Significant SNPs have limited prediction ability for thyroid cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Guo S
PROVIDER: S-EPMC4101765 | biostudies-literature | 2014 Jun
REPOSITORIES: biostudies-literature
Publications
Significant SNPs have limited prediction ability for thyroid cancer.
Guo Shicheng S Wang Yu-Long YL Li Yi Y Jin Li L Xiong Momiao M Ji Qing-Hai QH Wang Jiucun J
Cancer medicine 20140303 3
Recently, five thyroid cancer significantly associated genetic variants (rs965513, rs944289, rs116909374, rs966423, and rs2439302) have been discovered and validated in two independent GWAS and numerous case-control studies, which were conducted in different populations. We genotyped the above five single nucleotide polymorphisms (SNPs) in Han Chinese populations and performed thyroid cancer-risk predictions with nine machine learning methods. We found that four SNPs were significantly associate ...[more]
Similar Datasets
Project description:PurposeGenome-wide-association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) that are associated with an increased risk of breast cancer. Most of these studies were conducted primarily in postmenopausal breast cancer patients. Therefore, we set out to assess whether or not these breast cancer variants are also associated with an elevated risk of breast cancer in young premenopausal patients.MethodsIn 451 women of European ancestry who had prospectively enrolled in a longitudinal cohort study for women diagnosed with breast cancer at or under age 40, we genotyped 44 SNPs that were previously associated with breast cancer risk. A control group was comprised of 1142 postmenopausal healthy women from the Nurses' Health Study (NHS). We assessed if the frequencies of the adequately genotyped SNPs differed significantly (p≤0.05) between the cohort of young breast cancer patients and postmenopausal controls, and then we corrected for multiple testing.ResultsGenotyping of the controls or cases was inadequate for comparisons between the groups for seven of the 44 SNPs. 9 of the remaining 37 were associated with breast cancer risk in young women with a p-value <0.05: rs10510102, rs1219648, rs13387042, rs1876206, rs2936870, rs2981579, rs3734805, rs3803662 and rs4973768. The directions of these associations were consistent with those in postmenopausal women. However, after correction for multiple testing (Benjamini Hochberg) none of the results remained statistically significant.ConclusionAfter correction for multiple testing, none of the alleles for postmenopausal breast cancer were clearly associated with risk of premenopausal breast cancer in this relatively small study.
| S-EPMC6534300 | biostudies-literature
Project description:As a common malignant tumor disease, thyroid cancer lacks effective preventive and therapeutic drugs. Thus, it is crucial to provide an effective drug selection method for thyroid cancer patients. The connectivity map (CMAP) project provides an experimental validated strategy to repurpose and optimize cancer drugs, the rationale behind which is to select drugs to reverse the gene expression variations induced by cancer. However, it has a few limitations. Firstly, CMAP was performed on cell lines, which are usually different from human tissues. Secondly, only gene expression information was considered, while the information about gene regulations and modules/pathways was more or less ignored. In this study, we first measured comprehensively the perturbations of thyroid cancer on a patient including variations at gene expression level, gene co-expression level and gene module level. After that, we provided a drug selection pipeline to reverse the perturbations based on drug signatures derived from tissue studies. We applied the analyses pipeline to the cancer genome atlas (TCGA) thyroid cancer data consisting of 56 normal and 500 cancer samples. As a result, we obtained 812 up-regulated and 213 down-regulated genes, whose functions are significantly enriched in extracellular matrix and receptor localization to synapses. In addition, a total of 33,778 significant differentiated co-expressed gene pairs were found, which form a larger module associated with impaired immune function and low immunity. Finally, we predicted drugs and gene perturbations that could reverse the gene expression and co-expression changes incurred by the development of thyroid cancer through the Fisher's exact test. Top predicted drugs included validated drugs like baclofen, nevirapine, glucocorticoid, formaldehyde and so on. Combining our analyses with literature mining, we inferred that the regulation of thyroid hormone secretion might be closely related to the inhibition of the proliferation of thyroid cancer cells.
| S-EPMC6359462 | biostudies-literature
Project description:Lung cancer is the leading cause of cancer deaths in both men and women in the US. While most sporadic lung cancer cases are related to environmental factors such as smoking, genetic susceptibility may also play an important role and a number of lung cancer associated single-nucleotide polymorphisms (SNPs) have been identified although many remain to be found. The collective effects of genome-wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of complex traits and diseases. Here we studied MAC in lung cancer using previously published SNPs data sets (US and Finland samples) and found higher MAC in cases relative to matched controls. A set of 5400 SNPs with MA (MAF < 0.5) more common in cases (P < 0.08) and linkage disequilibrium (LD) r2 = 0.3 was found to have the best predictive accuracy. These results identify higher MAC in lung cancer susceptibility and provide a meaningful genetic method to identify those at risk of lung cancer.
| S-EPMC6221896 | biostudies-literature
Project description:For African American or Hispanic women, the extent to which clinical breast cancer risk prediction models are improved by including information on susceptibility single nucleotide polymorphisms (SNPs) is unknown, even though these women comprise increasing proportions of the US population and represent a large proportion of the world's population. We studied 7539 African American and 3363 Hispanic women from the Women's Health Initiative. The age-adjusted 5-year risks from the BCRAT and IBIS risk prediction models were measured and combined with a risk score based on >70 independent susceptibility SNPs. Logistic regression, adjusting for age group, was used to estimate risk associations with log-transformed age-adjusted 5-year risks. Discrimination was measured by the odds ratio (OR) per standard deviation (SD) and the area under the receiver operator curve (AUC). When considered alone, the ORs for African American women were 1.28 for BCRAT, and 1.04 for IBIS. When combined with the SNP risk score (OR 1.23), the corresponding ORs were 1.39 and 1.22. For Hispanic women the corresponding ORs were 1.25 for BCRAT, and 1.15 for IBIS. When combined with the SNP risk score (OR 1.39), the corresponding ORs were 1.48 and 1.42. There was no evidence that any of the combined models were not well calibrated. Including information on known breast cancer susceptibility loci provides approximately 10 and 19% improvement in risk prediction using BCRAT for African Americans and Hispanics, respectively. The corresponding figures for IBIS are approximately 18 and 26%, respectively.
| S-EPMC4661211 | biostudies-literature
Project description:BackgroundSince its inception, the Bethesda System for Reporting Thyroid Cytopathology (TBS) has been widely adopted. Each category conveys a risk of malignancy and recommended next steps, though it is unclear if each category also predicts the type and extent of malignancy. If so, this would greatly expand the utility of the TBS by providing prognostic information in addition to baseline cancer risk.MethodsAll patients prospectively enrolled into the authors' thyroid nodule database from 1995 to 2013 with histologically proven malignancy were analyzed. The primary ultrasound-guided fine-needle aspiration cytology (AUS, atypia of unknown significance; FN, follicular neoplasm; SUSP, suspicious; M, malignant) was correlated with the type of thyroid cancer and histological features known to impact prognosis and recurrence, including lymph node metastasis (LNM), lymphovascular invasion, and extrathyroidal extension (ETE). Primary cytology was separately correlated with higher risk malignancy.ResultsA total of 1291 malignancies were identified, with primary cytology AUS in 130 cases, FN in 241 cases, SUSP in 411 cases, and M in 509 cases. AUS, SUSP, and M cytology were progressively associated with an increasing risk of high-risk disease (p?<?0.001), LNM (p?<?0.001), ETE (p?<?0.001), and margin positivity (p?<?0.001). Notably, 71% of malignancies with AUS cytology were follicular variants of papillary thyroid cancer compared with 63% with SUSP cytology and only 20% with M cytology. In contrast, high-risk malignancies were diagnosed in only 4% with AUS cytology, but 9% and 27% with SUSP and M cytology, respectively. FN conveyed a significantly increased risk of follicular thyroid carcinoma compared with all other types (28% vs. 2%; p?<?0.001). A composite endpoint of recurrence, distant metastases, and death similarly increased as cytology progressed from AUS to SUSP to M (p?<?0.001).ConclusionIn addition to predicting cancer prevalence, the TBS also imparts important prognostic information about cancer type, variant, and risk of recurrence. These data extend the utility of TBS classification by fostering an improved understanding of the risk posed by any confirmed malignancy.
| S-EPMC4754507 | biostudies-literature
Project description:m6A methylation has been demonstrated to be one of the most important epigenetic regulation mechanisms in cell differentiation and cancer development especially m6A derived diagnostic and prognostic biomarkers have been identified in the past several years. However, systemic investigation to the interaction between germline single-nucleotide polymorphisms (SNPs) and m6A has not been conducted yet. In this study, we collected previous identified significant thyroid cancer associated SNPs from UKB cohort (358 cases and 407,399 controls) and ICR cohort (3,001 patients and 287,550 controls) and thyroid eQTL (sample size = 574 from GTEx project) and m6A-SNP (N = 1,678,126) were applied to prioritize the candidate SNPs. Finally, five candidate genes (PLEKHA8, SMUG1, CDC123, RMI2, ACSM5) were identified to be thyroid cancer associated m6A-related genetic susceptibility. Loss and gain function studies of m6A writer proteins confirm that ACSM5 is regulated by m6A methylation of mRNA. Moreover, ACSM5 is downregulated in thyroid cancer and inversely correlated with PTC malignancy and patient survival. Together, our study highlight mRNA-seq and m6A-seq double analysis provided a novel approach to identify cancer biomarkers and understanding the heterogeneity of human cancers.
| S-EPMC8640822 | biostudies-literature
Project description:Colorectal cancer risk stratification is crucial to improve screening and risk-reducing recommendations, and consequently do better than a one-size-fits-all screening regimen. Current screening guidelines in the UK, USA and Australia focus solely on family history and age for risk prediction, even though the vast majority of the population do not have any family history. We investigated adding a polygenic risk score based on 45 single-nucleotide polymorphisms to a family history model (combined model) to quantify how it improves the stratification and discriminatory performance of 10-year risk and full lifetime risk using a prospective population-based cohort within the UK Biobank. For both 10-year and full lifetime risk, the combined model had a wider risk distribution compared with family history alone, resulting in improved risk stratification of nearly 2-fold between the top and bottom risk quintiles of the full lifetime risk model. Importantly, the combined model can identify people (n = 72,019) who do not have family history of colorectal cancer but have a predicted risk that is equivalent to having at least one affected first-degree relative (n = 44,950). We also confirmed previous findings by showing that the combined full lifetime risk model significantly improves discriminatory accuracy compared with a simple family history model 0.673 (95% CI 0.664-0.682) versus 0.666 (95% CI 0.657-0.675), p = 0.0065. Therefore, a combined polygenic risk score and first-degree family history model could be used to improve risk stratified population screening programs.
| S-EPMC8443076 | biostudies-literature
Project description:Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving significant genes. To determine the impact of different imputation methods on the identification of important genes, the chi-squared test was used to compare the proportions of overlaps between significant genes detected from original data and those detected from the imputed datasets. Additionally, the significant genes are tested for their enrichment in important pathways, using the ConsensusPathDB. Our results showed that almost all the significant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no significant difference in the performance of various imputation methods tested. The source code and selected datasets are available on http://profiles.bs.ipm.ir/softwares/imputation_methods/.
| S-EPMC5828654 | biostudies-literature
Project description:Genome-wide association studies (GWAS) have consistently identified specific lung cancer susceptibility regions. We evaluated the lung cancer-predictive performance of single-nucleotide polymorphisms (SNPs) in these regions.Lung cancer cases (N = 778) and controls (N = 1166) were genotyped for 77 SNPs located in GWAS-identified lung cancer susceptibility regions. Variable selection and model development used stepwise logistic regression and decision-tree analyses. In a subset nested in the Pittsburgh Lung Screening Study, change in area under the receiver operator characteristic curve and net reclassification improvement were used to compare predictions made by risk factor models with and without genetic variables.Variable selection and model development kept two SNPs in each of three GWAS regions, rs2736100 and rs7727912 in 5p15.33, rs805297 and rs1802127 in 6p21.33, and rs8034191 and rs12440014 in 15q25.1. The ratio of cases to controls was three times higher among subjects with a high-risk genotype in every one as opposed to none of the three GWAS regions (odds ratio, 3.14; 95% confidence interval, 2.02-4.88; adjusted for sex, age, and pack-years). Adding a three-level classified count of GWAS regions with high-risk genotypes to an age and smoking risk factor-only model improved lung cancer prediction by a small amount: area under the receiver operator characteristic curve, 0.725 versus 0.717 (p = 0.056); overall net reclassification improvement was 0.052 across low-, intermediate-, and high- 6-year lung cancer risk categories (<3.0%, 3.0%-4.9%, ? 5.0%).Specifying genotypes for SNPs in three GWAS-identified susceptibility regions improved lung cancer prediction, but probably by an extent too small to affect disease control practice.
| S-EPMC4636453 | biostudies-literature
Project description:SummaryCombinatorial association mapping aims to assess the statistical association of higher-order interactions of genetic markers with a phenotype of interest. This article presents combinatorial association mapping (CASMAP), a software package that leverages recent advances in significant pattern mining to overcome the statistical and computational challenges that have hindered combinatorial association mapping. CASMAP can be used to perform region-based association studies and to detect higher-order epistatic interactions of genetic variants. Most importantly, unlike other existing significant pattern mining-based tools, CASMAP allows for the correction of categorical covariates such as age or gender, making it suitable for genome-wide association studies.Availability and implementationThe R and Python packages can be downloaded from our GitHub repository http://github.com/BorgwardtLab/CASMAP. The R package is also available on CRAN.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6662083 | biostudies-literature