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Expression sensitivity analysis of human disease related genes.

ABSTRACT: Background. Genome-wide association studies (GWAS) have shown its revolutionary power in seeking the influenced loci on complex diseases genetically. Thousands of replicated loci for common traits are helpful in diseases risk assessment. However it is still difficult to elucidate the variations in these loci that directly cause susceptibility to diseases by disrupting the expression or function of a protein currently. Results. We evaluate the expression features of disease related genes and find that different diseases related genes show different expression perturbation sensitivities in various conditions. It is worth noting that the expression of some robust disease-genes doesn't show significant change in their corresponding diseases, these genes might be easily ignored in the expression profile analysis. Conclusion. Gene ontology enrichment analysis indicates that robust disease-genes execute essential function in comparison with sensitive disease-genes. The diseases associated with robust genes seem to be relatively lethal like cancer and aging. On the other hand, the diseases associated with sensitive genes are apparently nonlethal like psych and chemical dependency diseases.

SUBMITTER: Ma LX

PROVIDER: S-EPMC3857905 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Expression sensitivity analysis of human disease related genes.

Ma Liang-Xiao LX Wang Ya-Jun YJ Wang Jing-Fang JF Li Xuan X Hao Pei P

BioMed research international 20131124

Background. Genome-wide association studies (GWAS) have shown its revolutionary power in seeking the influenced loci on complex diseases genetically. Thousands of replicated loci for common traits are helpful in diseases risk assessment. However it is still difficult to elucidate the variations in these loci that directly cause susceptibility to diseases by disrupting the expression or function of a protein currently. Results. We evaluate the expression features of disease related genes and find ...[more]

PMID: 24350280

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Project description:Schizophrenia (SCZ) is a severe psychiatric disorder affecting 0.7% of the population.[1] When inadequately treated, subjects with SCZ experience symptoms that render them dysfunctional and unable to discern aspects of reality. Despite the fact that the majority of subjects with SCZ are sporadic cases and do not have a known family history of SCZ, a family history is one of the largest risk factors for developing SCZ.[2-4] A large genome-wide association study (GWAS) recently pinpointed 108 significant loci within the human genome that contribute to SCZ pathogenesis.[5] While some loci include genes that have been previously implicated in SCZ, the majority, due to the unbiased nature of the genetic investigation, include genes with unknown relevance to SCZ. This investigation is based on the premise that: 1) at least one of the genes at the 108 loci contribute to SCZ etiology; 2) some of the genes contributing to SCZ etiology are in a common pathway; and 3) some genes in a common pathway will have correlated gene expression. Gene expression data available in the gene expression omnibus (GEO) was used to identify correlated expression among the 369 genes (853 isoforms) found at the 108 loci associated with SCZ. Expression data came from bone marrow CD34+ selected cells isolated from 66 individuals (GSE4619). First, correlation among genes related to the DRD2 pathway was analyzed to test the hypothesis that some SCZ genes are in a common pathway and have correlated expression. Then, two pathways were generated based on correlated expression of genes at the 108 loci. One pathway consisted of the largest number of genes with correlated expression (56) and included four genes from the DRD2 pathway and seven of the 33 genes that were previously implicated in SCZ. The second pathway, a novel pathway of 12 genes, was constructed by excluding both the 33 genes that were previously implicated in SCZ and other genes that exhibited significantly correlated expression with these 33 genes. Correlated expression analysis among SCZ-associated genes at the 108 loci provides evidence implicating those genes with correlated expression in SCZ pathogenesis. In addition, these analyses will facilitate pathway identification creating starting points for targeted experiments to verify or refute the hypothetical pathways generated here. Ultimately identifying the genes and pathways at the 108 loci involved in SCZ genesis will inform novel pharmaceutical development for treatment and prevention of SCZ.

Project description:BackgroundCervical cancer (CC) is the leading cause of cancer-related death in women. A limited number of studies have investigated whether immune-prognostic features can be used to predict the prognosis of CC. This study aimed to develop an improved prognostic risk scoring model (PRSM) for CC based on immune-related genes (IRGs) to predict survival and determine the key prognostic IRGs.MethodsWe downloaded the gene expression profiles and clinical data of CC patients from the TCGA and GEO databases. The ESTIMATE algorithm was used to calculate the score for both immune and stromal cells. Differentially expressed genes (DEGs) in different subpopulations were analyzed by "Limma". A weighted gene co-expression network analysis (WGCNA) was used to establish a DEG co-expression module related to the immune score. Immune-related gene pairs (IRGPs) were constructed, and univariate- and Lasso-Cox regression analyses were used to analyze prognosis and establish a PRSM. A log-rank test was used to verify the accuracy and consistency of the scoring model. Identification of the predicted key IRG was ensured by the application of functional enrichment, DisNor, protein-protein interactions (PPIs) and heatmap. Finally, we extracted the key prognostic immune-related genes from the gene expression data, validated the key genes by immunohistochemistry and analyzed the correlation between their expression and drug sensitivity.ResultsA new PRSM was developed based on 22 IRGPs. The prognosis of the low-risk group in the model group (P < 0.001) and validation group (P = 0.039) was significantly better than that in the high-risk group. Furthermore, M1 and M2 macrophages were highly expressed in the low-risk group. Retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs) and the Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway were significantly enriched in the low-risk group. Three representative genes (CD80, CD28, and LCP2) were markers of CC prognosis. CD80 and CD28 may more prominent represent important indicators to improve patient prognosis. These key genes was positively correlated with drug sensitivity. Finally, we found that differences in the sensitivity to JNK inhibitors could be distinguished based on the use and risk grouping of this PRSM.ConclusionsThe prognostic model based on the IRGs and key genes have potential clinical significance for predicting the prognosis of CC patients, providing a foundation for clinical prognosis judgment and individualized treatment.

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Expression sensitivity analysis of human disease related genes.

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Expression sensitivity analysis of human disease related genes.

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