ORIGINAL RESEARCH article

Front. Immunol.

Sec. Cancer Immunity and Immunotherapy

Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1603822

This article is part of the Research TopicAdvances in Immunity and Microbiome: Exploring Key Interactions and InnovationsView all 5 articles

Multi-omics analysis untangles the crosstalk between intratumor microbiome, lactic acid metabolism and immune status in lung squamous cell carcinoma

Provisionally accepted
  • Second Affiliated Hospital of Dalian Medical University, Dalian, China

The final, formatted version of the article will be published soon.

Cancer development is intricately linked with metabolic dysregulation, including lactic acid metabolism (LM), which plays a pivotal role in tumor progression and immune evasion. However, its specific implications in lung squamous cell carcinoma (LUSC) remain unclear. Here, we used numerous datasets encompassing bulk and single-cell transcriptome, genome, intratumor microbiome, and digital pathome to systematically investigate the LM patterns in LUSC. Two LM-based subtypes were discovered endowed with distinct clinical outcomes and biological peculiarities, such as overall survival, somatic mutations, and intratumor microbiota structure. Moreover, the histopathology image-based deep learning model accurately predicted our LM-based LUSC taxonomy, significantly improving its clinical utility. Machine learning models based on seven LM-related genes (CHEK2, LIPT1, TUFM, NDUFA10, AGK, PNPLA2, and GFM1) accurately predicted immunotherapy outcomes for multiple cancer types, including LUSC, and outperformed other currently known biomarkers. Furthermore, mediation analysis identified potential association pathways involving tumor-resident microbes, LM-related gene signatures, and antitumor immune cells. Overall, this study advanced the understanding of the relationship between LM patterns and LUSC tumor biology, as well as its potential clinical implications, which might advance the tailored management of LUSC.

Keywords: Lung squamous cell carcinoma, Lactic acid metabolism, Intratumor microbiome, Immunotherapy, machine learning, Tumor Microenvironment

Received: 01 Apr 2025; Accepted: 28 May 2025.

Copyright: © 2025 Qiu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dan Li, Second Affiliated Hospital of Dalian Medical University, Dalian, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.