Yuan Zhang

Association of aspirin and ibuprofen use with endometrial cancer risk in the PLCO dataset

Abstract Given the increasing incidence of endometrial cancer (EC) and the lack of improvement in survival rates, it is imperative to explore possible prevention methods. Studies on aspirin's effect on EC risk have been controversial; research on ibuprofen remains limited. We therefore aimed to investigate the relationship between aspirin and ibuprofen use and risk of EC through a cohort study. This analysis was based on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, which recruited participants aged 55–74 years from 1993 to 2001, with follow‐up for cancer incidence continuing until December 31, 2009. A total of 42,394 women were enrolled in this analysis, and 678 cases of EC were diagnosed during a median follow‐up period of 12.0 years. Compared with an intake of <4 pills per month, the use of ibuprofen ≥30 pills per month significantly reduced EC risk (fully‐adjusted hazard ratio [HR], 0.75; 95% confidence interval [CI]: 0.58–0.98), particularly in participants with a history of cardiovascular disease (fully‐adjusted HR, 0.57; 95% CI: 0.37–0.87). No evidence was found for an association between aspirin and EC occurrence in the general population (fully‐adjusted HR, 0.98; 95% CI: 0.81–1.19), nor in specific subgroups. In conclusion, frequent ibuprofen use, unlike aspirin, was linked to reduced EC risk. This protective effect of ibuprofen was enhanced in women with a history of cardiovascular disease. Additional well‐designed, prospective research is needed to validate these findings and explore the underlying mechanisms.

Development and validation of a prediction model for lymph node metastasis based on molecular typing in clinically early-stage endometrial carcinoma

To develop and externally validate a machine learning-based preoperative model integrating molecular typing and clinical features to predict lymph node metastasis (LNM) in patients with early-stage endometrial carcinoma (EC). This retrospective study included 465 patients with clinically early-stage EC treated at Qilu Hospital of Shandong University. Tumors were classified into molecular subtypes using The Cancer Genome Atlas-based methods. Least Absolute Shrinkage and Selection Operator regression identified five preoperative predictors: molecular typing (CN-H vs. non-CN-H), histological subtype, depth of myometrial invasion, neutrophil-to-lymphocyte ratio, and CA125 levels. Multiple machine learning algorithms were evaluated, and logistic regression (LR) was selected based on optimal discrimination and clinical applicability. Model performance was assessed using area under the curve (AUC), calibration plots, and decision curve analysis (DCA). A web-based nomogram was developed for clinical use. The LR model demonstrated excellent discrimination, with AUCs of 0.843 in the training cohort and 0.809 in the testing cohort. The CN-H subtype was significantly associated with increased LNM risk. The model enabled effective risk stratification and calibration curves and DCA confirmed the model's accuracy and clinical utility. By integrating molecular and preoperative clinical features, this model offers accurate LNM risk stratification for early-stage EC. It supports clinical decision-making and has been implemented as a user-friendly online tool. Further prospective multicenter validation is warranted.

[Retracted] Relationship between Prognosis, Immune Infiltration Level, and Differential Expression of PARVG Gene in Uterine Corpus Endometrial Carcinoma

Endometrial cancer (UCEC) is very common in gynecological diseases and ranks second in the death cause of gynecological cancer in developed countries. The connection between the overall survival of UCEC patients and immune invasion of the tumor microenvironment is positive. The PARVG gene has not been given notice in cancer, and its mechanism is unknown. The research utilized TCGA data to test the function of PARVG in UCEC. The manifestation of PARVG in UCEC was studied by GEPIA. By assessing the survival module, the authors learned the impact of PARVG on the survival of people with UCEC and then obtained UCEC information from TCGA. This study uses logistic regression to prove the possible relationship between PARVG expression and clinical information. From the research of Cox regression, clinicopathological characteristics of people with TCGA were connected with overall survival. Furthermore, the “correlation” module of GEPIA and CIBERSORT was used to study the association between cancer immune invasion and PARVG . Using univariate logistic regression analysis with PARVG expression as a categorical variable (median expression value of 2.5), the result suggested that raised PARVG expression was considerably connected with tumor status, pathological stage, and lymph nodes. Multiple factor studies have shown that upregulation of PARVG, distant metastasis, and negative pathological stage are absolute elements of excellent prognosis. In addition, CIBERSORT analysis was utilized to determine that raised PARVG expression has a positive connection with immune infiltration by T cells, mast cells, neutrophils, and B cells. This is recognized in GEPIA’s “correlation” module. The above outcomes show us that the raised expression of PARVG is associated with a good prognosis and it raises the proportion of immune cells (such as T cells, mast cells, neutrophils, and B cells) in UCEC. These outcomes tell us that PARVG can be utilized as a possible biomarker to evaluate UCEC’s immune infiltration levels and prognosis.

Glutamine metabolism prognostic index predicts tumour microenvironment characteristics and therapeutic efficacy in ovarian cancer

AbstractMounting evidence has highlighted the multifunctional characteristics of glutamine metabolism (GM) in cancer initiation, progression and therapeutic regimens. However, the overall role of GM in the tumour microenvironment (TME), clinical stratification and therapeutic efficacy in patients with ovarian cancer (OC) has not been fully elucidated. Here, three distinct GM clusters were identified and exhibited different prognostic values, biological functions and immune infiltration in TME. Subsequently, glutamine metabolism prognostic index (GMPI) was constructed as a new scoring model to quantify the GM subtypes and was verified as an independent predictor of OC. Patients with low‐GMPI exhibited favourable survival outcomes, lower enrichment of several oncogenic pathways, less immunosuppressive cell infiltration and better immunotherapy responses. Single‐cell sequencing analysis revealed a unique evolutionary trajectory of OC cells from high‐GMPI to low‐GMPI, and OC cells with different GMPI might communicate with distinct cell populations through ligand‐receptor interactions. Critically, the therapeutic efficacy of several drug candidates was validated based on patient‐derived organoids (PDOs). The proposed GMPI could serve as a reliable signature for predicting patient prognosis and contribute to optimising therapeutic strategies for OC.

Multispectral Image under Tissue Classification Algorithm in Screening of Cervical Cancer

The objectives of this study were to improve the efficiency and accuracy of early clinical diagnosis of cervical cancer and to explore the application of tissue classification algorithm combined with multispectral imaging in screening of cervical cancer. 50 patients with suspected cervical cancer were selected. Firstly, the multispectral imaging technology was used to collect the multispectral images of the cervical tissues of 50 patients under the conventional white light waveband, the narrowband green light waveband, and the narrowband blue light waveband. Secondly, the collected multispectral images were fused, and then the tissue classification algorithm was used to segment the diseased area according to the difference between the cervical tissues without lesions and the cervical tissues with lesions. The difference in the contrast and other characteristics of the multiband spectrum fusion image would segment the diseased area, which was compared with the results of the disease examination. The average gradient, standard deviation (SD), and image entropy were adopted to evaluate the image quality, and the sensitivity and specificity were selected to evaluate the clinical application value of discussed method. The fused spectral image was compared with the image without lesions, it was found that there was a clear difference, and the fused multispectral image showed a contrast of 0.7549, which was also higher than that before fusion (0.4716), showing statistical difference ( P < 0.05 ). The average gradient, SD, and image entropy of the multispectral image assisted by the tissue classification algorithm were 2.0765, 65.2579, and 4.974, respectively, showing statistical difference ( P < 0.05 ). Compared with the three reported indicators, the values of the algorithm in this study were higher. The sensitivity and specificity of the multispectral image with the tissue classification algorithm were 85.3% and 70.8%, respectively, which were both greater than those of the image without the algorithm. It showed that the multispectral image assisted by tissue classification algorithm can effectively screen the cervical cancer and can quickly, efficiently, and safely segment the cervical tissue from the lesion area and the nonlesion area. The segmentation result was the same as that of the doctor's disease examination, indicating that it showed high clinical application value. This provided an effective reference for the clinical application of multispectral imaging technology assisted by tissue classification algorithm in the early screening and diagnosis of cervical cancer.