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Tumor Microenvironment

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Unsupervised machine learning-based stratification and immune deconvolution of liver hepatocellular carcinoma.

BMC cancer
BACKGROUND: Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer and a leading cause of cancer-related deaths globally. The tumour microenvironment (TME) influences treatment response and prognosis, yet its heterogeneity remains ...

Multi-dimensional characterization of cellular states reveals clinically relevant immunological subtypes and therapeutic vulnerabilities in ovarian cancer.

Journal of translational medicine
BACKGROUND: Diverse cell types and cellular states in the tumor microenvironment (TME) are drivers of biological and therapeutic heterogeneity in ovarian cancer (OV). Characterization of the diverse malignant and immunology cellular states that make ...

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...

Optimizing Immunotherapy: The Synergy of Immune Checkpoint Inhibitors with Artificial Intelligence in Melanoma Treatment.

Biomolecules
Immune checkpoint inhibitors (ICIs) have transformed melanoma treatment; however, predicting patient responses remains a significant challenge. This study reviews the potential of artificial intelligence (AI) to optimize ICI therapy in melanoma by in...

Artificial Intelligence-Guided Cancer Engineering for Tumor Normalization Executed by Tumor Lysosomal-Triggered Supramolecular Chiral Peptide.

ACS nano
Cancer engineering for tumor normalization offers a promising therapeutic strategy to reverse malignant cells and their supportive tumor microenvironment into a more benign state. Herein, an artificial intelligence (AI) approach was developed using m...

Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.

Scientific reports
Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data ...

Machine learning and single-cell analysis uncover distinctive characteristics of CD300LG within the TNBC immune microenvironment: experimental validation.

Clinical and experimental medicine
Investigating the essential function of CD300LG within the tumor microenvironment in triple-negative breast cancer (TNBC). Transcriptomic and single-cell data from TNBC were systematically collected and integrated. Four machine learning algorithms we...