scholarly journals Evaluation of Gene Expression Profiles in Thyroid Nodule Biopsy Material to Diagnose Thyroid Cancer

2008 ◽  
Vol 93 (4) ◽  
pp. 1195-1202 ◽  
Author(s):  
Stéphanie Durand ◽  
Carole Ferraro-Peyret ◽  
Samia Selmi-Ruby ◽  
Christian Paulin ◽  
Michelle El Atifi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Normal Tissue ◽  
Large Scale ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Correct Diagnosis ◽  
Thyroid Tissue ◽  
Biopsy Material ◽  
Thyroid Carcinomas

Abstract Context: Detection of thyroid cancer among benign nodules on fine-needle aspiration biopsies (FNAB), which presently relies on cytological examination, is expected to be improved by new diagnostic tests set up from genomic data. Objective: The aim of the study was to use a set of genes discriminating benign from malignant tumors, on the basis of their expression levels, to build tumor classifiers and evaluate their capacity to predict malignancy on FNAB. Design: We analyzed the level of expression of 200 potentially informative genes in 56 thyroid tissue samples (benign or malignant tumors and paired normal tissue) using nylon macroarrays. Gene expression data were subjected to a weighted voting algorithm to generate tumor classifiers. The performances of the classifiers were evaluated on a series of 26 sham FNAB, i.e. FNAB carried out on thyroid nodules after surgical resection. Results: A series of 19 genes with a similar expression in follicular adenomas and normal tissue and discriminating follicular adenomas+normal tissue from the following: 1) follicular thyroid carcinomas (FTCs), 2) papillary thyroid carcinomas (PTCs), or 3) both FTCs and PTCs. These were used to generate four classifiers, the FTCs, PTCs, common (FTC+PTCs), and global classifiers. In 23 of the 26 sham FNAB, the four classifiers yielded a diagnosis in agreement with the diagnosis of the pathologist used as reference; in the three other cases, the correct diagnosis was given by three of four classifiers. Conclusions: We developed a procedure of molecular diagnosis of benign vs. malignant tumors applicable to the material collected by FNAB. The molecular test complied with a preclinical validation stage; it must be now evaluated on ultrasound-guided FNAB in a large-scale prospective study.

scholarly journals Molecular characteristics of papillary thyroid carcinomas without BRAF mutation or RET/PTC rearrangement: relationship with clinico-pathological features

2009 ◽  
Vol 16 (2) ◽  
pp. 467-481 ◽  
Author(s):  
Stéphanie Durand ◽  
Carole Ferraro-Peyret ◽  
Mireille Joufre ◽  
Annie Chave ◽  
Françoise Borson-Chazot ◽  
...  
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Expression Profiles ◽  
Strong Association ◽  
The Other ◽  
Marker Genes ◽  
Molecular Characteristics ◽  
Papillary Thyroid ◽  
Thyroid Carcinomas ◽  
Gene Alteration

About 60–70% of papillary thyroid carcinomas (PTC) present a BRAFT1799A gene mutation or a rearrangement of RET gene (RET/PTC). In this study, we examined whether PTC without BRAFT1799A mutation and without RET/PTC rearrangement named PTC-ga(−) were distinguishable from PTC-ga(+) (with one or the other gene alteration) on the basis of gene expression characteristics. We analyzed the mutational state of 116 PTC and we compared gene expression profiles of PTC-ga(+) and PTC-ga(−) from data of a 200 gene macroarray and quantitative PCR. Seventy five PTC were PTC-ga(+) and 41 were PTC-ga(−). Unsupervised analyses of macroarray data by hierarchical clustering led to a complete segregation of PTC-ga(+) and PTC-ga(−). In a series of 42 genes previously recognized as PTC ‘marker’ genes, 22 were found to be expressed at a comparable level in PTC-ga(−) and normal tissue. Thyroid-specific genes, TPO, TG, DIO1, and DIO2 were under-expressed in PTC-ga(+) but expressed at a normal level in PTC-ga(−). A few genes including DUOX1 and DUOX2 were selectively dys-regulated in PTC-ga(−). Tumor grade of PTC-ga(−) was lower than that of PTC-ga(+). There was a strong association between the mutational state and histiotype of PTC; 81% of PTC follicular variants were corresponded to PTC-ga(−), whereas 84% of PTC of classical form were PTC-ga(+). In conclusion, we show that PTC without BRAFT1799A mutation or RET/PTC rearrangement, mainly corresponding to follicular variants, maintain a thyroid differentiation expression level close to that of normal tissue and should be of better prognosis than PTC with one or the other gene alteration.

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

2021 ◽  
pp. 153537022110088
Author(s):  
Jinyi Tian ◽  
Yizhou Bai ◽  
Anyang Liu ◽  
Bin Luo
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Thyroid Tissue ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Tissue Samples

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein–protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.

scholarly journals LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanan Ren ◽  
Ting-You Wang ◽  
Leah C. Anderton ◽  
Qi Cao ◽  
Rendong Yang
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Samples ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Regulatory Pathways ◽  
Cancer Transcriptome ◽  
Versatile Tool

Abstract Background Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. Results As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. Conclusions LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA.

scholarly journals Identification of Gastric Cancer-Related Circular RNA through Microarray Analysis and Bioinformatics Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Gu ◽  
Ying Sun ◽  
Xiong Zheng ◽  
Jin Ma ◽  
Xiao-Ying Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Microarray Analysis ◽  
Cancer Progression ◽  
Pathway Analysis ◽  
Bioinformatics Analysis ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Circular Rnas ◽  
Target Mrnas

Gastric cancer is one of the common malignant tumors worldwide. Increasing studies have indicated that circular RNAs (circRNAs) play critical roles in the cancer progression and have shown great potential as useful markers and therapeutic targets. However, the precise mechanism and functions of most circRNAs are still unknown in gastric cancer. In the present study, we performed a microarray analysis to detect circRNA expression changes between tumor samples and adjacent nontumor samples. The miRNA expression profiles were obtained from the National Center of Biotechnology Information Gene Expression Omnibus (GEO). The differentially expressed circRNAs and miRNAs were identified through fold change filtering. The interactions between circRNAs and miRNAs were predicted by Arraystar’s home-made miRNA target prediction software. After circRNA-related miRNAs and dysregulated miRNAs were intersected, 23 miRNAs were selected. The target mRNAs of miRNAs were predicted by TarBase v7.0. Gene ontology (GO) enrichment analysis and pathway analysis were performed using standard enrichment computational methods for the target mRNAs. The results of pathway analysis showed that p53 signaling pathway and hippo signal pathway were significantly enriched and CCND2 was a cross-talk gene associated with them. Finally, a circRNA-miRNA-mRNA regulation network was constructed based on the gene expression profiles and bioinformatics analysis results to identify hub genes and hsa_circRNA_101504 played a central role in the network.

Gene Expression Profiles for Radiation-induced Thyroid Cancer

2011 ◽  
Vol 23 (4) ◽  
pp. 282-288 ◽  
Author(s):  
C. Maenhaut ◽  
V. Detours ◽  
G. Dom ◽  
D. Handkiewicz-Junak ◽  
M. Oczko-Wojciechowska ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Radiation Induced

scholarly journals Analysis of blood-based gene expression in idiopathic Parkinson disease

Neurology ◽  
2017 ◽  
Vol 89 (16) ◽  
pp. 1676-1683 ◽  
Author(s):  
Ron Shamir ◽  
Christine Klein ◽  
David Amar ◽  
Eva-Juliane Vollstedt ◽  
Michael Bonin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson Disease ◽  
Gene Networks ◽  
Large Scale ◽  
Expression Profiles ◽  
Area Under The Curve ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
Gene Profiles ◽  
Independent Test

Objective:To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples).Methods:Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks.Results:A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E–6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E–4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1, ATP5A1, and VDAC3.Conclusions:We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Real Gene ◽  
Large Scale Dataset ◽  
Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

Neural system-enriched gene expression: relationship to biological pathways and neurological diseases

2004 ◽  
Vol 18 (2) ◽  
pp. 167-183 ◽  
Author(s):  
Jianhua Zhang ◽  
Amy Moseley ◽  
Anil G. Jegga ◽  
Ashima Gupta ◽  
David P. Witte ◽  
...  
Keyword(s):  
Gene Expression ◽  
Nervous System ◽  
Intracellular Signaling ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene List ◽  
Structure And Function ◽  
System Structure ◽  
Psychiatric Disease ◽  
And Function

To understand the commitment of the genome to nervous system differentiation and function, we sought to compare nervous system gene expression to that of a wide variety of other tissues by gene expression database construction and mining. Gene expression profiles of 10 different adult nervous tissues were compared with that of 72 other tissues. Using ANOVA, we identified 1,361 genes whose expression was higher in the nervous system than other organs and, separately, 600 genes whose expression was at least threefold higher in one or more regions of the nervous system compared with their median expression across all organs. Of the 600 genes, 381 overlapped with the 1,361-gene list. Limited in situ gene expression analysis confirmed that identified genes did represent nervous system-enriched gene expression, and we therefore sought to evaluate the validity and significance of these top-ranked nervous system genes using known gene literature and gene ontology categorization criteria. Diverse functional categories were present in the 381 genes, including genes involved in intracellular signaling, cytoskeleton structure and function, enzymes, RNA metabolism and transcription, membrane proteins, as well as cell differentiation, death, proliferation, and division. We searched existing public sites and identified 110 known genes related to mental retardation, neurological disease, and neurodegeneration. Twenty-one of the 381 genes were within the 110-gene list, compared with a random expectation of 5. This suggests that the 381 genes provide a candidate set for further analyses in neurological and psychiatric disease studies and that as a field, we are as yet, far from a large-scale understanding of the genes that are critical for nervous system structure and function. Together, our data indicate the power of profiling an individual biologic system in a multisystem context to gain insight into the genomic basis of its structure and function.

CFTR ΔF508 mutation has minimal effect on the gene expression profile of differentiated human airway epithelia

2005 ◽  
Vol 289 (4) ◽  
pp. L545-L553 ◽  
Author(s):  
Joseph Zabner ◽  
Todd E. Scheetz ◽  
Hakeem G. Almabrazi ◽  
Thomas L. Casavant ◽  
Jian Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cystic Fibrosis ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Primary Cultures ◽  
Gene Expression Profiles ◽  
Filter Method ◽  
Tissue Destruction ◽  
Airway Epithelia

Cystic fibrosis (CF) is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial chloride channel regulated by phosphorylation. Most of the disease-associated morbidity is the consequence of chronic lung infection with progressive tissue destruction. As an approach to investigate the cellular effects of CFTR mutations, we used large-scale microarray hybridization to contrast the gene expression profiles of well-differentiated primary cultures of human CF and non-CF airway epithelia grown under resting culture conditions. We surveyed the expression profiles for 10 non-CF and 10 ΔF508 homozygote samples. Of the 22,283 genes represented on the Affymetrix U133A GeneChip, we found evidence of significant changes in expression in 24 genes by two-sample t-test ( P < 0.00001). A second, three-filter method of comparative analysis found no significant differences between the groups. The levels of CFTR mRNA were comparable in both groups. There were no significant differences in the gene expression patterns between male and female CF specimens. There were 18 genes with significant increases and 6 genes with decreases in CF relative to non-CF samples. Although the function of many of the differentially expressed genes is unknown, one transcript that was elevated in CF, the KCl cotransporter (KCC4), is a candidate for further study. Overall, the results indicate that CFTR dysfunction has little direct impact on airway epithelial gene expression in samples grown under these conditions.

scholarly journals Dynamical consequences of regional heterogeneity in the brain’s transcriptional landscape

2020 ◽  
Author(s):  
Gustavo Deco ◽  
Kevin Aquino ◽  
Aurina Arnatkevičiūtė ◽  
Stuart Oldham ◽  
Kristina Sabaroedin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Global Gene Expression ◽  
Brain Regions ◽  
Biophysical Model ◽  
Neuronal Dynamics ◽  
Regional Heterogeneity ◽  
Magnetic Resonance Imaging Mri

AbstractBrain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles and MRI-derived estimates of myeloarchitecture. We further show that regional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional activity timescales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptional data to constrain models of large-scale brain function.

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