ANLN: A New Hub in Glutamine Metabolism of Lung Adenocarcinoma by scRNA-Seq and Machine Learning.

Authors

Yiming Ma, Zhihan Zhang, Hongli Pan, Hailin Jiang, Lili Guo, Fengjie Guo

Published in

Oncology research. Volume 34. Issue 8. Pages 20. Epub Jul 16, 2026.

Abstract

Lung adenocarcinoma (LUAD) has a poor prognosis, and effective metabolic biomarkers are still few. Glutamine metabolism is one of the central features of tumor metabolic reprogramming, but the cellular heterogeneity and clinical significance of glutamine metabolism in the LUAD tumor microenvironment (TME) remain unknown. The goal of this paper was to define glutamine metabolism on a single-cell basis and determine major regulators that have predictive value.
A single-cell RNA sequencing dataset (GSE149655) was combined with The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) and Gene Expression Omnibus (GEO) datasets in order to evaluate the metabolic activity and intercellular communication. The prognostic model was constructed on weighted gene co-expression network analysis (WGCNA), machine learning-based approaches, and least absolute shrinkage and selection operator (LASSO)-Cox regression to examine the characteristics of the immune system and sensitivity to drugs. Carried out CRISPR/Cas9 knockout experiments to prove the function of ANLN.
Glutamine metabolism activity and increased cell-cell communication were observed in mast cells. A gene signature of 4 genes (ANLN, CIP2A, MEST, WDR76) divided the patients into high-risk and low-risk groups; the survival of these two groups, immunosuppressant features of TME, and susceptibility to dasatinib were different. ANLN was also determined to be an essential prognostic driver, and downregulating it inhibited glutamine metabolism and the invasion properties of LUAD cells.
Mast cells are metabolic centers of LUAD, whereas ANLN is a mediator of the association between glutamine metabolism and the development of tumors, which offers possible treatment options to achieve precise prognostication and treatment goals.

PMID:
42500572
Bibliographic data and abstract were imported from PubMed on 25 Jul 2026.

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