The thyroid carcinoma proteomeThyroid cancer is fairly common. The annual incidence is between 0.5-10 per 100,000 in various populations and 2-4 times more frequent in women compared to men. The most common form of thyroid cancer is papillary carcinoma (70-80%), followed by follicular carcinoma (10-20%), medullary carcinoma (5-10%) and anaplastic carcinoma (2-10%). The classification of thyroid cancer is dependent on histological features according to WHO. Thyroid tumors can also be classified according to aggressiveness into low-grade malignant, intermediate-grade malignant and high-grade malignant. The prognosis for thyroid cancer is good, with a 10-year relative survival rate of approximately 98% for papillary carcinomas. Apart from age, where young patients have a considerably better prognosis, the size of the primary tumor and the tumor stage are the most significant prognostic factors. For most thyroid tumors, diagnosis can be established by microscopic examination alone, although immunohistochemistry plays an important role in tumors exhibiting unusual morphological features. Here, we explore the thyroid carcinoma proteome using TCGA transcriptomics data and antibody-based protein data. 358 genes are suggested as prognostic based on transcriptomics data from 495 patients; 305 genes are associated with unfavorable prognosis and 53 genes are associated with favorable prognosis. TCGA data analysisIn this metadata study we used data from TCGA where transcriptomics data was available from 495 patients with thyroid carcinoma. The total dataset included 362 female and 133 males. Most of the patients (479 patients) were still alive at the time of data collection. The stage distribution was stage i) 278 patients, stage ii) 52 patients, stage iii) 110 patients, stage iv) 53 patients and 2 patients with missing stage information. Unfavorable prognostic genes in thyroid carcinomaFor unfavorable genes, higher relative expression levels at diagnosis give significantly lower overall survival for the patients. There are 305 genes associated with an unfavorable prognosis in thyroid carcinoma. In Table 1, the top 20 most significant genes related to an unfavorable prognosis are listed. CYCS is a gene associated with an unfavorable prognosis in thyroid carcinoma. The best separation is achieved by an expression cutoff at 66 TPM which divides the patients into two groups with 80% 5-year survival for patients with high expression versus 96% for patients with low expression, p-value: 5.34e-5. Immunohistochemical staining using an antibody targeting CYCS (CAB004222) shows a differential expression pattern in thyroid carcinoma samples.
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Table 1. The 20 genes with highest significance associated with an unfavorable prognosis in thyroid carcinoma.
Favorable prognostic genes in thyroid carcinomaFor favorable genes, higher relative expression levels at diagnosis give significantly higher overall survival for the patients. There are 53 genes associated with a favorable prognosis in thyroid carcinoma. In Table 2, the top 20 most significant genes related to a favorable prognosis are listed. TRIM21 is a gene associated with a favorable prognosis in thyroid carcinoma. The best separation is achieved by an expression cutoff at 12 TPM which divides the patients into two groups with 98% 5-year survival for patients with high expression versus 78% for patients with low expression, p-value: 2.64e-6. Immunohistochemical staining using an antibody targeting VAMP8 (HPA005673) shows a differential expression pattern in thyroid carcinoma samples.
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Table 2. The 20 genes with highest significance associated with a favorable prognosis in thyroid carcinoma.
The thyroid carcinoma transcriptomeThe transcriptome analysis shows that 67% (n=13541) of all human genes (n=20162) are expressed in thyroid carcinoma. All genes were classified according to the thyroid carcinoma-specific expression into one of five different categories, based on the ratio between mRNA levels in thyroid carcinoma compared to the mRNA levels in the other 16 analyzed cancer tissues.
Figure 1. The distribution of all genes across the five categories based on transcript abundance in thyroid carcinoma as well as in all other cancer tissues. 273 genes show some level of elevated expression in thyroid carcinoma compared to other cancers (Figure 1). The elevated category is further subdivided into three categories as shown in Table 3. Table 3. The number of genes in the subdivided categories of elevated expression in thyroid carcinoma.
Additional informationPapillary thyroid carcinoma is defined as a malignant epithelial tumor. Microscopically the tumor shows evidence of follicular cell differentiation, typically with papillary and follicular structures as well as characteristic changes in tumor cell nuclei. The key to an accurate diagnosis is nuclear characteristics, including a ground glass appearance, large size, pale staining and irregular outline with deep grooves and pseudoinclusions. Papillary thyroid carcinoma is an indolent cancer, with an excellent long-term prognosis, despite a propensity to invade locally and to spread metastatically to regional lymph nodes. Distant metastases are uncommon. Follicular carcinoma shows follicular differentiation but lacks the diagnostic features of papillary carcinoma. The incidence of follicular carcinoma is higher in areas of endemic goiter, and iodine deficiency appears to be the main contributing risk factor. In contrast to papillary carcinoma, the main mode of metastatic spread is hematogenous rather than through the lymphatic system. Follicular carcinoma is typically delimited by a fibrous capsule surrounding tightly packed follicles, trabeculae or solid sheets of tumor cells. Tumor cells are often cuboidal with dark or pale staining nuclei with inconspicuous nucleoli. Few follicular carcinomas may display nuclear pleomorphism. Antibodies used in diagnostics of thyroid tumors include thyroglobulin (TG), calcitonin (CALCA) and thyroid transcription factor (TTF1). Relevant links and publications Uhlen M et al., A pathology atlas of the human cancer transcriptome. Science. (2017) |