The effect of inflammatory cytokines on the risk of hypertrophic scar: A Mendelian Randomization Study

doi:10.21203/rs.3.rs-4576811/v1

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The effect of inflammatory cytokines on the risk of hypertrophic scar: A Mendelian Randomization Study

https://doi.org/10.21203/rs.3.rs-4576811/v1

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Objectives: Hypertrophic scar (HS) results from burns or trauma, causing aesthetic and functional issues. However, observational studies have linked inflammatory cytokines to HS, but the causal pathways involved are unclear. We aimed to determine how circulating inflammatory cytokines contribute to HS formation.

Methods:
Two-sample Mendelian randomization (MR) was used to identify genetic variants associated with hypertrophic scar in a comprehensive, publicly available genome-wide association study (GWAS) involving 766 patients and 207,482 controls of European descent. Additionally, data on 91 plasma proteins were drawn from a GWAS summary involving 14,824 healthy participants. Causal relationships between exposures and outcomes were investigated primarily using the inverse variance weighted (IVW) method. Furthermore, a suite of sensitivity analyses, including MR‒Egger and weighted median approaches, were concurrently employed to fortify the robustness of the conclusive findings. Finally, reverse MR analysis was conducted to evaluate the plausibility of reverse causation between hypertrophic scar and the cytokines identified in our study.

Results: In inflammatory cytokines, there was evidence of inverse associations of osteoprotegerin(OPG) levels(OR = 0.59, 95% CI =0.41~0.85, p=0.01), and leukemia inhibitory factor(LIF) levels(OR = 0.51, 95% CI =0.32~0.82, p=0.01) are a nominally negative association with hypertrophic scar risk, while CUBdomain-domain-containing protein 1(CDCP1) level(OR = 0.59, 95% CI =0.41~0.85, p=0.01) glial cell line-derived neurotrophic factor(GDNF) levels(OR = 1.42, 95% CI =1.03~1.96, p=0.01) and programmed cell death 1 ligand 1(PD-L1) levels(OR = 1.47, 95% CI =1.92~2.11, p=0.04) showed a positive association with hypertrophic scar risk. These associations were similar in the sensitivity analyses.

Conclusions: According to our MR findings, OPG and LIF have a protective effect on hypertrophic scar, while CDCP1, GDNF, and PD-L1 have a risk-increasing effect on Hypertrophic scar. Our study adds to the current knowledge on the role of specific inflammatory biomarker pathways in hypertrophic scar. Further validation is needed to assess the potential of these cytokines as pharmacological or lifestyle targets for hypertrophic scar prevention and treatment.

hypertrophic scar

Mendelian randomization (MR)

genome-wide association study (GWAS)

inflammatory cytokines

instrumental variable (IV)

Wound healing involves a complex orchestration of physiological events wherein an organism seeks to regenerate damaged or lost tissue, thereby restoring the defensive capabilities of the integumentary system. This intricate and stepwise progression encompasses hemostasis, inflammation, proliferation, and remodeling, necessitating synchronized interplay among diverse cellular populations and cytokine signaling pathways.(Reinke & Sorg, 2012) Deviation from this orderly and finite course can culminate in aberrant wound healing processes, manifesting as conditions such as Hypertrophic scar (Diegelmann & Evans, 2004). HS typically manifests postsurgery following burns, trauma, or other cutaneous injuries and is characterized by raised, erythematous, painful, pruritic, contractile lesions distinct from keloids confined to the injury site(Berman et al., 2017). Across joint surfaces, HS strongly constrain joint mobility. In addition to its cosmetic benefits, HS imposes significant physiological burdens(Chiang et al., 2016). Key risk factors for HS include deep dermal injuries, heightened mechanical stress, and youthfulness, among others(Butzelaar et al., 2016). Nevertheless, the precise etiology underlying HS formation remains incompletely elucidated.

Numerous investigations have demonstrated the involvement of inflammatory and immune mechanisms in regulating collagen synthesis, a pivotal process in HS formation(Bhattacharya & Ramachandran, 2023; Mak et al., 2009; Ogawa, 2017; Wang et al., 2007). Given the urgent need to address this currently incurable condition, a broad range of etiologies warrants consideration. γδT cells are believed to secrete specific growth factors that are responsible for facilitating epidermal migration. Naïve T helper (TH) cells, characterized by a CD4 + phenotype, can differentiate into TH1 cells when exposed to IL-12 or into TH2 cells in response to IL-4 exposure.(Chen et al., 2020) In hypertrophic scar (HS) patients, there is evidence of a shift toward a TH2 cell phenotype accompanied by elevated IL-4 production(Jeschke et al., 2023), indicating that certain cytokines could serve as predictive biomarkers and potential therapeutic targets in HS. However, the findings regarding the link between immune inflammation and HS have been inconsistent thus far, likely due to small sample sizes, deficiencies in study design, and confounding factors not fully addressed in existing research(Castagnoli et al., 1997). Given the ongoing debates surrounding inflammatory cytokines and the biases inherent in traditional observational study designs, there is still a need to assess the potential causality of inflammatory factors and circulating immune responses on HS.

Mendelian randomization (MR) analysis is a method that utilizes genetic variants as instrumental variables (IVs) for environmental factors, aiming to uncover their potential causal relationships with outcomes(Davey Smith & Hemani, 2014). The rationale behind MR lies in the assumption that genetic variants are inherited randomly and are not influenced by diseases, thus minimizing confounding factors and reverse causation bias(Emdin et al., 2017).

A recent genome-wide association study (GWAS) meta-analysis investigated the genetic underpinnings of 91 cytokines(Zhao et al., 2023), offering an opportunity to explore their associations with hypertrophic scar (HS). Additionally, we assessed the causal relationships between inflammatory cytokines and HS using large-scale GWAS summary statistics on blood traits(Vuckovic et al., 2020). Employing a two-sample MR design, we systematically examined the potential causal links between circulating inflammatory cytokines and HS risk, with the goal of determining whether there is a causal relationship between circulating immune cells and HS incidence. Furthermore, reverse MR analysis was conducted to evaluate the impact of HS on cytokines.

The MR analysis incorporated previously published GWAS summary statistics. Ethical approval and written informed consent were obtained from participants by the ethics committees of each institutional review board in separate studies. No additional ethical approval or informed consent was deemed necessary. The STROBE-MR (Skrivankova et al., 2021)checklist has been reviewed and is provided (supplementary data in Table S1).

MR Assumptions

Figure 1 illustrates our estimation of the causal link between inflammatory cytokines and HS using a classic MR model. MR analysis relies on three key IV assumptions(Collaborators, 2021): relevance, independence, and exclusion restrictions. Exclusion restriction(Emdin et al., 2017) posits that these variants are not linked with the outcome via pathways other than the risk factor for interest. Relevance signifies that the chosen genetic variants must be genuinely associated with inflammatory cytokines (as defined in this study by a genetic association p value < 5 × 10–6). In this bidirectional study, we employed two GWASs to select genetically significant SNPs for 91 inflammatory cytokines and HS.

Instrumental Variable Selection

Due to the limited number of SNPs identified for certain cytokines when they were considered as exposures, we opted for a higher cutoff of p < 5 × 10 − 6 instead of p < 5 × 10 − 8. Subsequently, to circumvent issues related to linkage disequilibrium, we aggregated these SNPs using the parameters kb = 10,000 and r2 = 0.001. Palindromic SNPs were excluded due to uncertainty regarding their alignment direction in relation to exposure and outcome in the GWASs of systemic inflammatory regulators. Finally, we computed the proportion of variance in exposure by utilizing the R2 value of each SNP while using the F-statistic to mitigate potential biases associated with weak instruments(Palmer et al., 2012; Pierce et al., 2011).

Selection of Genetic Predictors of Inflammatory Cytokine Levels and Outcomes

The data utilized in this MR analysis were sourced from two publicly available summarized GWAS datasets. The dataset for hypertrophic scar (HS) was acquired from the Finngen dataset, which includes 766 cases and 207,482 controls of European ancestry (finn-b-L12_HYPETROPHICSCAR). The detailed study design and data control procedures have been described elsewhere. For inflammatory cytokines, the data were derived from a study that reported genome variant associations with 91 plasma proteins in 14,824 participants of European ancestry(Zhao et al., 2023). All participants shared an identical genetic background (European ancestry). There was no sample overlap between the exposure and outcome GWAS datasets.

Statistical analysis

The causal association was assessed using the inverse variance weighted method (IVW), known for its high efficiency and statistical power, and other robust methods that do not require all genetic variants to be valid IVs were also utilized to ensure consistent estimates of the causal parameter. MR‒Egger regression was employed to evaluate the presence of directional pleiotropy, with statistical significance determined by P values for the intercept < 0.05(Bowden et al., 2016). The weighted median method, which offers greater tolerance for invalid IVs, can generate reliable estimates when more than half of the weight corresponds to valid IVs(Burgess et al., 2013). The MR-PRESSO method(Verbanck et al., 2018), which is capable of identifying outlier genetic variants with horizontal pleiotropy, was used, assuming that more than 50% of the instruments were valid.

The F-statistic was approximated based on summary-level data to ascertain the relevance of IV exposure. A value of F > 10 indicated a strong enough correlation to mitigate weak IV bias. Cochrane's Q-statistic from IVW was utilized to assess heterogeneity among estimates from each SNP(Verbanck et al., 2018) (Jin et al., 2020). SNPs serving as instrumental variables (IVs) with missing data in the exposure or outcome summaries were omitted. Other MR methods yielded similar magnitudes and directions as IVW(Powers, 2020), and there was no evidence of heterogeneity or horizontal pleiotropy (e.g., P heterogeneity, P intercept, and p value for MR-PRESSO global test > 0.05).

We accounted for multiple comparisons using the Bonferroni approach, setting statistical significance at a P value < 5.4 × 10 − 4 (0.05/91) based on the number of cytokines. A P value between 5.4 × 10 − 4 and 0.05 was considered suggestive evidence for a potential causal association(Larsson et al., 2017). All MR analyses were conducted using R software (Yavorska & Burgess, 2017). It is important to note that the study was not preregistered on any platform.

The results of the IVW analysis of the relationships between 91 cytokines and HS are presented in Fig. 2. After the Bonferroni correction, only 5 cytokines (OPG levels, LIF levels, CDCP1 levels, GDNF levels, and PD-L1 levels) showed suggestive associations with HS risk. Summary data for the associations of genetic variants are presented in the supplementary data (Table S2). Genetically determined higher circulating levels of LIF [odds ratio (OR): 0.51, 95% confidence interval (CI): 0.32–0.82, P = 0.01] and OPG [odds ratio (OR): 0.59, 95% confidence interval (CI): 0.41–0.85, P = 0.01] showed a suggestive inverse association with the risk of HS. The results from sensitivity analyses indicated comparable trends, albeit lacking statistical significance, as observed with the weighted median and MR‒Egger methods. Specifically, MR‒Egger regression analyses did not reveal evidence of potential directional pleiotropy across the SNPs, as indicated by an intercept P value > 0.05. We further scanned the IVs in the GWAS Catalog and did not find any of the SNPs for which pleiotropy was documented. In addition, our results revealed an adverse effect of the circulating level of CDCP1 (OR: 1.29, 95% CI: 1.02–1.64, P = 0.03), GDNF (OR: 1.42, 95% CI: 1.03–1.96, P = 0.03) and PD-L1 (OR: 1.47, 95% CI: 1.02–2.11, P = 0.04) on HS risk according to the IVW method. The results of the sensitivity analyses revealed similar but not statistically significant trends according to the weighted median and MR‒Egger results. We carried out a series of sensitivity analyses to evaluate heterogeneity and potential horizontal pleiotropy. Cochran's Q test showed no clear evidence for the presence of heterogeneity in the MR results for CDCP1, GDNF, PD-L1, LIF, and OPG. For the reverse MR analyses shown in Fig. 3, we did not observe statistically significant associations between HS and cytokine levels using the IVW method (P > 0.05). The scatter plots and funnel plots of MR analyses for the five significant inflammatory cytokines in HS are shown in Fig. 4 and Fig. 5.

In this study, we integrated data from two large-scale GWASs to investigate the causal effects of 91 genetically proxied inflammatory cytokines on hypertrophic scar (HS) using a Mendelian randomization (MR) design. We observed a significant positive correlation between OPG and LIF levels and HS. Specifically, OPG(Arnold et al., 2022) and type I and III collagen, which are specialized forms of HS, are overproduced in keloids(Lim et al., 2009). Interestingly, OPG was present in normal keratinocyte-fibroblast cocultures but absent in keloid keratinocyte-fibroblast cocultures. Additionally, the expression of OPG may be linked to the development of degenerative changes in articular cartilage(Kaneyama et al., 2003). Furthermore, proinflammatory cytokines such as TNF-α and IL-6 may influence OPG changes(El Amrousy & El-Afify, 2020).

LIF, a member of the IL-6 cytokine family, is widely expressed across various tissue types in the body. Studies have shown that Buyang Huanwu Decoction can reduce ICH-induced glial scarring by decreasing LIF expression in rats(Kang et al., 2019). In cases of cesarean scar pregnancy, the intensity of LIF expression is significantly lower than that in the uterine cavity in the cardiac side population group and in the case of a cesarean scar in normal pregnancy(Qian et al., 2017). Moreover, increased blood levels of LIF following LIF cDNA transfection lead to cell cycle reentry of the cardiac side population, expansion of cardiac stem cells, and an increase in new cardiomyocytes, along with a reduction in the size of myocardial infarction scars(Qian et al., 2017). Additionally, LIF(Kanda et al., 2016) has been found to inhibit the infiltration of inflammatory cells and decrease the expression of the proinflammatory cytokine IL-12 while simultaneously increasing the expression of the anti-inflammatory cytokine IL-10 in injured areas of the uterine horns(Xue et al., 2019).

CDCP1, GDNF, and PD-L1 levels are significantly correlated with hypertrophic scar (HS). CDCP1, a substrate of Src family kinases, plays a role in regulating resistance to cell death when detached from the extracellular matrix (anoikis), cell migration, and breakdown of the extracellular matrix during the invasion and spread of tumors, specifically through tyrosine phosphorylation(Uekita & Sakai, 2011). Elevated levels of signal peptide-CUB-EGF-like domain-containing protein 1 (SCUBE1) were observed in adults diagnosed with acute appendicitis, indicating its potential as a novel and promising biomarker for predicting inflammation(Altuntas et al., 2023). In another study(Tokime et al., 2008), neurotrophic factors such as nerve growth factor and GDNF were suggested to be involved in the mechanism underlying the overgrowth and hypersensitivity of sensory nerves in atopic dermatitis(Huang et al., 2020). The study indicated that, along with nerve growth factor, the increased production and release of GDNF in the skin, together with an excess of IL-18, may contribute significantly to itching in atopic dermatitis patients(Noskovicova et al., 2018). Furthermore, CDCP1 levels are significantly greater in human cervical cancer tissues than in normal cervical tissues, affecting cell adhesion and fibroblast activation(Li et al., 2021). This activation, which is crucial for excessive collagen formation in abnormal scars, also influences keratinocyte migration, a process relevant to HS formation(McGovern et al., 2013).

GDNF can play a pivotal role in promoting hair follicle neogenesis and facilitating skin regeneration after wounding by influencing the fate of dermal fibroblasts. This finding implies that stimulating the GDNF signaling pathway in fibroblasts, particularly in less-regenerative animals such as humans, has the potential to promote skin regeneration, morphogenesis, and scarless wound healing(Zhang et al., 2021). The elevated production and secretion of GDNF in the skin associated with the overproduction of IL-18 may also be a potent causative factor of itching in atopic dermatitis patients, suggesting that GDNF could be a potential factor of HS-induced itching.

PD-1/PD-L1 inhibitors are promising medications for cancer therapy due to their potent effects on the adaptive immune response. BMS-202, a PD-1/PD-L1 inhibitor, effectively suppressed the proliferation, migration, and extracellular matrix deposition of HS tissues, potentially through the regulation of the ERK and transforming growth factor beta 1 (TGFβ1)/Smad signaling pathways(Cai et al., 2022). Moreover, investigations have suggested that HS, a fibroproliferative disorder of the skin with tumor-like properties, is closely associated with dysregulated inflammation. PD-L1 in fibroblast-like cells plays a positive role in the regulation of wound healing by initiating the resolution of inflammation(Wang et al., 2022).

This study represents the first Mendelian randomization study aimed at evaluating the causal relationship between hypertrophic scar (HS) and 91 inflammatory cytokines. However, several limitations need to be considered. First, due to the inherent limitations of Mendelian randomization analysis, the second and third assumptions could not be thoroughly examined, potentially introducing bias into the results. Second, our investigation relied on data from two large-scale GWASs, and subgroup analyses were not feasible due to a lack of specific demographic information and clinical records for the study patients. Third, there may be ethnic bias because the study participants were of European descent, and caution should be exercised in applying the conclusions to other racial groups. Further research is warranted to validate our findings and explore their potential application in clinical diagnosis and therapeutic interventions.

Hypertrophic scar

IVW

Inverse-variance weighted

OPG

Osteoprotegerin

LIF

Leukemia Inhibitory Factor

CDCP1

CUB Domain-Containing Protein 1

GDNF

Glial cell line-derived neurotrophic factor

PD-L1

Programmed death-ligand 1

Odds ratio

Confidence interval

Mendelian randomization

STROBE-MR

Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization

Glial Scar

SNP

Single nucleotide polymorphism

NGF

Nerve Growth Factor

FUNDING INFORMATION

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

DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this article will be made available by the authors without undue reservation. Further inquiries can be directed to the corresponding author.

ETHICS STATEMENT

Ethical review and approval were not required for the study of human participants because of the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

AUTHOR CONTRIBUTIONS

Seven Qi and Ashia Ma: study design and conceptualization of the data. Hai Lin and Liangyuan Peng: drafted the manuscript for intellectual content. Eminlam Deng: data analysis and interpretation, manuscript revision.

ACKNOWLEDGMENTS

We thank all the GWASs for making the summary data publicly available, and we are grateful to all the investigators and participants who contributed to those studies.

SUPPLEMENTARY MATERIAL

The data generated or analyzed during this study are available in this published article and its supplementary information files.

COMPETING INTERESTS

The authors declare no competing interests.

CONSENT FOR PUBLICATION

Consent for publication was obtained from the participants.

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The effect of inflammatory cytokines on the risk of hypertrophic scar: A Mendelian Randomization Study

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