Identification of novel biomarkers and candidate small molecule drugs in non-small-cell lung cancer by integrated microarray analysis

Abstract

Background: Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC. Methods: The microarray datasets of GSE18842, GSE30219, GSE31210, GSE32863 and GSE40791 from Gene Expression Omnibus database were downloaded. The differential expressed genes (DEGs) between NSCLC and normal samples were identified by limma package. The construction of protein-protein interaction (PPI) network, module analysis and enrichment analysis were performed using bioinformatics tools. The expression and prognostic values of hub genes were validated by GEPIA database and real-time quantitative PCR. Based on these DEGs, the candidate small molecules for NSCLC were identified by the CMap database. Results: A total of 408 overlapping DEGs including 109 up-regulated and 296 down-regulated genes were identified; 300 nodes and 1283 interactions were obtained from the PPI network. The most significant biological process and pathway enrichment of DEGs were response to wounding and cell adhesion molecules, respectively. Six DEGs (PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5) which significantly up-regulated in NSCLC tissues, were selected as hub genes according to the results of module analysis. The GEPIA database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. Additionally, CMap predicted the 20 most significant small molecules as potential therapeutic drugs for NSCLC. DL-thiorphan was the most promising small molecule to reverse the NSCLC gene expression. Conclusions: Based on the gene expression profiles of 696 NSCLC samples and 237 normal samples, we first revealed that PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5 could act as the promising novel diagnostic and therapeutic targets for NSCLC. Our work will contribute to clarifying the molecular mechanisms of NSCLC initiation and progression.

Keywords: bioinformatics analysis; candidate small molecules; non-small-cell lung cancer; novel biomarkers; prognosis.

Figure 1

The workflow of this study.

Figure 2

(A) Volcano plot of gene expression profile data between NSCLC and normal tissues in each dataset. Red dots: significantly up-regulated genes in NSCLC; green dots: significantly down-regulated genes in NSCLC; black dots: non-differentially expressed genes. Adj. P<0.01 and |log₂ FC|>1 were considered as significant. (Ba) Venn diagram of 408 overlapping DEGs from GSE18842, GSE30219, GSE31210, GSE32863 and GSE40791 datasets. (Bb) Up-regulated DEGs (Bc) Down-regulated DEGs.

Figure 3

Functional and signaling pathway analysis of the overlapped DEGs in NSCLC. (A) Biological processes, (B) cellular components, (C) molecular function and (D) KEGG pathway.

Figure 4

The protein–protein interaction networks of overlapping DEGs.

Figure 5

The three most significant modules extracted from PPI network and KEGG pathway analysis of module genes.

Figure 6

(A, B) The heatmap of module genes between NSCLC (LUAD and LUSC) and normal samples. (C) The BiNGO revealed the biological process of module genes. The color depth of nodes represents the corrected P-value. The size of nodes represents the number of genes involved.

Figure 7

The expression level of hub genes according to the GEPIA database.

Figure 8

(A) The survival analysis for hub genes according to the GEPIA database. (B) The protein level expression of hub genes in NSCLC and normal tissues using immunohistochemistry.

Figure 9

(A) The network of module genes and their co-expression genes constructed by cBioPortal. Nodes with thick outline: hub genes; nodes with thin outline: co-expression genes. (B) The 20 most small molecules drugs identified by CMap database. (C) qPCR validation of these six hub genes in seven paired NSCLC samples. *P<0.05.

Figure S1

The potential transcription factors associated with the expression of hub genes.

Figure S2

A regulatory network of lncRNA-miRNA-mRNA constructed by GCBI. Purple nodes: related lncRNA; Blue nodes: targeted miRNA.