Search

Abstract Objective We have developed explainable machine learning models to predict the overall survival (OS) of retroperitoneal liposarcoma (RLPS) patients. This approach aims to enhance the explainability and transparency of our modeling results. Methods We collected clinicopathological information of RLPS patients from The Surveillance, Epidemiology, and End Results (SEER) database and allocated them into training and validation sets with a 7:3 ratio. Simultaneously, we obtained an external validation cohort from The First Affiliated Hospital of Naval Medical University (Shanghai, China). We performed LASSO regression and multivariate Cox proportional hazards analysis to identify relevant risk factors, which were then combined to develop six machine learning (ML) models: Cox proportional hazards model (Coxph), random survival forest (RSF), ranger, gradient boosting with component‐wise linear models (GBM), decision trees, and boosting trees. The predictive performance of these ML models was evaluated using the concordance index (C‐index), the integrated cumulative/dynamic area under the curve (AUC), and the integrated Brier score, as well as the Cox–Snell residual plot. We also used time‐dependent variable importance, analysis of partial dependence survival plots, and the generation of aggregated survival SHapley Additive exPlanations (SurvSHAP) plots to provide a global explanation of the optimal model. Additionally, SurvSHAP (t) and survival local interpretable model‐agnostic explanations (SurvLIME) plots were used to provide a local explanation of the optimal model. Results The final ML models are consisted of six factors: patient's age, gender, marital status, surgical history, as well as tumor's histopathological classification, histological grade, and SEER stage. Our prognostic model exhibits significant discriminative ability, particularly with the ranger model performing optimally. In the training set, validation set, and external validation set, the AUC for 1, 3, and 5 year OS are all above 0.83, and the integrated Brier scores are consistently below 0.15. The explainability analysis of the ranger model also indicates that histological grade, histopathological classification, and age are the most influential factors in predicting OS. Conclusions The ranger ML prognostic model exhibits optimal performance and can be utilized to predict the OS of RLPS patients, offering valuable and crucial references for clinical physicians to make informed decisions in advance.

Abstract Background Kinesin family member 4A (KIF4A) is upregulated in a variety of cancers. However, its expression and potential downstream targets in urothelial bladder carcinoma (UBC) remain unclear. Methods Expression data of KIF4A in UBC and noncancerous tissues were downloaded from the GEPIA database. Cell proliferation, migration, invasion, and apoptosis of T24 and 5637 UBC cells were examined using wound healing, transwell, colony formation, CCK‐8, and flow cytometry assays. KIF4A and potential downstream genes were analyzed using qRT‐PCR, western blot analysis, and immunohistochemistry. Results In UBC samples, KIF4A expression was significantly higher than in corresponding noncancerous samples. UBC patients with high KIF4A expression had poor cancer‐specific survival and overall survival. Knockdown of KIF4A significantly inhibited proliferation and promoted apoptosis of UBC cells, accompanied by dephosphorylation of AKT and increased the protein level of proapoptotic factors. Additionally, knockdown of KIF4A reduced migration and invasion of UBC cells whereas overexpression of KIF4A exhibited opposite effects, along with altered protein level in epithelial‐mesenchymal transition‐related genes. Furthermore, overexpression of YAP1 promoted KIF4A expression whereas knockdown of YAP1 suppressed KIF4A expression in UBC cells. Alternatively, KIF4A knockdown reduced YAP1 nuclear protein level whereas KIF4A overexpression suppressed YAP1 phosphorylation and facilitated YAP1 nuclear translocation. Conclusions KIF4A upregulation correlates with poor prognosis of UBC. Knockdown of KIF4A inhibits proliferation, migration, and invasion of UBC cells while inducing apoptosis possibly through dephosphorylation of AKT, changes in EMT‐related genes, and interaction with YAP1.