Inflammatory cytokines and oral lichen planus: a Mendelian randomization study

Abstract

Background: Inflammatory cytokines have long been considered closely related to the development of oral lichen planus (OLP), and we further explored the causal relationship between the two by Mendelian randomization (MR) method.

Methods: We performed bidirectional MR analyses by large genome-wide association studies (GWAS). The data included a large-scale OLP dataset, as well as datasets of 41 inflammatory cytokines. All data were obtained from the University of Bristol database, which includes 41 inflammatory cytokines, and the GWAS Catalog database, which includes 91 inflammatory cytokines. OLP data were obtained from the Finngen database, which includes 6411 cases and 405770 healthy controls. We used the inverse variance weighted (IVW) method, MR-Egger method, weighted median method, simple mode method and weighted mode method to analyze the causal relationship between inflammatory cytokines and OLP, and we also combined with sensitivity analysis to further verify the robustness of the results. We performed a meta-analysis of positive or potentially positive results for the same genes to confirm the reliability of the final results.

Results: We primarily used the IVW analysis method, corrected using the Benjamin Hochberg (BH) method. When p<0.00038 (0.05/132), the results are significantly causal; when 0.00038<p<0.05, the results are potentially causal. We found a total of 7 inflammatory cytokines with significant or potential associations with OLP (University of Bristol database: 2, GWAS Catalog database: 5). In the reverse analysis, we found that a total of 30 inflammatory cytokines were significantly or potentially associated with OLP (University of Bristol database: 5, GWAS Catalog database: 25). After sensitivity analysis and meta-analysis, we finally determined that there was a causal relationship between a total of 3 inflammatory cytokines and OLP in the forward analysis, the most significant of which was FGF21 (p=0.02954, odds ratio (OR): 1.113, 95% confidence interval (95%CI): 1.011-1.226). In the reverse analysis, 14 inflammatory cytokines were causally associated with OLP, the most significant of which was PLAU (p=0.00002, OR: 0.951, 95%CI: 0.930-0.973).

Conclusion: There is a causal association between OLP and some inflammatory cytokines, which may play an important role in the pathogenesis of OLP and require further attention.

Keywords: Mendelian randomization; immunity; inflammation; inflammatory cytokines; oral lichen planus.

Figure 1

Flowchart of the design.

Figure 2

The forest plot shows the causal associations between 132 inflammatory cytokines and OLP, we mainly used the IVW method. OR<1 indicates a negative association between exposure and outcome, while OR>1 indicates a positive association between exposure and outcome. (A) Inflammatory cytokines on OLP; (B) OLP on inflammatory cytokines.

Figure 3

Funnel plot showing heterogeneity analysis of inflammatory cytokines causally associated with OLP. In the analysis of inflammatory cytokines on OLP, we excluded results with heterogeneity and horizontal pleiotropy, and finally screened the results of 5 eligible inflammatory cytokines. (A) MIP1B on OLP; (B) MIF on OLP; (C) IL5 on OLP; (D) FGF21 on OLP; (E) IL10RA on OLP.

Figure 4

Funnel plot showing heterogeneity analysis of inflammatory cytokines causally associated with OLP. In the analysis of OLP on inflammatory cytokines, we excluded results with heterogeneity and horizontal pleiotropy, and finally screened the results of 19 eligible inflammatory cytokines. (A) OLP on HGF; (B) OLP on RANTES; (C) OLP on SCF (University of Bristol database); (D) OLP on PLAU; (E) OLP on SCF (GWAS Catalog database); (F) OLP on DNER; (G) OLP on IL2; (H) OLP on CXCL9; (I) OLP on PDCD1L1; (J) OLP on CCL11; (K) OLP on CD6; (L) OLP on ARTN; (M) OLP on CXCL10; (N) OLP on IL18; (O) OLP on IL8; (P) OLP on CCL23; (Q) OLP on NRTN; (R) OLP on MCP1; (S) OLP on VEGFA.

Figure 5

Scatter plot showing the analysis of horizontal pleiotropy of inflammatory cytokines causally associated with OLP. In the analysis of inflammatory cytokines on OLP, we excluded results with heterogeneity and horizontal pleiotropy, and finally screened the results of 5 eligible inflammatory cytokines. (A) MIP1B on OLP; (B) MIF on OLP; (C) IL5 on OLP; (D) FGF21 on OLP; (E) IL10RA on OLP.

Figure 6

Scatter plot showing the analysis of horizontal pleiotropy of inflammatory cytokines causally associated with OLP. In the analysis of OLP on inflammatory cytokines, we excluded results with heterogeneity and horizontal pleiotropy, and finally screened the results of 19 eligible inflammatory cytokines. (A) OLP on HGF; (B) OLP on RANTES; (C) OLP on SCF (University of Bristol database); (D) OLP on PLAU; (E) OLP on SCF (GWAS Catalog database); (F) OLP on DNER; (G) OLP on IL2; (H) OLP on CXCL9; (I) OLP on PDCD1L1; (J) OLP on CCL11; (K) OLP on CD6; (L) OLP on ARTN; (M) OLP on CXCL10; (N) OLP on IL18; (O) OLP on IL8; (P) OLP on CCL23; (Q) OLP on NRTN; (R) OLP on MCP1; (S) OLP on VEGFA.

Figure 7

The forest plot shows the results of leave-one-out analyses, where we found no SNPs that could bias the results in the analysis of inflammatory cytokines on OLP. (A) MIP1B on OLP; (B) MIF on OLP; (C) IL5 on OLP; (D) FGF21 on OLP; (E) IL10RA on OLP.

Figure 8

The forest plot shows the results of leave-one-out analyses, where we found no SNPs that could bias the results in the analysis of OLP on inflammatory cytokines. (A) OLP on HGF; (B) OLP on RANTES; (C) OLP on SCF (University of Bristol database); (D) OLP on PLAU; (E) OLP on SCF (GWAS Catalog database); (F) OLP on DNER; (G) OLP on IL2; (H) OLP on CXCL9; (I) OLP on PDCD1L1; (J) OLP on CCL11; (K) OLP on CD6; (L) OLP on ARTN; (M) OLP on CXCL10; (N) OLP on IL18; (O) OLP on IL8; (P) OLP on CCL23; (Q) OLP on NRTN; (R) OLP on MCP1; (S) OLP on VEGFA.

Figure 9

The forest plot shows the two sets of results from the analysis of inflammatory cytokines on OLP by meta-analysis methods combined to assess the reliability of positive or potentially positive results, and we mainly analyzed the results of the IVW method and the selection of effect models. The forest plot uses the effect point as the invalid line when it equals 1. When 95% of the effect size contains 1 or is equal to 1, i.e., when the diamond-shaped region in the forest plot intersects with the invalid line, it suggests that the combined results are not statistically significant. When the diamond-shaped region invalid line does not cross, it means that the mixed results are statistically significant. (A) The meta-analysis of MIP1B; (B) The meta-analysis of IL5.

Figure 10

The forest plot shows the two sets of results from the analysis of OLP on inflammatory cytokines by meta-analysis methods combined to assess the reliability of positive or potentially positive results. (A) The meta-analysis of SCF; (B) The meta-analysis of HGF; (C) The meta-analysis of IL2; (D) The meta-analysis of CXCL9; (E) The meta-analysis of CCL11; (F) The meta-analysis of IL18; (G) The meta-analysis of IL8; (H) The meta-analysis of MCP1; (I) The meta-analysis of VEGFA.