Xin Zhang

18F‐Fluoro‐2‐Deoxyglucose Positron Emission Tomography/Computed Tomography Measures of Spatial Heterogeneity for Predicting Platinum Resistance of High‐Grade Serous Ovarian Cancer

ABSTRACTBackgroundThe purpose of this study is to construct models for predicting platinum resistance in high‐grade serous ovarian cancer (HGSOC) derived from quantitative spatial heterogeneity indicators obtained from 18F‐FDG PET/CT images.MethodsA retrospective study was conducted on patients diagnosed with HGSOC. Quantitative indicators of spatial heterogeneity were generated using conventional features and Haralick texture features from both CT and PET images. Three groups of predictive models (conventional, heterogeneity, and integrated) were built. Each group's optimal model was the one with the highest area under curve (AUC). Postoperative immunohistochemical staining for Ki‐67 and p53 was conducted. The correlation between the heterogeneity indicators and scores for Ki‐67 and p53 was assessed by Spearman's correlation coefficient (ρ).ResultsA total of 286 patients (54.6 ± 9.3 years) were enrolled. And 107 spatial heterogeneity indicators were extracted. The optimal models for each group were obtained using the Gradient Boosting Machine (GBM) algorithm. There was an AUC of 0.790 (95% CI: 0.696, 0.885) in the conventional model for the validation set, and an AUC of 0.904 (95% CI: 0.842, 0.966) in the heterogeneity model for the validation set. The integrated model achieved the highest predictive performance, with an AUC value of 0.928 (95% CI: 0.872, 0.984) for the validation set. Spearman's correlation showed that HU_Kurtosis had the strongest correlation with p53 scores with ρ = 0.718, while cluster site entropy had the strongest correlation with Ki‐67 scores with ρ = 0.753.ConclusionsAdding quantitative spatial heterogeneity indicators derived from PET/CT images can improve the prediction of platinum resistance in patients with HGSOC. Spatial heterogeneity indicators were related to Ki‐67 and p53 scores.

The past, present, and future of FIGO staging of endometrial cancer

The International Federation of Gynecology and Obstetrics (FIGO) staging of endometrial cancer (EC) is regarded as a crucial tool for guiding treatment, evaluating prognosis, and advancing clinical research. It is a concept of shared importance among gynecologic oncologists, pathologists, and patients with EC. In June 2023, the International Federation of Gynecology and Obstetrics released a new staging system for EC. This review aims to discuss comprehensively the developmental trajectory of FIGO staging for EC, focusing on the differences between the 2023 FIGO and earlier staging systems, and delineating the advantages and disadvantages of incorporating various pathological factors and molecular subtypes into staging. The article emphasizes the progress made with the updated 2023 FIGO version in improving prognostic prediction accuracy for patients with EC. However, as the staging categories expand, their complexity becomes increasingly apparent, potentially impacting health care professionals' accurate understanding and application of staging. Moreover, unresolved issues persist regarding histological types and grading, lymphovascular space invasion, and molecular subtypes, as well as distinguishing between low-grade endometrioid carcinomas confined to the uterus and ovaries, which may affect the personalized management of patients with EC. In the future, these issues still require extensive clinical research and specific data for validation or confirmation, presenting a challenge shared by gynecologic oncologists and pathologists.

Single-cell analysis reveals landscape of endometrial cancer response to estrogen and identification of early diagnostic markers

Background The development of endometrial cancer (EC) is closely related to the abnormal activation of the estrogen signaling pathway. Effective diagnostic markers are important for the early detection and treatment of EC. Method We downloaded single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST) data of EC from public databases. Enrichment scores were calculated for EC cell subpopulations using the “AddModuleScore” function and the AUCell package, respectively. Six predictive models were constructed, including logistic regression (LR), Gaussian naive Bayes (GaussianNB), k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGB), and neural network (NK). Subsequently, receiver-operating characteristics with areas under the curves (AUCs) were used to assess the robustness of the predictive model. Result We classified EC cell coaggregation into six cell clusters, of which the epithelial, fibroblast and endothelial cell clusters had higher estrogen signaling pathway activity. We founded the epithelial cell subtype Epi cluster1, the fibroblast cell subtype Fib cluster3, and the endothelial cell subtype Endo cluster3 all showed early activation levels of estrogen response. Based on EC cell subtypes, estrogen-responsive early genes, and genes encoding Stage I and para-cancer differentially expressed proteins in EC patients, a total of 24 early diagnostic markers were identified. The AUCs values of all six classifiers were higher than 0.95, which indicates that the early diagnostic markers we screened have superior robustness across different classification algorithms. Conclusion Our study elucidates the potential biological mechanism of EC response to estrogen at single-cell resolution, which provides a new direction for early diagnosis of EC.

[Retracted] Systematic Analysis of Tumor Microenvironment Patterns and Oxidative Stress Characteristics of Endometrial Carcinoma Mediated by 5‐Methylcytosine Regulators

As a widely distributed RNA methylation modification, m5C is involved in the regulation of tumorigenesis. Nevertheless, its fundamental process is not clear. This research sought to examine the genetic properties of the 5‐methylcytosine (m5C) regulator in endometrial carcinoma, as well as the prognostic significance and impact of m5C regulators on oxidative stress. Therefore, the TCGA‐UCEC data set was used to explore the characteristics of 17 RNAm5C‐related genes in the transcriptome, genome, and regulatory network. The subtypes of RNAm5C in UCEC were identified based on the expression levels of 17 RNAm5C‐related genes. The prognosis of RNAm5C‐2 was significantly better than that of RNAm5C‐1. Then, we examined the differences (variations) across various subtypes in terms of immune cell infiltration (ICI) as well as the expression of immune‐related signal markers. The findings demonstrated that there were distinct variations in the infiltration level of immune cells in each subtype, which may be the reason for the differences in the prognosis of each subtype. In addition, the differentially expressed genes (DEGs) among RNAm5C subtypes of different UCEC tumors were identified, and the DEGs significant for survival were screened. After obtaining 34 prognostic genes, the dimensionality was reduced to construct an RNA methylation score (RS). As per the findings, RS is a more accurate marker for determining the prognosis for patients with endometrial cancer. The RS was used to categorize UCEC tumor samples, and these results led to the formation of high‐score and low‐score groups. The patients in the group with a high‐RNA methylation score exhibited a survival time that was considerably longer in contrast with those in the group with a low‐RNA methylation score. The capacity of RS to predict whether or not immunotherapy would be beneficial was explored further. In the group with a high‐RNA methylation score, the objective response rate to the anti‐PD‐L1 therapy was substantially greater compared to that observed in the subgroup with a low‐RNA methylation score. Additionally, there were variations across various RS groups in terms of clinical features, tumor mutation burden, and the infiltration level of immune cells. After binary tree analysis and PCR verification of 34 prognostic genes, it is finally found that the six genes of MAGOH3P, TRBJ2_3, YTHDF1P1, RP11_323D18.5, RP11_405M12.2, and ADAM30 are significantly overexpressed in cancer tissues. These genes can be used as potential biomarkers of endometrial cancer and provide data support for precise immunotherapy in UCEC tumors.

M6A modification regulates tumor suppressor DIRAS1 expression in cervical cancer cells

DIRAS family GTPase 1 (DIRAS1) has been reported as a potential tumor suppressor in other human cancer. However, its expression pattern and role in cervical cancer remain unknown. Knockdown of DIRAS1 significantly promoted the proliferation, growth, migration, and invasion of C33A and SiHa cells cultured

Moderate Static Magnet Fields Suppress Ovarian Cancer Metastasis via ROS‐Mediated Oxidative Stress

Metastasis is the leading cause of cancer patient death, which is closely correlated with reactive oxygen species (ROS) levels. It is well known that the effects of ROS on tumors are diverse, depending on ROS concentration and cell type. We found that ovarian cancer cells have significantly lower levels of ROS than normal ovarian cells. Moreover, increased ROS levels in ovarian cancer cells can substantially inhibit their migration and invasion ability. Furthermore, the results show that moderate static magnetic field (SMF) can inhibit ovarian cancer cell migration, invasion, and stemness in a ROS‐dependent manner. RNA sequencing results confirm that SMFs increased the oxidative stress level and reduced the stemness of ovarian cancer cells. Consistently, the expressions of stemness‐related genes were significantly decreased, including hyaluronan receptor (CD44), SRY‐box transcription factor 2 (Sox2), and cell myc proto‐oncogene protein (C‐myc). Furthermore, moderate SMFs provided by a superconducting magnet and permanent magnet have good biosafety and can both inhibit ovarian cancer metastasis in mice. Therefore, our study demonstrates the effects of SMFs on oxidative stress and metastasis in the ovarian cancer cells, which reveals the potential of applying SMF as a physical method in cancer therapy in the future.