Dataset Information

A Normalization-Free and Nonparametric Method Sharpens Large-Scale Transcriptome Analysis and Reveals Common Gene Alteration Patterns in Cancers.

ABSTRACT: Heterogeneity in transcriptional data hampers the identification of differentially expressed genes (DEGs) and understanding of cancer, essentially because current methods rely on cross-sample normalization and/or distribution assumption-both sensitive to heterogeneous values. Here, we developed a new method, Cross-Value Association Analysis (CVAA), which overcomes the limitation and is more robust to heterogeneous data than the other methods. Applying CVAA to a more complex pan-cancer dataset containing 5,540 transcriptomes discovered numerous new DEGs and many previously rarely explored pathways/processes; some of them were validated, both in vitro and in vivo, to be crucial in tumorigenesis, e.g., alcohol metabolism (ADH1B), chromosome remodeling (NCAPH) and complement system (Adipsin). Together, we present a sharper tool to navigate large-scale expression data and gain new mechanistic insights into tumorigenesis.

SUBMITTER: Li QG

PROVIDER: S-EPMC5562223 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

A Normalization-Free and Nonparametric Method Sharpens Large-Scale Transcriptome Analysis and Reveals Common Gene Alteration Patterns in Cancers.

Li Qi-Gang QG He Yong-Han YH Wu Huan H Yang Cui-Ping CP Pu Shao-Yan SY Fan Song-Qing SQ Jiang Li-Ping LP Shen Qiu-Shuo QS Wang Xiao-Xiong XX Chen Xiao-Qiong XQ Yu Qin Q Li Ying Y Sun Chang C Wang Xiangting X Zhou Jumin J Li Hai-Peng HP Chen Yong-Bin YB Kong Qing-Peng QP

Theranostics 20170708 11

Heterogeneity in transcriptional data hampers the identification of differentially expressed genes (DEGs) and understanding of cancer, essentially because current methods rely on cross-sample normalization and/or distribution assumption-both sensitive to heterogeneous values. Here, we developed a new method, Cross-Value Association Analysis (CVAA), which overcomes the limitation and is more robust to heterogeneous data than the other methods. Applying CVAA to a more complex pan-cancer dataset co ...[more]

PMID: 28824723

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Project description:BackgroundStudies have shown that more than half of the human genome expression is affected by circadian rhythms, which includes genes involved in cell cycle control, DNA repair and apoptosis that are critical in cancer biology. However, the roles of circadian rhythm-related genes (CRRGs) in cervical cancer (CC) and other common gynecologic cancers remain unclear.MethodsThe transcriptome data and clinical information related to CC and other common gynecologic cancers were extracted from the UCSC Xena and Gene Expression Omnibus (GEO) databases. In this study, the differentially expressed CRRGs of CC (target genes) were obtained, and the functional enrichment analysis of these target genes was performed by "clusterProfiler". Then, the biomarkers of CC were screened out to construct the survival risk model (risk score). Moreover, function and tumor micro-environment (TME) analyses in different risk groups were performed for further study of the potential mechanism of CC. Furthermore, the prognostic value and function analyses of biomarkers in three common gynecologic cancers were performed to reveal the potential agreement or heterogeneity regulations.ResultsA total of 19 target genes were associated with pyrimidine metabolism. The survival risk model was constructed with six biomarkers, including APOBEC3B, CDA, HELLS, RHOB, SLC15A3, and UPP1. Among these, APOBEC3B, HELLS, and SLC15A3 were identified as positive factors, while CDA, RHOB, and UPP1 were identified as negative factors in CC. It is notable that multiple immune-related signaling pathways were associated with the clinical risk of CC, and the immunotherapy sensitivity was worse in the high-risk group. In addition, we found that most of biomarkers had the prognostic values in other common gynecologic cancers. It was notable that the mechanisms by which these biomarkers influence gynecologic cancers were associated with extracellular matrix (ECM) receptor interaction, focal adhesion, etc.ConclusionThis study identified six circadian rhythm-related biomarkers, including APOBEC3B, CDA, HELLS, RHOB, SLC15A3, and UPP1, which were associated with the prognosis of CC. The mechanisms by which these biomarkers influence gynecologic cancers were associated with ECM receptor interaction, focal adhesion, and other functions. These findings might help to deepen the understanding of the agreement or heterogeneity of CRRGs in the pathological processes of common gynecologic cancers.

Project description:Genome-scale metabolic models (GEMs) cover the entire list of metabolic genes in an organism and associated reactions, in a tissue/condition non-specific manner. RNA-seq provides crucial information to make the GEMs condition-specific. Integrative Metabolic Analysis Tool (iMAT) and Integrative Network Inference for Tissues (INIT) are the two most popular algorithms to create condition-specific GEMs from human transcriptome data. The normalization method of choice for raw RNA-seq count data affects the model content produced by these algorithms and their predictive accuracy. However, a benchmark of the RNA-seq normalization methods on the performance of iMAT and INIT algorithms is missing in the literature. Another important phenomenon is covariates such as age and gender in a dataset, and they can affect the predictivity of analysis. In this study, we aimed to compare five different RNA-seq data normalization methods (TPM, FPKM, TMM, GeTMM, and RLE) and covariate adjusted versions of the normalized data by mapping them on a human GEM using the iMAT and INIT algorithms to generate personalized metabolic models. We used RNA-seq data for Alzheimer's disease (AD) and lung adenocarcinoma (LUAD) patients. The results demonstrated that RNA-seq data normalized by the RLE, TMM, or GeTMM methods enabled the production of condition-specific metabolic models with considerably low variability in terms of the number of active reactions compared to the within-sample normalization methods (FPKM, TPM). Using these models, we could more accurately capture the disease-associated genes (average accuracy of ~0.80 for AD and ~0.67 for LUAD) for the RLE, TMM, and GeTMM normalization methods. An increase in the accuracies was observed for all the methods when covariate adjustment was applied. We found a similar accuracy trend when we compared the metabolites of perturbed reactions to metabolome data for AD. Together, our benchmark study shows that the between-sample RNA-seq normalization methods reduce false positive predictions at the expense of missing some true positive genes when mapped on GEMs.

Dataset Information

A Normalization-Free and Nonparametric Method Sharpens Large-Scale Transcriptome Analysis and Reveals Common Gene Alteration Patterns in Cancers.

Publications

A Normalization-Free and Nonparametric Method Sharpens Large-Scale Transcriptome Analysis and Reveals Common Gene Alteration Patterns in Cancers.

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