Dataset Information

Disease-Associated SNPs in Inflammation-Related lncRNAs.

ABSTRACT: Immune-mediated diseases, such as celiac disease, type 1 diabetes or multiple sclerosis, are a clinically heterogeneous group of diseases that share many key genetic triggers. Although the pathogenic mechanisms responsible for the development of immune mediated disorders is not totally understood, high-throughput genomic studies, such as GWAS and Immunochip, performed in the past few years have provided intriguing hints about underlying mechanisms and pathways that lead to disease. More than a hundred gene variants associated with disease susceptibility have been identified through such studies, but the progress toward understanding the underlying mechanisms has been slow. The majority of the identified risk variants are located in non-coding regions of the genome making it difficult to assign a molecular function to the SNPs. However, recent studies have revealed that many of the non-coding regions bearing disease-associated SNPs generate long non-coding RNAs (lncRNAs). LncRNAs have been implicated in several inflammatory diseases, and many of them have been shown to function as regulators of gene expression. Many of the disease associated SNPs located in lncRNAs modify their secondary structure, or influence expression levels, thereby affecting their regulatory function, hence contributing to the development of disease.

SUBMITTER: Castellanos-Rubio A

PROVIDER: S-EPMC6418042 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Disease-Associated SNPs in Inflammation-Related lncRNAs.

Castellanos-Rubio Ainara A Ghosh Sankar S

Frontiers in immunology 20190308

Immune-mediated diseases, such as celiac disease, type 1 diabetes or multiple sclerosis, are a clinically heterogeneous group of diseases that share many key genetic triggers. Although the pathogenic mechanisms responsible for the development of immune mediated disorders is not totally understood, high-throughput genomic studies, such as GWAS and Immunochip, performed in the past few years have provided intriguing hints about underlying mechanisms and pathways that lead to disease. More than a h ...[more]

PMID: 30906297

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Project description:BACKGROUND:Recent genome-wide association (GWA) studies have provided compelling evidence of association between genetic variants and common complex diseases. These studies have made use of cases and controls almost exclusively from populations of European ancestry and little is known about the frequency of risk alleles in other populations. The present study addresses the transferability of disease associations across human populations by examining levels of population differentiation at disease-associated single nucleotide polymorphisms (SNPs). METHODS:We genotyped ~1000 individuals from 53 populations worldwide at 25 SNPs which show robust association with 6 complex human diseases (Crohn's disease, type 1 diabetes, type 2 diabetes, rheumatoid arthritis, coronary artery disease and obesity). Allele frequency differences between populations for these SNPs were measured using Fst. The Fst values for the disease-associated SNPs were compared to Fst values from 2750 random SNPs typed in the same set of individuals. RESULTS:On average, disease SNPs are not significantly more differentiated between populations than random SNPs in the genome. Risk allele frequencies, however, do show substantial variation across human populations and may contribute to differences in disease prevalence between populations. We demonstrate that, in some cases, risk allele frequency differences are unusually high compared to random SNPs and may be due to the action of local (i.e. geographically-restricted) positive natural selection. Moreover, some risk alleles were absent or fixed in a population, which implies that risk alleles identified in one population do not necessarily account for disease prevalence in all human populations. CONCLUSION:Although differences in risk allele frequencies between human populations are not unusually large and are thus likely not due to positive local selection, there is substantial variation in risk allele frequencies between populations which may account for differences in disease prevalence between human populations.

Project description:BackgroundsUterine corpus endometrial carcinoma (UCEC) is one of the greatest threats on the female reproductive system. The aim of this study is to explore the inflammation-related LncRNA (IRLs) signature predicting the clinical outcomes and response of UCEC patients to immunotherapy and chemotherapy.MethodsConsensus clustering analysis was employed to determine inflammation-related subtype. Cox regression methods were used to unearth potential prognostic IRLs and set up a risk model. The prognostic value of the prognostic model was calculated by the Kaplan-Meier method, receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. Differential abundance of immune cell infiltration, expression levels of immunomodulators, the status of tumor mutation burden (TMB), the response to immune checkpoint inhibitors (ICIs), drug sensitivity, and functional enrichment in different risk groups were also explored. Finally, we used quantitative real-time PCR (qRT-PCR) to confirm the expression patterns of model IRLs in clinical specimens.ResultsAll UCEC cases were divided into two clusters (C1 = 454) and (C2 = 57) which had significant differences in prognosis and immune status. Five hub IRLs were selected to develop an IRL prognostic signature (IRLPS) which had value in forecasting the clinical outcome of UCEC patients. Biological processes related to tumor and immune response were screened. Function enrichment algorithm showed tumor signaling pathways (ERBB signaling, TGF-β signaling, and Wnt signaling) were remarkably activated in high-risk group scores. In addition, the high-risk group had a higher infiltration level of M2 macrophages and lower TMB value, suggesting patients with high risk were prone to a immunosuppressive status. Furthermore, we determined several potential molecular drugs for UCEC.ConclusionWe successfully identified a novel molecular subtype and inflammation-related prognostic model for UCEC. Our constructed risk signature can be employed to assess the survival of UCEC patients and offer a valuable reference for clinical treatment regimens.

Project description:Background: Colorectal cancer is the fourth most deadly cancer worldwide. Although current treatment regimens have prolonged the survival of patients, the prognosis is still unsatisfactory. Inflammation and lncRNAs are closely related to tumor occurrence and development in CRC. Therefore, it is necessary to establish a new prognostic signature based on inflammation-related lncRNAs to improve the prognosis of patients with CRC. Methods: LASSO-penalized Cox analysis was performed to construct a prognostic signature. Kaplan-Meier curves were used for survival analysis and ROC curves were used to measure the performance of the signature. Functional enrichment analysis was conducted to reveal the biological significance of the signature. The R package "maftool" and GISTIC2.0 algorithm were performed for analysis and visualization of genomic variations. The R package "pRRophetic", CMap analysis and submap analysis were performed to predict response to chemotherapy and immunotherapy. Results: An effective and independent prognostic signature, IRLncSig, was constructed based on sixteen inflammation-related lncRNAs. The IRLncSig was proved to be an independent prognostic indicator in CRC and was superior to clinical variables and the other four published signatures. The nomograms were constructed based on inflammation-related lncRNAs and detected by calibration curves. All samples were classified into two groups according to the median value, and we found frequent mutations of the TP53 gene in the high-risk group. We also found some significantly amplificated regions in the high-risk group, 8q24.3, 20q12, 8q22.3, and 20q13.2, which may regulate the inflammatory activity of cancer cells in CRC. Finally, we identified chemotherapeutic agents for high-risk patients and found that these patients were more likely to respond to immunotherapy, especially anti-CTLA4 therapy. Conclusion: In short, we constructed a new signature based on sixteen inflammation-related lncRNAs to improve the outcomes of patients in CRC. Our findings have proved that the IRLncSig can be used as an effective and independent marker for predicting the survival of patients with CRC.

Dataset Information

Disease-Associated SNPs in Inflammation-Related lncRNAs.

Publications

Disease-Associated SNPs in Inflammation-Related lncRNAs.

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