Single-cell RNA-sequencing analysis reveals divergent transcriptome events between platinum-sensitive and platinum-resistant high-grade serous ovarian carcinoma.

BACKGROUND: Tumor resistance is one of the main reasons leading to the failure of ovarian cancer treatment. Overcoming platinum resistance remains the greatest challenge in the management of high-grade serous ovarian carcinoma (HGSC). METHODS: Small conditional RNA-sequencing is a powerful method for exploring the complexity of the cellular components and their interactions in the tumor microenvironment. We profiled the transcriptomes of 35,042 cells from two platinum-sensitive and three platinum resistance HGSC clinical cases downloaded from Gene Expression Omnibus (GSE154600) and annotated tumor cells as platinum-resistant or sensitive based on the clinical trait. The study systematically investigated the inter-tumoral (using differential expression analysis, CellChat, and SCENIC) and intra-tumoral heterogeneity (using enrichment analysis such as gene set enrichment analysis, as well as gene set variation analysis, weighted gene correlation network analysis, and Pseudo-time analysis) of HGSC. RESULTS: A cellular map of HGSC generated by profiling 30,780 cells was revisualized using Uniform Manifold Approximation and Projection. The inter-tumoral heterogeneity was demonstrated with intercellular ligand-receptor interactions of major cell types and regulons networks. FN1, SPP1, and COLLAGEN play important roles in the cross-talk between tumor cells and the tumor microenvironment. HOXA7, HOXA9_extended, TBL1XR1_extended, KLF5, SOX17, and CTCFL regulons consistent with the distribution of platinum-resistant HGSC cells were the high activity regions. The intra-tumoral heterogeneity of HGSC was presented with corresponding functional pathway characteristics, tumor stemness features, and the cellular lineage transition from platinum-sensitive to resistant condition. Epithelial-mesenchymal transition played an important role in platinum resistance, whereas oxidative phosphorylation was the opposite. There was a small subset of cells in platinum-sensitive samples that had transcriptomic characteristics similar to platinum-resistant cells, suggesting that the progression of platinum resistance in ovarian cancer is inevitable. CONCLUSIONS: The present study describes a view of HGSC at single-cell resolution that reveals the characteristics of the HGSC heterogeneity and provides a useful framework for future investigation of platinum-resistant.

Construction of a prognostic signature for serous ovarian cancer based on lactate metabolism-related genes.

Background: The key biochemical feature of malignant tumor is the conversion of energy metabolism from oxidative phosphorylation to glycolysis, which provides sufficient capacity and raw materials for tumor cell rapid growth. Our study aims to construct a prognostic signature for ovarian cancer based on lactate metabolism-related genes (LMRGs). Methods: Data of ovarian cancer and non-diseased ovarian data were downloaded from TCGA and the GTEx database, respectively. LMRGs were obtained from GeneCards and MSigDB databases, and the differentially expressed LMRGs were identified using limma and DESeq2 R packages. Cox regression analysis and LASSO were performed to determine the LMRGs associated with OS and develop the prognostic signature. Then, clinical significance of the prognostic signature in ovarian cancer was assessed. Results: A total of 485 differentially expressed LMRGs in ovarian tissue were selected for subsequent analysis, of which 324 were up-regulated and 161 were down regulated. We found that 22 LMRGs were most significantly associated with OS by using the univariate regression analysis. The prognostic scoring model was consisted of 12 LMRGs (SLCO1B3, ERBB4, SLC28A1, PDSS1, BDH1, AIFM1, TSFM, PPARGC1A, HGF, FGFR1, ABCC8, TH). Kaplan-Meier survival analysis indicated that poorer overall survival (OS) in the high-risk group patients (P<0.0001). This prognostic signature could be an independent prognostic indicator after adjusting to other clinical factors. The calibration curves of nomogram for the signature at 1, 2, and 3 years and the ROC curve demonstrated good agreement between the predicted and observed survival rates of ovarian cancer patients. Furthermore, the high-risk group patients have much lower expression level of immune checkpoint-TDO2 compared with the low-risk group (P=0.024). Conclusions: We established a prognostic signature based on LMRGs for ovarian cancer, and highlighted emerging evidence indicating that this prognostic signature is a promising approach for predicting ovarian cancer prognosis and guiding clinical therapy.

Development and Validation of an Immune-Related Prognostic Signature in Cervical Cancer.

Background: Cervical cancer is the fourth most frequent gynecological malignancy across the world. Immunotherapies have proved to improve prognosis of cervical cancer. However, few studies on immune-related prognostic signature had been reported in cervical cancer. Methods: Raw data and clinical information of cervical cancer samples were downloaded from TCGA and UCSC Xena website. Immunophenoscore of immune infiltration cells in cervical cancer samples was calculated through the ssGSEA method using GSVA package. WGCNA, Cox regression analysis, LASSO analysis, and GSEA analysis were performed to classify cervical cancer prognosis and explore the biological signaling pathway. Results: There were eight immune infiltration cells associated with prognosis of cervical cancer. Through WGCNA, 153 genes from 402 immune-related genes were significantly correlated with prognosis of cervical cancer. A 15-gene signature demonstrated powerful predictive ability in prognosis of cervical cancer. GSEA analysis showed multiple signaling pathways containing Programmed cell death ligand-1 (PD-L1) expression and PD-1 checkpoint pathway differences between high-risk and low-risk groups. Furthermore, the 15-gene signature was associated with multiple immune cells and immune infiltration in tumor microenvironment. Conclusion: The 15-gene signature is an effective potential prognostic classifier in the immunotherapies and surveillance of cervical cancer.

Dynamically Accumulating Homologous Recombination Deficiency Score Served as an Important Prognosis Factor in High-Grade Serous Ovarian Cancer.

Background: The homologous recombination (HR) pathway defects in cancers induced abrogation of cell cycle checkpoints, resulting in the accumulation of DNA damage, mitotic catastrophe, and cell death. Cancers with BRCA1/2 loss and other accumulation of similar genomic scars resulting in HRD displayed increased sensitivity to chemotherapy. Our study aimed to explore HRD score genetic mechanisms and subsequent clinical outcomes in human cancers, especially ovarian cancer. Methods: We analyzed TCGA data of HRD score in 33 cancer types and evaluated HRD score distribution and difference among tumor stages and between primary and recurrent tumor tissues. A weighted gene co-expression network analysis (WGCNA) was performed to identify highly correlated genes representing essential modules contributing to the HRD score and distinguish the hub genes and significant pathways. We verified HRD status predicting roles in patients' overall survival (OS) with univariate and multivariate Cox regression analyses and built the predicting model for patient survival. Results: We found that the HRD score increased with the rise in tumor stage, except for stage IV. The HRD score tended to grow up higher in recurrent tumor tissue than in their primary counterparts (p = 0.083). We constructed 15 co-expression modules with WGCNA, identified co-expressed genes and pathways impacting the HRD score, and concluded that the HRD score was tightly associated with tumor cells replication and proliferation. A combined HRD score ≥42 was associated with shorter OS in 33 cancer types (HR = 1.010, 95% CI: 1.008-1.011, p < 0.001). However, in ovarian cancer, which ranked the highest HRD score among other cancers, HRD ≥42 cohort was significantly associated with longer OS (HR = 0.99, 95% CI: 0.98-0.99, p < 0.0001). We also built a predicting model for 3 and 5 years survival in HGSC patients. Conclusion: A quantitative HRD score representing the accumulated genomic scars was dynamically increasing in proliferating tumor cells since the HRD score was tightly correlated to tumor cell division and replication. We highlighted HRD score biomarker role in prognosis prediction of ovarian cancer.

Construction of a ceRNA network of hub genes affecting immune infiltration in ovarian cancer identified by WGCNA.

BACKGROUND: Ovarian cancer is the leading cause of death among gynecological malignancies. Immunotherapy has demonstrated potential effects in ovarian cancer. However, few studies on immune-related prognostic signatures in ovarian cancer have been reported. This study aimed to identify hub genes associated with immune infiltrates to provide insight into the immune regulatory mechanisms in ovarian cancer. METHODS: Raw data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and University of California, Santa Cruz (UCSC) Xena websites. Single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were used to identify hub genes. Kaplan-Meier analysis and differential expression analysis were applied to explore the real hub genes. RESULTS: Through ssGSEA and WGCNA, 7 hub genes (LY9, CD5, CXCL9, IL2RG, SLAMF1, SLAMF6, and SLAMF7) were identified. Finally, LY9 and SLAMF1 were recognized as the real hub genes in immune infiltrates of ovarian cancer. LY9 and SLAMF1 are classified as SLAM family receptors involved in the activation of hematopoietic cells and the pathogenesis of multiple malignancies. Furthermore, 12 lncRNAs and 43 miRNAs significantly related to the 2 hub genes were applied to construct a lncRNA-miRNA-mRNA ceRNA network. The lncRNA-miRNA-mRNA ceRNA network shows upstream regulatory sites of the 2 hub genes. CONCLUSIONS: These findings improve our understanding of the regulatory mechanism of and reveal potential immune checkpoints for immunotherapy for ovarian cancer.

Overexpression of RIPK4 Predicts Poor Prognosis and Promotes Metastasis in Ovarian Cancer.

This study was conducted to evaluate the prognostic value of receptor-interacting protein kinase 4 (RIPK4) in ovarian cancer (OC) and its role in tumorigenesis. RNA expression and the corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The relationship between clinical-pathological characteristics and RIPK4 expression was analyzed using the Wilcoxon signed-rank test and logistic regression. The Cox regression and the Kaplan-Meier method were used to evaluate the relationship between clinicopathological features and overall survival (OS). Gene set enrichment analysis (GSEA) was performed using Molecular Signatures Database. Scratch assay, transwell assay, and cell transfection were used to verify the function of RIPK4. Overexpression of RIPK4 was associated with the stage of OC and distant metastasis. Survival analysis revealed that patients with OC and higher expression of RIPK4 had a poorer prognosis. Univariate and multivariate analyses indicated that high expression of RIPK4 was associated with poor OS, as well as age and stage of OC. The areas under the curve (AUC) at 1, 4, and 8 years were 0.737, 0.634, and 0.669, respectively, according to the established OS prediction model. GSEA revealed that adherens junction, cadherin binding, and Wnt signaling pathway were enriched in the high RIPK4 expression group. Cell transfection confirmed RIPK4 was involved in the Wnt signaling pathway. RIPK4 can act as a potential prognostic molecular marker for poor survival in OC. Moreover, RIPK4 is associated with tumor metastasis and implicated in the regulation of the Wnt signaling pathway.

Identification of hub genes in key hallmarks of ovarian cancer via bioinformatics analysis.

BACKGROUND: Ovarian cancer is one of the most lethal malignant gynecologic tumors worldwide. We aimed to identify critical hallmarks of the surface epithelium between normal ovaries and serous ovarian carcinomas and then obtain the hub genes associated with these critical hallmarks. METHODS: We chose GSE36668, GSE54388 and GSE69428 as data sources and then determined the common gene sets through gene set enrichment analysis (GSEA) to explore essential hallmarks and hub genes driving normal ovarian cells to evolve progressively into a neoplastic state. The differentially expressed genes (DEGs) extracted separately in each gene set were analyzed again through the Metascape website. Kaplan-Meier plotter was used to obtain significant prognostic information. The hub genes were obtained through protein-protein interaction (PPI) network by Cytoscape. Hub genes were confirmed to have higher mRNA and protein expression levels in ovarian cancer tissues than in normal tissues through public databases [Gene Expression Profiling Interactive Analysis (GEPIA) and The Human Protein Atlas]. Correlation analysis of six hub genes showed a strong correlation via R. RESULTS: We obtained 11 common hallmarks from GSEA of the three mentioned datasets and 22 hub genes that showed a significant association with poor survival. CONCLUSIONS: Not only the gene sets enriched by GSEA but also the hub genes extracted by the PPI network indicate that the most fundamental trait of ovarian cancer cells involves their ability to sustain chronic proliferation.

Cumulative Live Birth Rate and Cost-Effectiveness Analysis of Gonadotropin Releasing Hormone-Antagonist Protocol and Multiple Minimal Ovarian Stimulation in Poor Responders.

Background: The overall cumulative live birth rate (CLBR) of poor ovarian responders (POR) is extremely low. Minimal ovarian stimulation (MOS) provides a relatively realistic solution for ovarian stimulation in POR. Our study aimed to investigate whether multiple MOS strategies resulted in higher CLBR compared to conventional gonadotropin releasing hormone (GnRH) antagonists in POR. Methods: This retrospective study included 699 patients (1,058 cycles) from one center, who fulfilled the Bologna criteria between 2010 and 2018. Overall, 325 women (325 cycles) were treated with one-time conventional GnRH antagonist ovarian stimulation (GnRH-antagonist). Another 374 patients (733 cycles) were treated with multiple MOS including natural cycles. CLBR and time-and-cost-benefit analyses were compared between these two groups of women. Results: GnRH antagonists provided more retrieved oocytes, meiosis II oocytes, fertilized oocytes, and more viable embryos compared to both the first MOS (p < 0.001) and the cumulative corresponding numbers in multiple MOSs (p < 0.001). For the first in vitro fertilization (IVF) cycle, GnRH antagonists resulted in higher CLBR than MOS [12.92 versus 4.54%, adjusted OR (odds ratio) 2.606; 95% CI (confidence interval) 1.386, 4.899, p = 0.003]. The one-time GnRH-antagonist induced comparable CLBR (12.92 versus 7.92%, adjusted OR 1.702; 95% CI 0.971, 2.982, p = 0.063), but a shorter time to live birth [9 (8, 10.75) months versus 11 (9, 14) months, p = 0.014] and similar financial expenditure compared to repeated MOS [20,838 (17,953, 23,422) ¥ versus 21,261.5 (15,892.5, 35,140.25) ¥, p = 0.13]. Conclusion: Both minimal ovarian stimulation (MOS) and GnRH-antagonists provide low chances of live birth in poor responders. The GnRH antagonist protocol is considered a suitable choice for PORs with comparable CLBR, shorter times to live birth, and similar financial expenditure compared to repeated MOS.

[Wound information management system: a standardized scheme for acquisition, storage and management of wound information].

OBJECTIVE: To form a wound information management scheme with objectivity, standardization, and convenience by means of wound information management system. METHODS: A wound information management system was set up with the acquisition terminal, the defined wound description, the data bank, and related softwares. The efficacy of this system was evaluated in clinical practice. RESULTS: The acquisition terminal was composed of the third generation mobile phone and the software. It was feasible to get access to the wound information, including description, image, and therapeutic plan from the data bank by mobile phone. During 4 months, a collection of a total of 232 wound treatment information was entered, and accordingly standardized data of 38 patients were formed automatically. CONCLUSIONS: This system can provide standardized wound information management by standardized techniques of acquisition, transmission, and storage of wound information. It can be used widely in hospitals, especially primary medical institutions. Data resource of the system makes it possible for epidemiological study with large sample size in future.