scholarly journals An Integrative Analysis of microRNA and mRNA Expression–-A Case Study

2008 ◽  
Vol 6 ◽  
pp. CIN.S633 ◽  
Author(s):  
Li-Xuan Qin
Keyword(s):  
Gene Expression ◽  
Mrna Expression ◽  
Hierarchical Clustering ◽  
Human Cancer ◽  
Microrna Expression ◽  
Tissue Type ◽  
Integrated Analysis ◽  
Tumor Type ◽  
Cancer Data

Background MicroRNAs are believed to play an important role in gene expression regulation. They have been shown to be involved in cell cycle regulation and cancer. MicroRNA expression profiling became available owing to recent technology advancement. In some studies, both microRNA expression and mRNA expression are measured, which allows an integrated analysis of microRNA and mRNA expression. Results We demonstrated three aspects of an integrated analysis of microRNA and mRNA expression, through a case study of human cancer data. We showed that (1) microRNA expression efficiently sorts tumors from normal tissues regardless of tumor type, while gene expression does not; (2) many microRNAs are down-regulated in tumors and these microRNAs can be clustered in two ways: microRNAs similarly affected by cancer and microRNAs similarly interacting with genes; (3) taking let-7f as an example, targets genes can be identified and they can be clustered based on their relationship with let-7f expression. Discussion Our findings in this paper were made using novel applications of existing statistical methods: hierarchical clustering was applied with a new distance measure–the co-clustering frequency–to identify sample clusters that are stable; microRNA-gene correlation profiles were subject to hierarchical clustering to identify microRNAs that similarly interact with genes and hence are likely functionally related; the clustering of regression models method was applied to identify microRNAs similarly related to cancer while adjusting for tissue type and genes similarly related to microRNA while adjusting for disease status. These analytic methods are applicable to interrogate multiple types of -omics data in general.

scholarly journals Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yuanyuan Li ◽  
David M. Umbach ◽  
Juno M. Krahn ◽  
Igor Shats ◽  
Xiaoling Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Predictive Models ◽  
Drug Sensitivity ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
The Cancer Genome Atlas ◽  
Differential Sensitivity ◽  
Tumor Type

Abstract Background Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. Results In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

scholarly journals BIOM-16. IMMUNOMIC ANALYSIS OF GLIOBLASTOMA (GBM) USING GENE EXPRESSION PROFILING

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii5-ii5
Author(s):  
Michael Castro ◽  
Nilofar Badra-Azar ◽  
Thomas Kessler ◽  
Moritz Schütte ◽  
Bodo Lange ◽  
...  
Keyword(s):  
Gene Expression ◽  
Antigen Presentation ◽  
Hierarchical Clustering ◽  
Immune Evasion ◽  
Human Cancer ◽  
Progression Free Survival ◽  
Immune Checkpoints ◽  
Successful Implementation ◽  
Cell Trafficking ◽  
Box Plots

Abstract BACKGROUND Despite the success of immunotherapy across the spectrum of human cancer, a successful strategy has not emerged for GBM. While PD-L1 IHC and TMB have demonstrated some utility as predictors of immunotherapy benefit, responsiveness is complexly determined by factors affecting T cell trafficking, antigen presentation, other immune checkpoints, and mediators of immune exhaustion. Thus, we set out to to characterize mediators of immune resistance and their diversity in a population of GBM patients utilizing quantitative gene expression. METHODS A set of 54 immunotherapy and checkpoint relevant genes and seven genes related to immune failure were selected from the literature. RNA gene counts for TCGA glioblastoma multiforme samples (N=163) were downloaded from https://portal.gdc.cancer.gov/. Annotation on subtypes and PFS values were obtained from PMID: 24120142. Gene expression normalization as FPKM, hierarchical clustering and box-plots were performed using R-3.6.0. Statistical differences of gene expression between subtypes were quantified using a TurkeyHSD test. RESULTS A heatmap with hierarchical clustering for immune related genes for the TCGA GBM cohort was generated including colored annotation for the subtype and progression free survival. The graph shows a rough separation into two groups, where one group of the genes is tentatively associated with mesenchymal subtype and shorter survival and showing higher expression for most immune evasion genes. However, a heterogeneity of immune evasion signatures was identified within and across subtypes. Transcripts related to antigen presentation, EZH2, and LDHA varied significantly between GBM subtypes (p < 0.05). CONCLUSION Gene expression analysis has utility to identify specific mediators of immune evasion and to inform the selection of combination therapies for discrete subsets of patients. A Bayesian approach to patient selection for specific immunotherapy strategies may enhance the likelihood of successful implementation of immunotherapy in the clinic.

scholarly journals Differentially Methylated Super-Enhancers Regulate Target Gene Expression in Human Cancer

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Emily L. Flam ◽  
Ludmila Danilova ◽  
Dylan Z. Kelley ◽  
Elena Stavrovskaya ◽  
Theresa Guo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Target Genes ◽  
Human Cancer ◽  
Genetic Alterations ◽  
Tumor Type ◽  
Gene Pairs ◽  
Aberrant Gene Expression ◽  
Tumor Types ◽  
Aberrant Gene

Abstract Current literature suggests that epigenetically regulated super-enhancers (SEs) are drivers of aberrant gene expression in cancers. Many tumor types are still missing chromatin data to define cancer-specific SEs and their role in carcinogenesis. In this work, we develop a simple pipeline, which can utilize chromatin data from etiologically similar tumors to discover tissue-specific SEs and their target genes using gene expression and DNA methylation data. As an example, we applied our pipeline to human papillomavirus-related oropharyngeal squamous cell carcinoma (HPV + OPSCC). This tumor type is characterized by abundant gene expression changes, which cannot be explained by genetic alterations alone. Chromatin data are still limited for this disease, so we used 3627 SE elements from public domain data for closely related tissues, including normal and tumor lung, and cervical cancer cell lines. We integrated the available DNA methylation and gene expression data for HPV + OPSCC samples to filter the candidate SEs to identify functional SEs and their affected targets, which are essential for cancer development. Overall, we found 159 differentially methylated SEs, including 87 SEs that actively regulate expression of 150 nearby genes (211 SE-gene pairs) in HPV + OPSCC. Of these, 132 SE-gene pairs were validated in a related TCGA cohort. Pathway analysis revealed that the SE-regulated genes were associated with pathways known to regulate nasopharyngeal, breast, melanoma, and bladder carcinogenesis and are regulated by the epigenetic landscape in those cancers. Thus, we propose that gene expression in HPV + OPSCC may be controlled by epigenetic alterations in SE elements, which are common between related tissues. Our pipeline can utilize a diversity of data inputs and can be further adapted to SE analysis of diseased and non-diseased tissues from different organisms.

Integrated analysis of miRNA and mRNA expression profiles in development of porcine testes

RSC Advances ◽  
2015 ◽  
Vol 5 (78) ◽  
pp. 63439-63449 ◽  
Author(s):  
Maoliang Ran ◽  
Bin Chen ◽  
Maisheng Wu ◽  
Xiaochun Liu ◽  
Changqing He ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mrna Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Expression Profiles ◽  
Physiological Process ◽  
Integrated Analysis ◽  
Testis Development ◽  
P Gene

Gene expression profile in the development of porcine testes investigates the intricate physiological process in pig testis development and spermatogenesis.

scholarly journals Genetic and Epigenetic Alterations Induced by Pesticide Exposure: Integrated Analysis of Gene Expression, microRNA Expression and DNA Methylation Datasets

2021 ◽  
Vol 18 (16) ◽  
pp. 8697
Author(s):  
Federica Giambò ◽  
Gian Leone ◽  
Giuseppe Gattuso ◽  
Roberta Rizzo ◽  
Alessia Cosentino ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Pesticide Exposure ◽  
Methylation Status ◽  
Microrna Expression ◽  
Integrated Analysis ◽  
Epigenetic Alterations ◽  
Expression Levels ◽  
Prediction Tools

Environmental or occupational exposure to pesticides is considered one of the main risk factors for the development of various diseases. Behind the development of pesticide-associated pathologies, there are both genetic and epigenetic alterations, where these latter are mainly represented by the alteration in the expression levels of microRNAs and by the change in the methylation status of the DNA. At present, no studies have comprehensively evaluated the genetic and epigenetic alterations induced by pesticides; therefore, the aim of the present study was to identify modifications in gene miRNA expression and DNA methylation useful for the prediction of pesticide exposure. For this purpose, an integrated analysis of gene expression, microRNA expression, and DNA methylation datasets obtained from the GEO DataSets database was performed to identify putative genes, microRNAs, and DNA methylation hotspots associated with pesticide exposure and responsible for the development of different diseases. In addition, DIANA-miRPath, STRING, and GO Panther prediction tools were used to establish the functional role of the putative biomarkers identified. The results obtained demonstrated that pesticides can modulate the expression levels of different genes and induce different epigenetic alterations in the expression levels of miRNAs and in the modulation of DNA methylation status.

scholarly journals Pan-Cancer and Single-Cell modelling of genomic alterations through gene expression

2018 ◽  
Author(s):  
Daniele Mercatelli ◽  
Forest Ray ◽  
Federico M. Giorgi
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Prediction Models ◽  
Human Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Copy Number Variations ◽  
Tumor Type ◽  
Genomic Alterations ◽  
Pan Cancer

AbstractCancer is a disease often characterized by the presence of multiple genomic alterations, which trigger altered transcriptional patterns and gene expression, which in turn sustain the processes of tumorigenesis, tumor progression and tumor maintenance. The links between genomic alterations and gene expression profiles can be utilized as the basis to build specific molecular tumorigenic relationships. In this study we perform pan-cancer predictions of the presence of single somatic mutations and copy number variations using machine learning approaches on gene expression profiles. We show that gene expression can be used to predict genomic alterations in every tumor type, where some alterations are more predictable than others. We propose gene aggregation as a tool to improve the accuracy of alteration prediction models from gene expression profiles. Ultimately, we show how this principle can be beneficial in intrinsically noisy datasets, such as those based on single cell sequencing.Author SummaryIn this article we show that transcript abundance can be used to predict the presence or absence of the majority of genomic alterations present in human cancer. We also show how these predictions can be improved by aggregating genes into small networks to counteract the effects of transcript measurement noise.

scholarly journals Molecular correlates and therapeutic targets in T cell-inflamed versus non-T cell-inflamed tumors across cancer types

2019 ◽  
Author(s):  
Riyue Bao ◽  
Jason J. Luke
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Hedgehog Signaling ◽  
Human Cancer ◽  
Expression Patterns ◽  
Gene Expression Signature ◽  
Gene Signature ◽  
Type I ◽  
Tumor Type ◽  
Cancer Types

AbstractThe T cell-inflamed tumor microenvironment, characterized by CD8 T cells and type I/II interferon transcripts, is an important cancer immunotherapy biomarker. Tumor mutational profile may also dictate response with some oncogenes (i.e. WNT/β-catenin) known to mediate immuno-suppression. Building on these observations we performed a multi-omic analysis of human cancer correlating the T cell-inflamed gene expression signature with the somatic mutanome and transcriptome for different immune phenotypes, by tumor type and across cancers. Strong correlations were noted between mutations in oncogenes and non-T cell-inflamed tumors with examples including IDH1 and GNAQ as well as less well-known genes including KDM6A, CD11c and genes with unknown functions. Conversely, we observe many genes associating with the T cell-inflamed phenotype including VHL and PBRM1, among others. Analyzing gene expression patterns, we identify oncogenic mediators of immune exclusion broadly active across cancer types including HIF1A and MYC. Novel examples from specific tumors include sonic hedgehog signaling in ovarian cancer or hormone signaling and novel transcription factors across multiple tumors. Using network analysis, somatic and transcriptomic events were integrated, demonstrating that most non-T cell-inflamed tumors are influenced by multiple pathways. Validating these analyses, we observe significant inverse relationships between protein levels and the T cell-inflamed gene signature with examples including NRF2 in lung, ERBB2 in urothelial and choriogonadotropin in cervical cancer. Finally, we integrate available databases for drugs that might overcome or augment the identified mechanisms. These results nominate molecular targets and drugs potentially available for immediate translation into clinical trials for patients with cancer.

scholarly journals Differential expression of H2A isoforms contribute to tissue and lineage specificity with HIST2H2AC as a potential cancer biomarker

2021 ◽  
Author(s):  
Sanket Girish Shah ◽  
Mudasir Rashid ◽  
Abhiram Natu ◽  
Sanjay Gupta
Keyword(s):  
Expression Patterns ◽  
Human Tumor ◽  
Tissue Type ◽  
Tumor Type ◽  
Histone H2a ◽  
Specific Expression ◽  
Cancer Data ◽  
Lineage Specificity ◽  
Specific Association ◽  
Tumor Types

AbstractRecent advancements in the field of histone biology imply non-redundancy in the function of histone H2A isoforms; however, the expression of H2A isoforms in various normal tissue types, the correlation among organs and tumor/tumor type-specific expression remain poorly investigated. The profiling of sixteen H2A isoforms in eleven different normal human tissue types strongly suggests their tissue-specific or predominant expression. Further, clustering analysis shows a lineage-specific correlation of H2A isoforms. In continuation, the expression analysis in twelve human tumor types shows overexpression of HIST2H2AC. Moreover, overexpression was observed exclusively in tumor samples but not with fetal samples; highlighting the cancer-specific association of HIST2H2AC. Further, in silico analysis of TCGA pan-cancer data also showed tumor-specific over-expression of the HIST2H2AC isoform. Our findings provide insights into tissue-type-specificity of histone H2A isoforms expression patterns and advance our understanding of their importance in lineage specification and cancer.

scholarly journals Molecular correlates and therapeutic targets in T cell-inflamed versus non-T cell-inflamed tumors across cancer types

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Riyue Bao ◽  
Daniel Stapor ◽  
Jason J. Luke
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Hedgehog Signaling ◽  
Human Cancer ◽  
Expression Patterns ◽  
Gene Expression Signature ◽  
Type I ◽  
Tumor Type ◽  
Sonic Hedgehog Signaling ◽  
Cancer Types

Abstract Background The T cell-inflamed tumor microenvironment, characterized by CD8 T cells and type I/II interferon transcripts, is an important cancer immunotherapy biomarker. Tumor mutational burden (TMB) may also dictate response, and some oncogenes (i.e., WNT/β-catenin) are known to mediate immunosuppression. Methods We performed an integrated multi-omic analysis of human cancer including 11,607 tumors across multiple databases and patients treated with anti-PD1. After adjusting for TMB, we correlated the T cell-inflamed gene expression signature with somatic mutations, transcriptional programs, and relevant proteome for different immune phenotypes, by tumor type and across cancers. Results Strong correlations were noted between mutations in oncogenes and tumor suppressor genes and non-T cell-inflamed tumors with examples including IDH1 and GNAQ as well as less well-known genes including KDM6A, CD11c, and genes with unknown functions. Conversely, we observe genes associating with the T cell-inflamed phenotype including VHL and PBRM1. Analyzing gene expression patterns, we identify oncogenic mediators of immune exclusion across cancer types (HIF1A and MYC) as well as novel examples in specific tumors such as sonic hedgehog signaling, hormone signaling and transcription factors. Using network analysis, somatic and transcriptomic events were integrated. In contrast to previous reports of individual tumor types such as melanoma, integrative pan-cancer analysis demonstrates that most non-T cell-inflamed tumors are influenced by multiple signaling pathways and that increasing numbers of co-activated pathways leads to more highly non-T cell-inflamed tumors. Validating these analyses, we observe highly consistent inverse relationships between pathway protein levels and the T cell-inflamed gene expression across cancers. Finally, we integrate available databases for drugs that might overcome or augment the identified mechanisms. Conclusions These results nominate molecular targets and drugs potentially available for further study and potential immediate translation into clinical trials for patients with cancer.

scholarly journals Integrated analysis of microRNA and mRNA expression and association with HIF binding reveals the complexity of microRNA expression regulation under hypoxia

2014 ◽  
Vol 13 (1) ◽  
pp. 28 ◽  
Author(s):  
Carme Camps ◽  
Harpreet K Saini ◽  
David R Mole ◽  
Hani Choudhry ◽  
Martin Reczko ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Expression Regulation ◽  
Microrna Expression ◽  
Integrated Analysis
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