Therapeutic Challenges in Patients with Gynecologic Carcinosarcomas: Analysis of a Multicenter National Cohort Study from the French Prospective TMRG Network

Background: Gynecological carcinosarcomas are rare and aggressive diseases, with a poor prognosis. The rarity of these tumors explains the lack of robust and specific data available in the literature. The objective of this study was to investigate the impact of initial adjuvant treatment and recurrent therapeutic strategies. Patients and methods: A multicentric cohort study within the French national prospective Rare Malignant Gynecological Tumors (TMRG) network was conducted. Data from all included carcinosarcomas diagnosed between 2011 and 2018 were retrospectively collected. Results: 425 cases of uterine and ovarian carcinosarcomas (n = 313 and n = 112, respectively) were collected and analyzed from 12 participating centers. At diagnosis, 140 patients (48%) had a FIGO stage III–IV uterine carcinosarcoma (UCS) and 88 patients (83%) had an advanced ovarian carcinosarcoma (OCS) (FIGO stage ≥ III). Two hundred sixty-seven patients (63%) received adjuvant chemotherapy, most preferably carboplatin-paclitaxel regimen (n = 227, 86%). After a median follow-up of 47.4 months, the median progression-free survival (mPFS) was 15.1 months (95% CI 12.3–20.6) and 14.8 months (95% CI 13.1–17.1) for OCS and UCS, respectively. The median overall survival for OCS and UCS was 37.1 months (95% CI 22.2–49.2) and 30.6 months (95% CI 24.1–40.9), respectively. With adjuvant chemotherapy followed by radiotherapy, mPFS was 41.0 months (95% CI 17.0–NR) and 18.9 months (95% CI 14.0–45.6) for UCS stages I–II and stages III–IV, respectively. In the early stage UCS subgroup (i.e., stage IA, n = 86, 30%), mPFS for patients treated with adjuvant chemotherapy (n = 24) was not reached (95% CI 22.2–NR), while mPFS for untreated patients (n = 62) was 19.9 months (95% IC 13.9–72.9) (HR 0.44 (0.20–0.95) p = 0.03). At the first relapse, median PFS for all patients was 4.2 months (95% CI 3.5–5.3). In the first relapse, mPFS was 6.7 months (95% CI 5.1–8.5) and 2.2 months (95% CI 1.9–2.9) with a combination of chemotherapy or monotherapy, respectively (p < 0.001). Conclusions: Interestingly, this vast prospective cohort of gynecological carcinosarcoma patients from the French national Rare Malignant Gynecological Tumors network (i) highlights the positive impact of adjuvant CT on survival in all localized stages (including FIGO IA uterine carcinosarcomas), (ii) confirms the importance of platinum-based combination as an option for relapse setting, and (iii) reports median PFS for various therapeutic strategies in the relapse setting.

A Comprehensive Overview of circRNAs: Emerging Biomarkers and Potential Therapeutics in Gynecological Cancers

Circular RNA (circRNA) is a highly conserved, stable and abundant non-coding RNA (ncRNA). Also, some circRNAs play an essential part in the progression of human cancers. CircRNA is different from traditional linear RNA. CircRNA has a closed circular structure, so it is resistant to exonuclease-mediated degradation and is more stable than linear RNA. Numerous studies have found that many circRNAs can act as a microRNA (miRNA) sponge, interact with RNA-binding proteins, regulate gene transcription, affect alternative splicing and be translated into proteins. Recently, some studies have also indicated that circRNA participates in the progression of gynecological cancers. In addition, circRNA can act as a promising biomarker for the diagnosis of gynecological tumors. Additionally, they can also play a key role in the prognosis of gynecological tumors. Furthermore, to our delight, circRNA may be a potential therapeutic target in gynecological cancers and widely used in clinical practice. This article reviews the functions and related molecular mechanisms of circRNAs in gynecological tumors, and discusses their potential as biomarkers for diagnostic and prognostic and therapeutic targets for gynecological cancers.

Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics

Background Ovarian cancer is one of the most common gynecological tumors, and among gynecological tumors, its incidence and mortality rates are fairly high. However, the pathogenesis of ovarian cancer is not clear. The present study aimed to investigate the differentially expressed genes and signaling pathways associated with ovarian cancer by bioinformatics analysis. Methods The data from three mRNA expression profiling microarrays (GSE14407, GSE29450, and GSE54388) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes between ovarian cancer tissues and normal tissues were identified using R software. The overlapping genes from the three GEO datasets were identified, and profound analysis was performed. The overlapping genes were used for pathway and Gene Ontology (GO) functional enrichment analysis using the Metascape online tool. Protein–protein interactions were analyzed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Subnetwork models were selected using the plugin molecular complex detection (MCODE) application in Cytoscape. Kaplan–Meier curves were used to analyze the univariate survival outcomes of the hub genes. The Human Protein Atlas (HPA) database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to validate hub genes. Results In total, 708 overlapping genes were identified through analyses of the three microarray datasets (GSE14407, GSE29450, and GSE54388). These genes mainly participated in mitotic sister chromatid segregation, regulation of chromosome segregation and regulation of the cell cycle process. High CCNA2 expression was associated with poor overall survival (OS) and tumor stage. The expression of CDK1, CDC20, CCNB1, BUB1B, CCNA2, KIF11, CDCA8, KIF2C, NDC80 and TOP2A was increased in ovarian cancer tissues compared with normal tissues according to the Oncomine database. Higher expression levels of these seven candidate genes in ovarian cancer tissues compared with normal tissues were observed by GEPIA. The protein expression levels of CCNA2, CCNB1, CDC20, CDCA8, CDK1, KIF11 and TOP2A were high in ovarian cancer tissues, which was further confirmed via the HPA database. Conclusion Taken together, our study provided evidence concerning the altered expression of genes in ovarian cancer tissues compared with normal tissues. In vivo and in vitro experiments are required to verify the results of the present study.