Machine learning-driven prediction of brain metastasis in lung adenocarcinoma using miRNA profile and target gene pathway analysis of an mRNA dataset.

Journal: Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico

PMID: 38568412

Abstract

BACKGROUND: Brain metastasis (BM) is common in lung adenocarcinoma (LUAD) and has a poor prognosis, necessitating predictive biomarkers. MicroRNAs (MiRNAs) promote cancer cell growth, infiltration, and metastasis. However, the relationship between the miRNA expression profiles and BM occurrence in patients with LUAD remains unclear.

Authors

Young Wha Koh

Pathology.
Jae-Ho Han

Department of Pathology, Ajou University School of Medicine, 206 Worldcup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea.
Seokjin Haam

Department of Thoracic and Cardiovascular Surgery, Ajou University School of Medicine, Suwon-si, Republic of Korea.
Hyun Woo Lee

Division of Respiratory and Critical Care, Department of Internal Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, South Korea.

Keywords

Adenocarcinoma of Lung Aged Biomarkers, Tumor Brain Neoplasms Datasets as Topic Epithelial-Mesenchymal Transition Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Lung Neoplasms Machine Learning Male MicroRNAs Middle Aged Prognosis RNA, Messenger Vimentin

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View on PubMed Access via DOI PubMed (38568412)

Machine learning-driven prediction of brain metastasis in lung adenocarcinoma using miRNA profile and target gene pathway analysis of an mRNA dataset.

Abstract

Authors

Keywords

External Resources

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