Overexpression of TELO2 decreases survival in human high-grade gliomas

Exploring the Functional Roles of Telomere Maintenance 2 in the Tumorigenesis of Glioblastoma Multiforme and Drug Responsiveness to Temozolomide

Glioblastoma multiforme (GBM) is a grade IV human glioma. It is the most malignant primary central nervous system tumor in adults, accounting for around 15% of intracranial neoplasms and 40-50% of all primary malignant brain tumors. However, the median survival time of GBM patients is still less than 15 months, even after treatment with surgical resection, concurrent chemoradiotherapy, and adjuvant chemotherapy with temozolomide (TMZ). Telomere maintenance 2 (TELO2) mRNA is highly expressed in high-grade glioma patients, and its expression correlates with shorter survival outcomes. Hence, it is urgent to address the functional role of TELO2 in the tumorigenesis and TMZ treatment of GBM. In this study, we knocked down TELO2 mRNA in GBM8401 cells, a grade IV GBM, compared with TELO2 mRNA overexpression in human embryonic glial SVG p12 cells and normal human astrocyte (NHA) cells. We first analyzed the effect of TELO2 on the Elsevier pathway and Hallmark gene sets in GBM8401, SVG p12, and NHA via an mRNA array analysis. Later, we further examined and analyzed the relationship between TELO2 and fibroblast growth factor receptor 3, cell cycle progression, epithelial-mesenchymal transient (EMT), reactive oxygen species (ROS), apoptosis, and telomerase activity. Our data showed that TELO2 is involved in several functions of GBM cells, including cell cycle progression, EMT, ROS, apoptosis, and telomerase activity. Finally, we examined the crosstalk between TELO2 and the responsiveness of TMZ or curcumin mediated through the TELO2-TTI1-TTI2 complex, the p53-dependent complex, the mitochondrial-related complex, and signaling pathways in GBM8401 cells. In summary, our work provides new insight that TELO2 might modulate target proteins mediated through the complex of phosphatidylinositol 3-kinase-related kinases in its involvement in cell cycle progression, EMT, and drug response in GBM patients.

Studying the effects of haplotype partitioning methods on the RA-associated genomic results from the North American Rheumatoid Arthritis Consortium (NARAC) dataset

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Haplotype blocks methods plays a complementary role to the single-SNP approaches.

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CIT, FGT, SSLD, and single-SNP methods should be applied to discover the markers.

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Selection of the method used for the association has an impact on the biomarkers.

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SSLD method detected more significant SNPs than CIT, FGT, and single-SNP methods.

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The 383 SNPs discovered by all methods are significantly associated with RA.

The human genome, which includes thousands of genes, represents a big data challenge. Rheumatoid arthritis (RA) is a complex autoimmune disease with a genetic basis. Many single-nucleotide polymorphism (SNP) association methods partition a genome into haplotype blocks. The aim of this genome wide association study (GWAS) was to select the most appropriate haplotype block partitioning method for the North American Rheumatoid Arthritis Consortium (NARAC) dataset. The methods used for the NARAC dataset were the individual SNP approach and the following haplotype block methods: the four-gamete test (FGT), confidence interval test (CIT), and solid spine of linkage disequilibrium (SSLD). The measured parameters that reflect the strength of the association between the biomarker and RA were the P-value after Bonferroni correction and other parameters used to compare the output of each haplotype block method. This work presents a comparison among the individual SNP approach and the three haplotype block methods to select the method that can detect all the significant SNPs when applied alone. The GWAS results from the NARAC dataset obtained with the different methods are presented. The individual SNP, CIT, FGT, and SSLD methods detected 541, 1516, 1551, and 1831 RA-associated SNPs respectively, and the individual SNP, FGT, CIT, and SSLD methods detected 65, 156, 159, and 450 significant SNPs respectively, that were not detected by the other methods. Three hundred eighty-three SNPs were discovered by the haplotype block methods and the individual SNP approach, while 1021 SNPs were discovered by all three haplotype block methods. The 383 SNPs detected by all the methods are promising candidates for studying RA susceptibility. A hybrid technique involving all four methods should be applied to detect the significant SNPs associated with RA in the NARAC dataset, but the SSLD method may be preferred because of its advantages when only one method was used.