Identification of Potential Key Genes in Anaplastic Thyroid Cancer Using Bioinformatics AnalysisIdentification of Potential Key Genes in Anaplastic Thyroid Cancer using Bioinformatics Analysis

2021 ◽  
Author(s):  
Zhenzhen Li ◽  
Chaoliang Xiong ◽  
Jin Wei ◽  
Ping Chen ◽  
Yanping Zhang ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Thyroid Cancer ◽  
Collagen Type ◽  
Expression Profiles ◽  
Thyroid Tissue ◽  
Anaplastic Thyroid Cancer ◽  
Type I ◽  
Tissue Samples ◽  
Key Genes ◽  
Km Plotter

Abstract BackgroundAnaplastic thyroid cancer (ATC) has a high degree of malignancy and a poor prognosis. Its incidence accounts for approximately 10-15% of all thyroid cancers. The purpose of this study was to determine the differentially expressed genes (DEGs) of ATC through biometric analysis technology, clarify the potential interactions between them, and screen genes related to the prognosis of ATC.MethodsThe GSE29265, GSE65144, GSE33630, and GSE85457 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 117 tissue samples (81 normal thyroid tissue samples and 36 ATC samples). The four datasets were integrated and analyzed by the limma packages to obtain DEGs. With these DEGs, we performed gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway analyses using the Database for Annotation, Visualization and Integrated Discovery, protein-protein interaction (PPI) analysis using Cytoscape, and survival analysis using the Kaplan-Meier (KM) plotter.Results.After R integration analysis of the four datasets, 764 DEGs were obtained, i.e., 314 upregulated and 450 downregulated genes. Among the hub DEGs obtained in the PPI network, the expression levels of thymidylate synthase (TYMS), fibronectin 1, chordin-like 1, syndecan 2, integrin alpha 2, collagen type I alpha 1 chain, collagen type IX alpha 3 chain (COL9A3), and collagen type XXIII alpha 1 chain (COL23A1) were associated with ATC prognosis. These results showed that the overall survival and recurrence-free survival of TYMS, COL9A3, and COL23A1 were statistically significant in our KM plotter survival analysis; thus, these DEGs may be used as potential biomarkers of ATC.ConclusionThis study identified several potential target genes and pathways that may affect the development of ATC. These findings provide new insights for the detection of novel diagnostic and therapeutic biomarkers for ATC.

Identification of potential key genes in anaplastic thyroid cancer using bioinformatics analysis

2021 ◽  
Author(s):  
Zhenzhen Li ◽  
Xiong Chaoliang ◽  
Jin Wei ◽  
Ping Chen ◽  
Yanping Zhang ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Thyroid Cancer ◽  
Target Genes ◽  
Collagen Type ◽  
Expression Profiles ◽  
Thyroid Tissue ◽  
Anaplastic Thyroid Cancer ◽  
Type I ◽  
Tissue Samples ◽  
Km Plotter

Abstract Background Anaplastic thyroid cancer (ATC) has a high degree of malignancy and a poor prognosis. Its incidence accounts for approximately 10–15% of all thyroid cancers. The purpose of this study was to determine the differentially expressed genes (DEGs) of ATC through biometric analysis technology, clarify the potential interactions between them, and screen genes related to the prognosis of ATC. Methods The GSE29265, GSE65144, GSE33630, and GSE85457 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 117 tissue samples (81 normal thyroid tissue samples and 36 ATC samples). The four datasets were integrated and analyzed by the limma packages to obtain DEGs. With these DEGs, we performed gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway analyses using the Database for Annotation, Visualization and Integrated Discovery, protein-protein interaction (PPI) analysis using Cytoscape, and survival analysis using the Kaplan-Meier (KM) plotter. Results. After R integration analysis of the four datasets, 764 DEGs were obtained, i.e., 314 upregulated and 450 downregulated genes. Among the hub DEGs obtained in the PPI network, the expression levels of thymidylate synthase (TYMS), fibronectin 1, chordin-like 1, syndecan 2, integrin alpha 2, collagen type I alpha 1 chain, collagen type IX alpha 3 chain (COL9A3), and collagen type XXIII alpha 1 chain (COL23A1) were associated with ATC prognosis. These results showed that the overall survival and recurrence-free survival of TYMS, COL9A3, and COL23A1 were statistically significant in our KM plotter survival analysis; thus, these DEGs may be used as potential biomarkers of ATC. Conclusion This study identified several potential target genes and pathways that may affect the development of ATC. These findings provide new insights for the detection of novel diagnostic and therapeutic biomarkers for ATC.

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 Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Yujie Shen ◽  
Shikun Dong ◽  
Jinhui Liu ◽  
Liqing Zhang ◽  
Jiacheng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Small Molecules ◽  
Signaling Pathway ◽  
Pathway Analysis ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Receptor Interaction ◽  
Tissue Samples ◽  
Key Genes

Background. The molecular mechanisms and genetic markers of thyroid cancer are unclear. In this study, we used bioinformatics to screen for key genes and pathways associated with thyroid cancer development and to reveal its potential molecular mechanisms. Methods. The GSE3467, GSE3678, GSE33630, and GSE53157 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 164 tissue samples (64 normal thyroid tissue samples and 100 thyroid cancer samples). The four datasets were integrated and analyzed by the RobustRankAggreg (RRA) method to obtain differentially expressed genes (DEGs). Using these DEGs, we performed gene ontology (GO) functional annotation, pathway analysis, protein-protein interaction (PPI) analysis and survival analysis. Then, CMap was used to identify the candidate small molecules that might reverse thyroid cancer gene expression. Results. By integrating the four datasets, 330 DEGs, including 154 upregulated and 176 downregulated genes, were identified. GO analysis showed that the upregulated genes were mainly involved in extracellular region, extracellular exosome, and heparin binding. The downregulated genes were mainly concentrated in thyroid hormone generation and proteinaceous extracellular matrix. Pathway analysis showed that the upregulated DEGs were mainly attached to ECM-receptor interaction, p53 signaling pathway, and TGF-beta signaling pathway. Downregulation of DEGs was mainly involved in tyrosine metabolism, mineral absorption, and thyroxine biosynthesis. Among the top 30 hub genes obtained in PPI network, the expression levels of FN1, NMU, CHRDL1, GNAI1, ITGA2, GNA14 and AVPR1A were associated with the prognosis of thyroid cancer. Finally, four small molecules that could reverse the gene expression induced by thyroid cancer, namely ikarugamycin, adrenosterone, hexamethonium bromide and clofazimine, were obtained in the CMap database. Conclusion. The identification of the key genes and pathways enhances the understanding of the molecular mechanisms for thyroid cancer. In addition, these key genes may be potential therapeutic targets and biomarkers for the treatment of thyroid cancer.

scholarly journals Detection of SARS-COV-2 receptor ACE-2 mRNA in thyroid cells: a clue for COVID-19-related subacute thyroiditis

2020 ◽  
Author(s):  
M. Rotondi ◽  
F. Coperchini ◽  
G. Ricci ◽  
M. Denegri ◽  
L. Croce ◽  
...  
Keyword(s):  
Primary Cultures ◽  
Thyroid Tissue ◽  
Subacute Thyroiditis ◽  
Human Thyroid ◽  
Thyroid Cell ◽  
Type I ◽  
Rt Pcr ◽  
Follicular Cells ◽  
Thyroid Cells ◽  
Tissue Samples

Abstract Purpose SARS-COV-2 is a pathogenic agent belonging to the coronavirus family, responsible for the current global world pandemic. Angiotensin-converting enzyme 2 (ACE-2) is the receptor for cellular entry of SARS-CoV-2. ACE-2 is a type I transmembrane metallo-carboxypeptidase involved in the Renin-Angiotensin pathway. By analyzing two independent databases, ACE-2 was identified in several human tissues including the thyroid. Although some cases of COVID-19-related subacute thyroiditis were recently described, direct proof for the expression of the ACE-2 mRNA in thyroid cells is still lacking. Aim of the present study was to investigate by RT-PCR whether the mRNA encoding for ACE-2 is present in human thyroid cells. Methods RT-PCR was performed on in vitro ex vivo study on thyroid tissue samples (15 patients undergoing thyroidectomy for benign thyroid nodules) and primary thyroid cell cultures. Results The ACE-2 mRNA was detected in all surgical thyroid tissue samples (n = 15). Compared with two reporter genes (GAPDH: 0.052 ± 0.0026 Cycles−1; β-actin: 0.044 ± 0.0025 Cycles−1; ACE-2: 0.035 ± 0.0024 Cycles−1), the mean level of transcript expression for ACE-2 mRNA was abundant. The expression of ACE-2 mRNA in follicular cells was confirmed by analyzing primary cultures of thyroid cells, which expressed the ACE-2 mRNA at levels similar to tissues. Conclusions The results of the present study demonstrate that the mRNA encoding for the ACE-2 receptor is expressed in thyroid follicular cells, making them a potential target for SARS-COV-2 entry. Future clinical studies in patients with COVID-19 will be required for increase our understanding of the thyroid repercussions of SARS-CoV-2 infection.

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 CD74 expression and its therapeutic potential in thyroid carcinoma

2015 ◽  
Vol 22 (2) ◽  
pp. 179-190 ◽  
Author(s):  
Shih-Ping Cheng ◽  
Chien-Liang Liu ◽  
Ming-Jen Chen ◽  
Ming-Nan Chien ◽  
Ching-Hsiang Leung ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Therapeutic Potential ◽  
Thyroid Tissue ◽  
Anaplastic Thyroid Cancer ◽  
Tumor Stage ◽  
Migration And Invasion ◽  
Advanced Tumor Stage ◽  
Papillary Thyroid ◽  
Extrathyroidal Invasion ◽  
Advanced Tumor

CD74, the invariant chain of major histocompatibility complex class II, is also a receptor for macrophage migration inhibitory factor (MIF). CD74 and MIF have been associated with tumor progression and metastasis in hematologic and solid tumors. In this study, we found that 60 and 65% of papillary thyroid cancers were positive for CD74 and MIF immunohistochemical staining respectively. Anaplastic thyroid cancer was negative for MIF, but mostly positive for CD74 expression. Normal thyroid tissue and follicular adenomas were negative for CD74 expression. CD74 expression in papillary thyroid cancer was associated with larger tumor size (P=0.043), extrathyroidal invasion (P=0.021), advanced TNM stage (P=0.006), and higher MACIS score (P=0.026). No clinicopathological parameter was associated with MIF expression. Treatment with anti-CD74 antibody in thyroid cancer cells inhibited cell growth, colony formation, cell migration and invasion, and vascular endothelial growth factor secretion. In contrast, treatment with recombinant MIF induced an increase in cell invasion. Anti-CD74 treatment reduced AKT phosphorylation and stimulated AMPK activation. Our findings suggest that CD74 overexpression in thyroid cancer is associated with advanced tumor stage and may serve as a therapeutic target.

scholarly journals Integrated analysis of lncRNA-miRNA-mRNA ceRNA network and the potential prognosis indicators in sarcomas

2021 ◽  
Author(s):  
Lu Gao ◽  
Yu Zhao ◽  
Xuelei Ma ◽  
Ling Zhang
Keyword(s):  
Survival Analysis ◽  
Kegg Pathway ◽  
Gene Prediction ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Integrated Analysis ◽  
Ppi Network ◽  
Tissue Samples ◽  
Pathway Enrichment ◽  
Cerna Network

Abstract Background: Competitive endogenous RNA (ceRNA) has revealed a new mechanism of interaction between RNAs and been demonstrated to play crucial roles in multiple biological processes and in the development of neoplasms that potentially serve as diagnostic and prognosis markers as well as therapeutic targets.Methods：In this work, we identified differentially expressed mRNAs (DEGs), lncRNAs (DELs) and miRNAs (DEMs) in sarcoma by comparing the genes expression profiles between sarcoma samples and normal tissue samples in Gene Expression Omnibus (GEO) datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were applied to investigate the primary functions of the overlapped DEGs. Then, lncRNA-miRNA and miRNA-mRNA interactions were predicted, and the ceRNA regulatory network was constructed in Cytoscape. In addition, the protein-protein interaction (PPI) network was constructed and survival analysis was performed.Results: A total of 1296 DEGs were identified in sarcoma samples by combining the GO and KEGG pathway enrichment analyses, 338 DELs were discovered after the probes were reannotated, and 36 DEMs were ascertained through intersecting two different expression miRNAs sets. Further, through target gene prediction, a lncRNA-miRNA-mRNA ceRNA network that contained 113 mRNAs, 69 lncRNAs and 29 miRNAs was constructed. The PPI network identified the six most significant hub proteins. Survival analysis revealed that seven mRNAs, four miRNAs and one lncRNA were associated with overall survival of sarcoma patients.Conclusions: Overall, we constructed a ceRNA network in sarcomas, which likely provides insights for further research on the molecular mechanism and potential prognosis biomarkers.

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