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Updated: Dec 24, 2025
Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
Published on: May 21, 2019
Comprehensive Bioinformatics Analysis Identifies Tumor Microenvironment and Immune-related Genes in Small Cell Lung
Yongchun Song1, Yanqin Sun2, Tuanhe Sun1
1Department of Oncology Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China.
Background:
Tumor microenvironment (TME) cells play important roles in tumor progression. Accumulating evidence show that they can be exploited to predict the clinical outcomes and therapeutic responses of the tumor. However, the role of immune genes of TME in small cell lung cancer (SCLC) is currently unknown.
Objective:
To determine the role of immune genes in SCLC.
Methods:
We downloaded the expression profile and clinical follow-up data of SCLC patients from Gene Expression Omnibus (GEO), and TME infiltration profile data of 158 patients using CIBERSORT. The correlation between TME phenotypes, genomic features, and clinicopathological features of SCLC was examined. A gene signature was constructed based on TME genes to further evaluate the relationship between molecular subtypes of SCLC with the prognosis and clinical features.
Results:
We identified a group of genes that are highly associated with TME. Several immune cells in TME cells were significantly correlated with SCLC prognosis (p<0.0001). These immune cells displayed diverse immune patterns. Three molecular subtypes of SCLC (TMEC1-3) were identified on the basis of enrichment of immune cell components, and these subtypes showed dissimilar prognosis profiles (p=0.03). The subtype with the best prognosis, TMEC3, was enriched with immune activation factors such as oncogene M0, oncogene M2, T cells follicular helper, and T cells CD8 (p<0.001). The TMEC1 subtype with the worst prognosis was enriched with T cells CD4 naive, B cells memory and Dendritic cells activated cells (p<0.001). Further analysis showed that the TME was significantly enriched with immune checkpoint genes, immune genes, and immune pathway genes (p<0.01). From the gene expression data, we identified four TME-related genes, GZMB, HAVCR2, PRF1 and TBX2, which were significantly associated with poor prognosis in both the training set and the validation set (p<0.05). These genes may serve as markers for monitoring tumor responses to immune checkpoint inhibitors.
Conclusion:
This study shows that TME features may serve as markers for evaluating the response of SCLC cells to immunotherapy.
