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Potentially functional variants of ERAP1, PSMF1 and NCF2 in the MHC-I-related pathway predict non-small cell lung cancer survival

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Abstract

Background

Cellular immunity against tumor cells is highly dependent on antigen presentation by major histocompatibility complex class I (MHC-I) molecules. However, few published studies have investigated associations between functional variants of MHC-I-related genes and clinical outcomes of lung cancer patients.

Methods

We performed a two-phase Cox proportional hazards regression analysis by using two previously published genome-wide association studies to evaluate associations between genetic variants in the MHC-I-related gene set and the survival of non-small cell lung cancer (NSCLC) patients, followed by expression quantitative trait loci analysis.

Results

Of the 7811 single-nucleotide polymorphisms (SNPs) in 89 genes of 1185 NSCLC patients in the discovery dataset of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial, 24 SNPs remained statistically significant after validation in additional 984 NSCLC patients from the Harvard Lung Cancer Susceptibility Study. In a multivariate stepwise Cox model, three independent functional SNPs (ERAP1 rs469783 T > C, PSMF1 rs13040574 C > A and NCF2 rs36071574 G > A) remained significant with an adjusted hazards ratio (HR) of 0.83 [95% confidence interval (CI) = 0.77–0.89, P = 8.0 × 10–7], 0.86 (0.80–0.93, P = 9.4 × 10–5) and 1.31 (1.11–1.54, P = 0.001) for overall survival (OS), respectively. Further combined genotypes revealed a poor survival in a dose–response manner in association with the number of unfavorable genotypes (Ptrend < 0.0001 and 0.0002 for OS and disease-specific survival, respectively). Also, ERAP1 rs469783C and PSMF1 rs13040574A alleles were associated with higher mRNA expression levels of their genes.

Conclusion

These potentially functional SNPs of the MHC-I-related genes may be biomarkers for NSCLC survival, possibly through modulating the expression of corresponding genes.

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Abbreviations

AUC:

Area under the receiver operating characteristic curve

BFDP:

Bayesian false discovery probability

CI:

Confidence interval

DSS:

Disease-specific survival

EAF:

Effect allele frequency

ERAP1:

Endoplasmic reticulum aminopeptidase 1

eQTL:

Expression quantitative trait loci

FDR:

False discovery rate

GWAS:

Genome-Wide Association Study

HLCS:

Harvard Lung Cancer Susceptibility

HR:

Hazards ratio

LD:

Linkage disequilibrium

LUAD:

Lung adenocarcinoma

LUSC:

Lung squamous cell carcinoma

MHC-I:

Major histocompatibility complex class I

NADPH:

Nicotinamide adenine dinucleotide phosphate

NSCLC:

Non-small cell lung cancer

NCF2:

Neutrophil cytosolic factor 2

OS:

Overall survival

PSMF1:

Proteasome inhibitor subunit 1

ROC:

Receiver operating characteristic

ROC:

Receiver operating characteristic curve

SNPs:

Single nucleotide polymorphisms

TCGA:

The Cancer Genome Atlas

PLCO:

The Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial

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Acknowledgements

We thank all the participants of the PLCO Cancer Screening Trial. We also thank the National Cancer Institute for providing the access to the data collected by the PLCO trial. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by National Cancer Institute. The authors would also like to acknowledge dbGaP repository for providing cancer genotyping datasets. The accession numbers for the datasets of lung cancer are phs000336.v1.p1 and phs000093.v2.p2. A list of contributing investigators and funding agencies for these studies can be found in Supplemental Data.

Funding

This work was supported by the National Institutes of Health (grant number: CA092824, 5U01CA209414, CA074386, CA090578, R01NS091307 and R56AG062302); the V Foundation for Cancer Research (grant number: D2017-19); the Duke Cancer Institute as part of the P30 Cancer Center Support Grant (Grant ID: NIH/NCI CA014236); the short-term international training program for PhD from Zhengzhou University, P.R. China.

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Author notes

  1. Sen Yang and Dongfang Tang contributed equally to this work.

Authors and Affiliations

  1. Department of Internal Medicine, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China

    Sen Yang & Qiming Wang

  2. Duke University Medical Center and Department of Population Health Sciences, Duke Cancer Institute, Duke University School of Medicine, 905 S LaSalle Street, Durham, NC, 27710, USA

    Sen Yang, Dongfang Tang, Yu Chen Zhao, Hongliang Liu, Thomas E. Stinchcombe, Carolyn Glass & Qingyi Wei

  3. Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, 27710, USA

    Sen Yang, Dongfang Tang, Yu Chen Zhao, Hongliang Liu & Qingyi Wei

  4. Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27710, USA

    Sheng Luo

  5. Department of Medicine, Duke University Medical Center, Durham, NC, 27710, USA

    Thomas E. Stinchcombe & Qingyi Wei

  6. Department of Pathology, Duke ©University School of Medicine, Durham, NC, 27710, USA

    Carolyn Glass

  7. Departments of Environmental Health and Department of Epidemiology, Harvard School of Public Health, Boston, MA, 02115, USA

    Li Su, Sipeng Shen & David C. Christiani

  8. Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA

    David C. Christiani

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Contributions

SY, DT, QW, QW contributed to the study conception and design. Material preparation, data collection, and analysis were performed by SY, DT, YCZ, HL, SL, TES, CG, LS, SS, DCC, QW, and QW. The first draft of the manuscript was written by SY and DT and critically reviewed and revised by QW and QW, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Qiming Wang or Qingyi Wei.

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The authors declare no potential conflicts of interest.

Availability of data and material

The PLCO dataset used for the analyses described in the present study was obtained from dbGaP (http://www.ncbi.nlm.nih.gov/gap) through dbGaP accession number phs000336.v1.p1 and phs000093.v2.p2. The HLCS dataset used for the replication will be made available upon reasonable request.

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Yang, S., Tang, D., Zhao, Y.C. et al. Potentially functional variants of ERAP1, PSMF1 and NCF2 in the MHC-I-related pathway predict non-small cell lung cancer survival. Cancer Immunol Immunother 70, 2819–2833 (2021). https://doi.org/10.1007/s00262-021-02877-9

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