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Endometrial cancer (UCEC) is the most prevalent gynecological malignancy in high-income countries, and its incidence is rising globally. Although early-stage UCEC can be treated with surgery, advanced cases have a poor prognosis, highlighting the need for effective molecular biomarkers to improve diagnosis and prognosis. In this study, we analyzed mRNA and miRNA sequencing data from UCEC tissues and adjacent non-cancerous tissues from the TCGA database.

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Colon cancer, as a highly prevalent malignant tumor globally, poses a significant threat to human health. In recent years, ferroptosis and cuproptosis, as two novel forms of cell death, have attracted widespread attention for their potential roles in the development and treatment of colon cancer. However, the investigation into the subtypes and their impact on the survival of colon cancer patients remains understudied.

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In this study, spatial and single-cell transcriptome techniques were used to investigate the role of beta-galactoside alpha-2,6-sialyltransferase 1 (ST6GAL1) in promoting peritoneal metastasis in ovarian cancer epithelial cells. We collected single-cell transcriptomic (GSE130000) and spatial transcriptomic datasets (GSE211956) from the Gene Expression Omnibus and RNA-sequencing data from The Cancer Genome Atlas. The Robust Cell Type Decomposition (RCTD) approach was implemented to integrate spatial and single-cell transcriptomic data.

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Background: Herein, we aimed to examine the relationship between sarcopenia, neutrophil-lymphocyte ratio (NLR), Charlson comorbidity index (CCI), and prognostic nutritional index (PNI) in patients with superficial esophageal carcinoma who underwent definitive chemoradiotherapy (CRT).

Methods: We retrospectively analyzed 100 patients (87 males) diagnosed with cT1N0M0 esophageal squamous cell carcinoma. The included patients underwent CRT as an initial treatment.

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Objectives: We aimed to use artificial intelligence to accurately identify molecular subgroups of medulloblastoma (MB), predict clinical outcomes, and incorporate deep learning-based imaging features into the risk stratification.

Methods: The MRI features were extracted for molecular subgroups by a novel multi-parameter convolutional neural network (CNN) called Bi-ResNet-MB. Then, MR features were used to establish a prognosis model based on XGBoost.

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