Integrated bioinformatics analysis to identify key genes related to the prognosis of esophageal squamous cell carcinoma

Introduction

Esophageal cancer presents a serious threat to human health and life. In 2018, there were an estimated 572,034 new cases of esophageal cancer and 508,585 deaths, which saw this disease rank 7^th for incidence and 6^th for mortality out of all malignant tumors (1). In East Asia, esophageal squamous cell carcinoma (ESCC) is the major pathological type of esophageal cancer (2). Despite advancements in surgery, radiotherapy techniques, and systematic treatments, the prognosis of ESCC is still poor, with the 5-year survival rate standing at around 15% (3). Together with primary prevention, the promotion of early cancer screening programs, and the development of innovative therapy strategies, a comprehensive understanding of the genetic changes of ESCC would aid in improving the prognostic outlook for patients. Acquiring in-depth knowledge of the pathogenesis, progression, and prognosis of ESCC would help to deepen our perceptions of the disease, stratify patients by prognosis, and provide the foundation for precision medicine and targeted therapies. However, previous reports on ESCC genetic susceptibility genes have been inconclusive (4-8). More investigations on genetic alterations are needed to improve our understanding of ESCC and explore its underlying mechanisms; specifically, analysis of differentially expressed genes (DEGs) is an effective approach to identifying genes of interest in the disease.

Gene chip is a reliable and effective technique to quickly identify DEGs (9). DEGs identified using this technique can be deposited in public databanks for future reference. Access to such open data and integrated information can provide researchers with worthwhile hints for novel study ideas.

To identify genetic factors relating to the pathogenesis and prognosis of ESCC, and to explore the potential mechanisms, we conducted an integrated bioinformatics analysis based on the National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) microarray database. Unlike the previous studies (4,5), we selected microarray datasets based on the same gene chip to reduce heterogeneity generated from experimental tests. Also, we selected gene profiles with no restrictions on patients’ race, which might make our conclusion more generalizable. We present the following article in accordance with the MDAR checklist (available at http://dx.doi.org/10.21037/tcr-20-3220).

Methods

Microarray profiles

Six gene expression profiles were collected from that NCBI-GEO microarray database: GSE77861 (African Americans), GSE26886 (Germans), GSE17351 (Americans), and GSE100942, GSE45670, and GSE33810 (Chinese). Each of the 6 microarray profiles contained both ESCC and normal esophageal epitheliums tissues, and all expression data were built on GPL570 platform (HG-U133_Plus_2; Affymetrix Human Genome U133 Plus 2.0 Array).

Data processing of DEGs

GEO2R online tools were applied to identify DEGs between ESCC and normal esophageal epithelial tissues (10). The filter criteria included |logFC|>0 and adjusted P<0.05. Two Chinese datasets, GSE100942 and GSE33810, were excluded from further analysis, due to having an adjusted P value of ≥0.05. Finally, GSE77861, GSE26886, GSE17351, and GSE45670, including 49 cancer tissues and 41 normal tissues, were included in the genetic analysis. Common DEGs among the 4 microarray datasets were determined using a Venn diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/). DEGs were considered to be upregulated if logFC >0, and downregulated if logFC <0.

PPI network analysis

The protein–protein interaction (PPI) network of the identified DEGs was constructed by estimating the probable interactions between the DEGs using the online instrument Search Tool for the Retrieval of Interacting Genes (STRING) in Cytoscape under the conditions of maximum number of interactions 0 and confidence score ≥0.4 (11,12). The Cytoscape Molecular Complex Detection app was applied to build modules of the PPI network, with the degree cutoff set at 2, maximum depth at 100, k-core at 2, and node score cutoff at 0.2.

Gene Ontology (GO) and pathway enrichment analysis

Based on high-throughput transcriptomic or genomic data, we used the bioinformatics tool Database for Annotation, Visualization and Integrated Discovery (DAVID) to conduct a GO analysis of hub DEGs to analyze their genetic features, including molecular functions (MFs), cellular components (CCs), and biological processes (BPs) (Bonferroni-corrected P<0.05/counts) (13,14). DAVID was also employed to visualize Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways (P<0.05) (15,16).

Survival analysis

Associations between the identified hub genes and ESCC survival in public genome databases, including the European Genome-phenome Archive (EGA), The Cancer Genome Atlas (TCGA), and GEO (Affymetrix microarrays only), were evaluated using the online Kaplan-Meier plotter (17). The results were presented as hazard ratios (HRs) with 95% confidence intervals (CIs) and as the log-rank P value. Because of the large number of genes to be tested, we applied both Bonferroni-corrected P<0.05/the number of genes and false discovery rate (FDR) <0.2 to adjust for multiple comparisons.

This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). All data used in our study are available in public databases, including the NCBI-GEO microarray database, EGA, and TCGA. Individual consent for this analysis was waived.

Results

A total of 2,720 upregulated genes and 2,725 downregulated genes were obtained from GSE77861 (African Americans); 15,133 upregulated genes and 6,263 downregulated genes were obtained from GSE26886 (Germans); 567 upregulated genes and 301 downregulated genes were obtained from GSE17351 (Americans); and 6,428 upregulated genes and 5,572 downregulated genes were obtained from GSE45670 (Chinese). The DEGs identified from 49 cancer tissues and 41 normal matching tissues in the above 4 microarray profiles were compared. After the exclusion of unknown genes, 332 common DEGs, including 220 upregulated genes and 112 downregulated genes, were identified (Figure 1 and Table 1).

Figure 1 Common DEGs in the 4 microarray datasets via Venn diagram. Blue represents GSE77861, pink represents GSE45670, green represents GSE26886, and yellow represents GSE17351. (A) A total of 221 upregulated DEGs (included 1 unknown gene) and (B) 112 downregulated DEGs. DEGs, differentially expressed genes.

STRING was used to identify the nodes with the most connections as core function genes (Figure S1). As shown in Figure 2, there were 40 hub genes, all of which were upregulated.

Figure 2 The 40 most connected nodes in module analysis via Cytoscape. Each blue square represents a gene, and the connected lines represent interactions between genes.

The 40 core DEGs were subjected to GO and KEGG pathway enrichment analyses. In terms of BPs, the DEGs were significantly enriched in cell division, mitotic nuclear division, DNA replication and sister chromatid cohesion, chromosome segregation, DNA replication initiation, and G1/S transition of mitotic cell cycle. In regard to CCs, the DEGs were significantly enriched in the nucleoplasm, nucleus, chromosome, centromeric region, midbody, and spindle midzone. In terms of MFs, the DEGs were mostly involved in ATP binding, protein binding, ATP-dependent microtubule motor activity, plus-end-directed, and chromatin binding (Table 2).

The KEGG pathway enrichment analysis revealed that the DEGs were mostly involved in the DNA replication and cell cycle pathways. The implicated genes included replication factor C subunit 4 (RFC4), minichromosome maintenance complex component 2 (MCM2), MCM5, MCM6, cell division cycle 6 (CDC6), and TTK protein kinase (TTK) (Figure 3A,B; Table 3).

Figure 3 The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the 40 core differentially expressed genes in esophageal squamous cell carcinoma. (A) KEGG pathway enrichment of hsa03030: DNA replication. Involved genes were replication factor C subunit 4 (RFC4), minichromosome maintenance complex component 2 (MCM2), MCM5, MCM6. (B) KEGG pathway enrichment of hsa04110: cell cycle. Involved genes were cell division cycle 6 (CDC6), TTK protein kinase (TTK), MCM2, MCM5, and MCM6.

Finally, we conducted a survival analysis of 39 identified core DEGs, as PICALM interacting mitotic regulator (FAM64A) was excluded due to a lack of survival data. With P<0.05 indicating statistical significance, 15 genes were found to be statistically significantly related to survival and are shown in Figure 4. The survival curves of the other 24 genes are presented in Figure S2.

Figure 4 Kaplan-Meier plots of the 15 genes with statistical significance (P<0.05). Gene name is shown at the top of each figure part. Black line represents low expression of the identified gene, and red line represents high expression of the identified gene. Corresponding life table, hazard ratio, and log-rank P value are shown in each figure part.

The GO analysis of the 15 significant genes revealed that the most enriched BP, CC, and MF were cell division, nucleoplasm, and ATP binding, respectively (Table 4). KEGG pathway enrichment analysis again showed enrichment mainly in the cell cycle pathway (Figure 3B and Table 5).

For multiple comparisons, we applied a Bonferroni correction of 0.05/39=0.001282 as Bonferroni-adjusted statistical significance. We also applied FDR <0.20 as a second reference. Eventually, 2 genes were identified as being significantly related to survival: kinesin family member 18A (KIF18A) (HR =0.24, 95% CI: 0.10–0.59, log-rank P=0.00072, FDR =0.10) and TTK (HR =0.20, 95% CI: 0.07–0.55, log-rank P=0.00057, FDR =0.10). Patients with a high expression of these genes had a better prognosis than those with a low expression.

Discussion

By performing a gene enrichment bioinformatics analysis of ESCC which did not factor in race, we identified 332 common DEGs from 4 microarray datasets (GSE77861, GSE26886, GSE17351, and GSE45670). Through module analysis, we identified 40 upregulated genes as core function genes, and the most involved pathways were the DNA replication and cell cycle pathways, which supported our previous knowledge of carcinogenesis. In the prognostic analysis, we identified 15 genes associated with survival. For these survival-related genes, the most enriched BP, CC, and MF were cell division, nucleoplasm, and ATP binding, respectively, and the most enriched pathway was the cell cycle pathway. Furthermore, for multiple comparisons, we applied the Bonferroni correction and FDR, and identified 2 genes, KIF18A (HR =0.24, 95% CI: 0.10–0.59, log-rank P=0.00072, FDR =0.10) and TTK (HR =0.20, 95% CI: 0.07–0.55, log-rank P=0.00057, FDR =0.10), as prognosis-related genes.

Although our findings present associations between KIF18A/TTK and ESCC development and prognosis, the exact mechanisms still are unclear, especially as research on these aspects of ESCC is lacking. A number of in vitro studies have explored the functional role of TTK in cancer, and have discovered the major pathways involved to be the TTK–AKT–mechanistic target of rapamycin kinase (mTOR) pathway in hepatocellular carcinoma and TTK/AKT interaction with B-Raf/mitogen-activated protein kinase 1 (MAPK1) signals in melanoma (18,19). KIF18A, KIF18B, and KIF19 are kinesin-8 family members. KIF18A together with dyneins, that are as the key component of canonical plus-end stepping in the mitotic spindle and the depolymerization of microtubules, is primarily involved in mitotic chromosome alignment, maintenance of chromosomal stability, and cell division. It also acts as the major driving force for cell migration (20-26). KIF18A dysregulation may give rise to genomic instability (27), eventually resulting in neoplasm development and metastatic progression (28).

Earlier studies have shown that the overexpression of human KIF18A is related to a poor prognosis of colorectal cancer (20), invasive breast cancer (29), and primary hepatocellular carcinoma (30). However, studies on KIF18A in ESCC are scarce. To the best of our knowledge, the present study is the first to reveal the relationship of KIF18A with the prognosis of ESCC. Taken together, previously published findings (20,29,30) and the results of the present study imply that KIF18A is a possible prognostic predictor; however, the molecular mechanisms need further elucidation.

In terms of treatment, the mechanism and molecules relevant to mitosis are well-established targets for microtubule-binding chemotherapeutic agents, such as taxol and vinca alkaloids (31), which is consistent with the fact that regimens containing paclitaxel are the preferred chemotherapeutic treatment for ESCC in Chinese clinical practice (32). Additionally, mitotic kinesins have also been considered as potentially valuable targets in the development of anticancer drugs (33).

TTK is a cancer/testis antigen (CTA) and an HLA-A2402-restricted epitope peptide (34). As well as in the testes, early-evolving embryos, the thymus, and placentas, the expression of CTA can be detected in various malignant tumors, and it is rarely detected in benign tissues (35,36). TTK participates in cell proliferation and migration through the phosphorylation of multiple amino acids, such as serine, threonine, and tyrosine hydroxyamino acids, and functions to maintain genomic integrity through regulating the spindle assembly checkpoint (35,37-39).

Previous studies have revealed the associations between TTK overexpression and the growth of pancreatic ductal adenocarcinoma (40), hepatocellular carcinoma (18), breast cancer (41), and melanoma (19). A previously published study found that TTK was a key component in ESCC progression and predicted a poor prognosis, although the molecular mechanisms were unclear (4).

TTK inhibitors suppress the activity of monopolar spindle 1 kinase, specifically deactivate the spindle assembly checkpoint, which results in chromosome separation dysfunction, heteroploidy, and ultimately, cell death. Thus, TTK inhibition has arisen as a promising therapeutic strategy for triple-negative breast cancer and malignant mesothelioma (37,42-46). Furthermore, the testis is an immunoprivileged organ, and CTAs are ideal candidates as tumor-related antigens (47). TTK has emerged as a valuable target for tumor immunotherapy, including cancer vaccines, chimeric antigen receptor T-cell immunotherapy, and immune checkpoint inhibitors, due to its cancer-specific expression and strong in vivo immunogenicity, together with its critical roles in cell proliferation and migration, and spindle assembly checkpoint regulation (42,47,48). In several pharmaceutical clinical trials on ESCC, TTK expression was reported as both a marker for the treatment response and a potential cancer vaccine target (49,50).

Our study has several limitations. First, a previous published study conducted using the laiurger microarray dataset (GSE38129 and GSE20347) recognized that the TPX2 microtubule nucleation factor, cyclin dependent kinase 1, and centrosomal protein 55 were associated with relapse-free survival (51). Instead, we selected another microarray dataset built on GPL570 platform, including diverse races, and chose overall survival as our study endpoint rather than relapse-free survival, which resulted in our conclusion being inconsistent with those of earlier studies. However, the discrepancy between our research and previous research revealed that the bioinformatics analysis results were unstable and emphasized the significance of molecular mechanism studies. Second, the survival data on ESCC were limited, and the small sample size reduced the reliability of the results; therefore, larger sample sizes are required in future research. Third, for both KIF18A or TTK, molecular mechanism studies were limited and none of them focused on ESCC. It should be recognized that gene enrichment analysis can only provide clues, and our findings should be further verified by biological experiments. Finally, bioinformatics analysis is a growing field. New analyses and gene expression microarray studies are in development, which might affect our conclusions. The challenge for researchers performing bioinformatics analyses is to integrate up-to-date analytical methods and datasets accordingly.

In conclusion, our analysis was the first to identify KIF18A related to ESCC prognosis and verified the previous association between TTK and ESCC survival. KIF18A is a key component of mitotic activity, and TTK is a good candidate for tumor immunotherapy. The results of the present study were in accordance with those of studies on the clinical use of paclitaxel and also shed light on directions for future anticancer immunotherapy research and development. However, our conclusions need to be further validated in studies with larger sample sizes and biological experiments.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the MDAR checklist. Available at http://dx.doi.org/10.21037/tcr-20-3220

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tcr-20-3220). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). All data used in our study were available in public database, and individual consent for this analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editors: R. Scott and J. Reynolds)