Dataset Information

Potential five-mRNA signature model for the prediction of prognosis in patients with papillary thyroid carcinoma.

ABSTRACT: Although the mortality rate of papillary thyroid carcinoma (PTC) is relatively low, the recurrence rates of PTC remain high. The high recurrence rates are related to the difficulties in treatment. Gene expression profiles has provided novel insights into potential therapeutic targets and molecular biomarkers of PTC. The aim of the present study was to identify mRNA signatures which may categorize PTCs into high-and low-risk subgroups and aid with the predictions for prognoses. The mRNA expression profiles of PTC and normal thyroid tissue samples were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs were identified using the 'EdgeR' software package. Gene signatures associated with the overall survival of PTC were selected, and enrichment analysis was performed to explore the biological pathways and functions of the prognostic mRNAs using the Database for Visualization, Annotation and Integration Discovery. A signature model was established to investigate a specific and robust risk stratification for PTC. A total of 1,085 differentially expressed mRNAs were identified between the PTC and normal thyroid tissue samples. Among them, 361 mRNAs were associated with overall survival (P<0.05). A 5-mRNA prognostic signature for PTC (ADRA1B, RIPPLY3, PCOLCE, TEKT1 and SALL3) was identified to classify the patients into high-and low-risk subgroups. These prognostic mRNAs were enriched in Gene Ontology terms such as 'calcium ion binding', 'enzyme inhibitor activity', 'carbohydrate binding', 'transcriptional activator activity', 'RNA polymerase II core promoter proximal region sequence-specific binding' and 'glutathione transferase activity', and Kyoto Encyclopedia of Genes and Genomes signaling pathways such as 'pertussis', 'ascorbate and aldarate metabolism', 'systemic lupus erythematosus', 'drug metabolism-cytochrome P450 and 'complement and coagulation cascades'. The 5-mRNA signature model may be useful during consultations with patients with PTC to improve the prediction of their prognosis. In addition, the prognostic signature identified in the present study may reveal novel therapeutic targets for patients with PTC.

SUBMITTER: Zhong LK

PROVIDER: S-EPMC7400165 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Potential five-mRNA signature model for the prediction of prognosis in patients with papillary thyroid carcinoma.

Zhong Lin-Kun LK Gan Xiao-Xiong XX Deng Xing-Yan XY Shen Fei F Feng Jian-Hua JH Cai Wen-Song WS Liu Qiong-Yao QY Miao Jian-Hang JH Zheng Bing-Xing BX Xu Bo B

Oncology letters 20200626 3

Although the mortality rate of papillary thyroid carcinoma (PTC) is relatively low, the recurrence rates of PTC remain high. The high recurrence rates are related to the difficulties in treatment. Gene expression profiles has provided novel insights into potential therapeutic targets and molecular biomarkers of PTC. The aim of the present study was to identify mRNA signatures which may categorize PTCs into high-and low-risk subgroups and aid with the predictions for prognoses. The mRNA expressio ...[more]

PMID: 32782547

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Project description:BACKGROUND:Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC), accounting for more than 80% of all cases. Ferroptosis is a novel iron-dependent and Reactive oxygen species (ROS) reliant type of cell death which is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in PTC remains unclear. This study aims at exploring the expression of ferroptosis-related genes (FRG) and their prognostic values in PTC. METHODS:A ferroptosis-related gene signature was constructed using lasso regression analysis through the PTC datasets of the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT database. Finally, SDG were test in clinical PTC specimens and normal thyroid tissues. RESULTS:LASSO regression model was utilized to establish a novel FRG signature with 10 genes (ANGPTL7, CDKN2A, DPP4, DRD4, ISCU, PGD, SRXN1, TF, TFRC, TXNRD1) to predicts the prognosis of PTC, and the patients were separated into high-risk and low-risk groups by the risk score. The high-risk group had poorer survival than the low-risk group (p < 0.001). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Multivariate regression analysis identified the prognostic signature-based risk score was an independent prognostic indicator for PTC. The functional roles of the DEGs in the TGCA PTC cohort were explored using GO enrichment and KEGG pathway analyses. Immune related analysis demonstrated that the most types of immune cells and immunological function in the high-risk group were significant different with those in the low-risk group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) verified the SDG have differences in expression between tumor tissue and normal thyroid tissue. In addition, cell experiments were conducted to observe the changes in cell morphology and expression of signature's genes with the influence of ferroptosis induced by sorafenib. CONCLUSIONS:We identified differently expressed FRG that may involve in PTC. A ferroptosis-related gene signature has significant values in predicting the patients' prognoses and targeting ferroptosis may be an alternative for PTC's therapy.

Project description:Papillary thyroid carcinoma (PTC) is the fastest-growing disease caused by numerous molecular alterations in addition to previously reported DNA mutations. There is a compelling need to identify novel transcriptomic alterations that are associated with the pathogenesis of PTC with potential diagnostic and prognostic implications. Methods: We gathered and compared 242 expression profiles between paired PTC and adjacent normal tissues and identified and validated the coding and long non-coding RNAs (lncRNAs) associated with the extrathyroidal extension (ETE) of 655 PTC patients in two independent cohorts, followed by predicting their interactions with drugs. Co-expression, RNA interaction, Kaplan-Meier survival and multivariate Cox proportional regression analyses were performed to identify dysregulated lncRNAs and genes that correlated with clinical outcomes of PTC. Alternative splicing (AS), RNA circularization, and editing were also compared between transcriptomes to expand the repertoire of molecular alterations in PTC. Results: Numerous genes related to cellular microenvironment and steroid hormone response were associated with the ETE of PTC. Drug susceptibility predictions of the expression signature revealed two highly ranked compounds, 6-bromoindirubin-3'-oxime and lovastatin. Co-expression and RNA interaction analysis revealed the essential role of lncRNAs in PTC pathogenesis by modulating extracellular matrix and cell adhesion. Eight genes and two novel lncRNAs were identified that correlated with the aggressive nature and disease-free survival of PTC. Furthermore, this study provided the transcriptome-wide landscape of circRNAs in PTC and uncovered dissimilar expression profiles among circRNAs originating from the same host gene, suggesting the functional complexity of circRNAs in PTC carcinogenesis. The newly identified AS events in the SERPINA1 and FN1 genes may improve the sensitivity and specificity of these diagnostic biomarkers. Conclusions: Our study uncovered a comprehensive transcriptomic signature associated with the carcinogenesis and aggressive behavior of PTC, as well as presents a catalog of 10 potential biomarkers, which would facilitate PTC prognosis and development of new therapeutic strategies for this cancer.

Project description:The incidence rate of papillary thyroid carcinoma (PTC) has rapidly increased in the recent decades, and the microRNA (miRNA) is one of the potential biomarkers in this cancer. Despite good prognosis, certain features such as lymph node metastasis (LNM) and BRAF V600E mutation are associated with a poor outcome. More than 50% of PTC patients present with LNM and BRAF V600E is the most common mutation identified in this cancer. The molecular mechanisms underlying these features are yet to be elucidated. This study aims to elucidate miRNA-genes interaction networks in PTC with or without LNM and to determine the association of BRAF V600E mutation with miRNAs and genes expression profiles. Next generation sequencing was performed to characterize miRNA and gene expression profiles in 20 fresh frozen tumor and the normal adjacent tissues of PTC with LNM positive (PTC LNM-P) and PTC without LNM (PTC LNN). BRAF V600E was genotyped using Sanger sequencing. Bioinformatics integration and pathway analysis were performed to determine the regulatory networks involved. Based on network analysis, we then investigated the association between miRNA and gene biomarkers, and pathway enrichment analysis was performed to study the role of candidate biomarkers. We identified 138 and 43 significantly deregulated miRNAs (adjusted p value?<?0.05; log2 fold change???-1.0 or ?1.0) in PTC LNM-P and PTC LNN compared to adjacent normal tissues, respectively. Ninety-six miRNAs had significant expression ratios of 3p-to-5p in PTC LNM-P as compared to PTC LNN. In addition, ribosomal RNA-reduced RNA sequencing analysis revealed 699 significantly deregulated genes in PTC LNM-P versus normal adjacent tissues, 1,362 genes in PTC LNN versus normal adjacent tissue, and 1,576 genes in PTC LNM-P versus PTC LNN. We provide the evidence of miRNA and gene interactions, which are involved in LNM of papillary thyroid cancer. These findings may lead to better understanding of carcinogenesis and metastasis processes. This study also complements the existing knowledge about deregulated miRNAs in papillary thyroid carcinoma development.

Project description:Background: The incidence of papillary thyroid carcinoma (PTC) is high and increasing worldwide. Although prognosis is relatively good, it is important to select the minority of patients with poorer prognosis to avoid side effects associated with unnecessary over-treatment in low-risk patients; this requires accurate prognostic predictions. Materials and Methods: Six PTC expression datasets were obtained from the gene expression omnibus (GEO) database. Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) database. Through integrated analysis of these datasets, highly reliable differentially-expressed genes (DEGs) between tumor and normal tissue were identified and lasso Cox regression was applied to identify DEGs related to the progression-free interval (PFI) and to establish a prognostic gene signature. The performance of a five-gene signature was evaluated based on a Kaplan-Meier curve, receiver operating characteristic (ROC), and Harrell's concordance index (C-index). Multivariate Cox regression analysis was used to identify factors associated with PTC prognosis. Finally, a prognostic nomogram was established based on the TCGA-THCA dataset. Results: A novel five-gene signature was established to predict the PTC PFI, which included PLP2, LYVE1, FABP4, TGFBR3, and FXYD6, and the ROC curve and C-index showed good performance in both training and validation datasets. This could classify patients into high- and low-risk groups with distinct PFIs and differentiate PTC tumors from normal tissue. Univariate Cox regression revealed that this signature was an independent prognostic factor for PTC. The established nomogram, incorporating the prognostic gene signature and clinical parameters, was able to predict the PFI with high efficiency. The gene signature-based nomogram was superior to the American Thyroid Association (ATA) risk stratification to predict PTC PFI. Conclusions: Our study identified a five-gene signature and established a prognostic nomogram, which were reliable in predicting the PFI of PTC; this could be beneficial for individualized treatment and medical decision making.

Dataset Information

Potential five-mRNA signature model for the prediction of prognosis in patients with papillary thyroid carcinoma.

Publications

Potential five-mRNA signature model for the prediction of prognosis in patients with papillary thyroid carcinoma.

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