Gene expression differences between thyroid carcinoma, thyroid adenoma and normal thyroid tissue

Abstract

To identify differences in gene expression profiles of infected cells between thyroid carcinoma (C), thyroid adenoma (A) and normal thyroid (N) epithelial cells, differentially expressed genes were identified using three pairwise comparisons with the GEO2R online tool. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were used to classify them at the functional level. The most significant cluster in the N vs. A pairwise comparison had four hub genes: Insulin-like growth factor 2, Von Willebrand factor (VWF), multimerin 1 (MMRN1) and complement factor D (CFD). In N vs. C, the most significant cluster had 19 genes: IGF2, early growth response 2, transcription factor 3, KIT proto-oncogene receptor tyrosine kinase, SMAD family member 9, MLLT3 super elongation complex subunit, runt related transcription factor 1, CFD, actinin α 1, SWI/SNF related matrix associated actin dependent regulator of chromatin subfamily a member 4, JunD proto-oncogene AP-1 transcription factor subunit, serum response factor (SRF), FosB proto-oncogene, AP-1 transcription factor subunit, connective tissue growth factor (CTGF), SRC proto-oncogene, non-receptor tyrosine kinase, MMRN1, SRY-box 9, early growth response 3 and ETS variant 4. In A vs. C, the most significant cluster had 14 genes: BCL2-like 1, galectin 3, MCL1 BCL2 family apoptosis regulator, DNA damage inducible transcript 3, BCL2 apoptosis regulator, CTGF, matrix metallopeptidase 7, early growth response 1, kinase insert domain receptor, TIMP metallopeptidase inhibitor 1, apolipoprotein E, VWF, cyclin D1 and placental growth factor. Histological evidence was presented to confirm the makeup of the hubs prior to logistic regression analysis to differentiate benign and malignant neoplasms. The results of the present study may aid in the search for novel potential biomarkers for the differential diagnosis, prognosis and development of drug targets of thyroid neoplasm.

Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma

“…However, another important use of such data (and our main focus here) is to identify and characterise biological functions associated with these genes that may give insights into the biology and behaviour of FTC lesions themselves. Wang et al [36] examined on normal tissue vs. FTC and normal tissue vs. FTA in the microarray dataset accession number GSE27155. They identified some important pathways that included drug metabolism cytochrome p450, viral carcinogenesis, tyrosine metabolism, cytokine-cytokine receptor interaction, and cocaine Addition as significant pathways distinguishing normal from FTC tissue.…”

“…These latter genes were not identified by our analytical approach, but it should be noted that our study used very different analytic tools, focussing on identifying pathways and on finding evidence for gene functions using PPI and other resources. Our analytical approach was somewhat similar to that of Wang et al [36] who examined an older thyroid cancer dataset (GSE27155) containing 10 FTA, 14 FTC, and 4 normal thyroid tissue samples plus those of other types of thyroid lesions. While we employed a number of additional analytical tools (some developed previously by us), there were a number of significant genes and pathways that were also found in the work by Wang et al [36] where they compared FTC and FTA.…”

“…Our analytical approach was somewhat similar to that of Wang et al [36] who examined an older thyroid cancer dataset (GSE27155) containing 10 FTA, 14 FTC, and 4 normal thyroid tissue samples plus those of other types of thyroid lesions. While we employed a number of additional analytical tools (some developed previously by us), there were a number of significant genes and pathways that were also found in the work by Wang et al [36] where they compared FTC and FTA. These notably included the AP-1 TF and its components (e.g., JUN and FOS), BCL2, factors relating to DNA damage, and cyclin-related factors that regulate the cell cycle.…”

A system biology approach reveals cellular pathway differences between follicular thyroid carcinoma and follicular thyroid adenoma

“…These latter genes were not identified by our analytical approach, but it should be noted that our study used very different analytic tools, focussing on identifying pathways and on finding evidence for gene functions using PPI and other resources. Our analytical approach was somewhat more similar to that of Wang et al [49] who examined an older thyroid cancer dataset (GSE27155) containing 10 FTA, 14 FTC, 4 normal thyroid tissue samples plus other types of thyroid lesion. While we employed a number of additional analytical tools (some developed previously by us) there were a number of significant genes and pathways we found in common in the FTC/FTA analyses Wang et al [49].…”

“…Our analytical approach was somewhat more similar to that of Wang et al [49] who examined an older thyroid cancer dataset (GSE27155) containing 10 FTA, 14 FTC, 4 normal thyroid tissue samples plus other types of thyroid lesion. While we employed a number of additional analytical tools (some developed previously by us) there were a number of significant genes and pathways we found in common in the FTC/FTA analyses Wang et al [49]. These notably include the AP-1 TF and its components (e.g., JUN and FOS), BCL2, factors relating to DNA damage and cyclin-related factors that regulate cell cycle.…”