Yue Zhang

Comprehensive pan-cancer analysis of expression profiles and prognostic significance for NUMB and NUMBL in human tumors

NUMB has been initially identified as a critical cell fate determinant that modulates cell differentiation via asymmetrical partitioning during mitosis, including tumor cells. However, it remains absent that a systematic assessment of the mechanisms underlying NUMB and its homologous protein NUMBLIKE (NUMBL) involvement in cancer. This study aimed to investigate the prognostic significance for NUMB and NUMBL in pan-cancer. In this study, using the online databases TIMER2.0, gene expression profiling interactive analysis, cBioPortal, the University of ALabama at Birmingham CANcer data analysis Portal, SearchTool for the Retrieval of Interacting Genes/Proteins, and R software, we focused on the relevance between NUMB/NUMBL and oncogenesis, progression, mutation, phosphorylation, function and prognosis. This study demonstrated that abnormal expression of NUMB and NUMBL were found to be significantly associated with clinicopathologic stages and the prognosis of survival. Besides, genetic alternations of NUMB and NUMBL focused on uterine corpus endometrial carcinoma, and higher genetic mutations of NUMBL were correlated with more prolonged overall survival and disease-free survival in different cancers. Moreover, S438 locus of NUMB peptide fragment was frequently phosphorylated in 4 cancer types and relevant to its phosphorylation sites. Furthermore, endocytosis processing and neurogenesis regulation were involved in the functional mechanisms of NUMB and NUMBL separately. Additionally, the pathway enrichment suggested that NUMB was implicated in Hippo, Neurotrophin, Thyroid hormone, and FoxO pathways, while MAPK, Hippo, Rap1, mTOR, and Notch pathways were related to the functions of NUMBL. This study highlights the predictive roles of NUMB and NUMBL in pan-cancer, suggesting NUMB and NUMBL might be served as potential biomarkers for diagnosis and prognosis in various malignant tumors.

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.