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Head and neck carcinomas are the sixth most common cancers worldwide, with laryngeal squamous cell carcinoma (LSCC) being the second most prevalent subtype. Improving survival outcomes in LSCC patients remains a critical clinical challenge. This retrospective study aimed to develop a nomogram model integrating tumor-infiltrating lymphocytes (TILs) and clinicopathological characteristics to predict the prognosis of LSCC patients. The nomogram model was constructed using Cox and Lasso regression analyses and was subsequently evaluated through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were utilized for model validation and to further elucidate the role of TILs and immune responses in LSCC. This study cohort included LSCC patients diagnosed by pathological examination between 2011 and 2014 at Xiangya Hospital and Harbin Medical University Cancer Hospital. A total of 412 patients were assigned to the training cohort and 140 patients to the test cohort for validation. The final nomogram model integrated TNM stage, TILs, PLR, BMI, age, differentiation and NLR. The area under the curve (AUC) was 0.745, indicating strong calibration and clinical utility. Kaplan-Meier survival curves demonstrated significant discrimination. TILs were positively correlated with immune cell abundance and the expression of immune-related genes. In conclusion, the nomogram model based on TILs and clinicopathological features effectively predicts the prognosis of LSCC patients.
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http://dx.doi.org/10.62347/MKFI3976 | DOI Listing |
World J Pediatr
September 2025
Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, No. 79 Qingchun Road, Hangzhou 310003, China.
Background: Carbapenem-resistant Enterobacteriaceae (CRE) infections can pose a significant risk following pediatric liver transplantations. This study aimed to identify risk factors for CRE infections and develop prediction models for pediatric recipients.
Methods: This study enrolled pediatric patients who underwent liver transplantation between 2017 and 2023.
World J Urol
September 2025
Department of Clinical Laboratory, Fuzhou University Affiliated Provincial Hospital, Fuzhou, 350000, Fujian, China.
Objective: To develop and validate a prognostic nomogram for predicting the risk of proximal ureteral impacted calculi, supporting personalized clinical management.
Methods: This retrospective, multicenter study employed a continuous cohort of 391 patients with proximal ureteral stones treated between January 2021 and April 2024. Data from Longyan People's Hospital (affiliated with Xiamen Medical College) comprised the training set, while independent external validation was performed using data from The Fifth Affiliated Hospital of Fujian University of Traditional Chinese Medicine.
Abdom Radiol (NY)
September 2025
Department of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Background: We aimed to develop and validate a radiomics-based machine learning nomogram using multiparametric magnetic resonance imaging to preoperatively predict substantial lymphovascular space invasion in patients with endometrial cancer.
Methods: This retrospective dual-center study included patients with histologically confirmed endometrial cancer who underwent preoperative magnetic resonance imaging (MRI). The patients were divided into training and test sets.
Epigenomics
September 2025
College of Physical Education, Yangzhou University, Yangzhou, China.
Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder lacking objective biomarkers for early diagnosis. DNA methylation is a promising epigenetic marker, and machine learning offers a data-driven classification approach. However, few studies have examined whole-blood, genome-wide DNA methylation profiles for ASD diagnosis in school-aged children.
View Article and Find Full Text PDFBackground: The goal was to explore the impact of the NR1D1 gene on the occurrence, development, and prognosis of colorectal cancer (CRC) using bioinformatics approaches.
Methods: CRC transcriptomic and clinical data from TCGA were analyzed to compare NR1D1 expression in tumors and various clinical stages. Survival differences between high and low NR1D1 expression groups were assessed using the R survival package.