scholarly journals Identification and analysis of genes associated with papillary thyroid carcinoma by bioinformatics methods

2019 ◽  
Vol 39 (4) ◽  
Author(s):  
Shulong Zhang ◽  
Quan Wang ◽  
Qi Han ◽  
Huazhong Han ◽  
Pinxiang Lu
Keyword(s):  
Gene Expression ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Protein Interactions ◽  
Disease Free Survival ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Papillary Thyroid ◽  
Protein Protein Interactions ◽  
Search Tool

AbstractThe molecular mechanism of the occurrence and development of papillary thyroid carcinoma (PTC) has been widely explored, but has not been completely elucidated. The present study aimed to identify and analyze genes associated with PTC by bioinformatics methods. Two independent datasets were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between PTC tissues and matched non-cancerous tissues were identified using GEO2R tool. The common DEGs in the two datasets were screened out by VennDiagram package, and analyzed by the following tools: KOBAS, Database for Annotation, Visualization, and Integrated Discovery (DAVID), Search tool for the retrieval of interacting genes/proteins (STRING), UALCAN and Gene expression profiling interactive analysis (GEPIA). A total of 513 common DEGs, including 259 common up-regulated and 254 common down-regulated genes in PTC, were screened out. These common up-regulated and down-regulated DEGs were most significantly enriched in cytokine–cytokine receptor interaction and metabolic pathways, respectively. Protein–protein interactions (PPI) network analysis showed that the up-regulated genes: FN1, SDC4, NMU, LPAR5 and the down-regulated genes: BCL2 and CXCL12 were key genes. Survival analysis indicated that the high expression of FN1 and NMU genes significantly decreased disease-free survival of patients with thyroid carcinoma. In conclusion, the genes and pathways identified in the current study will not only contribute to elucidating the pathogenesis of PTC, but also provide prognostic markers and therapeutic targets for PTC.

scholarly journals Identification of Core Prognosis-Related Candidate Genes in Cervical Cancer via Integrated Bioinformatical Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Jianxia Wei ◽  
Yang Wang ◽  
Kejian Shi ◽  
Ying Wang
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Protein Interactions ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Protein Protein Interactions ◽  
Normal Tissues ◽  
Interactive Analysis ◽  
Kaplan Meier ◽  
Search Tool

Purposes. Cervical cancer (CC) is one of the highest frequently occurred malignant gynecological tumors with high rates of morbidity and mortality. Here, we aimed to identify significant genes associated with poor outcome. Materials and methods. Differentially expressed genes (DEGs) between CC tissues and normal cervical tissues were picked out by GEO2R tool and Venn diagram software. Database for Annotation, Visualization and Integrated Discovery (DAVID) was performed to analyze gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. The protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Afterwards, Kaplan-Meier analysis was applied to analyze the overall survival among these genes. The Gene Expression Profiling Interactive Analysis (GEPIA) was applied for further validation of the expression level of these genes. Results. The mRNA expression profile datasets of GSE63514, GSE27678, and GSE6791 were downloaded from the Gene Expression Omnibus database (GEO). In total, 76 CC tissues and 35 normal tissues were collected in the three profile datasets. There were totally 73 consistently expressed genes in the three datasets, including 65 up-regulated genes and 8 down-regulated genes. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 65 up-regulated genes and 4 down-regulated genes were selected. The results of the Kaplan-Meier survival analysis showed that 3 of the 65 up-regulated genes had a significantly worse prognosis, while 3 of the 4 down-regulated genes had a significantly better outcome. For validation in GEPIA, 4 of 6 genes (PLOD2, ANLN, AURKA, and AR) were confirmed to be significantly deregulated in CC tissues compared to normal tissues. Conclusion. We have identified three up-regulated (PLOD2, ANLN, and AURKA) and a down-regulated DEGs (AR) with poor prognosis in CC on the basis of integrated bioinformatical methods, which could be regarded as potential therapeutic targets for CC patients.

scholarly journals TERT Promoter Mutations and Their Impact on Gene Expression Profile in Papillary Thyroid Carcinoma

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1597 ◽  
Author(s):  
Dagmara Rusinek ◽  
Aleksandra Pfeifer ◽  
Marta Cieslicka ◽  
Malgorzata Kowalska ◽  
Agnieszka Pawlaczek ◽  
...  
Keyword(s):  
Gene Expression ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Distant Metastases ◽  
Gene Set Enrichment Analysis ◽  
Braf V600e ◽  
Papillary Thyroid ◽  
Mutational Status

Background: Telomerase reverse transcriptase promoter (TERTp) mutations are related to a worse prognosis in various malignancies, including papillary thyroid carcinoma (PTC). Since mechanisms responsible for the poorer outcome of TERTp(+) patients are still unknown, searching for molecular consequences of TERTp mutations in PTC was the aim of our study. Methods: The studied cohort consisted of 54 PTCs, among them 24 cases with distant metastases. BRAF V600E, RAS, and TERTp mutational status was evaluated in all cases. Differences in gene expression profile between TERTp(+) and TERTp(−) PTCs were examined using microarrays. The evaluation of signaling pathways and gene ontology was based on the Gene Set Enrichment Analysis. Results: Fifty-nine percent (32/54) of analyzed PTCs were positive for at least one mutation: 27 were BRAF(+), among them eight were TERTp(+), and 1 NRAS(+), whereas five other samples harbored RAS mutations. Expression of four genes significantly differed in BRAF(+)TERTp(+) and BRAF(+)TERTp(−) PTCs. Deregulation of pathways involved in key cell processes was observed. Conclusions: TERTp mutations are related to higher PTC aggressiveness. CRABP2 gene was validated as associated with TERTp mutations. However, its potential use in diagnostics or risk stratification in PTC patients needs further studies.

LncRNAs SNHG12 and LINC00152 were associated with progression of patients with papillary thyroid carcinoma

2019 ◽  
Vol 15 (36) ◽  
pp. 4167-4179 ◽  
Author(s):  
Jianqiu Liu ◽  
Xinyue Tang ◽  
Jing Lv ◽  
Xiaowei Peng ◽  
Ke Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Papillary Thyroid Carcinoma ◽  
Operating Characteristic ◽  
Thyroid Nodules ◽  
Diagnostic Value ◽  
Gene Expression Omnibus ◽  
Papillary Thyroid ◽  
Normal Tissues ◽  
Benign Thyroid Nodules ◽  
Benign Thyroid

Aim: To investigate the clinical roles of LINC00152 and SNHG12 in papillary thyroid carcinoma (PTC). Methods: LINC00152 and SNHG12 expression was sought and analysis in gene expression omnibus, The Cancer Genome Atlas and GEPIA datasets. Tumor and adjacent normal tissues were collected from 97 PTC and 44 benign thyroid nodules patients. The expression was evaluated by quantitative real-time polymerase chain reaction. The association between the expression level and clinicopathologic characteristics was analyzed by χ2 test. Receiver operating characteristic curves were plotted to evaluate the diagnostic value. Results: The expression of SNHG12 and LINC00152 were significantly higher in PTC tissues than in adjacent normal tissues not only in gene expression omnibus database but the validated samples. More interesting, LINC00152 expression level was also significantly higher in PTC tissues than that in benign thyroid nodules. The upregulation of LINC00152 and SNHG12 was associated with the malignant progression of PTC. Receiver operating characteristic curve analysis also demonstrated that there was a good trend, which indicates that they may have certain diagnostic value. Conclusion: LINC00152 and SNHG12 might serve as serve as potential related molecules of PTC.

scholarly journals A meta-analysis of prognostic roles of molecular markers in papillary thyroid carcinoma

2017 ◽  
Vol 6 (3) ◽  
pp. R8-R17 ◽  
Author(s):  
Huy Gia Vuong ◽  
Uyen N P Duong ◽  
Ahmed M A Altibi ◽  
Hanh T T Ngo ◽  
Thong Quang Pham ◽  
...  
Keyword(s):  
Molecular Markers ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Random Effect ◽  
Random Effect Model ◽  
Disease Free Survival ◽  
Papillary Thyroid ◽  
Promoter Mutation ◽  
Meta Analyses ◽  
The Impact

The prognostic role of molecular markers in papillary thyroid carcinoma (PTC) is a matter of ongoing debate. The aim of our study is to investigate the impact of RAS, BRAF, TERT promoter mutations and RET/PTC rearrangements on the prognosis of PTC patients. We performed a search in four electronic databases: PubMed, Scopus, Web of Science and Virtual Health Library (VHL). Data of hazard ratio (HR) and its 95% confidence interval (CI) for disease-specific survival (DSS) and disease-free survival (DFS) were directly obtained from original papers or indirectly estimated from Kaplan–Meier curve (KMC). Pooled HRs were calculated using random-effect model weighted by inverse variance method. Publication bias was assessed by using Egger’s regression test and visual inspection of funnel plots. From 2630 studies, we finally included 35 studies with 17,732 patients for meta-analyses. TERT promoter mutation was significantly associated with unfavorable DSS (HR = 7.64; 95% CI = 4.00–14.61) and DFS (HR = 2.98; 95% CI = 2.27–3.92). BRAF mutations significantly increased the risk for recurrence (HR = 1.63; 95% CI = 1.27–2.10) but not for cancer mortality (HR = 1.41; 95% CI = 0.90–2.23). In subgroup analyses, BRAF mutation only showed its prognostic value in short-/medium-term follow-up. Data regarding RAS mutations and RET/PTC fusions were insufficient for meta-analyses. TERT promoter mutation can be used as an independent and reliable marker for risk stratification and predicting patient’s outcomes. The use of BRAF mutation to assess patient prognosis should be carefully considered.

scholarly journals Gene expression in papillary thyroid carcinoma reveals highly consistent profiles

2001 ◽  
Vol 98 (26) ◽  
pp. 15044-15049 ◽  
Author(s):  
Y. Huang ◽  
M. Prasad ◽  
W. J. Lemon ◽  
H. Hampel ◽  
F. A. Wright ◽  
...  
Keyword(s):  
Gene Expression ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Papillary Thyroid

593 Gene expression profilling in papillary thyroid carcinoma: Are there different pathways of carcinogenesis?

2003 ◽  
Vol 1 (5) ◽  
pp. S179
Author(s):  
B. Jarzab ◽  
M. Wiench ◽  
J. Wloch ◽  
K. Fujarewicz ◽  
K. Simek ◽  
...  
Keyword(s):  
Gene Expression ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Papillary Thyroid

scholarly journals Identification of hub genes and pathways in nonalcoholic steatohepatitis by integrated bioinformatics analysis

2021 ◽  
Author(s):  
Pegah Einaliyan ◽  
Ali Owfi ◽  
Mohammadamin Mahmanzar ◽  
Taha Aghajanzadeh ◽  
Morteza Hadizadeh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Expression Profiles ◽  
Principal Component ◽  
Gene Expression Omnibus ◽  
P Value ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Alcoholic Fatty Liver ◽  
Critical Pathways

AbstractBackgroundCurrently, non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases in the world. Forecasting the short-term, up to 2025, NASH due to fibrosis is one of the leading causes of liver transplantation. Cohort studies revealed that non-alcoholic steatohepatitis (NASH) has a higher risk of fibrosis progression among NAFLD patients. Identifying differentially expressed genes helps to determine NASH pathogenic pathways, make more accurate diagnoses, and prescribe appropriate treatment.Methods and ResultsIn this study, we found 11 NASH datasets by searching in the Gene Expression Omnibus (GEO) database. Subsequently, NASH datasets with low-quality control scores were excluded. Four datasets were analyzed with packages of R/Bioconductor. Then, all integrated genes were Imported into Cytoscape to illustrate the protein-protein interactions network. All hubs and nodes degree has been calculated to determine the hub genes with critical roles in networks.Possible correlations between expression profiles of mutual DEGs were identified employing Principal Component Analysis (PCA). Primary analyzed data were filtered based on gene expression (logFC > 1, logFC < −1) and adj-P-value (<0.05). Ultimately, among 379 DEGs, we selected the top 10 genes (MYC, JUN, EGR1, FOS, CCL2, IL1B, CXCL8, PTGS2, IL6, SERPINE1) as candidates among up and down regulated genes, and critical pathways such as IL-6, IL-17, TGF β, and TNFα were identified.ConclusionThe present study suggests an important DEGs, biological processes, and critical pathways involved in the pathogenesis of NASH disease. Further investigations are needed to clarify the exact mechanisms underlying the development and progression of NASH disease.

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