scholarly journals Identification and Validation of Core Genes Involved in the Development of Papillary Thyroid Carcinoma via Bioinformatics Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaoyan Li ◽  
Jing He ◽  
Mingxia Zhou ◽  
Yun Cao ◽  
Yiting Jin ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Malignant Neoplasm ◽  
Papillary Thyroid ◽  
Ppi Network ◽  
Hub Genes ◽  
Clinical Biomarkers ◽  
Core Genes

Background. Papillary thyroid carcinoma (PTC) is a common endocrine malignant neoplasm, and its incidence increases continuously worldwide in the recent years. However, efficient clinical biomarkers were still deficient; the present research is aimed at exploring significant core genes of PTC. Methods. We integrated three cohorts to identify hub genes and pathways associated with PTC by comprehensive bioinformatics analysis. Expression profiles GSE33630, GSE35570, and GSE60542, including 114 PTC tissues and 126 normal tissues, were enrolled in this research. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were utilized to search for the crucial biological behaviors and pathways involved in PTC carcinogenesis. Protein-protein interaction (PPI) network was constructed, and significant modules were deeply studied. Results. A total of 831 differentially expressed genes (DEGs) were discovered, comprising 410 upregulated and 421 downregulated genes in PTC tissues compared to normal thyroid tissues. PPI network analysis demonstrated the interactions between those DEGs, and top 10 pivotal genes (TGFB1, CXCL8, LRRK2, CD44, CCND1, JUN, DCN, BCL2, ACACB, and CXCL12) with highest degree of connectivity were extracted from the network and verified by TCGA dataset and RT-PCR experiment of PTC samples. Four of the hub genes (CXCL8, DCN, BCL2, and ACACB) were linked to the prognosis of PTC patients and considered as clinically relevant core genes via survival analysis. Conclusion. In conclusion, we propose a series of key genes associated with PTC development and these genes could serve as the diagnostic biomarkers or therapeutic targets in the future treatment for PTC.

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

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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