Identification of Biomarkers Based on Differentially Expressed Genes in Papillary Thyroid Carcinoma

Objective: Next-generation sequencing (NGS) was performed to identify genes that were differentially expressed between normal thyroid tissue and papillary thyroid carcinoma (PTC). Materials & Methods: Six candidate genes were selected and further confirmed with quantitative real-time polymerase chain reaction (qRT-PCR), and immunohistochemistry in samples from 24 fresh thyroid tumors and adjacent normal tissues. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used to investigate signal transduction pathways of the differentially expressed genes. Results: In total, 1690 genes were differentially expressed between samples from patients with PTC and the adjacent normal tissue. Among these, SFRP4, ZNF90, and DCN were the top three upregulated genes, whereas KIRREL3, TRIM36, and GABBR2 were downregulated with the smallest p values. Several pathways were associated with the differentially expressed genes and involved in cellular proliferation, cell migration, and endocrine system tumor progression, which may contribute to the pathogenesis of PTC. Upregulation of SFRP4, ZNF90, and DCN at the mRNA level was further validated with RT-PCR, and DCN expression was further confirmed with immunostaining of PTC samples. Conclusion: These results provide new insights into the molecular mechanisms of PTC. Identification of differentially expressed genes should not only improve the tumor signature for thyroid tumors as a diagnostic biomarker but also reveal potential targets for thyroid tumor treatment.

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

Genes ◽

10.3390/genes10010045 ◽

2019 ◽

Vol 10 (1) ◽

pp. 45 ◽

Cited By ~ 5

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.

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

Clinical Hemorheology and Microcirculation ◽

10.3233/ch-201074 ◽

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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Identification of key genes of papillary thyroid carcinoma by integrated bioinformatics analysis

Bioscience Reports ◽

10.1042/bsr20201555 ◽

2020 ◽

Vol 40 (8) ◽

Cited By ~ 1

Author(s):

Gang Xue ◽

Xu Lin ◽

Jing-Fang Wu ◽

Da Pei ◽

Dong-Mei Wang ◽

...

Keyword(s):

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Differentially Expressed Genes ◽

Disease Free Survival ◽

Differentially Expressed ◽

Mrna Levels ◽

Papillary Thyroid ◽

Positive Role ◽

Immune Infiltration ◽

Key Genes

Abstract Background: Papillary thyroid carcinoma (PTC) is one of the fastest-growing malignant tumor types of thyroid cancer. Therefore, identifying the interaction of genes in PTC is crucial for elucidating its pathogenesis and finding more specific molecular biomarkers. Methods: Four pairs of PTC tissues and adjacent tissues were sequenced using RNA-Seq, and 3745 differentially expressed genes were screened (P<0.05, |logFC|>1). The enrichment analysis indicated that the vast majority of differentially expressed genes (DEGs) may play a positive role in the development of cancer. Then, the significant modules were analyzed using Cytoscape software in the protein–protein interaction network. Survival analysis, TNM analysis, and immune infiltration analysis of key genes were analyzed. And the expression of ADORA1, APOE, and LPAR5 genes were verified by qPCR in PTC compared with matching adjacent tissues. Results: Twenty-five genes were identified as hub genes with nodes greater than 10. The expression of 25 genes were verified by the GEPIA database, and the overall survival and disease-free survival analyses were conducted with Kaplan–Meier plotter. We found only three genes were confirmed with our validation and were statistically significant in PTC, namely ADORA1, APOE, and LPAR5. Further analysis found that the mRNA levels and methylation degree of these three genes were significantly correlated with the TNM staging of PTC. And these three genes were related to PTC immune infiltration. Verification of the expression of these three genes by RT-qPCR and Western blot further confirmed the reliability of our results. Conclusion: Our study identified three genes that may play key regulatory roles in the development, metastasis, and immune infiltration of papillary thyroid carcinoma.

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Integrated bioinformatics analysis and screening of hub genes in papillary thyroid carcinoma

PLoS ONE ◽

10.1371/journal.pone.0251962 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0251962

Author(s):

Rong Fan ◽

Lijin Dong ◽

Ping Li ◽

Xiaoming Wang ◽

Xuewei Chen

Keyword(s):

Gene Ontology ◽

Survival Analysis ◽

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Papillary Thyroid ◽

Hub Genes ◽

Online Software

Background With the increasing incidence of papillary thyroid carcinoma (PTC), PTC continues to garner attention worldwide; however its pathogenesis remains to be elucidated. The purpose of this study was to explore key biomarkers and potential new therapeutic targets for, PTC. Methods GEO2R and Venn online software were used for screening of differentially expressed genes. Hub genes were screened via STRING and Cytoscape, followed by Gene Ontology and KEGG enrichment analysis. Finally, survival analysis and expression validation were performed using the UALCAN online software and immunohistochemistry. Results We identified 334 consistently differentially expressed genes (DEGs) comprising 136 upregulated and 198 downregulated genes. Gene Ontology enrichment analysis results suggested that the DEGs were mainly enriched in cancer-related pathways and functions. PPI network visualization was performed and 17 upregulated and 13 downregulated DEGs were selected. Finally, the expression verification and overall survival analysis conducted using the Gene Expression Profiling Interactive Analysis Tool (GEPIA) and UALCAN showed that LPAR5, TFPI, and ENTPD1 were associated with the development of PTC and the prognosis of PTC patients, and the expression of LPAR5, TFPI and ENTPD1 was verified using a tissue chip. Conclusions In summary, the hub genes and pathways identified in the present study not only provide information for the development of new biomarkers for PTC but will also be useful for elucidation of the pathogenesis of PTC.

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Role of differentially expressed genes and long non-coding RNAs in papillary thyroid carcinoma diagnosis, progression, and prognosis

Journal of Cellular Biochemistry ◽

10.1002/jcb.26836 ◽

2018 ◽

Vol 119 (10) ◽

pp. 8249-8259 ◽

Cited By ~ 10

Author(s):

Wei-Yang Cai ◽

Xiong Chen ◽

Li-Ping Chen ◽

Qian Li ◽

Xiao-Jing Du ◽

...

Keyword(s):

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Differentially Expressed Genes ◽

Differentially Expressed ◽

Papillary Thyroid ◽

Non Coding Rnas

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

Disease Markers ◽

10.1155/2016/2832980 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 1

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.

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Progression Risk Assessment of Post-Surgical Papillary Thyroid Carcinoma Based on Circular RNA-Associated Competing Endogenous RNA Mechanisms

10.21203/rs.3.rs-47878/v1 ◽

2020 ◽

Author(s):

Mengwei Wu ◽

Rui Liu ◽

Hongwei Yuan ◽

Xiequn Xu ◽

Xiaobin Li ◽

...

Keyword(s):

Risk Assessment ◽

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Assessment Tool ◽

Cox Regression ◽

Circular Rna ◽

Differentially Expressed ◽

Papillary Thyroid ◽

Progression Risk ◽

Stratification System

Abstract BackgroundAccurate risk assessment of post-surgical progression in papillary thyroid carcinoma (PTC) patients is critical. Exploring key differentially expressed mRNAs (DE-mRNAs) regulated by differentially expressed circRNAs (DE-circRNAs) via the ceRNA mechanism could help establish a novel assessment tool. MethodsceRNA network was established based on differentially expressed RNAs and correlation analysis. DE-mRNAs within the ceRNA network associated with progression-free interval (PFI) of PTC were identified to construct a prognostic ceRNA regulatory subnetwork. LASSO-Cox regression was applied to identify hub DE-mRNAs and establish a novel DE-mRNA signature in predicting PFI of PTC.ResultsSix hub DE-mRNAs, namely CLCNKB, FXBO27, FXYD6, RIMS2, SPC24, and CDKN2A, were identified to be most significantly related to the PFI of PTC and a prognostic DE-mRNA signature was proposed. A nomogram incorporating the DE-mRNA signature and clinical parameters was established to improve the progression risk assessment in post-surgical PTC, which was superior to the ATA risk stratification system and MACIS score AJCC staging system.ConclusionsBased on the circRNA-associated ceRNA RNA mechanism, a DE-mRNA signature and prognostic nomogram was established, which may improve the progression risk assessment in post-surgical PTC.

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Identification and validation of an immune-related prognostic signature and key gene in papillary thyroid carcinoma

Cancer Cell International ◽

10.1186/s12935-021-02066-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Rujia Qin ◽

Chunyan Li ◽

Xuemin Wang ◽

Zhaoming Zhong ◽

Chuanzheng Sun

Keyword(s):

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Cox Regression ◽

Expression Profiles ◽

Tumor Therapy ◽

Differentially Expressed ◽

Expression Level ◽

Papillary Thyroid ◽

Ppi Network ◽

Immune Activity

Abstract Background Papillary thyroid carcinoma (PTC) is the most common pathological type of thyroid cancer. The effect of traditional anti-tumor therapy is not ideal for the patients with recurrence, metastasis and radioiodine resistance. The abnormal expression of immune-related genes (IRGs) has critical roles in the etiology of PTC. However, the effect of IRGs on PTC prognosis remains unclear. Methods Based on The Cancer Genome Atlas (TCGA) and ImmPort databases, we integrated IRG expression profiles and progression-free intervals (PFIs) of PTC patients. First, we identified the differentially expressed IRGs and transcription factors (TFs) in PTC. Subsequently, an IRG model that can predict the PFI was constructed by using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses of the differentially expressed IRGs in the TCGA. Additionally, a protein–protein interaction (PPI) network showed the interactions between the differentially expressed genes (DEGs), and the top 30 genes with the highest degree were extracted from the network. Then, the key IRG was identified by the intersection analysis of the PPI network and univariate Cox regression, which was verified the differential expression of by western blotting and immunohistochemistry (IHC). ssGSEA was performed to understand the correlation between the key IRG expression level and immune activity. Results A total of 355 differentially expressed IRGs and 43 differentially expressed TFs were identified in PTC patients. Then, eight IRGs were finally utilized to construct an IRG model. The respective areas under the curve (AUCs) of the IRG model reached 0.948, 0.820, and 0.831 at 1, 3 and 5 years in the training set. In addition, lactotransferrin (LTF) was determined as the key IRG related to prognosis. The expression level of LTF in tumor tissues was significantly lower than that in normal tissues. And the results of ssGSEA showed the expression level of LTF is closely related to immune activity. Conclusions These findings show that the prognostic model and key IRG may become promising molecular markers for the prognosis of PTC patients.

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Identification of an Autophagy-Related Signature Predicting Overall Survival for Papillary Thyroid Carcinoma

Dose-Response ◽

10.1177/1559325819899265 ◽

2020 ◽

Vol 18 (1) ◽

pp. 155932581989926 ◽

Cited By ~ 1

Author(s):

Gang Hu ◽

Hong-fang Feng ◽

Hui Zhan

Keyword(s):

Overall Survival ◽

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Scoring System ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Papillary Thyroid ◽

Cox Regression Analysis

Background: Papillary thyroid carcinoma usually shows an excellent prognosis. However, its recurrence or persistence rate is high. In this study, we used bioinformatics to identify autophagy-related genes (ARGs) and establish a novel scoring system for papillary thyroid carcinoma. Methods: We collected ARGs sequencing data of patients with papillary thyroid carcinoma from The Cancer Genome Atlas database. Differentially expressed ARGs were identified by the “Limma” package in R language. After univariate and multivariate Cox regression analysis, an ARG signature was developed. The established prognostic signature was evaluated by Kaplan-Meier curve and time-dependent receiver operating characteristic. Results: A sum of 26 differentially expressed ARGs were identified. Gene set enrichment analysis revealed that several significantly oncological signatures were enriched, such as autophagy, p53 signaling pathway, apoptosis, human cytomegalovirus infection, and platinum drug resistance. After univariate and multivariate analysis, 3 ARGs ( ITPR1, CCL2, and CDKN2A) were selected to develop autophagy-related signature. Patients with high risk had significantly shorter overall survival than those with low risk. The areas under the curve indicated that the signature showed good accuracy of prediction. Conclusions: We established a novel scoring system based on 3 ARGs, which provides a promising tool for the development of personalized therapy.

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