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A gene expression-based immune signature for lung adenocarcinoma prognosis

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Abstract

Background

Lung adenocarcinoma (LUAD) has become the most frequent histologic type of lung cancer in the past several decades. Recent successes with immune checkpoint blockade therapy have demonstrated that the manipulation of the immune system is a very potent treatment for LUAD. This study aims to explore the role of immune-related genes in the development of LUAD and establish a signature that can predict overall survival for LUAD patients.

Methods

To identify the differential expression genes (DEGs) between normal and tumor tissues, we developed an analysis strategy to combine an independent-sample design and a paired-sample design using RNA-seq transcriptomic profiling data of The Cancer Genome Atlas LUAD samples. Further, we selected prognostic markers from DEGs and evaluated their prognostic value in a prediction model.

Results

We identified and validated PD1, PDL1 and CTLA4 genes as prognostic markers, which are well-known immune checkpoints, and revealed two new potential prognostic immune checkpoints for LUAD, HHLA2 (logFC = 2.55, FDR = 1.89 × 10–6) and VTCN1 (logFC = −2.86, FDR = 1.72 × 10–11). Furthermore, we identified an 18-gene LUAD prognostic biomarker panel and observed that the classified high-risk group presented a significantly shorter overall survival time (HR = 3.57, p value = 4.07 × 10–10). The prediction model was validated in five independent high-throughput gene expression datasets.

Conclusions

The identified DEG features may serve as potential biomarkers for prognosis prediction of LUAD patients and immunotherapy. Based on that assumption, we identified a gene expression-based immune signature for lung adenocarcinoma prognosis.

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Abbreviations

BH:

Benjamin and Hochberg

CAMS:

Cell adhesion molecules

DEG:

Differential expression genes

DEIGs:

Differential expression immune genes

FDR:

False discovery rate

GEO:

Gene expression omnibus

HR:

Hazard ratio

IgA:

Immunoglobulin A

KEGG:

Kyoto encyclopedia of genes and genomes

LUAD:

Lung adenocarcinoma

RPKM:

Reads per kilobase per million

TCGA:

The Cancer Genome Atlas

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Acknowledgements

We thank the study participants and research staff for their contributions and commitment to this study.

Funding

The preparation of this manuscript was supported by internal funding from the University of South Carolina for Dr. Feifei **ao and Dr. Guoshuai Cai.

Author information

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA

    Lijuan Wang, **zhi Luo & Feifei **ao

  2. Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nan**g Medical University, Nan**g, 211166, China

    Lijuan Wang

  3. Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA

    Chao Cheng & Christopher I. Amos

  4. Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, 77030, USA

    Chao Cheng & Christopher I. Amos

  5. Department of Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA

    Guoshuai Cai

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Contributions

FX and GC made substantial contributions to the conception and design of the study. LW and XL performed all analyses. All authors made substantial contributions to the acquisition, analysis or interpretation of data for the work. LW wrote the first draft of the manuscript. FX, GC, CC and CIA revised the manuscript for important intellectual content. All authors approved the manuscript.

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Correspondence to Feifei **ao.

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Wang, L., Luo, X., Cheng, C. et al. A gene expression-based immune signature for lung adenocarcinoma prognosis. Cancer Immunol Immunother 69, 1881–1890 (2020). https://doi.org/10.1007/s00262-020-02595-8

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