Abstract
Cancer genomic research is a relatively new method. It has shown great potential but faces certain challenges. Researchers often have to deal with tens of thousands of genes with a relatively small sample size of patient cases—a dilemma referred to as the “Curse of Dimensionality” [1]—and it makes it hard to learn the data well because of relatively sparse data in high dimensional space. To deal with the dilemma, this study uses p-values of individual genes for pathway enrichment to find statistically significant pathways. The aim of this study is to find significant genes and biological pathways that are related to lung cancer by statistical method and pathway enrichment analysis. Several significant genes, such as WNT2B, VAV2, and significant pathways, such as Metabolism of xenobiotics by cytochrome P450-Homo sapiens (human) and Fatty acid degradation-Homo sapiens (human), are found to be both statistically significant and biological studies supported. Significant genes-including TESK2, C5orf43, and ZSCAN21—and significant pathways such as Pentose and glucoronate interconversions-Homo sapiens (human), are found to be new cancer-related genes and pathways that worth laboratory studies. The idea and method used in this research can be applied to find more significant genes and pathways that worth study experimentally.
Highlights
In the 21st century, cancer research, integrated with biology, genetics, cytology and statistics, continues to be a hot spot
The aim of this study is to find significant genes and biological pathways that are related to lung cancer by statistical method and pathway enrichment analysis
The top 10 genes are shown in Table 1 as following (Note that there are 528 genes that passed the False Discovery Rate (FDR) corrections, and p-values passed Bonferroni cutoff are highlighted): After studying existing literatures, we find some medical and laboratory evidence for correlation between some genes above and lung cancer
Summary
In the 21st century, cancer research, integrated with biology, genetics, cytology and statistics, continues to be a hot spot. Generic aspects of such, a relatively new method for learning causes and preventions for cancer, have begun to show its potential. Large-scale research projects have been launched but faced certain challenges. Acquiring high-quality biological samples needed for genomic studies, managing and analyzing the vast amounts of data involved, and having a converged genetic abnormality result all add challenges to genomic method of research [3] [4]
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