Development of A Three-Gene Signature Prediction Model for Lymph Node Metastasis in Papillary Thyroid Cancer

Abstract Background: Thyroid cancer is one of the most prevalent endocrine cancers with a rising incidence rate over the past years. Papillary thyroid cancer (PTC) is the dominant historical type of thyroid cancer. Early lymph node metastasis happens frequently in PTC. However, some of the lymph node metastasis may be troublesome for detecting because of limited methods.Methods: Robust rank aggregation afforded us the shared differential expression genes among multiple datasets. Gene ontology analysis was performed to identify potential functions. Weighted gene co-expression network analysis was used to research the correlations between gene expression patterns with clinical characteristic. Protein-protein interaction network was performed to identify the hub genes. The least absolute shrinkage and selection operator and Logistic regression were performed to construct a prediction model.Results: We developed a three-gene signature prediction model for lymph node metastasis in PTC through transcriptomic analysis. After quality control, we collected 8 microarray datasets from GEO database and an RNA sequencing dataset from TCGA database. We found the transcriptome profiles were correlated with lymph node metastasis and 3 genes were verified to be independent prediction factors towards those statistic approach. Afterwards, we designed a predicable risk score system and effectively confirmed the model in two independent papillary thyroid cancer cohorts.Conclusions: We recommended a successful predicable model of lymph node metastasis in papillary thyroid cancer patients with moderate accuracy.

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Transcriptome Analyses Identify a Metabolic Gene Signature Indicative of Dedifferentiation of Papillary Thyroid Cancer

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2018-02686 ◽

2019 ◽

Vol 104 (9) ◽

pp. 3713-3725 ◽

Cited By ~ 7

Author(s):

Ben Ma ◽

Hongyi Jiang ◽

Duo Wen ◽

Jiaqian Hu ◽

Litao Han ◽

...

Keyword(s):

Thyroid Cancer ◽

Lymph Node ◽

Lymph Node Metastasis ◽

Papillary Thyroid Cancer ◽

Gene Signature ◽

Metabolic Reprogramming ◽

Papillary Thyroid ◽

Metabolic Gene ◽

Node Metastasis ◽

Metabolic Genes

Abstract Context Metabolic reprogramming is a common feature of tumorigenesis. It remains unknown concerning the expression pattern of metabolism-associated genes in dedifferentiated thyroid cancer (DDTC). Objective This study aimed to identify a useful signature to indicate dedifferentiation of papillary thyroid cancer (PTC). Design and Setting We used one discovery and two validation cohorts to screen out aberrant metabolic genes in DDTC, and further used The Cancer Genome Atlas (TCGA) cohort to search for independent risk factors for the low-differentiated phenotype of PTC as a signature of dedifferentiation. The prediction of the signature for DDTC was validated in the TCGA cohort and the combined Gene Expression Omnibus cohort. We also analyzed the correlations of the signature risk score with clinicopathological features of PTC. Gene set enrichment analyses were performed in the TCGA cohort. Results Significant enrichment of metabolic pathways correlated with differentiation status of PTC. A signature of metabolic genes including LPCAT2, ACOT7, HSD17B8, PDE8B, and ST3GAL1 was discovered and validated across three cohorts. The signature was not only predictive of DDTC but also significantly associated with BRAFV600E mutation (P < 0.001), T3/T4 stage (P < 0.001), extrathyroidal extension (P < 0.001), lymph node metastasis (P < 0.001), and tumor/lymph node/metastasis III/IV stage (P < 0.001) in PTC. Downregulations of LPCAT2 expression (P = 0.009) and ST3GAL1 expression (P = 0.005) increased risks of decreased disease-free survival for patients. Furthermore, the signature was implicated in a number of oncogenic biological pathways. Conclusions Our findings suggest that metabolic deregulations mediate dedifferentiation of PTC, and that the metabolic gene signature can be used as a biomarker for DDTC.

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