AIMC Topic: Gene Expression Regulation, Neoplastic

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

Frontiers in immunology Apr 16, 2025
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...
Gene Expression Profiling Transcriptome Single-Cell Analysis RNA-Binding Proteins Gene Expression Regulation, Neoplastic Female Humans Artificial Intelligence Breast Neoplasms Machine Learning Tumor Microenvironment Prognosis Biomarkers, Tumor
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A Multi-Omics-Based Prognostic Model for Elderly Breast Cancer by Machine Learning: Insights From Hypoxia and Immunity of Tumor Microenvironment.

Clinical breast cancer Apr 15, 2025
INTRODUCTION: Older adult breast cancer (OABC) patients (≥ 65 years) frequently experience poorer prognoses compared to younger adults, attributed to complex tumor biology and age-related factors. The present study employs a multiomics approach combi...
Humans Machine Learning Prognosis Female Aged Middle Aged Multiomics Gene Expression Regulation, Neoplastic DNA Copy Number Variations Tumor Hypoxia Adult Breast Neoplasms Tumor Microenvironment Biomarkers, Tumor
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Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for Predicting the Prognosis of Neoadjuvant Chemotherapy in Breast Cancer.

International journal of molecular sciences Apr 13, 2025
Breast cancer (BRCA) continues to pose a serious risk to women's health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Nevertheless, the heterogeneity in treatment outcomes necessitates the identification of reliable biom...
Sequence Analysis, RNA Gene Expression Regulation, Neoplastic Neoadjuvant Therapy Cell Death Female Breast Neoplasms Biomarkers, Tumor Middle Aged Single-Cell Analysis Humans Machine Learning Prognosis
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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 Apr 13, 2025
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...
Middle Aged Gene Expression Regulation, Neoplastic Bayes Theorem Caspase 1 Female Biomarkers, Tumor Hematologic Neoplasms Male NF-kappa B p50 Subunit Artificial Intelligence Machine Learning Cytokines Humans
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Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning.

Scientific reports Apr 11, 2025
Over the past several decades, there has been a significant increase in the number of breast cancer patients. Among the four subtypes of breast cancer, Her2-positive breast cancer is one of the most aggressive breast cancers. In this study, we screen...
Female Breast Neoplasms Biomarkers, Tumor Gene Expression Profiling Computational Biology Immunotherapy Gene Expression Regulation, Neoplastic Protein Interaction Maps Gene Regulatory Networks Humans Machine Learning Macrophages Prognosis Receptor, ErbB-2
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Integrative Multi-Omics Analysis Reveals Molecular Subtypes of Ovarian Cancer and Constructs Prognostic Models.

Journal of immunotherapy (Hagerstown, Md. : 1997) Apr 9, 2025
Ovarian cancer (OV) remains the most lethal gynecological malignancy. The aim of this study was to identify molecular subtypes of OV through integrative multi-omics analysis and construct machine learning-based prognostic models for predicting the ef...
Databases, Genetic DNA Copy Number Variations Ovarian Neoplasms Mutation Biomarkers, Tumor Gene Expression Profiling Transcriptome Gene Regulatory Networks Immunotherapy Multiomics Gene Expression Regulation, Neoplastic Genomics Computational Biology Humans Machine Learning Prognosis Female
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Comprehensive analysis of scRNA-seq and bulk RNA-seq data via machine learning and bioinformatics reveals the role of lysine metabolism-related genes in gastric carcinogenesis.

BMC cancer Apr 9, 2025
BACKGROUND: Gastric cancer (GC) is a highly aggressive and heterogeneous cancer with extremely complex biological characteristics. Lysine and its metabolism are closely related to human cancer, but little is known about how lysine metabolism-related ...
Vimentin Lysine RNA-Seq Gene Regulatory Networks Single-Cell Gene Expression Analysis Computational Biology Nomograms Cell Proliferation Carcinogenesis Sequence Analysis, RNA Gene Expression Regulation, Neoplastic Stomach Neoplasms Biomarkers, Tumor Cell Line, Tumor Single-Cell Analysis Humans Machine Learning Prognosis
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Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma.

BMC cancer Apr 9, 2025
OBJECTIVE: The assessment of immunotherapy plays a pivotal role in the clinical management of skin melanoma. Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise i...
Prognosis Biomarkers, Tumor Melanoma Gene Expression Profiling Skin Neoplasms Transcriptome Tumor Microenvironment Computational Biology Humans Graph Neural Networks Neural Networks, Computer Immunotherapy Gene Expression Regulation, Neoplastic
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Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma.

BMC cancer Apr 7, 2025
BACKGROUND: Melanoma (SKCM) is an extremely aggressive form of cancer, characterized by high mortality rates, frequent metastasis, and limited treatment options. Our study aims to identify key target genes and enhance the diagnostic accuracy of melan...
Tumor Microenvironment Biomarkers, Tumor Gene Expression Profiling Transcriptome Prognosis Female Melanoma Skin Neoplasms Mutation Cell Line, Tumor Immunotherapy Multiomics Gene Expression Regulation, Neoplastic Humans Machine Learning
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Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer.

Cell communication and signaling : CCS Apr 5, 2025
BACKGROUND: Lysyl oxidases (LOX/LOXL1-4) are crucial for cancer progression, yet their transcriptional regulation, potential therapeutic targeting, prognostic value and involvement in immune regulation remain poorly understood. This study comprehensi...
Humans Machine Learning Computer Simulation Prognosis Neoplasms Computational Biology Gene Expression Regulation, Neoplastic Protein-Lysine 6-Oxidase
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