Association between circulating inflammatory markers and adult cancer risk: a Mendelian randomization analysis

Integrating Mendelian randomization and literature-mined evidence for breast cancer risk factors

OBJECTIVE

An increasing challenge in population health research is efficiently utilising the wealth of data available from multiple sources to investigate disease mechanisms and identify potential intervention targets. The use of biomedical data integration platforms can facilitate evidence triangulation from these different sources, improving confidence in causal relationships of interest. In this work, we aimed to integrate Mendelian randomization (MR) and literature-mined evidence from the EpiGraphDB biomedical knowledge graph to build a comprehensive overview of risk factors for developing breast cancer.

METHODS

We utilised MR-EvE ("Everything-vs-Everything") data to identify candidate risk factors for breast cancer and generate hypotheses for potential mediators of their effect. We also integrated this data with literature-mined relationships, which were extracted by overlapping literature spaces of risk factors and breast cancer. The literature-based discovery (LBD) results were followed up by validation with two-step MR to triangulate the findings from two data sources.

RESULTS

We identified 129 novel and established lifestyle risk factors and molecular traits with evidence of an effect on breast cancer, and made the MR results available in an R/Shiny app (https://mvab.shinyapps.io/MR_heatmaps/). We developed an LBD approach for identifying potential mechanistic intermediates of identified risk factors. We present the results of MR and literature evidence integration for two case studies (childhood body size and HDL-cholesterol), demonstrating their complementary functionalities.

CONCLUSION

We demonstrate that MR-EvE data offers an efficient hypothesis-generating approach for identifying disease risk factors. Moreover, we show that integrating MR evidence with literature-mined data may be used to identify causal intermediates and uncover the mechanisms behind the disease.

GWAS identifies genetic loci, lifestyle factors and circulating biomarkers that are risk factors for sarcoidosis

Sarcoidosis is a complex inflammatory disease with a strong genetic component. Here, we perform a genome-wide association study in 9755 sarcoidosis cases to identify risk loci and map associated genes. We then use transcriptome-wide association studies and enrichment analyses to explore pathways involved in sarcoidosis and use Mendelian randomization to examine associations with modifiable factors and circulating biomarkers. We identify 28 genomic loci associated with sarcoidosis, with the C1orf141-IL23R locus showing the largest effect size. We observe gene expression patterns related to sarcoidosis in the spleen, whole blood, and lung, and highlight 75 tissue-specific genes through transcriptome-wide association studies. Furthermore, we use enrichment analysis to establish key roles for T cell activation, leukocyte adhesion, and cytokine production in sarcoidosis. Additionally, we find associations between sarcoidosis and genetically predicted body mass index, interleukin-23 receptor, and eight circulating proteins.

Genetic evidence identifies a causal relationship between EBV infection and multiple myeloma risk

BACKGROUND

Previous observational studies have suggested a potential association between Epstein-Barr virus (EBV) infection and the development of multiple myeloma (MM), but this relationship is not clear. Therefore, we conducted a systematic Mendelian randomization (MR) analysis to investigate the causal relationship between EBV infection and the risk of MM, while exploring the possible mediating role of immune cells in this association.

METHODS

The study first conducted a two-sample MR analysis using the MM R11 dataset from the FinnGen Consortium to evaluate the causal relationship between five EBV infection-related antibodies (AEB-IgG, EA-D, EBNA-1, VCA-p18, and ZEBRA) and MM, with validation in the MM R10 dataset. A reverse MR analysis was then performed. For significant results, multivariable MR (MVMR) was used to adjust for the effects of confounding risk factors. Next, a two-step MR mediation analysis was applied to investigate the potential mediating role of 731 immune cell types between positive exposure and MM. Multiple sensitivity analyses were conducted to assess the robustness of the findings.

RESULTS

A two-sample MR study found that EBNA-1 antibodies (OR = 1.36, 95% CI: 1.06-1.73; P = 0.015) were associated with an increased risk of MM, with similar results observed in the FinnGen Consortium R10 replication study. Although the association did not remain statistically significant after false discovery rate (FDR) adjustment (P_fdr = 0.075), further adjustment for relevant confounders using multivariable MR (MVMR) demonstrated that EBNA-1 antibodies (OR = 1.33, 95% CI: 1.01-1.75; P = 0.041) were still significantly associated with an increased risk of MM. Reverse MR analysis indicated no causal effect of MM on EBV-related antibodies. A two-sample MR analysis involving 731 immune cell phenotypes identified 27 potential mediating cell types. Ultimately, two-step MR confirmed that HLA-DR on myeloid dendritic cells (HLA-DR⁺ mDC) serves as a mediating factor, with EBNA-1 antibodies downregulating HLA-DR⁺ mDC, thereby increasing MM risk. Multiple sensitivity analyses supported the robustness of these findings.

CONCLUSION

The findings of this study suggest that EBNA-1 antibodies may increase the risk of MM by downregulating HLA-DR⁺ mDC. This indicates that chronic EBV infection may contribute to an elevated risk of MM. We hope these results provide new insights for future research on the prevention and treatment of MM.

Mendelian randomization study of serum uric acid levels and urate-lowering drugs on pulmonary arterial hypertension outcomes

This study aims to explore the causal relationships between serum uric acid level and pulmonary arterial hypertension (PAH) using the Mendelian randomization (MR) approach, and to assess the therapeutic impacts of urate-lowering drugs on PAH. Utilizing published genome-wide association study (GWAS) data, we applied MR and colocalization analysis to assess the link between serum uric acid levesl and PAH across four GWAS datasets from two distinct European populations. The validity and reliability of these findings were confirmed through multiple statistical methods, along with an MR analysis of urate-lowering drug targets to investigate their potential effects on PAH treatment. MR analysis revealed a significant positive correlation between serum uric acid levels and PAH (odds ratio (OR) 1.106, 95% confidence intervals (CI) 1.021-1.200, P = 0.014), corroborated by a replication MR analysis (OR 1.859, 95% CI 1.130-3.057, P = 0.015). No significant heterogeneity or horizontal pleiotropy was found in the sensitivity analyses. However, urate-lowering drugs did not demonstrate a significant direct therapeutic effect on PAH. This study establishes a genetic basis for a causal link between serum uric acid levels and PAH. However, urate-lowering drugs do not appear to have a direct causal effect on improving PAH. These findings provide a novel reference point for developing future therapeutic strategies for PAH.

Prognostic evaluation of non-muscle invasive bladder cancer with P-CRP and its nomogram

Purpose

To investigate the impact of the product of preoperative platelet count and C-reactive protein (P-CRP) on the postoperative prognosis of patients with non-muscle invasive bladder cancer (NMIBC), and to construct a Nomogram to predict the recurrence-free survival (RFS) of NMIBC patients based on pathological data.

Methods

A retrospective analysis was conducted on the clinical data of 164 NMIBC patients who underwent transurethral resection of bladder tumors (TURBT) at the Second Affiliated Hospital of University of South China from January 2013 to December 2019. The endpoint of the study was the RFS. Kaplan-Meier (KM) method and Cox regression were used for analysis to identify independent factors affecting RFS. Then, the Nomogram was used to visualize the results of the multivariate analysis that were statistically significant and related to the RFS of NMIBC patients. Finally, the predictive ability of the model was evaluated using the concordance index (C-index) and calibration curves.

Results

Before the end of the follow-up, the RFS was 88.3% at 1 year, 75.5% at 2 years, and 58.5% at 3 years. KM curves showed that P-CRP (HR=0.357, 95% CI: 0.204-0.625, P<0.001), number of tumors (HR=2.658, 95% CI: 1.572-4.494, P<0.001), tumor size (HR=2.271, 95% CI: 1.377-3.745, P=0.001), T stage of the tumor (HR=2.026, 95% CI: 1.233-3.329, P=0.005), and tumor G grade (G2: HR=1.615, 95% CI: 0.48-5.433, G3: HR=3.361, 95% CI: 1.022-11.054) were independent factors affecting the RFS of NMIBC patients after TURBT. The Nomogram could estimate the risk of tumor recurrence at 1, 2, and 3 years postoperatively. The Nomogram model incorporating P-CRP parameters had a higher predictive accuracy than the classic model that only included EORTC risk group parameters.

Conclusion

Preoperative P-CRP has a certain impact on the RFS of NMIBC patients after TURBT. The Nomogram incorporating P-CRP, number of tumors, tumor size, T stage, and tumor pathological grading can better predict the postoperative recurrence risk of NMIBC patients.

Multi-omics analysis reveals novel causal pathways in psoriasis pathogenesis

BACKGROUND

To elucidate the genetic and molecular mechanisms underlying psoriasis by employing an integrative multi-omics approach, using summary-data-based Mendelian randomization (SMR) to infer causal relationships among DNA methylation, gene expression, and protein levels in relation to psoriasis risk.

METHODS

We conducted SMR analyses integrating genome-wide association study (GWAS) summary statistics with methylation quantitative trait loci (mQTL), expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) data. Publicly available datasets were utilized, including psoriasis GWAS data from the European Molecular Biology Laboratory-European Bioinformatics Institute and the UK Biobank. Heterogeneity in dependent instruments (HEIDI) test and colocalization analyses were performed to identify shared causal variants, and multi-omics integration was employed to construct potential regulatory pathways.

RESULTS

Our analyses identified significant causal associations between DNA methylation, gene expression, protein abundance, and psoriasis risk. We discovered two pathways involving the long non-coding RNA RP11-977G19.11 and apolipoprotein F (APOF). Methylation at sites cg26804944 and cg02705573 was negatively associated with RP11-977G19.11 expression. Reduced expression of RP11-977G19.11 was linked to increased APOF levels, which were positively associated with a higher risk of psoriasis. Methylation at sites cg00172967, cg00294382, and cg24773560 was positively associated with RP11-977G19.11 expression. Elevated expression of RP11-977G19.11 was associated with decreased APOF levels, reducing the risk of psoriasis. Colocalization analysis highlighted APOF as a key protein in psoriasis pathogenesis. Validation using skin tissue, EBV-transformed lymphocytes data and inflammation-related protein panels confirmed the associations of RP11-977G19.11 and APOF with psoriasis.

CONCLUSIONS

Our multi-omics analysis provides preliminary evidence for potential molecular mechanisms in psoriasis pathogenesis. Through the integration of GWAS and molecular QTL data, we identify candidate pathways that may be relevant to disease biology. While these findings require extensive experimental validation, they offer a framework for future investigations into the molecular basis of psoriasis.

Role of a Whole Plant Foods Diet in Breast Cancer Prevention and Survival

Breast cancer (BC) is one of the leading causes of death and morbidity among women worldwide. Epidemiologic evidence shows that the risk of BC and other chronic diseases decreases as the proportion of whole plant foods increases, while the proportion of animal foods (fish, meat, poultry, eggs, seafood, and dairy products) and non-whole plant foods (e.g., refined grains, added sugars, French fries) in the diet decreases. Whole plant foods include fruits, vegetables, roots, tubers, whole grains, legumes, nuts, and seeds from which no edible part has been removed and to which no non-whole food been added. A whole plant foods diet lowers insulin resistance, inflammation, excess body fat, cholesterol, and insulin-like growth factor 1 and sex hormone bioavailability; it also increases estrogen excretion, induces favorable changes in the gut microbiota, and may also favorably affect mammary microbiota composition and decrease the risk of early menarche, all contributing to reduced BC incidence, recurrence, and mortality. This review explores the connection between a whole plant foods diet and BC risk and mortality as well as the potential mechanisms involved. Additionally, this diet is compared with other dietary approaches recommended for BC. A whole plant foods diet seems the optimal dietary pattern for BC and overall disease prevention as it exclusively consists of whole plant foods which, based on existing evidence, lead to the best health outcomes.

Machine Learning and Mendelian Randomization Reveal Molecular Mechanisms and Causal Relationships of Immune-Related Biomarkers in Periodontitis

This study aimed to investigate the molecular mechanisms of periodontitis and identify key immune-related biomarkers using machine learning and Mendelian randomization (MR). Differentially expressed gene (DEG) analysis was performed on periodontitis datasets GSE16134 and GSE10334 from the Gene Expression Omnibus (GEO) database, followed by weighted gene co-expression network analysis (WGCNA) to identify relevant gene modules. Various machine learning algorithms were utilized to construct predictive models, highlighting core genes, while MR assessed the causal relationships between these genes and periodontitis. Additionally, immune infiltration analysis and single-cell sequencing were employed to explore the roles of key genes in immunity and their expression across different cell types. The integration of machine learning, MR, and single-cell sequencing represents a novel approach that significantly enhances our understanding of the immune dynamics and gene interactions in periodontitis. The study identified 682 significant DEGs, with WGCNA revealing seven gene modules associated with periodontitis and 471 core candidate genes. Among the 113 machine learning algorithms tested, XGBoost was the most effective in identifying periodontitis samples, leading to the selection of 19 core genes. MR confirmed significant causal relationships between CD93, CD69, and CXCL6 and periodontitis. Further analysis showed that these genes were correlated with various immune cells and exhibited specific expression patterns in periodontitis tissues. The findings suggest that CD93, CD69, and CXCL6 are closely related to the progression of periodontitis, with MR confirming their causal links to the disease. These genes have potential applications in the diagnosis and treatment of periodontitis, offering new insights into the disease's molecular mechanisms and providing valuable resources for precision medicine approaches in periodontitis management. Limitations of this study include the demographic and sample size constraints of the datasets, which may impact the generalizability of the findings. Future research is needed to validate these biomarkers in larger, diverse cohorts and to investigate their functional roles in the pathogenesis of periodontitis.

Mendelian randomization study on the association of circulating ketone bodies with lung cancer and respiratory diseases

The liver produces various ketone bodies (KBs) including 3-Hydroxybutyrate (3-OHB), acetoacetate (AcAc), and acetone, with 3-OHB being the major component. Previous studies have shown that KBs protect against respiratory diseases; however, there is no evidence of a genetic link. To avoid biases existing in traditional observational studies, a two-sample Mendelian randomization (MR) analysis was carried out to investigate genetic causation and novel therapeutic uses for KBs. This study used databases from genome-wide association studies (GWAS) and single nucleotide polymorphisms as instrumental variables for KBs from a recently published metabonomics study (n = 121,584) and respiratory diseases [lung cancer, n = 85,716; asthma, n = 127,669; chronic bronchitis, n = 450,422; chronic obstructive pulmonary disease (COPD), n = 468,475; FEV1/FVC < 0.7, n = 353,315] from their publicly available GWAS, respectively. Strong sets of instrumental variables (P < 5 × 10- 8) were selected, with inverse-variance weighted as the primary MR method. Sensitivity analyses included Cochran's Q test, MR Egger, MR-PRESSO, leave-one-out test, and funnel plots. The Steiger test and reversed MR were used to exclude reverse causality. Additionally, independent replication MR studies were conducted using databases from another large public GWAS and similar methods as described above. After MR analyses and sensitivity filtering, we discovered a protective effect of 3-OHB on lung cancer (odds ratio [OR] = 0.771; 95% confidence interval [CI] = 0.648-0.916; PFDR=0.006), small cell carcinoma (OR = 0.485, 95% CI = 0.301-0.781, PFDR=0.006), asthma (OR = 0.585, 95% CI = 0.395-0.867, PFDR=0.010), chronic bronchitis (OR = 0.753, 95% CI = 0.570-0.994, PFDR=0.045), COPD (OR = 0.690, 95% CI = 0.535-0.890, PFDR=0.008) and lung function (OR = 0.970, 95%CI = 0.950-0.990, PFDR =0.008). In summary, our findings suggest that 3-OHB acts as a protective factor against lung cancer and respiratory diseases. However, heterogeneity implies that other mechanisms may also be involved in COPD improvement by 3-OHB.

Prospective study on the association between 36 human blood cell traits and pan-cancer outcomes: a mendelian randomization analysis

BACKGROUND

Research on the link between blood cell traits and cancer risk has gained significant attention. Traditional epidemiological and cell biology studies, have identified correlations between blood traits and cancer risks. These findings are important as they suggest potential risk factors and biological mechanisms. However, these studies often can't confirm causality, pointing to the need for further investigation to understand these relationships better.

METHODS

Mendelian randomization (MR), utilizing single-nucleotide polymorphisms as instrumental variables, was employed to investigate blood cell trait causal effects on cancer risk. Thirty-six blood cell traits were analyzed, and their impact on 28 major cancer outcomes was assessed using data from the FinnGen cohort, with eight major cancer outcomes and 22 cancer subsets. Furthermore, 1,008 MR analyses were conducted, incorporating sensitivity analyses (weighted median, MR-Egger, and MR-PRESSO) to address potential pleiotropy and heterogeneity.

RESULTS

The analysis (data from 173,480 individuals primarily of European descent) revealed significant results. An increase in eosinophil count was associated with a reduced risk of colorectal malignancies (OR = 0.7702 per 1 SD higher level, 95% CI = 0.6852 to 0.8658; P = 1.22E-05). Similarly, an increase in total eosinophil and basophil count was linked to a decreased risk of colorectal malignancies (OR = 0.7798 per 1 SD higher level, 95% CI = 0.6904 to 0.8808; P = 6.30E-05). Elevated hematocrit (HCT) levels were associated with a reduced risk of ovarian cancer (OR = 0.5857 per 1 SD higher level, 95% CI = 0.4443 to 0.7721; P = 1.47E-04). No significant heterogeneity or horizontal pleiotropy was observed.

CONCLUSIONS

Our study highlights the complex and context-dependent roles of blood cell traits in cancers.

Multi-omics and multi-stages integration identified a novel variant associated with silicosis risk

Assessing the association between candidate single-nucleotide polymorphisms (SNPs) identified by multi-omics approaches and susceptibility to silicosis. RNA-seq analysis was performed to screen the differentially expressed mRNAs in the fibrotic lung tissues of mice exposed to silica particles. Following this, we integrated the SNPs located in the above human homologenes with the silicosis-related genome-wide association study (GWAS) data to select the candidate SNPs. Then, expression quantitative trait locus (eQTL)-SNPs were identified by the GTEx database. Next, we validated the associations between the functional eQTL-SNPs and silicosis susceptibility by additional case-control study. And the contribution of the identified SNP and its host gene in the fibrosis process was further validated by functional experiments. A total of 12 eQTL-SNPs were identified in the screening stage. The results of the validation stage suggested that the variant T allele of rs419540 located in IL12RB1 significantly increased the risk of developing silicosis [additive model: odds ratio (OR) = 1.78, 95% confidence interval (CI) 1.11-2.85, P = 0.017]. Furthermore, the combination of GWAS and the results of validation stage also indicated that the variant T allele of rs419540 in IL12RB1 was associated with increased silicosis risk (additive model: OR = 2.07, 95% CI 1.38-3.12, P < 0.001). Additionally, after knockdown or overexpression of IL12RB1, the levels of pro-inflammatory factors, such as IL-12, IFN-γ, and other pro-inflammatory factors, were correspondingly decreased or increased. The novel eQTL-SNP, rs419540, might increase the risk of silicosis by modulating the expression levels of IL12RB1.

Association between blood metabolites and basal cell carcinoma risk: a two-sample Mendelian randomization study

Background

Circulating metabolites, which play a crucial role in our health, have been reported to be disordered in basal cell carcinoma (BCC). Despite these findings, evidence is still lacking to determine whether these metabolites directly promote or prevent BCC's progression. Therefore, our study aims to examine the potential effects of circulating metabolites on BCC progression.

Material and methods

We conducted a two-sample Mendelian randomization (MR) analysis using data from two separate genome-wide association studies (GWAS). The primary study included data for 123 blood metabolites from a GWAS with 25,000 Finnish individuals, while the secondary study had data for 249 blood metabolites from a GWAS with 114,000 UK Biobank participants.GWAS data for BCC were obtained from the UK Biobank for the primary analysis and the FinnGen consortium for the secondary analysis. Sensitivity analyses were performed to assess heterogeneity and pleiotropy.

Results

In the primary analysis, significant causal relationships were found between six metabolic traits and BCC with the inverse variance weighted (IVW) method after multiple testing [P < 4 × 10-4 (0.05/123)]. Four metabolic traits were discovered to be significantly linked with BCC in the secondary analysis, with a significance level of P < 2 × 10-4 (0.05/249). We found that all the significant traits are linked to Polyunsaturated Fatty Acids (PUFAs) and their degree of unsaturation.

Conclusion

Our research has revealed a direct link between the susceptibility of BCC and Polyunsaturated Fatty Acids and their degree of unsaturation. This discovery implies screening and prevention of BCC.

Modifiable risk factors and inflammation-related proteins in polymyalgia rheumatica: genome-wide meta-analysis and Mendelian randomisation

OBJECTIVE

Polymyalgia rheumatica (PMR) is an age-related inflammatory disease of unknown cause. We aimed to identify potentially modifiable risk factors and therapeutic targets for preventing or treating PMR.

METHODS

We meta-analysed genetic association data from 8,156 cases of PMR (defined using diagnostic codes and self-report) and 416,495 controls of European ancestry from the UK Biobank and FinnGen. We then performed Mendelian randomization analyses to estimate the association between eight modifiable risk factors (using data from up to 1.2 million individuals) and 65 inflammation-related circulating proteins (up to 55,792 individuals), using the inverse variance weighted and pleiotropy robust methods.

RESULTS

We identified three novel genome-wide significant loci in the IL1R1, NEK6 and CCDC88B genes and confirmation of previously described associations with HLA-DRB1 and ANKRD55. Genetically predicted smoking intensity (OR 1.32; 95%CI 1.08-1.60; p = 0.006) and visceral adiposity (OR 1.22; 95%CI 1.10-1.37; p = 3.10x10-4) were associated with PMR susceptibility. Multiple circulating proteins related to IL-1 family signaling were associated with PMR. IL-1 receptor-like 2, also known as IL-36 receptor (OR 1.25; p = 1.89x10-32), serum amyloid A2 (OR 1.06, 9.91x10-10) and CXCL6 (OR 1.09, p = 4.85x10-7) retained significance after correction for multiple testing.

CONCLUSION

Reducing smoking and visceral adiposity at a population level might reduce incidence of PMR. We identified proteins that may play causal roles in PMR, potentially suggesting new therapeutic opportunities. Further research is needed before these findings are applied to clinical practice.

Adiposity and cancer: meta-analysis, mechanisms, and future perspectives

Obesity is a recognised risk factor for many cancers and with rising global prevalence, has become a leading cause of cancer. Here we summarise the current evidence from both population-based epidemiologic investigations and experimental studies on the role of obesity in cancer development. This review presents a new meta-analysis using data from 40 million individuals and reports positive associations with 19 cancer types. Utilising major new data from East Asia, the meta-analysis also shows that the strength of obesity and cancer associations varies regionally, with stronger relative risks for several cancers in East Asia. This review also presents current evidence on the mechanisms linking obesity and cancer and identifies promising future research directions. These include the use of new imaging data to circumvent the methodological issues involved with body mass index and the use of omics technologies to resolve biologic mechanisms with greater precision and clarity.