White blood cell traits and lung cancer risk: a two-sample mendelian randomization analysis

This study aimed to investigate the potential association between white blood cell counts and the risk of lung cancer, including its subtypes, through Mendelian randomization (MR) analysis. We conducted a two-sample MR analysis using genome-wide association study (GWAS) summary statistics for the both exposure traits (eosinophil count, neutrophil count, lymphocyte count, monocyte count, basophil count, and total white blood cell count) and outcome traits (lung cancer and its subtypes). The GWAS dataset for lung cancer included 29,266 cases (11273 lung adenocarcinoma (LUAD), 7426 lung squamous cell carcinoma (LSCC), 2664 small cell lung cancer (SCLC)) and 56,450 controls. In MR analysis, we employed methods such as Inverse variance weighted (IVW), weighted median, MR-Egger regression, MR pleiotropy residual sum and outlier. MR analysis revealed an elevated total white blood cell (WBC) count significantly increased the risk of LUAD (IVW: OR = 1.484, 95% CI = 1.219-1.749, p = 0.003). The results confirmed a causal relationship between monocyte count and LUAD (IVW: OR = 1.687, 95% CI:1.542-1.830, p < 0.001). An increased total WBC count was associated with a higher risk of LUAD. Additionally, analysis of WBC subtypes counts indicated that monocyte count plays an crucial role in the elevated risk of LUAD.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-02-04
DOI
https://doi.org/10.1038/s41598-025-88366-w
Citations
5
Primary Topic
Cancer-related molecular mechanisms research
Type
article
Field-Weighted Citation Impact
4.68
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

White blood cell traits and lung cancer risk: a two-sample mendelian randomization analysis

Kaijun Long, Gang Xu, Xi Zheng, Xixian Ke et al.
5 citations
Scientific Reports
Cancer-related molecular mechanisms research
4.68
article

White blood cell traits and lung cancer risk: a two-sample mendelian randomization analysis

Kaijun Long, Gang Xu, Xi Zheng, Xixian Ke, Zhengfeng Zhu, Cheng Chen
article en
5 citations

Abstract

This study aimed to investigate the potential association between white blood cell counts and the risk of lung cancer, including its subtypes, through Mendelian randomization (MR) analysis. We conducted a two-sample MR analysis using genome-wide association study (GWAS) summary statistics for the both exposure traits (eosinophil count, neutrophil count, lymphocyte count, monocyte count, basophil count, and total white blood cell count) and outcome traits (lung cancer and its subtypes). The GWAS dataset for lung cancer included 29,266 cases (11273 lung adenocarcinoma (LUAD), 7426 lung squamous cell carcinoma (LSCC), 2664 small cell lung cancer (SCLC)) and 56,450 controls. In MR analysis, we employed methods such as Inverse variance weighted (IVW), weighted median, MR-Egger regression, MR pleiotropy residual sum and outlier. MR analysis revealed an elevated total white blood cell (WBC) count significantly increased the risk of LUAD (IVW: OR = 1.484, 95% CI = 1.219-1.749, p = 0.003). The results confirmed a causal relationship between monocyte count and LUAD (IVW: OR = 1.687, 95% CI:1.542-1.830, p < 0.001). An increased total WBC count was associated with a higher risk of LUAD. Additionally, analysis of WBC subtypes counts indicated that monocyte count plays an crucial role in the elevated risk of LUAD.

Scientific ReportsVol. 15(1)
Zunyi Medical University (CN), Affiliated Hospital of Zunyi Medical College (CN)
Good health and well-being
Openalex Percentile: Top 6%
Cancer-related molecular mechanisms research
4.68
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.