Possible pathways used to predict different stages of lung adenocarcinoma

Abstract

We aimed to find some specific pathways that can be used to predict the stage of lung adenocarcinoma.RNA-Seq expression profile data and clinical data of lung adenocarcinoma (stage I [37], stage II 161], stage III [75], and stage IV [45]) were obtained from the TCGA dataset. The differentially expressed genes were merged, correlation coefficient matrix between genes was constructed with correlation analysis, and unsupervised clustering was carried out with hierarchical clustering method. The specific coexpression network in every stage was constructed with cytoscape software. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed with KOBAS database and Fisher exact test. Euclidean distance algorithm was used to calculate total deviation score. The diagnostic model was constructed with SVM algorithm.Eighteen specific genes were obtained by getting intersection of 4 group differentially expressed genes. Ten significantly enriched pathways were obtained. In the distribution map of 10 pathways score in different groups, degrees that sample groups deviated from the normal level were as follows: stage I < stage II < stage III < stage IV. The pathway score of 4 stages exhibited linear change in some pathways, and the score of 1 or 2 stages were significantly different from the rest stages in some pathways. There was significant difference between dead and alive for these pathways except thyroid hormone signaling pathway.Those 10 pathways are associated with the development of lung adenocarcinoma and may be able to predict different stages of it. Furthermore, these pathways except thyroid hormone signaling pathway may be able to predict the prognosis.

Figure 1

The coexpression networks for 4 stages (A: stage I, B: stage II, C: stage III, and D: stage IV).

Figure 2

The results of network analysis.

Figure 3

The distribution map of 10 pathways score in different groups.

Figure 4

The boxplot of 10 pathway score in different stages.

Figure 5

The receiver-operating characteristic (ROC) curve.

Figure 6

Correlation between the identified pathway and patients’ prognosis. The horizontal ordinate, patients’ prognosis; longitudinal coordinates, pathscore; read sample, dead; and green sample, alive.