Genetic variants in hypoxia-inducible factor pathway are associated with colorectal cancer risk and immune infiltration.

Hypoxia-inducible factor (HIF) pathway genes influence tumorigenesis and immune status. However, the associations between genetic variants in hypoxia-related genes and colorectal cancer risk and the immune status of hypoxia-associated genes in colorectal cancer have not been systematically characterized. The associations between genetic variants and colorectal cancer risk were evaluated in Chinese, Japanese and European populations using logistic regression analysis. The relationships between target genes and tumour immune infiltration were predicted by Tumour Immune Estimation Resource (TIMER). We found that rs34533650 in EPAS1 was associated with colorectal cancer risk (OR = 1.43, 95% CI = 1.20-1.70, P(FDR) = 8.35 × 10-4 ), and this finding was validated in two independent populations (Japanese: OR = 1.07, 95% CI = 1.01-1.15, p = 3.38 × 10-2 ; European: OR = 1.11, 95% CI = 1.03-1.19, p = 6.04 × 10-3 ). EPAS1-associated genes were enriched in immune-related pathways. In addition, we found that EPAS1 copy number variation (CNV) was associated with the degree of infiltration of immune cells and observed correlations between EPAS1 expression and immune cell infiltration levels in colorectal cancer. These results highlight that genetic variants of hypoxia-related genes play roles in colorectal cancer risk and provide new insight that EPAS1 might be a promising predictor of colorectal cancer susceptibility and immune status.

Enhancing H2O2 Electrosynthesis at Industrial-Relevant Current in Acidic Media on Diatomic Cobalt Sites.

Electrocatalytic synthesis of hydrogen peroxide (H2O2) in acidic media is an efficient and eco-friendly approach to produce inherently stable H2O2, but limited by the lack of selective and stable catalysts under industrial-relevant current densities. Herein, we report a diatomic cobalt catalyst for two-electron oxygen reduction to efficiently produce H2O2 at 50-400 mA cm-2 in acid. Electrode kinetics study shows a >95% selectivity for two-electron oxygen reduction on the diatomic cobalt sites. In a flow cell device, a record-high production rate of 11.72 mol gcat-1 h-1 and exceptional long-term stability (100 h) are realized under high current densities. In situ spectroscopic studies and theoretical calculations reveal that introducing a second metal into the coordination sphere of the cobalt site can optimize the binding strength of key H2O2 intermediates due to the downshifted d-band center of cobalt. We also demonstrate the feasibility of processing municipal plastic wastes through decentralized H2O2 production.

Hydrophilic Peptide and Glycopeptide as Immobilized Sorbents for Glycosylation Analysis.

Hydrophilic interaction liquid chromatography (HILIC) is widely used for glycopeptide enrichment in shot-gun glycoproteomics to enhance the glycopeptide signal and minimize the ionization competition of peptides. In this work, we have developed a novel hydrophilic material (glycoHILIC) based on glycopeptides and peptides to provide hydrophilic properties. GlycoHILIC was synthesized by oxidizing cotton and then reacting the resulting aldehyde with the N-terminus of the glycopeptide or peptide by reductive amination. Due to the large amount of hydrophilic carbohydrates and hydrophilic amino acids contained in glycopeptides, glycoHILIC showed significantly better enrichment of glycopeptides than cotton itself. Our results demonstrate that glycoHILIC has high selectivity, a low detection limit, and good stability. Over 257 unique N-linked glycosylation sites in 1477 intact N-glycopeptides from 146 glycoproteins were identified from 1 µL of human serum using glycoHILIC. Serum analysis of pancreatic cancer patients found that 38 N-glycopeptides among 21 glycoproteins changed significantly, of which 7 N-glycopeptides increased and 31 N-glycopeptides decreased. These results demonstrate that glycoHILIC can be used for glycopeptide enrichment and analysis.

Microbial synthesis of gallic acid and its glucoside ß-glucogallin.

Gallic acid (GA) and ß-glucogallin (BGG) are natural products with diverse uses in pharmaceutical, food, chemical and cosmetic industries. They are valued for their wide-ranging properties such as antioxidant, antibacterial, antidiabetic, and anticancer properties. Despite their significant importance, microbial production of GA and BGG faces challenges such as limited titers and yields, along with the incomplete understanding of BGG biosynthesis pathways in microorganisms. To address these challenges, we developed a recombinant Escherichia coli strain capable of efficiently producing GA. Our approach involved screening efficient pathway enzymes, integrating biosynthetic pathway genes into the genome while balancing carbon flux via adjusting expression levels, and strengthening the shikimate pathway to remove bottlenecks. The resultant strain achieved impressive results, producing 51.57 g/L of GA with a carbon yield of 0.45 g/g glucose and a productivity of 1.07 g/L/h. Furthermore, we extended this microbial platform to biosynthesize BGG by screening GA 1-O-glucosyltransferase, leading to the de novo production of 92.42 mg/L of BGG. This work establishes an efficient chassis for producing GA at an industrial level and provides a microbial platform for generating GA derivatives.

Factor-augmented transformation models for interval-censored failure time data.

Interval-censored failure time data frequently arise in various scientific studies where each subject experiences periodical examinations for the occurrence of the failure event of interest, and the failure time is only known to lie in a specific time interval. In addition, collected data may include multiple observed variables with a certain degree of correlation, leading to severe multicollinearity issues. This work proposes a factor-augmented transformation model to analyze interval-censored failure time data while reducing model dimensionality and avoiding multicollinearity elicited by multiple correlated covariates. We provide a joint modeling framework by comprising a factor analysis model to group multiple observed variables into a few latent factors and a class of semiparametric transformation models with the augmented factors to examine their and other covariate effects on the failure event. Furthermore, we propose a nonparametric maximum likelihood estimation approach and develop a computationally stable and reliable expectation-maximization algorithm for its implementation. We establish the asymptotic properties of the proposed estimators and conduct simulation studies to assess the empirical performance of the proposed method. An application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study is provided. An R package ICTransCFA is also available for practitioners. Data used in preparation of this article were obtained from the ADNI database.

Identifying drug targets with thermal proteome profiling using IBT-16plex.

RATIONALE: Thermal proteome profiling (TPP) has been widely used for the identification of drug targets for several years, and TMTpro-16plex has recently been evaluated for TPP of vehicle- and drug-treated samples in a single labeling process to reduce missing values and save instrument time. A novel isobaric labeling reagent, IBT-16plex, was developed with slightly better performance in protein identification and quantification than the commercially available TMTpro-16plex. METHODS: In this study, we applied the newly developed IBT-16plex for target identification of methotrexate and panobinostat using TPP. RESULTS: The known targets of these two drugs were successfully identified with elevated melting temperatures, and some known off-targets and potential new off-targets were also identified. CONCLUSIONS: IBT-16plex can be a cost-effective replacement for TMTpro-16plex for TPP applications.

Exploring the novel benefits of leucine: Protecting nitrite-induced liver damage in sub-adult grass carp (Ctenopharyngodon idella) through regulating mitochondria quality control.

Leucine is an essential amino acid for fish. The ability of leucine to resist stress in fish has not been reported. Nitrite is a common pollutant in the aquatic environment. Therefore, we investigated the effects of dietary leucine on growth performance and nitrite-induced liver damage, mitochondrial dysfunction, autophagy, and apoptosis for sub-adult grass carp. A total of 450 grass carp (615.91 ± 1.15 g) were selected and randomly placed into 18 net cages. The leucine contents of the six diets were 2.91, 5.90, 8.92, 11.91, 14.93, and 17.92 g/kg, respectively. After a 9-week feeding trial, the nitrite exposure experiment was set up for 96 h. These results indicated that dietary leucine significantly promoted FW, WG, PWG, and SGR of sub-adult grass carp (P < 0.05). Appropriate levels of dietary leucine (11.91-17.92 g/kg) decreased the activities of serum parameters (glucose, cortisol, and methemoglobin contents, glutamic oxaloacetic transaminase, glutamic pyruvic transaminase, and lactate dehydrogenase), the contents of reactive oxygen species (ROS), nitric oxide (NO) and peroxynitrite (ONOO-). In addition, appropriate levels of dietary leucine (11.91-17.92 g/kg) increased the mRNA levels of mitochondrial biogenesis genes (PGC-1α, Nrf1/2, TFAM), fusion-related genes (Opa1, Mfn1/2) (P < 0.05), and decreased the mRNA levels of caspase 3, caspase 8, caspase 9, fission-related gene (Drp1), mitophagy-related genes (Pink1, Parkin) and autophagy-related genes (Beclin1, Ulk1, Atg5, Atg7, Atg12) (P < 0.05). Appropriate levels of dietary leucine (8.92-17.92 g/kg) also increased the protein levels of AMP-activated protein kinase (AMPK), prostacyclin (p62) and decreased the protein levels of protein light chain 3 (LC3), E3 ubiquitin ligase (Parkin), and Cytochrome c (Cytc). Appropriate levels of leucine (8.92-17.92 g/kg) could promote growth performance and alleviate nitrite-induced mitochondrial dysfunction, autophagy, apoptosis for sub-adult grass carp. Based on quadratic regression analysis of PWG and serum GPT activity, dietary leucine requirements of sub-adult grass carp were recommended to be 12.47 g/kg diet and 12.55 g/kg diet, respectively.

Interactions between polycyclic aromatic hydrocarbons and genetic variants in the cGAS-STING pathway affect the risk of colorectal cancer.

The cGAS-STING pathway plays an essential role in the activation of tumor immune cells. Polycyclic aromatic hydrocarbons (PAHs) are environmental pollutants with potential carcinogenicity, and their exposure is associated with the development of colorectal cancer. However, the impacts of genetic factors in the cGAS‒STING pathway and gene‒environment interactions on colorectal cancer remain understudied. We used logistic regression models and interaction analysis to evaluate the impact of genetic variants on colorectal cancer risk and gene‒environment interactions. We analysed the expression patterns of candidate genes based on the RNA-seq data. Molecular biology experiments were performed to investigate the impact of PAHs exposure on candidate gene expression and the progression of colorectal cancer. We identified the susceptibility locus rs3750511 in the cGAS‒STING pathway, which is associated with colorectal cancer risk. A negative interaction between TRAF2 rs3750511 and PAHs exposure was also identified. Single-cell RNA-seq analysis revealed significantly elevated expression of TRAF2 in colorectal cancer tissues compared with normal tissues, especially in T cells. BPDE exposure increased TRAF2 expression and the malignant phenotype of colorectal cancer cells. The treatment also further increased the expression of the TRAF2 downstream gene NF-κB and decreased the expression of Caspase8. Our results suggest that the genetic variant of rs3750511 affects the expression of TRAF2, thereby increasing the risk of colorectal cancer through interaction with PAHs. Our study provides new insights into the influence of gene‒environment interactions on the risk of developing colorectal cancer.

Cellular senescence gene TACC3 associated with colorectal cancer risk via genetic and DNA methylated alteration.

Cell senescence genes play a vital role in the pathogenesis of colorectal cancer, a process that may involve the triggering of genetic variations and reversible phenotypes caused by epigenetic modifications. However, the specific regulatory mechanisms remain unclear. Using CellAge and The Cancer Genome Atlas databases and in-house RNA-seq data, DNA methylation-modified cellular senescence genes (DMCSGs) were validated by Support Vector Machine and correlation analyses. In 1150 cases and 1342 controls, we identified colorectal cancer risk variants in DMCSGs. The regulatory effects of gene, variant, and DNA methylation were explored through dual-luciferase and 5-azacytidine treatment experiments, complemented by multiple database analyses. Biological functions of key gene were evaluated via cell proliferation assays, SA-ß-gal staining, senescence marker detection, and immune infiltration analyses. The genetic variant rs4558926 in the downstream of TACC3 was significantly associated with colorectal cancer risk (OR = 1.35, P = 3.22 × 10-4). TACC3 mRNA expression increased due to rs4558926 C > G and decreased DNA methylation levels. The CpG sites in the TACC3 promoter region were regulated by rs4558926. TACC3 knockdown decreased proliferation and senescence in colorectal cancer cells. In addition, subjects with high-TACC3 expression presented an immunosuppressive microenvironment. These findings provide insights into the involvement of genetic variants of cellular senescence genes in the development and progression of colorectal cancer.

Aflatoxin B1 inhibited the development of primary myoblasts of grass carp (Ctenopharyngodon idella) by degrading extracellular matrix.

According to the International Agency for Research on Cancer (IARC), aflatoxin B1 (AFB1) has been recognized as a major contaminant in food and animal feed and which is a common mycotoxin with high toxicity. Previous research has found that AFB1 inhibited zebrafish muscle development. However, the potential mechanism of AFB1 on fish muscle development is unknown, so it is necessary to conduct further investigation. In the present research, the primary myoblast of grass carp was used as a model, we treated myoblasts with AFB1 for 24 h. Our results found that 5 µM AFB1 significantly inhibited cell proliferation and migration (P < 0.05), and 10 µM AFB1 promoted lactate dehydrogenase (LDH) release (P < 0.05). Reactive oxygen species (ROS), protein carbonyl (PC) and malondialdehyde (MDA) levels were increased in 15, 5 and 10 µM AFB1 (P < 0.05), respectively. Catalase (CAT), glutathione peroxidase (GPx) and total superoxide dismutase (T-SOD) activities were decreased in 10, 10 and 15 µM AFB1 (P < 0.05), respectively. Furthermore, 15 µM AFB1 induced oxidative damage by Nrf2 pathway, also induced apoptosis in primary myoblast of grass carp. Meanwhile, 15 µM AFB1 decreased MyoD gene and protein expression (P < 0.05). Importantly, 15 µM AFB1 decreased the protein expression of collagen Ⅰ and fibronectin (P < 0.05), and increased the protein levels of urokinase plasminogen activator (uPA), matrix metalloproteinase 9 (MMP-9), matrix metalloproteinase 2 (MMP-2), and p38 mitogen-activated protein kinase (p38MAPK) (P < 0.05). As a result, our findings suggested that AFB1 damaged the cell morphology, induced oxidative damage and apoptosis, degraded ECM components, in turn inhibiting myoblast development by activating the p38MAPK/urokinase-type plasminogen activator (uPA)/matrix metalloproteinase (MMPs)/extracellular matrix (ECM) signaling pathway.

Hyperplexing Approaches for up to 45-Plex Quantitative Proteomic Analysis.

Isobaric labeling has emerged as an indispensable quantitative proteomic approach for its unprecedented multiplexing capacity in a single analysis. Currently, different hyperplexing approaches have been developed to meet the demand for the increasing sample size in large-scale cohort analysis. In this report, we present a tribrid hyperplexing approach by the combinatorial use of three types of isobaric reagents, a novel isobaric tag 16-plex (IBT16) reagent and the widely used tandem mass tag (TMT; TMT11) and TMTpro (TMT18) reagents. After the determination of labeling efficiency and the optimization of testing conditions, we systematically evaluated the identification and quantification performance of the three labeling reagents in both independent and combinatorial manners using the mixtures of E. coli and HeLa peptides with different ratios. Our results reveal that the three reagents are quite similar in all testing aspects despite some differences, and the combination use of the three reagents could expand the multiplexing capacity to up to 45-plex. Furthermore, we conclude the advantages of IBT16 in the combination use and the preferred combinations for different practical applications. Data are available via ProteomeXchange with identifier PXD037498.

New Set of Isobaric Labeling Reagents for Quantitative 16Plex Proteomics.

Peptide labeling by isobaric tags is a powerful approach for the relative quantitative analysis of proteomes in multiple groups. There has been a revolution in the innovation of new isobaric reagents; however, great effort is being made to expand simultaneous labeling groups to identify more labeled peptides and reduce reporter ion signal suppression. We redesigned the original chemical structure of the deuterium isobaric amine-reactive tag developed in our laboratory. We optimized the synthetic pathway to create a new set of 16-plex isobaric tags (IBT-16plex). The novel reagent enabled almost complete labeling of peptides within 90 min, with all labeling reporter ions exhibiting comparable MS/MS signals. Compared to a typical 16plex reagent, TMTpro-16plex, the peptides and proteins identified by IBT-16plex in trypsinized HeLa cells were significantly increased by 14.8 and 8.6%, respectively. Moreover, differences in peptide abundance within 10-fold among multiple groups were barely suppressed in IBT-16plex, whereas the dynamic range in TMTpro-16plex-labeled groups was smaller. After quantitative examination of MCF7 cell proteins, IBT-16plex was confirmed as feasible and useful for evaluating protein responses of glucose-starved MCF7 cells to a glucose-rich medium.

Capturing Protein-Protein Interactions with Acidic Amino Acids Reactive Cross-Linkers.

Acidic residues (Asp and Glu) have a high prevalence on protein surfaces, but cross-linking reactions targeting these residues are limited. Existing methods either require high-concentration coupling reagents or have low structural compatibility. Here a previously reported "plant-and-cast" strategy is extended to develop heterobifunctional cross-linkers. These cross-linkers first react rapidly with Lys sidechains and then react with Asp and Glu sidechains, in a proximity-enhanced fashion. The cross-linking reaction proceeds at neutral pH and room temperature without coupling reagents. The efficiency and robustness of cross-linking using model proteins, ranging from small monomeric proteins to large protein complexes are demonstrated. Importantly, it is shown that this type of cross-linkers are efficient at identifying protein-protein interactions involving acidic domains. The Cross-linking mass spectrometry (XL-MS) study with p53 identified 87 putative binders of the C-terminal domain of p53. Among them, SARNP, ZRAB2, and WBP11 are shown to regulate the expression and alternative splicing of p53 target genes. Thus, these carboxylate-reactive cross-linkers will further expand the power of XL-MS in the analysis of protein structures and protein-protein interactions.

Instrumental variable estimation of complier causal treatment effect with interval-censored data.

Assessing causal treatment effect on a time-to-event outcome is of key interest in many scientific investigations. Instrumental variable (IV) is a useful tool to mitigate the impact of endogenous treatment selection to attain unbiased estimation of causal treatment effect. Existing development of IV methodology, however, has not attended to outcomes subject to interval censoring, which are ubiquitously present in studies with intermittent follow-up but are challenging to handle in terms of both theory and computation. In this work, we fill in this important gap by studying a general class of causal semiparametric transformation models with interval-censored data. We propose a nonparametric maximum likelihood estimator of the complier causal treatment effect. Moreover, we design a reliable and computationally stable expectation-maximization (EM) algorithm, which has a tractable objective function in the maximization step via the use of Poisson latent variables. The asymptotic properties of the proposed estimators, including the consistency, asymptotic normality, and semiparametric efficiency, are established with empirical process techniques. We conduct extensive simulation studies and an application to a colorectal cancer screening data set, showing satisfactory finite-sample performance of the proposed method as well as its prominent advantages over naive methods.

Semiparametric probit regression model with misclassified current status data.

Current status data arise when each subject under study is examined only once at an observation time, and one only knows the failure status of the event of interest at the observation time rather than the exact failure time. Moreover, the obtained failure status is frequently subject to misclassification due to imperfect tests, yielding misclassified current status data. This article conducts regression analysis of such data with the semiparametric probit model, which serves as an important alternative to existing semiparametric models and has recently received considerable attention in failure time data analysis. We consider the nonparametric maximum likelihood estimation and develop an expectation-maximization (EM) algorithm by incorporating the generalized pool-adjacent-violators (PAV) algorithm to maximize the intractable likelihood function. The resulting estimators of regression parameters are shown to be consistent, asymptotically normal, and semiparametrically efficient. Furthermore, the numerical results in simulation studies indicate that the proposed method performs satisfactorily in finite samples and outperforms the naive method that ignores misclassification. We then apply the proposed method to a real dataset on chlamydia infection.

Semiparametric regression analysis of length-biased and partly interval-censored data with application to an AIDS cohort study.

Length-biased data occur often in many scientific fields, including clinical trials, epidemiology surveys and genome-wide association studies, and many methods have been proposed for their analysis under various situations. In this article, we consider the situation where one faces length-biased and partly interval-censored failure time data under the proportional hazards model, for which it does not seem to exist an established method. For the estimation, we propose an efficient nonparametric maximum likelihood method by incorporating the distribution information of the observed truncation times. For the implementation of the method, a flexible and stable EM algorithm via two-stage data augmentation is developed. By employing the empirical process theory, we establish the asymptotic properties of the resulting estimators. A simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well and is more efficient than the conditional likelihood approach. An application to an AIDS cohort study is also provided.

A pairwise pseudo-likelihood approach for regression analysis of left-truncated failure time data with various types of censoring.

BACKGROUND: Failure time data frequently occur in many medical studies and often accompany with various types of censoring. In some applications, left truncation may occur and can induce biased sampling, which makes the practical data analysis become more complicated. The existing analysis methods for left-truncated data have some limitations in that they either focus only on a special type of censored data or fail to flexibly utilize the distribution information of the truncation times for inference. Therefore, it is essential to develop a reliable and efficient method for the analysis of left-truncated failure time data with various types of censoring. METHOD: This paper concerns regression analysis of left-truncated failure time data with the proportional hazards model under various types of censoring mechanisms, including right censoring, interval censoring and a mixture of them. The proposed pairwise pseudo-likelihood estimation method is essentially built on a combination of the conditional likelihood and the pairwise likelihood that eliminates the nuisance truncation distribution function or avoids its estimation. To implement the presented method, a flexible EM algorithm is developed by utilizing the idea of self-consistent estimating equation. A main feature of the algorithm is that it involves closed-form estimators of the large-dimensional nuisance parameters and is thus computationally stable and reliable. In addition, an R package LTsurv is developed. RESULTS: The numerical results obtained from extensive simulation studies suggest that the proposed pairwise pseudo-likelihood method performs reasonably well in practical situations and is obviously more efficient than the conditional likelihood approach as expected. The analysis results of the MHCPS data with the proposed pairwise pseudo-likelihood method indicate that males have significantly higher risk of losing active life than females. In contrast, the conditional likelihood method recognizes this effect as non-significant, which is because the conditional likelihood method often loses some estimation efficiency compared with the proposed method. CONCLUSIONS: The proposed method provides a general and helpful tool to conduct the Cox's regression analysis of left-truncated failure time data under various types of censoring.

Metal Exposure Promotes Colorectal Tumorigenesis via the Aberrant N6-Methyladenosine Modification of ATP13A3.

Element contamination, including that from heavy metals, is associated with gastrointestinal tumorigenesis, but the effects and mechanisms of crucial element exposure associated with colorectal cancer remain unclear. We profiled 56 elements by ICP-MS and used logistic regression, LASSO, BKMR, and GAM to identify colorectal cancer-relevant elements. A series of biochemical experiments were performed to demonstrate the cytotoxicity and the mechanisms of malignant transformation after metal exposure. Using an elementomics approach, we first found that the metal thallium (Tl) was positively correlated with many toxic metals and was associated with a significantly increased risk of colorectal cancer. Acute exposure to Tl induced cytotoxicity and cell death by accelerating the generation of reactive oxygen species and DNA damage. Chronic exposure to Tl led to the inhibition of cell death and thereby induced the malignant transformation of normal colon cells and xenograft tumor formation in nude mice. Furthermore, we describe the first identification of a significant metal quantitative trait locus for the novel colorectal cancer susceptibility locus rs1511625 near ATP13A3. Mechanistically, Tl increased the level of aberrant N6-methyladenosine (m6A) modification of ATP13A3 via the METLL3/METTL14/ALKBH5-ATP13A3 axis to promote colorectal tumorigenesis. This study provides a basis for the development of public health strategies for reducing metal exposure among populations at high risk for colorectal cancer.

Systematic evaluation of the effects of genetic variants on PIWI-interacting RNA expression across 33 cancer types.

PIWI-interacting RNAs (piRNAs) are an emerging class of non-coding RNAs involved in tumorigenesis. Expression quantitative trait locus (eQTL) analysis has been demonstrated to help reveal the genetic mechanism of single nucleotide polymorphisms (SNPs) in cancer etiology. However, there are no databases that have been constructed to provide an eQTL analysis between SNPs and piRNA expression. In this study, we collected genotyping and piRNA expression data for 10 997 samples across 33 cancer types from The Cancer Genome Atlas (TCGA). Using linear regression cis-eQTL analysis with adjustment of appropriate covariates, we identified millions of SNP-piRNA pairs in tumor (76 924 831) and normal (24 431 061) tissues. Further, we performed differential expression and survival analyses, and linked the eQTLs to genome-wide association study (GWAS) data to comprehensively decipher the functional roles of identified cis-piRNA eQTLs. Finally, we developed a user-friendly database, piRNA-eQTL (http://njmu-edu.cn:3838/piRNA-eQTL/), to help users query, browse and download corresponding eQTL results. In summary, piRNA-eQTL could serve as an important resource to assist the research community in understanding the roles of genetic variants and piRNAs in the development of cancers.

Effect of polycyclic aromatic hydrocarbons on cancer risk causally mediated via vitamin D levels.

Polycyclic aromatic hydrocarbons (PAHs) widely exist in environmental substrates and are closely related to individual circulating vitamin D levels and tumorigenesis. Therefore, we proposed to evaluate the relationship between PAH exposure, vitamin D, and the risks for 14 cancer types via a causal inference framework underlying the mediation analysis. We evaluated seven urine monohydroxylated PAH (OH-PAH) and serum vitamin D concentrations of 3306 participants from the National Health and Nutrition Examination Survey between the 2013 and 2016 survey cycles and measured PAH concentrations in 150 subjects from the Nanjing cohort. We observed a significant negative dose-response relationship between increased OH-PAH levels and vitamin D deficiency. Each unit increase in ∑OH-PAHs could lead to a decrease in vitamin D levels (ßadj = -0.98, Padj = 2.05 × 10-4 ). Body mass index could have interaction effects with ∑OH-PAHs and affect vitamin D levels. Coexposure to naphthalene and fluorene metabolites mutually affected vitamin D levels. Notably, vitamin D could causally mediate the relationship between OH-PAHs and nine types of cancer (e.g., colorectal cancer, liver cancers, etc.). This study first emphasizes the causal cascade of individual OH-PAHs, vitamin D, and cancer risk, providing insights into prevention via the environment.