MicroRNA expression integrated analysis and identification of novel biomarkers in small cell lung cancer: a meta-analysis

Abstract

Background: Small cell lung cancer (SCLC) is an aggressive and recalcitrant cancer. In recent years, studies focused on the abnormal expression of microRNA which has proven valuable in terms of prognosis, diagnosis and treatment in SCLC. To address the limitations of independent studies data, a meta-analysis seems necessary for further exploration of microRNA as biological target and regulatory factor in SCLC.

Methods: We performed comprehensive literature retrieval in GEO database and EBI ArrayExpress database. The microRNA expression data was extracted from 4 related researches (GSE15008, GSE74190, GSE19945, GSE77380), which was obtained from GEO database. In each included study, the R. Affymetrix Expression Console's Limma package and RMA algorithms were used to screen for raw data for gene chip quality control, standardization, log2 conversion and differential expression of the gene chip, respectively. Significant microRNA meta-signatures were identified by Robust Rank Aggregation method. Subsequently, gene ontology (GO) enrichment analysis and pathway analysis were performed using bioinformatics tools.

Results: We found a significant microRNA meta-signature of six up-regulated (hsa-miR-182-5p, hsa-miR-96-5p, hsa-miR-7-5p, hsa-miR-301b-3p, hsa-miR-130b-3p, hsa-miR-210-3p) and four down-regulated (hsa-miR-126-3p, hsa-miR-451a, hsa-miR-145-5p, hsa-miR-486-5p) microRNA s in meta-analysis approaches. GO analysis showed that target gene of meta-signatures microRNA was mainly enriched in endosome, chordate embryonic development and transforming growth factor beta receptor. The related functional gene of microRNA meta signature synergistically targeting SCLC signaling pathway was confirmed by enrichment analysis. In particular, neurotrophin and TGF-beta signaling pathway play the most important roles in the pathway network.

Conclusions: Our study identified 10 highly significant and consistently dysregulated microRNA s from 4 datasets, which offering convincing molecular targets and regulatory factors in future research of SCLC.

Keywords: biomarker; meta-analysis; microRNA; small cell lung cancer (SCLC).

Figure 1

Flowchart of study selection process in this meta-analysis.

Figure 2

Distribution of tumor-specific microRNA alterations in small cell lung cancer. Red and blue vertical bars indicate up-regulated and down-regulated microRNAs, respectively. MicroRNAs are aligned according to miRBase release 22, containing 381mature human microRNAs. The number of microRNAs analyzed in each study is graphically depicted on the right. The positions of small cell lung cancer meta-signature microRNAs are shown.

Figure 3

The size of deregulated microRNA lists varies greatly across the studies. Vertical boxes designate the number of significantly up-regulated (A) or down-regulated (B) microRNAs. The rank scale is shown on the left. Positions of small cell lung cancer meta-signature microRNAs, identified by robust rank aggregation analysis are highlighted.

Figure 4

Target counts of meta-signature microRNAs.

Figure 5

Twenty gene ontology (GO) processes most strongly enriched by meta-signature microRNA targets. (A) GO covering the domains of molecular functions (MF). (B) GO covering the domains of biological processes (BP). The most relevant top 20 enrichment analysis results (with smaller p-values) from the DAVIA. The bubbles represent the top 20 pathways. The bubble size represents enrichment target gene number in the process; The bubble color represents −log₁₀(P value), from green to red, the P value decreases; the Y-axis represents the enrichment target of GO. The X-axis is Richfactor: it’s counts divided by the third column.

Figure 6

Twenty gene ontology (GO) processes and enrichment scores for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways strongest enriched by meta-signature microRNA targets. (A) GO covering the domains of cellular components (CC). (B) KEGG pathways that were the most significantly up-regulated pathways during SCLC. The most relevant top 20 enrichment analysis results (with smaller P values) from the DAVIA. The bubbles represent the top 20 pathways. The bubble size represents enrichment target gene number in the process; The bubble color represents −log₁₀(P value), from green to red, the p-value decreases; the Y-axis represents the enrichment target of GO or pathway. The X-axis is Richfactor: it’s counts divided by the third column. SCLC, small cell lung cancer.