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Gene Expression Regulation, Neoplastic

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Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.

PloS one
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...

Feature gene selection and functional validation of SH3KBP1 in infantile hemangioma using machine learning.

Biochemical and biophysical research communications
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 ...

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
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...

A machine learning-based investigation of integrin expression patterns in cancer and metastasis.

Scientific reports
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 ...

Machine Learning Accurately Predicts Muscle Invasion of Bladder Cancer Based on Three miRNAs.

Journal of cellular and molecular medicine
The aim of this study was to validate the diagnostic potential of four previously identified miRNAs in two independent cohorts and to develop accurate classification models to predict invasiveness of bladder cancer. Furthermore, molecular subtypes we...

Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer.

Scientific reports
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...

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
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...

Identifying potential signatures of immune cells in hepatocellular carcinoma using integrative bioinformatics approaches and machine-learning strategies.

Immunologic research
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...