Search

In 2020, 878,348 newly reported cases and 444,347 deaths related to head and neck cancer were reported. These numbers suggest that there is still a need for molecular biomarkers for the diagnosis and prognosis of the disease. In this study, we aimed to analyze mitochondria-related mitochondrial transcription factor A (TFAM) and DNA polymerase γ (POLG) single-nucleotide polymorphisms (SNPs) in the head and neck cancer patient group and evaluate associations between SNPs, disease characteristics, and patient outcomes. Genotyping was performed using TaqMan probes with Real-Time polymerase chain reaction. We found associations between TFAM gene SNPs rs11006129 and rs3900887 and patient survival status. We found that patients with the TFAM rs11006129 CC genotype and non-carriers of the T allele had longer survival times than those with the CT genotype or T-allele carriers. Additionally, patients with the TFAM rs3900887 A allele tended to have shorter survival times than non-carriers of the A allele. Our findings suggest that variants in the TFAM gene may play an important role in head and neck cancer patient survival and could be considered and further evaluated as prognostic biomarkers. However, due to the limited sample size (n = 115), further studies in larger and more diverse cohorts are needed to confirm these findings.

Most glioblastoma studies incorporate the layer of tumor molecular subtype based on the four-subtype classification system proposed in 2010. Nevertheless, there is no universally recognized and convenient tool for glioblastoma molecular subtyping, and each study applies a different set of markers and/or approaches that cause inconsistencies in data comparability and reproducibility between studies. Thus, this study aimed to create an applicable user-friendly tool for glioblastoma classification, with high accuracy, while using a significantly smaller number of variables. The study incorporated a TCGA microarray, sequencing datasets, and an independent cohort of 56 glioblastomas (LUHS cohort). The models were constructed by applying the Agilent G4502 dataset, and they were tested using the Affymetrix HG-U133a and Illumina Hiseq cohorts, as well as the LUHS cases. Two classification models were constructed by applying a logistic regression classification algorithm, based on the mRNA levels of twenty selected genes. The classifiers were translated to a RT-qPCR assay and validated in an independent cohort of 56 glioblastomas. The classification accuracy of the 20-gene and 5-gene classifiers varied between 90.7–91% and 85.9–87.7%, respectively. With this work, we propose a cost-efficient three-class (classical, mesenchymal, and proneural) tool for glioblastoma molecular classification based on the mRNA analysis of only 5–20 genes, and we provide the basic information for classification performance starting from the wet-lab stage. We hope that the proposed classification tool will enable data comparability between different research groups.