Identification of Potential Genes Associated with Vemurafenib Efficacy and Melanoma Prognosis

2021 ◽  
Vol 24 ◽  
Author(s):  
Yue Qi ◽  
GuiE Ma
Keyword(s):  
Signaling Pathway ◽  
Estrogen Signaling ◽  
Akt Signaling Pathway ◽  
Ppi Network ◽  
Hub Genes ◽  
Ampk Signaling ◽  
Melanoma Patients ◽  
Key Genes ◽  
Neurotrophin Signaling ◽  
Upregulated Genes

Objective: This work aimed to investigate the molecular mechanisms underlying the efficacy of vemurafenib as a treatment for melanoma. Methods: The GSE52882 dataset, which includes A375 and A2058 melanoma cell lines treated with vemurafenib and dimethyl sulfoxide (DMSO), and clinical information associated with melanoma patients, were acquired from the Gene Expression Omnibus (GEO) database and University of California Santa Cruz (UCSC), respectively. Functional enrichment analysis, protein-protein interaction (PPI) network construction, sub-module analysis, and transcriptional regulation analysis were performed on overlapping differentially expressed genes (DEGs) identified in both cell lines. Finally, we performed a survival analysis based on the genes identified. Results: A total of 447 consistently overlapping DEGs (176 up- and 271 down-regulated DEGs) were screened. Upregulated genes were enriched in pathways of neurotrophin signaling, estrogen signaling, and transcriptional misregulation in cancer. Downregulated DEGs played essential roles in melanogenesis, pathways of cancer, PI3K-Akt signaling pathway, and AMPK signaling pathway. Upregulated (MMP2, JUN, KAT28, and PIK3R3) and downregulated genes (CXCL8, CCND1, IGF1R, and ITGB3) were considered as hub genes in the PPI network. Additionally, PIK3R3 and LEF1 served as key genes in the regulatory network. The overexpression of MMP2 and CXCL8 was associated with a poor prognosis in melanoma patients. Results: A total of 447 consistently overlapping DEGs (176 up- and 271 down-regulated DEGs) were screened. Upregulated genes were enriched in pathways of neurotrophin signaling, estrogen signaling, and transcriptional misregulation in cancer. Downregulated DEGs played essential roles in melanogenesis, pathways of cancer, PI3K-Akt signaling pathway, and AMPK signaling pathway. Upregulated (MMP2, JUN, KAT28, and PIK3R3) and downregulated genes (CXCL8, CCND1, IGF1R, and ITGB3) were considered as hub genes in the PPI network. Additionally, PIK3R3 and LEF1 served as key genes in the regulatory network. The overexpression of MMP2 and CXCL8 was associated with a poor prognosis in melanoma patients. Conclusion: MMP2, CXCL8, PIK3R3, ITGB3, and LEF1 may play roles in the efficacy of vemurafenib treatment in melanoma; for example, MMP2 and PIK3R3 are likely associated with vemurafenib resistance. These findings will contribute to the development of novel therapies for melanoma.

Abstract TP276: Identification of Potential Key Genes and Pathways in Ischemic Stroke by Analysis of Human High Throughput Sequencing Data

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Yufeng Li ◽  
Dan Lu ◽  
Anding Xu
Keyword(s):  
Ischemic Stroke ◽  
Signaling Pathway ◽  
High Throughput Sequencing ◽  
Ras Signaling ◽  
Akt Signaling Pathway ◽  
Sequencing Data ◽  
Hub Genes ◽  
High Throughput Sequencing Data ◽  
Ras Signaling Pathway ◽  
Key Genes

Background and Purpose: Ischemic stroke (IS) is one of the most common causes of human death worldwide, but the molecular mechanisms of IS are still unclear. To explore the key genes and pathways involved in the pathogenic process of IS, we did a bioinformatics analysis of high throughput sequencing data of IS for the first time, which compared the expression profiles of brain tissues in IS patients and health controls. Methods: The dataset SRP040622 analyzed of expression profiling in 13 samples by deep sequencing in the platform of Illumina HiSeq 2000, including seven cortical ischemic stroke tissues and six control cortex tissues. These data from SRP040622 were downloaded from Sequence Read Archive (SRA) by using Prefech. We adopted fastq-dump, Hisat2, samtools, HTseq and DEseq2 to get the quantification of gene expression from raw data. The differentially expressed genes (DEGs) were identified by limma package, and function enrichment analyses were performed in DAVID website. In addition, the protein-protein interaction (PPI) network was constructed by using STRING and Cytoscape. Results: A total 880 DEGs were identified, including 350 up-regulated genes and 530 down-regulated genes. These DEGs enriched in biological processes(BP) mainly associated with signal transduction, multicellular organism development, positive regulation of cell proliferation, protein phosphorylation, cell-cell adhesion and angiogenesis. And KEGG pathway which DEGS were mainly enriched in is PI3K-Akt signaling pathway, Focal adhesion and Ras signaling pathway. In addition, 34 hub genes were selected by using CytoHubba with degrees >10. Conclusions: Our data reflects that PI3K-Akt signaling pathway, Focal adhesion and Ras signaling pathway may play significant roles in IS, and the hub genes among DEGs(including KDR, ESR1, MMP2, CAV1, PTK2, VWF) may be the potential targets for diagnosis and treatment of IS.

scholarly journals Exploration of the Molecular Mechanism of Polygonati Rhizoma in the Treatment of Osteoporosis Based on Network Pharmacology and Molecular Docking

2022 ◽  
Vol 12 ◽  
Author(s):  
Jinlong Zhao ◽  
Fangzheng Lin ◽  
Guihong Liang ◽  
Yanhong Han ◽  
Nanjun Xu ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Signaling Pathway ◽  
Akt Signaling ◽  
Estrogen Signaling ◽  
Network Pharmacology ◽  
Akt Signaling Pathway ◽  
Ppi Network ◽  
Active Components ◽  
Treatment Of Osteoporosis ◽  
Effective Components

ObjectiveTo explore the effective components and mechanism of Polygonati Rhizoma (PR) in the treatment of osteoporosis (OP) based on network pharmacology and molecular docking methods.MethodsThe effective components and predicted targets of PR were obtained through the Traditional Chinese Medicine Systems Pharmacology and Analysis Platform (TCMSP) database. The disease database was used to screen the disease targets of OP. The obtained key targets were uploaded to the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database for protein-protein interaction (PPI) network analysis. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of key targets. Analysis and docking verification of chemical effective drug components and key targets were performed with IGEMDOCK software.ResultsA total of 12 chemically active components, 84 drug target proteins and 84 common targets related to drugs and OP were obtained. Key targets such as JUN, TP53, AKT1, ESR1, MAPK14, AR and CASP3 were identified through PPI network analysis. The results of enrichment analysis showed that the potential core drug components regulate the HIF-1 signaling pathway, PI3K-Akt signaling pathway, estrogen signaling pathway and other pathways by intervening in biological processes such as cell proliferation and apoptosis and estrogen response regulation, with an anti-OP pharmacological role. The results of molecular docking showed that the key targets in the regulatory network have high binding activity to related active components.ConclusionsPR may regulate OP by regulating core target genes, such as JUN, TP53, AKT1, ESR1, AR and CASP3, and acting on multiple key pathways, such as the HIF-1 signaling pathway, PI3K-Akt signaling pathway, and estrogen signaling pathway.

scholarly journals Identification of Key Genes and Pathways in Breast Cancer Based on Bioinformatic Analysis

2020 ◽  
Author(s):  
Chi Pan ◽  
Qingtao Ni
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Signaling Pathway ◽  
Human Tumor ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Cancer Type ◽  
Hub Genes ◽  
Ampk Signaling ◽  
Key Genes

Abstract Breast cancer(BC) is the most frequent cancer type in women. However, the pathogenesis of BC is still not well understood. Thus, we aim to explore key genes and pathways in relation to BC. We used the Gene Expression Omnibus (GEO) database to identify the differently expression of genes in the carcinogenesis and progression of BC. Then, bioinformatics analysis was performed to determine the key genes targets and pathways associated with BC. The gene expression profile of the hub genes in human tumor was displayed using GEPIA. Finally, the expression of hub genes, correlation between genes and miRNA were screened via the miRDB, MirTarBase and DIANA Tools. We screened 159 downregulated genes and 55 upregulated genes in BC patients among the 4 datasets. The enriched functions of the DEGs involved in cell proliferation, positive regulation of Akt signaling and extracellular exosome, PPAR signaling pathway and AMPK signaling pathway. 3 hub genes were screened out by construction of PPI network. MELK were found to be upregulated, and CLU and NTRK2 were downregulated. Further verification showed that MELK displayed higher levels in almost all tumors. We found correlation between these hube genes and the miRNAs. All in all, three key genes closely related to the incidence of BC were identified, and the results could provide new potential molecular targets for the diagnosis and treatment of BC. In particular, MELK is regulated by multiple miRNAs and participate in the development of BC.

scholarly journals Identification of Key Genes and Pathways Associated With Irradiation in Breast Cancer Tissue and Breast Cancer Cell Lines

Dose-Response ◽  
2020 ◽  
Vol 18 (2) ◽  
pp. 155932582093125
Author(s):  
Changchun Zhu ◽  
Chang Ge ◽  
Junbo He ◽  
Xueying Zhang ◽  
Guoxing Feng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Signaling Pathway ◽  
Breast Cancer Cell Lines ◽  
Cancer Tissue ◽  
Interleukin 17 ◽  
Ppi Network ◽  
Hub Genes ◽  
Data Set ◽  
Key Genes

Radiotherapy is mainly a traditional treatment for breast cancer; however, the key genes and pathways in breast cancer associated with irradiation are not clear. In this study, we aimed to explore the messenger RNA expression changes between preradiation and postradiation breast cancer. The gene expression data set (GSE59733) was downloaded from Gene Expression Omnibus database. According to |log2FC (fold change) | ≥ 1 and with false discovery rate adjusted P value <.05, differentially expressed genes (DEGs) were screened and annotated by R programming software. The protein–protein interaction (PPI) network was conducted through STRING database, and subnetworks and hub genes were extracted by plug-in in Cytoscape. A total of 82 DEGs (74 upregulated and 8 downregulated genes) were identified. These DEGs mainly enriched in an intrinsic apoptotic signaling pathway and G-protein-coupled receptor binding. What’s more, tumor necrosis factor signaling pathway and interleukin 17 signaling pathway abnormally activated in postradiation tumor samples. Two characteristic subnetworks and 3 hub genes ( FOS, CCL2, and CXCL12) were strongly distinguished in PPI network. Moreover, the expression level of the hub genes was confirmed in irradiated MCF-7 cell and SUM-159 cell using quantitative real-time polymerase chain reaction assay. These findings imply that these hub genes may play momentous function in breast cancer to irradiation.

scholarly journals Study on potential differentially expressed genes in stroke by bioinformatics analysis

2021 ◽  
Author(s):  
Xitong Yang ◽  
Pengyu Wang ◽  
Shanquan Yan ◽  
Guangming Wang
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Normal Control ◽  
Enrichment Analysis ◽  
Normal Control Group ◽  
Control Group ◽  
Ppi Network ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Key Genes

AbstractStroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein–protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine − cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

2022 ◽  
Vol 12 (3) ◽  
pp. 523-532
Author(s):  
Xin Yan ◽  
Chunfeng Liang ◽  
Xinghuan Liang ◽  
Li Li ◽  
Zhenxing Huang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Adrenocortical Carcinoma ◽  
Gene Expression Omnibus ◽  
Heparin Binding ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Upregulated Genes

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

scholarly journals Identification of Hub Genes and Immune Infiltration in Psoriasis by Bioinformatics Method

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenxing Su ◽  
Yuqian Wei ◽  
Biao Huang ◽  
Jiang Ji
Keyword(s):  
T Cells ◽  
B Cells ◽  
Nk Cells ◽  
Signaling Pathway ◽  
Control Sample ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Chemokine Signaling

BackgroundPsoriasis is a chronic, prolonged, and recurrent skin inflammatory disease. However, the pathogenesis of psoriasis is not completely clear, thus we aimed to explore potential molecular basis of it.MethodsTwo datasets were downloaded from the Gene Expression Omnibus database. After identifying the differentially expressed genes of psoriasis skin lesion samples and healthy controls, three kinds of analyses, namely functional annotation, protein-protein interaction (PPI) network, and immune infiltration analyses, were performed.ResultsA total of 152 up-regulated genes and 38 down-regulated genes were selected for subsequent analyses. Evaluation of the PPI network identified the most important module containing 13 hub genes. Gene ontology analysis showed that the hub genes have a significant enrichment effect on positive regulation of cell migration, defense response to the other organism and epithelial cell differentiation. KEGG signaling pathway analysis showed that the hub genes were significantly enriched in chemokine signaling, Toll-like receptor signaling pathway, and IL-17 signaling pathway. Compared with the normal control sample, naive B cells, CD8+ T cells, activated memory CD4+ T cells, follicular helper T cells, gamma delta T cells, resting NK cells, monocytes, M0 macrophages, M1 macrophages, activated dendritic cells and neutrophils infiltrated more, while memory B cells, naive CD4+ T cells, regulatory T cells (Tregs), activated NK cells, resting mast cells, and eosinophils infiltrated less.ConclusionTo conclude, the hub genes and pathways identified from psoriasis lesions and normal controls along with the immune infiltration profile may provide new insights into the study of psoriasis.

scholarly journals Weighted gene co-expression network analysis to define pivotal modules and genes in diabetic heart failure

2020 ◽  
Vol 40 (7) ◽  
Author(s):  
Weiwei Liang ◽  
FangFang Sun
Keyword(s):  
Heart Failure ◽  
Kegg Pathway ◽  
Diabetic Heart ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Light Yellow ◽  
Go Enrichment ◽  
Key Genes ◽  
Functional Analyses

Abstract This research was carried out to reveal specific hub genes involved in diabetic heart failure, as well as remarkable pathways that hub genes locate. The GSE26887 dataset from the GEO website was downloaded. The gene co-expression network was generated and central modules were analyzed to identify key genes using the WGCNA method. Functional analyses were conducted on genes of the clinical interest modules via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene ontology (GO) enrichment, associated with protein–protein interaction (PPI) network construction in a sequence. Centrality parameters of the PPI network were determined using the CentiScape plugin in Cytoscape. Key genes, defined as genes in the ≥95% percentile of the degree distribution of significantly perturbed networks, were identified. Twenty gene co-expression modules were detected by WGCNA analysis. The module marked in light yellow exhibited the most significant association with diabetes (P=0.08). Genes involved in this module were primarily located in immune response, plasma membrane and receptor binding, as shown by the GO analysis. These genes were primarily assembled in endocytosis and phagosomes for KEGG pathway enrichment. Three key genes, STK39, HLA-DPB1 and RAB5C, which may be key genes for diabetic heart failure, were identified. To our knowledge, our study is the first to have constructed the co-expression network involved in diabetic heart failure using the WGCNA method. The results of the present study have provided better understanding the molecular mechanism of diabetic heart failure.

scholarly journals Identification of Key Genes and Prognostic Value Analysis in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Meng Wang ◽  
Licheng Wang ◽  
Shusheng Wu ◽  
Dongsheng Zhou ◽  
Xianming Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Expression Profiles ◽  
Cyclin B1 ◽  
Ppi Network ◽  
Hub Genes ◽  
Altered Expression ◽  
Value Analysis ◽  
Kaplan Meier ◽  
Key Genes

Emerging evidence indicates that various functional genes with altered expression are involved in the tumor progression of human cancers. This study is aimed at identifying novel key genes that may be used for hepatocellular carcinoma (HCC) diagnosis, prognosis, and targeted therapy. This study included 3 expression profiles (GSE45267, GSE74656, and GSE84402), which were obtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the differentially expressed genes (DEGs) between HCC and normal samples. The functional and pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identified DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identified. ONCOMINE and CCLE databases were used to verify the expression of the hub genes in HCC tissues and cells. Kaplan-Meier plotter was used to assess the effects of the hub genes on the overall survival of HCC patients. A total of 99 DEGs were identified from the 3 expression profiles. These DEGs were enriched with functional processes and pathways related to HCC pathogenesis. From the PPI network, 5 hub genes were identified. The expression of the 5 hub genes was all upregulated in HCC tissues and cells compared with the control tissues and cells. Kaplan-Meier survival curves indicated that high expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), MAD2 mitotic arrest deficient-like 1 (MAD2L1), and topoisomerase IIα (TOP2A) predicted poor overall survival in HCC patients (all log-rank P<0.01). These results revealed that the DEGs may serve as candidate key genes during HCC pathogenesis. The 5 hub genes, including CDK1, CCNB1, CCNB2, MAD2L1, and TOP2A, may serve as promising prognostic biomarkers in HCC.

scholarly journals Integrated Bioinformatics Analysis of Master Regulators in Anaplastic Thyroid Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Zongfu Pan ◽  
Lu Li ◽  
Qilu Fang ◽  
Yangyang Qian ◽  
Yiwen Zhang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Thyroid Carcinoma ◽  
Signaling Pathway ◽  
Regulatory Network ◽  
Anaplastic Thyroid Carcinoma ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Thyroid ◽  
Master Regulators

Anaplastic thyroid carcinoma (ATC) is one of the most aggressive and rapidly lethal tumors. However, limited advances have been made to prolong the survival and to reduce the mortality over the last decades. Therefore, identifying the master regulators underlying ATC progression is desperately needed. In our present study, three datasets including GSE33630, GSE29265, and GSE65144 were retrieved from Gene Expression Omnibus with a total of 32 ATC samples and 78 normal thyroid tissues. A total of 1804 consistently changed differentially expressed genes (DEGs) were identified from three datasets. KEGG pathways enrichment suggested that upregulated DEGs were mainly enriched in ECM-receptor interaction, cell cycle, PI3K-Akt signaling pathway, focal adhesion, and p53 signaling pathway. Furthermore, key gene modules in PPI network were identified by Cytoscape plugin MCODE and they were mainly associated with DNA replication, cell cycle process, collagen fibril organization, and regulation of leukocyte migration. Additionally, TOP2A, CDK1, CCNB1, VEGFA, BIRC5, MAPK1, CCNA2, MAD2L1, CDC20, and BUB1 were identified as hub genes of the PPI network. Interestingly, module analysis showed that 8 out of 10 hub genes participated in Module 1 network and more than 70% genes of Module 2 consisted of collagen family members. Notably, transcription factors (TFs) regulatory network analysis indicated that E2F7, FOXM1, and NFYB were master regulators of Module 1, while CREB3L1 was the master regulator of Module 2. Experimental validation showed that CREB3L1, E2F7, and FOXM1 were significantly upregulated in ATC tissue and cell line when compared with normal thyroid group. In conclusion, the TFs regulatory network provided a more detail molecular mechanism underlying ATC occurrence and progression. TFs including E2F7, FOXM1, CREB3L1, and NFYB were likely to be master regulators of ATC progression, suggesting their potential role as molecular therapeutic targets in ATC treatment.

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