Integrating multi-omics and machine learning methods reveals the metabolism of amino acids and derivatives-related signature in colorectal cancer
Jian Yue, Huiying Fang, Qian Yang, Rui Feng, Guosheng Ren

TL;DR
This study explores how amino acid metabolism relates to colorectal cancer, identifying a key gene that could be a new treatment target.
Contribution
The study introduces a novel MAAD-related gene, LSM8, as a potential therapeutic target for colorectal cancer.
Findings
A prognostic model based on MAAD-related genes showed better survival outcomes for low-risk patients.
High-risk patients exhibited worse immunotherapy response and higher tumor heterogeneity.
Knocking down the gene LSM8 reduced the malignancy of colorectal cancer cells.
Abstract
The metabolism of amino acids and derivatives (MAAD) is closely related to the occurrence and development of colorectal cancer (CRC), but the specific regulatory mechanisms are not yet clear. This study aims to explore the role of MAAD in the progression of colorectal cancer and ultimately identify key molecules that may become potential therapeutic targets for CRC. This study integrates bulk transcriptome and single-cell transcriptome to analyze and identify key MAAD-related genes from multiple levels. Subsequently, numerous machine learning methods were incorporated to construct MAAD-related prognostic models, and the infiltration of immune cells, tumor heterogeneity, tumor mutation burden, and potential pathway changes under different modes were analyzed. Finally, key molecules were identified for experimental validation. We successfully constructed prognostic models and Nomograms…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsFerroptosis and cancer prognosis · Cancer Genomics and Diagnostics · Epigenetics and DNA Methylation
