Li Guo

m5C-Modified lncRNA SNHG15 Promotes Ovarian Cancer Progression Via the miR-545-3p/PD-L1 Axis

Ovarian cancer (OC) poses a health burden of women. Long non-coding RNA SNHG15 has been reported to promote OC progression; however, its effect on immune evasion remains unknown. Here, we investigated the effect of SNHG15 on OC cell immune evasion and the underlying mechanism. Cell phenotypes were assessed using cell counting kit-8, colony formation, lactate dehydrogenase cytotoxicity, and enzyme-linked immunosorbent assays. The mechanism was evaluated by dual-luciferase reporter assay, RNA pull-down, RNA immunoprecipitation (RIP), methylated RIP, and RNA stability assay. The role in vivo was analyzed using tumor-bearing mice. The results showed that SNHG15 and PD-L1 levels were increased, while miR-545-3p expression was reduced in OC. SNHG15 was a sponge of miR-545-3p, and PD-L1 was a downstream target of SNHG15. Knockdown of SNHG15 suppressed OC cell proliferation, and enhanced cytotoxicity and pro-inflammatory response of CD8

Screening for ferroptosis genes related to endometrial carcinoma and predicting of targeted drugs based on bioinformatics

Endometrial carcinoma is one of most common malignant tumors in women, and ferroptosis is closely related to the development and treatment of endometrial carcinoma. The aim of this study was to screen ferroptosis-related genes associated with endometrial carcinoma and predict targeted drugs through bioinformatics. 761 differentially expressed genes were obtained by the dataset GSE63678 from the GEO database, and most of the genes were enriched in the KEGG_CELL_CYCLE and KEGG_OOCYTE_MEIOSIS signaling pathways. 22 ferroptosis-differentially expressed genes were obtained by intersection with the FerrDb database. These genes were involved in biological processes including macromolecular complex assembly and others, and involved in signal pathways including glutathione metabolism, p53 signaling pathway and others. CDKN2A, IDH1, NRAS, TFRC and GOT1 were obtained as hub genes by PPI network analysis. GEPIA showed that CDKN2A, IDH1, NRAS and TFRC were significantly expressed in endometrial carcinoma. Immunohistochemical results showed that CDKN2A, NRAS and TFRC were significantly expressed in endometrial carcinoma clinical tissue samples. The ROC constructed by TCGA database showed that CDKN2A, NRAS and TFRC had significant value in the diagnosis of endometrial carcinoma, and all had prognostic efficacy. 136,572-09-3 BOSS and others were identified as potential targeted drugs for endometrial carcinoma targeting ferroptosis. Our study has shown that ferroptosis-related genes CDKN2A, NRAS and TFRC are diagnostic markers of endometrial carcinoma, and 136,572-09-3 BOSS, methyprylon BOSS, daunorubicin CTD 00005752, nitroglycerin BOSS and dUTP BOSS, IRON BOSS, Imatinib mesylate BOSS, 2-Butanone BOSS, water BOSS, and L-thyroxine BOSS may be potential therapeutic drugs.

Tumor-like ovarian endometriosis with pregnancy decidua reaction: A case report and review of the literature

We describe a case of bilateral ovarian tumor-like lesions detected during pregnancy. It is important to highlight that these masses were not detected for the first time during pregnancy; the patient had already been aware of them 2 years prior, during pregnancy preparation, when an ultrasound examination revealed bilateral space-occupying ovarian lesions. These lesions did not exhibit any increase in size during regular follow-ups until pregnancy. At 17 weeks of gestation, fetal ultrasound showed significant enlargement of the bilateral ovarian lesions. The patient underwent pelvic magnetic resonance imaging, which revealed cystic masses in both the ovaries with septations and multiple nodular and flocculent projections on the walls and septations, exhibiting features resembling malignant tumors. The cystic fluid within each cyst predominantly showed slightly short T1 and long T2 signal characteristics. The final diagnosis of lesions occupying the ovarian space was endometriotic cysts with a decidual reaction associated with pregnancy, which was confirmed on postoperative pathological examination. Subsequently, at 19 weeks of gestation, the patient underwent a “laparoscopic excision of the left ovarian lesion and right ovarian lesion stripping.” The patient recovered well postoperatively and successfully delivered a baby at 39 weeks of gestation. Endometriosis with decidual reaction during pregnancy is rare and ectopic decidual tissue can easily be confused with neoplastic lesions using imaging results. In addition, clinicians must remain vigilant about the special conditions that ectopic decidual tissue may cause, such as cyst rupture, massive hemorrhage, dystocia, and even fetal death.

Application of convolutional neural network for differentiating ovarian thecoma-fibroma and solid ovarian cancer based on MRI

Background Ovarian thecoma-fibroma and solid ovarian cancer have similar clinical and imaging features, and it is difficult for radiologists to differentiate them. Since the treatment and prognosis of them are different, accurate characterization is crucial. Purpose To non-invasively differentiate ovarian thecoma-fibroma and solid ovarian cancer by convolutional neural network based on magnetic resonance imaging (MRI), and to provide the interpretability of the model. Material and Methods A total of 156 tumors, including 86 ovarian thecoma-fibroma and 70 solid ovarian cancer, were split into the training set, the validation set, and the test set according to the ratio of 8:1:1 by stratified random sampling. In this study, we used four different networks, two different weight modes, two different optimizers, and four different sizes of regions of interest (ROI) to test the model performance. This process was repeated 10 times to calculate the average performance of the test set. The gradient weighted class activation mapping (Grad-CAM) was used to explain how the model makes classification decisions by visual location map. Results ResNet18, which had pre-trained weight, using Adam and one multiple ROI circumscribed rectangle, achieved best performance. The average accuracy, precision, recall, and AUC were 0.852, 0.828, 0.848, and 0.919 ( P < 0.01), respectively. Grad-CAM showed areas associated with classification appeared on the edge or interior of ovarian thecoma-fibroma and the interior of solid ovarian cancer. Conclusion This study shows that convolution neural network based on MRI can be helpful for radiologists in differentiating ovarian thecoma-fibroma and solid ovarian cancer.

Discriminating Between Benign and Malignant Solid Ovarian Tumors Based on Clinical and Radiomic Features of MRI

To develop and validate a combined model integrating clinical and radiomic features to non-invasive discriminate between the benign and malignant solid ovarian tumors. A total of 148 patients with 156 solid ovarian tumors (86 benign and 70 malignant tumors) were included in this study. The dataset was split into the training and the test set with a ratio of 8:2 using stratified random sampling. 12 clinical features and 1612 radiomic features were extracted from each tumor. These features were selected by least absolute shrinkage and selection operator (Lasso). Three classification models were built using extreme gradient boosting (XGB) algorithm: clinical model, radiomic model, combined model. The area under the receiver operating characteristic curve (AUC), accuracy, precision and sensitivity were analyzed to evaluate the performance of these models. All of the three models obtained good performances in differentiating benign with malignant solid ovarian tumors in both training and test sets. The AUC, accuracy, precision, sensitivity of clinical model and radiomic model in test set were 0.847 (95% confidence interval (CI), 0.707-0.986, p <0.01), 0.774, 0.769, 0.714, and 0.807 (95%CI, 0.652-0.961, p <0.05), 0.677, 0.643, 0.643, respectively. Combined model had the best prediction results, the AUC, accuracy, precision and sensitivity were 0.954 (95%CI, 0.862-1.0, p <0.01), 0.839, 0.909 and 0.714 in test set. Radiomics based on machine learning can be helpful for radiologists in differentiating the benign and malignant solid ovarian tumors.

Comparing the effects of argon plasma coagulation and interferon therapy in patients with vaginal intraepithelial neoplasia: a single-center retrospective study

Abstract Purpose This study aimed to evaluate the clinical efficacy and safety of argon plasma coagulation (APC) therapy and interferon therapy in patients with grade I and II vaginal intraepithelial neoplasia (VaIN). Methods A total of 112 patients with VaIN were diagnosed via colposcopy-induced biopsy and classified into the APC group ( n  = 77) and interferon group ( n  = 35). Clinical data including age, grade, symptoms, historical or concomitant neoplasia of the lower genital tract, indications for hysterectomy, pregnancy history, cytology, human papillomavirus (HPV) subtype, treatment modalities, and clinical outcomes were analyzed, retrospectively. Complications and clinical outcomes were assessed at 6- and 12-month follow-ups. Results There was no significant difference in the HPV clearance rate between the APC (53.42%) and interferon (33.33%) groups at 6 months after treatment. However, the 12-month follow-up of the APC group showed a significantly higher HPV clearance rate as compared to the interferon group (87.67% vs. 51.52%, P  &lt; 0.05). The APC group exhibited a significantly higher cure rate (79.22% vs. 40.0%) and lower persistence rate (12.99% vs. 37.14%) than the interferon group ( P  &lt; 0.05). Adverse reaction analysis revealed that the primary reaction in the APC group was vaginal drainage, in contrast to the increased vaginal discharge in the interferon group; though the difference was significant (68.83% vs. 28.57%, P  &lt; 0.05), no serious complications were observed. Conclusions Treatment with APC is a safe and more effective procedure against VaIN I and II, compared to interferon. APC may serve as a viable alternative to other physiotherapies.