Multi-dimensional characterization of the tumor microenvironment profiles in lung squamous cell carcinoma.

Abstract

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. Cluster1 and cluster3 exhibited substantial tumor immune infiltration, suggesting a better response to immunotherapy. Relatively worse survival was observed in cluster4, probably due to higher angiogenesis. Besides, differentially expressed genes in cluster1, cluster2 and cluster3 were prominently enriched in immune-related pathways, while extracellular matrix-related pathways were enriched for cluster4. Genomic data analyses showed significant variations in tumor mutational burden and mutational frequency of several genes, such as TP53, among the four subtypes. Additionally, 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.