Intratumoral Microbiota-Host Interactions Shape the Variability of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma in Recurrence and Metastasis

Sep 24, 2026

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Differences in tissue microbiota–host interaction between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) regarding recurrence and metastasis remain insufficiently characterized. Patients were stratified into recurrence/metastasis (RM) and non-recurrence/non-metastasis (non-RM) groups within 3 years after surgery. Bioinformatics analyses identified genes and microbes linked to RM. LUAD and LUSC showed distinct gene expression and microbial abundance, and a multimodal gene–microbe model predicted LUSC RM risk with AUC 0.81, with predicted scores correlated to survival.

LanguageEnglish

CategoryResearch & Report

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Pages17

Reading Time43 min

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Abstract

Importance

Background: Lung cancer subtypes and rationale

Study design and comparison of RM vs non-RM

Molecular analyses: gene expression and microbial abundance

Machine learning prediction model for LUAD recurrence/metastasis risk

Clinical relevance: association with patient survival

RESEARCH ARTICLE Intratumoral Microbiota-Host Interactions Shape the Variability of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma in Recurrence and Metastasis Xiangfeng Zhou,a,b Lei Ji,b,c Yanyu Ma,c,d Geng Tian,b,c Kebo Lv,a Jialiang Yangb,c,e,f a Department of Mathematics, Ocean University of China, Qingdao, China bGeneis Beijing Co., Ltd., Beijing, China cQingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China dDepartment of Mathematics, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China e Chifeng Municipal Hospital, Chifeng, Inner Mongolia, China fAcademician Workstation, Changsha Medical University, Changsha, China ABSTRACT Differences in tissue microbiota-host interaction between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) about recurrence and metastasis have not been well studied. In this study, we performed bioinformatics analyses to identify the genes and tissue microbes signiﬁcantly associated with recurrence or metastasis. All lung cancer patients were divided into the recurrence or metastasis (RM) group and the nonrecurrence and nonmetastasis (non-RM) group according to whether or not they had recurred or metastasized within 3 years after the initial surgery. Results showed that there were signiﬁcant differences between LUAD and LUSC in gene expression and microbial abundance associated with recurrence and metastasis. Compared with non-RM, the bacterial community of RM had a lower richness in LUSC. In LUSC, host genes signiﬁcantly correlated with tissue microbe, whereas host-tissue microbe interaction in LUAD was rare. Then, we established a novel multimodal machine learning model based on genes and microbes to predict the recurrence and metastasis risk of a LUSC patient, which achieves an area under the curve (AUC) of 0.81. In addition, the predicted risk score was signiﬁcantly associated with the patient’s survival. IMPORTANCE Our study elucidates signiﬁcant differences in RM-associated host-microbe interactions between LUAD and LUSC. Besides, the microbes in tumor tissue could be used to predict the RM risk of LUSC, and the predicted risk score is associated with patients’ survival. KEYWORDS non-small cell lung cancer, recurrence and metastasis, multi-omics, hostmicrobe interactions Lung cancer is the most common cancer in men worldwide, which mainly consists of two subtypes, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). NSCLC can be further divided into lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and large cell lung carcinoma (LCLC) (1). LUAD and LUSC combinedly account for around 70% of all lung cancer incidents (2). LUAD is a type of alveolar epithelial cell carcinoma derived from glandular differentiated or mucus-producing cancer cells, while LUSC isan epithelial basal cell carcinoma displaying keratinization and intercellular bridges (3). LUSC exhibits a higher progression rate than LUAD during tumor development, resulting in poorer outcomes for LUSC patients (4, 5). Therefore, it is essential to explore the differences between LUAD and LUSC to identify biomarkers guiding more personalized therapy. In recent years, the comparisons between LUAD and LUSC have been widely studied at multiple molecular levels. For example, several studies have shown that there Editor Se-Ran Jun, University of Arkansas for Medical Sciences Copyright © 2023 Zhou et al. This is an openaccess article distributed under the terms of the Creative Commons Attribution 4 .0 International license. Address correspondence to Kebo Lv, [kewave@ouc.edu.cn](kewave@ouc.edu.cn), or Jialiang Yang, [yangjl@geneis.cn](yangjl@geneis.cn). The authors declare no conﬂict of interest. Received 16 September 2022 Accepted 10 March 2023 Published 19 April 2023 May/June 2023 Volume 11 Issue 3 10.1128/spectrum.03738-22 1 are signiﬁcant mutational differences between LUSC and LUAD, with a signiﬁcant

FAQs

How were patients classified for recurrence and metastasis analysis?

Patients were divided into the recurrence or metastasis (RM) group and the non-RM group based on whether they had recurred or metastasized within 3 years after initial surgery.

What key differences were found between LUAD and LUSC in the study?

The study found significant differences between LUAD and LUSC in gene expression and microbial abundance associated with recurrence and metastasis.

How was recurrence and metastasis risk predicted for LUAD, and how well did it perform?

A multimodal machine learning model using genes and microbes was built to predict recurrence and metastasis risk in LUSC, achieving an AUC of 0.81, and the predicted risk score was significantly associated with patient survival.

Sep 24, 2026

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