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Comprehensive Analysis of Mrna Expression Profiles in Head and Neck Cancer by Using Robust Rank Aggregation and Weighted Gene Coexpression Network Analysis

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Comprehensive Analysis of Mrna Expression Profiles in Head and Neck Cancer by Using Robust Rank Aggregation and Weighted Gene Coexpression Network Analysis Hindawi BioMed Research International Volume 2020, Article ID 4908427, 21 pages https://doi.org/10.1155/2020/4908427 Research Article Comprehensive Analysis of mRNA Expression Profiles in Head and Neck Cancer by Using Robust Rank Aggregation and Weighted Gene Coexpression Network Analysis Zaizai Cao , Yinjie Ao , Yu Guo , and Shuihong Zhou Department of Otolaryngology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang Province 310003, China Correspondence should be addressed to Shuihong Zhou; [email protected] Received 3 June 2020; Revised 2 November 2020; Accepted 23 November 2020; Published 10 December 2020 Academic Editor: Hassan Dariushnejad Copyright © 2020 Zaizai Cao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Head and neck squamous cell cancer (HNSCC) is the sixth most common cancer in the world; its pathogenic mechanism remains to be further clarified. Methods. Robust rank aggregation (RRA) analysis was utilized to identify the metasignature dysregulated genes, which were then used for potential drug prediction. Weighted gene coexpression network analysis (WGCNA) was performed on all metasignature genes to find hub genes. DNA methylation analysis, GSEA, functional annotation, and immunocyte infiltration analysis were then performed on hub genes to investigate their potential role in HNSCC. Result. A total of 862 metasignature genes were identified, and 6 potential drugs were selected based on these genes. Based on the result of WGCNA, six hub genes (ITM2A, GALNTL1, FAM107A, MFAP4, PGM5, and OGN) were selected (GS > 0:1, MM > 0:75,GSp value < 0.05, and MM p value < 0.05). All six genes were downregulated in tumor tissue (FDR < 0:01) and were related to the clinical stage and prognosis of HNSCC in different degrees. Methylation analysis showed that the dysregulation of ITM2A, GALNTL1, FAM107A, and MFAP4 may be caused by hypermethylation. Moreover, the expression level of all 6 hub genes was positively associated with immune cell infiltration, and the result of GSEA showed that all hub genes may be involved in the process of immunoregulation. Conclusion. All identified hub genes could be potential biomarkers for HNSCC and provide a new insight into the diagnosis and treatment of head and neck tumors. 1. Introduction different screening criteria have a great impact on the research results. To solve this problem and obtain stable bio- Head and neck squamous cell carcinoma (HNSC) is the sixth markers, researchers proposed a novel rank aggregation most common cancer in the world [1]. Worldwide, more method: robust rank aggregation (RRA) [5], which has been than 300000 patients die of HNSC every year [2]. Although implemented as an R package (RobustRankAggreg) [5], to many treatments for HNSC such as surgery, chemotherapy, identify the overlapping genes among ranked gene lists [6], and radiotherapy have obtained some success, the 5-year sur- thus making the result more reliable. vival rate is still only 40-50% [3]. The chances of survival for WGCNA [7] is an effective method to find the clusters of patients with HNSCC depend largely on the initial stage of highly correlated genes and identify the hub genes of each cancer. Therefore, early detection and accurate diagnosis cluster [7]. This method has been widely applied in various are crucial for patients with HNSCC to receive treatment. biological contexts. In our study, a total of 24 independent In the past twenty years, with the application of microar- gene datasets were included in RRA analysis to identify ray and next-generation sequencing technologies, a great robust DEGs. We used these DEGs to predict the potential number of novel diagnostic or therapeutic biomarkers have small molecular drugs. The coexpression network was then been identified in HNSCC [4]. However, small samples in established by WGCNA to identify hub genes in these robust independent research, different platform technologies, and DEGs. The role of all hub genes in HNSCC was then 2 BioMed Research International 24 HNSCC GEO datasets Differential analysis (p value < 0.05) Differentially expressed genes in each GEO datasets RRA (adj p value < 0.05) Screen related small GO and KEGG Metasignature DEGs molecule drugs (CMap) enrichment analysis WGCNA in GSE65858 Key module : blue GS and MM filtering Explore the correlation Function annotation by 6 hub genes between hub genes and GSEA immunocyte infiltration Explore the correlation (i) Validation of the diagnostic role of hub genes. between methylation and (TCGA and GSE30784) expression of hub genes (ii) Validation of the correlation between hub genes and clinical characteristic (TCGA) (iii) III Explore the expression of hub genes in dysplasia tissue (GSE30784) 4 Explore the prognostic role of hub genes in HNSCC (TCGA) Figure 1: Simple flow chart of the entire study. validated by other independent databases. Furthermore, we MAL (FDR = 8:50e − 35), CRISP3 (FDR = 1:60e − 34), also utilized multiple online tools such as DiseaseMeth [8], CRNN (FDR = 4:32e − 33), and KRT4 (FDR = 1:24e − 30) MEXPRESS [9], and MethSurv [10] to evaluate the methyla- were the most significant downregulated genes. The chromo- tion level of hub genes. TIMER was used to assess the associ- somal locations and expression patterns of the top 100 DEGs ation between immune infiltration and hub genes. GSEA [11] are visualized in Figure 2. Chromosome 1 contains most analysis was applied to explore the biological functions of metasignature DEGs while X and Y chromosome contains these hub genes. To the best of our knowledge, this is the first no DEGs. It is clear that almost all displayed genes have the time to utilize RRA and WGCNA simultaneously for screen- same expression pattern in most of the independent studies, ing biomarkers of HNSCC. which indicates the reliability of the RRA analysis result. 2. Result 2.2. Functional Enrichment Analysis. We select the top 300 DEGs to perform GO and KEGG enrichment analyses. 2.1. Metasignature DEGs Identified by RRA Analysis. The Among the KEGG pathway database, we can find that these workflow of our study is shown in Figure 1, 24 independent DEGs were enriched in multiple cancer-related pathways like studies were used in RRA analysis, and a total of 466 upreg- focal adhesion, PI3K-Akt signal pathway, pathway in cancer, ulated genes and 396 downregulated genes were identified. small-cell lung cancer, transcriptional misregulation in can- The top 5 upregulated genes in tumor tissue were MMP1 cer, and chemical carcinogenesis (Figure 3(a)). Furthermore, (FDR = 4:77e − 53), MMP10 (FDR = 8:25e − 40), PTHLH in all terms of KEGG and GO, we found that these meta- (FDR = 1:48e − 38), MMP3 (FDR = 5:38e − 38), and SPP1 signature genes mostly involved in pathways associated with (FDR = 2:79e − 37) while TMPRSS11B (FDR = 1:53e − 36), the construction of ECM such as ECM receptor interaction, BioMed Research International 3 ISG15 PDPN MFAP2 IFI6 CLCA4 TGFBR3 SLC16A1 CRNN S100A7A DPT FMO2 LAMC2 KIF14 IL24 Heatmap APOL1 LAMB3 MMP11 8.49 RSAD2 FAM3B MAL FAP HOXD10 COL5A2 TPX2 MYO1B TGM3 C2orf54 CEACAM1 CEACAM5 CEACAM7 BST2 FUT3 KAT2B GPD1L LAMA3 FAM4107A FAM3D MGLL ABCA8 RBP1 HLF COL1A1 KRT16 KRT13 MFAP4 TMPRSS11B CDH3 IL8 GDPD3 CXCL10 PPL CXCL11 TNFRSF12A SPP1 ADH7 TDO2 0 HPGD RHCG TRIP13 TGFBI SPINK5 PAX9 SPINK7 COL4A2 PTK7 COL4A1 SCEL PLA2G7 CRISP3 POSTIN SH3BGRL2 DCBLD1 WDR66 KRT78 KRT4 FSCN1 ENDOU DFNA5 PTHLH INHBA FOXM1 CYP3A5 SERPINE1 ATP6V0A4 MMP13 –8.49 MMP12 MMP3 MMP1 LOXL2 EPHX2 SNAI2 SLURP1 SERPINH1 MAMDC2 TNC IFT3 PLAU PPP1R3C Figure 2: Circular visualization of expression patterns and chromosomal positions of top 100 DEGs. Red indicates gene upregulation, blue indicates downregulation, and white indicates genes that do not exist in a given dataset. extracellular matrix organization, collagen catabolic process, expression data of metasignature DEGs. The correlation collagen binding, collagen trimer, extracellular region, and coefficients between each module and each clinical trait were extracellular exosome (Figure 3). calculated, and it is clear that only the blue module and gray module were significantly associated with T grade of HNSCC 2.3. Screen the Candidate Small Molecule Drugs for HNSCC. (Figure 4(e)). Because genes in the gray module are not sig- According to our screen criteria, 6 small molecule drugs (repa- nificantly coexpressed with each other, we only chose the glinide ES = −0:848, thiostrepton ES = −0:863,levamisoleES blue module as a key module. A total of 102 genes were = −0:75, cortisone ES = −0:866, zimeldine ES = −0:784,and included in blue modules, and the result of enrichment anal- cyproterone ES = −0:742) were identified (Table 1). Their 2D ysis for these genes showed that the most significant GO and structures are visualized in Supplementary Fig 1. These poten- KEGG terms were related to cell metabolism, chemokine tial drugs can to some extent reverse the robust dysregulated activity, and transmembrane transport (Supplementary Fig genes in HNSCC, thus providing suggestions for us to develop 2). According to the value of GS and MM (GS > 0:1, MM > targeted drugs. 0:75,GSp value < 0.05, and MM p value < 0.05), 6 genes (ITM2A, GALNTL1, OGN, FAM107A, MFAP4, and PGM5), 2.4. Identification of Hub Genes in HNSCC Patients. To iden- which were also significantly correlated with each other tify the hub genes, we performed WGCNA on the GSE65858, (Figure
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