Evidence map›Paper›PMID 41234824›Full record

ArticleTranslational cancer research2025

Integrated multi-omic analysis unravels the characteristics of the metabolism-related intratumoral microbes and establishes a novel signature for predicting prognosis and therapeutic response in lung adenocarcinoma.

Huan Liu, Yueguang Liu, Yixuan Dai, Lei Zhang, Mei Long

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Article in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Huan LiuKey Laboratory of Cancer Prevention and Treatment, Huaihua Central Hospital, Huaihua, China.
Yueguang LiuKey Laboratory of Cancer Prevention and Treatment, Huaihua Central Hospital, Huaihua, China.
Yixuan DaiKey Laboratory of Cancer Prevention and Treatment, Huaihua Central Hospital, Huaihua, China.
Lei ZhangChina-Sweden International Joint Research Center for Brain Diseases, Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China.ORCID https://orcid.org/0000-0001-7438-0489
Mei LongKey Laboratory of Cancer Prevention and Treatment, Huaihua Central Hospital, Huaihua, China.

Funding

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6 · The paper itself

Abstract

Background: Cellular metabolic irregularities are intricately associated with the initiation and progression of tumors. Emerging evidence suggests that interactions between intratumoral microbiomes and host mediate this process. However, a comprehensive understanding of the role of metabolism-related intratumoral microbes (MRIMs) in lung adenocarcinoma (LUAD) is still lacking. This study aimed to investigate the characteristics and prognostic significance of MRIMs, as well as elucidate their potential implications in relation to the microenvironment in LUAD. Methods: Integrated analyses were conducted using accessible datasets of the microbiome, bulk and single-cell transcriptomes. Spearman's coefficient between metabolic activity score and microbial abundance was used to identify MRIMs. An unsupervised clustering approach was utilized to distinguish the MRIMs-featured subtypes in LUAD samples. The Scissor algorithm was executed to select the cell subpopulations featured by MRIMs, and the underlying regulatory network in MRIMs-featured cells was explored. Additionally, a prognostic signature based on the microbial abundance of MRIMs was developed, and comprehensive analyses were subsequently carried out to reveal the correlation between MRIMs and LUAD microenvironment. Results: Ten microbial species were identified as MRIMs, enabling the classification of LUAD samples into two distinct subtypes that showed significantly associated with clinical features and survival outcomes. The scRNA-seq analysis revealed notable differences in T cells, ciliated cells, mast cells, endothelial cells, and fibroblasts between MRIM+ and MRIM- subpopulations. BCL3, KLF3, and NFKB2 were the regulons in the regulatory network of MRIM-featured cells. Additionally, a microbial prognostic-predictive signature was established comprising Conclusions: This study identified intratumoral microbes associated with metabolism, revealed distinct subtypes and their roles in LUAD, and established a predictive signature for the prognosis and therapeutic responsiveness of LUAD.

Indexed as

drug sensitivityimmune microenvironmentlung adenocarcinoma (LUAD)Metabolism-related intratumoral microbes (MRIMs)scRNA-seq

Identifiers

PMID41234824
PMCPMC12605661

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