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.

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.

Molecular Classification of Multiple Myeloma: A Distinct Transcriptional Profile Characterizes Patients Expressing CCND1 and Negative for 14q32 Translocations.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1548-1548
Author(s):  
Luca Agnelli ◽  
Silvio Bicciato ◽  
Michela Mattioli ◽  
Sonia Fabris ◽  
Daniela Intini ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Expression Profiles ◽  
Gene Expression Pattern ◽  
Marker Genes ◽  
Molecular Characteristics ◽  
Cyclin D ◽  
Expression Data ◽  
Chromosome 13Q ◽  
13Q Deletion

Abstract The deregulation of CCND1, CCND2 and CCND3 genes represents a common event in multiple myeloma (MM), being at least one of them deregulated in almost all MM tumors. A recently proposed TC classification1 grouped MM patients into five classes on the basis of their cyclins D expression profiles and the presence of the main translocations involving the immunoglobulin heavy-chain (IGH) locus at 14q32. The aim of our study was to identify the putative transcriptional fingerprints associated with the deregulation of the different D-type cyclins and the presence of IGH translocations. The cyclin D expression levels obtained by high-density oligonucleotide microarray analysis of purified plasma cells from 50 MM cases were used to stratify the samples into the five TC classes, along with the molecular characteristics. The cyclin D expression data were validated by means of real-time quantitative PCR analysis; fluorescence in-situ hybridization was used to investigate the cyclin D loci arrangements, and to detect the main IGH translocations and the chromosome 13q deletion. A multi-class classification analysis was performed on the gene expression data and used to identify the transcriptional fingerprints of the 5 TC groups. 112 probe sets were selected as characterizing the TC1, TC2, TC4 and TC5 groups, whereas the TC3 samples showed heterogeneous phenotypes and no marker genes. In particular, TC1, TC4 and TC5 groups were characterized by the molecular signatures associated with the primary IGH translocations target genes. The TC2 group, showing significantly extra copies of the CCND1 locus (P=5.9×10−3) and neither IGH translocations nor the chromosome 13q deletion (P=1.7×10−3), was characterized by the overexpression of 30 genes, mainly involved in protein biosynthesis at translational level. Among the most specifically modulated transcripts within the group we identified a novel gene containing a BTB/POZ domain, typical of many zinc finger transcription factors and associated with transcriptional repression activity. A meta-analysis performed on two publicly available MM datasets, containing almost 250 cases, validated the identified gene expression signatures with a global classification rate (indicating the correct prediction of the TC class for the independent set) of 86% and 90%, respectively. Our data contribute to the understanding of the molecular and biological features of distinct MM subtypes; the identification of a distinctive gene expression pattern in TC2 patients may improve risk stratification and indicate novel therapeutic targets.

scholarly journals Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

2021 ◽  
Vol 20 ◽  
pp. 117693512110024
Author(s):  
Jason D Wells ◽  
Jacqueline R Griffin ◽  
Todd W Miller
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Human Tumors ◽  
Molecular Characteristics ◽  
Survival Times ◽  
Chemotherapy Drugs ◽  
Testing Dataset ◽  
Cancer Models ◽  
Cancer Types ◽  
Pan Cancer

Motivation: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor. Results: Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line–derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times ( P = .048) and in patients with pancreatic cancer treated with gemcitabine ( P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

Distinctive gene expression profiles of CD34 cells from patients with myelodysplastic syndrome characterized by specific chromosomal abnormalities

Blood ◽  
2004 ◽  
Vol 104 (13) ◽  
pp. 4210-4218 ◽  
Author(s):  
Guibin Chen ◽  
Weihua Zeng ◽  
Akira Miyazato ◽  
Eric Billings ◽  
Jaroslaw P. Maciejewski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Small Sample ◽  
Hematopoietic Progenitor Cell ◽  
Molecular Characteristics ◽  
Hematopoietic Progenitor ◽  
Trisomy 8 ◽  
Monosomy 7 ◽  
Cd34 Cells

Abstract Aneuploidy, especially monosomy 7 and trisomy 8, is a frequent cytogenetic abnormality in the myelodysplastic syndromes (MDSs). Patients with monosomy 7 and trisomy 8 have distinctly different clinical courses, responses to therapy, and survival probabilities. To determine disease-specific molecular characteristics, we analyzed the gene expression pattern in purified CD34 hematopoietic progenitor cells obtained from MDS patients with monosomy 7 and trisomy 8 using Affymetrix GeneChips. Two methods were employed: standard hybridization and a small-sample RNA amplification protocol for the limited amounts of RNA available from individual cases; results were comparable between these 2 techniques. Microarray data were confirmed by gene amplification and flow cytometry using individual patient samples. Genes related to hematopoietic progenitor cell proliferation and blood cell function were dysregulated in CD34 cells of both monosomy 7 and trisomy 8 MDS. In trisomy 8, up-regulated genes were primarily involved in immune and inflammatory responses, and down-regulated genes have been implicated in apoptosis inhibition. CD34 cells in monosomy 7 showed up-regulation of genes inducing leukemia transformation and tumorigenesis and apoptosis and down-regulation of genes controlling cell growth and differentiation. These results imply distinct molecular mechanisms for monosomy 7 and trisomy 8 MDS and implicate specific pathogenic pathways.

scholarly journals Gene Expression in RET/PTC3 and E7 Transgenic Mouse Thyroids: RET/PTC3 But Not E7 Tumors Are Partial and Transient Models of Human Papillary Thyroid Cancers

Endocrinology ◽  
2008 ◽  
Vol 149 (10) ◽  
pp. 5107-5117 ◽  
Author(s):  
Agnès Burniat ◽  
Ling Jin ◽  
Vincent Detours ◽  
Natacha Driessens ◽  
Jean-Christophe Goffard ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fusion Gene ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Human Thyroid ◽  
Matrix Remodeling ◽  
Biological Processes ◽  
Papillary Thyroid ◽  
Thyroid Cancers ◽  
Transient Models

We studied gene expression profiles in two mouse models of human thyroid carcinoma: the Tg-RET/PTC3 (RP3) and Tg-E7 mice. RP3 fusion gene is the most frequent mutation found in the first wave post-Chernobyl papillary thyroid cancers (PTCs). E7 is an oncoprotein derived from the human papillomavirus 16 responsible for most cervical carcinoma in women. Both transgenic mice develop thyroid hyperplasia followed by solid differentiated carcinoma in older animals. To understand the different steps leading to carcinoma, we analyzed thyroid gene expression in both strains at different ages by microarray technology. Important biological processes were differentially regulated in the two tumor types. In E7 thyroids, cell cycle was the most up-regulated process, an observation consistent with the huge size of these tumors. In RP3 thyroids, contrary to E7 tumors, several human PTC characteristics were observed: overexpression of many immune-related genes, regulation of human PTC markers, up-regulation of EGF-like growth factors and significant regulation of angiogenesis and extracellular matrix remodeling-related genes. However, similarities were incomplete; they did not concern the overall gene expression and were not conserved in old animals. Therefore, RP3 tumors are partial and transient models of human PTC. They constitute a good model, especially in young animals, to study the respective role of the biological processes shared with human PTC and will allow testing drugs targeting these validated variables.

scholarly journals Differences in miRNA expression profiles between wild-type and mutated NIFTPs

2017 ◽  
Vol 24 (10) ◽  
pp. 543-553 ◽  
Author(s):  
Maria Denaro ◽  
Clara Ugolini ◽  
Anello Marcello Poma ◽  
Nicla Borrelli ◽  
Gabriele Materazzi ◽  
...  
Keyword(s):  
Expression Profile ◽  
Mirna Expression ◽  
Fatty Acid Biosynthesis ◽  
Expression Profiles ◽  
Receptor Interaction ◽  
Papillary Thyroid ◽  
Wild Type ◽  
Thyroid Carcinomas ◽  
Mutational Status ◽  
Mirna Expression Profiles

Noninvasive encapsulated follicular variants of papillary thyroid carcinomas have been recently reclassified as noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTPs). NIFTPs exhibit a behavior that is very close to that of follicular adenomas but different from the infiltrative and invasive follicular variants of papillary thyroid carcinomas (FVPTCs). The importance of miRNAs to carcinogenesis has been reported in recent years. miRNAs seem to be promising diagnostic and prognostic molecular markers for thyroid cancer, and the combination of miRNA expression and mutational status might improve cytological diagnosis. The aim of the present study was to evaluate the miRNA expression profile in wild-type, RAS- or BRAF-mutated NIFTPs, infiltrative and invasive FVPTCs, and follicular adenomas using the nCounter miRNA Expression assay (NanoString Technologies). To identify the significant Kyoto Encyclopedia of Genes and Genomes (KEGG) molecular pathways associated with deregulated miRNAs, we used the union of pathways option in DNA Intelligent Analysis (DIANA) miRPath software. We have shown that the miRNA expression profiles of wild-type and mutated NIFTPs could be different. The expression profile of wild-type NIFTPs seems comparable to that of follicular adenomas, whereas mutated NIFTPs have an expression profile similar to that of infiltrative and invasive FVPTCs. The upregulation of 4 miRNAs (miR-221-5p, miR-221-3p, miR-222-3p, miR-146b-5p) and the downregulation of 8 miRNAs (miR-181a-3p, miR-28-5p, miR-363-3p, miR-342-3p, miR-1285-5p, miR-152-3p, miR-25-3p, miR-30e-3) in mutated NIFTPs compared to wild-type ones suggest a potential invasive-like phenotype by deregulating the specific pathways involved in cell adhesion and cell migration (Hippo signaling pathway, ECM-receptor interaction, adherens junction, regulation of actin cytoskeleton, fatty acid biosynthesis and metabolism).

scholarly journals Analyzing cancer gene expression data through the lens of normal tissue-specificity

2021 ◽  
Author(s):  
H. Robert Frost
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Specificity ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Cancer Types

AbstractThe genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

scholarly journals MON-500 Cell Adhesion Molecules mRNA Expression in Thyroid Tumors

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Larissa Teodoro ◽  
Karina Colombera Peres ◽  
Matheus Nascimento ◽  
Elisangela Souza Teixeira ◽  
Icleia Siqueira Barreto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Adhesion ◽  
Mrna Expression ◽  
Adhesion Molecules ◽  
Cell Adhesion Molecules ◽  
Cancer Biology ◽  
Thyroid Nodules ◽  
Thyroid Tumors ◽  
Papillary Thyroid ◽  
Thyroid Carcinomas

Abstract Thyroid cancer biology is extremely diverse. While some cases never progress clinically or do so indolently, others evolve aggressively and may even lead to death. Cell adhesion molecules are glycoproteins present in the cell membrane and play an important role in inflammatory and neoplastic diseases by recruiting immune cells to these sites. The aim of the present study was to investigate the role of mRNA expression of SELL, ICAM1 and ITGAL in thyroid tumors and their relationship with lymphocyte infiltration. We evaluated by RT-qPCR technique 191 thyroid nodules including 97 benign (79 females, 17 males; 49.8±12.5 years old) and 94 malignant (71 females, 23 males; 48.3±15.5years old) cases. Clinical and pathology data were obtained from 47 goiters; 50 follicular adenomas (FA); 74 papillary thyroid carcinomas (PTC), including: 29 classic papillary thyroid carcinomas (CPTC), 21 follicular variant of PTC (FVPTC), 12oxifilic variant of PTC (OVPTC), 12 tall cell papillary thyroid carcinomas (TCPTC); and 20 follicular thyroid carcinomas (FTC). All patients were managed according to a standard protocol based on current guidelines and followed-up for 78.7±54.2 months. SELL was more expressed in malignant (0.85±1.54 UA) than in benign (0.54±0.71 UA, p=0.0027) nodules. The same occurred with ICAM1 (0.99±1.41 vs. 0.46±0.85, p=0.0001), but not with ITGAL gene expression (1.04±1.63 vs. 0.76±1.21, p=0.2131). In addition, the expression of SELL was different when we compared PTC with FA (0.94±1.62 UA vs. 0.47±0.72 UA, p=0.0018) and FTC with FA (0.82±2.38 UA vs. 0.47±0.72 UA, p=0.0078). ICAM1 expression was lower in goiters (0.46±0.90 UA) when compared with PTC (0.93±1.22 UA, p=0.0030) and FTC (1.03±3.30 UA, p=0.0207). Higher expression of ICAM1 (1.16±3.04 UA vs. 0.52±0.96 UA, p=0.0064) and ITGAL (1.17±1.54 UA vs. 0.49±1.39 UA, p=0.0244) was observed in tumors with lymphocyte infiltrate. Also, ITGAL gene expression was higher in tumors that had distant metastasis at diagnosis (1.53±2.18 UA vs. 0.57±1.10 UA, p=0.0217). We were not able to demonstrate any association between any of the investigated molecules and patients’ outcome. In conclusion, our data suggest that cell adhesion molecules may play an important role in neoplastic thyroid cells proliferation. In addition, our findings show that gene expression of SELL and ICAM1 may assist in the histological characterization of follicular patterned thyroid nodules.

scholarly journals Gene Expression Profiles of Papillary Thyroid Microcarcinoma

2017 ◽  
Vol 102 (1-2) ◽  
pp. 39-46 ◽  
Author(s):  
Woo Young Kim ◽  
Jae Bok Lee ◽  
Seung Pil Jung ◽  
Hoon Yub Kim ◽  
Sang Uk Woo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Differentially Expressed Genes ◽  
Expression Profile ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Papillary Thyroid Microcarcinoma ◽  
Papillary Thyroid ◽  
Normal Thyroid ◽  
Thyroid Microcarcinoma

The objective was to identify gene expression profile of papillary thyroid microcarcinoma. To help improve diagnosis of papillary thyroid microcarcinoma, we performed gene expression profiling and compared it to pair normal thyroid tissues. We performed microarray analysis with 6 papillary thyroid microcarcinoma and 6 pair normal thyroid tissues. Differentially expressed genes were selected using paired t test, linear models for microarray data, and significance analysis of microarrays. Real-time quantitative reverse transcription–polymerase chain reaction was used to validate the representative 10 genes (MET, TIMP1, QPCT, PROS1, LRP4, SDC4, CITED1, DPP4, LRRK2, RUNX2). We identified 91 differentially expressed genes (84 upregulated and 7 downregulated) in the gene expression profile and validated 10 genes of the profile. We identified a significant genetic difference between papillary thyroid microcarcinoma and normal tissue by 10 upregulated genes greater than 2-fold (P < 0.05).

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