Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Jun 4;24(1):680.
doi: 10.1186/s12885-024-12449-6.

Systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for lung cancer

Wenfu Song  1   2 , Yingying Li  1   2 , Yaxuan Yao  2 , Shiling Sun  3   4 , Xutao Guan  1   2 , Bing Wang  1
Affiliations

Systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for lung cancer

Wenfu Song et al. BMC Cancer. .

Abstract

Background: Drug repurposing provides a cost-effective approach to address the need for lung cancer prevention and therapeutics. We aimed to identify actionable druggable targets using Mendelian randomization (MR).

Methods: Summary-level data of gene expression quantitative trait loci (eQTLs) were sourced from the eQTLGen resource. We procured genetic associations with lung cancer and its subtypes from the TRICL, ILCCO studies (discovery) and the FinnGen study (replication). We implemented Summary-data-based Mendelian Randomization analysis to identify potential therapeutic targets for lung cancer. Colocalization analysis was further conducted to assess whether the identified signal pairs shared a causal genetic variant.

Findings: In the main analysis dataset, we identified 55 genes that demonstrate a causal relationship with lung cancer and its subtypes. However, in the replication cohort, only three genes were found to have such a causal association with lung cancer and its subtypes, and of these, HYKK (also known as AGPHD1) was consistently present in both the primary analysis dataset and the replication cohort. Following HEIDI tests and colocalization analyses, it was revealed that HYKK (AGPHD1) is associated with an increased risk of squamous cell carcinoma of the lung, with an odds ratio and confidence interval of OR = 1.28,95%CI = 1.24 to 1.33.

Interpretation: We have found that the HYKK (AGPHD1) gene is associated with an increased risk of squamous cell carcinoma of the lung, suggesting that this gene may represent a potential therapeutic target for both the prevention and treatment of lung squamous cell carcinoma.

Keywords: Colocalization; Druggable target; Gene expression; Lung cancer; Mendelian randomization.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
A Study design. SMR, summary-based Mendelian randomization; QTL, quantitative trait loci; LC, Lung Cancer; PPH4, posterior probability of H4. B cis-eQTL analysis
Fig. 2
Fig. 2
A Associations of predicted gene expression with lung cancer in Mendelian randomization analysis. B Associations of predicted gene expression with lung adenocarcinoma cancer in Mendelian randomization analysis. C Associations of predicted gene expression with lung squamous cell carcinoma cancer in Mendelian randomization analysis. D Associations of predicted gene expression with small cell lung cancer in Mendelian randomization analysis. E Associations of predicted gene expression with lung cancer (replication) in Mendelian randomization analysis. OR, odds ratio; CI, confidence interval
Fig. 3
Fig. 3
Scatter plot for associations between HYKK (AGPHD1) gene and lung squamous cell carcinoma
Fig. 4
Fig. 4
Scatter plot for associations between PSMA4 gene and lung adenocarcinoma
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Ferlay J, et al. GLOBOCAN 2012 v1.0, cancer incidence and mortality worldwide: IARC cancer base no. 11. Lyon: International Agency for Research on Cancer; 2013.
    1. Miller KD, et al. Cancer treatment and survivorship statistics, 2022. CA Cancer J Clin. 2022;72:409–436. doi: 10.3322/caac.21731. - DOI - PubMed
    1. Reck M, Heigener DF, Mok T, Soria JC, Rabe KF. Management of non-small-cell lung cancer: recent developments. Lancet. 2013;382:709–719. doi: 10.1016/S0140-6736(13)61502-0. - DOI - PubMed
    1. Chen S, Wu S. Identifying lung cancer risk factors in the elderly using deep neural networks: quantitative analysis of web-based survey data. J Med Internet Res. 2020;22:e17695. doi: 10.2196/17695. - DOI - PMC - PubMed
    1. Legha SS, Muggia FM, Carter SK. Adjuvant chemotherapy in lung cancer: review and prospects. Cancer. 1977;39(4):1415–1424. doi: 10.1002/1097-0142(197704)39:4<1415::AID-CNCR2820390410>3.0.CO;2-O. - DOI - PubMed

MeSH terms