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Tumour necrosis factor (TNF) plays a critical role in tumour progression, but the specific involvement of mRNA in this process, particularly in kidney renal clear cell carcinoma (KIRC) remains insufficiently understood. Our study aims to develop a TNF-related mRNA (TRmRNA) model to predict prognosis and inform treatment strategies in KIRC. KIRC expression data from The Cancer Genome Atlas (TCGA) and TNF-related genes (TRGs) from the Genecards database were used to construct and validate a TRmRNA prognostic model. A nomogram integrating clinical features with the risk model was also developed to enhance prognostic accuracy. Enrichment analysis, drug sensitivity analysis and RT-qPCR validation were performed to further explore the biological mechanisms and clinical applicability of the model. A prognostic signature consisting of nine TRmRNAs was identified. Kaplan-Meier analysis showed that the high-risk (HRK) group had significantly shorter overall survival (OS) compared to the low-risk (LRG) group (p < 0.001). The nomogram, incorporating the risk model, yielded an area under the curve (AUC) of 0.766, indicating robust prognostic accuracy. Enrichment analysis identified solute sodium symporter and proximal tubule transport pathways enriched in the LRG group, whereas the HRK group exhibited enrichment in CD22-mediated BCR regulation and immunoglobulin complex pathways. The HRK group also showed a higher tumour mutational burden (TMB), correlating with a poorer prognosis. RT-qPCR confirmed the differential expression of mRNAs in KIRC cells. The TRmRNA-based prognostic model holds significant promise for predicting patient outcomes and guiding personalised treatment strategies in KIRC.
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http://dx.doi.org/10.1111/jcmm.70657 | DOI Listing |
Ann Surg Oncol
September 2025
Orthopaedic Oncology Service, Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, USA.
Background: Undifferentiated pleomorphic sarcoma (UPS) is a prevalent soft tissue sarcoma subtype associated with poor prognosis. Current prognostic tools lack the ability to incorporate personalized data for predicting survival. Machine learning (ML) offers a potential solution to enhance survival prediction accuracy.
View Article and Find Full Text PDFProstate Cancer Prostatic Dis
September 2025
Department of Urology, Department of Health Science, University of Milan, ASST Santi Paolo e Carlo, Milan, Italy.
Introduction: The introduction of novel robotic platforms has expanded surgical options for robot-assisted radical prostatectomy (RARP). However, comparative outcomes with da Vinci multiport (MP) system remain unclear. This systematic review and network meta-analysis aimed to compare perioperative, early oncological, and functional outcomes of RARP performed with novel robotic platforms versus the da Vinci MP system.
View Article and Find Full Text PDFJ Int Med Res
September 2025
Department of Hematology, Guangzhou Eighth People's Hospital, Guangzhou Medical University, China.
ObjectiveAccurate prognostication is crucial for managing human immunodeficiency virus (HIV)-associated cutaneous T-cell lymphoma. In this study, we aimed to develop an improved machine learning-based prognostic model for predicting the 5-year survival rates in HIV-associated cutaneous T-cell lymphoma patients.MethodsWe derived and tested machine learning models using algorithms including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest.
View Article and Find Full Text PDFZhonghua Bing Li Xue Za Zhi
September 2025
Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
To investigate the clinicopathological features, diagnosis, and prognosis of aggressive natural killer-cell leukemia (ANKL). A retrospective analysis was conducted on 27 ANKL patients treated at the First Affiliated Hospital of Nanjing Medical University from 2014 to 2024. Their clinical data, histomorphology, and immunophenotype were reviewed.
View Article and Find Full Text PDFRadiother Oncol
September 2025
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA. Electronic address:
Purpose: To predict metastasis-free survival (MFS) for patients with prostate adenocarcinoma (PCa) treated with androgen deprivation therapy (ADT) and external radiotherapy using clinical factors and radiomics extracted from primary tumor and node volumes in pre-treatment PSMA PET/CT scans.
Materials/methods: Our cohort includes 134 PCa patients (nodal involvement in 28 patients). Gross tumor volumes of primary tumor (GTVp) and nodes (GTVn) on CT and PET scans were segmented.