7028 ONCOLOGY LETTERS 15: 7028-7036, 2018 SPP1 and AGER as potential prognostic biomarkers for lung adenocarcinoma WEIGUO ZHANG, JUNLI FAN, QIANG CHEN, CAIPENG LEI, BIN QIAO and QIN LIU Henan Key Laboratory of Cancer Epigenetics, Department of Oncology Surgery, Cancer Institute and College of Clinical Medicine, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan 471003, P.R. China Received August 11, 2017; Accepted January 5, 2018 DOI: 10.3892/ol.2018.8235 Abstract. Overdue treatment and prognostic evaluation lead Introduction to low survival rates in patients with lung adenocarcinoma (LUAD). To date, effective biomarkers for prognosis are still Lung adenocarcinoma (LUAD) is one of the three major required. The aim of the present study was to screen differen- histopathological subtypes along with squamous cell carci- tially expressed genes (DEGs) as biomarkers for prognostic noma (SqCLC) and large cell carcinoma (1). Currently, LUAD evaluation of LUAD. DEGs in tumor and normal samples has become the most common lung cancer with increasing were identified and analyzed for Kyoto Encyclopedia of Genes morbidity. This uptrend of incidence may be due to increasing and Genomes/Gene Ontology functional enrichments. The smoking rate and air pollution (2). Furthermore, LUAD is common genes that are up and downregulated were selected diagnosed at late stages (stage III and IV), when the cancer for prognostic analysis using RNAseq data in The Cancer has spread to nearby tissues and metastasis has occurred (3). Genome Atlas. Differential expression analysis was performed As well as overdue diagnosis that leads to delayed treatment, with 164 samples in GSE10072 and GSE7670 datasets. A total the failure of prognostic evaluation also contributes to the low of 484 DEGs that were present in GSE10072 and GSE7670 survival rate of LUAD (4,5). Therefore, there remains to be datasets were screened, including secreted phosphopro- an urgent requirement for effective biomarkers for prognosis, tein 1 (SPP1) that was highly expressed and DEGs ficolin 3, where the outcome of patients with LUAD can be evaluated in advanced glycosylation end‑product specific receptor (AGER), time to provide adjuvant therapy. transmembrane protein 100 that were lowly expressed in Due to cancer largely being a genetic disease, the latest tumor tissues. These four key genes were subsequently verified approaches to identify genes as biomarkers are based on using an independent dataset, GSE19804. The gene expression microarray technology. Oncogenomic analysis has the ability model was consistent with GSE10072 and GSE7670 data- to generate a wealth of data, which identifies the complex gene sets. The dysregulation of highly expressed SPP1 and lowly expression patterns in cancer (6,7). These gene expression expressed AGER significantly reduced the median survival patterns, including point mutations, structural variants at the time of patients with LUAD. These findings suggest that SPP1 DNA level and changes at the epigenetic level, are frequently and AGER are risk factors for LUAD, and these two genes may studied to identify the association with the occurrence, devel- be utilized in the prognostic evaluation of patients with LUAD. opment or the survival time of patients with cancer (8,9). A Additionally, the key genes and functional enrichments may number of studies have identified specific genes that function provide a reference for investigating the molecular expression as diagnostic or prognostic biomarkers for several types of mechanisms underlying LUAD. cancer, including in LUAD (10-12). Additionally, gene expres- sion profiling by microarray technology has been used to investigate the molecular mechanisms underlying a number of other diseases as well (13). The reliability of biomarker identification relies on not only the analysis methods but also the database (14). The Cancer Correspondence to: Dr Weiguo Zhang, Henan Key Laboratory Genome Atlas (TCGA) provides comprehensive data on the of Cancer Epigenetics, Department of Oncology Surgery, Cancer molecular basis of various types of cancer (15). Significant gene Institute and College of Clinical Medicine, The First Affiliated patterns observed in one or several independent databases may Hospital of Henan University of Science and Technology, 24 Jinghua Road, Jianxi, Luoyang, Henan 471003, P.R. China also act in a similar genetic subtype of cancer (16). This may also E‑mail: [email protected] be the case for different types of cancer. However, researchers must have the ability to evaluate and determine if this common Key words: lung adenocarcinoma, differentially expressed genes, pattern affects clinical phenotypes, including survival by using functional analysis, prognostic biomarker survival or clustering analysis with another independent dataset. In the present study, the common differentially expressed genes (DEGs) were screened, including secreted phospho- protein 1 (SPP1) with high expression and ficolin 3 (FCN3), ZHANG et al: PROGNOSTIC BIOMARKERS FOR LUNG ADENOCARCINOMA 7029 advanced glycosylation end‑product specific receptor (AGER; of the selected samples was analyzed. A correlation chart of RAGE), transmembrane protein 100 (TMEM100) demon- all the samples was obtained using Pearson's correlation coef- strating low expression in tumor tissues. These four key genes ficient (22). (SPP1, FCN3, AGER and TMEM100) were verified using an additional dataset. The gene expression model was consistent Screening of DEGs. The DEGs of the samples between tumor with the two previous datasets that were analyzed. The dysreg- and normal samples were identified using the limma package ulation of highly expressed SPP1 and lowly expressed AGER in R (http://bioconductor.org/packages/release/bioc/html/limma. significantly reduced the median survival time of patients with html) (23). The threshold used is as follows: (fold change, logFC) LUAD. These two genes were verified and indicated stable log2>1 and P<0.05. Then, the differential expression of these significance as risk factors for LUAD. This discovery enables genes was visualized in a Venn diagram, ʻVolcano plotsʼ or these prognostic marker genes to be utilized in the evaluation of heatmaps were generated using ggplot2 in R (24). patients with LUAD. Additionally, the key genes and functional enrichments may provide reference to investigate the molecular Functional analysis of DEGs. To further understand the expression mechanism underlying LUAD. potential pathways that these DEGs may participate in or regulate, functional analysis was performed in The Database Materials and methods for Annotation, Visualization and Integrated Discovery (DAVID) (25). All DEGs were firstly annotated using the Data source. Gene expression profiling of th ree individual data- functional annotation tool and then analyzed for enriched sets for microarray analysis [GSE19804 (17), GSE10072 (18) pathways in Gene Ontology (GO) and Kyoto Encyclopedia and GSE7670 (19)] were downloaded from Gene Expression of Genes and Genomes (KEGG) terms. For every functional Omnibus (https://www.ncbi.nlm.nih.gov/geo/). There were item, DAVID calculated the P-value of the enrichment and 120 samples in GSE19804, including 60 LUAD tissues and false discovery rate using Benjamini correction. The threshold 60 normal tissues. GSE10072 consisted of 107 samples for significantly enriched pathways was P<0.05. (49 tumor samples and 58 normal tissues). GSE7670 contained 54 samples in total (27 tumor tissues and 27 normal lung Verification analysis. The common up- and down-regulated tissues). Datasets GSE10072 and GSE7670 were employed for genes were selected from the top DEGs in two individual the identification of DEGs on the Affymetrix Gene Chip™ datasets (GSE7670 and GSE10072). To evaluate the expres- Human Genome U133A arrayplatform (Affymetrix; Thermo sion of these candidate DEGs for the prognosis of patients Fisher Scientific, Inc., Waltham, MA, USA). GSE19804 was with LUAD, an independent dataset GSE19804 containing used to verify the significance of selected DEGs. This verifica- 120 samples (60 tumor tissues and 60 normal tissues) was tion was based on Affymetrix Gene Chip Human Genome U133 analyzed using Affymetrix Human Genome U133 Plus 2.0. Plus 2.0 (Affymetrix; Thermo Fisher Scientific, Inc.). Finally, the prognostic analysis of the selected genes was performed Prognostic and statistical analysis. Survival analysis was with the gene expression data and follow-ups of patients with performed by using SPSS version 24 (26,27) to investigate the LUAD by using RNAseq (Illumina HiSeq 2500; Illumina, prognostic impact of selected DEGs on patients with LUAD. Inc., San Diego CA, USA) in TCGA (https://cancergenome. The data are presented as the mean ± standard deviation. nih.gov/). Significance analysis was performed using Kaplan‑Meier analysis with the log-rank test. Log-rank (Mantel-Cox) test Quality control. The quality assessment of the Affymetrix was used with a significance level of P<0.05. All statistical Gene Chip datasets was performed using the affyPLM package analysis was performed with GraphPad Prism (GraphPad in R software (version 3.6; https://www.r-project.org) (20). In Software, Inc., La Jolla, CA, USA). The P-value of the enrich- the present analysis, the linear modeling procedures were ment and false discovery rate was obtained using Benjamini applied at a probe-level. The Relative Log Expression (RLE) correlation. P<0.05 was considered to indicate a statistically