Search

Perspective on This Article from Anthropometric Measures, Physical Activity, and Risk of Glioma and Meningioma in a Large Prospective Cohort Study

age at allergy onset

Background: Several previous studies have found inverse associations between glioma susceptibility and a history of allergies or other atopic conditions. Some evidence indicates that respiratory allergies are likely to be particularly relevant with regard to glioma risk. Using data from the Glioma International Case-Control Study (GICC), we examined the effects of respiratory allergies and other atopic conditions on glioma risk.Methods: The GICC contains detailed information on history of atopic conditions for 4,533 cases and 4,171 controls, recruited from 14 study sites across five countries. Using two-stage random-effects restricted maximum likelihood modeling to calculate meta-analysis ORs, we examined the associations between glioma and allergy status, respiratory allergy status, asthma, and eczema.Results: Having a history of respiratory allergies was associated with an approximately 30% lower glioma risk, compared with not having respiratory allergies (mOR, 0.72; 95% confidence interval, 0.58–0.90). This association was similar when restricting to high-grade glioma cases. Asthma and eczema were also significantly protective against glioma.Conclusion: A substantial amount of data on the inverse association between atopic conditions and glioma has accumulated, and findings from the GICC study further strengthen the existing evidence that the relationship between atopy and glioma is unlikely to be coincidental.Impact: As the literature approaches a consensus on the impact of allergies in glioma risk, future research can begin to shift focus to what the underlying biologic mechanism behind this association may be, which could, in turn, yield new opportunities for immunotherapy or cancer prevention. Cancer Epidemiol Biomarkers Prev; 25(2); 282–90. ©2016 AACR.

Gains and losses at chromosome 3p12-21 are common in breast tumors and associated with patient outcomes. We hypothesized that the LRIG1 gene at 3p14.1, whose product functions in ErbB-family member degradation, is a critical tumor modifier at this locus. We analyzed 971 stage I/II breast tumors using Affymetrix Oncoscan molecular inversion probe arrays that include 12 probes located within LRIG1. Copy number results were validated against gene expression data available in the public database. By partitioning the LRIG1 probes nearest exon 12/13, we confirm a breakpoint in the gene and show that gains and losses in the subregions differ by tumor and patient characteristics including race/ethnicity. In analyses adjusted for known prognostic factors, loss of LRIG1 was independently associated with risk of any relapse (HR, 1.90; 95% CI, 1.32–2.73), relapse ≥ 5 years (HR, 2.39; 95% CI, 1.31–4.36), and death (HR, 1.55; 95% CI, 1.11–2.16). Analyses of copy number across chromosome 3, as well as expression data from pooled, publicly available datasets, corroborated the hypothesis of an elevated and persistent risk among cases with loss of or low LRIG1. We concluded that loss/low expression of LRIG1 is an independent risk factor for breast cancer metastasis and death in stage I/II patients. Increased hazard in patients with loss/low LRIG1 persists years after diagnosis, suggesting that LRIG1 is acting as a critical suppressor of tumor metastasis and is an early clinical indicator of risk for late recurrences in otherwise low-risk patients. Cancer Res; 74(11); 2928–35. ©2014 AACR.

The supplemental files provide details on the specific sub-studies used in the expression based replication analysis as well as detailed information on LRIG1 probe location on the MIP array and sensitivity analyses that were conducted relative to treatment type. Table 1. Gene expression data set description Table 2. Probe sets for LRIG1 and SLC25A26 Table 3. Datasets with available data for SLC25A26 and LRIG1 Supplemental Figure 1. LRIG1 segment lengths and probe count Supplemental Figure 2. LRIG1 gene and MIP probe location Supplemental Figure 3. Box plots of the entire cohort Supplemental Figure 4. Computed average overlap with LRIG1 across all samples. Supplemental Figure 5. observed no evidence for an association between the level of expression of SLC25A26 and DMFS

Despite advances in treatment, colorectal cancer remains one of the leading causes of cancer-specific death. To decrease colorectal cancer mortality, better and more cost-efficient tools for risk prediction and early detection are needed. One area that has shown promise is using protein biomarkers, measured in blood, to detect individuals with early-stage disease or at increased risk. The primary aim of this study was therefore to identify novel protein biomarkers associated with colorectal cancer risk and the secondary aim was to validate protein biomarkers that had been described in previous studies. We included data and samples from 185 incident colorectal cancer cases and 185 controls (matched on age, sex, sampling date, and the number of freeze-thaw cycles) from the Northern Sweden Health and Disease Study (NSHDS). Samples were collected between six years and three months prior to the diagnosis date of each case and concentrations of 1536 individual proteins were measured in plasma samples using the Olink Explore 1536 platform (Olink Proteomics). Prior to statistical analyses, 249 proteins were excluded, either due to not passing quality control or having more than 50% of the concentrations below the limit of detection. In addition, we also excluded extreme outliers. Conditional logistic regression was used to estimate Odds Ratios (ORs) and 95% confidence intervals (95% CI) for the association between protein concentrations and colorectal cancer risk. Multivariable models were adjusted for predetermined colorectal cancer risk factors, including body mass index, alcohol consumption, physical activity, smoking status, and level of education. When considering p Citation Format: Justin Harbs, Matilda Rentoft, Xijia Liu, Beatrice S. Melin, Sophia Harlid, Bethany Van Guelpen. Identifying colorectal cancer biomarkers using a targeted proteomics platform with extensive coverage [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3023.

BackgroundThe functions of most glioma risk alleles are unknown. Very few studies had evaluated expression quantitative trait loci (eQTL), and insights of susceptibility genes were limited due to scarcity of available brain tissues. Moreover, no prior study had examined the effect of glioma risk alleles on alternative RNA splicing.ObjectiveThis study explored splicing quantitative trait loci (sQTL) as molecular QTL and improved the power of QTL mapping through meta-analyses of both cis eQTL and sQTL.MethodsWe first evaluated eQTLs and sQTLs of the CommonMind Consortium (CMC) and Genotype-Tissue Expression Project (GTEx) using genotyping, or whole-genome sequencing and RNA-seq data. Alternative splicing events were characterized using an annotation-free method that detected intron excision events. Then, we conducted meta-analyses by pooling the eQTL and sQTL results of CMC and GTEx using the inverse variance-weighted model. Afterward, we integrated QTL meta-analysis results (Q < 0.05) with the Glioma International Case Control Study (GICC) GWAS meta-analysis (case:12,496, control:18,190), using a summary statistics-based mendelian randomization (SMR) method.ResultsBetween CMC and GTEx, we combined the QTL data of 354 unique individuals of European ancestry. SMR analyses revealed 15 eQTLs in 11 loci and 32 sQTLs in 9 loci relevant to glioma risk. Two loci only harbored sQTLs (1q44 and 16p13.3). In seven loci, both eQTL and sQTL coexisted (2q33.3, 7p11.2, 11q23.3 15q24.2, 16p12.1, 20q13.33, and 22q13.1), but the target genes were different for five of these seven loci. Three eQTL loci (9p21.3, 20q13.33, and 22q13.1) and 4 sQTL loci (11q23.3, 16p13.3, 16q12.1, and 20q13.33) harbored multiple target genes. Eight target genes of sQTLs (C2orf80, SEC61G, TMEM25, PHLDB1, RP11-161M6.2, HEATR3, RTEL1-TNFRSF6B, and LIME1) had multiple alternatively spliced transcripts.ConclusionOur study revealed that the regulation of transcriptome by glioma risk alleles is complex, with the potential for eQTL and sQTL jointly affecting gliomagenesis in risk loci. QTLs of many loci involved multiple target genes, some of which were specific to alternative splicing. Therefore, quantitative trait loci that evaluate only total gene expression will miss many important target genes.

Background An inverse relationship between allergies with glioma risk has been reported in several but not all epidemiological observational studies. We performed an analysis of genetic variants associated with atopy to assess the relationship with glioma risk using Mendelian randomisation (MR), an approach unaffected by biases from temporal variability and reverse causation that might have affected earlier investigations. Methods Two-sample MR was undertaken using genome-wide association study data. We used single nucleotide polymorphisms (SNPs) associated with atopic dermatitis, asthma and hay fever, IgE levels, and self-reported allergy as instrumental variables. We calculated MR estimates for the odds ratio (OR) for each risk factor with glioma using SNP-glioma estimates from 12,488 cases and 18,169 controls, using inverse-variance weighting (IVW), maximum likelihood estimation (MLE), weighted median estimate (WME) and mode-based estimate (MBE) methods. Violation of MR assumptions due to directional pleiotropy were sought using MR-Egger regression and HEIDI-outlier analysis. Results Under IVW, MLE, WME and MBE methods, associations between glioma risk with asthma and hay fever, self-reported allergy and IgE levels were non-significant. An inverse relationship between atopic dermatitis and glioma risk was found by IVW (OR 0.96, 95% confidence interval (CI) 0.93–1.00, P = 0.041) and MLE (OR 0.96, 95% CI 0.94–0.99, P = 0.003), but not by WME (OR 0.96, 95% CI 0.91–1.01, P = 0.114) or MBE (OR 0.97, 95% CI 0.92–1.02, P = 0.194). Conclusions Our investigation does not provide strong evidence for relationship between atopy and the risk of developing glioma, but findings do not preclude a small effect in relation to atopic dermatitis. Our analysis also serves to illustrate the value of using several MR methods to derive robust conclusions. Electronic supplementary material The online version of this article (10.1186/s12916-018-1027-5) contains supplementary material, which is available to authorized users.