ABSTRACT
OBJECTIVE: In this study, crucial genes and microRNAs (miRNAs) associated with the progression, staging, and prognosis of papillary thyroid cancer (PTC) were identified. METHODS: Four PTC datasets, including our own mRNA-sequencing (mRNA-seq) dataset and three public datasets downloaded from Gene Expression Omnibus and The Cancer Genome Atlas, were used to analyze differentially expressed genes (DEGs) and miRNAs (DEMs) between PTC tumor tissues and paired normal tissues (control). Gene ontology (GO) terms and pathways associated with these DEGs were identified, and protein-protein interactions (PPIs) were analyzed. Additionally, an miRNA-mRNA regulatory network was constructed and the functions of DEMs were explored. Finally, miRNAs/mRNAs associated with tumor staging and prognosis were identified. The expression levels of several key genes and miRNAs were validated by qRT-PCR. RESULTS: Numerous DEGs and DEMs were identified between tumor and control groups in four datasets. The DEGs were significantly enriched in cell adhesion and cancer-related GO terms and pathways. In the constructed PPI network, ITGA2, FN1, ICAM1, TIMP1 and CDH2 were hub proteins. In the miRNA-mRNA negative regulatory networks, miR-204-5p regulated the largest number of target genes, such as TNFRSF12A. miR-146b, miR-204, miR-7-2, and FN1 were associated with tumor stage in PTC, and TNFRSF12A and CLDN1 were related to prognosis. CONCLUSIONS: Our results suggested the important roles of ITGA2, FN1, ICAM1, TIMP1 and CDH2 in the progression of PTC. miR-204-5p, miR-7-2, and miR-146b are potential biomarkers for PTC staging and FN1, CLDN1, and TNFRSF12A may serve as markers of prognosis in PTC.
Subject(s)
Biomarkers, Tumor/genetics , Gene Expression Regulation, Neoplastic/genetics , MicroRNAs/genetics , Thyroid Cancer, Papillary/genetics , Thyroid Neoplasms/genetics , Datasets as Topic , Gene Expression Profiling , Humans , Neoplasm Staging , Survival Analysis , Thyroid Cancer, Papillary/diagnosis , Thyroid Cancer, Papillary/mortality , Thyroid Neoplasms/diagnosis , Thyroid Neoplasms/mortalityABSTRACT
OBJECTIVE: In this study, crucial genes and microRNAs (miRNAs) associated with the progression, staging, and prognosis of papillary thyroid cancer (PTC) were identified. METHODS: Four PTC datasets, including our own mRNA-sequencing (mRNA-seq) dataset and three public datasets downloaded from Gene Expression Omnibus and The Cancer Genome Atlas, were used to analyze differentially expressed genes (DEGs) and miRNAs (DEMs) between PTC tumor tissues and paired normal tissues (control). Gene ontology (GO) terms and pathways associated with these DEGs were identified, and protein-protein interactions (PPIs) were analyzed. Additionally, an miRNA-mRNA regulatory network was constructed and the functions of DEMs were explored. Finally, miRNAs/mRNAs associated with tumor staging and prognosis were identified. The expression levels of several key genes and miRNAs were validated by qRT-PCR. RESULTS: Numerous DEGs and DEMs were identified between tumor and control groups in four datasets. The DEGs were significantly enriched in cell adhesion and cancer-related GO terms and pathways. In the constructed PPI network, ITGA2, FN1, ICAM1, TIMP1 and CDH2 were hub proteins. In the miRNA-mRNA negative regulatory networks, miR-204-5p regulated the largest number of target genes, such as TNFRSF12A. miR-146b, miR-204, miR-7-2, and FN1 were associated with tumor stage in PTC, and TNFRSF12A and CLDN1 were related to prognosis. CONCLUSIONS: Our results suggested the important roles of ITGA2, FN1, ICAM1, TIMP1 and CDH2 in the progression of PTC. miR-204-5p, miR-7-2, and miR-146b are potential biomarkers for PTC staging and FN1, CLDN1, and TNFRSF12A may serve as markers of prognosis in PTC.
Subject(s)
Humans , Thyroid Neoplasms/genetics , Biomarkers, Tumor/genetics , Gene Expression Regulation, Neoplastic/genetics , MicroRNAs/genetics , Thyroid Cancer, Papillary/genetics , Thyroid Neoplasms/diagnosis , Thyroid Neoplasms/mortality , Survival Analysis , Gene Expression Profiling , Datasets as Topic , Thyroid Cancer, Papillary/diagnosis , Thyroid Cancer, Papillary/mortality , Neoplasm StagingABSTRACT
As the predominant thyroid cancer, papillary thyroid cancer (PTC) accounts for 7585% of thyroid cancer cases. This research aimed to investigate transcriptomic changes and key genes in PTC. Using RNAsequencing technology, the transcriptional profiles of 5 thyroid tumor tissues and 5 adjacent normal tissues were obtained. The single nucleotide polymorphisms (SNPs) were identified by SAMtools software and then annotated by ANNOVAR software. After differentially expressed genes (DEGs) were selected by edgR software, they were further investigated by enrichment analysis, protein domain analysis, and proteinprotein interaction (PPI) network analysis. Additionally, the potential gene fusion events were predicted using FusionMap software. A total of 70,172 SNPs and 2,686 DEGs in the tumor tissues, as well as 83,869 SNPs in the normal tissues were identified. In the PPI network, fibronectin 1 (FN1; degree=31) and transforming growth factor ß receptor 1 (TGFßR1; degree=22) had higher degrees. A total of 7 PPI pairs containing the nonsynonymous risk SNP loci in the interaction domains were identified. Particularly, the interaction domains involved in the interactions of FN1 and 5 other proteins (such as FN1tenascin C, TNC) had nonsynonymous risk SNP loci. Furthermore, 11 and 4 gene fusion events were identified in all of the tumor tissues and normal tissues, respectively. Additionally, the NK2 homeobox 1surfactant associated 3 (NKX21SFTA3) gene fusion was identified in both tumor and normal tissues. These results indicated that TGFßR1 and the NKX21SFTA3 gene fusion may be involved in PTC. Furthermore, FN1 and TNC containing the nonsynonymous risk SNP loci might serve a role in PTC by interacting with each other.
Subject(s)
Carcinoma, Papillary/genetics , Gene Expression Regulation, Neoplastic , Oncogene Proteins, Fusion/genetics , Pulmonary Surfactant-Associated Proteins/genetics , Thyroid Neoplasms/genetics , Thyroid Nuclear Factor 1/genetics , Transcriptome , Adult , Carcinoma, Papillary/metabolism , Carcinoma, Papillary/pathology , Carcinoma, Papillary/surgery , Cytokines/genetics , Cytokines/metabolism , Female , Fibronectins , Gene Expression Profiling , Gene Library , High-Throughput Nucleotide Sequencing , Humans , Lymph Node Excision , Lymph Nodes/metabolism , Lymph Nodes/pathology , Lymph Nodes/surgery , Male , Oncogene Proteins, Fusion/metabolism , Polymorphism, Single Nucleotide , Protein Interaction Mapping , Protein Serine-Threonine Kinases/genetics , Protein Serine-Threonine Kinases/metabolism , Pulmonary Surfactant-Associated Proteins/metabolism , RNA, Neoplasm/genetics , RNA, Neoplasm/metabolism , Receptor, Transforming Growth Factor-beta Type I , Receptors, Transforming Growth Factor beta/genetics , Receptors, Transforming Growth Factor beta/metabolism , Signal Transduction , Tenascin/genetics , Tenascin/metabolism , Thyroid Cancer, Papillary , Thyroid Neoplasms/metabolism , Thyroid Neoplasms/pathology , Thyroid Neoplasms/surgery , Thyroid Nuclear Factor 1/metabolism , ThyroidectomyABSTRACT
PURPOSE: The study aims to uncover molecular mechanisms of PTC (papillary thyroid carcinoma) progression and provide therapeutic biomarkers. METHODS: The paired tumor and control tissues were obtained from 5 PTC patients. RNA was extracted and cDNA libraries were constructed. RNA-sequencing (RNA-seq) was performed on the Illumina HiSeq2000 platform using paired-end method. After preprocessing of the RNA-seq data, gene expression value was calculated by RPKM. Then the differentially expressed genes (DEGs) were identified with edgeR. Functional enrichment and protein-protein interaction (PPI) network analyses were conducted for the DEGs. Module analysis of the PPI network was also performed. Transcription factors (TFs) of DEGs were predicted. RESULTS: A cohort of 496 up-regulated DEGs mainly correlating with the ECM degradation pathways, and 440 down-regulated DEGs predominantly enriching in transmembrane transport process were identified. Hub nodes in the PPI network were RRM2 and a set of collagens (COL1A1, COL3A1 and COL5A1), which were also remarkable in module 3 and module 5, respectively. Genes in module 3 were associated with cell cycle pathways, while in module 5 were related to ECM degradation pathways. PLAU, PSG1 and EGR2 were the crucial TFs with higher transcriptional activity in PTC than in control. CONCLUSION: Several genes including COL1A1, COL3A1, RRM2, PLAU, and EGR2 might be used as biomarkers of PTC therapy. Among them, COL1A1 and COL3A1 might exert their functions via involving in ECM degradation pathway, while RRM2 through cell cycle pathway. PLAU might be an active TF, whereas EGR2 might be a tumor suppressor.
