AIMC Topic: Gene Expression Regulation, Neoplastic

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Integrating single-cell sequencing and machine learning to uncover the role of mitophagy in subtyping and prognosis of esophageal cancer.

Apoptosis : an international journal on programmed cell death Feb 13, 2025
Globally, esophageal cancer stands as a prominent contributor to cancer-related fatalities, distinguished by its poor prognosis. Mitophagy has a significant impact on the process of cancer progression. This study investigated the prognostic significa...
Mitophagy Prognosis Female Tumor Microenvironment Biomarkers, Tumor Male Single-Cell Analysis Gene Expression Regulation, Neoplastic Esophageal Neoplasms Humans Machine Learning
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Unveiling the crucial role of glycosylation modification in lung adenocarcinoma metastasis through artificial neural network-based spatial multi-omics single-cell analysis and Mendelian randomization.

BMC cancer Feb 13, 2025
BACKGROUND: Investigations into the intricacies of glycosylation modifications, a prevalent post-translational alteration observed in neoplasms, especially remain elusive in the context of lung adenocarcinoma. Through the integration of multiple omic...
Humans Neural Networks, Computer Gene Expression Profiling Mendelian Randomization Analysis Multiomics Adenocarcinoma of Lung Transcriptome Gene Expression Regulation, Neoplastic Single-Cell Analysis Glycosylation Lung Neoplasms Protein Processing, Post-Translational
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A machine learning-based investigation of integrin expression patterns in cancer and metastasis.

Scientific reports Feb 12, 2025
Integrins, a family of transmembrane receptor proteins, are well known to play important roles in cancer development and metastasis. However, a comprehensive understanding of these roles has not been achieved due to the complex relationships between ...
Databases, Genetic Neoplasm Metastasis Integrins Male Neoplasms Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Machine Learning Female
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Feature gene selection and functional validation of SH3KBP1 in infantile hemangioma using machine learning.

Biochemical and biophysical research communications Feb 10, 2025
BACKGROUND: Infantile hemangioma (IH) is a prevalent vascular tumor in infancy with a complex pathogenesis that remains unclear. This study aimed to investigate the underlying mechanisms of IH using comprehensive bioinformatics analyses and in vitro ...
Adaptor Proteins, Signal Transducing Hemangioma Gene Regulatory Networks Computational Biology Gene Expression Regulation, Neoplastic Humans Male MicroRNAs Machine Learning Infant Female Gene Expression Profiling Child, Preschool
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Differential gene expression analysis and machine learning identified structural, TFs, cytokine and glycoproteins, including SOX2, TOP2A, SPP1, COL1A1, and TIMP1 as potential drivers of lung cancer.

Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals Feb 10, 2025
BACKGROUND: Lung cancer is a primary global health concern, responsible for a considerable portion of cancer-related fatalities worldwide. Understanding its molecular complexities is crucial for identifying potential targets for treatment. The goal i...
Gene Expression Profiling Protein Interaction Maps Gene Regulatory Networks Lung Neoplasms Humans Machine Learning Biomarkers, Tumor SOXB1 Transcription Factors Poly-ADP-Ribose Binding Proteins Collagen Type I, alpha 1 Chain DNA Topoisomerases, Type II Gene Expression Regulation, Neoplastic Osteopontin Collagen Type I Tissue Inhibitor of Metalloproteinase-1
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Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.

PloS one Feb 5, 2025
Hepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, and its mortality rate is gradually increasing worldwide. Existing studies used genetic datasets, taken from various platforms, but focused only on common different...
Biomarkers, Tumor Protein Interaction Maps Humans Gene Expression Profiling Gene Regulatory Networks Machine Learning Liver Neoplasms Gene Expression Regulation, Neoplastic Prognosis Carcinoma, Hepatocellular Databases, Genetic
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Machine learning-based integration reveals immunological heterogeneity and the clinical potential of T cell receptor (TCR) gene pattern in hepatocellular carcinoma.

Apoptosis : an international journal on programmed cell death Feb 4, 2025
The T Cell Receptor (TCR) significantly contributes to tumor immunity, whereas the intricate interplay with the Hepatocellular Carcinoma (HCC) microenvironment and clinical significance remains largely unexplored. Here, we aimed to examine the functi...
Prognosis Female Tumor Microenvironment Male Humans Machine Learning Receptors, Antigen, T-Cell Middle Aged Gene Expression Regulation, Neoplastic Cell Line, Tumor Signal Transduction Liver Neoplasms Carcinoma, Hepatocellular
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Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer.

Scientific reports Feb 4, 2025
Gastric cancer (GC) is one of the most common tumors; one of the reasons for its poor prognosis is that GC cells can resist normal cell death process and therefore develop distant metastasis. Cuproptosis is a novel type of cell death and a limited nu...
Transcriptome ROC Curve Gene Expression Regulation, Neoplastic Nomograms Biomarkers, Tumor Male Middle Aged Gene Expression Profiling Random Forest Kaplan-Meier Estimate Humans Machine Learning Prognosis Female Stomach Neoplasms
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Identifying potential signatures of immune cells in hepatocellular carcinoma using integrative bioinformatics approaches and machine-learning strategies.

Immunologic research Feb 4, 2025
Hepatocellular carcinoma (HCC) is a malignant tumor regulated by the immune system. Immunotherapy using checkpoint inhibitors has shown encouraging outcomes in a subset of HCC patients. The main challenges in checkpoint immunotherapy for HCC are to e...
Machine Learning Biomarkers, Tumor Gene Expression Profiling Gene Expression Regulation, Neoplastic Transcriptome Liver Neoplasms Carcinoma, Hepatocellular Gene Regulatory Networks Computational Biology Humans
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Integrating multiomics analysis and machine learning to refine the molecular subtyping and prognostic analysis of stomach adenocarcinoma.

Scientific reports Jan 30, 2025
Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and a lack of highly precise treatment options. We downloaded the multiomics data of STAD patients in The Cancer Genome Atlas (TCGA)-STAD cohort, which included mRNA, microR...
Gene Expression Profiling Multiomics Gene Expression Regulation, Neoplastic Adenocarcinoma Stomach Neoplasms Humans Biomarkers, Tumor Machine Learning Male Prognosis Middle Aged Kaplan-Meier Estimate Female DNA Methylation MicroRNAs RNA, Long Noncoding Mutation
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