Dataset Information

Identification of Prognostic Genes and Immune Landscape Signatures Based on Tumor Microenvironment in Lung Adenocarcinoma.

ABSTRACT:

Background

Lung adenocarcinoma is the most common lung cancer subtype and accounts for the highest proportion of cancer-related deaths. The tumor microenvironment influences prognostic outcomes in lung adenocarcinoma (LUAD).

Materials and methods

We used the ESTIMATE algorithm (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) to investigate the role of microenvironment-related genes and stromal cells in lung adenocarcinoma prognosis. This analysis was done on lung adenocarcinoma cases from The Cancer Genome Atlas (TCGA). The cases were divided into high and low groups on the basis of immune and stromal scores, respectively.

Results

There were close correlations between immune scores with prognosis and disease stage. There were 367 differentially expressed genes. Combining the Gene Expression Omnibus (GEO) database, we found 14 prognosis-related genes. Results. Based on the enrichment levels of the immune cell types, we clustered LUAD into Immunity_H and Immunity_L subtypes. Most of these genes were upregulated in Immunity_H subtype. Finally, using the Human Protein Atlas (HPA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, most of the proteins corresponding to prognostic genes were verified to be differentially expressed between the tumor and normal groups.

Conclusions

The key genes identified in this study are involved in molecular mechanisms of LUAD.

SUBMITTER: Han P

PROVIDER: S-EPMC9411923 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Identification of Prognostic Genes and Immune Landscape Signatures Based on Tumor Microenvironment in Lung Adenocarcinoma.

Han Pengkai P Fan Yunxiu Y Liu Qiping Q Zhou Junhao J

Disease markers 20220818

<h4>Background</h4>Lung adenocarcinoma is the most common lung cancer subtype and accounts for the highest proportion of cancer-related deaths. The tumor microenvironment influences prognostic outcomes in lung adenocarcinoma (LUAD).<h4>Materials and methods</h4>We used the ESTIMATE algorithm (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) to investigate the role of microenvironment-related genes and stromal cells in lung adenocarcinoma prognosis. This an ...[more]

PMID: 36033829

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Project description: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.

Dataset Information

Identification of Prognostic Genes and Immune Landscape Signatures Based on Tumor Microenvironment in Lung Adenocarcinoma.

Background

Materials and methods

Results

Results

Conclusions

Publications

Identification of Prognostic Genes and Immune Landscape Signatures Based on Tumor Microenvironment in Lung Adenocarcinoma.

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