Wei Ren

Development and validation of a prognostic prediction model for endometrial cancer based on CD8+ T cell infiltration-related genes

Endometrial cancer (EC) is the most common gynecologic malignancy with increasing incidence and mortality. The tumor immune microenvironment significantly impacts cancer prognosis. Weighted Gene Co-Expression Network Analysis (WGCNA) is a systems biology approach that analyzes gene expression data to uncover gene co-expression networks and functional modules. This study aimed to use WGCNA to develop a prognostic prediction model for EC based on immune cell infiltration, and to identify new potential therapeutic targets. WGCNA was performed using the Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma dataset to identify hub modules associated with T-lymphocyte cell infiltration. Prognostic models were developed using LASSO regression based on genes in these hub modules. The Search Tool for the Retrieval of Interacting Genes/Proteins was used for protein–protein interaction network analysis of the hub module. Gene Set Variation Analysis identified differential gene enrichment analysis between high- and low-risk groups. The relationship between the model and microsatellite instability, tumor mutational burden, and immune cell infiltration was analyzed using The Cancer Genome Atlas data. The model’s correlation with chemotherapy and immunotherapy resistance was examined using the Genomics of Drug Sensitivity in Cancer and Cancer Immunome Atlas databases. Immunohistochemical staining of EC tissue microarrays was performed to analyze the relationship between the expression of key genes and immune infiltration. The green-yellow module was identified as a hub module, with 4 genes (ARPC1B, BATF, CCL2, and COTL1) linked to CD8+ T cell infiltration. The prognostic model constructed from these genes showed satisfactory predictive efficacy. Differentially expressed genes in high- and low-risk groups were enriched in tumor immunity-related pathways. The model correlated with EC-related phenotypes, indicating its potential to predict immunotherapeutic response. Basic leucine zipper activating transcription factor-like transcription factor(BATF) expression in EC tissues positively correlated with CD8+ T cell infiltration, suggesting BATF’s crucial role in EC development and antitumor immunity. The prognostic model comprising ARPC1B, BATF, CCL2, and COTL1 can effectively identify high-risk EC patients and predict their response to immunotherapy, demonstrating significant clinical potential. These genes are implicated in EC development and immune infiltration, with BATF emerging as a potential therapeutic target for EC.

Predicting complete response to concurrent chemoradiotherapy in locally advanced cervical squamous cell carcinoma using multi-sequence MRI data and a 2.5D deep learning algorithm integrated with crossformer model

Objective Despite advances in prevention, cervical cancer remains a serious global health issue. Concurrent chemoradiation is the standard treatment for locally advanced squamous cell carcinoma, yet 20–30% of patients develop persistent cervical cancer due to incomplete response, resulting in poor outcomes. This study aims to develop a predictive model for persistent cervical cancer in patients with locally advanced cervical squamous cell carcinoma following concurrent chemoradiation therapy, leveraging pretreatment multisequence magnetic resonance imaging data and advanced deep learning techniques. Methods This retrospective study included 259 patients with locally advanced cervical squamous cell carcinoma who underwent concurrent chemoradiation therapy at two centres. Four magnetic resonance imaging sequences were used to generate 2.5D data. A deep learning model incorporating Crossformer was developed and compared with radiomics and clinical models. Model performance was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis. Results CrossFormer model outperformed the traditional convolutional neural network models in slice-level analysis across all cohorts, achieving an area under the curve of 0.775 in the test cohorts. The deep learning model achieved high predictive accuracy, with area under the curves of 0.884, 0.833, and 0.814 in the training, validation, and test cohorts, respectively, outperforming both the clinical and radiomics models. Combining clinical features with the deep learning model further improved performance, yielding area under the curves of 0.914, 0.868, and 0.839 in the respective cohorts. Conclusion The developed model, utilizing 2.5D multi-sequence magnetic resonance imaging data and the deep learning technology that incorporated Crossformer, demonstrated strong predictive performance for persistent cervical cancer in patients with locally advanced cervical squamous cell carcinoma following concurrent chemoradiation therapy. This approach offers a promising and clinically applicable tool for treatment decision-making.

Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer

AbstractCervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This research aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology. The AICCS system was trained and validated using various datasets, including retrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide image (WSIs). The AICCS consistently showed high accuracy in predicting cytology grades across different datasets. In the prospective assessment, it achieved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AUC, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity. Thus, AICCS holds promise as an additional tool for accurate and efficient cervical cancer screening.