To investigate the function of Laminin G domain(LG domain) of STGC3 in human nasopharyngeal carcinoma cell line CNE2.Recombinant plasmids pcDNA3.1(+)-STGC3 and pcDNA3.1(+)-STGC3△1～42AA were respectively transfected into CNE2 cells by liposome-mediated transfection.Therefore,the CNE2/pcDNA3.1(+)-STGC3 and CNE2/pcDNA3.1(+)-STGC3△1～42AA cell lines of stable expression STGC3 were established.Here,it is reported that deletion of LG domain in this STGC3 reduces the tumor suppression activity of it,as demonstrated by drawing growth curve,experimenting plate clone formation as well as detecting cell cycle distribution.The results showed: the ability of STGC3△1～42AA suppressing CNE2 cell proliferation was obviously decreased to compare with wild type STGC3(n=4,P 0.05).These results indicate that the LG domain is necessary for the tumor suppression activity of the STGC3.
Abstract: Artificial intelligence (AI) is a sort of new technical science which can simulate, extend and expand human intelligence by developing theories, methods and application systems. In the last five years, the application of AI in medical research has become a hot topic in modern science and technology. Gynecological malignant tumors involves a wide range of knowledge, and AI can play an important part in these aspects, such as medical image recognition, auxiliary diagnosis, drug research and development, treatment scheme formulation and other fields. The purpose of this paper is to describe the progress of AI in gynecological malignant tumors and discuss some problems in its application. It is believed that AI improves the efficiency of diagnosis, reduces the burden of clinicians, and improves the effect of treatment and prognosis. AI will play an irreplaceable role in the field of gynecological malignant oncology and will promote the development of medicine and further promote the transformation from traditional medicine to precision medicine and preventive medicine. However, there are also some problems in the application of AI in gynecologic malignant tumors. For example, AI, inseparable from human participation, still needs to be more "humanized", and needs to further protect patients' privacy and health, improve legal and insurance protection, and further improve according to local ethnic conditions and national conditions. However, it is believed that with the continuous development of AI, especially ensemble classifier, and deep learning will have a profound influence on the future of medical technology, which is a powerful driving force for future medical innovation and reform. Keywords: artificial intelligence, gynecological malignant tumor, diagnosis, treatment, prognosis
Ovarian cancer (OC) is a gynecological oncology that has a poor prognosis and high mortality. This study is conducted to identify the key genes implicated in the prognosis of OC by bioinformatic analysis.Gene expression data (including 568 primary OC tissues, 17 recurrent OC tissues, and 8 adjacent normal tissues) and the relevant clinical information of OC patients were downloaded from The Cancer Genome Atlas database. After data preprocessing, cluster analysis was conducted using the ConsensusClusterPlus package in R. Using the limma package in R, differential analysis was performed to identify feature genes. Based on Kaplan-Meier (KM) survival analysis, prognostic seed genes were selected from the feature genes. After key prognostic genes were further screened by cluster analysis and KM survival analysis, they were performed functional enrichment analysis and multivariate survival analysis. Using the survival package in R, cox regression analysis was conducted for the microarray data of GSE17260 to validate the key prognostic genes.A total of 3668 feature genes were obtained, among which 75 genes were identified as prognostic seed genes. Then, 25 key prognostic genes were screened, including AXL, FOS, KLF6, WDR77, DUSP1, GADD45B, and SLIT3. Especially, AXL and SLIT3 were enriched in ovulation cycle. Multivariate survival analysis showed that the key prognostic genes could effectively differentiate the samples and were significantly associated with prognosis. Additionally, GSE17260 confirmed that the key prognostic genes were associated with the prognosis of OC.AXL, FOS, KLF6, WDR77, DUSP1, GADD45B, and SLIT3 might affect the prognosis of OC.
Ovarian cancer is a serious threat to women's health. Multidrug resistance is a major cause of post-treatment relapse, metastasis, and even mortality. This characteristic severely restricts the survival of patients with ovarian cancer. Integrin α-6 (ITGA6) is a member of the adhesion molecule family that conducts signals through interactions between the extracellular domain and the matrix, serving important roles in cell adhesion-mediated drug resistance, which is considered to have a critical function in ovarian cancer drug resistance. The association between ITGA6 and ovarian cancer multidrug resistance has been investigated only rarely, to the best of our knowledge. Using RT-qPCR and immunohistochemistry, it was identified that ITGA6 is a central drug resistance gene, and that its expression was upregulated in cisplatin-resistant SKOV3 (SKOV3/DDP2), cisplatin-resistant A2780 (A2780/DDP) cells, and in 54 cases of drug-resistant tissues, as compared with in the controls. Furthermore, bioinformatics and text mining performed by Coremine Medical (http://www.coremine.com/medical/#search) confirmed that ITGA6 was significantly associated with ovarian cancer and drug resistance. Additionally, the high expression of ITGA6 is associated with a poor outcome. The present study provides the basis for further understanding the role of ITGA6 in the regulation of drug resistance in ovarian cancer, and demonstrates that it could be a potential marker for the prognosis of ovarian cancer.
This study aimed to evaluate the efficacy and safety of bevacizumab in the treatment of recurrent ovarian cancer.The Cochrane Library, MEDLINE, and EMBASE were searched. Data regarding the use of bevacizumab in recurrent ovarian cancer were collected from randomized controlled trials (RCTs). Data were evaluated with the Cochrane systematic method, and statistical analysis was performed with the RevMan 5.2 software. Two RCTs comprising a total of 845 patients were included.Bevacizumab combined with conventional chemotherapy prolonged the progression-free survival (PFS) (hazard ratio [HR] 0.48; 95% confidence interval [CI], 0.41-0.56), without significantly altering the overall survival (OS) (HR 1.03; 95% CI 0.79-1.33). Adverse events (NCI-CTCAE v.4.0) associated with bevacizumab were ≥ grade 3 hypertension (relative risk [RR] 2.30; 95% CI 1.39-3.83) and bleeding (RR 4.76; 95% CI 1.38-16.37).Bevacizumab prolonged the PFS of patients with recurrent ovarian cancer. Additional high-quality randomized controlled trials are needed to verify these results.
To determine the accuracy and clinical value of combining 64 multi-slice spiral computer tomography (MSCT) and serum amyloid A protein (SAA) in the preoperative staging of rectal cancer.Prospectively enrolled patients with rectal cancer from October 2007 to October 2008. The patients were randomly assigned into two groups: MSCT and SAA combined group: both MSCT and SAA combinative assessment were performed for preoperative evaluation; MSCT group: only MSCT was performed preoperatively for tumor staging. The accuracy of the preoperative T, N, M, and TNM staging and the concordance rate of predictive operative strategy were compared between the two groups.Total of 225 cases with rectal cancer were enrolled in this study. There were 110 cases in MSCT and SAA combined group and 115 cases in MSCT group. The baseline characteristics was comparable between the two groups. For MSCT and SAA combined group, the accuracies of preoperative staging of T, N, M and TNM was 87.3%, 85.2%, 100% and 86.4%, respectively; and for MSCT group, the corresponding rates was 85.2%, 67.0%, 100% and 66.1%, respectively. Statistical differences was found in the accuracy of preoperative N and TNM staging between the two groups (P = 0.009 and 0.001, respectively). In addition, there was statistical difference in the accuracy of prediction to operative procedures between the two groups (94.7% vs. 81.7%, P = 0.003).Combinative assessment of MSCT and SAA could improve the accuracy of preoperative staging, and thus provide higher predictive coincidence rate of operative procedures.
Objective:To observe the expression of matrix metalloproteinase-7(MMP-7) and tissue inhibitors of matrix metalloproteinase-1(TIMP-1) in endmetriosis.and to study the possible role of MMP-7 and TIMP-1 in the pathogenesis of endmetriosis.Methods:The Expression of MMP-7 and TIMP-1 in 35 ectopic endometrium and 35 eutopic endometrium in endmetriosis,were detected by immunohistochemical methods in comparison with 20 women without endometriosis and cancer and the results were analyzed with SPSS statistical software.Results:①The expression levels of MMP-7 in ectopic and eutopic adenomyosis tissues were higher than those in the normal endometrium.The positive rates of MMP-7 were 57.1%,45.7% and 40.0% respectively(P0.05).Density of MMP-7expression in endometrium from endometriosis patients was significantly higher compared with endometrium without endometriosis(P0.05).② Expression of TIMP-1 was detected in all samples.Density of TIMP-1 expression in endometrium from endometriosis patients was lower compared with endometrium without endometriosis(P0.05).Conclusion:MMP-7 plays an important role in endometriosis.An increase of MMP-7expression of eutopic endometrium with endometriosis and decrease of TIMP-1expression of ectopic lesions may participate in the process of invasion and tissue modeling that is hypothesized to occur in the pathogenesis of endometriosis.
Objectives:To study the value of high-frequency ultrasonic and CDFI for the diagnosis of the endometriosis in the scar abdominal wall after caesarean section.Methods:26 patients who had been authenticated with the endometriosis in the scar abdominal wall by pathological examination were investigated via ultrasonography.Results:An intermediate frequency echo or low frequency echo conglomeration had been found in the scar abdominal wall,and the border was blurry,the shape was erose,the envelope was not found,and flecky liquid dark areas with low frequency echo photic points and zonary low frequency echo could be found in the conglomeration.CDFI: punctate and short club-shape or zonary blood stream signal could be found in the conglomeration.PW showed low speed and high resistance characteristic,Vs 5.0~20 cm/s,RI 0.65~8.2.Conclusion:Using high frequency ultrasonography combined with CDFI and PW,the exact diagnosis can be given to the endometriosis in the scar abdominal wall,thus good advise is provided for the clinical diagnosis.
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