Abstract
Lung adenocarcinoma is often diagnosed at an advanced stage with poor prognosis. Patients with different clinical outcomes may have similar clinico-pathological characteristics. The results of previous studies for biomarkers for lung adenocarcinoma have generally been inconsistent and limited in clinical application. In this study, we used inverse-variance weighting to combine the hazard ratios for the four datasets and performed pathway analysis to identify prognosis-associated gene signatures. A total of 2,418 genes were found to be significantly associated with overall survival. Of these, a 21-gene signature in the HMGB1/RAGE signalling pathway and a 31-gene signature in the clathrin-coated vesicle cycle pathway were significantly associated with prognosis of lung adenocarcinoma across all four datasets (all p-values < 0.05, log-rank test). We combined the scores for the three pathways to derive a combined pathway-based risk (CPBR) score. Three pathway-based signatures and CPBR score also had more predictive power than single genes. Finally, the CPBR score was validated in two independent cohorts (GSE14814 and GSE13213 in the GEO database) and had significant adjusted hazard ratios 2.72 (p-value < 0.0001) and 1.71 (p-value < 0.0001), respectively. These results could provide a more complete picture of the lung cancer pathogenesis.
Highlights
VVVVV on individual genes, as individual functionally associated genes that often show only moderate differential expression can act co-ordinately in the cell, magnifying the effect[12,13]
The study population consisted of 443 patients with lung adenocarcinoma from the University of Michigan Cancer Center (UM) (n = 1 78), the Moffitt Cancer Center (HLM) (n = 7 9), the Memorial Sloan-Kettering Cancer Center (MSK) (n = 1 04) and the Dana-Farber Cancer Institute (CAN/DF) (n = 82)[7]
Pathway-based risk scores were computed using the level of expression of genes in the same pathway weighted by regression coefficients
Summary
VVVVV on individual genes, as individual functionally associated genes that often show only moderate differential expression can act co-ordinately in the cell, magnifying the effect[12,13]. Classification of patient risk using a single biomarker that is strongly associated with disease outcome might not be a good strategy, as a dysregulated gene that may not show any obvious association with disease on its own may interact with others in the same pathway, resulting in carcinogenesis or drug resistance[14,15,16]. It is worth identifying particular sets of genes showing unusual expression that act in the same cancer-associated pathway. In this study, using data from the public gene expression and clinical data on the caArray database of the National Cancer Institute[7], cancer-associated pathway-based approaches were used to identify pathway-based gene signatures, which may have potential for prognosis prediction and therapeutic target identification in lung adenocarcinoma
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