Zhou Yi, Chai Lixun, Wang Yuyao, Zhang Hongguang
Department of Thoracic Surgery, First Hospital of Shanxi Medical University, Taiyuan, People's Republic of China.
Department of Thoracic Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, People's Republic of China.
Physiol Genomics. 2025 Aug 1;57(8):485-497. doi: 10.1152/physiolgenomics.00042.2025. Epub 2025 Jun 25.
Tumor microenvironment (TME) plays an important role in tumorigenesis, development, metastasis, and drug sensitivity, but little is known about it in lung squamous cell carcinoma (LUSC). Here, the RNA-sequencing data, clinical and survival data of patients with LUSC in The Cancer Genome Atlas, and six independent datasets were collected. Based on the unsupervised clustering of knowledge-based functional gene expression signatures, LUSC was classified into four subtypes. and exhibited substantial tumor immune infiltration, suggesting a better response to immunotherapy. Relatively worse survival was observed in , probably due to higher angiogenesis. Besides, differentially expressed genes in , , and were prominently enriched in immune-related pathways, whereas extracellular matrix-related pathways were enriched for . Genomic data analyses showed significant variations in tumor mutational burden and mutational frequency of several genes, such as tumor protein P53 (), among the four subtypes. In addition, the four subtypes exhibited heterogeneity in the sensitivity of commonly used chemotherapy drugs for lung cancer and the intratumor microbiome profile. Finally, a prognostic model was developed, and its performance and generalization ability were independently validated in multiple datasets. Overall, our study advances the understanding of the TME in LUSC and proposes a prognostic model that facilitates clinical decision-making. This study obtained four immunological subtypes exhibiting substantial difference in the tumor microenvironment (TME), immune-related pathways, tumor mutational burden, drug sensitivity, and intratumor microbiome. Furthermore, we developed a novel prognostic model consisting of 11 signature genes showing excellent performance in predicting prognosis. Our study deepens the understanding of the heterogeneity of the TME in lung squamous cell carcinoma (LUSC) and contributes to the precision therapy of patients with LUSC.
肿瘤微环境（TME）在肿瘤发生、发展、转移及药物敏感性方面发挥着重要作用，但在肺鳞状细胞癌（LUSC）中人们对其了解甚少。在此，我们收集了癌症基因组图谱中LUSC患者的RNA测序数据、临床及生存数据，以及六个独立数据集。基于基于知识的功能基因表达特征的无监督聚类，LUSC被分为四种亚型。其中两种亚型表现出大量肿瘤免疫浸润，提示对免疫治疗有更好的反应。第三种亚型的生存情况相对较差，可能是由于血管生成较高。此外，第一种、第二种和第四种亚型中差异表达基因显著富集于免疫相关途径，而细胞外基质相关途径在第三种亚型中富集。基因组数据分析显示，四种亚型之间肿瘤突变负荷及几个基因（如肿瘤蛋白P53）的突变频率存在显著差异。此外，四种亚型在肺癌常用化疗药物敏感性及肿瘤内微生物组图谱方面表现出异质性。最后，我们开发了一个预后模型，并在多个数据集中独立验证了其性能和泛化能力。总体而言，我们的研究推进了对LUSC中TME的理解，并提出了一个有助于临床决策的预后模型。本研究获得了四种在肿瘤微环境（TME）、免疫相关途径、肿瘤突变负荷、药物敏感性和肿瘤内微生物组方面存在显著差异的免疫亚型。此外，我们开发了一个由11个特征基因组成的新型预后模型，该模型在预测预后方面表现出色。我们的研究加深了对肺鳞状细胞癌（LUSC）中TME异质性的理解，并有助于LUSC患者的精准治疗。
