Identification of key mRNAs, miRNAs, and mRNA-miRNA network involved in papillary thyroid carcinoma

2020 ◽  
Vol 15 ◽  
Author(s):  
Wei Han ◽  
Dongchen Lu ◽  
Chonggao Wang ◽  
Mengdi Cui ◽  
Kai Lu
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Differential Expression Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Integrated Analysis ◽  
Papillary Thyroid ◽  
Gene Set Enrichment ◽  
Mirna Expression Data

Background: In the past decades, the incidence of thyroid cancer (TC) has been gradually increasing, owing to the widespread use of ultrasound scanning devices. However, the key mRNAs, miRNAs, and mRNA-miRNA network in papillary thyroid carcinoma (PTC) has not been fully understood. Material and Methods: In this study, multiple bioinformatics methods were employed, including differential expression analysis, gene set enrichment analysis, and miRNA-mRNA interaction network construction. Results: First, we investigated the key miRNAs that regulated significantly more differentially expressed genes based on GSEA method. Second, we searched for the key miRNAs based on the mRNA-miRNA interaction subnetwork involved in PTC. We identified hsa-mir-1275, hsa-mir-1291, hsa-mir-206 and hsa-mir-375 as the key miRNAs involved in PTC pathogenesis. Conclusion: The integrated analysis of the gene and miRNA expression data not only identified key mRNAs, miRNAs, and mRNA-miRNA network involved in papillary thyroid carcinoma, but also improved our understanding of the pathogenesis of PTC.

Immune cell confrontation in the papillary thyroid carcinoma microenvironment

2020 ◽  
Author(s):  
Zhenyu Xie ◽  
Xin Li ◽  
Yuzhen He ◽  
Song Wu ◽  
Shiyue Wang ◽  
...  
Keyword(s):  
T Cells ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Immune Cell Infiltration ◽  
M2 Macrophages ◽  
Papillary Thyroid ◽  
Gene Set Enrichment

Abstract Background Papillary thyroid carcinoma (PTC) is classified as an inflammation-driven cancer. A systematic understanding of immune cell infiltration in PTC is essential for subsequent immune research and new diagnostic and therapeutic strategies. Methods Three different algorithms, single-sample gene set enrichment analysis (ssGSEA), immune cell marker and CIBERSORT, were used to evaluate the immune cell infiltration levels (abundance and proportion) in 10 data sets (The Cancer Genome Atlas [TCGA], GSE3467, GSE3678, GSE5364, GSE27155, GSE33630, GSE50901, GSE53157, GSE58545, and GSE60542; a total of 799 PTC and 194 normal thyroid samples). Consensus unsupervised clustering divided PTC patients into low-immunity and high-immunity groups. Weighted gene coexpression network analysis (WGCNA) and gene set enrichment analysis (GSEA) were used to analyze the potential mechanisms that cause differences in the immune response. Results Compared with normal tissues, PTC tissues had a higher overall immune level, and the M2 macrophages, Tregs, monocytes, neutrophils, dendritic cells (DCs), mast cells (MCs), and M0 macrophages had higher abundances and proportions in PTC tissues. Compared with early PTC, advanced PTC had higher immune infiltration, and M2 macrophages, Tregs, monocytes, neutrophils, DCs, MCs, and M0 macrophages had higher abundances and proportions in advanced PTC. Compared to the low-immunity group patients, the high-immunity group patients presented with a more advanced stage, a larger tumor size, greater lymph node metastasis, higher tall-cell PTC, lower follicular PTC proportions, more BRAF mutations and fewer RAS mutations. Epstein-Barr virus (EBV) infection was the most significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway for key module genes. Conclusions In human PTC, M2 macrophages, Tregs, monocytes, neutrophils, DCs, MCs, and M0 macrophages played a tumor-promoting role, while M1 macrophages, CD8 + T cells, B cells, NK cells, and T follicular helper (TFH) cells (including eosinophils, γδ T cells, and Th17 cells, with weak supporting evidence) played an antitumor role. During the occurrence and development of PTC, the overall immune level was increased, and the abundance and proportion of tumor-promoting immune cells were significantly increased, indicating that immune escape had aggravated. Finally, we speculate that EBV may play an important role in changing the immune microenvironment of PTC tumors.

scholarly journals Network Analyses of Integrated Differentially Expressed Genes in Papillary Thyroid Carcinoma to Identify Characteristic Genes

Genes ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 45 ◽  
Author(s):  
Junliang Shang ◽  
Qian Ding ◽  
Shasha Yuan ◽  
Jin-Xing Liu ◽  
Feng Li ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Differentially Expressed Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Papillary Thyroid ◽  
Topological Properties ◽  
Network Analyses ◽  
Genetic Mechanisms

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Identifying characteristic genes of PTC are of great importance to reveal its potential genetic mechanisms. In this paper, we proposed a framework, as well as a measure named Normalized Centrality Measure (NCM), to identify characteristic genes of PTC. The framework consisted of four steps. First, both up-regulated genes and down-regulated genes, collectively called differentially expressed genes (DEGs), were screened and integrated together from four datasets, that is, GSE3467, GSE3678, GSE33630, and GSE58545; second, an interaction network of DEGs was constructed, where each node represented a gene and each edge represented an interaction between linking nodes; third, both traditional measures and the NCM measure were used to analyze the topological properties of each node in the network. Compared with traditional measures, more genes related to PTC were identified by the NCM measure; fourth, by mining the high-density subgraphs of this network and performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, several meaningful results were captured, most of which were demonstrated to be associated with PTC. The experimental results proved that this network framework and the NCM measure are useful for identifying more characteristic genes of PTC.

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.

scholarly journals Identification of Differentially Expressed Kinase and Screening Potential Anticancer Drugs in Papillary Thyroid Carcinoma

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Huairong Zhang ◽  
Bo Gao ◽  
Bingyin Shi
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Signaling Pathway ◽  
Protein Kinases ◽  
Molecular Mechanisms ◽  
Kinase Inhibitors ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Papillary Thyroid

Aim. We aim to identify protein kinases involved in the pathophysiology of papillary thyroid carcinoma (PTC) in order to provide potential therapeutic targets for kinase inhibitors and unfold possible molecular mechanisms.Materials and Methods. The gene expression profile of GSE27155 was analyzed to identify differentially expressed genes and mapped onto human protein kinases database. Correlation of kinases with PTC was addressed by systematic literature search, GO and KEGG pathway analysis.Results. The functional enrichment analysis indicated that “mitogen-activated protein kinases pathway” expression was extremely enriched, followed by “neurotrophin signaling pathway,” “focal adhesion,” and “GnRH signaling pathway.” MAPK, SRC, PDGFRa, ErbB, and EGFR were significantly regulated to correct these pathways. Kinases investigated by the literature on carcinoma were considered to be potential novel molecular therapeutic target in PTC and application of corresponding kinase inhibitors could be possible therapeutic tool.Conclusion. SRC, MAPK, and EGFR were the most important differentially expressed kinases in PTC. Combined inhibitors may have high efficacy in PTC treatment by targeting these kinases.

scholarly journals A Novel Metastasis-related Genes Based Signature for Predicting the Progression-free Interval of Patients With Papillary Thyroid Carcinoma

2022 ◽  
Author(s):  
Rui Liu ◽  
Zhen Cao ◽  
Meng-wei Wu ◽  
Xiao-bin Li ◽  
Hong-wei Yuan ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Cell Lines ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Functional Enrichment ◽  
Clinical Parameters ◽  
Papillary Thyroid ◽  
Free Interval

Abstract Background: We aimed to build a novel model with metastasis-related genes (MTGs) signature and relevant clinical parameters for predicting progression-free interval (PFI) after surgery for papillary thyroid carcinoma (PTC).Methods: We performed a bioinformatic analysis of integrated PTC datasets with the MTGs to identify differentially expressed MTGs (DE-MTGs). Then we generated PFI-related DE-MTGs and established a novel MTGs based signature. After that, we validated the signature on multiple datasets and PTC cell lines. Further, we carried out uni- and multivariate analysis to identify independent prognostic characters. Finally, we established a signature and clinical parameters-based nomogram for predicting the PFI of PTC. Results: We identified 155 DE-MTGs related to PFI in PTC. The functional enrichment analysis showed that the DE-MTGs were associated with an essential oncogenic process. Consequently, we found a novel 10-gene signature and could distinguish patients with poorer prognoses and predicted PFI accurately. The novel signature had a C-index of 0.76 and the relevant nomogram had a C-index of 0.80. Also, it was closely related to pivotal clinical characters of datasets and invasiveness of cell lines. And the signature was confirmed a significant independent prognostic factor in PTC. Finally, we built a nomogram by including the signature and relevant clinical factors. Validation analysis showed that the nomogram's efficacy was satisfying in predicting PTC’s PFI. Conclusions: The MTG signature and nomogram were closely associated with PTC prognosis and may help clinicians improve the individualized prediction of PFI, especially for high-risk patients after surgery.

scholarly journals Identification of a Hypoxia - Related lncRNA Signature For The Prognosis of Colorectal Cancer

2021 ◽  
Author(s):  
HUA HUANG ◽  
Shanshan Xu ◽  
Youran Li ◽  
Yunfei Gu ◽  
Lijiang Ji
Keyword(s):  
Colorectal Cancer ◽  
Roc Analysis ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Receptor Interaction ◽  
Prognostic Signature ◽  
Gene Set Enrichment ◽  
Gene Set

Abstract Background: Colorectal cancer (CRC), the commonly seen malignancy, ranks the 3rd place among the causes of cancer-associated mortality. As suggested by more and more studies, long coding RNAs (lncRNAs) have been considered as prognostic biomarkers for CRC. But the significance of hypoxic lncRNAs in predicting CRC prognosis remains unclear.Methods: The gene expressed profiles for CRC cases were obtained based on the Cancer Genome Atlas (TCGA) and applied to estimate the hypoxia score using a single-sample gene set enrichment analysis (ssGSEA) algorithm. Overall survival (OS) of high- and low-hypoxia score group was analyzed by the Kaplan–Meier (KM) plot. To identify differentially expressed lncRNAs (DELs) between two hypoxia score groups, this study carried out differential expression analysis, and then further integrated with the DELs between controls and CRC patients to generate the hypoxia-related lncRNAs for CRC. Besides, prognostic lncRNAs were screened by the univariate Cox regression, which were later utilized for constructing the prognosis nomogram for CRC by adopting the least absolute shrinkage and selection operator (LASSO) algorithm. In addition, both accuracy and specificity of the constructed prognostic signature were detected through the receiver operating characteristic (ROC) analysis. Moreover, our constructed prognosis signature also was validated in the internal testing test. This study operated gene set enrichment analysis (GSEA) for exploring potential biological functions associated with the prognostic signature. Finally, the ceRNA network of the prognostic lncRNAs was constructed.Results: Among 2299 hypoxia-related lncRNAs of CRC in total, LINC00327, LINC00163, LINC00174, SYNPR-AS1, and MIR31HG were identified as prognostic lncRNAs by the univariate Cox regression, and adopted for constructing the prognosis signature for CRC. ROC analysis showed the predictive power and accuracy of the prognostic signature. Additionally, the GSEA revealed that ECM-receptor interaction, PI3K-Akt pathway, phagosome, and Hippo pathway were mostly associated with the high-risk group. 352 miRNAs-mRNAs pairs and 177 lncRNAs-miRNAs were predicted.Conclusion: To conclude , we identified 5 hypoxia-related lncRNAs to establish an accurate prognostic signature for CRC, providing important prognostic markers and therapeutic target.

scholarly journals Identification of Potential Prognostic Biomarker for Predicting Survival in Multiple Myeloma Using Bioinformatics Analysis and Experiments

2021 ◽  
Vol 12 ◽  
Author(s):  
Jian Zhou ◽  
Menghui Zhang ◽  
Yan Zhang ◽  
Xi Shi ◽  
Linlin Liu ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Plasma Cells ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
New Biomarkers ◽  
Protein Protein Interaction Network

Multiple myeloma (MM) is a malignant disease of plasma cells, which remains incurable because of its unclear mechanism and drug resistance. Herein, we aimed to explore new biomarkers and therapeutic targets in MM. After screening differentially expressed genes (DEGs) in GSE6477 and GSE13591 dataset, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of DEGs using DAVID online database. The results indicated that the downregulated DEGs were mainly enriched in the immune-associated biological process. The protein–protein interaction network was constructed by STRING database, on which we performed module analysis and identified key genes. Gene set enrichment analysis (GSEA) and Kaplan–Meier analysis showed that RRM2 could be a novel biomarker in MM diagnosis. We further confirmed that novel RRM2 inhibitor osalmid inhibited MM cell proliferation and triggered cell cycle S phase arrest. Targeting RRM2 was expected to develop new therapeutic strategies for malignant MM.

Identification of Genes Associated with Papillary Thyroid Carcinoma (PTC) for Diagnosis by Integrated Analysis

2016 ◽  
Vol 48 (04) ◽  
pp. 226-231 ◽  
Author(s):  
W.-B. Li ◽  
J. Zhou ◽  
L. Xu ◽  
X.-L. Su ◽  
Q. Liu ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Integrated Analysis ◽  
Papillary Thyroid

Identification of biomarkers associated with cervical lymph node metastasis in papillary thyroid carcinoma: Evidence from an integrated bioinformatic analysis

2021 ◽  
pp. 1-10
Author(s):  
Zheng Zhang ◽  
Shuangshuang Zhao ◽  
Keke Wang ◽  
Mengyuan Shang ◽  
Zheming Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lymph Node ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Differentially Expressed Genes ◽  
Cervical Lymph Node ◽  
Receptor Activity ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Papillary Thyroid

Integrated analysis of accumulated data is an effective way to obtain reliable potential diagnostic molecular of cervical lymph node metastases (LNM) in papillary thyroid carcinoma (PTC). The benefits of prophylactic lymph node dissection (PLND) for these clinically node-negative (cN0) patients remained considerable controversies. Hence, elucidation of the mechanisms of LNM and exploration of potential biomarkers and prognostic indicators are essential for accurate diagnosis of LNM in PTC patients. Up to date, advanced microarray and bioinformatics analysis have advanced an understanding of the molecular mechanisms of disease occurrence and development, which are necessary to explore genetic changes and identify potential diagnostic biomarkers. In present study, we performed a comprehensive analysis of the differential expression, biological functions, and interactions of LNM-related genes. Two publicly available microarray datasets GSE60542 and GSE129562 were available from Gene Expression Omnibus (GEO) database. Differentially expressed genes between clinically node-positive (cN1) and cN0 PTC samples were screened by an integrated analysis of multiple gene expression profile after gene reannotation and batch normalization. Our results identified 48 differentially expressed genes (DEGs) genetically associated with LNM in PTC patients. Gene ontology (GO) analyses revealed the changes in the modules were mostly enriched in the regulation of MHC class II receptor activity, the immune receptor activity, and the peptide antigen binding. Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of DEGs displayed that the intestinal immune network for IgA production, staphylococcus aureus infection, and cell adhesion molecules (CAMs). To screen core genes related to LNM of PTC from the protein-protein interaction network, top 10 hub genes were identified with highest scores. Our results help us understand the exact mechanisms underlying the metastasis of cervical LNM in PTC tissues and pave an avenue for the progress of precise medicine for individual patients.

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