Global burden, risk factors, clinicopathological characteristics, molecular biomarkers and outcomes of microsatellite instability-high gastric cancer.

Microsatellite instability-high (MSI-H) has gained considerable interests since it was approved as a tumor-agnostic biomarker in immunotherapy. However, the reported characteristics of MSI-H gastric cancer (GC) are inconsistent due to the biological complexity. Here, we aim to clarify the prevalence, risk factors, clinicopathological/molecular features and outcomes of MSI-H GC though a comprehensive review on 43246 patients from 134 cohorts. Overall, the proportion of MSI-H GC was 14.5% (95% CI, 13.3%-15.8%). Patients with MSI-H GC were less likely to have Epstein-Barr virus infection. High incidences of MSI-H GC were associated with female, older age, lower gastric body, Lauren intestinal histology, WHO tubular and mucinous subtypes, and early disease stage. Additionally, patients with MSI-H GC harbored more KRAS mutation, PD-L1 positivity, CD8 overexpression, and higher TMB, but less HER2 positivity and TP53 mutation. When treated with conventional strategy, the 5-year survival rates in MSI-H patients (70.3%) and MSI-L/MSS patients (43.7%) were significantly different (p<0.001). Patients with MSI-H GC derived larger benefit from immunotherapy in term of overall survival (pInteraction<0.001) and objective response (pInteraction=0.02). Since the prevalence of MSI-H GC is relatively high and associated with distinct clinicopathological and molecular characteristics, MSI testing should be conducted during standard diagnostical activity. Moreover, giving MSI-H tumors are often diagnosed at early stage and have favorable outcomes, less aggressive treatment strategies may be considered in clinical practice. In summary, this panoramic review may assist in design and/or interpretation of clinical trials, provide references in drug development, and constitute complementary information in drafting the clinical practice guideline.

Maternal fasting glucose levels throughout the pregnancy and risk of adverse birth outcomes in newborns: a birth cohort study in Foshan city, Southern China.

OBJECTIVE: We aimed to investigate the associations between maternal fasting plasma glucose (FPG) levels and glycemic fluctuations during different trimesters and adverse birth outcomes among newborns. METHODS: This cohort study used data from 63 213 pregnant women and their offspring in Foshan city from November 2015 to January 2019. Associations between maternal FPG and glycemic fluctuations during different trimesters and adverse birth outcomes [congenital heart defect (CHD), macrosomia, small/large for gestational age (SGA/LGA), and preterm birth (PTB)] in newborns were estimated using mixed-effects logistic regression models. RESULTS: A total of 45 516 participants accepted at least one FPG test throughout pregnancy, and 7852 of whom had glycemic trajectory data. In the adjusted model, higher maternal FPG throughout the pregnancy was associated with an increased risk of adverse birth outcomes (except for SGA). Each 1 mmol/L increase in maternal FPG during trimester 1 was associated with higher odds of CHD (OR = 1.14 (95% CI: 1.02, 1.26)). The same increase in maternal FPG during trimester 3 was associated with a higher risk of PTB (OR = 1.05 (95% CI: 1.01, 1.10)). Increment of maternal FPG during trimester 2 and trimester 3 was associated with a higher risk of macrosomia and LGA. Increase in FPG throughout the pregnancy was associated with slightly lower odds of SGA. Similar results were observed when analyzing the associations between glycemic fluctuations during different trimesters and adverse birth outcomes. CONCLUSIONS: Our findings indicate higher maternal FPG levels during different trimesters were associated with different adverse birth outcomes, which suggests the importance of glycemic management throughout the pregnancy.

A Large-Scale Genome-Wide Gene-Gene Interaction Study of Lung Cancer Susceptibility in Europeans With a Trans-Ethnic Validation in Asians.

INTRODUCTION: Although genome-wide association studies have been conducted to investigate genetic variation of lung tumorigenesis, little is known about gene-gene (G × G) interactions that may influence the risk of non-small cell lung cancer (NSCLC). METHODS: Leveraging a total of 445,221 European-descent participants from the International Lung Cancer Consortium OncoArray project, Transdisciplinary Research in Cancer of the Lung and UK Biobank, we performed a large-scale genome-wide G × G interaction study on European NSCLC risk by a series of analyses. First, we used BiForce to evaluate and rank more than 58 billion G × G interactions from 340,958 single-nucleotide polymorphisms (SNPs). Then, the top interactions were further tested by demographically adjusted logistic regression models. Finally, we used the selected interactions to build lung cancer screening models of NSCLC, separately, for never and ever smokers. RESULTS: With the Bonferroni correction, we identified eight statistically significant pairs of SNPs, which predominantly appeared in the 6p21.32 and 5p15.33 regions (e.g., rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.17, p = 6.57 × 10-13; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.17, p = 2.43 × 10-13; rs2858859HLA-DQA1 and rs9275572HLA-DQA2, ORinteraction = 1.15, p = 2.84 × 10-13; rs2853668TERT and rs62329694CLPTM1L, ORinteraction = 0.73, p = 2.70 × 10-13). Notably, even with much genetic heterogeneity across ethnicities, three pairs of SNPs in the 6p21.32 region identified from the European-ancestry population remained significant among an Asian population from the Nanjing Medical University Global Screening Array project (rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.13, p = 0.008; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.11, p = 5.23 × 10-4; rs3135369BTNL2 and rs9271300HLA-DQA1, ORinteraction = 0.89, p = 0.006). The interaction-empowered polygenetic risk score that integrated classical polygenetic risk score and G × G information score was remarkable in lung cancer risk stratification. CONCLUSIONS: Important G × G interactions were identified and enriched in the 5p15.33 and 6p21.32 regions, which may enhance lung cancer screening models.

Circulating C-reactive protein increases lung cancer risk: Results from a prospective cohort of UK Biobank.

Chronic inflammation has been associated with the development of lung cancer. In this study, we examined the association between C-reactive protein (CRP) and lung cancer in a prospective cohort study and used Mendelian randomization (MR) to clarify the causality. We included 420 977 participants from the UK Biobank (UKB) in the analyses; 1892 thereof were diagnosed with lung cancer during the follow-up. Hazards ratios (HRs) of CRP concentrations were estimated by Cox proportional hazard models and two approaches of MR analysis were performed. Besides, we added CRP concentrations to epidemiological model of lung cancer to evaluate its prediagnostic role through time-dependent receiver operating characteristic curve analysis. Elevated CRP levels were associated with a 22% increased lung cancer risk per 1 SD increase (HR = 1.22, 95% confidence interval [CI] = 1.18-1.26). Positive associations were observed in small cell lung cancer (HR = 1.21, 95% CI = 1.10-1.33), lung adenocarcinoma (HR = 1.17, 95% CI = 1.11-1.23) and lung squamous cell carcinoma (HR = 1.22, 95% CI = 1.14-1.31). No genetical association of circulating CRP levels and lung cancer risk was observed in MR analysis. When added to a risk model of lung cancer, CRP improved the performance of model as long as 8 years among current smokers (basic model: C-statistic = 0.78 [95% CI = 0.75-0.80]; CRP model: C-statistic = 0.79 [95% CI = 0.76-0.81]; Pnonadjusted  = .003, Padjusted  = .014). Our results did not support the causal association of circulating CRP with lung cancer risk. However, circulating CRP could be a prediagnostic marker of lung cancer as long as 8 years in advance for current smokers.

Diet and Risk of Incident Lung Cancer: A Large Prospective Cohort Study in UK Biobank.

BACKGROUND: Epidemiological evidence remains conflicting regarding diet and risk of lung cancer. OBJECTIVES: We sought to systematically investigate whether dietary factors are associated with the risk of incident lung cancer in the UK Biobank. METHODS: A total of 416,588 participants (54% women) from the UK Biobank were included in the present study. Based on baseline data from FFQs, 3 main dietary patterns were identified by using principal component analysis. Cox proportional hazards models were used to investigate the association of individual food groups and dietary patterns with lung cancer risk. RESULTS: During a median follow-up of 7.13 y, 1782 incident lung cancer cases were documented. The association analysis showed high intake of red meat and processed meat was associated with an increased risk of lung cancer (HRper 50 g/d: 1.36; 95% CI: 1.13, 1.65 for red meat; HRper 25 g/d: 1.30; 95% CI: 1.10, 1.53 for processed meat). However, the consumption of fruits (HRper 100 g/d: 0.90; 95% CI: 0.84, 0.95), vegetables (HRper 100 g/d: 0.89; 95% CI: 0.81, 0.99), breakfast cereals (HRper 50 g/d: 0.81; 95% CI: 0.74, 0.89), and dietary fiber (HRper 5 g/d: 0.76; 95% CI: 0.69, 0.84) was inversely associated with the risk of lung cancer. For the dietary pattern analysis [quartile (Q) comparison], high adherence to the Prudent pattern (HRQ4 compared with Q1: 0.84; 95% CI: 0.73, 0.96) was associated with a lower risk of lung cancer, whereas the Western pattern (HRQ4 compared with Q1: 1.27; 95% CI: 1.11, 1.46) was associated with a higher risk of lung cancer. CONCLUSIONS: Our study indicated that a diet characterized by high intake of fruits, vegetables, breakfast cereals, and dietary fiber, as well as low intake of red meat and processed meat, was associated with a lower risk of lung cancer.

Genome-wide gene-smoking interaction study identified novel susceptibility loci for non-small cell lung cancer in Chinese populations.

Gene-smoking interactions play important roles in the development of non-small cell lung cancer (NSCLC). To identify single-nucleotide polymorphisms (SNPs) that modify the association of smoking behavior with NSCLC risk, we conducted a genome-wide gene-smoking interaction study in Chinese populations. The genome-wide interaction analysis between SNPs and smoking status (ever- versus never-smokers) was carried out using genome-wide association studies of NSCLC, which included 13 327 cases and 13 328 controls. Stratified analysis by histological subtypes was also conducted. We used a genome-wide significance threshold of 5 × 10-8 for identifying significant gene-smoking interactions and 1 × 10-6 for identifying suggestive results. Functional annotation was performed to identify potential functional SNPs and target genes. We identified three novel loci with significant or suggestive gene-smoking interaction. For NSCLC, the interaction between rs2746087 (20q11.23) and smoking status reached genome-wide significance threshold [odds ratio (OR) = 0.63, 95% confidence interval (CI): 0.54-0.74, P = 3.31 × 10-8], and the interaction between rs11912498 (22q12.1) and smoking status reached suggestive significance threshold (OR = 0.72, 95% CI: 0.63-0.82, P = 8.10 × 10-7). Stratified analysis by histological subtypes identified suggestive interactions between rs459724 (5q11.2) and smoking status (OR = 0.61, 95% CI: 0.51-0.73, P = 7.55 × 10-8) in the risk of lung squamous cell carcinoma. Functional annotation indicated that both classic and novel biological processes, including nicotine addiction and airway clearance, may modulate the susceptibility to NSCLC. These novel loci provide new insights into the biological mechanisms underlying NSCLC risk. Independent replication in large-scale studies is needed and experimental studies are warranted to functionally validate these associations.

Air Pollution, Genetic Factors, and the Risk of Lung Cancer: A Prospective Study in the UK Biobank.

Rationale: Both genetic and environmental factors contribute to lung cancer, but the degree to which air pollution modifies the impact of genetic susceptibility on lung cancer remains unknown. Objectives: To investigate whether air pollution and genetic factors jointly contribute to incident lung cancer. Methods: We analyzed data from 455,974 participants (53% women) without previous cancer at baseline in the UK Biobank. The concentrations of particulate matter (PM) (PM ⩽2.5 µm in aerodynamic diameter [PM2.5], coarse PM between 2.5 µm and 10 µm in aerodynamic diameter [PMcoarse], and PM ⩽10 µm in aerodynamic diameter [PM10]), nitrogen dioxide (NO2), and nitrogen oxides (NOx) were estimated by using land-use regression models, and the association between air pollutants and incident lung cancer was investigated by using a Cox proportional hazard model. Furthermore, we constructed a polygenic risk score and evaluated whether air pollutants modified the effect of genetic susceptibility on the development of lung cancer. Measurements and Main Results: The results showed significant associations between the risk of lung cancer and PM2.5 (hazard ratio [HR], 1.63; 95% confidence interval [CI], 1.33-2.01; per 5 µg/m3), PM10 (HR, 1.53; 95% CI, 1.20-1.96; per 10 µg/m3), NO2 (HR, 1.10; 95% CI, 1.05-1.15; per 10 µg/m3), and NOx (HR, 1.13; 95% CI, 1.07-1.18; per 20 µg/m3). There were additive interactions between air pollutants and the genetic risk. Compared with participants with low genetic risk and low air pollution exposure, those with high air pollution exposure and high genetic risk had the highest risk of lung cancer (PM2.5: HR, 1.71; 95% CI, 1.45-2.02; PM10: HR, 1.77; 95% CI, 1.50-2.10; NO2: HR, 1.77; 95% CI, 1.42-2.22; NOx: HR, 1.67; 95% CI, 1.43-1.95). Conclusions: Long-term exposure to air pollution may increase the risk of lung cancer, especially in those with high genetic risk.

Relationships between sleep traits and lung cancer risk: a prospective cohort study in UK Biobank.

STUDY OBJECTIVES: To prospectively investigate the association between sleep traits and lung cancer risk, accounting for the interactions with genetic predisposition of lung cancer. METHODS: We included 469 691 individuals free of lung cancer at recruitment from UK Biobank, measuring sleep behaviors with a standardized questionnaire and identifying incident lung cancer cases through linkage to national cancer and death registries. We estimated multivariable-adjusted hazard ratios (HRs) for lung cancer (2177 incident cases) across four sleep traits (sleep duration, chronotype, insomnia, and snoring) and examined the interaction and joint effects with a lung cancer polygenic risk score. RESULTS: A U-shaped association was observed for sleep duration and lung cancer risk, with an 18% higher risk (95% confidence interval [CI]: 1.07 to 1.30) for short sleepers and a 17% higher risk (95% CI: 1.02 to 1.34) for long sleepers compared with normal sleepers (7-8 h/day). Evening preference was associated with elevated lung cancer risk compared with morning preference (HR: 1.25; 95% CI: 1.07 to 1.46), but no association was found for insomnia or snoring. Compared with participants with favorable sleep traits and low genetic risk, those with both unfavorable sleep duration (<7 hours or >8 hours) or evening preference and high genetic risk showed the greatest lung cancer risk (HRsleep duration: 1.83; 95% CI: 1.47 to 2.27; HRchronotype: 1.85; 95% CI: 1.34 to 2.56). CONCLUSIONS: Both unfavorable sleep duration and evening chronotype were associated with increased lung cancer incidence, especially for those with low to moderate genetic risk. These results indicate that sleep behaviors as modifiable risk factors may have potential implications for lung cancer risk.

Integrated gene-based and pathway analyses using UK Biobank data identify novel genes for chronic respiratory diseases.

BACKGROUND: Chronic respiratory diseases have become a non-negligible cause of death globally. Although smoking and environmental exposures are primary risk factors for chronic respiratory diseases, genetic factors also play an important role in determining individual's susceptibility to diseases. Here we performed integrated gene-based and pathway analyses to systematically illuminate the heritable characteristics of chronic respiratory diseases. METHODS: UK (United Kingdom) Biobank is a very large, population-based prospective study with over 500,000 participants, established to allow detailed investigations of the genetic and nongenetic determinants of the diseases. Utilizing the GWAS-summarized data downloaded from UK Biobank, we conducted gene-based analysis to obtain associations of susceptibility genes with asthma, chronic obstructive pulmonary disease(COPD) and pneumonia using FUSION and MAGMA software. Across the identified susceptibility regions, functional annotation integrating multiple functional data sources was performed to explore potential regulatory mechanisms with INQUISIT algorithm. To further detect the biological process involved in the development of chronic respiratory diseases, we undertook pathway enrichment analysis with the R package (clusterProfiler). RESULTS: A total of 195 susceptibility genes were identified significantly associated with chronic respiratory diseases (Pbonferroni < 0.05), and 24/195 located out of known susceptibility regions (e.g. WDPCP in 2p15). Within the identified susceptibility regions, functional annotation revealed an aggregation of credible variants in promoter-like and enhancer-like histone modification regions and such regulatory mechanisms were specific to lung tissues. Furthermore, 110 genes with INQUISIT score ≥1 may influence diseases susceptibility through exerting effects on coding sequences, proximal promoter and distal enhancer regulations. Pathway enrichment results showed that these genes were enriched in immune-related processes and nicotinic acetylcholine receptors pathways. CONCLUSIONS: This study implemented an integrated gene-based and pathway strategy to explore the underlying biological mechanisms and our findings may serve as promising targets for future clinical treatments of chronic respiratory diseases.

Cross-Cancer Pleiotropic Analysis Reveals Novel Susceptibility Loci for Lung Cancer.

Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with cancer risk, several of which have shown pleiotropic effects across cancers. Therefore, we performed a systematic cross-cancer pleiotropic analysis to detect the effects of GWAS-identified variants from non-lung cancers on lung cancer risk in 12,843 cases and 12,639 controls from four lung cancer GWASs. The overall association between variants in each cancer and risk of lung cancer was explored using sequential kernel association test (SKAT) analysis. For single variant analysis, we combined the result of specific study using fixed-effect meta-analysis. We performed functional exploration of significant associations based on features from public databases. To further detect the biological mechanism underlying identified observations, pathway enrichment analysis were conducted with R package "clusterProfiler." SNP-set analysis revealed the overall associations between variants of 8 cancer types and lung cancer risk. Single variant analysis identified 6 novel SNPs related to lung cancer risk after multiple correction (P fdr < 0.10), including rs1707302 (1p34.1, OR = 0.93, 95% CI: 0.90-0.97, P = 7.60 × 10-4), rs2516448 (6p21.33, OR = 1.07, 95% CI: 1.03-1.11, P = 1.00 × 10-3), rs3869062 (6p22.1, OR = 0.91, 95% CI: 0.86-0.96, P = 7.10 × 10-4), rs174549 (11q12.2, OR = 0.90, 95% CI: 0.87-0.94, P = 1.00 × 10-7), rs7193541 (16q23.1, OR = 0.93, 95% CI: 0.90-0.96, P = 1.20 × 10-4), and rs8064454 (17q12, OR = 1.07, 95% CI: 1.03-1.11, P = 4.30 × 10-4). The eQTL analysis and functional annotation suggested that these variants might modify lung cancer susceptibility through regulating the expression of related genes. Pathway enrichment analysis showed that genes modulated by these variants play important roles in cancer carcinogenesis. Our findings demonstrate the pleiotropic associations between non-lung cancer susceptibility loci and lung cancer risk, providing important insights into the shared mechanisms of carcinogenesis across cancers.