AIMC Topic: Gene Expression Regulation, Neoplastic

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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...
Gene Expression Regulation, Neoplastic Humans Receptor, ErbB-2 Machine Learning Macrophages Prognosis Protein Interaction Maps Female Gene Regulatory Networks Breast Neoplasms Computational Biology Biomarkers, Tumor Immunotherapy Gene Expression Profiling
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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...
Biomarkers, Tumor Gene Expression Profiling Transcriptome Gene Regulatory Networks Humans Gene Expression Regulation, Neoplastic Machine Learning Prognosis Databases, Genetic Female DNA Copy Number Variations Genomics Ovarian Neoplasms Computational Biology Mutation Immunotherapy Multiomics
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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 ...
Humans Machine Learning Prognosis Stomach Neoplasms Biomarkers, Tumor Cell Line, Tumor Cell Proliferation Computational Biology Carcinogenesis Single-Cell Analysis Sequence Analysis, RNA Vimentin RNA-Seq Gene Expression Regulation, Neoplastic Lysine Gene Regulatory Networks Nomograms Single-Cell Gene Expression Analysis
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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...
Computational Biology Immunotherapy Gene Expression Regulation, Neoplastic Graph Neural Networks Tumor Microenvironment Prognosis Biomarkers, Tumor Melanoma Gene Expression Profiling Skin Neoplasms Transcriptome Humans Neural Networks, Computer
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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...
Multiomics Gene Expression Regulation, Neoplastic Mutation Gene Expression Profiling Transcriptome Cell Line, Tumor Immunotherapy Humans Machine Learning Prognosis Female Melanoma Skin Neoplasms Tumor Microenvironment Biomarkers, Tumor
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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 Computational Biology Gene Expression Regulation, Neoplastic Protein-Lysine 6-Oxidase Machine Learning Computer Simulation Prognosis Neoplasms
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Identification of thyroid cancer biomarkers using WGCNA and machine learning.

European journal of medical research Apr 5, 2025
OBJECTIVE: The incidence of thyroid cancer (TC) is increasing in China, largely due to overdiagnosis from widespread screening and improved ultrasound technology. Identifying precise TC biomarkers is crucial for accurate diagnosis and effective treat...
Humans Machine Learning Gene Regulatory Networks Biomarkers, Tumor Gene Expression Regulation, Neoplastic Gene Expression Profiling Thyroid Neoplasms
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Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and experiments.

Frontiers in immunology Apr 4, 2025
INTRODUCTION: Early diagnosis of Ewing sarcoma (ES) is critical for improving patient prognosis. However, the accurate diagnosis of ES remains challenging, underscoring the need for novel diagnostic biomarkers to enhance diagnostic precision and reli...
Cell Movement Bone Neoplasms Gene Expression Profiling Homeodomain Proteins Humans Cell Line, Tumor Sarcoma, Ewing Machine Learning Gene Expression Regulation, Neoplastic Reproducibility of Results Cell Proliferation Biomarkers, Tumor
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Mitigating ambient RNA and doublets effects on single cell transcriptomics analysis in cancer research.

Cancer letters Apr 3, 2025
In cancer biology, where understanding the tumor microenvironment at high resolution is vital, ambient RNA contamination becomes a considerable problem. This hinders accurate delineation of intratumoral heterogeneity, complicates the identification o...
Biomarkers, Tumor Neoplasms Gene Expression Profiling Single-Cell Analysis Transcriptome Humans RNA-Seq Tumor Microenvironment Computational Biology Gene Expression Regulation, Neoplastic
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Development and validation of machine learning models for early diagnosis and prognosis of lung adenocarcinoma using miRNA expression profiles.

Cancer biomarkers : section A of Disease markers Apr 2, 2025
ObjectiveStudy aims to develop diagnostic and prognostic models for lung adenocarcinoma (LUAD) using Machine learning（ML）algorithms, aiming to enhance clinical decision-making accuracy.MethodsData from The Cancer Genome Atlas (TCGA) for LUAD patients...
Gene Expression Profiling ROC Curve Lung Neoplasms Gene Expression Regulation, Neoplastic Aged Humans Nomograms Biomarkers, Tumor Machine Learning Early Detection of Cancer Male Prognosis MicroRNAs Middle Aged Female Adenocarcinoma of Lung
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