Evidence map›Paper›PMID 39148731›Full record

ArticleFrontiers in immunology2024

Integrated bioinformatics analysis of nucleotide metabolism based molecular subtyping and biomarkers in lung adenocarcinoma.

Dayuan Luo, Haohui Wang, Zhen Zeng, Jiajing Chen, Haiqin Wang

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Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Dayuan Luo *Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Haohui Wang *Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Zhen ZengDepartment of Geriatrics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Jiajing ChenDepartment of Geriatrics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Haiqin WangDepartment of Geriatrics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung adenocarcinoma (LUAD), a predominant subtype of non-small cell lung cancers, continues to challenge treatment outcomes due to its heterogeneity and complex tumor microenvironment (TME). Dysregulation in nucleotide metabolism has been identified as a significant factor in tumorigenesis, suggesting its potential as a therapeutic target. Methods: This study analyzed LUAD samples from The Cancer Genome Atlas (TCGA) using Non-negative Matrix Factorization (NMF) clustering, Weighted Correlation Network Analysis (WGCNA), and various machine learning techniques. We investigated the role of nucleotide metabolism in relation to clinical features and immune microenvironment through large-scale data analysis and single-cell sequencing. Using Results: Nucleotide metabolism genes classified LUAD patients into two distinct subtypes with significant prognostic differences. The 'C1' subtype associated with active nucleotide metabolism pathways showed poorer prognosis and a more aggressive tumor phenotype. Furthermore, a nucleotide metabolism-related score (NMRS) calculated from the expression of 28 key genes effectively differentiated between patient outcomes and predicted associations with oncogenic pathways and immune responses. By integrating various immune infiltration algorithms, we delineated the associations between nucleotide metabolism signature genes and the tumor microenvironment, and characterized their distribution differences at the cellular level by analyzing single-cell sequencing dataset related to immunochemotherapy. Finally, we demonstrated the differential expression of the key nucleotide metabolism gene AUNIP acts as an oncogene to promote LUAD cell proliferation and is associated with tumor immune infiltration. Conclusion: The study underscores the pivotal role of nucleotide metabolism in LUAD progression and prognosis, highlighting the NMRS as a valuable biomarker for clinical outcomes and therapeutic responses. Specifically, AUNIP functions as a critical oncogene, offering a promising target for novel treatment strategies in LUAD.

Indexed as

Adenocarcinoma of LungBiomarkers, TumorComputational BiologyLung NeoplasmsNucleotidesTumor MicroenvironmentAnimalsCell Line, TumorGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMicePrognosisBiomarkers, TumorNucleotidesAUNIPimmune microenvironmentlung adenocarcinomanucleotide metabolismtumor microenvironment

Identifiers

PMID39148731
PMCPMC11324481

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.