AIMC Topic: Gene Expression Regulation, Neoplastic
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Predicting breast cancer prognosis based on a novel pathomics model through CHEK1 expression analysis using machine learning algorithms.
PloS one
Jan 1, 2025
BACKGROUND: Checkpoint kinase 1 (CHEK1) is often overexpressed in solid tumors. Nonetheless, the prognostic significance of CHEK1 in breast cancer (BrC) remains unclear. This study used pathomics leverages machine learning to predict BrC prognosis ba...
Integrative Machine Learning of Glioma and Coronary Artery Disease Reveals Key Tumour Immunological Links.
Journal of cellular and molecular medicine
Jan 1, 2025
It is critical to appreciate the role of the tumour-associated microenvironment (TME) in developing strategies for the effective therapy of cancer, as it is an important factor that determines the evolution and treatment response of tumours. This wor...
Bayesian-optimized deep learning for identifying essential genes of mitophagy and fostering therapies to combat drug resistance in human cancers.
Journal of cellular and molecular medicine
Jan 1, 2025
Dysregulated mitophagy is essential for mitochondrial quality control within human cancers. However, identifying hub genes regulating mitophagy and developing mitophagy-based treatments to combat drug resistance remains challenging. Herein, BayeDEM (...
Diagnostic Power of MicroRNAs in Melanoma: Integrating Machine Learning for Enhanced Accuracy and Pathway Analysis.
Journal of cellular and molecular medicine
Jan 1, 2025
This study identifies microRNAs (miRNAs) with significant discriminatory power in distinguishing melanoma from nevus, notably hsa-miR-26a and hsa-miR-211, which have exhibited diagnostic potential with accuracy of 81% and 78% respectively. To enhance...
Immunoinfiltration Analysis of Mitochondrial Damage-Related Genes in Lung Adenocarcinoma and Construction of a Classification and Prognostic Model Integrated With WGCNA and Machine Learning Algorithms.
Cancer medicine
Jan 1, 2025
BACKGROUND: Lung adenocarcinoma (LUAD) exhibits molecular heterogeneity, with mitochondrial damage affecting progression. The relationship between mitochondrial damage and immune infiltration, and Weighted Gene Co-expression Network Analysis (WGCNA)-...
Artificial Intelligence-Guided Identification of IGFBP7 as a Critical Indicator in Lactic Metabolism Determines Immunotherapy Response in Stomach Adenocarcinoma.
Journal of cellular and molecular medicine
Jan 1, 2025
Due to considerable tumour heterogeneity, stomach adenocarcinoma (STAD) has a poor prognosis and varies in response to treatment, making it one of the main causes of cancer-related mortality globally. Recent data point to a significant role for metab...
TPD52 as a Therapeutic Target Identified by Machine Learning Shapes the Immune Microenvironment in Breast Cancer.
Journal of cellular and molecular medicine
Jan 1, 2025
Breast cancer (BRCA) is one of the most common malignancies and a leading cause of cancer-related mortality among women globally. Despite advances in diagnosis and treatment, the heterogeneity of BRCA presents significant challenges for effective man...
Identification of miR-20a as a Diagnostic and Prognostic Biomarker in Colorectal Cancer: MicroRNA Sequencing and Machine Learning Analysis.
MicroRNA (Shariqah, United Arab Emirates)
Jan 1, 2025
INTRODUCTION: The differential expression of miRNAs, a key regulator in many cell signaling pathways, has been studied in various malignancies and may have an important role in cancer progression, including colorectal cancer (CRC).
Developing a Predictive Model for Metastatic Potential in Pancreatic Neuroendocrine Tumor.
The Journal of clinical endocrinology and metabolism
Dec 18, 2024
CONTEXT: Pancreatic neuroendocrine tumors (PNETs) exhibit a wide range of behavior from localized disease to aggressive metastasis. A comprehensive transcriptomic profile capable of differentiating between these phenotypes remains elusive.
[Screening of characteristic genes of salivary gland adenoid cystic carcinoma based on weighted co-expression network and machine learning].
Shanghai kou qiang yi xue = Shanghai journal of stomatology
Dec 1, 2024
PURPOSE: To identify potential biomarkers of salivary gland adenoid cystic carcinoma to further understand the potential pathogenesis of adenoid cystic carcinoma.
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