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Biomarkers, Tumor

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MANIFEST: Multiomic Platform for Cancer Immunotherapy.

Cancer discovery
Immunotherapy has revolutionized survival outcomes for many patients diagnosed with cancer. However, biomarkers that can reliably distinguish treatment responders from nonresponders, predict potential life-threatening and life-changing drug-induced t...

Inside a Metastatic Fracture: Molecular Bases and New Potential Therapeutic Targets.

Cancer medicine
INTRODUCTION: Bone metastases and pathological fractures significantly impact the prognosis and quality of life in cancer patients. However, clinical and radiological features alone have been shown to fail to predict skeletal related events of a bone...

Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer.

Frontiers in immunology
BACKGROUND: Breast cancer is the most common malignancy in women globally, with significant heterogeneity affecting prognosis and treatment. RNA-binding proteins play vital roles in tumor progression, yet their prognostic potential remains unclear. T...

Analysis of the Relationship Between and Cytokine Gene Expression in Hematological Malignancy: Leveraging Explained Artificial Intelligence and Machine Learning for Small Dataset Insights.

International journal of medical sciences
This study measures expression of () and related cytokine genes in bone marrow mononuclear cells in patients with hematological malignancies, analyzing the relationship between them with an integrated framework of statistical analyses, machine learn...

The Role of Eosinophils, Eosinophil-Related Cytokines and AI in Predicting Immunotherapy Efficacy in NSCLC Cancer.

Biomolecules
Immunotherapy and chemoimmunotherapy are standard treatments for non-oncogene-addicted advanced non-small cell lung cancer (NSCLC). Currently, a limited number of biomarkers, including programmed death-ligand 1 (PD-L1) expression, microsatellite inst...

Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification.

BMC cancer
BACKGROUND: Advancements in the management of gastric cancer (GC) and innovative therapeutic approaches highlight the significance of the role of biomarkers in GC prognosis. Machine-learning (ML)-based methods can be applied to identify the most impo...

Identification of M2 macrophage-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches.

Biology direct
M2 macrophages play a crucial role in the initiation and progression of various tumors, including diffuse large B-cell lymphoma (DLBCL). However, the characterization of M2 macrophage-related genes in DLBCL remains incomplete. In this study, we downl...

Selection of neuroendocrine markers in diagnostic workup of neuroendocrine neoplasms: The real-world data and machine learning model algorithms.

Cancer cytopathology
BACKGROUND: Accurate diagnosis of neuroendocrine neoplasms (NENs) is challenging, especially in poorly differentiated neuroendocrine carcinomas (NECs). This study was aimed to search the best or best combination of neuroendocrine markers in the diagn...