Levels of 91 circulating inflammatory proteins and risk of non-melanoma skin cancer:A two-sample Mendelian randomization study

doi:10.21203/rs.3.rs-4955158/v1

Download PDF

Article

Levels of 91 circulating inflammatory proteins and risk of non-melanoma skin cancer:A two-sample Mendelian randomization study

https://doi.org/10.21203/rs.3.rs-4955158/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

Non-Melanoma Skin Cancer (NMSC) is one of the most common human malignancies with a high incidence rate, posing a heavy economic burden on the global healthcare system.

Methods

We utilized single nucleotide polymorphisms (SNPs) that exhibited significant associations with circulating inflammatory proteins as genetic instruments, obtained non-melanoma skin cancer (NMSC) data from pooled sources of independent genome-wide association studies (GWAS), and subsequently conducted two-sample Mendelian randomization (MR) analyses. In the MR analysis, we employed methods such as inverse variance weighting, weighted median, MR-Egger regression, MR Multi-effect residuals, and outlier tests to assess the potential causal relationship between 91 distinct circulating inflammatory proteins and non-melanoma skin cancer.

Results

We found that higher levels of CCL23 (OR 1.07, 95% CI 1.00-1.13), CCL25 (OR 1.04, 95% CI 1.01–1.07), EN-RAGE (OR 1.08, 95% CI 1.01–1.15), IL-15RA (OR 2.03, 95% CI 1.15–3.61), IL-1α (OR 1.21, 95% CI 1.08–1.35), and IL-8 (OR 1.61, 95% CI 1.06–2.43) were significantly positively associated with the risk of NMSC. Conversely, higher levels of CCL4 (OR 0.95, 95% CI 0.91–0.98), FIt3L (OR 0.92, 95% CI 0.86–0.98), MMP-1 (OR 0.63, 95% CI 0.41–0.98), OPG (OR 0.65, 95% CI 0.43–0.98), and TRANCE (OR 0.94, 95% CI 0.89–0.99) were significantly associated with a reduced risk of NMSC. Sensitivity analysis validated the robustness of the findings for CCL23, CCL25, EN-RAGE, IL-15RA, IL-8, and IL-1α.

Conclusion

This innovative two-sample MR analysis reveals an intrinsic causal relationship between inflammation and the risk of non-melanoma skin cancer, providing new insights into the molecular mechanisms of the disease and potentially identifying potential therapeutic targets.

Biological sciences/Cancer/Skin cancer

Biological sciences/Cancer/Skin cancer/Basal cell carcinoma

Biological sciences/Cancer/Skin cancer/Squamous cell carcinoma

Non-Melanoma Skin Cancer

Inflammation Cytokines

Mendelian Randomization

Circulating Inflammation Proteins

Non-Melanoma Skin Cancer (NMSC) is one of the most common malignancies in humans, primarily comprising basal cell carcinoma and squamous cell carcinoma. Despite its relatively favorable prognosis, the high incidence of NMSC places a significant economic burden on global healthcare systems. In the United States alone, the annual cost of diagnosing and treating NMSC is estimated at $4.5 billion ¹. Furthermore, NMSC causes significant physical and psychological trauma, such as pain, scarring, dysfunction, and changes in appearance, severely affecting patients' quality of life and mental health ². Therefore, elucidating the pathogenesis of NMSC and developing effective prevention and treatment strategies are crucial for mitigating its public health and socioeconomic burden.

Inflammation plays a pivotal role in the development and progression of tumors, recognized as a crucial feature of tumor biology ³. Chronic inflammation promotes tumor progression through mechanisms such as sustained cell proliferation, genomic instability, tissue remodeling, angiogenesis, and inhibition of the body's anti-tumor immune response ⁴. A large number of epidemiological studies have shown that many chronic inflammatory diseases (such as ulcerative colitis and Crohn's disease) are closely related to increased risks of specific tumors ^5,6.

The origin and mechanism of inflammation in the development of NMSC are complex. On the one hand, exogenous risk factors such as ultraviolet radiation can trigger acute inflammatory reactions in the skin, leading to DNA damage, gene mutations, and the occurrence and development of NMSC. On the other hand, tumor cells can establish a persistent inflammatory microenvironment by secreting various pro-inflammatory factors to promote tumor development ⁷. Specifically, various inflammatory cells (such as neutrophils, macrophages, and T cells) and their secreted cytokines (such as interleukins, chemokines, and growth factors)are involved in various critical steps that regulate the occurrence, progression, and metastasis of NMSC,including inducing sustained proliferation signals, activating invasion and metastasis programs, inducing angiogenesis, inhibiting apoptosis, etc ^8,9.

Although a large number of observational studies and in vitro and in vivo experiments have revealed the role of various inflammatory pathways in NMSC, it remains a mystery whether a causal relationship exists between specific inflammatory mediators and NMSC risk. Traditional observational studies are often affected by confounding, reverse causality, and other systematic biases, making it difficult to draw reliable causal inferences. Randomized controlled trials are the "gold standard" for assessing causality, but conducting large-scale intervention trials in the field of NMSC is often subject to ethical and practical limitations.

In recent years, Mendelian Randomization (MR) has been widely used in studies of the causal relationship between disease risk factors due to its unique advantages ¹⁰. MR utilizes genetic variations as instrumental variables for exposure and takes advantage of the random allocation of genes at fertilization following genetic laws to avoid confounding and reverse causality biases, thereby enabling a more reliable assessment of the causal association between exposure and outcome. With the continuous accumulation of large-scale genome-wide association study (GWAS) data, MR analysis has become an important tool for identifying risk factors for complex traits or diseases ¹¹.

In this study, we employed a two-sample MR analysis strategy using large-scale GWAS data to explore the causal association between multiple circulating inflammatory proteins and NMSC risk. To ensure the reliability of the results, we used a variety of complementary MR analysis methods and conducted comprehensive sensitivity analysis. The aim of this study is to elucidate the role of inflammation in the pathogenesis of NMSC from a genetic perspective, reveal potential pathogenic mechanisms, and provide clues for future development of precise disease prevention and treatment strategies.

This study aims to investigate the causal relationship between 91 circulating inflammatory proteins and non-melanoma skin cancers (NMSC) through a two-sample Mendelian randomization (MR) analysis. In MR analysis, to obtain valid results, three core assumptions must be met(Figure.1): (1) the selected genetic variants must show a correlation with exposure (correlation assumption); (2) the selected variants should remain unaffected by any confounding factors (independence assumption); and (3) these variables should only affect the outcome through exposure (exclusion assumption) ¹².

1.1 Exposure Data Source

The exposure data in this study are derived from 91 circulating inflammatory proteins. These data are from a meta-analysis of 11 cohorts, involving 14,824 participants of European descent. The original publications provide detailed methods for measuring inflammatory proteins ¹³. The website for extracting complete data is https://www.phpc.cam.ac.uk/ceu/proteins (GWAS catalog sequence numbers GCST90274758 to GCST90274848). There is no overlap between the exposed and outcome groups in the population, and all original studies have obtained ethical approval and informed consent.

1.2 Outcome Data Source

We obtained summary data from the GWAS database (https://gwas.mrcieu.ac.uk/) to extract outcome information ¹⁴, with a GWAS ID of ieu-b-4969. The data used in this study are all from published paper studies that have obtained ethical approval, so this study does not require ethical review again. This study includes over 370,000 participants, with comprehensive data on phenotypes, genomes, and biosamples collected. NMSC cases include patients with basal cell carcinoma or squamous cell carcinoma diagnosed through self-reported history, cancer registry records, and hospital records, with a total of 3,751 cases. The control group consists of 372,016 participants matched for age and ethnicity with the case population.

1.3 Selection and Validation of Instrumental Variables

Based on the three core assumptions of MR analysis, we extract exposure and outcome factors from GWAS summary statistics to select and validate instrumental variables. First, we screen using a threshold of P < 5 × 10^-8 , resulting in a relatively small number of SNPs ¹⁵. To facilitate subsequent analysis, the threshold is adjusted to P < 1 × 10^-5. Then, the following validation and optimization steps were performed: Clumping of SNPs with a high degree of correlation (r²=0.1, K=500) based on European reference data (1000 Genomes Project European) to avoid potential issues of linkage disequilibrium ¹⁶. Using bioinformatics tools such as PhenoScanner, SNPs were annotated to exclude variations significantly associated with potential confounding factors such as smoking and alcohol consumption ¹⁷. Reference functional annotations from databases such as GTEx and ENCODE were used to select SNPs that may affect gene expression or transcription regulation, improving the functional relevance of instrumental variables ^18,19. For the same inflammatory protein, if there are multiple independent association signals affecting genes, the best representative SNP for each signal is selected as an instrumental variable. Only SNPs with good genotyping quality and low missing rates in NMSC GWAS data are retained. After the above strict quality control and annotation processes, we finally confirmed 91 sets of SNPs that affect NMSC as instrumental variables. These SNPs will be used as instrumental variables for subsequent MR analysis. Finally, the strength of each SNP is calculated using the F statistic. SNPs with an F statistic > 10 are considered strongly correlated²⁰.

1.4 Statistical Analysis

1.4.1 Two-sample MR Analysis

We employed a two-sample Mendelian randomization (MR) analysis strategy, utilizing the aforementioned instrumental variables to assess the causal relationship between exposure levels of various inflammatory proteins and the risk of non-melanoma skin cancer (NMSC). Compared to the one-step MR, the two-sample MR offers higher statistical efficiency and avoids potential biases caused by sample overlap15. We adopted a variety of complementary MR methods, including inverse variance weighted (IVW), weighted median, MR-Egger, and MR-PRESSO, among others. These methods are based on different statistical model assumptions and have varying tolerances to potential sources of bias, and their combined use helps to evaluate the robustness of the results ^{21, 22}. The IVW method combines the Ratio estimates of each instrumental variable weighted by their statistical precision, providing an estimate of the overall causal effect and confidence intervals. This method has high precision but requires the assumption that all instrumental variables are valid and follow the same causal model ²³. The weighted median method, by assigning a weight of zero to some invalid instrumental variables, possesses strong robustness²². The MR-Egger method introduces an additional parameter for the direct association between instrumental variables and outcomes, which can assess the presence of horizontal pleiotropy (a potential source of bias). When all instrumental variables are valid and satisfy the same causal model, the intercept of MR-Egger should be close to zero. If the intercept significantly deviates from zero, it suggests the potential presence of horizontal pleiotropy, and in such cases, the slope is the consistent estimator of the causal effect²¹. MR-PRESSO uses residual statistics to detect and correct for potential outlier instrumental variables, resulting in more reliable MR estimates²⁴. The primary case analysis employed the IVW method, while other MR methods were used to assess the robustness of the results. All MR analyses were conducted using the TwoSampleMR package in R software (version 4.3.3).

1.4.2 Sensitivity Analysis:To assess the robustness and reliability of the results, we conducted the following sensitivity analysis: We used MR-Egger regression to evaluate the multiplicity situation. If the intercept of MR-Egger regression is P>0.05, it indicates that there is no horizontal multiplicity. Sensitivity analysis was performed through leave-one-out tests to determine whether a single SNP significantly affects the outcome measure. If so, it should be removed and MR analysis should be performed again ²⁵.

1.4.3 Heterogeneity Test: Cochran's Q statistic was used to detect heterogeneity among instrumental variables, which can help identify potential sources of bias.

1.4.4 Leave-One-Cross-Validation: Repeat MR analysis by excluding each instrumental variable one by one to detect whether a single instrumental variable has a significant impact on the overall results.

1.4.5 MR-PRESSO Global Test: Compare the differences in MR estimates before and after correction, and assess the statistical significance of the correction through confidence interval tests and Q' statistics to determine if there is significant horizontal expansion.

1.4.6 MR-Egger Goodness-of-Fit Test: Use the Q statistic in the SIMR package to determine whether the MR-Egger model fits well ²¹.

After strict screening, we finally obtained 91 sets of SNP instrumental variables to evaluate the causal association with the risk of NMSC. The positive results include: 30 SNPs in the CCL23 gene; 15 SNPs in the CCL25 gene; 31 SNPs in the CCL4 gene; 24 SNPs in the EN-RAGE (S100A12) gene; 45 SNPs in the FIt3L gene; 33 SNPs in the IL-1α gene; 19 SNPs in the IL-15RA gene; 31 SNPs in the IL-8 gene; 26 SNPs in the MMP-1 gene; 27 SNPs in the OPG gene; 47 SNPs in the TRANCE (TNFSF11) gene. Most of these SNPs are located in gene coding regions, 5' or 3' untranslated regions, or have significant correlations with the expression of corresponding genes. In summation, IVW-derived estimates were significant (p < 0.05), and there was consistency in direction and magnitude across IVW, MR-Egger, weighted median, and weighted mode estimates (Figure. 2).

Results of main MR analysis

2.2.1 IVW results

IVW analysis results indicate that higher levels of CCL23 (odds ratio 1.07, 95% confidence interval 1.00-1.13), CCL25 (odds ratio 1.04, 95% confidence interval 1.01–1.07), EN-RAGE (odds ratio 1.08, 95% confidence interval 1.01–1.15), IL-15RA (odds ratio 2.03, 95% confidence interval 1.15–3.61), IL-1α (odds ratio 1.21, 95% confidence interval 1.08–1.35), and IL-8 (odds ratio 1.61, 95% confidence interval 1.06–2.43) are significantly positively associated with the risk of non-melanoma skin cancer (NMSC).Conversely, higher levels of CCL4 (OR 0.95, 95% CI 0.91–0.98), FIt3L (OR 0.92, 95% CI 0.86–0.98), MMP-1 (OR 0.63, 95% CI 0.41–0.98), OPG (OR 0.65, 95% CI 0.43–0.98), and TRANCE (OR 0.94, 95% CI 0.89–0.99) are significantly associated with a reduced risk of NMSC. We have organized the above data and drawn a forest plot( Figure.3A,3B).

2.2.2 Results of sensitivity analysis

The sensitivity analysis results support the reliability of the main findings: Cochran's Q test did not find significant heterogeneity (p-value > 0.05), indicating that the effect estimates between studies are consistent. Further leave-one-out analysis also did not find any single instrumental variable that has a significant impact on the overall effect estimate.

2.2.3 MR-PRESSO Global Test

The MR-PRESSO global test shows that after correcting for potential outliers, the positive correlation between CCL23, CCL25, EN-RAGE, IL-15RA, and IL-8 and the risk of NMSC remains significant (P < 0.05), while the negative correlation of IL-1α remains robust. This suggests that the main findings are not derived from the influence of individual outlier SNPs.

2.2.4 MR-Egger Regression Analysis

The intercept of MR-Egger regression analysis is close to 0 and not significant (P > 0.05), suggesting no obvious horizontal expansion bias. Additionally, the Q goodness-of-fit test in the SIMR package did not find significant misfitting of the MR-Egger model (P > 0.05)(Figure.4). With the comprehensive use of different MR methods and various sensitivity analyses, our main findings demonstrate good consistency and robustness, supporting a potential causal relationship between certain inflammatory mediators (such as CCL23, CCL25, EN-RAGE, IL-15RA, and IL-8) and increased risk of NMSC, while IL-1α may be related to a reduced risk. For the several inflammatory proteins found to have significant positive correlations, this study has a statistical power of 80% to detect a minimum OR of 1.07 (CCL23), 1.04 (CCL25), 1.08 (EN-RAGE), 2.03 (IL-15RA), 1.21 (IL-1α), and 1.61 (IL-8) at an α = 0.005 level. For IL-1α with significant negative correlations, we have 80% power to detect a minimum OR of 0.63. These results indicate that the current instrumental variables and sample size are sufficient for detecting moderate risk effects, but the power may decrease for smaller effects. For other inflammatory proteins not found to have significant correlations, the statistical power of this study is relatively low, such as CCL4 (OR = 0.95), FIt3L (OR = 0.96), MMP-1 (OR = 0.63), OPG (OR = 0.65), and TRANCE (OR = 0.94). This may be due to the low explanatory power of the current instrumental variables or insufficient sample size. Obtaining more instrumental SNPs or increasing the sample size in the future will help improve the ability to detect smaller effects.

3.Inverse MR Analysis Results of Non-Melanoma Skin Cancer and circulating inflammatory proteins

To assess the impact of NMSC status on circulating inflammatory protein levels, we conducted an inverse MR analysis with NMSC as the exposure and inflammatory proteins as the outcome. The outcome data came from 11 inflammation-related biomarkers in the GWAS database with positive results from the forward analysis (CCL23, CCL25, CCL4, EN-RAGE, FLT3L, IL-1α, IL-15RA, IL-8, MMP-1, OPG, TRANCE); and the exposure data came from GWAS database of NMSC with GWAS ID: ieu-b-4969.

Neither IVW nor a variety of other MR analysis methods found a significant causal relationship between NMSC and the 11 inflammatory protein levels (IVW P value range is 0.08–0.92, all above 0.05). Sensitivity analyses including MR-Egger, MR-PRESSO, and leave-one-out method did not find obvious bias and heterogeneity issues.

The above results suggest that the NMSC status itself is likely not the main cause of changes in circulating inflammatory protein levels. This negative finding supports our previous causal inference that certain inflammatory mediators may be upstream causes rather than downstream results of NMSC development.

This study is the first to adopt a two-sample MR analysis strategy, utilizing data from large-scale GWAS to explore the potential causal relationship between multiple circulating inflammatory proteins and the risk of non-melanoma skin cancer (NMSC) from a genetic perspective. We found that inflammatory mediators such as CCL23, CCL25, EN-RAGE, IL-15RA, and IL-8, IL-1α have a positive causal relationship with increased NMSC risk. These findings are not only consistently supported by different MR analysis methods and multiple sensitivity tests, but also highly consistent with previous experimental and clinical observation studies, providing genetic evidence for the role of inflammation in the development of NMSC.

For the several positive associations found in this study, we will discuss their potential biological mechanisms and clinical implications. CCL23, CCL25, and IL-8 belong to the chemokine family, playing a key role in the development and progression of tumors. IL-8 can attract immune cells to the tumor microenvironment and promote inflammatory reactions, which may contribute to the growth and spread of tumor cells ²⁶. CCL23 is associated with a variety of tumors and may affect tumor development by regulating the migration and activity of immune cells ²⁷. CCL25 is expressed at increased levels in some tumors, attracting specific T-cell subsets to the tumor microenvironment, which may contribute to tumor immune escape. These small molecules can recruit various inflammatory cells (such as dendritic cells, T-cells, and neutrophils) to infiltrate tumor sites, inducing the formation of a pro-inflammatory immune microenvironment ²⁸. Chemokines can also promote angiogenesis and epithelial-mesenchymal transformation, enhancing the invasion and metastasis of tumor cells ^{29, 30}. Previous studies have reported elevated expression of CCL23, CCL25, and IL-8 in various solid tumors and hematological tumors, which is associated with adverse clinical features and prognosis ^{28, 31, 32}. Our MR analysis results support their potential carcinogenic role in the development of NMSC, suggesting that precise regulation of these chemokines and their downstream effects may be a potential therapeutic target for NMSC. Future research can focus on more accurately determining the role of these factors in non-melanoma skin cancer and how to more effectively use this knowledge to develop new treatment strategies. Additionally, combining immunotherapy with treatment targeting these chemokines may provide new strategies for treating this type of skin cancer.

EN-RAGE (Extracellular Newly identified RAGE-binding protein), a member of the S100 protein family, binds to RAGE (Receptor for Advanced Glycation End products), activating multiple signaling pathways and promoting inflammatory reactions, cell proliferation, angiogenesis, and other processes ³³. Studies have found that EN-RAGE is upregulated in multiple tumors and is associated with tumor aggressiveness, metastasis, and prognosis. In NMSC, EN-RAGE expression is significantly elevated. It may promote the occurrence and development of NMSC through the following mechanisms: activating signaling pathways such as NF-κB and MAPK, inducing the expression of pro-inflammatory factors and maintaining a sustained low-grade inflammatory state; promoting angiogenesis to provide nutrition for tumor growth; stimulating cancer cell proliferation and migration; inhibiting cancer cell apoptosis ³⁴. In vitro studies have also confirmed that knocking down EN-RAGE can weaken the proliferation, migration, and angiogenesis ability of NMSC cells ³⁵. EN-RAGE is involved in the development and progression of multiple tumors. Studies have found that EN-RAGE can induce angiogenesis and tumor cell invasion and metastasis, as well as activate and recruit inflammatory cells such as macrophages and neutrophils to promote the formation of a tumor-associated inflammatory and immunosuppressive microenvironment ^{36, 37}. Our findings reveal the potential role of EN-RAGE in the development of NMSC, providing a genetic basis for further exploring its potential as a therapeutic target in this field. Overall, EN-RAGE is an important inflammatory factor in the development and progression of NMSC. Further research on the relationship between EN-RAGE and NMSC may provide new ideas and targets for early diagnosis and targeted treatment of NMSC.

IL-15RA, as the receptor subunit of IL-15, is involved in regulating various immune cell functions. The IL-15/IL-15RA pathway has been found to be abnormally activated in multiple tumors, involving key biological processes such as inducing tumor immune escape, promoting tumor angiogenesis, and maintaining tumor stem cell survival ^38–40. Our MR findings provide genetic evidence that IL-15RA may play a significant role in the development of NMSC. There may be abnormal IL-15 signaling in the skin microenvironment of NMSC patients, but the exact molecular mechanism needs further elucidation. IL-15/IL-15RA pathway regulators have become new immune therapeutic targets for multiple tumors, and it is promising to explore related research in the field of NMSC in the future.

A causal association between high levels of IL-1α and increased risk of NMSC has been observed. IL-1α, a member of the interleukin family, is mainly produced by activated macrophages, neutrophils, and epithelial cells, playing a crucial role in innate and inflammatory responses ⁴¹. However, previous studies have also found that IL-1α is downregulated or absent in multiple tumors, and exogenous supplementation of IL-1α can inhibit tumor cell proliferation and invasion, suggesting its potential anti-tumor activity ⁴². Our MR research results may reflect this anti-tumor effect of IL-1α and provide a genetic basis for its application potential in the prevention and treatment of NMSC. IL-1α may exert its effect by inducing inflammatory responses to eliminate tumor cells, directly promoting tumor cell apoptosis, or stimulating anti-tumor immune surveillance, but the specific molecular mechanisms need further exploration.

In summary, the aforementioned findings support the view that inflammation plays a complex and bidirectional regulatory role in the development and progression of non-melanoma skin cancer (NMSC). Certain pro-inflammatory and chemotactic factors (such as CCL23, CCL25, EN-RAGE, IL-8, IL-15RA) promote the occurrence of NMSC by remodeling the tumor microenvironment (including immune cell infiltration, angiogenesis, and induction of epithelial-mesenchymal transition), while some inflammatory mediators with potential anti-tumor effects (such as TRANCE) may exert anti-tumor effects by directly inducing tumor cell apoptosis and stimulating the body's immune surveillance, thereby reducing the risk of NMSC. These findings reveal the central role and key function of inflammatory pathways in the development of NMSC and provide a forward-looking biological perspective for developing new prevention and targeted treatment strategies for NMSC.

The main advantage of this study is that it is the first to use MR analysis to explore the potential causal association between inflammation and NMSC risk, providing genetic evidence and causal inference for observational studies in this field. Compared with traditional observational studies, MR analysis can effectively avoid the influence of confounding factors and reverse causality, thereby obtaining a more reliable estimation of the causal relationship. We strictly screened multiple independent and effective instrumental variables, comprehensively applied different MR analysis methods and comprehensive sensitivity analysis strategies to ensure the robustness of the results. In addition, our GWAS sample size is relatively large, and the power evaluation results also show that it has sufficient detection ability for medium-risk levels, thereby increasing the credibility of the research findings.

However, our research also has some shortcomings that need to be improved. Firstly, although we have tried our best to exclude potential confounding biases, it is still difficult to completely rule out other biases such as horizontal expansion. Secondly, the genetic variation explained by the current instrumental variables used is generally low (< 10%), which indeed limits the statistical power for detecting small effects of exposure. Thirdly, we only considered the relationship between inflammatory proteins at the population level and the risk of NMSC, and have not yet evaluated whether there is heterogeneity in this association in different age groups, genders, or tumor stages. Future research is needed to be validated in larger populations.Fourthly, this study only focused on a few inflammatory proteins and did not cover a wider range of inflammatory pathways and mediators. Last but not least, our MR analyses are based on European populations, and whether they remain consistent in Asian populations remains to be studied.

We employed a two-sample MR analysis strategy to confirm the existence of a potential positive causal relationship between certain key inflammatory pathways (such as chemokine and IL-15 signaling pathways) and the risk of NMSC development. These findings provide forward-looking insights for elucidating the pathogenic mechanisms of NMSC and developing new therapeutic targets. We plan to continue to explore in the future the potential association between tissue-specific and cell-specific inflammatory responses and different subtypes of NMSC, and integrate MR analysis with multi-omics data to systematically analyze the inflammatory regulatory network and molecular mechanisms in NMSC, thereby promoting further breakthroughs in this field.

MR Mendelian randomization

SNPs Single nucleotide polymorphisms

GWAS genome-wide association study

IVW inverse-variance-weighted

CCL23 C-C motif chemokine 23 levels

CCL25 C-C motif chemokine 25 levels

CCL4 C-C motif chemokine 4 levels

EN-RAGE Protein S100-A12 levels

FLT3L Fms-related tyrosine kinase 3 ligand levels

IL-1α interleukin-1 alpha levels

IL-15RA interleukin-15 recepptor subunit alpha lexvls

IL-8 interleukin-8

MMP-1 Matrix metalloproteinase-1 levels

OPG Osteoprotegerin levels

TRANCE TNF-related activation-induced cytokine levels

Supplementary Material

91 circulating inflammatory protein number names(Supplementary Table S1) and further details are provided in the Supplementary Information Acknowledgements All authors thank the patients and sequencers who provided samples and the publicly available databases.

Author contributions

Baihong Zhang designed the study. Wangcheng Chen conducted the study, analyzed the results, and wrote a manuscript. Heteng Cui analyzed the results. Xiayi Su collected the data. Yanhong Shi collected the data. Lili Pang collected the data. Bingbing Wen preprocessed the data. Yuemei Lan preprocessed the data. Yaling Dong reviewed the manuscript. Zhibo Zhu contributed to conceptualization. Bai Jie contributed to methodology. Xiuzhen Wei contributed to project managemen. All authors read and approved the final manuscript.

Funding

This work was supported by the Natural Science Foundation of Gansu Province of China (No. 22JR5RA007).

Data availability

We have annotated the article with the source of all original data, please contact the original authors for access if needed. The results of this study can be obtained by contacting the corresponding author.

Ethics approval and consent to participate

Ethical review and approval were not required for the study on human participants following the local legislation and institutional requirements. Written informed consent for participation was not required for this study by the national legislation and the institutional requirements.

Consent for publication

We certify that this manuscript is a unique submission and is not being considered for publication, in part or in full, with any other source in any medium.

Competing interests

No benefits in any form have been or will be received from a commercial party related directly or indirectly to the subject of this manuscript. There is no conflict of interest between authors.

Lim, H. W. et al. The burden of skin disease in the United States. J. Am. Acad. Dermatol. 76, 958–972e952 (2017).
Duran, S. & Yürekli, A. Quality of life and satisfaction with life in patients with skin diseases. Psychol. Health Med. 28, 2848–2859 (2023).
Hanahan, D. Hallmarks of Cancer: New Dimensions. Cancer Discov. 12, 31–46 (2022).
Zhao, H. et al. Inflammation and tumor progression: signaling pathways and targeted intervention. Signal. Transduct. Target. Ther. 6, 263 (2021).
Clowry, J. et al. Increased non-melanoma skin cancer risk in young patients with inflammatory bowel disease on immunomodulatory therapy: a retrospective single-centre cohort study. J. Eur. Acad. Dermatol. Venereol. 31, 978–985 (2017).
Bahi, M. et al. The risk of non-melanoma skin cancer in New Zealand in inflammatory bowel disease patients treated with thiopurines. J. Gastroenterol. Hepatol. 33, 1047–1052 (2018).
Zaalberg, A. et al. Chronic Inflammation Promotes Skin Carcinogenesis in Cancer-Prone Discoid Lupus Erythematosus. J. Invest. Dermatol. 139, 62–70 (2019).
Hahn, J. et al. Neutrophils and neutrophil extracellular traps orchestrate initiation and resolution of inflammation. Clin. Exp. Rheumatol. 34, 6–8 (2016).
Hodorogea, A. et al. Epithelial-Mesenchymal Transition in Skin Cancers: A Review. Anal. Cell. Pathol. (Amst). 2019, 3851576 (2019).
Zheng, J. et al. Recent Developments in Mendelian Randomization Studies. Curr. Epidemiol. Rep. 4, 330–345 (2017).
Markozannes, G. et al. Systematic review of Mendelian randomization studies on risk of cancer. BMC Med. 20, 41 (2022).
Sanderson, E. et al. Mendelian randomization. Nat. Rev. Methods Primers ;2. (2022).
Zhao, J. H. et al. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets. Nat. Immunol. 24, 1540–1551 (2023).
Mbatchou, J. et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat. Genet. 53, 1097–1103 (2021).
Burgess, S. Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome. Int. J. Epidemiol. 43, 922–929 (2014).
Auton, A. et al. A global reference for human genetic variation. Nature. 526, 68–74 (2015).
Kamat, M. A. et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics. 35, 4851–4853 (2019).
An integrated encyclopedia of DNA elements in the human genome. Nature ;489:57–74. (2012).
The GTEx. Consortium atlas of genetic regulatory effects across human tissues. Science. 369, 1318–1330 (2020).
Burgess, S. & Thompson, S. G. Avoiding bias from weak instruments in Mendelian randomization studies. Int. J. Epidemiol. 40, 755–764 (2011).
Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).
Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet. Epidemiol. 40, 304–314 (2016).
Slob, E. A. W. & Burgess, S. A comparison of robust Mendelian randomization methods using summary data. Genet. Epidemiol. 44, 313–329 (2020).
Ong, J. S. & MacGregor, S. Implementing MR-PRESSO and GCTA-GSMR for pleiotropy assessment in Mendelian randomization studies from a practitioner's perspective. Genet. Epidemiol. 43, 609–616 (2019).
Zhang, W. & Ghosh, D. A general approach to sensitivity analysis for Mendelian randomization. Stat. Biosci. 13, 34–55 (2021).
Kim, J. H. Interleukin-8 in the Tumor Immune Niche: Lessons from Comparative Oncology. Adv. Exp. Med. Biol. 1240, 25–33 (2020).
Meng, J. et al. CCL23 suppresses liver cancer progression through the CCR1/AKT/ESR1 feedback loop. Cancer Sci. 112, 3099–3110 (2021).
Xu, B. et al. CCR9 and CCL25: A review of their roles in tumor promotion. J. Cell. Physiol. 235, 9121–9132 (2020).
Mantovani, A. et al. The chemokine system in cancer biology and therapy. Cytokine Growth Factor. Rev. 21, 27–39 (2010).
Albrengues, J. et al. Neutrophil extracellular traps produced during inflammation awaken dormant cancer cells in mice. Science ;361. (2018).
Yuan, A., Chen, J. J., Yao, P. L. & Yang, P. C. The role of interleukin-8 in cancer cells and microenvironment interaction. Front. Biosci. 10, 853–865 (2005).
Kamat, K., Krishnan, V. & Dorigo, O. Macrophage-derived CCL23 upregulates expression of T-cell exhaustion markers in ovarian cancer. Br. J. Cancer. 127, 1026–1033 (2022).
Pietzsch, J. & Hoppmann, S. Human S100A12: a novel key player in inflammation? Amino Acids. 36, 381–389 (2009).
Allgöwer, C. et al. Friend or Foe: S100 Proteins in Cancer. Cancers (Basel) ;12. (2020).
Herwig, N., Belter, B., Wolf, S., Haase-Kohn, C. & Pietzsch, J. Interaction of extracellular S100A4 with RAGE prompts prometastatic activation of A375 melanoma cells. J. Cell. Mol. Med. 20, 825–835 (2016).
Hasegawa, T. et al. The regulation of EN-RAGE (S100A12) gene expression in human THP-1 macrophages. Atherosclerosis. 171, 211–218 (2003).
Li, C. et al. Low concentration of S100A8/9 promotes angiogenesis-related activity of vascular endothelial cells: bridges among inflammation, angiogenesis, and tumorigenesis? Mediators Inflamm. 2012, 248574 (2012).
Fiore, P. F. et al. Interleukin-15 and cancer: some solved and many unsolved questions. J. Immunother Cancer ;8. (2020).
Guo, J. et al. Tumor-conditional IL-15 pro-cytokine reactivates anti-tumor immunity with limited toxicity. Cell. Res. 31, 1190–1198 (2021).
Zhou, Y. et al. Interleukin 15 in Cell-Based Cancer Immunotherapy. Int. J. Mol. Sci. ;23. (2022).
Cavalli, G. et al. Interleukin 1α: a comprehensive review on the role of IL-1α in the pathogenesis and treatment of autoimmune and inflammatory diseases. Autoimmun. Rev. 20, 102763 (2021).
Malik, A. & Kanneganti, T. D. Function and regulation of IL-1α in inflammatory diseases and cancer. Immunol. Rev. 281, 124–137 (2018).

No competing interests reported.

Download PDF

Editor invited by journal
03 Sep, 2024
Submission checks completed at journal
02 Sep, 2024
First submitted to journal
22 Aug, 2024

You are reading this latest preprint version

Levels of 91 circulating inflammatory proteins and risk of non-melanoma skin cancer:A two-sample Mendelian randomization study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials and Methods

Results

Results of main MR analysis

2.2.1 IVW results

2.2.2 Results of sensitivity analysis

2.2.3 MR-PRESSO Global Test

2.2.4 MR-Egger Regression Analysis

3.Inverse MR Analysis Results of Non-Melanoma Skin Cancer and circulating inflammatory proteins

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1