Combinatorial Chemistry & High Throughput Screening

A Five-microRNA Signature as Risk Stratification System in Uterine Corpus Endometrial Carcinoma

Background: MicroRNAs (miRs) have been shown to play important roles in various cancers and may be a reliable prognostic marker. However, its prognostic value in endometrial carcinoma (UCEC) needs to be further explored. Objectives: The aim of this study was to create a miR-based signature to effectively predict the prognosis for patients with uterine corpus endometrial carcinoma (UCEC). Methods: Using UCEC data set in TCGA, we identified differentially expressed miRs between UCEC and healthy endometrial tissues. The LASSO method was used to construct a miR-based signature prognosis index for predicting prognosis in the training cohort. The miR-based signature prognosis index was validated in an independent test cohort. MiRNet tool was applied to perform functional enrichment analysis of this miR-based signature. Results: A total of 208 miRs were differentially expressed between UCEC and healthy endometrial tissues. Five miRs (miR-652, miR-3170, miR-195, miR-34a, and miR-934) were identified to generate a prognosis index (PI). The five-miR signature is a promising biomarker for predicting the 5-year-survival rate of UCEC with AUC = 0.730. The PI remained an independent prognostic factor adjusted by routine clinicopathologic factors. Using the PI, we could successfully identify the high-risk individuals, furthermore, it still worked in an independent test cohort. The five miRs involved in various pathways associated with cancer. Conclusions: We proposed and validated a five-miR signature that could serve as an independent prognostic predictor of UCECs.

Development and Validation of a Five-immune Gene Pair Signature in Endometrial Carcinoma

Background: Endometrial cancer (EC) is a common gynecological malignancy worldwide. Immunity is closely related to the occurrence and prognosis of EC. At the same time, immune-related genes have great potential as prognostic markers in many types of cancer. Objective: Therefore, we attempt to develop immune-related gene markers to enhance prognosis prediction of EC. Methods: 542 samples of EC gene expression data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA). The samples were randomly divided into two groups, one group as a training set (N=271), and one set as a validation set. (N=271). In the training set, the gene pairs were established based on the relative expression levels of 271 immune genes, and the prognosis-related gene pairs were screened. The lasso was used to select the features, and finally, the robust biomarkers were screened. Finally, the prognostic model of the immune gene pair was established and verified by the validation data set. Results: 10030 immune gene pair (IRGPs) were obtained, and univariate survival analysis was used to identify 1809 prognostic-related IRGPs (p<0.05). 5-IRGPs were obtained by lasso regression feature selection, and multivariate regression was used to establish 5-IRGPs signature, 5-IRGPs signature is an independent prognostic factor for EC patients, and could be risk stratified in patients with TCGA datasets, age, ethnicity, stage, and histological classification (p#60;0.05). The mean AUC of survival in both the training set and the validation set was greater than 0.7, indicating that 5-IRGPs signature has superior classification performance in patients with EC. In addition, 5-IRGPs have the highest average C index (0.795) compared to the prognostic characteristics of the three endometrial cancers reported in the past and Stage and Age. Conclusion: This study constructed a 5-IRGPs signature as a novel prognostic marker for predicting survival in patients with EC.

Exploring the Mechanism of Brucea Javanica against Ovarian Cancer based on Network Pharmacology and the Influence of Luteolin on the PI3K/AKT Pathway

Background: Ovarian cancer (OC) is a commonly diagnosed female cancer around the world. The Chinese herbal medicine Brucea javanica has an anti-cancer effect. However, there is no relevant report on whether Brucea javanica is effective in treating OC, and the corresponding mechanism is also unknown. Objective: This study was projected to excavate the active components and underpinned molecular mechanisms of Brucea javanica in treating ovarian cancer (OC) through network pharmacology combined with in vitro experiments. Methods: The essential active components of Brucea javanica were selected using the TCMSP database. The OC-related targets were selected by GeneCards, intersecting targets were obtained by Venn Diagram. The core targets were obtained through the PPI network and Cytoscape, and the key pathway was gained through GO and KEGG enrichment analyses. Meanwhile, docking conformation was observed as reflected by molecular docking. MTT, colony formation assay and flow cytometer (FCM) analysis were performed to determine cell proliferation and apoptosis, respectively. Finally, Levels of various signaling proteins were evaluated by western blotting. Results: Luteolin, β-sitosterol and their corresponding targets were selected as the essential active components of Brucea javanica. 76 intersecting targets were obtained by Venn Diagram. TP53, AKT1, and TNF were obtained through the PPI network and Cytoscape, and the key pathway PI3K/AKT was gained through GO and KEGG enrichment analyses. A good docking conformation was observed between luteolin and AKT1. Luteolin could hinder A2780 cell proliferation, induce cell apoptosis and enhance the inhibition of the PI3K/AKT pathway. Conclusion: It was verified in vitro that luteolin could hinder OC cell proliferation and activate the PI3K/AKT pathway to lead to apoptosis.

Cervical Cancer Diagnosis: Insights into Biochemical Biomarkers and Imaging Techniques

Cervical malignancy is known as one of the important cancers which is originated from cervix. This malignancy has been observed in women infected with papillomavirus who had regular oral contraceptives, multiple pregnancies, and sexual relations. Early and fast cervical cancer diagnosis is known as two important aspects of cervical cancer therapy. Several investigations indicated that early and fast detection of cervical cancer could be associated with better treatment process and increasing survival rate of patients with this malignancy. Imaging techniques are very important diagnosis tools that could be employed for diagnosis and following responses to therapy in various cervical cancer stages. Multiple lines of evidence indicated that utilization of imaging techniques is related to some limitations (i.e. high cost, and invasive effects). Hence, it seems that along with using imaging techniques, finding and developing new biomarkers could be useful in the diagnosis and treatment of subjects with cervical cancer. Taken together, many studies showed that a variety of biomarkers including, several proteins, mRNAs, microRNAs, exosomes and polymorphisms might be introduced as prognostic, diagnostic and therapeutic biomarkers in cervical cancer therapy. In this review article, we highlighted imaging techniques as well as novel biomarkers for the diagnosis of cervical cancer.

Screening of Significant Biomarkers Related to Prognosis of Cervical Cancer and Functional Study Based on lncRNA-associated ceRNA Regulatory Network

Background:Cervical cancer (CESC), which threatens the health of women, has a very high recurrence rate.Purposes:This study aimed to identify the signature long non-coding RNAs (lncRNAs) associated with the prognosis of CESC and predict the prognostic survival rate with the clinical risk factors.Methods:The CESC gene expression profiling data were downloaded from TCGA database and NCBI Gene Expression Omnibus. Afterwards, the differentially expressed RNAs (DERs) were screened using limma package of R software. R package “survival” was then used to screen the signature lncRNAs associated with independently recurrence prognosis, and a nomogram recurrence rate model based on these signature lncRNAs was constructed to predict the 3-year and 5-year survival probability of CESC. Finally, a competing endogenous RNAs (ceRNA) regulatory network was proposed to study the functions of these genes.Results:We obtained 305 DERs significantly associated with prognosis. Afterwards, a risk score (RS) prediction model was established using the screened 5 signature lncRNAs associated with independently recurrence prognosis (DLEU1, LINC01119, RBPMS-AS1, RAD21-AS1 and LINC00323). Subsequently, a nomogram recurrence rate model, proposed with Pathologic N and RS model status, was found to have a good prediction ability for CESC. In ceRNA regulatory network, LINC00323 and DLEU1 were hub nodes which targeted more miRNAs and mRNAs. After that, 15 GO terms and 3 KEGG pathways were associated with recurrence prognosis and showed that the targeted genes PTK2, NRP1, PRKAA1 and HMGCS1 might influence the prognosis of CESC.Conclusion:The signature lncRNAs can help improve our understanding of the development and recurrence of CESC and the nomogram recurrence rate model can be applied to predict the survival rate of CESC patients in clinical practice.

Expression of MCMs in Endometrial Cancer and its Biological Correlation Analysis

Purpose: Minichromosome maintenance (MCM) has been demonstrated to be involved in tumorigenesis and pathogenesis of many cancer types. However, the role of MCMs in endometrial cancer (EC) has not been elucidated. Materials and Methods: We first employed GEPIA, cBioPortal, and R software to perform the differential expression analysis, survival analysis, and gene alteration analysis of the MCMs family. Then, GSE17025 and GSE63678 datasets and CTPAC were used to verify the mRNA and protein expression levels of MCM4. In addition, the internal mechanism of the MCM4 was investigated by comparing MCM4 expression-correlated differentially expressed genes (DEGs) from GEPIA and MCM4-interacted genes from STRING. Last, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to identify MCM4-related biological processes. Results: Compared with normal tissues, only MCM2 and MCM4 expression were significantly upregulated in EC tissues. High expression of MCM4 was related to worse clinicopathological features and poor prognosis in EC cohorts. Additionally, a certain degree of gene alterations in the MCM2-7 gene was observed. By comparing MCM4 expression-correlated DEGs and MCM4- interacted genes, six genes were obtained: SSRP1, ORC1, GINS1, CDK2, DBF4 and GINS3. Functional enrichment analysis suggested that MCM4 may be involved in regulating the biological processes of DNA replication and the p53 signaling pathway. Conclusion: This was the first comprehensive study to disclose the biological effects of MCMs in EC, indicating that MCM4 could be used as a new prognostic biomarker and potential therapeutic target for EC.

A Novel Risk Model of SUMOylation-related Genes Associated with Prognosis in Endometrial Cancer

Background: Endometrial cancer is ranked fourth in women's cancers worldwide. SUMOylation is a process of post-translational modification and some evidence indicate that SUMOylation may influence the occurrence and development of cancer. Until now, the prognostic value of SUMOylation-related genes in endometrial cancer remains unclear. Therefore, we aimed at exploring the prognostic value of SUMOylation-related genes in endometrial cancer in this study. Methods: The transcriptome of endometrial cancer from TCGA database was downloaded and then differentially expressed SUMOylation-related genes were extracted. The risk model was constructed with the use of the least absolute shrinkage and selection operator Cox regression. Samples were divided into low-risk and high-risk group based on the risk score. Survival analysis and Cox analysis were performed between groups. A validation cohort from Fudan University Shanghai Cancer Center were obtained to verify the model. Gene ontology and Kyoto Encylopedia of Genes and Genomes analyses were conducted based on differentially expressed genes between groups. Results: Samples in low-risk group possess better outcome than in high-risk group. (P<0.001) The results of univariate (P<0.001) and multivariate (P=0.018) analysis showed that the risk score was independently correlated to worse outcome for patients with endometrial cancer. In Fudan University Shanghai Cancer Center validation cohort, the low-risk group possessed better survival outcome than the high-risk group (P=0.0393). Functional analysis demonstrated that most of the immune cell infiltration levels and immune pathways activity in low-risk group were higher than in high-risk group. Conclusions: In short, the SUMOylation-related signature had good predictability in endometrial cancer and SUMOylation-related genes play important roles in tumour immunity. Also, our study might have some merits in elucidating potential mechanism of SUMOylation in endometrial cancer.