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Use of Bioinformatic Database Analysis and Specimen Verification Journal of Cancer 2021, Vol. 12 5967 Ivyspring International Publisher Journal of Cancer 2021; 12(19): 5967-5976. doi: 10.7150/jca.58768 Research Paper Use of bioinformatic database analysis and specimen verification to identify novel biomarkers predicting gastric cancer metastasis Weimin Wang1,2*, Ke Min2*, Gaoyang Chen3*, Hui Zhang4, Jianliang Deng1, Mengying Lv2, Zhihong Cao1, Yan Zhou1,2 1. Department of Oncology, Yixing Hospital Affiliated to Medical College of Yangzhou University, Yangzhou University, Jiangsu, China. 2. Institute of Combining Chinese Traditional and Western Medicine, Medical College, Yangzhou University, Jiangsu, China. 3. Department of Oncology, The second People’s Hospital of Taizhou City, Jiangsu, China. 4. Department of Nursing, SuZhou Vocational Health College, Jiangsu, China. *WW, KM and GC contributed equally to this work. Corresponding authors: Professor Zhihong Cao, E-mail: [email protected]; Professor Yan Zhou, E-mail: [email protected]; Professor Weimin Wang, E-mail: [email protected]. © The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions. Received: 2021.01.28; Accepted: 2021.07.20; Published: 2021.08.13 Abstract Background: Gastric cancer (GC) is a common gastrointestinal tumor, and its metastasis has led to a significant increase in the death rate. The mechanisms of GC metastasis remain unclear. Methods: The differentially expressed genes (DmRs) and lncRNAs (DlncRs) of GC were selected from The Cancer Genome Atlas (TCGA) database. We applied the weighted gene co-expression network analysis (WGCNA) to construct co-expression modules related with GC metastasis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) method analyzed the functional regions and signal pathways of genes in vital modules. DmRs-DlncRs co-expression network were drawn for finding out hub nodes. Survival analyses of significant biomarkers were analyzed by Kaplan-Meier (KM) method. Finally, the expressions of selected biomarkers were validated in cell lines and caner tissues by quantitative real-time PCR (qRT-PCR), in GC tissue microarray by Fluorescence in situ hybridization (FISH). Results: 4776 DmRs and 213 DlncRs were involved the construction of WGCNA network, and MEyellow module was identified to have more significant correlation with GC metastasis. DmRs and DlncRs of MEyellow module were proved to be involved in the processes of cancer pathogenesis by GO and KEGG pathway analysis. Through the DmRs-DlncRs co-expression network, 7 DmRs and 1 DlncRs were considered as hub nodes. Besides, the high expression of TIMD4, CETP, KRT27, PTGDS, FAM30A was worse than low expression in GC patients survival, respectively; However, LRRC26 was opposite trend. FAM30A and TIMD4 were all significant biomarkers of GC survival and hub genes. Simultaneously, TIMD4, CETP, KRT27, PTGDS, FAM30A were increased in GC cell lines and tissues compared with GES-1 and normal tissues, respectively; the expression of LRRC26 was reduced in GC cell lines and tissues. Conclusion: This study identified 6 genes as new biomarkers affecting the metastasis of GC. Especially, FAM30A and TIMD4 might be an effective marker for predicting the prognosis and a potential- therapeutic target in GC. Key words: Gastric cancer, Metastasis, Prognosis, Biomarker, WGCNA Introduction Gastric cancer (GC) is a common malignant include surgery, chemotherapy, radiotherapy, tumor of the digestive system. The mortality rate of targeted therapy, and immunotherapy [2]. Although GC ranks second among all malignant tumors these treatment methods could reduce the recurrence worldwide [1]. With the diversification of treatment rate of GC, the 5-year survival rate of this cancer is modes, the treatment methods for GC primarily still low [3]. The occurrence and development of GC is http://www.jcancer.org Journal of Cancer 2021, Vol. 12 5968 a continuous, multistage, multifactor process. The (FISH). This comprehensive analysis might provide pathogenesis of GC is complicated and involves potential biomarkers or therapeutic targets for future genetic and epigenetic changes, such as research investigating GC metastasis at the protein-encoding genes, lncRNAs, and miRNAs [4]. transcriptomic level. Therefore, it is particularly important to uncover effective biomarkers associated with GC metastasis to Materials and methods improve the overall survival (OS) of GC in this Study design and collection of datasets process. In recent years, an increasing number of studies The design of this article is outlined in Fig. 1. The have focused on RNA sequencing (RNA-Seq), which datasets on the expression of mRNAs and lncRNAs is a rapidly maturing second-generation sequencing from GC patients were obtained from the TCGA technology. The Cancer Genome Atlas (TCGA), as the database through the Illumina-HiSeq RNA-Seq world’s largest public tumor database, provides an platform. Excluding patients with other tumors or RNA-Seq platform that contains mRNA, lncRNA, and patients without metastatic clinical information, the miRNA data for various cancers. With these mRNA and lncRNA expression data included 367 GC sequencing results, we identified new biomarkers to nonmetastasis samples, 27 metastasis samples and 35 predict tumor metastasis and improve OS through normal samples. bioinformatic analysis. Weighted gene coexpression network analysis (WGCNA) is an important bioinformatic analysis method that can design clusters or modules of highly similar biomolecules and identify internal modular “hubs”, including mRNA, lncRNA, and miRNA [5-7]. Furthermore, these modules and sample features were analyzed by WGCNA, which was able to investigate the mechanism underlying certain features [8]. WGCNA was employed to construct coexpression modules to identify essential genes in human osteosarcoma [9]. The WGCNA method was utilized to determine that SERP2, EFEMP2, FBN1, SPARC, and LINC0219 were recurrence-related molecules and prognostic markers in colon cancer [10]. However, studies employing WGCNA to investigate GC metastasis have not been reported. In our study, we comprehensively analyzed Figure 1. Flow chart of data processing, analysis and verification. RNA-Seq data of GC patients in the TCGA database and successfully identified a group of differentially expressed mRNAs (DmRs) and lncRNAs (DlncRs). Identification of DmRs and DlncRs After merging the DmRs and DlncRs of GC, we The limma package of R was used to analyze the conducted WGCNA and module-trait relationship DmRs and DlncRs between GC metastasis, analyses to illustrate significant modules related to nonmetastasis and normal samples according to a GC metastasis. Immediately, cell functional areas and false discovery rate (FDR) < 0.05 and a fold change signaling pathways of important modules were (FC) > 2. These samples were divided into two excavated by Gene Ontology (GO) and Kyoto groups: A1 (GC nonmetastasis vs. normal tissues) and Encyclopedia of Genes and Genomes (KEGG) A2 (GC metastasis vs. normal tissues). Then, we analyses. A DmR-DlncR coexpression network merged the datasets of DmRs and DlncRs of GC for analysis and a survival analysis of biomarkers in WGCNA analysis. significant modules were performed. We chose 6 candidate biomarkers to be validated in GC cell lines WGCNA analysis and GES-1 cells, fresh GC tissues and normal tissues Using WGCNA (version 1.61), the merged DmRs by quantitative real-time PCR (qRT-PCR). Finally, and DlncRs of GC were applied to construct after combining the results of DmR-DlncR coexpression modules and perform network analysis coexpression and survival curves, we used the GC [11]. First, we selected the soft threshold for network population sample database to verify 2 candidate construction, which made the adjacency matrix a biomarkers by fluorescence in situ hybridization continuous value between 0 and 1. According to this http://www.jcancer.org Journal of Cancer 2021, Vol. 12 5969 method, the coexpression network conformed to the Gene Expression Profiling Interactive Analysis power law distribution and was closer to the actual (GEPIA) database of TCGA [14]. A total of 408 GC biological network state. Second, the samples were included, which were divided into blockwiseModules function was employed to high-expression and low-expression groups construct a scale-free network. The gene coexpression according to the median value. P < 0.05 was modules were constructed by module allocation considered to be significant. analysis. We used the dynamic tree cutting algorithm to cut the clustering tree into branches to define these Cell culture and fresh GC tissue acquisition modules and assigned them to different colors for GC cell lines (AGS, HGC27 and MKN45) and visualization [12]. The module eigengene (ME) was normal gastric mucosa cells (GES-1) were purchased calculated to represent the expression level of each from the Cell Bank of the Chinese Academy of module. The correlation between ME and clinical Sciences, Shanghai Institute of Cell Biology (Shanghai, traits was calculated. Finally, we identified genes with China). Cells were inoculated into RPMI-1640 significant differences for further analysis. medium (HyClone, Thermo Fisher Scientific Biochemical Products Co., Ltd., China) supplemented Recognition of significant modules associated with 10% fetal bovine serum (Thermo Scientific with GC metastasis HyClone, Logan, UT, USA).
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