Genotype-Tissue Expression Datasets Research Articles

RNA-Binding Proteins Play an Important Role in the Prognosis of Patients With Testicular Germ Cell Tumor.

Testicular germ cell tumors (TGCTs) are common urological neoplasms in young adult males. The outcome of TGCT depends on pathologic type and tumor stage. RNA-binding proteins (RBPs) influence numerous cancers via post-transcriptional regulation. The prognostic importance of RBPs in TGCT has not been fully investigated. In this study, we set up a prognostic risk model of TGCT using six significantly differentially expressed RBPs, namely, TRMT61A, POLR2J, DIS3L2, IFIH1, IGHMBP2, and NPM2. The expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression datasets. We observed by performing least absolute shrinkage and selection operator (LASSO) regression analyses that in the training cohort, the expression of six RBPs was correlated with disease-free survival in patients with TGCT. We assessed the specificity and sensitivity of 1-, 3-, 5-, and 10-year survival status prediction using receiver operating characteristic curve analysis and successfully validated using the test cohorts, the entire TCGA cohort, and Gene Expression Omnibus (GEO) datasets. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analyses were carried out to seek the possible signaling pathways related with risk score. We also examined the association between the model based on six RBPs and different clinical characteristics. A nomogram was established for TGCT recurrence prediction. Consensus clustering analysis was carried out to identify the clusters of TGCT with different clinical outcomes. Ultimately, external validations of the six-gene risk score were performed by using the GSE3218 and GSE10783 datasets downloaded from the GEO database. In general, our study constructed a prognostic model based on six RBPs, which could serve as independent risk factor in TGCT, especially in seminoma, and might have brilliant clinical application value.

Prognostic Signatures of Metabolic Genes and Metabolism-Related Long Non-coding RNAs Accurately Predict Overall Survival for Osteosarcoma Patients.

In this study, we identified eight survival-related metabolic genes in differentially expressed metabolic genes by univariate Cox regression analysis based on the therapeutically applicable research to generate effective treatments (n = 84) data set and genotype tissue expression data set (n = 396). We also constructed a six metabolic gene signature to predict the overall survival of osteosarcoma (OS) patients using least absolute shrinkage and selection operator (Lasso) Cox regression analysis. Our results show that the six metabolic gene signature showed good performance in predicting survival of OS patients and was also an independent prognostic factor. Stratified correlation analysis showed that the metabolic gene signature accurately predicted survival outcomes in high-risk and low-risk OS patients. The six metabolic gene signature was also verified to perform well in predicting survival of OS patients in an independent cohort (GSE21257). Then, using univariate Cox regression and Lasso Cox regression analyses, we identified an eight metabolism-related long noncoding RNA (lncRNA) signature that accurately predicts overall survival of OS patients. Gene set variation analysis showed that the apical surface and bile acid metabolism, epithelial mesenchymal transition, and P53 pathway were activated in the high-risk group based on the eight metabolism-related lncRNA signature. Furthermore, we constructed a competing endogenous RNA (ceRNA) network and conducted immunization score analysis based on the eight metabolism-related lncRNA signature. These results showed that the six metabolic gene signature and eight metabolism-related lncRNA signature have good performance in predicting the survival outcomes of OS patients.

A 4-gene signature predicts prognosis of uterine serous carcinoma

BackgroundUterine serous carcinoma (USC) is an aggressive type of endometrial cancer that accounts for up to 40% of endometrial cancer deaths, creating an urgent need for prognostic biomarkers.MethodsUSC RNA-Seq data and corresponding patients’ clinical records were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression datasets. Univariate cox, Lasso, and Multivariate cox regression analyses were conducted to forge a prognostic signature. Multivariable and univariable cox regression analysis and ROC curve evaluated the prediction efficiency both in the training and testing sets.ResultsWe uncovered 1385 genes dysregulated in 110 cases of USC tissue relative to 113 cases of normal uterine tissue. Functional enrichment analysis of these genes revealed the involvement of various cancer-related pathways in USC. A novel 4-gene signature (KRT23, CXCL1, SOX9 and ABCA10) of USC prognosis was finally forged by serial regression analyses. Overall patient survival (OS) and recurrence-free survival (RFS) were significantly lower in the high-risk group relative to the low-risk group in both the training and testing sets. The area under the ROC curve of the 4-gene signature was highest among clinicopathological features in predicting OS and RFS. The 4-gene signature was found to be an independent prognostic indicator in USC and was a superior predictor of OS in early stage of USC.ConclusionsOur findings highlight the potential of the 4-gene signature as a guide for personalized USC treatment.

SNPs associated with colorectal cancer at 15q13.3 affect risk enhancers that modulate GREM1 gene expression.

Several genome wide association studies of colorectal cancer (CRC) have identified single nucleotide polymorphisms (SNPs) on chromosome 15q13.3 associated with CRC risk. To identify functional variant(s) underlying this association, we investigated SNPs in linkage disequilibrium with the risk‐associated SNP rs4779584 that overlapped regulatory regions/enhancer elements characterized in colon‐related tissues and cells. We identified several SNP‐containing regulatory regions that exhibited enhancer activity in vitro, including one SNP (rs1406389) that correlated with allele‐specific effects on enhancer activity. Deletion of either this enhancer or another enhancer that had previously been reported in this region correlated with decreased expression of GREM1 following CRISPR/Cas9 genome editing. That GREM1 is one target of these enhancers was further supported by an expression quantitative trait loci correlation between rs1406389 and GREM1 expression in the transverse but not sigmoid colon in the Genotype‐Tissue Expression dataset. Taken together, we conclude that the 15q13.3 region contains at least two functional variants that map to distinct enhancers and impact CRC risk through modulation of GREM1 expression.

M6A RNA methylation regulators play an important role in the prognosis of patients with testicular germ cell tumor

BackgroundN6-methyladenosine (m6A) is found to be associated with promoting tumorigenesis in different types of cancers, however, the function of m6A-related genes in testicular germ cell tumors (TGCT) development remains to be illuminated. This study aimed to investigated the prognostic value of m6A RNA methylation regulators in TGCT.MethodsWe collected TGCT patients’ information about clinicopathologic parameters and twenty-two m6A regulatory genes expression from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx). We analyzed the differentially expressed m6A RNA methylation regulators between tumor tissues and normal tissues, as well as the correlation of m6A RNA methylation regulators. By using Cox univariate analysis, last absolute shrinkage and selection operator (LASSO) Cox regression algorithm and Cox multivariate proportional hazards regression analysis, a risk score was constructed based on a TCGA training cohort, and further verified in the TCGA testing cohort. Then, univariate and multivariate Cox regression analyses were used to evaluate the relationship between risk score and progression-free survival (PFS) in TGCT. Finally, the six-gene risk score was further verified by two gene expression profiles (GSE3218 and GSE10783) as an independent external validation cohort.ResultsDistinct expression patterns of m6A regulatory genes were identified between TGCT tissues and normal tissues in TCGA and GTEx datasets. To predict prognosis of TGCT patients, a risk score was calculated based on six selected m6A RNA methylation regulators (YTHDF1, RBM15, IGF2BP1, ZC3H13, METTL3, and FMR1). Additionally, we found significant differences between the high-risk and low-risk groups in serum marker study levels and histologic subtype. Univariate and multivariate analysis indicated that high risk score was associated with unfavorable PFS. Ultimately, the risk score was further verified by two gene expression profiles (GSE3218 and GSE10783).ConclusionsBased on six selected m6A RNA methylation regulators, we developed a m6A methylation related risk score that can independently predict the prognosis of TGCT patients, and verify the prediction efficiency in TCGA and GEO datasets. Patients in high-risk group were associated with serum tumor marker study levels beyond the normal limits, non-seminoma, and unfavorable survival time. However, further prospective experiments should be carried out to verify our results.

A Novel Seventeen-Gene Metabolic Signature for Predicting Prognosis in Colon Cancer

A metabolic disorder is considered one of the hallmarks of cancer. Multiple differentially expressed metabolic genes have been identified in colon cancer (CC), and their biological functions and prognostic values have been well explored. The purpose of the present study was to establish a metabolic signature to optimize the prognostic prediction in CC. The related data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, and Gene Expression Omnibus (GEO) combined with GSE39582 set, GSE17538 set, GSE33113 set, and GSE37892 set. The differentially expressed metabolic genes were selected for univariate Cox regression and lasso Cox regression analysis using TCGA and GTEx datasets. Finally, a seventeen-gene metabolic signature was developed to divide patients into a high-risk group and a low-risk group. Patients in the high-risk group presented poorer prognosis compared to the low-risk group in both TCGA and GEO datasets. Moreover, gene set enrichment analyses demonstrated multiple significantly enriched metabolism-related pathways. To sum up, our study described a novel seventeen-gene metabolic signature for prognostic prediction of colon cancer.

Integrated analysis of lymphocyte infiltration-associated lncRNA for ovarian cancer via TCGA, GTEx and GEO datasets.

BackgroundAbnormal expression of long non-coding RNAs (lncRNA) play a significant role in the incidence and progression of high-grade serous ovarian cancer (HGSOC), which is a leading cause of mortality among gynecologic malignant tumor patients. In this study, our aim is to identify lncRNA-associated competing endogenous RNA (ceRNA ) axes that could define more reliable prognostic parameters of HGSOC, and to investigate the lncRNAs’ potential mechanism of in lymphocyte infiltration.MethodsThe RNA-seq and miRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database; while for obtaining the differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs), we used edgeR, limma and DESeq2. After validating the RNA, miRNA and gene expressions, using integrated three RNA expression profiles (GSE18520, GSE27651, GSE54388) and miRNA profile (GSE47841) from the Gene Expression Omnibus (GEO) database, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analyses through ClusterProfiler. The prognostic value of these genes was determined with Kaplan–Meier survival analysis and Cox regression analysis. The ceRNA network was constructed using Cytoscape. The correlation between lncRNAs in ceRNA network and immune infiltrating cells was analyzed by using Tumor IMmune Estimation Resource (TIMER), and gene markers of tumor-infiltrating immune cells were identified using Spearman’s correlation after removing the influence of tumor purity.ResultsA total of 33 DELs (25 upregulated and eight downregulated), 134 DEMs (76 upregulated and 58 downregulated), and 1,612 DEGs (949 upregulated and 663 downregulated) were detected that could be positively correlated with overall survival (OS) of HGSOC. With the 1,612 analyzed genes, we constructed a ceRNA network, which indicated a pre-dominant involvement of the immune-related pathways. Furthermore, our data revealed that LINC00665 influenced the infiltration level of macrophages and dendritic cells (DCs). On the other hand, FTX and LINC00665, which may play their possible roles through the ceRNA axis, demonstrated a potential to inhibit Tregs and prevent T-cell exhaustion.ConclusionWe defined several prognostic biomarkers for the incidence and progression of HGSOC and constructed a network for ceRNA axes; among which three were indicated to have a positive correlation with lymphocyte infiltration, namely: FTX-hsa-miR-150-5p-STK11, LINC00665-hsa-miR449b-5p-VAV3 and LINC00665-hsa-miR449b-5p-RRAGD.

Consistent RNA sequencing contamination in GTEx and other data sets

A challenge of next generation sequencing is read contamination. We use Genotype-Tissue Expression (GTEx) datasets and technical metadata along with RNA-seq datasets from other studies to understand factors that contribute to contamination. Here we report, of 48 analyzed tissues in GTEx, 26 have variant co-expression clusters of four highly expressed and pancreas-enriched genes (PRSS1, PNLIP, CLPS, and/or CELA3A). Fourteen additional highly expressed genes from other tissues also indicate contamination. Sample contamination is strongly associated with a sample being sequenced on the same day as a tissue that natively expresses those genes. Discrepant SNPs across four contaminating genes validate the contamination. Low-level contamination affects ~40% of samples and leads to numerous eQTL assignments in inappropriate tissues among these 18 genes. This type of contamination occurs widely, impacting bulk and single cell (scRNA-seq) data set analysis. In conclusion, highly expressed, tissue-enriched genes basally contaminate GTEx and other datasets impacting analyses.

B7-H3 and B7-H4 Expression in Breast Cancer and Their Association with Clinicopathological Variables and T Cell Infiltration

Objectives: B7-H3 and B7-H4 proteins are expressed in breast cancer tissues, but their relationships with respect to tumor immune surveillance and outcomes in breast cancer are not conclusive. Methods: We first examined B7-H3 and B7-H4 mRNA expression in the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets. Next, mRNA and protein expression were assessed by RNAscope in situ hybridization (ISH) and immunohistochemistry in 10 pairs of breast cancer and matched normal tissues. Immunohistochemical staining of B7-H3, B7-H4, CD3, and CD8 was performed in tissue microarray slides containing 198 breast cancer samples. Association of B7-H3 and B7-H4 expression with survival was verified using the publicly accessible BreastMark tool. Results: B7-H3 and B7-H4 mRNA expression were significantly higher in breast cancer samples in the TCGA dataset than in normal breast tissues in the GTEx dataset. RNAscope ISH and immunohistochemistry showed that B7-H3 and B7-H4 mRNA and protein appeared to be mainly expressed in cancer cells. Expression of B7-H3 and B7-H4 tended to be associated with low-density scores of stromal tumor-infiltrating lymphocytes (TILs) as well as molecular subtypes. Expressions of B7-H3 and B7-H4 were negatively correlated with stromal CD3+ and CD8+ T cell infiltration density. B7-H3 and B7-H4 expression was not associated with survival, which was verified by BreastMark analysis. Conclusion: Expression levels of B7-H3 and B7-H4 were independent of clinical outcomes of breast cancer. There was an inverse relationship between the expression of B7-H3 and B7-H4 in breast cancer and the density of stromal TILs and CD8+ T lymphocytes. This inverse relationship may represent a promising target in the field of breast cancer immunotherapy.

Uncovering biomarker genes with enriched classification potential from Hallmark gene sets

Given the complex relationship between gene expression and phenotypic outcomes, computationally efficient approaches are needed to sift through large high-dimensional datasets in order to identify biologically relevant biomarkers. In this report, we describe a method of identifying the most salient biomarker genes in a dataset, which we call “candidate genes”, by evaluating the ability of gene combinations to classify samples from a dataset, which we call “classification potential”. Our algorithm, Gene Oracle, uses a neural network to test user defined gene sets for polygenic classification potential and then uses a combinatorial approach to further decompose selected gene sets into candidate and non-candidate biomarker genes. We tested this algorithm on curated gene sets from the Molecular Signatures Database (MSigDB) quantified in RNAseq gene expression matrices obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data repositories. First, we identified which MSigDB Hallmark subsets have significant classification potential for both the TCGA and GTEx datasets. Then, we identified the most discriminatory candidate biomarker genes in each Hallmark gene set and provide evidence that the improved biomarker potential of these genes may be due to reduced functional complexity.

1722-P: Colocalization of TOPMed Whole Genome Sequencing Analysis and Tissue-Specific eQTL Signals Detects Target Genes for Type 2 Diabetes Risk

Type 2 diabetes (T2D) risk is highly heritable, and although many associated loci have been identified, analyses of regulatory regions in most whole-genome sequencing (WGS) studies have not been specific to relevant tissues. The availability of tissue-specific expression quantitative trait (eQTL) information can further elucidate the functional role of non-coding regulatory variants. A WGS (>38x sequencing depth) association study was performed for variants in 9,663 cases and 35,050 controls from the NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program. eQTL data from 48 different tissues was obtained from the Genotype-Tissue Expression (GTEx) Portal. To generate loci of interest, 200-kb windows were considered upstream and downstream of each variant found to be genome-wide significant (p < 5x10^-8) in both the TOPMed and GTEx datasets. A total of 11 regions in different tissues were significantly associated with T2D status, centered around variants such as rs6585201 in the ascending aorta (TCF7L2, TOPMed p = 1.18x10^-9, GTEx p = 1.12x10^-10, but did not localize to the strongest signal in the region), rs76895963 in the cerebellum (CCND2, TOPMed p = 3.51x10^-9, GTEx p = 4.40x10^-17), and rs4898431 in ovarian tissue (DUSP9, TOPMed p = 1.88x10^-8, GTEx p = 4.39x10^-8). We plan to implement eCAVIAR to examine causal variant colocalization, with the advantages of leveraging summary statistics and accounting for the possibility of more than one causal variant per locus. These results confirm previously published studies, but provide validation of methods we will extend to larger samples and diabetes-related outcomes. Disclosure M.D. Szeto: None. H.M. Highland: None. A. Manning: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (U01DK078616, K01DK107836)

Role of ATG10 expression quantitative trait loci in non-small cell lung cancer survival.

The aim of this article was to evaluate whether genetic variants in autophagy-related genes affect the overall survival (OS) of non-small cell lung cancer (NSCLC) patients. We analyzed 14 single nucleotide polymorphisms (SNPs) in core autophagy-related genes for OS in 1,001 NSCLC patients. Three promising SNPs in ATG10 were subsequently annotated by the expression quantitative trait loci (eQTL) and methylation quantitative trait loci (meQTL) analyses based on Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets. We observed that the variants of rs10514231, rs1864182 and rs1864183 were associated with poor lung cancer survival (HR = 1.33, 95% CI = 1.07-1.65; HR = 1.43, 95% CI = 1.13-1.81; HR = 1.38, 95% CI = 1.14-1.68, respectively) and positively correlated with ATG10 expression (all p < 0.05) from GTEx and TCGA datasets. The elevated expression of ATG10 may predict shorter survival time in lung cancer patients in TCGA dataset (HR = 2.10, 95% CI = 1.33-3.29). Moreover, the variants of rs10514231 and rs1864182 were associated with the increased methylation levels of cg17942617 (meQTL), which in turn contributed to the elevated ATG10 expression and decreased survival time. Further functional assays revealed that ATG10 facilitated lung cancer cell proliferation and migration. Our findings suggest that eQTL/meQTL variations of ATG10 could influence lung cancer survival through regulating ATG10 expression.