHG12 silencing and overexpression of miR-4429 markedly suppressed proliferation, migration and invasion of RL95-2 cells, arrested cells in the G₁ phase, and markedly downregulated the expression of MMP2 and MMP9. The opposite effects were observed in miR-4429 mimic-transfected RL95-2 cells after SNHG12 was overexpressed. The findings of the present study established the role of SNHG12 and miR-4429 in EC. Therefore, targeting the SNHG12/miR-4429 axis could serve as a potential future therapeutic target for treatment of EC.

Simultaneous FTIR and Raman Spectroscopy in Endometrial Atypical Hyperplasia and Cancer

Currently, endometrial carcinoma (EC) is the most common genital cancer in high-income countries. Some types of endometrial hyperplasia (EH) may be progressing to this malignancy. The diagnosis of EC and EH is based on time consuming histopathology evaluation, which is subjective and causes discrepancies in reassessment. Therefore, there is a need to create methods of objective evaluation allowing the diagnosis of early changes. The study aimed to simultaneously asses Fourier Transform Infrared (FTIR) and Raman spectroscopy combined with multidimensional analysis to identify the tissues of endometrial cancer, atypical hyperplasia and the normal control group, and differentiate them. The results of FTIR and Raman spectroscopy revealed quantitative and qualitative changes in the nucleic acid and protein in the groups of cancer and atypical hyperplasia, in comparison with the control group. Changes in the lipid region were also observed in Raman spectra. Pearson correlation coefficient demonstrated a statistically significant correlation between Raman spectra for the cancer and atypical hyperplasia groups (0.747, p < 0.05) and for atypical hyperplasia and the controls (0.507, p < 0.05), while FTIR spectra demonstrated a statistically significant positive correlation for the same group as in Raman data and for the control and cancer groups (0.966, p < 0.05). To summarize, the method of spectroscopy enables differentiation of atypical hyperplasia and endometrial cancer tissues from the physiological endometrial tissue.

A novel risk score system of immune genes associated with prognosis in endometrial cancer

Background

Endometrial cancer was the commonest gynecological malignancy in developed countries. Despite striking advances in multimodality management, however, for patients in advanced stage, targeted therapy still remained a challenge. Our study aimed to investigate new biomarkers for endometrial cancer and establish a novel risk score system of immune genes in endometrial cancer.

Methods

The clinicopathological characteristics and gene expression data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) of immune genes between tumors and normal tissues were identified. Protein–protein interaction (PPI) network of immune genes and transcriptional factors was integrated and visualized in Cytoscape. Univariate and multivariate analysis were employed for key genes to establish a new risk score system. Receiver operating characteristic (ROC) curve and survival analysis were performed to investigate the prognostic value of the model. Association between clinical characteristics and the model was analyzed by logistic regression. For validation, we identified 34 patients with endometrial cancer from Fudan University Shanghai Cancer Center (FUSCC). We detected 14-genes mRNA expression and calculated the risk scores of each patients and we performed survival analysis between the high-risk group and the low-risk group.

Results

23 normal tissues and 552 tumor tissues were obtained from TCGA database. 410 immune-related DEGs was identified by difference analysis and correlation analysis. KEGG and GO analysis revealed these DEGs were enriched in cell adhesion, chemotaxis, MAPK pathways and PI3K-Akt signaling pathway, which might regulate tumor progression and migration. All genes were screened for risk model construction and 14 hub immune-related genes (HTR3E, CBLC, TNF, PSMC4, TRAV30, PDIA3, FGF8, PDGFRA, ESRRA, SBDS, CRHR1, LTA, NR2F1, TNFRSF18) were prognostic in endometrial cancer. The area under the curve (AUC) was 0.787 and the high-risk group estimated by the model possessed worse outcome (P < 0.001). Multivariate analysis suggested that the model was indeed an independent prognostic factor (high-risk vs. low-risk, HR = 1.14, P < 0.001). Meanwhile, the high-risk group was prone to have higher grade (P = 0.002) and advanced clinical stage (P = 0.018). In FUSCC validation set, the high-risk group had worse survival than the low-risk group (P < 0.001).

Conclusions

In conclusion, the novel risk model of immune genes had some merits in predicting the prognosis of endometrial cancer and had strong correlation with clinical outcomes. Furthermore, it might provide new biomarkers for targeted therapy in endometrial cancer.