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The differences of regulatory networks between papillary and anaplastic thyroid carcinoma: an integrative transcriptomics study.

ABSTRACT:

Background

Unlike papillary thyroid cancer (PTC), anaplastic thyroid carcinoma (ATC) is extremely aggressive and rapidly lethal without effective therapies. However, the differences of master regulators and regulatory networks between PTC and ATC remain unclear. Methods: Three representative datasets comprising 32 ATC, 69 PTC, and 78 normal thyroid tissue samples were combined to form a large dataset. Differentially expressed genes (DEGs) were identified and enriched by limma package and gene set enrichment analysis, respectively. Subsequently, protein-protein interaction network and transcription factors (TFs) regulatory network were constructed to identify gene modules and master regulators. Further, master regulators were validated by RT-PCR and western blot. Finally, Kaplan-Meier plotter was applied to evaluate their prognostic values. Results: A total of 560 DEGs were identified as ATC-specific malignant signature. The regulatory network analysis showed that nine master regulators were significantly correlated with three gene modules and potentially regulated the expression of DEGs in three gene modules, respectively. Furthermore, CREB3L1, FOSL2, E2F1 and CAT were significantly associated with overall survival of thyroid cancer patients. FOXM1, FOSL2, MYBL2, AVEN and E2F1 were unfavorable factors of recurrence-free survival (RFS), while CAT was a favorable factor of RFS. RT-PCR and western blot confirmed that six TFs were obviously up-regulated in ATC tissues/cell line as compared with PTC and normal thyroid tissues/cell lines, respectively. In addition, 19 ATC-specific kinases were identified to illustrate the potential post-translational modification. Conclusions: Our findings provide a comprehensive insight into malignant mechanism of ATC, which may indicate their value in the future investigation of ATC.

SUBMITTER: Pan Z

PROVIDER: S-EPMC7537793 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:In order to better understand basic mechanisms of tumor development and identify potential new biomarkers, we have performed difference gel electrophoresis (DIGE) and peptide mass fingerprinting on pooled protein extracts from patients with papillary thyroid carcinoma (PTC) compared with matched normal thyroid tissue. Image analysis of DIGE gels comparing PTC and matched normal thyroid tissue protein indicated that 25% of the protein spots were differentially expressed at a 2.5-fold cutoff and 35% at two-fold. Comparison between two different pools of protein from normal thyroid tissues revealed differential protein expression of only 4% at 2.5-fold and 6% at two-fold cutoff. One hundred ninety-two protein spots were identified by MALDI-TOFMS, representing 90 distinct proteins. Excluding albumin, globins and thyroglobulin, imaging software determined 31 proteins to be differentially expressed at the two-fold (or greater) level. Individual gel comparisons (PTC vs. matched normal) from five patients established that 15/31 (48%) of these proteins exhibited statistically significant differential expression. Previously identified molecular markers in this group of proteins include cathepsin B, cytokeratin 19, and galectin-3. Novel differentially expressed proteins include S100A6, moesin, HSP70 (BiP), peroxiredoxin 2, protein phosphatase 2, selenium binding protein 1, vitamin D binding protein, and proteins involved in mitochondrial function. The use of two-dimensional gel electrophoresis (2DGE) revealed a significantly altered protein mass and/or pI in 10%-15% of proteins, suggesting alternatively spliced forms and other posttranslational modification of proteins revealed by this approach. We confirmed S100A6 as a potentially useful biomarker using immunohistochemical analysis (85% sensitivity and 69% specificity for distinguishing benign from malignant thyroid neoplasms). In summary, proteomic analysis of PTC using DIGE and mass spectrometry has confirmed several known biomarkers, uncovered novel potential biomarkers, and provided insights into global pathophysiologic changes in PTC. Many of the differences observed would not have been detected by genomic or other proteomic approaches.

Project description:The present study aimed to screen all types of RNAs involved in the development of papillary thyroid carcinoma (PTC). RNA‑sequencing data of PTC and normal samples were used for screening differentially expressed (DE) microRNAs (DE‑miRNAs), long non‑coding RNAs (DE‑lncRNAs) and genes (DEGs). Subsequently, lncRNA‑miRNA, miRNA‑gene (that is, miRNA‑mRNA) and gene‑gene interaction pairs were extracted and used to construct regulatory networks. Feature genes in the miRNA‑mRNA network were identified by topological analysis and recursive feature elimination analysis. A support vector machine (SVM) classifier was built using 15 feature genes, and its classification effect was validated using two microarray data sets that were downloaded from the Gene Expression Omnibus (GEO) database. In addition, Gene Ontology function and Kyoto Encyclopedia Genes and Genomes pathway enrichment analyses were conducted for genes identified in the ceRNA network. A total of 506 samples, including 447 tumor samples and 59 normal samples, were obtained from The Cancer Genome Atlas (TCGA); 16 DE‑lncRNAs, 917 DEGs and 30 DE‑miRNAs were screened. The miRNA‑mRNA regulatory network comprised 353 nodes and 577 interactions. From these data, 15 feature genes with high predictive precision (>95%) were extracted from the network and were used to form an SVM classifier with an accuracy of 96.05% (486/506) for PTC samples downloaded from TCGA, and accuracies of 96.81 and 98.46% for GEO downloaded data sets. The ceRNA regulatory network comprised 596 lines (or interactions) and 365 nodes. Genes in the ceRNA network were significantly enriched in 'neuron development', 'differentiation', 'neuroactive ligand‑receptor interaction', 'metabolism of xenobiotics by cytochrome P450', 'drug metabolism' and 'cytokine‑cytokine receptor interaction' pathways. Hox transcript antisense RNA, miRNA‑206 and kallikrein‑related peptidase 10 were nodes in the ceRNA regulatory network of the selected feature gene, and they may serve import roles in the development of PTC.

Project description:BackgroundPapillary thyroid carcinoma (PTC) is the most common malignant endocrine tumour, and its incidence and prevalence are increasing considerably. Cellular heterogeneity in the tumour microenvironment is important for PTC prognosis. Spatial transcriptomics is a powerful technique for cellular heterogeneity study.MethodsIn conjunction with a clinical pathologist identification method, spatial transcriptomics was employed to characterise the spatial location and RNA profiles of PTC-associated cells within the tissue sections. The spatial RNA-clinical signature genes for each cell type were extracted and applied to outlining the distribution regions of specific cells on the entire section. The cellular heterogeneity of each cell type was further revealed by ContourPlot analysis, monocle analysis, trajectory analysis, ligand-receptor analysis and Gene Ontology enrichment analysis.ResultsThe spatial distribution region of tumour cells, typical and atypical follicular cells (FCs and AFCs) and immune cells were accurately and comprehensively identified in all five PTC tissue sections. AFCs were identified as a transitional state between FCs and tumour cells, exhibiting a higher resemblance to the latter. Three tumour foci were shared among all patients out of the 13 observed. Notably, tumour foci No. 2 displayed elevated expression levels of genes associated with lower relapse-free survival in PTC patients. We discovered key ligand-receptor interactions, including LAMB3-ITGA2, FN1-ITGA3 and FN1-SDC4, involved in the transition of PTC cells from FCs to AFCs and eventually to tumour cells. High expression of these patterns correlated with reduced relapse-free survival. In the tumour immune microenvironment, reduced interaction between myeloid-derived TGFB1 and TGFBR1 in tumour focus No. 2 contributed to tumourigenesis and increased heterogeneity. The spatial RNA-clinical analysis method developed here revealed prognosis-associated cellular heterogeneity in the PTC microenvironment.ConclusionsThe occurrence of tumour foci No. 2 and three enhanced ligand-receptor interactions in the AFC area/tumour foci reduced the relapse-free survival of PTC patients, potentially leading to improved prognostic strategies and targeted therapies for PTC patients.

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The differences of regulatory networks between papillary and anaplastic thyroid carcinoma: an integrative transcriptomics study.

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