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Background: The incidence and mortality of cervical cancer remain high in female malignant tumors worldwide. There is still a lack of diagnostic and prognostic markers for cervical carcinoma. This study aimed to screen differentially expressed genes (DEGs) between normal and cervical cancer tissues to identify candidate genes for further research. Methods: Uterine cervical specimens were resected from our clinical patients after radical hysterectomy. Three patients' transcriptomic datasets were built by the next generation sequencing (NGS) results. DEGs were selected through the edgeR and DESeq2 packages in the R environment. Functional enrichment analysis, including GO/DisGeNET/KEGG/Reactome enrichment analysis, was performed. Normal and cervical cancer tissue data from the public databases TCGA and GTEx were collected to compare the expression levels of 10 selected DEGs in tumor and normal tissues. ROC curve and survival analysis were performed to compare the diagnostic and prognostic values of each gene. The expression levels of candidate genes were verified in 15 paired clinical specimens via quantitative real-time polymerase chain reaction. Results: There were 875 up-regulated and 1,482 down-regulated genes in cervical cancer samples compared with the paired adjacent normal cervical tissues according to the NGS analysis. The top 10 DEGs included APOD, MASP1, ACKR1, C1QTNF7, SFRP4, HSPB6, GSTM5, IGFBP6, F10 and DCN. GO, DisGeNET and Reactome analyses revealed that the DEGs were related to extracellular matrix and angiogenesis which might influence tumorigenesis. KEGG enrichment showed that PI3K-Akt signaling pathway might be involved in cervical cancer tumorigenesis and progression. The expression levels of selected genes were decreased in tumors in both the public database and our experimental clinical specimens. All the candidate genes showed excellent diagnostic value, and the AUC values exceeded 0.90. Additionally, APOD, ACKR1 and SFRP4 expression levels could help predict the prognosis of patients with cervical cancer. Conclusions: In this study, we selected the top 10 DEGs which were down-regulated in cervical cancer tissues. All of them had dramatically diagnostic value. APOD, ACKR1 and SFRP4 were associated with the survivals of cervical cancer. C1QTNF7, HSPB6, GSTM5, IGFBP6 and F10 were first reported to be candidate genes of cervical carcinoma. [ABSTRACT FROM AUTHOR]

Background: Despite breakthroughs in treatment, ovarian cancer (OC) remains one of the most lethal gynecological malignancies, with an increasing age-standardized mortality rate. This underscores an urgent need for novel biomarkers and therapeutic targets. Although growth factor receptor-bound protein 7 (GRB7) is implicated in cell signaling and tumorigenesis, its expression pattern and clinical implications in OC remain poorly characterized. Methods: To systematically investigate GRB7's expression in OC, our study utilized extensive datasets from TCGA, GTEx, CCLE, and GEO. The prognostic significance of GRB7 was evaluated by means of Kaplan–Meier and Cox regression analyses. Using a correlation analysis and gene set enrichment analysis, relationships between GRB7's expression and gene networks, immune cell infiltration and immunotherapy response were investigated. In vitro experiments were conducted to confirm GRB7's function in the biology of OC. Results: Compared to normal tissues, OC tissues exhibited a substantial upregulation of GRB7. Reduced overall survival, disease-specific survival, and disease-free interval were all connected with high GRB7 mRNA levels. The network study demonstrated that GRB7 is involved in pathways relevant to the course of OC and has a positive connection with several key driver genes. Notably, GRB7's expression was linked to the infiltration of M2 macrophage and altered response to immunotherapy. Data from single-cell RNA sequencing data across multiple cancer types indicated GRB7's predominant expression in malignant cells. Moreover, OC cells with GRB7 deletion showed decreased proliferation and migration, as well as increased susceptibility to T cell-mediated cytotoxicity. Conclusion: With respect to OC, our results validated GRB7 as a viable prognostic biomarker and a promising therapeutic target, providing information about its function in tumorigenesis and immune modulation. GRB7's preferential expression in malignant cells highlights its significance in the biology of cancer and bolsters the possibility that it could be useful in enhancing the effectiveness of immunotherapy. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 (COVID-19) had become a health care event endangering humans globally. It takes up a large number of healthcare resources. We studied the impact of COVID-19 on patients with ovarian cancer by comprehensively analyzing their admissions before and after the epidemic, and made reasonable suggestions to improve their current situation.Methods: We randomly divided the enrolled patients into three groups, PreCOVID-19 Group (PCG) (2019.8.20–2020.1.20), COVID-19 Group (CG) (2020.1.21–2020.6.14), and Secondary Outbreak COVID-19 Group (SOCG) (2020.6.15–2020.10.10). One-way ANOVA and chi-square test were used for analysis.Results: The number of patients from other provinces decreased significantly (p < 0.05). The total hospital stay during the epidemic was substantially more extended (p < 0.05). Before the epidemic, our department performed more open surgery while during the epidemic outbreak, we tended to choose laparoscopy (p < 0.01). We took a longer surgery time (P < 0.05). Patients had significantly less post-operative fever during the epidemic (p < 0.001).Conclusion: During the COVID-19 epidemic, no patient was infected with COVID-19, and no patient experienced severe post-operative complications. We recommend maintaining the admissions of patients with ovarian cancer during the epidemic following the rules: 1. The outpatients must complete a nucleic acid test and chest CT in the outpatient clinic; 2. Maintain full daily disinfection of the ward and insist that health care workers disinfect their hands after contact with patients; 3. Increase the use of minimally invasive procedures, including laparoscopy and robotics; 4. Disinfect the ward twice a day with UV light and sodium hypochlorite disinfectant; 5. Patients need to undergo at least three nucleic acid tests before entering the operating room.

Ovarian cancer (OC) is characterised by heterogeneity that complicates the prediction of patient survival and treatment outcomes. Here, we conducted analyses to predict the prognosis of patients from the Genomic Data Commons database and validated the predictions by fivefold cross-validation and by using an independent dataset in the International Cancer Genome Consortium database. We analysed the somatic DNA mutation, mRNA expression, DNA methylation, and microRNA expression data of 1203 samples from 599 serous ovarian cancer (SOC) patients. We found that principal component transformation (PCT) improved the predictive performance of the survival and therapeutic models. Deep learning algorithms also showed better predictive power than the decision tree (DT) and random forest (RF). Furthermore, we identified a series of molecular features and pathways that are associated with patient survival and treatment outcomes. Our study provides perspective on building reliable prognostic and therapeutic strategies and further illuminates the molecular mechanisms of SOC. What is already known on this subject? Recent studies have focussed on predicting cancer outcomes based on omics data. But the limitation is the performance of single-platform genomic analyses or the small numbers of genomic analyses. What do the results of this study add? We analysed multi-omics data, found that principal component transformation (PCT) significantly improved the predictive performance of the survival and therapeutic models. Deep learning algorithms also showed better predictive power than did decision tree (DT) and random forest (RF). Furthermore, we identified a series of molecular features and pathways that are associated with patient survival and treatment outcomes. What are the implications of these findings for clinical practice and/or further research? Our study provides perspective on how to build reliable prognostic and therapeutic strategies and further illuminates the molecular mechanisms of SOC for future studies. [ABSTRACT FROM AUTHOR]

Background: This study compared the survival outcomes of abdominal radical hysterectomy (ARH) (N = 32), laparoscopic radical hysterectomy (LRH) (N = 61), robot-assisted radical hysterectomy (RRH) (N = 100) and vaginal radical hysterectomy (VRH) (N = 45) approaches for early-stage cervical cancer to identify the surgical approach that provides the best survival. Methods: Disease-free survival (DFS) and overall survival (OS) were calculated using the Kaplan–Meier method, and survival curves were compared using the log-rank test. Results: The volume of intraoperative blood loss was greater in the ARH group than in the LRH group, the RRH group or the VRH group [(712.50 ± 407.59) vs. (224.43 ± 191.89), (109.80 ± 92.98) and (216.67 ± 176.78) ml, respectively; P < 0.001]. Total 5-year OS was significantly different among the four groups (ARH, 96.88%; LRH, 82.45%; RRH, 94.18%; VRH, 91.49%; P = 0.015). However, no significant difference in 5-year DFS was observed among the four groups (ARH, 96.88%; LRH, 81.99%; RRH, 91.38%; VRH, 87.27%; P = 0.061). Conclusion: This retrospective study demonstrated that ARH and RRH achieved higher 5-year OS rates than LRH for early-stage cervical cancer. [ABSTRACT FROM AUTHOR]