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Meta-Analysis
. 2024 Sep 26:15:1434737.
doi: 10.3389/fimmu.2024.1434737. eCollection 2024.

Causal effect of thyroid cancer on secondary primary malignancies: findings from the UK Biobank and FinnGen cohorts

Zhengshi Wang #  1   2 , Youlutuziayi Rixiati #  3 , Chengyou Jia #  4 , Yong Xu  5 , Zhiqiang Yin  1   2 , Junwen Huang  1   2 , Jiaqi Dai  1   2 , Yun Zhang  1   2
Affiliations
Meta-Analysis

Causal effect of thyroid cancer on secondary primary malignancies: findings from the UK Biobank and FinnGen cohorts

Zhengshi Wang et al. Front Immunol. .

Abstract

Background: Existing epidemiological data indicated a correlation between thyroid cancer (THCA) and the risk of secondary primary malignancies (SPMs). However, the correlation does not always imply causality.

Methods: The Mendelian randomization (MR) analyses were performed to investigate the causal relationships between THCA and SPMs based on international multicenter data. Odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated. The Cancer Genome Atlas (TCGA) was used to explore potential mechanisms shared by THCA and bladder cancer (BLCA).

Results: Summary datasets of genome-wide association studies (GWAS) on 30 types of cancers were obtained from the United Kingdom Biobank (UKB) and FinnGen database. Meta-analysis of the UKB and FinnGen results revealed that THCA was significantly positively correlated with BLCA (OR = 1.140; 95% CI, 1.072-1.212; P < 0.001). Four genes, including WNT3, FAM171A2, MLLT11, and ULBP1, were identified as key genes shared by both TCHA and BLCA. Correlation analysis indicated that THCA may increase the risk of secondary BLCA through augmentation of N2 neutrophil infiltration.

Conclusions: This study showed that THCA was causally related to BLCA. It is recommended to conduct more rigorous screenings for BLCA during the follow-up of THCA patients.

Keywords: GWAS; Mendelian randomization; SNP; bladder cancer; thyroid cancer.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Design and analysis workflow.
Figure 2
Figure 2
MR results from the UKB database.
Figure 3
Figure 3
MR results from the FinnGen database.
Figure 4
Figure 4
Meta-analyses of the UKB and FinnGen cohorts. (A) bladder cancer; (B) chronic lymphocytic leukemia; (C)ovarian cancer; (D) brain malignancy; (E) kidney cancer; (F) malignancy of bronchus and lung; (G) rectal cancer.
Figure 5
Figure 5
Key genes shared by both THCA and BLCA. (A) Softpower of WGCNA for THCA; (B) Module correlation plot of WGCNA for THCA; (C) Module–trait relationships for THCA; (D) Softpower of WGCNA for BLCA; (E) Module correlation plot of WGCNA for BLCA; (F) Module–trait relationships for BLCA; (G) Venn diagram of hub genes shared by THCA and BLCA; (H) Volcano map of DEGs between THCA and normal thyroid tissues; (I) Volcano map of DEGs between BLCA and normal bladder tissues; (J) Venn diagram of DEGs shared by THCA and BLCA; (K) Venn diagram of key genes shared by THCA and BLCA based on the results from WGCNA and DEGs.
Figure 6
Figure 6
Potential mechanisms shared by both THCA and BLCA. (A) The correlation between shared genes and immunoinfiltration; (B) The correlation between shared genes and TGFB1 (a key marker of N2 neutrophil infiltration); (C) The correlation between shared genes and IFNG (a key marker of N1 neutrophil infiltration).
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