Abstract

BackgroundPrognostic genes in the tumor microenvironment play an important role in immune biological processes and the response of cancer to immunotherapy. Thus, we aimed to assess new biomarkers that are associated with immune/stromal cells in lung adenocarcinomas (LUAD) using the ESTIMATE algorithm, which also significantly affects the prognosis of cancer.MethodsThe RNA sequencing (RNA-Seq) and clinical data of LUAD were downloaded from the the Cancer Genome Atlas (TCGA ). The immune and stromal scores were calculated for each sample using the ESTIMATE algorithm. The LUAD gene chip expression profile data and the clinical data (GSE37745, GSE11969, and GSE50081) were downloaded from the Gene Expression Omnibus (GEO) for subsequent validation analysis. Differentially expressed genes were calculated between high and low score groups. Univariate Cox regression analysis was performed on differentially expressed genes (DEGs) between the two groups to obtain initial prognosis genes. These were verified by three independent LUAD cohorts from the GEO database. Multivariate Cox regression was used to identify overall survival-related DEGs. UALCAN and the Human Protein Atlas were used to analyze the mRNA /protein expression levels of the target genes. Immune cell infiltration was evaluated using the Tumor Immune Estimation Resource (TIMER) and CIBERSORT methods, and stromal cell infiltration was assessed using xCell.ResultsIn this study, immune scores and stromal scores are significantly associated with the clinical characteristics of LUAD, including T stage, M stage, pathological stage, and overall survival time. 530 DEGs (18 upregulated and 512 downregulated) were found to coexist in the difference analysis with the immune scores and stromal scores subgroup. Univariate Cox regression analysis showed that 286 of the 530 DEGs were survival-related genes (p < 0.05). Of the 286 genes initially identified, nine prognosis-related genes (CSF2RB, ITK, FLT3, CD79A, CCR4, CCR6, DOK2, AMPD1, and IGJ) were validated from three separate LUAD cohorts. In addition, functional analysis of DEGs also showed that various immunoregulatory molecular pathways, including regulation of immune response and the chemokine signaling pathways, were involved. Five genes (CCR6, ITK, CCR4, DOK2, and AMPD1) were identified as independent prognostic indicators of LUAD in specific data sets. The relationship between the expression levels of these genes and immune genes was assessed. We found that CCR6 mRNA and protein expression levels of LUAD were greater than in normal tissues. We evaluated the infiltration of immune cells and stromal cells in groups with high and low levels of expression of CCR6 in the TCGA LUAD cohort. In summary, we found a series of prognosis-related genes that were associated with the LUAD tumor microenvironment.

Highlights

  • Lung cancer is a malignant disease with the highest morbidity and mortality worldwide in recent years (Bray et al, 2018)

  • From the perspective of the tumor microenvironment (TME), we identified a series of genes related to the overall survival of patients with lung adenocarcinomas (LUAD) based on data in the the Cancer Genome Atlas (TCGA) database

  • By analyzing differences in the overall expression profiles of patients of LUAD cases in high and low scoring groups using the ESTIMATE algorithm, we identified 286 prognostic genes involved in immune response and chemokine signaling

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Summary

Introduction

Lung cancer is a malignant disease with the highest morbidity and mortality worldwide in recent years (Bray et al, 2018). Despite important advances in targeted molecular therapies and immunotherapy for lung cancer, drug resistance still limits success (Ascierto et al, 2019), and new biomarkers are warranted for different populations This will have important implications for improving patients’ early diagnosis rates and for discovering novel targeted therapies. We aimed to assess new biomarkers that are associated with immune/stromal cells in lung adenocarcinomas (LUAD) using the ESTIMATE algorithm, which significantly affects the prognosis of cancer. Univariate Cox regression analysis was performed on differentially expressed genes (DEGs) between the two groups to obtain initial prognosis genes. These were verified by three independent LUAD cohorts from the GEO database.

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