Pengpeng Qu

Improved practical value of the FIGO 2023 staging system in evaluating prognosis of early stages endometrial carcinoma

AbstractObjectiveTo investigate the practical value of the International Federation of Gynecology and Obstetrics (FIGO) 2023 endometrial cancer (EC) staging system.MethodsData on clinicopathological characteristics of patients diagnosed with EC at Tianjin Central Hospital of Gynecology Obstetrics from January 2015 to December 2017 were collected. Initial staging was performed using the FIGO 2009 system, followed by revised staging with the 2023 FIGO system. The oncological outcomes of patients under the different staging systems were analyzed.ResultsA total of 671 patients with EC were included in this study, and after applying the 2023 FIGO staging system, the staging of 119 (17.73%) patients changed, with 11 (1.64%) patients experiencing downstaging and 108 (16.10%) patients experiencing upstaging; 5‐year progression‐free survival (PFS) and overall survival (OS) rates changed in stage I (97.75%–98.55% and 98.79%–99.38%, respectively) and stage II (91.39%–93.16% and 95.65%–95.72%, respectively) patients, and the differences in PFS (P = 0.060 and P = 0.001, respectively) and OS (P = 0.349 and P = 0.003, respectively) between stage I and stage II patients became statistically significant. After the restaging of FIGO 2009 stage I patients, there were statistically significant differences in PFS (P = 0.002 and P = 0.024, respectively) and OS (P = 0.002 and P = 0.002, respectively) between stage IIB and IA1 and IA2 patients, and significant differences in PFS (P = 0.022) and OS (P = 0.048) were observed between stage IIC and IA1 patients. In FIGO 2023 stage IIB and IIC patients, the differences in PFS (P = 0.39 and P = 0.39, respectively) and OS (P = 0.78 and P = 0.5, respectively) were not statistically significant among the various FIGO 2009 stages.ConclusionIn the FIGO 2023 EC staging system, stage I and II staging criteria are more reasonable, and the addition of stage IIB and IIC helps to better evaluate patient prognosis.

PD‐1 Coexpression Gene Analysis and the Regulatory Network in Endometrial Cancer Based on Bioinformatics Analysis

Gynecological malignancies are tumors of the female reproductive system, mainly cervical cancer, endometrial cancer, and ovarian cancer. Endometrial cancer (EC) is the most common gynecological malignant tumor in developed countries. The aim of this study was to construct a network of programmed cell death protein 1 (PD‐1) coexpressed genes through bioinformatics analysis and screen the potential biomarkers of PD‐1 in endometrial cancer. In addition, genes and pathways involved in PD‐1 and modulating tumor immune status were identified. We select the EC transcriptomic dataset in TCGA to retrieve gene sets on the cBioPortal platform, and the PD‐1 coexpressed genes were obtained on the platform. GO and KEGG enrichment analysis of coexpressed genes was performed using the DAVID database. The target protein‐protein interaction (PPI) network was constructed using Cytoscape 3.7.1 software, and the hub genes were then screened. A total of 976 coexpression genes were obtained. The enrichment analysis showed that PD‐1 coexpressed genes were significantly enriched in overall components of the cell structure, the interaction of cytokines with cytokine receptors, chemokine signaling pathways, and cell adhesion molecules (CAMs). Ten hub genes were obtained by node degree analysis. CD3E gene is involved in the prognosis and immune process of EC, and the expression level is related to PD‐1 (Pearson correlation coefficient is 0.82, P < 0.01). Patients with low CD3E gene expression in EC have a poor prognosis. The coexpression hub genes of PD‐1 are related to immunity, in which CD3E is a prognostic marker that is involved in the PD‐1/PD‐L1‐induced tumor immune escape. This study provides a new area to study the mechanism of PD‐1/PD‐L1 in EC and the precise treatment with targeted drugs.

Proteomic Analysis of Human Endometrial Tissues Reveals the Roles of PI3K/AKT/mTOR Pathway and Tumor Angiogenesis Molecules in the Pathogenesis of Endometrial Cancer

As one major gynecological malignancy, endometrial cancer (EC) has been widely studied recently. However, its pathogenesis is still unclear to date. In this study, we identified differentially expressed proteins between 30 endometrial cancer tissues and 30 matched normal controls using 2D LC‐MS/MS quantitative proteomics. As a result, we identified 619 differentially expressed proteins among all 2521 proteins being quantified. Further analyses suggested that the changes of fat, amino acid metabolism, peroxisome, extracellular signal, cytoskeleton, and other signaling or metabolic pathways may be closely related to the development of this cancer. Particularly, the PI3K/AKT/mTOR pathway‐related molecules including PI3K and mTOR, ERK (the molecule of the ERK pathway), SPP1, and ANGPT2 (angiogenesis‐related molecules) are highly associated with the pathogenesis of EC, which were reconfirmed by western blot and immunohistochemistry (IHC) analysis. In summary, our study revealed that the PI3K/AKT/mTOR pathway and tumor angiogenesis molecules contribute to the pathogenesis of endometrial cancer.

Molecular Features, Prognostic Value, and Cancer Immune Interactions of Angiogenesis-Related Genes in Ovarian Cancer

Angiogenesis is crucial to tumor growth and metastasis; it plays a key role in various cancers development and progression. However, the potential effects of angiogenesis-related genes (ARGs) in ovarian cancer (OC) remain to be further investigated. We discussed the characteristics changes of ARGs in 784 OC samples from genomic and transcriptional levels, as well as their expression patterns based on four distinct datasets. First, 784 OC patients were divided into three molecular subtypes, and the findings indicated that ARG changes were correlated with clinicopathological parameters, prognosis, and immune cell-infiltrating tumor microenvironment (TME). OC patients were subsequently divided into two gene subtypes depending on differentially expressed genes (DEGs) of the abovementioned molecular subtypes. We also established an ARGs-related score (ARGs score) model for evaluating overall survival (OS) and determining the immunological landscape of OC patients, therefore predicting patients' prognosis and therapeutic responses. A lower ARGs' score accompanied by a high mutation frequency implies a higher probability of survival. Furthermore, the ARG score was correlated with the cancer stem cell (CSC) index and chemotherapeutic sensitivity. The significant involvement of ARGs in the tumor-immune-stromal microenvironment, clinicopathological characteristics, and prognosis were established in our systematic investigation of ARGs for OC patients. These discoveries might help us to better understand the role of ARGs in OC, as well as give new insight for predicting the prognosis and providing promising immunotherapy.

Bioinformatics Analysis Reveals MCM3 as an Important Prognostic Marker in Cervical Cancer

The minichromosome maintenance complex 3 (MCM3) is essential for the regulation of DNA replication and cell cycle progression. However, the expression and prognostic values of MCM3 in cervical cancer (CC) have not been well-studied. Herein, we investigated the expression patterns and survival data of MCM3 in cervical cancer patients from the ONCOMINE, GEPIA, Human Protein Atlas, UALCAN, Kaplan-Meier Plotter, and LinkedOmics databases. The expression level of MCM3 is negatively correlated with advanced tumor stage and metastatic status. Specifically, MCM3 is significantly differentially expressed between patients in stage 1 and stage 3 cervical cancer with p value 0.0138. Similarly, the p values between stage 1 and stage 4 cervical cancer, between stage 2 and stage 3, and between stage 2 and stage 4 are 0.00089, 0.0244, and 0.00197, respectively. Not only that, cervical cancer patients with high mRNA expression of MCM3 may indicate longer overall survival but indicate shorter relapse-free survival. PRIM2 and MCM6 are positively correlated genes of MCM3. Bioinformatics analysis revealed that MCM3 might be considered a biological indicator for prognostic evaluation of cervical cancer. However, it is currently limited to bioinformatics analysis, and more clinical tissue specimens and cell experiments are needed to further explore the role of MCM3 in the occurrence and progression of cervical cancer.