Palve Vinayak, Pareek Manisha, Krishnan Neeraja M, Siddappa Gangotri, Suresh Amritha, Kuriakose Moni A, Panda Binay
Ganit Labs, Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology, Bangalore, India.
Head and Neck Oncology, Mazumdar Shaw Centre for Translational Research, Bangalore, India.
PeerJ. 2018 Aug 14;6:e5207. doi: 10.7717/peerj.5207. eCollection 2018.
Selection of the right reference gene(s) is crucial in the analysis and interpretation of gene expression data. The aim of the present study was to discover and validate a minimal set of internal control genes in head and neck tumor studies. We analyzed data from multiple sources (in house whole-genome gene expression microarrays, previously published quantitative real-time PCR (qPCR) data and RNA-seq data from TCGA) to come up with a list of 18 genes (discovery set) that had the lowest variance, a high level of expression across tumors, and their matched normal samples. The genes in the discovery set were ranked using four different algorithms (BestKeeper, geNorm, NormFinder, and comparative delta Ct) and a web-based comparative tool, RefFinder, for their stability and variance in expression across tissues. Finally, we validated their expression using qPCR in an additional set of tumor:matched normal samples that resulted in five genes (, , , , and ), out of which and were most stable and were abundantly expressed across the tissues. Our data suggest that or in combination with either or or can be used as a minimal set of control genes in head and neck tumor gene expression studies.
选择合适的参考基因对于基因表达数据的分析和解读至关重要。本研究的目的是在头颈部肿瘤研究中发现并验证一组最小化的内参基因。我们分析了多个来源的数据（内部全基因组基因表达微阵列、先前发表的定量实时PCR（qPCR）数据以及来自TCGA的RNA测序数据），得出了一份包含18个基因的列表（发现集），这些基因具有最低的变异性、在肿瘤及其匹配的正常样本中具有高水平的表达。使用四种不同的算法（BestKeeper、geNorm、NormFinder和比较ΔCt）以及一个基于网络的比较工具RefFinder对发现集中的基因进行排序，以评估它们在不同组织中的表达稳定性和变异性。最后，我们在另一组肿瘤：匹配的正常样本中使用qPCR验证了它们的表达，结果得到了五个基因（、、、和），其中和最稳定且在各组织中大量表达。我们的数据表明，或与或或组合可作为头颈部肿瘤基因表达研究中的一组最小化对照基因。
