Combination the Gene Expression of Adjacent Normal and Tumor Makes more Robust Gene Signature as Cancer Prognosis Biomarker
Abstract Purpose: We tried to explore new gene signature via the combination of tumor-derived expression profile and the adjacent normal-derived expression profile to find more robust cancer biomarker. Methods: Log2 transformed ratio of tumor tissue and the adjacent normal tissue (Log2TN) expression, tumor-derived expression, and normal-derived expression were used to do univariate Cox regression in The Cancer Genome Atlas (TCGA) lung squamous cell carcinoma (LUSC) respectively. Then, we used factor analysis and least absolute shrinkage and selection operator Cox (LASSO-Cox) to select gene signature in TCGA LUSC for Log2TN, tumor, and adjacent normal respectively.Results: By comparing Log2TN with tumor and adjacent normal in LUSC, we found that genes derived from Log2TN show more robust (p = 0.006 and p = 0.001) and have lower p-values (p < 0.001). Gene signature selected from Log2TN shows the best generalization in the three GEO datasets even though only tumor-derived expression profiles were available in the three datasets. Enrichment analysis showed that the tumor cells mainly focus on proliferation with losing functional of metabolism.Conclusions: These results indicate that (1) Log2TN could get more robust genes and gene signature than tumor-derived expression profiles used traditionally; (2) the adjacent-normal tissue may also play an important role in the progress and outcome of the tumor.Implications for Cancer Survivors: By combined of tumor-derived expression profile and the adjacent normal-derived expression profile, we could find more robust gene signature than traditionally method. Using these robust gene signatures, robust cancer biomarkers could be constructed and will do great help to improve cancer prognosis.