PancanQTL: systematic identification of cis-eQTLs and trans-eQTLs in 33 cancer types

tRic: a user-friendly data portal to explore the expression landscape of tRNAs in human cancers

Transfer RNAs (tRNAs) play critical roles in human cancer. Currently, no database provides the expression landscape and clinical relevance of tRNAs across a variety of human cancers. Utilizing miRNA-seq data from The Cancer Genome Atlas, we quantified the relative expression of tRNA genes and merged them into the codon level and amino level across 31 cancer types. The expression of tRNAs is associated with clinical features of patient smoking history and overall survival, and disease stage, subtype, and grade. We further analysed codon frequency and amino acid frequency for each protein coding gene and linked alterations of tRNA expression with protein translational efficiency. We include these data resources in a user-friendly data portal, tRic (tRNA in cancer, https://hanlab.uth.edu/tRic/ or http://bioinfo.life.hust.edu.cn/tRic/), which can be of significant interest to the research community.

Functional variants of autophagy-related genes are associated with the development of hepatocellular carcinoma

AIMS

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, and accounts for substantial morbidity and mortality. Autophagy plays an essential role in the development and progression of HCC. This study aims to evaluate whether genetic variants in autophagy-related genes (ATGs) affect the development of HCC.

MATERIALS AND METHODS

We conducted a case-control study with 986 HCC cases and 1000 healthy controls to analyze 14 functional variants of five ATGs (ATG3, ATG5, ATG10, ATG12 and ATG16L1) among a Chinese population.

KEY FINDINGS

We found ATG5 rs17067724 (G vs A: OR = 0.80; 95% CI = 0.65-0.98; P = 0.031), ATG10 rs1864183 (G vs A: OR = 1.29; 95% CI = 1.07-1.57; P = 0.009), ATG10 rs10514231 (C vs T: OR = 1.41; 95% CI = 1.15-1.73; P = 0.001), ATG12 rs26537 (C vs T: OR = 1.16; 95% CI = 1.02-1.33; P = 0.030), and ATG16L1 rs4663402 (T vs A: OR = 1.28; 95% CI = 1.01-1.63; P = 0.044) were significantly associated with HCC risk. Specifically, ATG10 rs10514231 kept significant association even adjusted for Bonferroni correction (P = 0.001 × 14 = 0.014). Bioinformatics analyses showed that allele C of ATG10 rs10514231 was significantly correlated with higher expression of ATG10 gene in both HCC tissues and normal liver tissues. Dual-luciferase reporter assay presented that cell lines transfected with vectors containing the risk allele C of rs10514231 showed higher relative luciferase activity compared to that containing the allele T.

SIGNIFICANCE

These results suggested that ATG10 rs10514231 might contribute to an allele-specific effect on the expression of host gene ATG10 and explain a fraction of HCC genetic susceptibility. Our study would benefit the construction of early warning model, early prevention, screening, even therapeutic target of HCC.

The prostate cancer risk variant rs55958994 regulates multiple gene expression through extreme long-range chromatin interaction to control tumor progression

Genome-wide association studies identified single-nucleotide polymorphism (SNP) rs55958994 as a significant variant associated with increased susceptibility to prostate cancer. However, the mechanisms by which this SNP mediates increased risk to cancer are still unknown. In this study, we show that this variant is located in an enhancer active in prostate cancer cells. Deletion of this enhancer from prostate tumor cells resulted in decreased tumor initiation, tumor growth, and invasive migration, as well as a loss of stem-like cells. Using a combination of capture chromosome conformation capture (Capture-C) and RNA sequencing, we identified genes on the same and different chromosomes as targets regulated by the enhancer. Furthermore, we show that expression of individual candidate target genes in an enhancer-deleted cell line rescued different aspects of tumorigenesis. Our data suggest that the rs55958994-associated enhancer affects prostate cancer progression by influencing expression of multiple genes via long-range chromatin interactions.

Genome-wide association study of cervical cancer suggests a role for ARRDC3 gene in human papillomavirus infection

The development of cervical cancer is initiated by human papillomavirus (HPV) infection and involves both viral and host genetic factors. Genome-wide association studies (GWAS) of cervical cancer have identified associations in the HLA locus and two loci outside HLA, but the principal genes that control infection and pathogenesis have not been identified. In the present study, we performed GWAS of cervical cancer in East Asian populations, involving 2609 cases and 4712 controls in the discovery stage and 1461 cases and 3295 controls in the follow-up stage. We identified novel-significant associations at 5q14 with the lead single nucleotide polymorphism (SNP) rs59661306 (P = 2.4 × 10-11) and at 7p11 with the lead SNP rs7457728 (P = 1.2 × 10-8). In 5q14, the chromatin region of the GWAS-significant SNPs was found to be in contact with the promoter of the ARRDC3 (arrestin domain-containing 3) gene. In our functional studies, ARRDC3 knockdown in HeLa cells caused significant reductions in both cell growth and susceptibility to HPV16 pseudovirion infection, suggesting that ARRDC3 is involved in the infectious entry of HPV into the cell. Our study advances the understanding of host genes that are responsible for cervical cancer susceptibility and guides future research on HPV infection and cancer development.

Allele-specific binding of RNA-binding proteins reveals functional genetic variants in the RNA

Allele-specific protein-RNA binding is an essential aspect that may reveal functional genetic variants (GVs) mediating post-transcriptional regulation. Recently, genome-wide detection of in vivo binding of RNA-binding proteins is greatly facilitated by the enhanced crosslinking and immunoprecipitation (eCLIP) method. We developed a new computational approach, called BEAPR, to identify allele-specific binding (ASB) events in eCLIP-Seq data. BEAPR takes into account crosslinking-induced sequence propensity and variations between replicated experiments. Using simulated and actual data, we show that BEAPR largely outperforms often-used count analysis methods. Importantly, BEAPR overcomes the inherent overdispersion problem of these methods. Complemented by experimental validations, we demonstrate that the application of BEAPR to ENCODE eCLIP-Seq data of 154 proteins helps to predict functional GVs that alter splicing or mRNA abundance. Moreover, many GVs with ASB patterns have known disease relevance. Overall, BEAPR is an effective method that helps to address the outstanding challenge of functional interpretation of GVs.

SAA1 gene polymorphisms in osteoporosis patients

Background: Serum amyloid A (SAA1) is an apolipoprotein that maintains glucose and lipid homeostasis. Its polymorphisms are associated with risks of myocardial infarction and coronary artery disease (CAD). Methods: However, little is known about the associations of these polymorphisms with susceptibility to osteoporosis, which we evaluated in this hospital-based case–control study involving 300 osteoporosis patients and 350 controls. Three single-nucleotide polymorphisms (SNPs) (rs183978373, rs12218, and rs10832915) were genotyped using MALDI TOF MS. Results: There were no differences in the rs183978373 and rs12218 polymorphisms between the osteoporosis group and controls. The SAA1 gene rs10832915 polymorphism increased the risk of osteoporosis in our Chinese population. The genotypes of the rs10832915 polymorphism were not significantly associated with clinical parameters (age, body mass index (BMI), high- and low-density lipoprotein (LDL), total cholesterol (TC), and T-score). Haplotype analysis revealed that the ATT haplotype had a significant correlation with a decreased risk of osteoporosis. Conclusion: In conclusion, the SAA1 rs10832915 polymorphism and its haplotypes are associated with osteoporosis, but this finding should be confirmed in large well-designed studies.

SEdb: a comprehensive human super-enhancer database

Super-enhancers are important for controlling and defining the expression of cell-specific genes. With research on human disease and biological processes, human H3K27ac ChIP-seq datasets are accumulating rapidly, creating the urgent need to collect and process these data comprehensively and efficiently. More importantly, many studies showed that super-enhancer-associated single nucleotide polymorphisms (SNPs) and transcription factors (TFs) strongly influence human disease and biological processes. Here, we developed a comprehensive human super-enhancer database (SEdb, http://www.licpathway.net/sedb) that aimed to provide a large number of available resources on human super-enhancers. The database was annotated with potential functions of super-enhancers in the gene regulation. The current version of SEdb documented a total of 331 601 super-enhancers from 542 samples. Especially, unlike existing super-enhancer databases, we manually curated and classified 410 available H3K27ac samples from >2000 ChIP-seq samples from NCBI GEO/SRA. Furthermore, SEdb provides detailed genetic and epigenetic annotation information on super-enhancers. Information includes common SNPs, motif changes, expression quantitative trait locus (eQTL), risk SNPs, transcription factor binding sites (TFBSs), CRISPR/Cas9 target sites and Dnase I hypersensitivity sites (DHSs) for in-depth analyses of super-enhancers. SEdb will help elucidate super-enhancer-related functions and find potential biological effects.

Germline genetic polymorphisms influence tumor gene expression and immune cell infiltration

Cancer immunotherapy has emerged as an effective therapy in a variety of cancers. However, a key challenge in the field is that only a subset of patients who receive immunotherapy exhibit durable response. It has been hypothesized that host genetics influences the inherent immune profiles of patients and may underlie their differential response to immunotherapy. Herein, we systematically determined the association of common germline genetic variants with gene expression and immune cell infiltration of the tumor. We identified 64,094 expression quantitative trait loci (eQTLs) that associated with 18,210 genes (eGenes) across 24 human cancers. Overall, eGenes were enriched for their being involved in immune processes, suggesting that expression of immune genes can be shaped by hereditary genetic variants. We identified the endoplasmic reticulum aminopeptidase 2 (ERAP2) gene as a pan-cancer type eGene whose expression levels stratified overall survival in a subset of patients with bladder cancer receiving anti-PD-L1 (atezolizumab) therapy. Finally, we identified 103 gene signature QTLs (gsQTLs) that were associated with predicted immune cell abundance within the tumor microenvironment. Our findings highlight the impact of germline SNPs on cancer-immune phenotypes and response to therapy; and these analyses provide a resource for integration of germline genetics as a component of personalized cancer immunotherapy.

False positives in trans-eQTL and co-expression analyses arising from RNA-sequencing alignment errors

Sequence similarity among distinct genomic regions can lead to errors in alignment of short reads from next-generation sequencing. While this is well known, the downstream consequences of misalignment have not been fully characterized.  We assessed the potential for incorrect alignment of RNA-sequencing reads to cause false positives in both gene expression quantitative trait locus (eQTL) and co-expression analyses. Trans-eQTLs identified from human RNA-sequencing studies appeared to be particularly affected by this phenomenon, even when only uniquely aligned reads are considered. Over 75% of trans-eQTLs using a standard pipeline occurred between regions of sequence similarity and therefore could be due to alignment errors. Further, associations due to mapping errors are likely to misleadingly replicate between studies. To help address this problem, we quantified the potential for "cross-mapping'' to occur between every pair of annotated genes in the human genome. Such cross-mapping data can be used to filter or flag potential false positives in both trans-eQTL and co-expression analyses. Such filtering substantially alters the detection of significant associations and can have an impact on the assessment of false discovery rate, functional enrichment, and replication for RNA-sequencing association studies.

False positives in trans-eQTL and co-expression analyses arising from RNA-sequencing alignment errors

Sequence similarity among distinct genomic regions can lead to errors in alignment of short reads from next-generation sequencing. While this is well known, the downstream consequences of misalignment have not been fully characterized.  We assessed the potential for incorrect alignment of RNA-sequencing reads to cause false positives in both gene expression quantitative trait locus (eQTL) and co-expression analyses. Trans-eQTLs identified from human RNA-sequencing studies appeared to be particularly affected by this phenomenon, even when only uniquely aligned reads are considered. Over 75\% of trans-eQTLs using a standard pipeline occurred between regions of sequence similarity and therefore could be due to alignment errors. Further, associations due to mapping errors are likely to misleadingly replicate between studies. To help address this problem, we quantified the potential for "cross-mapping'' to occur between every pair of annotated genes in the human genome. Such cross-mapping data can be used to filter or flag potential false positives in both trans-eQTL and co-expression analyses. Such filtering substantially alters the detection of significant associations and can have an impact on the assessment of false discovery rate, functional enrichment, and replication for RNA-sequencing association studies.

The rs878081 polymorphism of AIRE gene increases the risk of rheumatoid arthritis in a Chinese Han population: a case-control study

The autoimmune regulator (AIRE), a transcriptional regulator expressed in medullary thymic epithelial cells, plays an important role in thymocyte education and negative selection. Several citations studying the association between the rs878081 exon polymorphism of the AIRE gene and the risk of rheumatoid arthritis (RA) in different populations have yielded conflicting findings. Thus, this case-control study involving 300 RA cases and 300 controls was aimed to identify whether such association existed in a Chinese Han population from East China. The rs878081 polymorphism of the AIRE gene was genotyped. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using the chi-squared test, genetic model analysis, and stratification analysis. Genetic model analysis showed significant correlations between the TT genotype and the risk of RA (OR: 1.89, 95%CI: 1.03-3.47 in TT vs CC; OR: 1.84, 95%CI: 1.02-3.31 in TT vs CC+TC). Stratification analyses of sex, age, smoking, and alcoholism suggested that the rs878081 polymorphism of the AIRE gene increased RA risk among non-smokers. In conclusion, rs878081 polymorphism of AIRE gene increases the risk of RA in a Chinese Han population.

Maximizing the Utility of Cancer Transcriptomic Data

Transcriptomic profiling has been applied to large numbers of cancer samples, by large-scale consortia, including The Cancer Genome Atlas, International Cancer Genome Consortium, and Cancer Cell Line Encyclopedia. Advances in mining cancer transcriptomic data enable us to understand the endless complexity of the cancer transcriptome and thereby to discover new biomarkers and therapeutic targets. In this paper, we review computational resources for deep mining of transcriptomic data to identify, quantify, and determine the functional effects and clinical utility of transcriptomic events, including noncoding RNAs, post-transcriptional regulation, exogenous RNAs, and transcribed genetic variants. These approaches can be applied to other complex diseases, thereby greatly leveraging the impact of this work.

The association between COX-2 gene rs5275 polymorphism and Nasopharyngeal carcinoma risk

The overexpression of cyclooxygenase-2 (COX-2) was correlated with the invasion and lymphatic metastasis and with the clinical stage of Nasopharyngeal carcinoma (NPC). The C allele of COX-2 gene rs5275 polymorphism disrupts miR-542-3p function to promote COX-2 overexpression. To examine the role of COX-2 gene rs5275 polymorphism in NPC, we determined COX-2 gene rs5275 polymorphism by using a custom-by-design 48-Plex single nucleotide polymorphism (SNP) Scan™ Kit. We found that C allele or CC genotype of rs5275 polymorphism in COX-2 gene was associated with an increased risk of NPC. In stratified analyses, COX-2 gene rs5275 polymorphism was associated with the risk of NPC among females, smokers, and drinkers. Based on these results, we concluded that COX-2 gene rs5275 variant contributes to NPC risk in a Chinese population. Larger studies with more diverse ethnic populations are needed to confirm these results.

Exome sequencing study in patients with multiple sclerosis reveals variants associated with disease course

Background

It remains unclear whether disease course in multiple sclerosis (MS) is influenced by genetic polymorphisms. Here, we aimed to identify genetic variants associated with benign and aggressive disease courses in MS patients.

Methods

MS patients were classified into benign and aggressive phenotypes according to clinical criteria. We performed exome sequencing in a discovery cohort, which included 20 MS patients, 10 with benign and 10 with aggressive disease course, and genotyping in 2 independent validation cohorts. The first validation cohort encompassed 194 MS patients, 107 with benign and 87 with aggressive phenotypes. The second validation cohort comprised 257 patients, of whom 224 patients had benign phenotypes and 33 aggressive disease courses. Brain immunohistochemistries were performed using disease course associated genes antibodies.

Results

By means of single-nucleotide polymorphism (SNP) detection and comparison of allele frequencies between patients with benign and aggressive phenotypes, a total of 16 SNPs were selected for validation from the exome sequencing data in the discovery cohort. Meta-analysis of genotyping results in two validation cohorts revealed two polymorphisms, rs28469012 and rs10894768, significantly associated with disease course. SNP rs28469012 is located in CPXM2 (carboxypeptidase X, M14 family, member 2) and was associated with aggressive disease course (uncorrected p value < 0.05). SNP rs10894768, which is positioned in IGSF9B (immunoglobulin superfamily member 9B) was associated with benign phenotype (uncorrected p value < 0.05). In addition, a trend for association with benign phenotype was observed for a third SNP, rs10423927, in NLRP9 (NLR family pyrin domain containing 9). Brain immunohistochemistries in chronic active lesions from MS patients revealed expression of IGSF9B in astrocytes and macrophages/microglial cells, and expression of CPXM2 and NLRP9 restricted to brain macrophages/microglia.

Conclusions

Genetic variants located in CPXM2, IGSF9B, and NLRP9 have the potential to modulate disease course in MS patients and may be used as disease activity biomarkers to identify patients with divergent disease courses. Altogether, the reported results from this study support the influence of genetic factors in MS disease course and may help to better understand the complex molecular mechanisms underlying disease pathogenesis.

Electronic supplementary material

The online version of this article (10.1186/s12974-018-1307-1) contains supplementary material, which is available to authorized users.

Genome-Wide Expression Quantitative Trait Loci Analysis Using Mixed Models

Expression quantitative trait loci (eQTLs) are important for understanding the genetic basis of cellular activities and complex phenotypes. Genome-wide eQTL analyses can be effectively conducted by employing a mixed model. The mixed model includes random polygenic effects with variability, which can be estimated by the covariance structure of pairwise genomic similarity among individuals based on genotype information for nucleotide sequence variants. This increases the accuracy of identifying eQTLs by avoiding population stratification. Its extensive use will accelerate our understanding of the genetics of gene expression and complex phenotypes. An overview of genome-wide eQTL analyses using mixed model methodology is provided, including discussions of both theoretical and practical issues. The advantages of employing mixed models are also discussed in this review.

The Association Between AURKA Gene rs2273535 Polymorphism and Gastric Cancer Risk in a Chinese Population

The Aurora kinase A (AURKA) gene is frequently amplified and overexpressed in gastric cancer (GC). The overexpression of AURKA promotes inflammation and tumorigenesis in GC. We performed co-expression analysis to identify genes associated with AURKA and speculated its function through the COXPRESdb and STRING databases. We also conducted a hospital-based case-control study involving 385 GC cases and 470 controls in a Chinese population to evaluate the role of AURKA gene rs2273535 polymorphism in the risk of GC. Genotyping was performed using a custom-by-design 48-Plex single nucleotide polymorphism (SNP) Scan™ Kit. Co-expression analysis indicated that the overexpression of AURKA may be associated with poor prognosis of GC. In addition, TT genotypes of rs2273535 polymorphism increased the risk of GC by 72% compared to the AA genotypes. This significant correlation was also observed in the allelic and dominant models. The stratified analysis suggested that TT+AT genotypes showed positive correlation with the risk of GC among female, age <55 years group and non-smokers compared to AA genotypes. In conclusion, AURKA plays an important role in the development of GC. Larger studies with more diverse ethnic populations are needed to confirm these results.

Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients

Our comprehensive analysis of alternative splicing across 32 The Cancer Genome Atlas cancer types from 8,705 patients detects alternative splicing events and tumor variants by reanalyzing RNA and whole-exome sequencing data. Tumors have up to 30% more alternative splicing events than normal samples. Association analysis of somatic variants with alternative splicing events confirmed known trans associations with variants in SF3B1 and U2AF1 and identified additional trans-acting variants (e.g., TADA1, PPP2R1A). Many tumors have thousands of alternative splicing events not detectable in normal samples; on average, we identified ≈930 exon-exon junctions ("neojunctions") in tumors not typically found in GTEx normals. From Clinical Proteomic Tumor Analysis Consortium data available for breast and ovarian tumor samples, we confirmed ≈1.7 neojunction- and ≈0.6 single nucleotide variant-derived peptides per tumor sample that are also predicted major histocompatibility complex-I binders ("putative neoantigens").

Host genetics and microbiome associations through the lens of genome wide association studies

Recent studies indicate that the gut microbiome is partially heritable, motivating the need to investigate microbiome-host genome associations via microbial genome-wide association studies (mGWAS). Existing mGWAS demonstrate that microbiome-host genotype associations are typically weak and are spread across multiple variants, similar to associations often observed in genome-wide association studies (GWAS) of complex traits. Here we reconsider mGWAS by viewing them through the lens of GWAS, and demonstrate that there are striking similarities between the challenges and pitfalls faced by the two study designs. We further advocate the mGWAS community to adopt three key lessons learned over the history of GWAS: firstly, adopting uniform data and reporting formats to facilitate replication and meta-analysis efforts; secondly, enforcing stringent statistical criteria to reduce the number of false positive findings; and thirdly, considering the microbiome and the host genome as distinct entities, rather than studying different taxa and single nucleotide polymorphism (SNPs) separately. Finally, we anticipate that mGWAS sample sizes will have to increase by orders of magnitude to reproducibly associate the host genome with the gut microbiome.

Relationship Between Tumor Necrosis Factor-α rs361525 Polymorphism and Gastric Cancer Risk: A Meta-Analysis

Tumor necrosis factor (TNF)-α, a major part in inflammatory, infectious and tumor processes, and is pivotal at the early stages of gastric cancer. Relationship between its risk and TNF-α rs361525 polymorphism has been demonstrated, but remains conflicting and controversial. Therefore, a meta-analysis was conducted to more precisely estimate this relationship. PubMed, Web of Science, EMBASE and CNKI were comprehensively searched to find out relevant articles through October 5, 2017. The strength of the relationship was assessed using pooled odds ratios and 95% confidence intervals. Totally 20 articles were included involving 4,084 cases and 7,010 controls. No significant relationship between TNF-α rs361525 polymorphism and increased GC risk was found in the whole populations. Subgroup analyses uncovered TNF-α rs361525 polymorphism intensified the risk of GC among Asians under five models, but decreased the risk of GC among Caucasiansin the allelic and dominant models. Subgroup analysis by genotyping methods revealed increased risk for other methods. In conclusion, this meta-analysis suggests TNF-α rs361525 polymorphism is related to the risk of GC, especially for Asians.