Clinical significance and potential regulatory mechanism of overexpression of pituitary tumor-transforming gene transcription factor in bladder cancer.

BACKGROUND: Pituitary tumor transforming gene-1 (PTTG1) transcription factor is identified as carcinogenic and associated with tumor invasiveness, but its role in bladder cancer (BLCA) remains obscure. This research is intended to analyze the aberrant expression and clinical significance of PTTG1 in BLCA, explore the relationship between PTTG1 and tumor microenvironment characteristics and predict its potential transcriptional activity in BLCA tissue. METHODS: We compared the expression discrepancy of PTTG1 mRNA in BLCA and normal bladder tissue, using the BLCA transcriptomic datasets from GEO, ArrayExpress, TCGA, and GTEx. In-house immunohistochemical staining was implemented to determine the PTTG1 protein intensity. The prognostic value of PTTG1 was evaluated using the Kaplan-Meier Plotter. CRISPR screen data was utilized to estimate the effect PTTG1 interference has on BLCA cell lines. We predicted the abundance of the immune cells in the BLCA tumor microenvironment using the microenvironment cell populations-counter and ESTIMATE algorithms. Single-cell RNA sequencing data was applied to identify the major cell types in BLCA, and the dynamics of BLCA progression were revealed using pseudotime analysis. PTTG1 target genes were predicted by CistromeDB. RESULTS: The elevated expression level of PTTG1 was confirmed in 1037 BLCA samples compared with 127 non-BLCA samples, with a standardized mean difference value of 1.04. Higher PTTG1 expression status exhibited a poorer BLCA prognosis. Moreover, the PTTG1 Chronos genetic effect scores were negative, indicating that PTTG1 silence may inhibit the proliferation and survival of BLCA cells. With PTTG1 mRNA expression level increasing, higher natural killer, cytotoxic lymphocyte, and monocyte lineage cell infiltration levels were observed. A total of four candidate targets containing CHEK2, OCIAD2, UBE2L3, and ZNF367 were determined ultimately. CONCLUSIONS: PTTG1 mRNA over-expression may become a potential biomarker for BLCA prognosis. Additionally, PTTG1 may correlate with the BLCA tumor microenvironment and exert transcriptional activity by targeting CHEK2, OCIAD2, UBE2L3, and ZNF367 in BLCA tissue.

The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study.

OBJECTIVE: To develop and validate a pretreatment magnetic resonance imaging (MRI)-based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models. METHODS: We retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs. a responding group according to the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. For each tumor, 960 features were extracted from the MRI sequence. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. A support vector machine (SVM) model incorporating clinical factors and radiomic features wase used to construct the radiomic-clinical model. SHAP method explained the SVM model by prioritizing the importance of features, in terms of assessment contribution. RESULTS: Three radiomic features and three clinical factors were identified to build the model. Radiomic-clinical model yielded AUCs of 0.928 (95%CI 0.901-0.949) and 0.851 (95%CI 0.816-0.886) for assessing the treatment response in the training cohort and validation cohort, respectively. SHAP summary plot illustrated the feature's value affected the feature's impact attributed to model, and SHAP force plot showed the integration of features' impact attributed to individual response. CONCLUSION: The radiomic-clinical model with the SHAP method can be useful for assessing the treatment response of WBRT and may assist clinicians in directing personalized WBRT strategies in an understandable manner. KEY POINTS: • Radiomic-clinical model can be useful for assessing the treatment response of WBRT. • SHAP could explain and visualize radiomic-clinical machine learning model in a clinician-friendly way.