Identification of Two Novel Biomarkers of Rectal Carcinoma Progression and Prognosis Via Co-Expression Network Analysis

www.impactjournals.com/oncotarget/ Oncotarget, 2017, Vol. 8, (No. 41), pp: 69594-69609 Research Paper Identification of two novel biomarkers of rectal carcinoma progression and prognosis via co-expression network analysis Min Sun1,*, Taojiao Sun2,*, Zhongshi He3 and Bin Xiong1 1Department of Oncology, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological Behaviors & Hubei Cancer Clinical Study Center, Wuhan 430071, P.R. China 2Department of Stomatology, Zhongnan Hospital of Wuhan University, Wuhan 430071, P.R. China 3Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan 430071, P.R. China *These authors have contributed equally to this work Correspondence to: Bin Xiong, email: [email protected] Keywords: rectal cancer, The Cancer Genome Atlas, weighted gene co-expression network analysis, prognosis, disease progression Received: March 06, 2017 Accepted: May 22, 2017 Published: June 27, 2017 Copyright: Sun et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT mRNA expression profiles provide important insights on a diversity of biological processes involved in rectal carcinoma (RC). Our aim was to comprehensively map complex interactions between the mRNA expression patterns and the clinical traits of RC. We employed the integrated analysis of five microarray datasets and The Cancer Genome Atlas rectal adenocarcinoma database to identify 2118 consensual differentially expressed genes (DEGs) in RC and adjacent normal tissue samples, and then applied weighted gene co-expression network analysis to parse DEGs and eight clinical traits in 66 eligible RC samples. A total of 16 co-expressed gene modules were identified. The green-yellow and salmon modules were most appropriate to the pathological stage (R = 0.36) and the overall survival (HR =13.534, P = 0.014), respectively. A diagnostic model of the five pathological stage hub genes (SCG3, SYP, CDK5R2, AP3B2, and RUNDC3A) provided a powerful classification accuracy between localized RC and non-localized RC. We also found increased Secretogranin III (SCG3) expression with higher pathological stage and poorer prognosis in the test and validation set. The increased Homer scaffolding protein 2 (HOMER2) expression with the favorable survival prediction efficiency significantly correlated with the markedly reduced overall survival of RC patients and the higher pathological stage during the test and validation set. Our findings indicate that theSCG3 and HOMER2 mRNA levels should be further evaluated as predictors of pathological stage and survival in patients with RC. INTRODUCTION for RC, especially for modules having a strong correlation between genes with similar expression patterns, to predict Accurate staging of rectal cancer (RC) is essential the pathological stage and survival outcome, which could for carrying out precise therapies that increase the help to understand disease pathogenesis and provide survival rate of patients [1]. Previous studies have personalized treatment, have been rarely reported. found no differences in specific clinicopathological risk However, previous research based on examining genetic factors between patients with a high or low risk of RC mutations and different expression patterns associating progression [2]. Recent technological breakthroughs with colorectal carcinogenesis has largely ignored the in genome-wide sequencing have shed new insights relationship between the genes and clinical characteristics on deregulated mRNAs that have been identified and [4, 5]. In addition, RC progression involves several critical characterized in the past few years [3]. Genetic biomarkers stages, although most studies have only evaluated the www.impactjournals.com/oncotarget 69594 Oncotarget Table 1: Quality control results of RC in the different datasets No. Study IQC EQC CQCg CQCp AQCg AQCp Rank 1 GSE75548 3.92 2 1.8* 2.23 1.9* 1.13* 1.83 2 GSE12225 4.74 2 0.68* 1.66* 1.08* 2.38 2.00 3 GSE35982 5.62 2 0.02* 10.03 0* 0.07* 2.67 4 GSE34472 1.3* 0.4* 0.24* 0.36* 0.56* 0.11* 3.50 RC, rectal cancer; GSE, GEO dataset; IQC, internal quality control indexes; EQC, external quality control indexes; CQCg and CQCp, consistency of differential expression quality control indexes for genes and pathways; AQCg and AQCp, accuracy quality control indexes for genes and pathways. * P-value not significant after Bonferroni correction. differences between RC and adjacent normal tissue (ANT), colon cancer, there was very limited mRNA expression regardless of the intermediate stages [6]. Therefore, there data relating to rectal cancer. We investigated and is an urgency to include stage and prognostic predictive manually curated four public datasets (GSE12225, modules to the current staging system, which could GSE34472, GSE35982, and GSE75548) as the training be achieved by combining information on the clinical set (Supplementary Table 1) [12–15]. characteristics and validated gene-specific biomarkers. The training set calculated the six quantitative For these reasons, we aimed to establish comprehensive quality control (QC) measures by standardized mean ranks mRNA expression patterns of the module genes and and principal component analysis (PCA) biplots within clinical traits, especially those relating to multi-stage MetaQC (Table 1 and Figure 1A) [16]. GSE34472 was disease progression which directly affects the prognosis detected in low quality RC samples and was omitted from through weighted gene co-expression network analysis the training set [17]. The training set finally consisted of 65 (WGCNA) [7]. RC samples and 42 ANTs, and it had 8153 gene symbols WGCNA results in the construction of free-scale in common. A total of 6603 gene symbol sets passed the gene co-expression networks that explore the relationships filtering criteria. We identified 4091 mRNAs that were between the gene sets and clinical features [8]. WGCNA consistently DEGs using the moderated t test, Fisher’s groups prerogative genes into modules based on their co- method by summarizing -log(p-value) across studies and expression similarities and analogous functions across a running 300 permutations for the meta-analysis to infer population of samples [9–11]. the P-values (Figure 1B) [18]. As expected, hierarchical In the present work, we carried out the pooled clustering of the three datasets using the remaining 4091 analysis of RC mRNA raw microarrays and the network DEGs distinguished RC from ANT samples (Figure 2A). level analysis of The Cancer Genome Atlas (TCGA) The raw level-3 RNAseq data from 20,501 rectal adenocarcinoma (READ) data to decipher the mRNAs in 95 RC samples and 10 ANT samples were relationships between the module genes and clinical traits. downloaded from TCGA [1, 8, 10]. A total of 2177 DEGs To the best of our knowledge, we are the first group to use were identified by linear models within the microarray meta-analysis and WGCNA to identify modules displaying analysis (LIMMA), among which 1092 were up-regulated a nominal evidence association with RC clinical traits. By and 1085 were down-regulated in RC versus ANT characterizing module content and topology, we identified (Supplementary Table 2). This mRNA signature allowed clinical traits, modules, and network concepts that play for the separation of RC samples from ANT samples in important roles in the regulation of RC at the level of the the 2-way hierarchical cluster (Figure 2B) and in the PCA differentially expressed genes (DEGs). Moreover, to define plot (Figure 2C). A total of 2118 overlapping DEGs were the prognostic value of the cancer-specific module, which chosen by both training and test sets (Figure 2D). was related to tumor progression, further analysis was performed to validate candidate markers by combining Co-expression network construction and module survival analysis with an independent validation cohort. preservation analysis RESULTS We included 2118 DEGs from 70 RC patients with complete clinical traits and prognostic information of the Identification of consensual DEGs in training TCGA-READ test set to construct co-expression networks and test sets in rectal cancer patients via WGCNA. After four outlier samples were discarded, the connections between the genes in the gene network The flow chart outlining the methods used in this were in line with a scale-free network distribution when study is shown in Supplementary Figure 1. In contrast to the soft threshold power β was set at 3 (Supplementary www.impactjournals.com/oncotarget 69595 Oncotarget Figure 2A-2D). The dynamic tree cut method identified yielded significant PCC with the pathological stage (R modules with similar expression profiles (Figure 3A, = 0.25, P = 0.04) and pathology T stage (R = 0.33, P = 3B). After highly similar modules were merged (Figure 0.007). The red (126 genes) module yielded significant 3B), a total of 16 co-expressed modules were identified, PCC with the pathological stage (R = 0.37, P = 0.002), ranging in size from 11 to 623 genes, whereas the “grey” the pathology T stage (R = 0.29, P = 0.02) and pathology module was reserved for genes that were not co-expressed N stage (R = 0.31, P = 0.01). The tan (44 genes) module (Figure 3C). yielded significant PCC with the pathological stage (R = After comparing the TCGA-READ test set with the 0.31, P = 0.01), the pathology T stage (R = 0.26, P = 0.03), validation set (GSE29621) [19], the summary preservation pathology N stage (R = 0.38, P = 0.002), pathology M statistics used in determining whether a reference network stage (R = 0.32, P = 0.009) and lymphatic invasion (R is found in another test network were visualized [20]. The = 0.27, P = 0.03). The green-yellow (54 genes) module green-yellow and salmon modules were well preserved, yielded significant PCC with the pathological stage (R with low median Rank statistics and Z-summary statistics = 0.38, P = 0.002), the pathology T stage (R = 0.34, P larger than 10 (Supplementary Figure 3) [7, 21].

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