Screening of Differential Expressed Genes from Gene Chip and Sequencing Data and Evaluate Its Prognostic Values in Males with Digestive System Neoplasms

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Original Article

Screening of differential expressed genes from gene chip and sequencing data and evaluate its prognostic values in males with digestive system neoplasms

Kai Fang1#, Caixia Hu1#, Xiufen Zhang1, Zijian Guo2, Lihua Li1

1Oncology Institute, 2Department of Oncological Surgery, The Affiliated Hospital of Jiangnan University, Wuxi 214062, China Contributions: (I) Conception and design: K Fang, C Hu; (II) Administrative support: L Li, Z Guo; (III) Provision of study materials or patients: K Fang, L Li; (IV) Collection and assembly of data: K Fang, C Hu, X Zhang; (V) Data analysis and interpretation: K Fang, C Hu; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #These authors contributed equally to this work. Correspondence to: Lihua Li. Oncology Institute, The Affiliated Hospital of Jiangnan University, Wuxi 214062, China. Email: [email protected]; Zijian Guo. Department of Oncological Surgery, The Affiliated Hospital of Jiangnan University, Wuxi 214062, China. Email: [email protected].

Background: Digestive system neoplasm is a common cancer in males worldwide. This study aimed to explore the commonalities in males with digestive system neoplasms (MDSN) and its clinical relevance. Methods: A total of 46 differential expressed genes (DEGs) in MDSN were identified shared in TCGA and GEO databases. Results: These DEGs significantly affected a variety of cell function and signaling pathways. Of which, a hub of 7 genes (CCNB1, MAD2L1, BUB1, CHEK1, MCM2, CCNA2 and CDC25B) were interacted with each other in protein level and significantly enriched in cell cycle pathway. Further methylation analysis, we found that BUB1, MAD2L1 and MCM2 were hypomethylation via m6A modification. Besides, BUB1, MAD2L1 and MCM2 were co-expressed in mRNA level and up-regulation of them led to worse prognostic in hepatocellular carcinoma, while caused a better prognostic in stomach adenocarcinoma (STAD), and had a race difference between white and Asian people in STAD. Medicine molecules, I-threonine (ID: PA451673) and enzymes (ID: PA164712734) might be efficient medicines in BUB1, MAD2L1 and MCM2 up-regulated MDSN patients. Conclusions: Taken together, hypomethylation via m6A modification might cause BUB1, MAD2L1 and MCM2 up-regulation in MDSN. Dysregulation of BUB1, MAD2L1 and MCM2 function as contrast prognostic in liver hepatocellular carcinoma and STAD. Our study provides more accurate therapeutic targets and prognostic biomarkers for specific cancer types.

Keywords: BUB1; MAD2L1; MCM2; m6A modification; liver hepatocellular carcinoma; stomach adenocarcinoma (STAD)

Submitted Apr 09, 2019. Accepted for publication Sep 09, 2019. doi: 10.21037/tcr.2019.09.58 View this article at: http://dx.doi.org/10.21037/tcr.2019.09.58

Introduction genetic characteristics of cells, and forming tumor cells with different morphology, metabolism and function from Tumors are hereditary diseases whose biological basis is genetic abnormality. Oncogenic factors lead to somatic normal cells. Genetic abnormalities of different genes lead cell gene mutations, resulting in dysfunction and disorder to different tumors (1). The incidence and mortality rate of gene expression, affecting the biological activity and of tumors in male is higher than that in female, and half of

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2019;8(6):2339-2349 | http://dx.doi.org/10.21037/tcr.2019.09.58 2340 Fang et al. Hypomethylation via m6A modification might cause BUB1, MAD2L1 and MCM2 up-regulation in MDSN tumors in male occur in the respiratory tract and digestive Methods tract (2). We sought to identify genes that are differential TCGA database expressed in male with digestive system tumors (MDSN). Recently, gene expression profiling and bioinformatics RNA sequence expression profiles of digestive system analysis have been widely used to identify potential biomarkers neoplasms in males were obtained from TCGA database, for various cancers (3,4). LncRNA CTB-193M12.5 was consists of 976 male tumour tissues and 81 normal tissues, found associated with prognosis of lung adenocarcinoma by including esophageal carcinoma (ESCA), STAD, liver integrated The Cancer Genome Atlas (TCGA) analysis (5). hepatocellular carcinoma (LIHC), colon adenocarcinoma Similarly, an in-depth understanding of the notable signaling and rectum adenocarcinoma (COAD and READ) (Table S1). pathway was achieved through the analysis of pan-carcinoma using TCGA data (6). Besides, approximately 895 genes and GEO database their associated pathways were identified by analyzing the Gene Expression Omnibus (GEO) datasets of 7,168 tumour Microarray expression profiles of GSE41258, GSE23400, samples from 16 independent cancer types (7). However, GSE3335 and GSE84402 were obtained from GEO there are still limited studies on MDSN. database and consisted of 277 tumour tissues and 143 DNA methylation is a chemically modified form of DNA normal tissues (Table S1). Methylation expression profiles of that alters hereditary properties without altering DNA GSE73003 were downloaded in GEO database, containing sequences and is one of the earliest epigenetic regulatory 20 paired tumour tissues and non-tumour tissues. mechanisms that have been discovered. DNA methylation causes changes in chromatin structure, DNA conformation, Identification of DEGs DNA stability and DNA-protein interactions, thereby controlling gene expression (8). In general, hypermethylation The DEGs between tumour tissues and adjacent non- inhibits gene expression, while hypomethylation leads to tumorous tissues in MDSN in TCGA were identified using gene over-expression. The N6-methyladenosine (m6A) limma package by R software 3.5.1. While the DEGs in modification is the most common methylation modification GEO were performed by GEO2R (https://www.ncbi.nlm. in transcripts and involves almost all aspects of mRNA nih.gov/geo/geo2r/). |log FC|>2 and P<0.01 was selected regulation (9). Documents revealed that the modification of as significant. m6A is involved in all processes of mRNA transcription and translation, involving various cancers (10,11). Bioinformatics analysis In these studies, the raw gene expression data of TCGA database and GEO database were analyzed, and 46 shared DAVID (https://david.ncifcrf.gov/home.jsp) was used to differential expressed genes (DEGs) were found in MDSN conducted Gene ontology (GO) and Kyoto Encyclopedia of tumour tissue samples compared with adjacent normal Genes and Genomes (KEGG) pathway enrichment analysis samples. Therein, a hub of 7 genes (CCNB1, MAD2L1, of DEGs. The threshold of FDR <0.05, P value<0.05 was BUB1, CHEK1, MCM2, CCNA2 and CDC25B) were regarded as significantly enrichment. STRING (https:// found to be enriched in cell cycle pathway and the 7 genes string-db.org/) was performed to construct a protein- were found to be interacted with each other in protein protein interaction (PPI) network for DEGs. P<0.001 and level. Furthermore, MAD2L1, BUB1 and MCM2 was combined score >0.9 were selected as significant. hypomethylation via m6A modification. Survival analysis The RMBase (http://rna.sysu.edu.cn/rmbase) and of MAD2L1, BUB1 and MCM2 suggested that these three m6AVar (http://m6avar.renlab.org/index.html) was used to genes have a promising prognostic values in MDSN. investigate modification type of genes. Particular, in stomach adenocarcinoma (STAD), there was a race difference between white and Asian people. Statistic analysis and clinical relevance Medicine molecules related analysis shown that medicine molecules, I-threonine (ID: PA451673) and enzymes (ID: Kaplan-Meier plotter was used to predict the prognostic PA164712734) might be efficient medicines in these three values of genes in MDSN and the race difference between genes up-regulated MDSN patients. white and Asian people. Correlation analysis of genes was

A ESCA STAD LIHC COAD and READ Volcano Volcano Volcano Volcano 10 10 n=1,095 10 n=1,154 10 n=544 n=1,903 5 5 5 5

0 0 0 0

n=946 LogFC n=1,573 LogFC LogFC LogFC –5 –5 –5 –5 n=1,287 n=666 –10 –10 –10 –10 0 10 20 30 40 0 20 30 60 80 0 20 40 60 80 100 0 50 100 150 -Log10 (FDR) -Log10 (FDR) -Log10 (FDR) -Log10 (FDR)

B C D ESCA LIHC GSE41258 GSE23400 TCGA GEO

COAD/READ STAD GSE884402 GSE33335

Figure 1 DEGs of MDSN in TCGA and GEO database. (A) Volcano plots were shown the DEGs in esophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), liver hepatocellular carcinoma (LIHC) and colon adenocarcinoma and rectum adenocarcinoma (COAD and READ); (B) Venn diagrams were shown the shared DEGs in TCGA; (C) Venn diagrams were shown the shared DEGs in GEO dataset; (D) Venn diagrams were shown the DEGs shared in TCGA and GEO. P<0.001, logFC ≥2 was regarded as significant. DEGs, differential expressed genes. performed in Graphpad Prism 5 software. GEPIA (http:// DEGs in MDSN, including mRNAs and lncRNAs. In gepia.cancer-pku.cn/) was used for clinical relevance MDSN, 4 cancer types were included, ESCA, STAD, analysis. The data are presented as the mean ± standard LIHC, COAD and READ. All DEGs were shown in deviation. P<0.05 was set as the cutoff criterion. volcano plots (Figure 1A). A total of 195 shared DEGs are common in MDSN in TCGA dataset (Figure 1B). We further screened DEGs in GEO dataset by GEO2R, DEGs related medicine analysis 289 shared DEGs were found (Figure 1C). In TCGA and WebGestalt (http://www.webgestalt.org/option.php) is a GEO database, 46 common DEGs were found in MDSN, functional web tool for enrichment analysis. Information including 29 up-regulated genes and 17 down-regulated in this web was collected from NCBI GEO, DrugBank, genes (Figure 1D). KEGG and dbSNP. In Gene Set Enrichment Analysis part, medicine molecules related DEGs were screened. P value <0.05 was regarded as significant. Bioinformatics analysis of 46 DEGs in MDSN GO and KEGG analysis was conducted on DAVID's Results website tool and it was found that these 46 DEGs significantly affected GO function and KEGG signaling Identification of DEGs in MDSN pathways (Figure 2). In GO terms, as to molecular function, A total of 976 cancerous and 81 para-cancerous raw mRNA the DEGs were significantly enriched in DNA clamp loader expression data of MDSN were downloaded from the activity, cell cycle, ATPase activity, single-stranded DNA- TCGA database. R language software was used to screen dependent ATPase activity, microtubule motor activity,

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2019;8(6):2339-2349 | http://dx.doi.org/10.21037/tcr.2019.09.58 2342 Fang et al. Hypomethylation via m6A modification might cause BUB1, MAD2L1 and MCM2 up-regulation in MDSN Fold enrichment 0 50 100 150 200 cfa04110: Cell cycle GO:0005819~spindle GO:0005634~nucleus GO:0030496~midbody GO:0000785~chromatin GO:0048477~oogenesis GO:0005737~cytoplasm GO:0000910~cytokinesis GO:0000776~kinetochore GO:0005874~microtubule GO:0005813~centrosome GO:0005524~ATP binding GO:0005524~ATP cfa03030: DNA replication GO:0000922~spindle pole GO:0005643~nuclear pore cfa03430: Mismatch repair GO:0005654~nucleoplasm GO:0016887~ATPase activity GO:0016887~ATPase GO:0042393~histone binding GO:0007144~female meiosis I GO:0005871~kinesin complex GO:0001556~oocyte maturation GO:0000281~mitotic cytokinesis GO:0006468~protein phosphorylation GO:0006468~protein GO:0097149~centralspindlin complex GO:0031390~Ctf18 RFC-like complex GO:0005881~cytoplasmic microtubule GO:0090307~mitotic spindle assembly GO:0000793~condensed chromosome GO:0007059~chromosome segregation GO:0007059~chromosome GO:0001958~endochondral ossification GO:0003689~DNA clamp loader activity GO:0003777~microtubule motor activity GO:0003777~microtubule GO:0030199~collagen fibril organization GO:0030574~collagen catabolic process GO:0050840~extracellular matrix binding GO:0031572~G2 DNA damage checkpoint GO:0001578~microtubule bundle formation GO:0001578~microtubule GO:0007018~microtubule-based movement GO:0007018~microtubule-based GO:0035987~endodermal cell differentiation GO:0006261~DNA-dependent DNA replication GO:0032467~positive regulation of cytokinesis GO:0032467~positive regulation GO:0000794~condensed nuclear chromosome GO:0000775~chromosome, centromeric region centromeric GO:0000775~chromosome, GO:0005663~DNA replication factor C complex GO:0005663~DNA replication GO:0005578~proteinaceous extracellular matrix GO:0005578~proteinaceous GO:0032147~activation of protein kinase activity GO:0032147~activation of protein GO:0000086~G2/M transition of mitotic cell cycle GO:0007094~mitotic spindle assembly checkpoint GO:0000777~condensed chromosome kinetochore GO:0000777~condensed chromosome GO:0031536~positive regulation of exit from mitosis of exit from GO:0031536~positive regulation GO:0045931~positive regulation of mitotic cell cycle GO:0045931~positive regulation GO:0043065~positive regulation of apoptotic process GO:0043065~positive regulation GO:0006268~DNA unwinding involved in DNA replication GO:0046602~regulation of mitotic centrosome separation of mitotic centrosome GO:0046602~regulation GO:0043142~single-stranded DNA-dependent ATPase activity GO:0043142~single-stranded DNA-dependent ATPase GO function and KEGG pathway analysis of 46 shared DEGs of MDSN in TCGA and GEO. P<0.001 was regarded as significant enrichment. Red bar represent GO:0051988~regulation of attachment of spindle microtubules to kinetochore of attachment spindle microtubules GO:0051988~regulation GO:1900264~positive regulation of DNA-directed DNA polymerase activity of DNA-directed GO:1900264~positive regulation Figure 2 KEGG pathway. Green bar represent molecular function. bar Yellow represent cellular component. Blue bar represent biological process. GO, Gene ontology; DEGs, differential expressed genes.

A B Gene Gene Combined score RFC3 RFC4 0.999 BUB1 MAD2L1 0.999 CCNA2 CCNB1 0.998 NEK2 MAD2L1 0.997 MAD2L1 CCNB1 0.997 KIF2C BUB1 0.996 MAD2L1 CCNA2 0.996 DSCC1 RFC4 0.996 RFC3 DSCC1 0.995 CHEK1 MCM2 0.995 BIRC5 CCNB1 0.995 KIF23 ECT2 0.995 CENPF BUB1 0.994 BUB1 CCNB1 0.994 BIRC5 MAD2L1 0.993 UBE2C CCNB1 0.992 UBE2C MAD2L1 0.992 BUB1 BIRC5 0.992 RFC4 MCM2 0.991 CCNA2 MCM2 0.991

C m6A methylation

MCM2

BUB1

MAD2L1

0 10 20 30 40 50 60 70

MAD2L1 BUB1 MCM2 Sources of m6AVar 9 18 58 Sources of RMBase 6 20 28

Sources of m6AVar Sources of RMBase

Figure 3 PPI network of 46 shared DEGs and m6A modification of MCM2, BUB1 and MAD2L1. (A) Interaction of 46 DEGs in protein level; (B) top 20 combined genes; (C) m6A modification of MCM2, BUB1 and MAD2L1 in m6AVar and RMBase websites. Combined score >0.9 was selected as significant. PPI, protein-protein interaction; DEGs, differential expressed genes.

etc. About cellular component, DNA replication factor CDC25B) were included. C complex, ATP binding, microtubule, proteinaceous Then, PPI network of STRING result was constructed extracellular matrix, condensed chromosome kinetochore, to predict the interaction between these 46 DEGs. A total etc., were significantly enriched with these DEGs. In of 153 protein interaction nodes were shown in the PPI addition, the most enriched GO terms in biological process network, among them CCNB1, MAD2L1, BUB1, CHEK1, were DNA unwinding involved in DNA replication, positive MCM2, CCNA2 and CDC25B were found to be interaction regulation of DNA-directed DNA polymerase activity, G2/ with each other in protein level (Figure 3A). The connected M transition of mitotic cell cycle, positive regulation of nodes in the network are at required interaction score >0.9. mitotic cell cycle, positive regulation of apoptotic process, The top 20 combined score genes were selected (Figure 3B) etc. In KEGG pathway terms, DEGs were enriched in cell and CCNB1, MAD2L1, BUB1, CHEK1, MCM2 and CCNA2 cycle, DNA replication and mismatch repair. Cell cycle is were included in the top 20 genes. Combined with KEGG the most DEGs enrichment pathway and a hub of 7 genes and PPI analysis, we selected the cell cycle pathway for (CCNB1, MAD2L1, BUB1, CHEK1, MCM2, CCNA2 and further study.

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2019;8(6):2339-2349 | http://dx.doi.org/10.21037/tcr.2019.09.58 2344 Fang et al. Hypomethylation via m6A modification might cause BUB1, MAD2L1 and MCM2 up-regulation in MDSN

Methylation status analysis of cell cycle pathway related 7 genes and enzymes (ID: PA164312734) are closely related to BUB1, MAD2L1 and MCM2. For BUB1 and MAD2L1, they We downloaded the raw mRNA methylation expression data are significantly enriched with taxanes (ID: PA150481189), from the GEO database, and R language was used to found paclitaxel (ID: PA450761), calcium (ID: PA164712582) and differential expressed CpG methylation sites. In GSE73003, a I-serine (ID: PA451330). However, tumor detection (ID: total of 13,660 CpG methylation sites were found differential PA164713367) and dactinomycin (ID: PA151917012) were expressed, of which MAD2L1, BUB1 and MCM2 was only correlated with MCM2, while cisplatin (ID: PA449014) hypomethylation. Further investigation in RMBase and and vinblastine (ID: PA451877) were correlated with m6AVar, we found that MAD2L1, BUB1 and MCM2 was MAD2L1, respectively. hypomethylation via m6A modification Figure( 3C).

Discussion Survival analysis and clinical relevance Colorectal cancer is the third most common cancer After verifying BUB1, MAD2L1 and MCM2 were up- regulation in MDSN than adjacent normal tissues diagnosed in men worldwide, while liver, stomach and (Figure S1). We predicted the prognostic value of BUB1, esophageal cancers are the fourth, fifth and ninth cancers, MAD2L1 and MCM2 in Kaplan-Meier plotter. Over- respectively. Liver cancer is the second leading cause of expression of BUB1, MAD2L1 and MCM2 significantly death, while gastric, colorectal and esophageal cancers rank lead to a worse prognostic in liver hepatocellular carcinoma third, fourth and sixth globally (2). These four types of (Figure 4A), while causing a better prognostic in STAD cancers all belonged to digestive system neoplasms. So we (Figure 4B). Due to the number limited of males patient, committed to identifying the commonality in MDSN and BUB1, MAD2L1 and MCM2 tends to be better prognostic give an insight to its mechanism. in rectum adenocarcinoma, however the difference is not TCGA database contains a variety of original expression statistically significant. data of tumors, including mRNA expression data, For ESCA, there is no significantly prognostic value methylation data, gene mutation data, and related clinical of BUB1, MAD2L1 and MCM2 (Figure S2). Further co- data (12). Increasing literature report that through the expression analysis were performed, we found that BUB1, analysis of the TCGA database, a large number of new MAD2L1 and MCM2 co-expression with each other research directions emerge. Previous researchers analyzed in mRNA level (Figure 4C,D). The co-expression and the TCGA database to determine a targeted treatment interaction (Figure 3A) of BUB1, MAD2L1 and MCM2 trial and a roadmap for stratifying gastric adenocarcinoma might increase the prognostic value in liver hepatocellular patients (13). Through systematic analysis of the TCGA carcinoma and STAD. Further, clinical relevance analysis database, biomarkers and transgenes were identified to was performed in Kaplan-Meier plotter and GEPIA. For point to relevant cellular processes and new therapeutic survival analysis of different race, high expression of BUB1, targets (14). With the development of scientific research MAD2L1 and MCM2 leading a poorer prognostic in and detection technology, the GEO database has become an hepatocellular carcinoma in both Asian and white people important data tool to make up for the defects of the TCGA (Figure 5A,B). In STAD, up-regulation of BUB1, MAD2L1 database. GEO has more accurate expression data, more and MCM2 causing a better prognostic in Asian (Figure 5C); precise experimental conditions, and further advancing the so did MCM2 in white people, while BUB1 and MAD2L1 development of bioinformatics analysis. The combination has no significant prognostic value (Figure 5D). Besides, of TCGA and GEO will provide more accurate and reliable in GEPIA, we found that BUB1, MAD2L1 and MCM2 clinical guidance for scientific research (15). We downloaded expression elevated in stage I to stage III, while down- raw gene expression profiles from the TCGA database and regulated in stage IV (Figure 5E). GEO database, then DEGs analysis was performed, and 46 shared DEGs were found in the MDSN. Combined with bioinformatics analysis, we found that these 46 DEGs Medicine molecules related to BUB1, MAD2L1 and MCM2 significantly enriched in GO and KEGG pathways. Of Based on the above studies, molecule drugs of BUB1, which, a hub of 7 genes (CCNB1, MAD2L1, BUB1, CHEK1, MAD2L1 and MCM2 was identified in WebGestalt MCM2, CCNA2 and CDC25B) were interacted with each (Table 1). It shown that both I-threonine (ID: PA451673) other in protein level and significantly enriched in cell cycle

A BUB1 MAD2L1 MCM2

1.0 HR=2.59 (1.55–4.34) 1.0 HR=2.77 (1.68–4.57) 1.0 HR=2.25 (1.43–3.52) logrank P=0.00018 logrank P=3.4e–05 logrank P=0.00029 0.8 0.8 0.8 n=100 n=106 n=139 0.6 0.6 0.6

0.4 n=149 0.4 0.4 n=143 n=110 Survival rates Survival rates Survival rates 0.2 0.2 0.2 Expression Expression Expression low low low 0.0 high 0.0 high 0.0 high 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120

BUB1 B MAD2L1 MCM2 1.0 HR=0.49 (0.3–0.81) 1.0 HR=0.7 (0.46–1.06) 1.0 HR=0.56 (0.37–0.83) logrank P=0.0047 logrank P=0.088 logrank P=0.0034 0.8 0.8 n=62 0.8

0.6 0.6 0.6 n=158 0.4 0.4 n=167 0.4 Survival rates Survival rates Survival rates

0.2 Expression n=176 0.2 Expression 0.2 Expression low low low high n=71 high n=80 0.0 0.0 high 0.0 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 C 10 60 N=281 60 N=281 8 N=281 Pearson r=0.7395 40 Pearson r=0.6886 Pearson r=0.7751 6 P<0.0001 P<0.0001 40 P<0.0001 BUB1 4 MCM2 20 2 MCM2 20 0 0 0 2 4 6 8 10 0 2 4 6 8 10 0 MAD2L1 MAD2L1 0 2 4 6 8 10 BUB1 D 25 50 50 20 N=263 40 N=263 40 N=263 Pearson r=0.6679 Pearson r=0.4845 15 30 30 Pearson r=0.6131 P<0.0001 P<0.0001

MCM2 P<0.0001

BUB1 10 20

MCM2 20 5 10 10 0 0 0 0 10 20 30 0 10 20 30 0 5 10 15 20 15 MAD2L1 MAD2L1 BUB1

Figure 4 Survival and co-expression analysis of BUB1, MAD2L1 and MCM2 in Kaplan-Meier plotters for hepatocellular carcinoma and gastric adenocarcinoma. (A) Up-regulation of BUB1, MAD2L1 and MCM2 significantly leads to a worse prognosis for hepatocellular carcinoma; (B) up-regulation of BUB1, MAD2L1 and MCM2 significantly leads to a better prognosis for gastric adenocarcinoma; (C,D) BUB1, MAD2L1 and MCM2 are co-expressed at the mRNA level. pathway. We hypothesized that the dysregulation of the methylation of its promoter significantly down-regulated downstream cell cycle pathway may play an important role miR-486-5p and promoted cell proliferation and invasion in in the tumorigenesis and development of MDSN. rectal cancer by activating the PLAGL2/IGF2/beta-catenin DNA methylation changes in promoter and genomic signaling pathway (17). Compared with radiation-sensitive regions produce cancer phenotypes by affecting gene cells in squamous cell carcinoma of the head and neck, transcription (16). Previous studies have shown that DNA DNA methylation increased in radiation-resistant cells, so

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2019;8(6):2339-2349 | http://dx.doi.org/10.21037/tcr.2019.09.58 2346 Fang et al. Hypomethylation via m6A modification might cause BUB1, MAD2L1 and MCM2 up-regulation in MDSN

A BUB1 MAD2L1 MCM2 1.0 1.0 1.0 n=85 n=91 n=90 0.8 0.8 0.8

0.6 0.6 0.6

0.4 0.4 n=33 0.4 n=38 Survival rates Survival rates Survival rates n=32 0.2 0.2 0.2 Expression Expression HR=5.56 (2.77–11.17) Expression HR=3.98 (2.03–7.79) low low low HR=4.88 (2.45–9.73) logrank P=6.8e–08) logrank P=1.4e–05) high logrank P=6.9e–07) 0.0 high 0.0 high 0.0 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 Time (months) Time (months) Time (months) B BUB1 MAD2L1 MCM2 1.0 1.0 1.0 HR=3.17 (1.49–6.77) HR=2.06 (1.04–4.09) HR=3.27 (1.43–7.49) logrank P=0.0016) logrank P=0.034 logrank P=0.003 0.8 0.8 0.8

0.6 0.6 0.6 n=47 n=52 n=39 0.4 0.4 0.4

Survival rates n=56 Survival rates n=51 Survival rates n=64 0.2 0.2 0.2 Expression Expression Expression low low low 0.0 high 0.0 high 0.0 high 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Time (months) Time (months) Time (months)

C BUB1 MAD2L1 MCM2 1.0 1.0 1.0 n=18 n=17 n=15 0.8 0.8 0.8 HR=0.1 (0.01–0.8) HR=0.17 (0.04–0.77) HR=0.21 (0.05–0.95) logrank P=0.026) logrank P=0.0081) 0.6 logrank P=0.0092) 0.6 0.6

n=32 0.4 n=30 n=29 0.4 0.4 Survival rates Survival rates Survival rates 0.2 Expression 0.2 Expression 0.2 Expression low low low high high high 0.0 0.0 0.0 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 Time (months) Time (months) Time (months)

D BUB1 MAD2L1 MCM2 1.0 HR=0.55 (0.3–1.01) 1.0 HR=0.75 (0.45–1.25) 1.0 HR=0.48 (0.29–0.8) logrank P=0.051) logrank P=0.27) logrank P=0.0036) 0.8 0.8 0.8 n=40 0.6 0.6 0.6 n=88

0.4 0.4 n=98 0.4 n=108 Survival rates Survival rates Survival rates n=60 0.2 0.2 Expression n=50 0.2 Expression Expression low low high low 0.0 high 0.0 0.0 high 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Time (months) Time (months) Time (months)

E BUB1 MAD2L1 MCM2 6 5 F value=9.36 F value=10.2 7 F value=6.34 Pr(>F) =5.82e–06 Pr(>F) =1.94e–06 Pr(>F) =0.00034 5 6 4 4 5 3 3 4 2 3 2 2 1 1 1

Stage I Stage II Stage III Stage IV Stage I Stage II Stage III Stage IV Stage I Stage II Stage III Stage IV

Figure 5 Clinical significance of BUB1, MAD2L1 and MCM2 in hepatocellular carcinoma and gastric adenocarcinoma. (A,B) For different race of Asian and white people, high expression of BUB1, MAD2L1 and MCM2 leading a poorer prognostic in hepatocellular carcinoma in both Asian (A) and white (B) people. (C) The up-regulation of BUB1, MAD2L1 and MCM2 in gastric adenocarcinoma has significantly improved prognosis in Asian, but only MCM2 has a better prognosis for gastric adenocarcinoma in whites (D), and BUB1 and MAD2L1 have no significant prognostic value. (E) BUB1, MAD2L1 and MCM2 expression elevated in stage I to stage III, while down-regulated in stage IV.

Table 1 Related medicine molecules to BUB1, MAD2L1 and MCM2 Drug ID Parameters Genes

Taxanes PA150481189 R=76.66; P value =2.23e-04; FDR =1.71e-01 MAD2L1; BUB1

Paclitaxel PA450761 R=76.66; P value =2.23e-04; FDR =1.71e-01 MAD2L1; BUB1

l-threonine PA451673 R=14.98; P value =2.96e-04; FDR =1.71e-01 MAD2L1; BUB1; MCM2

Enzymes PA164712734 R=6.19; P value =4.2e-03; FDR =1e+00 MAD2L1; BUB1; MCM2

Vinblastine PA451877 R=98.16; P value =1.02e-02; FDR =1e+00 MAD2L1

Calcium PA164712582 R=10.23; P value =1.22e-02; FDR =1e+00 MAD2L1; BUB1

l-serine PA451330 R=8.85; P value =1.62e-02; FDR =1e+00 MAD2L1; BUB1

Cisplatin PA449014 R=27.01; P value =3.66e-02; FDR =1e+00 MAD2L1

Tumour detection PA164713367 R=21.99; P value =4.48e-02; FDR =1e+00 MCM2

Dactinomycin PA151917012 R=20.53; P value =4.79e-02; FDR =1e+00 MCM2

as to further understand the DNA methylation status as a gene expression. Besides, studies have shown that binding biomarker of radiation-resistant cells (18). We downloaded proteins selectively recognize dynamic m6A modifications DNA methylation expression data from the GEO database to regulate mRNA translation status and longevity (22). to investigate differential methylated CPG sites and found Changes in m6A modification are observed to be involved that MAD2L1, BUB1and MCM2 was hypomethylation in multiple cellular processes, which might have impacts in DMSN tumour tissues. Among the more than 100 on several human diseases (23). In order to study the m6A chemical modifications that may occur in various types modification state of MAD2L1, BUB1 and MCM2, RMbase of RNA molecules, m6A is the most abundant internal and m6AVar were used for analysis, and it was found that chemical modification of mRNA, which has the greatest MAD2L1, BUB1 and MCM2 were hypomethylated by influence on its dynamic regulation (19-21). Ubiquitous m6A modification. Therefore, we speculate that the up- m6A modification plays a fundamental regulatory role in regulation of MAD2L1, BUB1 and MCM2 in MDSN may

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2019;8(6):2339-2349 | http://dx.doi.org/10.21037/tcr.2019.09.58 2348 Fang et al. Hypomethylation via m6A modification might cause BUB1, MAD2L1 and MCM2 up-regulation in MDSN be the result of m6A-modified hypomethylation. Ethical Statement: The authors are accountable for all aspects Shi et al. reported (24) that MAD2L1 was up-regulated of the work in ensuring that questions related to the accuracy in cancer tissue, elevated expression of MAD2L1 increased or integrity of any part of the work are appropriately the risk of cancer recurrence and caused a worse survival in investigated and resolved. The study was conducted in lung adenocarcinoma. Up-regulated MAD2L1 enhanced accordance with the Declaration of Helsinki (as revised the proliferation, invasion and migration of hepatocellular in 2013). The institutional ethical approval and individual carcinoma cells and inhibited apoptosis and cell cycle informed consent were waived due to the nature of the study. arrest (25). As for BUB1, it is elevated expressed in non- small cell lung cancer and has a poor prognosis (26) similar Open Access Statement: This is an Open Access article to prostate cancer (27). However, in gastric adenocarcinoma, distributed in accordance with the Creative Commons elevated BUB1 expression can predict prognosis well (28). Attribution-NonCommercial-NoDerivs 4.0 International In terms of MCM2, high expression of MCM2 leading a License (CC BY-NC-ND 4.0), which permits the non- worse prognostic in hepatocellular carcinoma (29), while commercial replication and distribution of the article with causing a better prognostic in cervical cancer (30). Although the strict proviso that no changes or edits are made and the MAD2L1, BUB1 and MCM2 up-regulation in various original work is properly cited (including links to both the cancers, they play opposite prognostic in different cancer. formal publication through the relevant DOI and the license). In this study, we revealed that up-regulation of MAD2L1, See: https://creativecommons.org/licenses/by-nc-nd/4.0/. BUB1 and MCM2 lead to a worse prognostic in liver hepatocellular carcinoma, while causing a better prognostic References in STAD, which provide a exact prognostic for clinical biomarkers. Not only that, we further estimate the survival 1. Xia LC, Bell JM, Wood-Bouwens C, et al. Identification rates in white and Asian people, we found in hepatocellular of large rearrangements in cancer genomes with barcode carcinoma, elevated MAD2L1, BUB1 and MCM2 function linked reads. Nucleic Acids Res 2018;46:e19. as the poorer prognostic in both races. However, in STAD, 2. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer elevated MAD2L1, BUB1 and MCM2 signify a better statistics 2018: GLOBOCAN estimates of incidence and prognostic only in Asian patients but not in white persons. mortality worldwide for 36 cancers in 185 countries. CA Chemotherapy resistance is one of the major obstacle in Cancer J Clin 2018;68:394-424. cancer treatment (31-34). We investigated the MAD2L1, 3. Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: BUB1 and MCM2 related anti-cancer drugs and found archive for functional genomics data sets--update. Nucleic I-threonine, enzymes, taxanes, paclitaxel, calcium, I-serine, Acids Res 2013;41:D991-5. cisplatin and vinblastine are closely related to BUB1, 4. Ryan M, Wong WC, Brown R, et al. TCGASpliceSeq MAD2L1 and MCM2. It is speculated that increased a compendium of alternative mRNA splicing in cancer. expression of BUB1, MAD2L1 and MCM2 can provide Nucleic Acids Res 2016;44:D1018-22. guidance for the use of these drug molecules. 5. Wang X, Li G, Luo Q, et al. Integrated TCGA analysis implicates lncRNA CTB-193M12.5 as a prognostic factor in lung adenocarcinoma. Cancer Cell Int 2018;18: 27. Acknowledgments 6. Neapolitan R, Horvath CM, Jiang X. Pan-cancer analysis Funding: This work was supported by Grants from the of TCGA data reveals notable signaling pathways. BMC National Natural Science Foundation of China (81472485) Cancer 2015;15:516. and the Project of the Wuxi Health and Family Planning 7. Cava C, Bertoli G, Colaprico A, et al. Integration of Commission (YGZXM14038). multiple networks and pathways identifies cancer driver genes in pan-cancer analysis. BMC Genomics 2018;19:25. 8. Estève PO, Zhang G, Ponnaluri VK, et al. Binding of 14- Footnote 3-3 reader proteins to phosphorylated DNMT1 facilitates Conflicts of Interest: The authors have completed the aberrant DNA methylation and gene expression. Nucleic ICMJE uniform disclosure form (available at http://dx.doi. Acids Res 2016;44:1642-56. org/10.21037/tcr.2019.09.58). The authors have no conflicts 9. Huang J, Yin P. Structural Insights into N(6)- of interest to declare. methyladenosine (m(6)A) Modification in the Transcriptome.

Genomics Proteomics Bioinformatics 2018;16:85-98. 24. Shi YX, Zhu T, Zou T, et al. Prognostic and predictive 10. Roundtree IA, Evans ME, Pan T, et al. Dynamic RNA values of CDK1 and MAD2L1 in lung adenocarcinoma. Modifications in Gene Expression Regulation. Cell Oncotarget 2016;7:85235-43. 2017;169:1187-200. 25. Li Y, Bai W, Zhang J. MiR-200c-5p suppresses 11. Wang S, Sun C, Li J, et al. Roles of RNA methylation proliferation and metastasis of human hepatocellular by means of N(6)-methyladenosine (m(6)A) in human carcinoma (HCC) via suppressing MAD2L1. Biomed cancers. Cancer Lett 2017;408:112-20. Pharmacother 2017;92:1038-44. 12. Tomczak K, Czerwinska P, Wiznerowicz M. The Cancer 26. Huang R, Gao L. Identification of potential diagnostic and Genome Atlas (TCGA): an immeasurable source of prognostic biomarkers in non-small cell lung cancer based knowledge. Contemp Oncol (Pozn) 2015;19:A68-77. on microarray data. Oncol Lett 2018;15:6436-42. 13. Cancer Genome Atlas Research Network. Comprehensive 27. Fu X, Chen G, Cai ZD, et al. Overexpression of BUB1B molecular characterization of gastric adenocarcinoma. contributes to progression of prostate cancer and predicts Nature 2014;513: 202-9. poor outcome in patients with prostate cancer. Onco 14. Rykunov D, Beckmann ND, Li H, et al. A new molecular Targets Ther 2016;9:2211-20. signature method for prediction of driver cancer pathways 28. Stahl D, Braun M, Gentles AJ, et al. Low BUB1 expression from transcriptional data. Nucleic Acids Res 2016;44:e110. is an adverse prognostic marker in gastric adenocarcinoma. 15. Zhang H, Liu T, Zhang Z, et al. Integrated Proteogenomic Oncotarget 2017;8:76329-39. Characterization of Human High-Grade Serous Ovarian 29. Liu Z, Li J, Chen J, et al. MCM family in HCC: MCM6 Cancer. Cell 2016;166:755-65. indicates adverse tumour features and poor outcomes 16. Molnár B, Galamb O, Peterfia B, et al. Gene promoter and promotes S/G2 cell cycle progression. BMC Cancer and exon DNA methylation changes in colon cancer 2018;18:200. development - mRNA expression and tumour mutation 30. Wang M, Li L, Liu J, et al. A gene interaction network alterations. BMC Cancer 2018;18:695. based method to measure the common and heterogeneous 17. Liu X, Chen X, Zeng K, et al. DNA-methylation- mechanisms of gynecological cancer. Mol Med Rep mediated silencing of miR-486-5p promotes colorectal 2018;18:230-42. cancer proliferation and migration through activation of 31. Chen J, Yang X, Huang L, et al. Development of dual-drug- PLAGL2/IGF2/beta-catenin signal pathways. Cell Death loaded stealth nanocarriers for targeted and synergistic anti- Dis 2018;9:1037. lung cancer efficacy. Drug Deliv 2018;25:1932-42. 18. Chen X, Liu L, Mims J, et al. Analysis of DNA 32. Huang H, Li T, Chen M, et al. Identification and methylation and gene expression in radiation-resistant validation of NOLC1 as a potential target for enhancing head and neck tumors. Epigenetics 2015;10:545-61. sensitivity in multidrug resistant non-small cell lung cancer 19. Ianniello Z, Fatica A. N6-Methyladenosine Role in Acute cells. Cell Mol Biol Lett 2018;23:54. Myeloid Leukaemia. Int J Mol Sci 2018. doi: 10.3390/ 33. Zeng L, Liao Q, Zou Z, et al. Long Non-Coding RNA ijms19082345. XLOC_006753 Promotes the Development of Multidrug 20. Golovina AY, Dzama MM, Petriukov KS, et al. Method for Resistance in Gastric Cancer Cells Through the PI3K/ site-specific detection of m6A nucleoside presence in RNA AKT/mTOR Signaling Pathway. Cell Physiol Biochem based on high-resolution melting (HRM) analysis. Nucleic 2018;51:1221-36. Acids Res 2014;42:e27. 34. Leonetti A, Assaraf YG, Veltsista PD, et al. MicroRNAs 21. Bartosovic M, Molares HC, Gregorova P, et al. N6- as a drug resistance mechanism to targeted therapies in methyladenosine demethylase FTO targets pre-mRNAs EGFR-mutated NSCLC: Current implications and future and regulates alternative splicing and 3'-end processing. directions. Drug Resist Updat 2019;42:1-11. Nucleic Acids Res 2017;45:11356-70. 22. Huang Y, Yan J, Li Q, et al. Meclofenamic acid selectively inhibits FTO demethylation of m6A over ALKBH5. Cite this article as: Fang K, Hu C, Zhang X, Guo Z, Li L. Nucleic Acids Res 2015;43:373-84. Screening of differential expressed genes from gene chip and 23. He L, Li J, Wang X, et al. The dual role of N6- sequencing data and evaluate its prognostic values in males with methyladenosine modification of RNAs is involved in digestive system neoplasms. Transl Cancer Res 2019;8(6):2339- human cancers. J Cell Mol Med 2018;22:4630-9. 2349. doi: 10.21037/tcr.2019.09.58

Table S1 Resources of TCGA and GEO dataset TCGA Tumor tissues (n) Normal tissues (n) GEO Tumor tissues (n) Normal tissues (n)

ESCA 139 8 GSE41258 185 54

STAD 241 22 GSE23400 53 50

LIHC 253 28 GSE3335 25 25

COAD and READ 343 23 GSE84402 14 14

A 80 60 12.5 50 50 10 60 40 40 7.5 40 30 30 5 20 20 20 10 2.5 0 10 0

Transcript per million Transcript 0 Transcript per million Transcript Transcript per million Transcript per million Transcript –20 –10 –2.5 0 Normal Male Normal Male Normal Male Normal Male (n=11) (n=157) (n=34) (n=268) (n=50) (n=245) (n=10) (n=90) B 125 125 30 125 100 100 25 100

75 75 20 75 15 50 50 50 10 25 25 25 5 0 0 0 0 Transcript per million Transcript Transcript per million Transcript Transcript per million Transcript Transcript per million Transcript –25 –25 –5 –25 Normal Male Normal Male Normal Male Normal Male (n=11) (n=157) (n=34) (n=268) (n=50) (n=245) (n=10) (n=90) C 175 100 50 100 150 80 40 80 125 60 30 100 60 75 40 20 40 50 20 10 25

Transcript per million Transcript 20 0 0 Transcript per million Transcript

0 per million Transcript per million Transcript –25 –20 –10 0 Normal Male Normal Male Normal Male Normal Male (n=11) (n=157) (n=34) (n=268) (n=50) (n=245) (n=10) (n=90)

Figure S1 MAD2L1, BUB1 and MCM2 high expression in tumour tissues than normal tissues in UALCAN database. MAD2L1 and Figure S2

high low Number atrisk Probability high low Number atrisk 0.2 0.4 0.6 0.8 1.0 0.0 Probability 0.0 0.2 0.4 0.6 0.8 1.0 MCM2 53 17510 85 2971 0 24 14610 66 3710210 Expression 0 Expression and high low high low Survival analysis of tend to have better prognosis in rectal adenocarcinoma, but no statistically significant difference has been found; (B) found; been has difference significant statistically no but adenocarcinoma, rectal in prognosis better have to tend 20 406080 20 40 60 80 MCM2 Time (months) Time (months) havenosignificantprognosticvalueinesophagealcancer. BUB1 BUB1 logrank P=0.17 HR=0.68 (0.39–1.19) logrank P=0.068 HR=0.18 (0.02–1.42) MAD2L1 100 120 , BUB1

Probability high low Number atrisk and high low

Number atrisk Probability 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 102 26610 36 2061 0 38 2511210 52 26510 0 MCM2 Expression Expression high low high low 20 406080 in rectumadenocarcinoma and esophageal carcinoma. (A) B 20 40 60 80 Time (months) Time (months) MAD2L1 MAD2L1 logrank P=0.06 HR=0.32 (0.09–1.1) logrank P=0.13 HR=1.58 (0.87–2.87) 100 120 high low Number atrisk

high low Number atrisk Probability Probability 0.0 0.2 0.4 0.6 0.8 1.0 0.8 1.0 0.0 0.2 0.4 0.6 40 25810 50 268210 0 50 920 88 371021 Expression 0 Expression high low 20 406080 high low 20 40 60 80 Time (months) Time (months) MCM2 MCM2 logrank P=0.33 HR=0.56 (0.17–1.84) logrank P=0.42 HR=1.26 (0.71–2.23) UB1, 100 120

MAD2L1 BUB1 ,