CYP2A6 Activity and Cigarette Consumption Interact in Smoking-Related Lung Cancer Susceptibility.

Cigarette smoke, containing both nicotine and carcinogens, causes lung cancer. However, not all smokers develop lung cancer, highlighting the importance of the interaction between host susceptibility and environmental exposure in tumorigenesis. Here, we aimed to delineate the interaction between metabolizing ability of tobacco carcinogens and smoking intensity in mediating genetic susceptibility to smoking-related lung tumorigenesis. Single-variant and gene-based associations of 43 tobacco carcinogen-metabolizing genes with lung cancer were analyzed using summary statistics and individual-level genetic data, followed by causal inference of Mendelian randomization, mediation analysis, and structural equation modeling. Cigarette smoke-exposed cell models were used to detect gene expression patterns in relation to specific alleles. Data from the International Lung Cancer Consortium (29,266 cases and 56,450 controls) and UK Biobank (2,155 cases and 376,329 controls) indicated that the genetic variant rs56113850 C>T located in intron 4 of CYP2A6 was significantly associated with decreased lung cancer risk among smokers (OR = 0.88, 95% confidence interval = 0.85-0.91, P = 2.18 × 10-16), which might interact (Pinteraction = 0.028) with and partially be mediated (ORindirect = 0.987) by smoking status. Smoking intensity accounted for 82.3% of the effect of CYP2A6 activity on lung cancer risk but entirely mediated the genetic effect of rs56113850. Mechanistically, the rs56113850 T allele rescued the downregulation of CYP2A6 caused by cigarette smoke exposure, potentially through preferential recruitment of transcription factor helicase-like transcription factor. Together, this study provides additional insights into the interplay between host susceptibility and carcinogen exposure in smoking-related lung tumorigenesis. SIGNIFICANCE: The causal pathway connecting CYP2A6 genetic variability and activity, cigarette consumption, and lung cancer susceptibility in smokers highlights the need for behavior modification interventions based on host susceptibility for cancer prevention.

Zn2+ @Polyvinylpyrrolidone and Urushiol Preparation of Nanofibrous Membranes and Their Synergistic Effect.

In this study, lacquer is gathered from a lacquer tree and rotary evaporation is used to remove impurities to obtain urushiol. Next, 10 mL of anhydrous ethanol serves as the solvent for blending polyvinylpyrrolidone (PVP) at a specified content (0.7 g and 0.2-0.7 g urushiol) to form an electrospinning solution. Electrospinning is carried out with a voltage of 18 kV to prepare PVP/urushiol nanofibrous membranes. At a ratio of 7/4, the PVP/urushiol nanofibrous membranes are not eroded in 98% sulfuric acid and these membranes also demonstrate a 50-60% antibacterial effect against Staphylococcus aureus and Escherichia coli. Moreover, the antibacterial effect can be boosted to 98% with the incorporation of zinc ions. The results indicate that anhydrous ethanol can remove the sensitization of urushiol from PVP/urushiol membranes. Furthermore, animal test results indicate that when rats are in contact with PVP/urushiol anhydrous ethanol for 48 h, their skins are free from dark brown skin allergy. The presence of PVP eliminates the sensitization of urushiol, and the nanofibrous membranes demonstrate low toxicity. Hence, urushiol is the only natural material that enables PVP to withstand 98% sulfuric acid as well as acquire hydrolyzability, thereby qualify PVP as a medical material.

Semi-supervised calibration of noisy event risk (SCANER) with electronic health records.

OBJECTIVE: Electronic health records (EHR), containing detailed longitudinal clinical information on a large number of patients and covering broad patient populations, open opportunities for comprehensive predictive modeling of disease progression and treatment response. However, since EHRs were originally constructed for administrative purposes not for research, in the EHR-linked studies, it is often not feasible to capture reliable information for analytical variables, especially in the survival setting, when both accurate event status and event times are needed for model building. For example, progression-free survival (PFS), a commonly used survival outcome for cancer patients, often involves complex information embedded in free-text clinical notes and cannot be extracted reliably. Proxies of PFS time such as time to the first mention of progression in the notes are at best good approximations to the true event time. This leads to difficulty in efficiently estimating event rates for an EHR patient cohort. Estimating survival rates based on error-prone outcome definitions can lead to biased results and hamper the power in the downstream analysis. On the other hand, extracting accurate event time information via manual annotation is time and resource intensive. The objective of this study is to develop a calibrated survival rate estimator using noisy outcomes from EHR data. MATERIALS AND METHODS: In this paper, we propose a two-stage semi-supervised calibration of noisy event rate (SCANER) estimator that can effectively overcome censoring induced dependency and attains more robust performance (i.e., not sensitive to misspecification of the imputation model) by fully utilizing both a small-labeled set of gold-standard survival outcomes annotated via manual chart review and a set of proxy features automatically captured via EHR in the unlabeled set. We validate the SCANER estimator by estimating the PFS rates for a virtual cohort of lung cancer patients from one large tertiary care center and the ICU-free survival rates for COVID patients from two large tertiary care centers. RESULTS: In terms of survival rate estimates, the SCANER had very similar point estimates compared to the complete-case Kaplan Meier estimator. On the other hand, other benchmark methods for comparison, which fail to account for the induced dependency between event time and the censoring time conditioning on surrogate outcomes, produced biased results across all three case studies. In terms of standard errors, the SCANER estimator was more efficient than the KM estimator, with up to 50% efficiency gain. CONCLUSION: The SCANER estimator achieves more efficient, robust, and accurate survival rate estimates compared to existing approaches. This promising new approach can also improve the resolution (i.e., granularity of event time) by using labels conditioning on multiple surrogates, particularly among less common or poorly coded conditions.

Temporal Trends in Clinical Evidence of 5-Year Survival Within Electronic Health Records Among Patients With Early-Stage Colon Cancer Managed With Laparoscopy-Assisted Colectomy vs Open Colectomy.

Importance: Temporal shifts in clinical knowledge and practice need to be adjusted for in treatment outcome assessment in clinical evidence. Objective: To use electronic health record (EHR) data to (1) assess the temporal trends in treatment decisions and patient outcomes and (2) emulate a randomized clinical trial (RCT) using EHR data with proper adjustment for temporal trends. Design, Setting, and Participants: The Clinical Outcomes of Surgical Therapy (COST) Study Group Trial assessing overall survival of patients with stages I to III early-stage colon cancer was chosen as the target trial. The RCT was emulated using EHR data of patients from a single health care system cohort who underwent colectomy for early-stage colon cancer from January 1, 2006, to December 31, 2017, and were followed up to January 1, 2020, from Mass General Brigham. Analyses were conducted from December 2, 2019, to January 24, 2022. Exposures: Laparoscopy-assisted colectomy (LAC) vs open colectomy (OC). Main Outcomes and Measures: The primary outcome was 5-year overall survival. To address confounding in the emulation, pretreatment variables were selected and adjusted. The temporal trends were adjusted by stratification of the calendar year when the colectomies were performed with cotraining across strata. Results: A total of 943 patients met key RCT eligibility criteria in the EHR emulation cohort, including 518 undergoing LAC (median age, 63 [range, 20-95] years; 268 [52%] women; 121 [23%] with stage I, 165 [32%] with stage II, and 232 [45%] with stage III cancer; 32 [6%] with colon adhesion; 278 [54%] with right-sided colon cancer; 18 [3%] with left-sided colon cancer; and 222 [43%] with sigmoid colon cancer) and 425 undergoing OC (median age, 65 [range, 28-99] years; 223 [52%] women; 61 [14%] with stage I, 153 [36%] with stage II, and 211 [50%] with stage III cancer; 39 [9%] with colon adhesion; 202 [47%] with right-sided colon cancer; 39 [9%] with left-sided colon cancer; and 201 [47%] with sigmoid colon cancer). Tests for temporal trends in treatment assignment (χ2 = 60.3; P < .001) and overall survival (χ2 = 137.2; P < .001) were significant. The adjusted EHR emulation reached the same conclusion as the RCT: LAC is not inferior to OC in overall survival rate with risk difference at 5 years of -0.007 (95% CI, -0.070 to 0.057). The results were consistent for stratified analysis within each temporal period. Conclusions and Relevance: These findings suggest that confounding bias from temporal trends should be considered when conducting clinical evidence studies with long time spans. Stratification of calendar time and cotraining of models is one solution. With proper adjustment, clinical evidence may supplement RCTs in the assessment of treatment outcome over time.

Preparation and Characterization of PEDOT:PSS/TiO2 Micro/Nanofiber-Based Gas Sensors.

In this study, we employed electrospinning technology and in situ polymerization to prepare wearable and highly sensitive PVP/PEDOT:PSS/TiO2 micro/nanofiber gas sensors. PEDOT, PEDOT:PSS, and TiO2 were prepared via in situ polymerization and tested for characteristic peaks using energy-dispersive X-ray spectroscopy (EDS) and Fourier transform infrared spectroscopy (FT-IR), then characterized using a scanning electron microscope (SEM), a four-point probe resistance measurement, and a gas sensor test system. The gas sensitivity was 3.46-12.06% when ethanol with a concentration between 12.5 ppm and 6250 ppm was measured; 625 ppm of ethanol was used in the gas sensitivity measurements for the PEDOT/composite conductive woven fabrics, PVP/PEDOT:PSS nanofiber membranes, and PVP/PEDOT:PSS/TiO2 micro/nanofiber gas sensors. The latter exhibited the highest gas sensitivity, which was 5.52% and 2.35% greater than that of the PEDOT/composite conductive woven fabrics and PVP/PEDOT:PSS nanofiber membranes, respectively. In addition, the influence of relative humidity on the performance of the PVP/PEDOT:PSS/TiO2 micro/nanofiber gas sensors was examined. The electrical sensitivity decreased with a decrease in ethanol concentration. The gas sensitivity exhibited a linear relationship with relative humidity lower than 75%; however, when the relative humidity was higher than 75%, the gas sensitivity showed a highly non-linear correlation. The test results indicated that the PVP/PEDOT:PSS/TiO2 micro/nanofiber gas sensors were flexible and highly sensitive to gas, qualifying them for use as a wearable gas sensor platform at room temperature. The proposed gas sensors demonstrated vital functions and an innovative design for the development of a smart wearable device.

Association between circulating vitamin E and ten common cancers: evidence from large-scale Mendelian randomization analysis and a longitudinal cohort study.

BACKGROUND: The association between vitamin E and cancer risk has been widely investigated by observational studies, but the findings remain inconclusive. Here, we aimed to evaluate the causal effect of circulating vitamin E on the risk of ten common cancers, including bladder, breast, colorectal, esophagus, lung, oral and pharynx, ovarian, pancreatic, prostate, and kidney cancer. METHODS: A Mendelian randomization (MR) analytic framework was applied to data from a cancer-specific genome-wide association study (GWAS) comprising a total of 297,699 cancer cases and 304,736 controls of European ancestry. Three genetic instrumental variables associated with circulating vitamin E were selected. Summary statistic-based methods of inverse variance weighting (IVW) and likelihood-based approach, as well as the individual genotyping-based method of genetic risk score (GRS) were used. Multivariable IVW analysis was further performed to control for potential confounding effects. Furthermore, the UK Biobank cohort was used as external validation, supporting 355,543 European participants (incident cases ranged from 437 for ovarian cancer to 4882 for prostate cancer) for GRS-based estimation of circulating vitamin E, accompanied by a one-sample MR analysis of dietary vitamin E intake underlying the time-to-event analytic framework. RESULTS: Specific to cancer GWAS, we found that circulating vitamin E was significantly associated with increased bladder cancer risk (odds ratios [OR]IVW = 6.23, PIVW = 3.05×10-3) but decreased breast cancer risk (ORIVW = 0.68, PIVW = 8.19×10-3); however, the significance of breast cancer was dampened (Pmultivariable IVW > 0.05) in the subsequent multivariable MR analysis. In the validation stage of the UK Biobank cohort, we did not replicate convincing causal effects of genetically predicted circulating vitamin E concentrations and dietary vitamin E intake on the risk of ten cancers. CONCLUSIONS: This large-scale population study upon data from cancer-specific GWAS and a longitudinal biobank cohort indicates plausible non-causal associations between circulating vitamin E and ten common cancers in the European populations. Further studies regarding ancestral diversity are warranted to validate such causal associations.

High-density lipoprotein, low-density lipoprotein and triglyceride levels and upper gastrointestinal cancers risk: a trans-ancestry Mendelian randomization study.

OBJECTIVE: This study was conducted to explore the causal associations of high-density lipoprotein (HDL), low-density lipoprotein (LDL) and triglyceride (TG) with the risk of upper gastrointestinal cancers (esophageal cancer [EC] and gastric cancer [GC]). METHODS: A total of 5623 Chinese and 4133 Europeans afforded the individual-level genotyping data, and 203,608 Japanese from Biobank Japan project and 393,926 Europeans from UK Biobank supported summary statistics of cancer genetic associations. Mendelian randomization (MR) analyses, including weighted genetic risk scores (wGRSs), inverse-variance weighted (IVW), weighted median and Egger-regression, were utilized to evaluate the causal effects of three blood lipids on upper gastrointestinal cancers risk. RESULTS: There was no significantly causal relationships between three blood lipids and EC or GC risk among Chinese or Europeans but a potential causal association between TG and GC risk among Japanese (IVW: odds ratio [OR] = 1.11, P = 0.034; Phet = 0.679). In stratified subgroups, higher genetically predicted TG levels were causally associated with an increased risk of GC among Chinese males (wGRS: OR = 1.61, P = 0.021; IVW: OR = 1.57, P = 0.009; Phet = 0.653) and Japanese females (IVW: OR = 1.33, P = 0.024; Phet = 0.378). CONCLUSION: This trans-ancestry MR study suggested null significant causality between serum HDL, LDL or TG and the risk of upper gastrointestinal cancers among Chinese and Europeans, but provided evidence for a causal role of TG involved in GC etiology in Japanese (especially females), which would support a prevention guide for high-risk groups of GC. Further research with more comprehensive information is needed to explore the underlying mechanism.

Postdiagnosis BMI Change Is Associated with Non-Small Cell Lung Cancer Survival.

BACKGROUND: Body mass index (BMI) change after a lung cancer diagnosis has been associated with non-small cell lung cancer (NSCLC) survival. This study aimed to quantify the association based on a large-scale observational study. METHODS: Included in the study were 7,547 patients with NSCLC with prospectively collected BMI data from Massachusetts General Hospital and Brigham and Women's Hospital/Dana-Farber Cancer Institute. Cox proportional hazards regression with time-dependent covariates was used to estimate effect of time-varying postdiagnosis BMI change rate (% per month) on overall survival (OS), stratified by clinical subgroups. Spline analysis was conducted to quantify the nonlinear association. A Mendelian Randomization (MR) analysis with a total of 3,495 patients further validated the association. RESULTS: There was a J-shape association between postdiagnosis BMI change and OS among patients with NSCLC. Specifically, a moderate BMI decrease [0.5-2.0; HR = 2.45; 95% confidence interval (CI), 2.25-2.67] and large BMI decrease (≥2.0; HR = 4.65; 95% CI, 4.15-5.20) were strongly associated with worse OS, whereas moderate weight gain (0.5-2.0) reduced the risk for mortality (HR = 0.78; 95% CI, 0.68-0.89) and large weight gain (≥2.0) slightly increased the risk of mortality without reaching statistical significance (HR = 1.10; 95% CI, 0.86-1.42). MR analyses supported the potential causal roles of postdiagnosis BMI change in survival. CONCLUSIONS: This study indicates that BMI change after diagnosis was associated with mortality risk. IMPACT: Our findings, which reinforce the importance of postdiagnosis BMI surveillance, suggest that weight loss or large weight gain may be unwarranted.

Preparation of Ag@ZIF-8@PP Melt-Blown Nonwoven Fabrics: Air Filter Efficacy and Antibacterial Effect.

Serving as matrices, polypropylene (PP) melt-blown nonwoven fabrics with 4% electrostatic electret masterbatch were incorporated with a 6%, 10%, 14%, or 18% phosphorus-nitrogen flame retardant. The test results indicate that the incorporation of the 6% flame retardant prevented PP melt-blown nonwoven fabrics from generating a molten drop, which, in turn, hampers the secondary flame source while increasing the fiber diameter ratio. With a combination of 4% electrostatic electret masterbatch and the 6% flame retardant, PP melt-blown nonwoven fabrics were grafted with ZIF-8 and Ag@ZIF-8. The antibacterial effect of ZIF-8 and Ag@ZIF-8 was 40% and 85%, respectively. Moreover, four reinforcing measures were used to provide Ag@ZIF-8 PP melt-blown nonwoven fabrics with synergistic effects, involving lamination, electrostatic electret, and Ag@ZIF-8 grafting, as well as a larger diameter because of the addition of phosphorus-nitrogen flame retardants. As specified in the GB2626-2019 and JIS T8151-2018 respiratory resistance test standards, with a constant 60 Pa, Ag@ZIF-8 PP melt-blown nonwoven membranes were tested for a filter effect against PM 0.3. When the number of lamination layers was five, the filter effect was 88 ± 2.2%, and the respiratory resistance was 51 ± 3.6 Pa.

Genomic-Metabolomic Associations Support the Role of LIPC and Glycerophospholipids in Age-Related Macular Degeneration.

PURPOSE: Large-scale genome-wide association studies (GWAS) have reported important single nucleotide polymorphisms (SNPs) with significant associations with age-related macular degeneration (AMD). However, their role in disease development remains elusive. This study aimed to assess SNP-metabolite associations (i.e., metabolite quantitative trait loci [met-QTL]) and to provide insights into the biological mechanisms of AMD risk SNPs. DESIGN: Cross-sectional multicenter study (Boston, Massachusetts, and Coimbra, Portugal). PARTICIPANTS: Patients with AMD (n = 388) and control participants (n = 98) without any vitreoretinal disease (> 50 years). METHODS: Age-related macular degeneration grading was performed using color fundus photographs according to the Age-Related Eye Disease Study classification scheme. Fasting blood samples were collected and evaluated with mass spectrometry for metabolomic profiling and Illumina OmniExpress for SNPs profiling. Analyses of met-QTL of endogenous metabolites were conducted using linear regression models adjusted for age, gender, smoking, 10 metabolite principal components (PCs), and 10 SNP PCs. Additionally, we analyzed the cumulative effect of AMD risk SNPs on plasma metabolites by generating genetic risk scores and assessing their associations with metabolites using linear regression models, accounting for the same covariates. Modeling was performed first for each cohort, and then combined by meta-analysis. Multiple comparisons were accounted for using the false discovery rate (FDR). MAIN OUTCOME MEASURES: Plasma metabolite levels associated with AMD risk SNPs. RESULTS: After quality control, data for 544 plasma metabolites were included. Meta-analysis of data from all individuals (AMD patients and control participants) identified 28 significant met-QTL (ß = 0.016-0.083; FDR q-value < 1.14 × 10-2), which corresponded to 5 metabolites and 2 genes: ASPM and LIPC. Polymorphisms in the LIPC gene were associated with phosphatidylethanolamine metabolites, which are glycerophospholipids, and polymorphisms in the ASPM gene with branched-chain amino acids. Similar results were observed when considering only patients with AMD. Genetic risk score-metabolite associations further supported a global impact of AMD risk SNPs on the plasma metabolome. CONCLUSIONS: This study demonstrated that genomic-metabolomic associations can provide insights into the biological relevance of AMD risk SNPs. In particular, our results support that the LIPC gene and the glycerophospholipid metabolic pathway may play an important role in AMD, thus offering new potential therapeutic targets for this disease.

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Identify and Estimate Survival in a Longitudinal Cohort of Patients With Lung Cancer.

Importance: Electronic health records (EHRs) provide a low-cost means of accessing detailed longitudinal clinical data for large populations. A lung cancer cohort assembled from EHR data would be a powerful platform for clinical outcome studies. Objective: To investigate whether a clinical cohort assembled from EHRs could be used in a lung cancer prognosis study. Design, Setting, and Participants: In this cohort study, patients with lung cancer were identified among 76 643 patients with at least 1 lung cancer diagnostic code deposited in an EHR in Mass General Brigham health care system from July 1988 to October 2018. Patients were identified via a semisupervised machine learning algorithm, for which clinical information was extracted from structured and unstructured data via natural language processing tools. Data completeness and accuracy were assessed by comparing with the Boston Lung Cancer Study and against criterion standard EHR review results. A prognostic model for non-small cell lung cancer (NSCLC) overall survival was further developed for clinical application. Data were analyzed from March 2019 through July 2020. Exposures: Clinical data deposited in EHRs for cohort construction and variables of interest for the prognostic model were collected. Main Outcomes and Measures: The primary outcomes were the performance of the lung cancer classification model and the quality of the extracted variables; the secondary outcome was the performance of the prognostic model. Results: Among 76 643 patients with at least 1 lung cancer diagnostic code, 42 069 patients were identified as having lung cancer, with a positive predictive value of 94.4%. The study cohort consisted of 35 375 patients (16 613 men [47.0%] and 18 756 women [53.0%]; 30 140 White individuals [85.2%], 1040 Black individuals [2.9%], and 857 Asian individuals [2.4%]) after excluding patients with lung cancer history and less than 14 days of follow-up after initial diagnosis. The median (interquartile range) age at diagnosis was 66.7 (58.4-74.1) years. The area under the receiver operating characteristic curves of the prognostic model for overall survival with NSCLC were 0.828 (95% CI, 0.815-0.842) for 1-year prediction, 0.825 (95% CI, 0.812-0.836) for 2-year prediction, 0.814 (95% CI, 0.800-0.826) for 3-year prediction, 0.814 (95% CI, 0.799-0.828) for 4-year prediction, and 0.812 (95% CI, 0.798-0.825) for 5-year prediction. Conclusions and Relevance: These findings suggest the feasibility of assembling a large-scale EHR-based lung cancer cohort with detailed longitudinal clinical measurements and that EHR data may be applied in cancer progression with a set of generalizable approaches.

Circular RNAs in body fluids as cancer biomarkers: the new frontier of liquid biopsies.

Cancer is a leading cause of death worldwide, particularly because of its high mortality rate in patients who are diagnosed at late stages. Conventional biomarkers originating from blood are widely used for cancer diagnosis, but their low sensitivity and specificity limit their widespread application in cancer screening among the general population. Currently, emerging studies are exploiting novel, highly-accurate biomarkers in human body fluids that are obtainable through minimally invasive techniques, which is defined as liquid biopsy. Circular RNAs (circRNAs) are a newly discovered class of noncoding RNAs generated mainly by pre-mRNA splicing. Following the rapid development of high-throughput transcriptome analysis techniques, numerous circRNAs have been recognized to exist stably and at high levels in body fluids, including plasma, serum, exosomes, and urine. CircRNA expression patterns exhibit distinctly differences between patients with cancer and healthy controls, suggesting that circRNAs in body fluids potentially represent novel biomarkers for monitoring cancer development and progression. In this study, we summarized the expression of circRNAs in body fluids in a pan-cancer dataset and characterized their clinical applications in liquid biopsy for cancer diagnosis and prognosis. In addition, a user-friendly web interface was developed to visualize each circRNA in fluids ( https://mulongdu.shinyapps.io/circrnas_in_fluids/ ).

Tumor evolutionary trajectories during the acquisition of invasiveness in early stage lung adenocarcinoma.

The evolutionary trajectories of early lung adenocarcinoma (LUAD) have not been fully elucidated. We hypothesize that genomic analysis between pre-invasive and invasive components will facilitate the description of LUAD evolutionary patterns. We micro-dissect malignant pulmonary nodules (MPNs) into paired pre-invasive and invasive components for panel-genomic sequencing and recognize three evolutionary trajectories. Evolutionary mode 1 (EM1) demonstrates none of the common driver events between paired components, but another two modes, EM2A and EM2B, exhibit critical private alterations restricted to pre-invasive and invasive components, respectively. When ancestral clones harbor EGFR mutations, truncal mutation abundance significantly decrease after the acquisition of invasiveness, which may be associated with the intratumoral accumulation of infiltrated B cells. Harboring EGFR mutations is critical to the selective pressure and further impacts the prognosis. Our findings extend the understanding of evolutionary trajectories during invasiveness acquisition in early LUAD.

Epigenomic analysis of 5-hydroxymethylcytosine (5hmC) reveals novel DNA methylation markers for lung cancers.

BACKGROUND: DNA methylation at the fifth position of cytosine (5mC) is a common epigenetic alteration affecting a range of cellular processes. In recent years, 5-hydroxymethylcytosine (5hmC), an oxidized form of 5mC, has risen broad interests as a potential biomarker for lung cancer diagnosis and survival. METHODS: We analyzed the epigenome-wide 5hmC profiles of paired lung tumor and adjacent normal tissues, using the TET-Assisted Bisulfite (TAB) array - Infinium MethylationEPIC BeadChip (EPIC) approach. The differentially methylated CpG sites were identified, and the biological relevance of 5hmC was assessed by differential methylation regions (DMR) analysis and gene set analysis (GSA). RESULTS: We observed global hypomethylation of 5hmC comparing tumor to normal tissues, and hypermethylated 5hmC were enriched in CpG islands and gene upstream. Comparison of 5hmC and 5modC (total methylation: 5mC + 5hmC) profiling showed low correlation, and only a small proportion of the significant 5hmC loci overlapped with the significant total methylation loci. GSA analysis suggested that 5hmC was mainly involved in biological processes such as cellular process, biological regulation, and metabolic process. CONCLUSION: This is the first study to analyze the epigenome-wide 5hmC profiles among paired lung tumor and normal tissues. We observed global hypomethylation of 5hmC in lung cancers, and hypermethylated 5hmC enriched in CpG islands and gene upstream. We found that the genome-wide 5hmC levels do not correlate with the total methylation, and the GSA suggested different biological functions of 5hmC compared to 5modC. Overall, our results demonstrate the potential of 5hmC as a novel biomarker for lung cancer.

A multi-omic study reveals BTG2 as a reliable prognostic marker for early-stage non-small cell lung cancer.

B-cell translocation gene 2 (BTG2) is a tumour suppressor protein known to be downregulated in several types of cancer. In this study, we investigated a potential role for BTG2 in early-stage non-small cell lung cancer (NSCLC) survival. We analysed BTG2 methylation data from 1230 early-stage NSCLC patients from five international cohorts, as well as gene expression data from 3038 lung cancer cases from multiple cohorts. Three CpG probes (cg01798157, cg06373167, cg23371584) that detected BTG2 hypermethylation in tumour tissues were associated with lower overall survival. The prognostic model based on methylation could distinguish patient survival in the four cohorts [hazard ratio (HR) range, 1.51-2.21] and the independent validation set (HR = 1.85). In the expression analysis, BTG2 expression was positively correlated with survival in each cohort (HR range, 0.28-0.68), which we confirmed with meta-analysis (HR = 0.61, 95% CI 0.54-0.68). The three CpG probes were all negatively correlated with BTG2 expression. Importantly, an integrative model of BTG2 methylation, expression and clinical information showed better predictive ability in the training set and validation set. In conclusion, the methylation and integrated prognostic signatures based on BTG2 are stable and reliable biomarkers for early-stage NSCLC. They may have new applications for appropriate clinical adjuvant trials and personalized treatments in the future.