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.
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