A genetic association analysis of polymorphisms, rs2282695 and rs12373539, in the FOSB gene and papillary thyroid cancer

High dimensional model representation of log likelihood ratio: binary classification with SNP data

BACKGROUND

Developing binary classification rules based on SNP observations has been a major challenge for many modern bioinformatics applications, e.g., predicting risk of future disease events in complex conditions such as cancer. Small-sample, high-dimensional nature of SNP data, weak effect of each SNP on the outcome, and highly non-linear SNP interactions are several key factors complicating the analysis. Additionally, SNPs take a finite number of values which may be best understood as ordinal or categorical variables, but are treated as continuous ones by many algorithms.

METHODS

We use the theory of high dimensional model representation (HDMR) to build appropriate low dimensional glass-box models, allowing us to account for the effects of feature interactions. We compute the second order HDMR expansion of the log-likelihood ratio to account for the effects of single SNPs and their pairwise interactions. We propose a regression based approach, called linear approximation for block second order HDMR expansion of categorical observations (LABS-HDMR-CO), to approximate the HDMR coefficients. We show how HDMR can be used to detect pairwise SNP interactions, and propose the fixed pattern test (FPT) to identify statistically significant pairwise interactions.

RESULTS

We apply LABS-HDMR-CO and FPT to synthetically generated HAPGEN2 data as well as to two GWAS cancer datasets. In these examples LABS-HDMR-CO enjoys superior accuracy compared with several algorithms used for SNP classification, while also taking pairwise interactions into account. FPT declares very few significant interactions in the small sample GWAS datasets when bounding false discovery rate (FDR) by 5%, due to the large number of tests performed. On the other hand, LABS-HDMR-CO utilizes a large number of SNP pairs to improve its prediction accuracy. In the larger HAPGEN2 dataset FTP declares a larger portion of SNP pairs used by LABS-HDMR-CO as significant.

CONCLUSION

LABS-HDMR-CO and FPT are interesting methods to design prediction rules and detect pairwise feature interactions for SNP data. Reliably detecting pairwise SNP interactions and taking advantage of potential interactions to improve prediction accuracy are two different objectives addressed by these methods. While the large number of potential SNP interactions may result in low power of detection, potentially interacting SNP pairs, of which many might be false alarms, can still be used to improve prediction accuracy.

FosB recruits KAT5 to potentiate the growth and metastasis of papillary thyroid cancer in a DPP4-dependent manner

OBJECTIVE

Dipeptidyl peptidase IV (DPP4) has been indicated as a possible prognostic biomarker in papillary thyroid cancer (PTC). However, the mechanism of DPP4 during metastasis of PTC remains unclear. In this study, we investigated whether lysine acetyltransferase 5 (KAT5) and FBJ murine osteosarcoma viral oncogene homolog B (FosB) synergistically regulate high DPP4 expression in PTC.

METHODS

PTC tissues and matched paracancerous tissues were harvested, followed by the establishment of IHH-4 and TPC-1 cells with downregulation of DPP4. The relevance of DPP4 on the metastasis of PTC cells was assessed. Subsequently, the effect of KAT5 on the transcription of DPP4 was verified. The binding relationship between FosB and DPP4 was predicted by a bioinformatics website. Functional rescue experiments were performed to evaluate cell activities after overexpression of KAT5 or FosB in cells with DPP4 knockdown.

RESULTS

DPP4 was overexpressed in PTC tissues and cell lines, which was correlated with higher risks for metastases and poorer survival. DPP4 downregulation curtailed cell growth and metastasis. Moreover, KAT5 acetylated DPP4 promoter histone, which promoted transcription activation of DPP4. Subsequently, FosB recruited KAT5 at the DPP4 promoter, thereby enhancing DPP4 transcriptional activation. Further overexpression of KAT5 or FosB in cells with low expression of DPP4 promoted cell activity. Finally, DPP4 expedited p62 nuclear translocation to elevate Keap1/Nrf2 expression, thus facilitating the growth and metastasis of PTC cells.

CONCLUSION

FosB enhanced the growth and metastasis of PTC cells by recruiting histone acetyltransferases KAT5 to increase DPP4 transcription and activate the p62/Keap1/Nrf2 signaling.

Network analysis of DEGs and verification experiments reveal the notable roles of PTTG1 and MMP9 in lung cancer

Lung cancer, a malignant tumor, is the most frequently fatal cancer, with poor survival rates in the advanced stages. In order to improve the understanding of this disease, and to improve the outcomes of patients, additional studies are required. In the present study, differentially expressed genes (DEGs) in patients with lung cancer compared with controls were identified. To understand how these DEGs act together to account for the initiation of lung cancer, a protein interaction network and a transcriptional regulatory network were constructed to explore the clusters and pathways in lung cancer, and the results indicated that PTTG1 and MMP9 served major roles in the development of lung cancer in the regulatory system. Consistent with this, mRNA and protein expression levels of PTTG1 and MMP9 were significantly upregulated in lung cancer tissues compared with normal lung tissues. The overexpression of PTTG1 or MMP9 was induced in the human bronchial epithelial BEAS-2B cell line, indicating that increased PTTG1 or MMP9 alone may not only facilitate cell migration, proliferation and induce colony formation, but also suppress cell apoptosis. In summary, PTTG1 and MMP9 were identified as potential targets for therapeutic intervention through gene therapy in lung cancer.

A Comprehensive Meta-analysis of Case-Control Association Studies to Evaluate Polymorphisms Associated with the Risk of Differentiated Thyroid Carcinoma

BACKGROUND

Linkage analyses and association studies suggested that inherited genetic variations play a role in the development of differentiated thyroid carcinoma (DTC).

METHODS

We combined the results from a genome-wide association study (GWAS) performed by our group and from published studies on DTC. With a first approach, we evaluated whether a SNP published as associated with the risk of DTC could replicate in our GWAS (using FDR as adjustment for multiple comparisons). With the second approach, meta-analyses were performed between literature and GWAS when both sources suggested an association, increasing the statistical power of the analysis.

RESULTS

rs1799814 (CYP1A1), rs1121980 (FTO), and 3 SNPs within 9q22 (rs965513, rs7048394, and rs894673) replicated the associations described in the literature. In addition, the meta-analyses between literature and GWAS revealed 10 more SNPs within 9q22, six within FTO, two within SOD1, and single variations within HUS1, WDR3, UGT2B7, ALOX12, TICAM1, ATG16L1, HDAC4, PIK3CA, SULF1, IL11RA, VEGFA, and 1p31.3, 2q35, 8p12, and 14q13.

CONCLUSION

This analysis confirmed several published risk loci that could be involved in DTC predisposition.

IMPACT

These findings provide evidence for the role of germline variants in DTC etiology and are consistent with a polygenic model of the disease. Cancer Epidemiol Biomarkers Prev; 25(4); 700-13. ©2016 AACR.