[Expression and significance of PD-1 and PD-L1 in the specimens of epithelial ovarian cancer]

Evaluation of immunotherapy efficacy in gynecologic cancer

Various immunotherapies have demonstrated remarkable success over the past few decades, and have been approved for the treatment of different cancer types. However, patient responses to immunotherapy are variable, and approximately 50% of cases are refractory to these agents. Tumor biomarker-based stratification of cases may therefore help identify subpopulations that are sensitive/resistant to immunotherapy; it may also improve prediction of response in various cancers including gynecologic cancer. These biomarkers include the tumor mutational burden, microsatellite instability, mismatch repair deficiency, T cell-inflamed gene expression profile, programmed cell death protein 1 ligand 1, tumor-infiltrating lymphocytes, and numerous other genomic alterations. Future directions in the treatment of gynecologic cancer include the utilization of these biomarkers to select ideal candidates. This review focused on recent advances in the predictive ability of molecular biomarkers in patients with gynecologic cancer who undergo immunotherapy. The most recent developments in combined immunotherapy and targeted therapy strategies and novel immune interventions against gynecologic cancers have also been discussed.

Systematic analysis of the relationship between ovarian cancer prognosis and alternative splicing

Background

Ovarian cancer(OC) is the gynecological tumor with the highest mortality rate, effective biomarkers are of great significance in improving its prognosis. In recent years, there have been many studies on alternative splicing (AS) events, and the role of AS events in tumor has become a focus of attention.

Methods

Data were downloaded from the TCGA database and Univariate Cox regression analysis was performed to determine AS events associated with OC prognosis.Eight prognostic models of OC were constructed in R package, and the accuracy of the models were evaluated by the time-dependent receiver operating characteristic (ROC) curves.Eight types of survival curves were drawn to evaluate the differences between the high and low risk groups.Independent prognostic factors of OC were analyzed by single factor independent analysis and multi-factor independent prognostic analysis.Again, Univariate Cox regression analysis was used to analyze the relationship between splicing factors(SF) and AS events, and Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis were performed on OS-related SFs to understand the pathways.

Results

Univariate Cox regression analysis showed that among the 15,278 genes, there were 31,286 overall survival (OS) related AS events, among which 1524 AS events were significantly correlated with OS. The area under the time-dependent receiver operating characteristic curve (AUC) of AT and ME were the largest and the RI was the smallest,which were 0.757 and 0.68 respectively. The constructed models have good value for the prognosis assessment of OC patients. Among the eight survival curves, AP was the most significant difference between the high and low risk groups, with a P value of 1.61e − 1.The results of single factor independent analysis and multi-factor independent prognostic analysis showed that risk score calculated by the model and age could be used as independent risk factors.According to univariate COX regression analysis,109 SFs were correlated with AS events and adjusted in two ways: positive and negative.

Conclusions

SFs and AS events can directly or indirectly affect the prognosis of OC patients. It is very important to find effective prognostic markers to improve the survival rate of OC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-021-00866-1.