AIMC Topic: Tumor Microenvironment

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Precise Electromagnetic Modulation of the Cell Cycle and Its Applications in Cancer Therapy.

International journal of molecular sciences
Precise modulation of the cell cycle via electromagnetic (EM) control presents a groundbreaking approach for cancer therapy, especially in the development of personalized treatment strategies. EM fields can precisely regulate key cellular homeostatic...

PFHxA and PFHxS promote breast cancer progression in 3D culture: MEX3C-associated immune infiltration revealed by bioinformatics and machine learning.

Journal of hazardous materials
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with widespread use and bioaccumulative potential. Short-chain PFAS such as perfluorohexanoic acid (PFHxA) and perfluorohexane sulfonate (PFHxS) have been introduced...

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 ...

Decoding the crossroads of aging and cancer through single-cell analysis: Implications for precision oncology.

International journal of cancer
Single-cell analysis is a transformative approach to understanding cellular heterogeneity in aging and cancer, interconnected processes driven by mechanisms like senescence and immune modulation. This review explores how aging influences cancer initi...

Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning.

Scientific reports
As a newly discovered histone modification, abnormal lactation has been found to be present in and contribute to the development of various cancers. The aim of this study was to investigate the potential role between lactylation and the prognosis of ...

Machine learning-based characterization of stemness features and construction of a stemness subtype classifier for bladder cancer.

BMC cancer
BACKGROUND: Bladder cancer (BLCA) is a highly heterogeneous disease that presents challenges in predicting prognosis and treatment response. Cancer stem cells are key drivers of tumor development, progression, metastasis, and treatment resistance. Th...

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-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...