scholarly journals The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data

2020 ◽  
Vol 21 (S21) ◽  
Author(s):  
Laurence de Torrenté ◽  
Samuel Zimmerman ◽  
Masako Suzuki ◽  
Maximilian Christopeit ◽  
John M. Greally ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profile ◽  
Cancer Biology ◽  
Predictive Accuracy ◽  
The Cancer Genome Atlas ◽  
Marker Genes ◽  
Transcriptomic Data ◽  
Statistical Classifiers ◽  
Distribution Shape ◽  
The Impact

Abstract Background In genomics, we often assume that continuous data, such as gene expression, follow a specific kind of distribution. However we rarely stop to question the validity of this assumption, or consider how broadly applicable it may be to all genes that are in the transcriptome. Our study investigated the prevalence of a range of gene expression distributions in three different tumor types from the Cancer Genome Atlas (TCGA). Results Surprisingly, the expression of less than 50% of all genes was Normally-distributed, with other distributions including Gamma, Bimodal, Cauchy, and Lognormal also represented. Most of the distribution categories contained genes that were significantly enriched for unique biological processes. Different assumptions based on the shape of the expression profile were used to identify genes that could discriminate between patients with good versus poor survival. The prognostic marker genes that were identified when the shape of the distribution was accounted for reflected functional insights into cancer biology that were not observed when standard assumptions were applied. We showed that when multiple types of distributions were permitted, i.e. the shape of the expression profile was used, the statistical classifiers had greater predictive accuracy for determining the prognosis of a patient versus those that assumed only one type of gene expression distribution. Conclusions Our results highlight the value of studying a gene’s distribution shape to model heterogeneity of transcriptomic data and the impact on using analyses that permit more than one type of gene expression distribution. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of distribution in a patient cohort.

scholarly journals The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data

2019 ◽  
Author(s):  
Laurence de Torrenté ◽  
Samuel Zimmerman ◽  
Masako Suzuki ◽  
Maximilian Christopeit ◽  
John M. Greally ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Biology ◽  
Relevant Information ◽  
The Cancer Genome Atlas ◽  
Good Survival ◽  
Transcriptomic Data ◽  
Survival Status ◽  
Specific Distribution ◽  
Distribution Shape ◽  
Normal Gene

AbstractIn genomics, we often impose the assumption that gene expression data follows a specific distribution. However, rarely do we stop to question this assumption or consider its applicability to all genes in the transcriptome. Our study investigated the prevalence of genes with expression distributions that are non-Normal in three different tumor types from the Cancer Genome Atlas (TCGA). Surprisingly, less than 50% of all genes were Normally-distributed, with other distributions including Gamma, Bimodal, Cauchy, and Lognormal were represented. Relevant information about cancer biology was captured by the genes with non-Normal gene expression. When used for classification, the set of non-Normal genes were able to discriminate between cancer patients with poor versus good survival status. Our results highlight the value of studying a gene’s distribution shape to model heterogeneity of transcriptomic data. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of distribution in a patient cohort.

scholarly journals Identification and Validation of a Predictive Immune-related Genes Signature for the Prognosis of Cholangiocarcinoma

2020 ◽  
Author(s):  
Rui Zhang ◽  
Chen Chen ◽  
Qi Li ◽  
Jialu Fu ◽  
Dong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data.Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score.Results: We identified that 223 IRGs were significantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells.Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

Spatial sampling approach to unravel the impact of soil texture and root genotype on maize root gene expression profiles

2020 ◽  
Author(s):  
Minh Ganther ◽  
Marie-Lara Bouffaud ◽  
Lucie Gebauer ◽  
François Buscot ◽  
Doris Vetterlein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Soil Texture ◽  
Expression Profiles ◽  
Soil Column ◽  
Column Experiment ◽  
Spatial Sampling ◽  
Marker Genes ◽  
Sampling Depth ◽  
Plant Growth Hormones ◽  
The Impact

<p>The complex interactions between plant roots and soil microbes enable a range of beneficial functions such as nutrient acquisition, defense against pathogens and production of plant growth hormones. The role of soil type and plant genotype in shaping rhizosphere communities has been explored in the past, but often without spatial context. The spatial resolution of rhizosphere processes enables us to observe pattern formation in the rhizosphere and investigate how spatial soil organization is shaped through soil–plant–microbiome interactions.</p><p>We applied spatial sampling in a standardized soil column experiment with two maize genotypes (wildtype vs. <em>roothairless3</em>) and two different soil textures (loam vs. sand) in order to investigate how in particular functions of the maize roots relating to nutrient/water uptake, immunity/defense, stress and exudation are affected. RNA sequencing and differential gene expression analysis were used to dissect impact of soil texture, root genotype and sampling depth. Our results indicate that variance in gene expression is predominantly explained by soil texture as well as sampling depth, whereas genotype appears to play a less pronounced role at the analyzed depths. Gene Ontology enrichment analysis of differentially expressed genes between soil textures revealed several functional categories and pathways relating to phytohormone-mediated signaling, cell growth, secondary metabolism, and water homeostasis. Community analysis of rhizosphere derived ACC deaminase active (acdS gene including) plant beneficial bacteria, which suppress the phytohormone ethylene production, suggests that soil texture and column depth are the major factors that affect acdS community composition.</p><p>From the comprehensive gene expression analyses we aim to identify maize marker genes from the relevant core functional groups. These marker genes will be potentially useful for future experiments; such as field plot experiments for investigation of later-emerging plant properties.</p><p>This research was conducted within the research program “Rhizosphere Spatiotemporal Organisation – a Key to Rhizosphere Functions” of the German Science Foundation (TA 290/5-1).</p>

scholarly journals Gene expression and survival analysis in cancer research using online open access platforms: a comparative analysis

2020 ◽  
Vol 17 (2) ◽  
pp. 1-19
Author(s):  
Sofia Ramos ◽  
Ana Sofia Fernandes ◽  
Nuno Saraiva
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Open Access ◽  
Cancer Biology ◽  
Patient Survival ◽  
Statistical Tests ◽  
Expression Patterns ◽  
The Impact ◽  
Tumor Gene Expression ◽  
Tumor Gene

The development of genomics and transcriptomics and the potential associated with sharing data related with cancer, led to a growing understanding of cancer biology and to the identification of new biomarkers. Analysis of tumor gene expression and associated patient survival rate enables the dissection of the impact of certain genes in cancer patient ́s survival. For that purpose, it is essential to choose user-friendly platforms, where it is easy to analyze, compare and collect information for a certain set of genes. The goal of this article is to compare the content and utility of five open access online platforms for tumor gene expression and patient survival analysis from TCGA datasets – cBioPortal, USCS Xena, GEPIA, UALCAN and ONCOLNC. We explore these platforms from the point of view of a lay user, assessing their applicability to study differences in gene expression in tumor vs normal tissues, or according to cancer stage, and the impact of such expression patterns on patient survival. Although all five platforms are very intuitive and access to the data is easy, they vary in the information available, results visualization, and statistical tests performed. Therefore, the choice of a platform must take into account the study goals. For some purposes, a combination of platforms may be required.

scholarly journals Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers

2021 ◽  
Author(s):  
Yifeng Tao ◽  
Xiaojun Ma ◽  
Georgios I. Laliotis ◽  
Adler Guerrero Zuniga ◽  
Drake Palmer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Cancer Cell Line ◽  
Attention Mechanism ◽  
The Cancer Genome Atlas ◽  
Patient Specific ◽  
Open Chromatin ◽  
Therapeutic Decisions ◽  
Somatic Alterations ◽  
The Impact

AbstractCancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Ultimately, interpreting patient somatic alterations within context-specific regulatory programs will facilitate personalized therapeutic decisions for each individual. Towards this goal, we develop a partially interpretable neural network model with encoder-decoder architecture, called Chromatin-informed Inference of Transcriptional Regulators Using Self-attention mechanism (CITRUS), to model the impact of somatic alterations on cellular states and further onto downstream gene expression programs. The encoder module employs a self-attention mechanism to model the contextual impact of somatic alterations in a tumor-specific manner. Furthermore, the model uses a layer of hidden nodes to explicitly represent the state of transcription factors (TFs), and the decoder learns the relationships between TFs and their target genes guided by the sparse prior based on TF binding motifs in the open chromatin regions of tumor samples. We apply CITRUS to genomic, mRNA sequencing and ATAC-seq data from tumors of 17 cancer types profiled by The Cancer Genome Atlas. Our computational framework enables us to share information across tumors to learn patient-specific TF activities, revealing regulatory program similarities and differences between and within tumor types. We show that CITRUS not only outperforms the competing models in predicting RNA expression, but also yields biological insights in delineating TFs associated with somatic alterations in individual tumors. We also validate the differential activity of TFs associated with mutant PIK3CA in breast cancer cell line and xenograft models using a panel of PI3K pathway inhibitors.

scholarly journals TFEB is a master regulator of tumor-associated macrophages in breast cancer

2020 ◽  
Vol 8 (1) ◽  
pp. e000543 ◽  
Author(s):  
Yong Li ◽  
Johnie Hodge ◽  
Qing Liu ◽  
Junfeng Wang ◽  
Yuzhen Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Tumor Growth ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Breast Tumor ◽  
Tumor Development ◽  
Tumor Associated Macrophages ◽  
And Function ◽  
The Impact

BackgroundTumor-associated macrophages (TAMs) play key roles in the development of many malignant solid tumors including breast cancer. They are educated in the tumor microenvironment (TME) to promote tumor growth, metastasis, and therapy resistance. However, the phenotype of TAMs is elusive and how to regulate them for therapeutic purpose remains unclear; therefore, TAM-targeting therapies have not yet achieved clinical success. The purposes of this study were to examine the role of transcription factor EB (TFEB) in regulating TAM gene expression and function and to determine if TFEB activation can halt breast tumor development.MethodsMicroarrays were used to analyze the gene expression profile of macrophages (MΦs) in the context of breast cancer and to examine the impact of TFEB overexpression. Cell culture studies were performed to define the mechanisms by which TFEB affects MΦ gene expression and function. Mouse studies were carried out to investigate the impact of MΦ TFEB deficiency or activation on breast tumor growth. Human cancer genome data were analyzed to reveal the prognostic value of TFEB and its regulated genes.ResultsTAM-mimic MΦs display a unique gene expression profile, including significant reduction in TFEB expression. TFEB overexpression favorably modulates TAM gene expression through multiple signaling pathways. Specifically, TFEB upregulates suppressor of cytokine signaling 3 (SOCS3) and peroxisome proliferator-activated receptor γ (PPARγ) expression and autophagy/lysosome activities, inhibits NLRP3 (NLR Family Pyrin Domain Containing 3) inflammasome and hypoxia-inducible factor (HIF)-1α mediated hypoxia response, and thereby suppresses an array of effector molecules in TAMs including arginase 1, interleukin (IL)-10, IL-1β, IL-6 and prostaglandin E2. MΦ-specific TFEB deficiency promotes, while activation of TFEB using the natural disaccharide trehalose halts, breast tumor development by modulating TAMs. Analysis of human patient genome database reveals that expression levels of TFEB, SOCS3 and PPARγ are positive prognostic markers, while HIF-1α is a negative prognostic marker of breast cancer.ConclusionsOur study identifies TFEB as a master regulator of TAMs in breast cancer. TFEB controls TAM gene expression and function through multiple autophagy/lysosome-dependent and independent pathways. Therefore, pharmacological activation of TFEB would be a promising therapeutic approach to improve the efficacy of existing treatment including immune therapies for breast cancer by favorably modulating TAM function and the TME.

scholarly journals Prognostic Significance of Expression of a Single MicroRNA,miR-181a, in Cytogenetically Normal Acute Myeloid Leukemia: A Cancer and Leukemia Group B Study

2010 ◽  
Vol 28 (36) ◽  
pp. 5257-5264 ◽  
Author(s):  
Sebastian Schwind ◽  
Kati Maharry ◽  
Michael D. Radmacher ◽  
Krzysztof Mrózek ◽  
Kelsi B. Holland ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Myeloid Leukemia ◽  
Prognostic Significance ◽  
Comprehensive Cancer Center ◽  
Wild Type ◽  
The Impact ◽  
Acute Myeloid

PurposeTo evaluate the prognostic significance of expression levels of a single microRNA, miR-181a, in the context of established molecular markers in cytogenetically normal acute myeloid leukemia (CN-AML), and to gain insight into the leukemogenic role of miR-181a.Patients and MethodsmiR-181a expression was measured in pretreatment marrow using Ohio State University Comprehensive Cancer Center version 3.0 arrays in 187 younger (< 60 years) adults with CN-AML. Presence of other molecular prognosticators was assessed centrally. A gene-expression profile associated with miR-181a expression was derived using microarrays and evaluated by Gene-Ontology analysis.ResultsHigher miR-181a expression associated with a higher complete remission (CR) rate (P = .04), longer overall survival (OS; P = .01) and a trend for longer disease-free survival (DFS; P = .09). The impact of miR-181a was most striking in poor molecular risk patients with FLT3-internal tandem duplication (FLT3-ITD) and/or NPM1 wild-type, where higher miR-181a expression associated with a higher CR rate (P = .009), and longer DFS (P < .001) and OS (P < .001). In multivariable analyses, higher miR-181a expression was significantly associated with better outcome, both in the whole patient cohort and in patients with FLT3-ITD and/or NPM1 wild-type. These results were also validated in an independent set of older (≥ 60 years) patients with CN-AML. A miR-181a-associated gene-expression profile was characterized by enrichment of genes usually involved in innate immunity.ConclusionTo our knowledge, we provide the first evidence that the expression of a single microRNA, miR-181a, is associated with clinical outcome of patients with CN-AML and may refine their molecular risk classification. Targeted treatments that increase endogenous levels of miR-181a might represent novel therapeutic strategies.

scholarly journals Gene expression profile of human thyroid cancer in relation to its mutational status

2011 ◽  
Vol 47 (3) ◽  
pp. R91-R103 ◽  
Author(s):  
Dagmara Rusinek ◽  
Sylwia Szpak-Ulczok ◽  
Barbara Jarzab
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Cancer Biology ◽  
Mapk Pathway ◽  
Human Thyroid ◽  
Thyroid Cell ◽  
Thyroid Cancers ◽  
The Difference

This review describes the gene expression profile changes associated with the presence of different mutations that contribute to thyroid cell carcinogenesis. The results are discussed in the context of thyroid cancer biology and of the implications for disease prognosis, while the diagnostic aspect has been omitted. For papillary thyroid cancer (PTC), the most characteristic gene expression profile is associated with the presence ofBRAFmutation. BRAF-associated PTC differ profoundly from RET/PTC or RAS-associated cancers. Simultaneously, they retain many characteristic gene expression features common for all PTCs, induced by the alternative mutations activating MAPK pathway. Although the difference between papillary and follicular thyroid cancer (FTC) is significant at the gene expression profile level, surprisingly, the RAS-related signature of FTC is not well specified.PAX8/peroxisome proliferator-activated receptor γ (PPARγ) rearrangements, which occur in FTC as an alternative to theRASmutation, are associated with specific changes in gene expression. Furthermore, the difference between well-differentiated thyroid cancers and poorly differentiated and anaplastic thyroid cancers is mainly a reflection of tumor degree of differentiation and may not be attributed to the presence of characteristic mutations.

scholarly journals Effects of histamine and various histamine receptor antagonists on gene expression profiles of macrophages during compressive strain

2021 ◽  
Author(s):  
Agnes Schröder ◽  
Catharina Petring ◽  
Anna Damanaki ◽  
Jonathan Jantsch ◽  
Peter Proff ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Periodontal Ligament ◽  
Tooth Movement ◽  
Expression Profiles ◽  
Quantitative Polymerase Chain Reaction ◽  
Compressive Strain ◽  
Orthodontic Tooth Movement ◽  
The Impact

Abstract Purpose Tissue hormone histamine can accumulate locally within the periodontal ligament via nutrition or may be released during allergic reactions by mast cells, which may have an impact on orthodontic tooth movement. In addition to periodontal ligament fibroblasts, cells of the immune system such as macrophages are exposed to compressive strain. The aim of this study was thus to investigate the impact of histamine on the gene expression profile of macrophages in the context of simulated orthodontic compressive strain. Methods Macrophages were incubated with different histamine concentrations (50, 100, 200 µM) for 24 h and then either left untreated or compressed for another 4 h. To assess the role of different histamine receptors, we performed experiments with antagonists for histamine 1 receptor (cetirizine), histamine 2 receptor (ranitidine) and histamine 4 receptor (JNJ7777120) under control and pressure conditions. We tested for lactate dehydrogenase release and analyzed the expression of genes involved in inflammation and bone remodeling by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results Histamine elevated gene expression of tumor necrosis factor under control conditions and in combination with pressure application. Increased prostaglandin-endoperoxide synthase‑2 mRNA was observed when histamine was combined with compressive force. Interleukin‑6 gene expression was not affected by histamine treatment. In macrophages, compressive strain increased osteoprotegerin gene expression. Histamine further elevated this effect. Most of the observed histamine effects were blocked by the histamine 1 receptor antagonist cetirizine. Conclusions Histamine has an impact on the gene expression profile of macrophages during compressive strain in vitro, most likely having an impairing effect on orthodontic tooth movement by upregulation of osteoprotegerin expression.

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