Shuang Li

CT-based radiomic analysis for categorization of ovarian sex cord-stromal tumors and epithelial ovarian cancers

To evaluate the diagnostic potential of radiomic analyses based on machine learning that rely on contrast-enhanced computerized tomography (CT) for categorizing ovarian sex cord-stromal tumors (SCSTs) and epithelial ovarian cancers (EOCs). We included a total of 225 patients with 230 tumors, who were randomly divided into training and test cohorts with a ratio of 8:2. Radiomic features were extracted from each tumor and dimensionally reduced using LASSO. We used univariate and multivariate analyses to identify independent predictors from clinical features and conventional CT parameters. Clinic-radiological model, radiomics model and mixed model were constructed respectively. We evaluated model performance via analysis of the receiver operating characteristic (ROC) curve and area under ROC curves (AUCs), and compared it across models using the Delong test. We selected a support vector machine as the best classifier. Both radiomic and mixed model achieved good classification accuracy with AUC values of 0.923/0.930 in the training cohort, and 0.879/0.909 in the test cohort. The mixed model performed significantly better than the model based on clinical radiological information, with AUC values of 0.930 versus 0.826 (p = 0.000) in the training cohort and 0.905 versus 0.788 (p = 0.042) in the test cohort. Radiomic analysis based on CT images is a reliable and noninvasive tool for identifying SCSTs and EOCs, outperforming experience radiologists.

From Viral Infection to Genome Reshaping: The Triggering Role of HPV Integration in Cervical Cancer

Human papillomavirus (HPV) integration is recognized as a hallmark event in cervical carcinogenesis. However, it does not represent a routine phase of the viral life cycle but rather a stochastic occurrence, often constituting a dead-end pathway for the virus. High-risk human papillomavirus (hr-HPV) exhibits a greater propensity for integration. The progression from initial infection to genomic integration constitutes a dynamic multi-step oncogenic process in the development of cervical cancer (CC). This process involves viral entry, immune evasion, persistent infection, and ultimately integration. This article innovatively provides a comprehensive overview of this multi-stage mechanism: HPV, via the L1/L2 proteins, mediates internalization and establishes infection. Subsequently, under the influence of factors such as the host’s genetic background, vaginal microbiota imbalance, and immune evasion, the host’s DNA damage response (DDR) pathways are activated. Viral DNA integrates into host genome vulnerable sites (e.g., 3q28 and 8q24) through microhomology-mediated end joining (MMEJ) or other alternative pathways. Following integration, the expression of viral oncogenes persists, triggering host genomic rearrangements, aberrant epigenetic modifications, and immune microenvironment remodeling, all of which collectively drive cervical cancer progression. The study further reveals the clinical potential of HPV integration as a highly specific molecular biomarker, offering new perspectives for precision screening and targeted therapy. This dynamic model deepens our understanding of the HPV carcinogenic mechanism and provides a theoretical basis for intervention strategies.

Molybdenum Disulfide Supported on Metal–Organic Frameworks as an Ultrasensitive Layer for the Electrochemical Detection of the Ovarian Cancer Biomarker CA125

Metal-organic frameworks (MOFs) are composed of metal ions/clusters and organic ligands, showing accessible functional sites, ultra-high porosity, and large specific surface area. Tricopper benzene-1,3,5-tricarboxylate (CuBTC), as a three-dimensional MOF architecture with an open and robust micro-/nanoconfiguration, possesses excellent catalytic performance and superior electric conductivity as compared to bulk MOF. In this study, CuBTC was used as a substrate on which molybdenum disulfide (MoS

A novel subtype to predict prognosis and treatment response with DNA driver methylation–transcription in ovarian cancer

GJA1 Expression and Its Prognostic Value in Cervical Cancer

Gap Junction Protein Alpha 1 (GJA1) belongs to the gap junction family and has been widely studied in cancers. We evaluated the role of GJA1 in cervical cancer (CC) using public data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. The difference of GJA1 expression level between CC and normal tissues was analyzed by the Gene Expression Profiling Interactive Analysis (GEPIA), six GEO datasets, and the Human Protein Atlas (HPA). The relationship between clinicopathological features and GJA1 expression was analyzed by the chi‐squared test and the logistic regression. Kaplan–Meier survival analysis and Cox proportional hazard regression analysis were used to assessing the effect of GJA1 expression on survival. Gene set enrichment analysis (GSEA) was used to screen the signaling pathways regulated by GJA1. Immune Cell Abundance Identifier (ImmuCellAI) was chosen to analyze the immune cells affected by GJA1. The expression of GJA1 in CC was significantly lower than that in normal tissues based on the GEPIA, GEO datasets, and HPA. Both the chi‐squared test and the logistic regression showed that high‐GJA1 expression was significantly correlated with keratinization, hormone use, tumor size, and FIGO stage. The Kaplan–Meier curves suggested that high‐GJA1 expression could indicate poor prognosis (p = 0.0058). Multivariate analysis showed that high‐GJA1 expression was an independent predictor of poor overall survival (HR, 4.084; 95% CI, 1.354‐12.320; p = 0.013). GSEA showed many cancer‐related pathways, such as the p53 signaling pathway and the Wnt signaling pathway, were enriched in the high‐GJA1‐expression group. Immune cell abundance analysis revealed that the abundance of CD8 naive, DC, and neutrophil was significantly increased in the high‐GJA1‐expression group. In conclusion, GJA1 can be regarded as a potential prognostic marker of poor survival and therapeutic target in CC. Moreover, many cancer‐related pathways may be the critical pathways regulated by GJA1. Furthermore, GJA1 can affect the abundance of immune cells.

Aptamer-based carbohydrate antigen 125 sensor with molybdenum disulfide functional hybrid materials

Epithelial ovarian cancer is a malignant tumor of the female reproductive system with insidious symptoms, aggressiveness, risk of metastasis, and high mortality. Carbohydrate antigen 125 (CA125), a standard biomarker for screening epithelial ovarian cancer, can be applied to track cancer progression and treatment response. Here, we constructed an aptamer-based electrochemical biosensor to achieve sensitive detection of CA125. Molybdenum disulfide (MoS