cis

1. what is ncRNA-eQTL?

Numerous studies indicate that ncRNAs have critical functions across biological processes, and single nucleotide polymorphisms (SNPs) could contribute to diseases or traits through influencing ncRNA expression. However, the associations between SNPs and ncRNA expressions are largely unknown. Therefore, genome-wide expression quantitative trait loci (eQTL) analysis, especially the use of multiple cancer type data to assess the effect of SNPs on ncRNA expression, will help to understand how genetic risk alleles contribute towards tumorigenesis and cancer development.

ncRNA-eQTL aims to comprehensively provide ncRNA related cis-eQTLs (SNPs affect local ncRNA gene expression) and trans-eQTLs (SNPs affect distant ncRNA gene expression) in different cancer types from The Cancer Genome Atlas (TCGA). We have identified 6,045,445 and 715,952 eQTL−gene pairs at FDR < 0.05 in 33 cancer types in cis-eQTL and trans-eQTL analysis, respectively. To prioritize promising ncRNA-eQTLs, we identified ncRNA-eQTLs associated with patient survival times (survival-eQTLs) and located in GWAS linkage disequilibrium (LD) regions (GWAS-eQTLs). In survival-eQTL, we identified 8,235 eQTLs associated with patient overall survival times, and in GWAS-eQTL, we identified 1,709,372 eQTLs that overlap with GWAS linkage disequilibrium (LD) regions.

In ncRNA-eQTL database, users can :

  • Browse or search ncRNA-eQTLs across different cancer types
  • Browse or search ncRNA-eQTLs associated with patient overall survival times across different cancer types
  • Browse or search ncRNA-eQTLs in GWAS linkage disequilibrium (LD) regions
  • Download figures and all eQTL results

2. Database construction pipeline

3. About the search result

Cis/Trans-eQTL

SNP ID: Reference id from dbSNP.
SNP Position: The position of SNP including chromosme.
Alleles: SNP alleles.
Gene Position: Strand : Gene genomic position including chromosome, gene region and strand.
r: The correlation coefficient.
Beta: Effect size of SNP on gene expression (calculated by linear regression using a computationally efficient eQTL analysis called Matrix eQTL).
P-value: P-value calculated by Matrix eQTL.
Display: Diagram of boxplot to display the association between SNP genotypes and gene expression.

GWAS-eQTL

LD: Linkage disequilibrium.
GWAS Traits: Traits linked with SNP.

Survival-eQTL

Sample Sizes: Number of samples in a cancer type.
Median Survival Time XX : Median survival times of different genotypes.
Display: Embedd diagram of the Kaplan–Meier plot to display the association between SNP genotypes and overall survival times..

4. Web introduction

5. Data summary

Cancer type No. of samples No. of ncRNAs No. of genotypes Cis-pairs Cis-ncRNAs Cis-eQTL Trans-pairs Trans-ncRNAs Trans-eQTL
ACC 77 10673 3567953 6906 229 6547 1030 49 934
BLCA 403 12090 4191159 205824 4077 156228 29840 935 23714
BRCA 1067 13170 2745615 498969 8124 308016 62764 2328 46137
CESC 242 12410 4276554 101968 2626 82242 20779 735 17555
CHOL 36 12217 4012151 0 0 0 0 0 0
COAD 282 11063 4505758 169256 3558 129931 25618 859 20423
DLBC 47 11447 4819767 122 8 121 82 4 82
ESCA 148 19921 4431385 44450 1238 37484 6177 218 4723
GBM 139 15247 4525414 74593 1743 61211 6109 202 5518
HNSC 492 11768 4249925 294234 4698 217470 38283 1075 31312
KICH 65 11736 3755519 9289 265 8075 382 26 320
KIRC 520 14537 4578071 558380 7216 380619 57962 1492 47020
KIRP 287 12554 4881400 228243 4350 175762 28242 905 22640
LAML 96 19856 5078753 44537 1069 34233 5570 148 4802
LGG 498 14213 4626469 723868 7844 465249 71517 1595 54899
LIHC 367 9691 4157271 171255 3333 128444 22425 764 17725
LUAD 506 13624 4384017 347537 5714 249589 39040 1130 31147
LUSC 495 14319 3745439 321036 5556 226779 44235 1236 38111
MESO 81 12393 4759523 15045 372 14140 2154 97 1779
OV 251 16660 2966217 102136 3423 79467 11534 515 9317
PAAD 177 13298 4991769 139332 2623 112129 16561 494 13926
PCPG 174 11971 4709166 119083 2571 93599 16630 552 14066
PRAD 478 12945 4822300 604359 7181 412073 69412 1686 56307
READ 91 11298 4540674 18312 539 16750 4645 153 4138
SARC 257 11454 4087361 105751 2607 85278 17121 600 14261
SKCM 103 11315 4854570 14150 427 13014 4856 159 4501
STAD 371 19117 4300207 175519 3637 128258 21276 635 15847
TGCT 148 14304 4811363 95579 1989 79971 11477 325 9839
THCA 495 12874 4876701 702674 7426 464482 57645 1353 44077
THYM 119 13223 4930920 92773 1907 74540 9355 330 7029
UCEC 173 11548 4957767 44594 1327 38605 9933 411 8385
UCS 55 13439 3871537 51 5 51 0 0 0
UVM 77 9182 4692767 15620 405 14530 3300 128 2993

6. Full name of cancer types

Cancer type Disease full name
ACC Adrenocortical carcinoma
BLCA Bladder urothelial carcinoma
BRCA Breast invasive carcinoma
CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma
CHOL Cholangiocarcinoma
COAD Colon adenocarcinoma
DLBC Lymphoid neoplasm diffuse large B-cell lymphoma
ESCA Esophageal carcinoma
GBM Glioblastoma multiforme
HNSC Head and neck squamous cell carcinoma
KICH Kidney chromophobe
KIRC Kidney renal clear cell carcinoma
KIRP Kidney renal papillary cell carcinoma
LAML Acute myeloid leukemia
LGG Lower grade glioma
LIHC Liver hepatocellular carcinoma
LUAD Lung adenocarcinoma
LUSC Lung squamous cell carcinoma
MESO Mesothelioma
OV Ovarian serous cystadenocarcinoma
PAAD Pancreatic adenocarcinoma
PCPG Pheochromocytoma and paraganglioma
PRAD Prostate adenocarcinoma
READ Rectum adenocarcinoma
SARC Sarcoma
SKCM Skin cutaneous melanoma
STAD Stomach adenocarcinoma
TGCT Testicular germ cell tumors
THCA Thyroid carcinoma
THYM Thymoma
UCEC Uterine corpus endometrial carcinoma
UCS Uterine carcinosarcoma
UVM Uveal melanoma

7. The eQTL boxplot and survival Kaplan-Meier plot

eQTL boxplot

Kaplan-Meier plot

8. The identification of GWAS-related eQTLs

eQTLs that overlaps with tagSNPs and/or linkage disequilibrium regions were extracted as GWAS-related eQTLs.

Data source

  • GWAS tagSNPs: GWAS catalog website (http://www.ebi.ac.uk/gwas/)
  • GWAS linkage disequilibrium regions: SNAP database (https://personal.broadinstitute.org/plin/snap/ldsearch.php)
  • The TCGA data portal (https://tcga-data.nci.nih.gov/tcga/)
  • GENCODE (version 22) website (https://www.gencodegenes.org/)
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