Lin Xie

Characterization and clinical significance of the CADM1/HER2/STAT3 axis in serous ovarian tumors

Abstract The subtypes of serous ovarian tumors (SOTs), including benign serous cystadenoma, serous borderline tumor (SBT), low-grade serous ovarian carcinoma (LGSC), and high-grade serous ovarian carcinoma (HGSC), remain poorly understood. Herein, we aimed to characterize the cell adhesion molecule 1 (CADM1)/signal transducer and activator of transcription 3 (STAT3)/human epidermal growth factor receptor 2 (HER2) axis and identify its clinical significance in patients with serous cystadenoma, SBT, LGSC, and HGSC. The immunohistochemical expression of CADM1, HER2, and STAT3 was assessed in 180 SOT specimens, and its association with clinical data was determined. High levels of CADM1 expression were detected in 100% of serous cystadenomas and 83.33% of SBTs, while a loss of CADM1 expression was observed in 44% of LGSCs and 72.5% of HGSCs. Relative to the levels in benign cystadenomas and SBTs, higher levels of HER2 and STAT3 expression were observed in LGSCs and aggressive HGSCs. Furthermore, the expression profile of the CADM1/HER2/STAT3 axis was significantly associated with histologic type, International Federation of Gynecology and Obstetrics stage, and lymph node metastasis in patients with SOT. Our study identified the changes in the CADM1/HER2/STAT3 axis that were closely associated with the clinical behavior of SOTs. These molecular data may provide new insights into SOT carcinogenesis and aid in the diagnosis and treatment of patients with SOT.

Risk Stratification of Early-Stage Cervical Cancer with Intermediate-Risk Factors: Model Development and Validation Based on Machine Learning Algorithm

Abstract Background Adjuvant therapy for patients with cervical cancer (CC) with intermediate-risk factors remains controversial. The objectives of the present study are to assess the prognoses of patients with early-stage CC with pathological intermediate-risk factors and to provide a reference for adjuvant therapy choice. Materials and Methods This retrospective study included 481 patients with stage IB–IIA CC. Cox proportional hazards regression analysis, machine learning (ML) algorithms, Kaplan-Meier analysis, and the area under the receiver operating characteristic curve (AUC) were used to develop and validate prediction models for disease-free survival (DFS) and overall survival (OS). Results A total of 35 (7.3%) patients experienced recurrence, and 20 (4.2%) patients died. Two prediction models were built for DFS and OS using clinical information, including age, lymphovascular space invasion, stromal invasion, tumor size, and adjuvant treatment. Patients were divided into high-risk or low-risk groups according to the risk score cutoff value. The Kaplan-Meier analysis showed significant differences in DFS (p = .001) and OS (p = .011) between the two risk groups. In the traditional Sedlis criteria groups, there were no significant differences in DFS or OS (p > .05). In the ML-based validation, the best AUCs of DFS at 2 and 5 years were 0.69/0.69, and the best AUCs of OS at 2 and 5 years were 0.88/0.63. Conclusion Two prognostic assessment models were successfully established, and risk grouping stratified the prognostic risk of patients with CC with pathological intermediate-risk factors. Evaluation of long-term survival will be needed to corroborate these findings. Implications for Practice The Sedlis criteria are intermediate-risk factors used to guide postoperative adjuvant treatment in patients with cervical cancer. However, for patients meeting the Sedlis criteria, the choice of adjuvant therapy remains controversial. This study developed two prognostic models based on pathological intermediate-risk factors. According to the risk score obtained by the prediction model, patients can be further divided into groups with high or low risk of recurrence and death. The prognostic models developed in this study can be used in clinical practice to stratify prognostic risk and provide more individualized adjuvant therapy choices to patients with early-stage cervical cancer.