Tumor microenvironment signatures enhances lung adenocarcinoma prognosis prediction: Implication of intratumoral microbiota
Authors:Fei Zhao1,#, Lei Wang2,3,4,#, Dongjie Du5, Heaven Zhao6,7, Geng Tian6,7, Yufeng Li2,3,8, Yankun Liu2,8,9, Zhiwu Wang2,3,10, Dasheng Liu11, Jingwu Li2,3,12, Lei Ji6,7 and Hong Zhao1
1 School of Mathematical Sciences, Ocean University of China, Qingdao, 266100, China. 2 Hebei Key Laboratory of Molecular Oncology, Hebei, 063000, China. 3 Tangshan Key Laboratory of Cancer Prevention and Therapy, Hebei, 063000, China. 4 Department of Pathology, Tangshan People’s Hospital, Hebei, 063000, China. 5 Department of Vascular Surgery, Hebei General Hospital, Shijiazhuang, 050051, China. 6 Geneis Beijing Co., Ltd., Beijing, 100102, China. 7 Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao 266000, China. 8 The Cancer Institute, Tangshan People’s Hospital, Hebei, 063000, China. 9 Tangshan Key Laboratory of Precision Medicine Testing, Hebei, 063000, China. 10 Department of Chemoradiotherapy, Tangshan People’s Hospital, Hebei, 063000, China. 11 Department of Thoracic Surgery, Jiamusi Central Hospital, Jiamusi, 154000, China. 12 Department of Gastrointestinal Surgery, Tangshan People’s Hospital, Hebei, 063000, China.
#The authors contributed equally to this work.
Keywords:
intratumoral microbiome, lung adenocarcinoma, machine learning, prognosis, tumor microenvironment.
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Conflict of interest statement:
The authors declare no competing interests.
Please cite this article as:
Fei Zhao, Lei Wang, Dongjie Du, Heaven Zhao, Geng Tian, Yufeng Li, Yankun Liu, Zhiwu Wang, Dasheng Liu, Jingwu Li, Lei Ji and Hong Zhao (2025). Tumor microenvironment signatures enhances lung ade-nocarcinoma prognosis prediction: Implication of intratumoral microbiota. Microbial Cell 12: 182-194. doi: 10.15698/mic2025.08.855
© 2025 Zhao et al. This is an open-access article released under the terms of the Creative Commons Attribution (CC BY) license, which allows the unrestricted use, distribution, and reproduction in any medium, provided the original author and source are acknowledged.
Abstract:
The interaction between intratumoral microbiome and the tumor microenvironment (TME) has furthered our understanding of tumor ecology. Yet, the implications of their interaction for lung cancer management remain unclear. In the current work, we collected host transcriptome samples and matched intratumoral microbiome samples, as well as detailed clinical metadata from The Cancer Genome Atlas (TCGA) of 478 patients with lung adenocarcinoma (LUAD). Utilizing the multiomics integration approach, we comprehensively investigated the crosstalk between the TME and intratumoral microbiome in patients with LUAD. First, we developed a prognostic model based on the TME signatures (TMEindex) that clearly distinguished clinical, survival, and response to immunotherapy of patients with LUAD. Additionally, we found profound differences in intratumoral microbiota signatures, including alpha- and beta-diversity, among patients with different survival risks based on the TME signatures. In depth, we detected that genera Luteibacter and Chryseobacterium were strongly negatively and positively associated with patients’ survival risk, respectively, suggesting their opposing roles in cancer progression. Moreover, we developed a model that fused intratumoral microbial abundance information with TME signatures, called intratumoral microbiome-modified TMEindex (IMTMEindex), leading in predicting patient overall survival at 1-, 3-, and 5-years. Future clinical profiling of the specific intratumoral microbes in the TME could improve prognosis, inform immunotherapy, and facilitate the development of novel therapeutics for LUAD.
doi: 10.15698/mic2025.08.855
Volume 12, pp. 182 to 194, published 11/08/2025.