Identification of potential therapeutic targets for breast cancer using Mendelian randomization analysis and drug target prediction.

Breast cancer stands as the foremost cause of cancer-related mortality among women, presenting a substantial economic impact on society. The limitations in current therapeutic options, coupled with poor patient tolerance, underscore the urgent need for novel treatments. Our study embarked on a genomic association exploration of breast cancer, leveraging whole-genome sequencing data from the Finngen database, complemented by expression quantitative trait loci (eQTL) insights from the eQTLGen and GTEx Consortiums. An initial investigation was conducted through summary-based Mendelian randomization (MR) to pinpoint primary eQTLs. Analysis of blood specimens revealed 103 eQTLs significantly correlated with breast cancer. Focusing our efforts, we identified 19 candidates with potential therapeutic significance. Further scrutiny via two-sample MR pinpointed UROD, LMO4, HORMAD1, and ZSWIM5 as promising targets for breast cancer therapy. Our research sheds light on new avenues for the treatment of breast cancer, highlighting the potential of genomic association studies in uncovering viable therapeutic targets.

Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework.

BACKGROUND: The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies. METHODS: We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies. RESULTS: We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue. CONCLUSION: Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.

Germline structural variation globally impacts the cancer transcriptome including disease-relevant genes.

Germline variation and somatic alterations contribute to the molecular profile of cancers. We combine RNA with whole genome sequencing across 1,218 cancer patients to determine the extent germline structural variants (SVs) impact expression of nearby genes. For hundreds of genes, recurrent and common germline SV breakpoints within 100 kb associate with increased or decreased expression in tumors spanning various tissues of origin. A significant fraction of germline SV expression associations involves duplication of intergenic enhancers or 3' UTR disruption. Genes altered by both somatic and germline SVs include ATRX and CEBPA. Genes essential in cancer cell lines include BARD1 and IRS2. Genes with both expression and germline SV breakpoint patterns associated with patient survival include GCLM. Our results capture a class of phenotypic variation at work in the disease setting, including genes with cancer roles. Specific germline SVs represent potential cancer risk variants for genetic testing, including those involving genes with targeting implications.

Single Nucleotide Polymorphisms [SNPs] in the Shadows: Uncovering their Function in Non-Coding Region of Esophageal Cancer.

Esophageal cancer is a complex disease influenced by genetic and environmental factors. Single nucleotide polymorphisms [SNPs] in non-coding regions of the genome have emerged as crucial contributors to esophageal cancer susceptibility. This review provides a comprehensive overview of the role of SNPs in non-coding regions and their association with esophageal cancer. The accumulation of SNPs in the genome has been implicated in esophageal cancer risk. Various studies have identified specific locations in the genome where SNPs are more likely to occur, suggesting a location-specific response. Chromatin conformational studies have shed light on the localization of SNPs and their impact on gene transcription, posttranscriptional modifications, gene expression regulation, and histone modification. Furthermore, miRNA-related SNPs have been found to play a significant role in esophageal squamous cell carcinoma [ESCC]. These SNPs can affect miRNA binding sites, thereby altering target gene regulation and contributing to ESCC development. Additionally, the risk of ESCC has been linked to base excision repair, suggesting that SNPs in this pathway may influence disease susceptibility. Somatic DNA segment alterations and modified expression quantitative trait loci [eQTL] have also been associated with ESCC. These alterations can lead to disrupted gene expression and cellular processes, ultimately contributing to cancer development and progression. Moreover, SNPs have been found to be associated with the long non-coding RNA HOTAIR, which plays a crucial role in ESCC pathogenesis. This review concludes with a discussion of the current and future perspectives in the field of SNPs in non-coding regions and their relevance to esophageal cancer. Understanding the functional implications of these SNPs may lead to the identification of novel therapeutic targets and the development of personalized approaches for esophageal cancer prevention and treatment.

De novo identification of complex traits associated with asthma.

Introduction: Asthma is a heterogeneous inflammatory disease often associated with other complex phenotypes. Identifying asthma-associated diseases and uncovering the molecular mechanisms mediating their interaction can help detangle the heterogeneity of asthma. Network analysis is a powerful approach for untangling such inter-disease relationships. Methods: Here, we integrated information on physical contacts between common single nucleotide polymorphisms (SNPs) and gene expression with expression quantitative trait loci (eQTL) data from the lung and whole blood to construct two tissue-specific spatial gene regulatory networks (GRN). We then located the asthma GRN (level 0) within each tissue-specific GRN by identifying the genes that are functionally affected by asthma-associated spatial eQTLs. Curated protein interaction partners were subsequently identified up to four edges or levels away from the asthma GRN. The eQTLs spatially regulating genes on levels 0-4 were queried against the GWAS Catalog to identify the traits enriched (hypergeometric test; FDR ≤ 0.05) in each level. Results: We identified 80 and 82 traits significantly enriched in the lung and blood GRNs, respectively. All identified traits were previously reported to be comorbid or associated (positively or negatively) with asthma (e.g., depressive symptoms and lung cancer), except 8 traits whose association with asthma is yet to be confirmed (e.g., reticulocyte count). Our analysis additionally pinpoints the variants and genes that link asthma to the identified asthma-associated traits, a subset of which was replicated in a comorbidity analysis using health records of 26,781 asthma patients in New Zealand. Discussion: Our discovery approach identifies enriched traits in the regulatory space proximal to asthma, in the tissue of interest, without a priori selection of the interacting traits. The predictions it makes expand our understanding of possible shared molecular interactions and therapeutic targets for asthma, where no cure is currently available.

Refined expression quantitative trait locus analysis on adenocarcinoma at the gastroesophageal junction reveals susceptibility and prognostic markers.

Objectives: This study aimed to explore cell type level expression quantitative trait loci (eQTL) in adenocarcinoma at the gastroesophageal junction (ACGEJ) and identify susceptibility and prognosis markers. Methods: Whole-genome sequencing (WGS) was performed on 120 paired samples from Chinese ACGEJ patients. Germline mutations were detected by GATK tools. RNA sequencing (RNA-seq) data on ACGEJ samples were taken from our previous studies. Public single-cell RNA sequencing (scRNA-seq) data were used to produce the proportion of epithelial cells. Matrix eQTL and a linear mixed model were used to identify condition-specific cis-eQTLs. The R package coloc was used to perform co-localization analysis with the public data of genome-wide association studies (GWASs). Log-rank and Cox regression tests were used to identify survival-associated eQTL and genes. Functions of candidate risk loci were explored by experimental validation. Results: Refined eQTL analyses of paired ACGEJ samples were performed and 2,036 potential ACGEJ-specific eQTLs with East Asian specificity were identified in total. ACGEJ-gain eQTLs were enriched at promoter regions more than ACGEJ-loss eQTLs. rs658524 was identified as the top eQTL close to the transcription start site of its paired gene (CTSW). rs2240191-RASAL1, rs4236599-FOXP2, rs4947311-PSORS1C1, rs13134812-LOC391674, and rs17508585-CDK13-DT were identified as ACGEJ-specific susceptibility eQTLs. rs309483-LINC01355 was associated with the overall survival of ACGEJ patients. We explored functions of candidate eQTLs such as rs658524, rs309483, rs2240191, and rs4947311 by experimental validation. Conclusion: This study provides new risk loci for ACGEJ susceptibility and effective disease prognosis biomarkers.

Identification and Interpretation of eQTL and eGenes for Hodgkin Lymphoma Susceptibility.

Genome-wide association studies (GWAS) have revealed approximately 100 genomic signals associated with Hodgkin lymphoma (HL); however, their target genes and underlying mechanisms causing HL susceptibility remain unclear. In this study, transcriptome-wide analysis of expression quantitative trait loci (eQTL) was conducted to identify target genes associated with HL GWAS signals. A mixed model, which explains polygenic regulatory effects by the genomic covariance among individuals, was implemented to discover expression genes (eGenes) using genotype data from 462 European/African individuals. Overall, 80 eGenes were identified to be associated with 20 HL GWAS signals. Enrichment analysis identified apoptosis, immune responses, and cytoskeletal processes as functions of these eGenes. The eGene of rs27524 encodes ERAP1 that can cleave peptides attached to human leukocyte antigen in immune responses; its minor allele may help Reed-Sternberg cells to escape the immune response. The eGene of rs7745098 encodes ALDH8A1 that can oxidize the precursor of acetyl-CoA for the production of ATP; its minor allele may increase oxidization activity to evade apoptosis of pre-apoptotic germinal center B cells. Thus, these minor alleles may be genetic risk factors for HL susceptibility. Experimental studies on genetic risk factors are needed to elucidate the underlying mechanisms of HL susceptibility and improve the accuracy of precision oncology.

Gene signatures derived from transcriptomic-causal networks stratified colorectal cancer patients for effective targeted therapy.

Predictive and prognostic gene signatures derived from interconnectivity among genes can tailor clinical care to patients in cancer treatment. We identified gene interconnectivity as the transcriptomic-causal network by integrating germline genotyping and tumor RNA-seq data from 1,165 patients with metastatic colorectal cancer (CRC). The patients were enrolled in a clinical trial with randomized treatment, either cetuximab or bevacizumab in combination with chemotherapy. We linked the network to overall survival (OS) and detected novel biomarkers by controlling for confounding genes. Our data-driven approach discerned sets of genes, each set collectively stratify patients based on OS. Two signatures under the cetuximab treatment were related to wound healing and macrophages. The signature under the bevacizumab treatment was related to cytotoxicity and we replicated its effect on OS using an external cohort. We also showed that the genes influencing OS within the signatures are downregulated in CRC tumor vs. normal tissue using another external cohort. Furthermore, the corresponding proteins encoded by the genes within the signatures interact each other and are functionally related. In conclusion, this study identified a group of genes that collectively stratified patients based on OS and uncovered promising novel prognostic biomarkers for personalized treatment of CRC using transcriptomic causal networks.

Examination of eQTL Polymorphisms Associated with Increased Risk of Progressive Complicated Sarcoidosis in European and African Descent Subjects.

Background: A limited pool of SNPs are linked to the development and severity of sarcoidosis, a systemic granulomatous inflammatory disease. By integrating genome-wide association studies (GWAS) data and expression quantitative trait loci (eQTL) single nuclear polymorphisms (SNPs), we aimed to identify novel sarcoidosis SNPs potentially influencing the development of complicated sarcoidosis. Methods: A GWAS (Affymetrix 6.0) involving 209 African-American (AA) and 193 European-American (EA, 75 and 51 complicated cases respectively) and publicly-available GWAS controls (GAIN) was utilized. Annotation of multi-tissue eQTL SNPs present on the GWAS created a pool of ~46,000 eQTL SNPs examined for association with sarcoidosis risk and severity (Logistic Model, Plink). The most significant EA/AA eQTL SNPs were genotyped in a sarcoidosis validation cohort (n=1034) and cross-validated in two independent GWAS cohorts. Results: No single GWAS SNP achieved significance (p<1x10-8), however, analysis of the eQTL/GWAS SNP pool yielded 621 eQTL SNPs (p<10-4) associated with 730 genes that highlighted innate immunity, MHC Class II, and allograft rejection pathways with multiple SNPs validated in an independent sarcoidosis cohort (105 SNPs analyzed) (NOTCH4, IL27RA, BTNL2, ANXA11, HLA-DRB1). These studies confirm significant association of eQTL/GWAS SNPs in EAs and AAs with sarcoidosis risk and severity (complicated sarcoidosis) involving HLA region and innate immunity. Conclusion: Despite the challenge of deciphering the genetic basis for sarcoidosis risk/severity, these results suggest that integrated eQTL/GWAS approaches may identify novel variants/genes and support the contribution of dysregulated innate immune responses to sarcoidosis severity.

Identifying cancer tissue-of-origin by a novel machine learning method based on expression quantitative trait loci.

Cancer of unknown primary (CUP) refers to cancer with primary lesion unidentifiable by regular pathological and clinical diagnostic methods. This kind of cancer is extremely difficult to treat, and patients with CUP usually have a very short survival time. Recent studies have suggested that cancer treatment targeting primary lesion will significantly improve the survival of CUP patients. Thus, it is critical to develop accurate yet fast methods to infer the tissue-of-origin (TOO) of CUP. In the past years, there are a few computational methods to infer TOO based on single omics data like gene expression, methylation, somatic mutation, and so on. However, the metastasis of tumor involves the interaction of multiple levels of biological molecules. In this study, we developed a novel computational method to predict TOO of CUP patients by explicitly integrating expression quantitative trait loci (eQTL) into an XGBoost classification model. We trained our model with The Cancer Genome Atlas (TCGA) data involving over 7,000 samples across 20 types of solid tumors. In the 10-fold cross-validation, the prediction accuracy of the model with eQTL was over 0.96, better than that without eQTL. In addition, we also tested our model in an independent data downloaded from Gene Expression Omnibus (GEO) consisting of 87 samples across 4 cancer types. The model also achieved an f1-score of 0.7-1 depending on different cancer types. In summary, eQTL was an important information in inferring cancer TOO and the model might be applied in clinical routine test for CUP patients in the future.

Exploring Potential Causal Genes for Uterine Leiomyomas: A Summary Data-Based Mendelian Randomization and FUMA Analysis.

Objective: To explore potential causal genetic variants and genes underlying the pathogenesis of uterine leiomyomas (ULs). Methods: We conducted the summary data-based Mendelian randomization (SMR) analyses and performed functional mapping and annotation using FUMA to examine genetic variants and genes that are potentially involved in the pathogenies of ULs. Both analyses used summarized data of a recent genome-wide association study (GWAS) on ULs, which has a total sample size of 244,324 (20,406 cases and 223,918 controls). We performed separate SMR analysis using CAGE and GTEx eQTL data. Results: Using the CAGE eQTL data, our SMR analysis identified 13 probes tagging 10 unique genes that were pleiotropically/potentially causally associated with ULs, with the top three probes being ILMN_1675156 (tagging CDC42, PSMR = 8.03 × 10-9), ILMN_1705330 (tagging CDC42, PSMR = 1.02 × 10-7) and ILMN_2343048 (tagging ABCB9, PSMR = 9.37 × 10-7). Using GTEx eQTL data, our SMR analysis did not identify any significant genes after correction for multiple testing. FUMA analysis identified 106 independent SNPs, 24 genomic loci and 137 genes that are potentially involved in the pathogenesis of ULs, seven of which were also identified by the SMR analysis. Conclusions: We identified many genetic variants, genes, and genomic loci that are potentially involved in the pathogenesis of ULs. More studies are needed to explore the exact underlying mechanisms in the etiology of ULs.

Novel MicroRNA-Regulated Transcript Networks Are Associated with Chemotherapy Response in Ovarian Cancer.

High-grade serous ovarian cancer (HGSOC) is a highly lethal gynecologic cancer, in part due to resistance to platinum-based chemotherapy reported among 20% of patients. This study aims to generate novel hypotheses of the biological mechanisms underlying chemotherapy resistance, which remain poorly understood. Differential expression analyses of mRNA- and microRNA-sequencing data from HGSOC patients of The Cancer Genome Atlas identified 21 microRNAs associated with angiogenesis and 196 mRNAs enriched for adaptive immunity and translation. Coexpression network analysis identified three microRNA networks associated with chemotherapy response enriched for lipoprotein transport and oncogenic pathways, as well as two mRNA networks enriched for ubiquitination and lipid metabolism. These network modules were replicated in two independent ovarian cancer cohorts. Moreover, integrative analyses of the mRNA/microRNA sequencing and single-nucleotide polymorphisms (SNPs) revealed potential regulation of significant mRNA transcripts by microRNAs and SNPs (expression quantitative trait loci). Thus, we report novel transcriptional networks and biological pathways associated with resistance to platinum-based chemotherapy in HGSOC patients. These results expand our understanding of the effector networks and regulators of chemotherapy response, which will help to improve the management of ovarian cancer.

The Expression Quantitative Trait Loci in Immune Response Genes Impact the Characteristics and Survival of Colorectal Cancer.

The impact of germline variants on the regulation of the expression of tumor microenvironment (TME)-based immune response genes remains unclear. Expression quantitative trait loci (eQTL) provide insight into the effect of downstream target genes (eGenes) regulated by germline-associated variants (eVariants). Through eQTL analyses, we illustrated the relationships between germline eVariants, TME-based immune response eGenes, and clinical outcomes. In this study, both RNA sequencing data from primary tumor and germline whole-genome sequencing data were collected from patients with stage III colorectal cancer (CRC). Ninety-nine high-risk subjects were subjected to immune response gene expression analyses. Seventy-seven subjects remained for further analysis after quality control, of which twenty-two patients (28.5%) experienced tumor recurrence. We found that 65 eQTL, including 60 germline eVariants and 22 TME-based eGenes, impacted the survival of cancer patients. For the recurrence prediction model, 41 differentially expressed genes (DEGs) achieved the best area under the receiver operating characteristic curve of 0.93. In total, 19 survival-associated eGenes were identified among the DEGs. Most of these genes were related to the regulation of lymphocytes and cytokines. A high expression of HGF, CCR5, IL18, FCER1G, TDO2, IFITM2, and LAPTM5 was significantly associated with a poor prognosis. In addition, the FCER1G eGene was associated with tumor invasion, tumor nodal stage, and tumor site. The eVariants that regulate the TME-based expression of FCER1G, including rs2118867 and rs12124509, were determined to influence survival and chromatin binding preferences. We also demonstrated that FCER1G and co-expressed genes in TME were related to the aggregation of leukocytes via pathway analysis. By analyzing the eQTL from the cancer genome using germline variants and TME-based RNA sequencing, we identified the eQTL in immune response genes that impact colorectal cancer characteristics and survival.

Mendelian Randomization Analysis Identified Potential Genes Pleiotropically Associated with Polycystic Ovary Syndrome.

Polycystic ovary syndrome (PCOS) is a common endocrine disorder with unclear etiology. Some genes may be pleiotropically or potentially causally associated with PCOS. In the present study, the summary data-based Mendelian randomization (SMR) method integrating genome-wide association study (GWAS) for PCOS and expression quantitative trait loci (eQTL) data was applied to identify genes that were pleiotropically associated with PCOS. Separate SMR analysis was performed using eQTL data in the ovary and whole blood. Although no genes showed significant pleiotropic association with PCOS after correction for multiple testing, some of the genes exhibited suggestive significance. RPS26 showed the strongest suggestive pleiotropic association with PCOS in both SMR analyses (ß[SE]=0.10[0.03], PSMR=1.72×10-4 for ovary; ß[SE]=0.11[0.03], PSMR=1.40×10-4 for whole blood). PM20D1 showed the second strongest suggestive pleiotropic association with PCOS in the SMR analysis using eQTL data for the whole blood and was also among the top ten hit genes in the SMR analysis using eQTL data for the ovary. Two other genes, including CTC-457L16.2 and NEIL2, were among the top ten hit genes in both SMR analyses. In conclusion, this study revealed multiple genes that were potentially involved in the pathogenesis of PCOS.

Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies.

BACKGROUND: Integrating functional annotations into SNP-set association studies has been proven a powerful analysis strategy. Statistical methods for such integration have been developed for continuous and binary phenotypes; however, the SNP-set integrative approaches for time-to-event or survival outcomes are lacking. METHODS: We here propose IEHC, an integrative eQTL (expression quantitative trait loci) hierarchical Cox regression, for SNP-set based survival association analysis by modeling effect sizes of genetic variants as a function of eQTL via a hierarchical manner. Three p-values combination tests are developed to examine the joint effects of eQTL and genetic variants after a novel decorrelated modification of statistics for the two components. An omnibus test (IEHC-ACAT) is further adapted to aggregate the strengths of all available tests. RESULTS: Simulations demonstrated that the IEHC joint tests were more powerful if both eQTL and genetic variants contributed to association signal, while IEHC-ACAT was robust and often outperformed other approaches across various simulation scenarios. When applying IEHC to ten TCGA cancers by incorporating eQTL from relevant tissues of GTEx, we revealed that substantial correlations existed between the two types of effect sizes of genetic variants from TCGA and GTEx, and identified 21 (9 unique) cancer-associated genes which would otherwise be missed by approaches not incorporating eQTL. CONCLUSION: IEHC represents a flexible, robust, and powerful approach to integrate functional omics information to enhance the power of identifying association signals for the survival risk of complex human cancers.

Association of genetic variants of FBXO32 and FOXO6 in the FOXO pathway with breast cancer risk.

Forkhead box class O (FOXO) transcription factors play a pivotal role in regulating a variety of biological processes, including organismal development, cell signaling, cell metabolism, and tumorigenesis. Therefore, we hypothesize that genetic variants in FOXO pathway genes are associated with breast cancer (BC) risk. To test this hypothesis, we conducted a large meta-analysis using 14 published genome-wide association study (GWAS) data sets in the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study. We assessed associations between 5214 (365 genotyped in DRIVE and 4849 imputed) common single-nucleotide polymorphisms (SNPs) in 55 FOXO pathway genes and BC risk. After multiple comparison corrections by the Bayesian false-discovery probability method, we found five SNPs to be significantly associated with BC risk. In stepwise multivariate logistic regression analysis with adjustment for age, principal components, and previously published SNPs in the same data set, three independent SNPs (i.e., FBXO32 rs10093411 A>G, FOXO6 rs61229336 C>T, and FBXO32 rs62521280 C>T) remained to be significantly associated with BC risk (p = 0.0008, 0.0011, and 0.0017, respectively). Additional expression quantitative trait loci analysis revealed that the FBXO32 rs62521280 T allele was associated with decreased messenger RNA (mRNA) expression levels in breast tissue, while the FOXO6 rs61229336 T allele was found to be associated with decreased mRNA expression levels in the whole blood cells. Once replicated by other investigators, these genetic variants may serve as new biomarkers for BC risk.

MiR-3130-5p is an intermediate modulator of 2q33 and influences the invasiveness of lung adenocarcinoma by targeting NDUFS1.

Genome-wide association studies (GWAS) have reported a handful of loci associated with lung cancer risk, of which the pathogenic pathways are largely unknown. We performed cis-expression quantitative trait loci (eQTL) mapping for 376 lung cancer related GWAS loci in 227 TCGA lung adenocarcinoma (LUAD) and reported two risk loci as eQTL of miRNA. Among the miRNAs in association with lung cancer risk, we further predicted and validated miR-3130-5p as an intermediate modulator of risk loci 2q33 and the tumor suppressor NDUFS1. We assessed the phenotypic impacts of the interaction between miR-3130-5p and NDUFS1 in both lung cancer cell lines and mice xenograft models. As a result, miR-3130-5p directly regulates the expression of NDUFS1 and the corresponding tumor invasiveness, migration and epithelial-mesenchymal transition (EMT). Our findings provide important clues for the pathogenic mechanism of 2q33 in lung carcinogenesis which informs clinical diagnosis and prognosis of LUAD. We performed a cis-eQTL analysis for 376 lung cancer risk loci based on the expression profiles of 251 miRNAs in a cohort of 227 TCGA lung adenocarcinoma. We report a novel pathogenic pathway of 2q33 via miR-3130-5p and NDUFS1.

A systematic comparison of normalization methods for eQTL analysis.

Expression quantitative trait loci (eQTL) analysis has been widely used in interpreting disease-associated loci through correlating genetic variant loci with the expression of specific genes. RNA-sequencing (RNA-Seq), which can quantify gene expression at the genome-wide level, is often used in eQTL identification. Since different normalization methods of gene expression have substantial impacts on RNA-seq downstream analysis, it is of great necessity to systematically compare the effects of these methods on eQTL identification. Here, by using RNA-seq and genotype data of four different cancers in The Cancer Genome Atlas (TCGA) database, we comprehensively evaluated the effect of eight commonly used normalization methods on eQTL identification. Our results showed that the application of different methods could cause 20-30% differences in the final results of eQTL identification. Among these methods, COUNT, Median of Ratio (MED) and Trimmed Mean of M-values (TMM) generated similar results for identifying eQTLs, while Fragments Per Kilobase Million (FPKM) or RANK produced more differential results compared with other methods. Based on the accuracy and receiver operating characteristic (ROC) curve, the TMM method was found to be the optimal method for normalizing gene expression data in eQTLs analysis. In addition, we also evaluated the performance of different pairwise combinations of these methods. As a result, compared with single normalization methods, the combination of methods can not only identify more cis-eQTLs, but also improve the performance of the ROC curve. Overall, this study provides a comprehensive comparison of normalization methods for identifying eQTLs from RNA-seq data, and proposes some practical recommendations for diverse scenarios.

Association of genetic variants of TMEM135 and PEX5 in the peroxisome pathway with cutaneous melanoma-specific survival.

BACKGROUND: Peroxisomes are ubiquitous and dynamic organelles that are involved in the metabolism of reactive oxygen species (ROS) and lipids. However, whether genetic variants in the peroxisome pathway genes are associated with survival in patients with melanoma has not been established. Therefore, our aim was to identify additional genetic variants in the peroxisome pathway that may provide new prognostic biomarkers for cutaneous melanoma (CM). METHODS: We assessed the associations between 8,397 common single-nucleotide polymorphisms (SNPs) in 88 peroxisome pathway genes and CM disease-specific survival (CMSS) in a two-stage analysis. For the discovery, we extracted the data from a published genome-wide association study from The University of Texas MD Anderson Cancer Center (MDACC). We then replicated the results in another dataset from the Nurse Health Study (NHS)/Health Professionals Follow-up Study (HPFS). RESULTS: Overall, 95 (11.1%) patients in the MDACC dataset and 48 (11.7%) patients in the NHS/HPFS dataset died of CM. We found 27 significant SNPs in the peroxisome pathway genes to be associated with CMSS in both datasets after multiple comparison correction using the Bayesian false-discovery probability method. In stepwise Cox proportional hazards regression analysis, with adjustment for other covariates and previously published SNPs in the MDACC dataset, we identified 2 independent SNPs (TMEM135 rs567403 C>G and PEX5 rs7969508 A>G) that predicted CMSS (P=0.003 and 0.031, respectively, in an additive genetic model). The expression quantitative trait loci analysis further revealed that the TMEM135 rs567403 GG and PEX5 rs7969508 GG genotypes were associated with increased and decreased levels of mRNA expression of their genes, respectively. CONCLUSIONS: Once our findings are replicated by other investigators, these genetic variants may serve as novel biomarkers for the prediction of survival in patients with CM.

Genome-wide association analysis reveals regulation of at-risk loci by DNA methylation in prostate cancer.

Epigenetic changes are potentially important for the ontogeny and progression of tumors but are not usually studied because of the complexity of analyzing transcript regulation resulting from epigenetic alterations. Prostate cancer (PCa) is characterized by variable clinical manifestations and frequently unpredictable outcomes. We performed an expression quantitative trait loci (eQTL) analysis to identify the genomic regions that regulate gene expression in PCa and identified a relationship between DNA methylation and clinical information. Using multi-level information published in The Cancer Genome Atlas, we performed eQTL-based analyses on DNA methylation and gene expression. To better interpret these data, we correlated loci and clinical indexes to identify the important loci for both PCa development and progression. Our data demonstrated that although only a small proportion of genes are regulated via DNA methylation in PCa, these genes are enriched in important cancer-related groups. In addition, single nucleotide polymorphism analysis identified the locations of CpG sites and genes within at-risk loci, including the 19q13.2-q13.43 and 16q22.2-q23.1 loci. Further, an epigenetic association study of clinical indexes detected risk loci and pyrosequencing for site validation. Although DNA methylation-regulated genes across PCa samples are a small proportion, the associated genes play important roles in PCa carcinogenesis.