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Original Article

CXCL8 is a potential biomarker for predicting disease progression in gastric carcinoma

Wen-Qian Qi, Qian Zhang, Jiang-Bin Wang

Department of Gastroenterology, China-Japan Union Hospital, Jilin University, Changchun 130033, China Contributions: (I) Conception and design: WQ Qi, Q Zhang; (II) Administrative support: None; (III) Provision of study materials or patients: None; (IV) Collection and assembly of data: WQ Qi, Q Zhang; (V) Data analysis and interpretation: All authors; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. Correspondence to: Jian-Bin Wang; Qian Zhang. Department of Gastroenterology, China-Japan Union Hospital, Jilin University, Changchun 130033, China. Email: [email protected]; [email protected].

Background: To find potential biomarkers for predicting disease progression in gastric cancer (GC). Methods: An extensive bioinformatics study of the Cancer Genome Atlas (TCGA) and Oncomine datasets was conducted to define potential mRNA biomarkers for GC. The mRNA expression profiles of 375 GC and 32 neighboring noncancerous adrenal tissues were analyzed. The Oncomine database was used to validate the hub . The correlation between candidate hub expression and survival of GC patients was analyzed using the Kaplan-Meier method. Results: Ten differentially expressed genes were identified as hub genes, and CXCL8 was the only gene validated as being up-regulated in GC tissues compared to control tissues using both the TCGA and Oncomine databases. Immunofluorescence staining showed that CXCL8 was expressed in GC tissues, and its higher expression predicted worse relapse-free survival in GC patients. Conclusions: CXCL8 is a potential biomarker for predicting disease progression in GC.

Keywords: Gastric cancer; CXCL8; disease progression; diagnosis marker; prognosis value

Submitted Jul 04, 2019. Accepted for publication Nov 29, 2019. doi: 10.21037/tcr.2019.12.52 View this article at: http://dx.doi.org/10.21037/tcr.2019.12.52

Introduction improve prognosis of GC patients. However, because of the lack of specific symptoms and signs in early stages of GC, it Gastric cancer (GC) is one of the leading causes of cancer- is difficult to diagnose most patients. Exploring economic, related mortality worldwide (1), and has the second highest effective, and safe screening methods for the early diagnosis incidence rate of all cancers (2,3). As the most common of GC and predicting the prognosis of GC patients is malignancy in the digestive system, GC is a major public extremely important. health concern (4), especially in China which is among CXC chemokine 8 (CXCL8), which belongs to the family the countries with the highest incidence of GC (5-7). of CXC chemokines (C is cysteine, X refers to an arbitrary Although the etiology of GC has not been fully elucidated, ), is generated by many cells (10). CXCL8 has its pathogenesis is believed to be associated with extremely a chemotactic effect on specific white blood cells and can complicated processes that involve multiple factors (8). To be stimulated by phytohemagglutinin, lipopolysaccharide, a large extent, early diagnosis significantly determines the bacterial virus products, tumor necrosis factor-α (TNF-α), prognosis of GC, as the 5-year survival rate of early GC and interleukin (IL). Also known as IL-8, CXCL8 activates is more than 90%, while that of advanced GC is less than and promotes a series of inflammatory responses, such as 50% (9). Therefore, timely detection of GC-specific tumor chemotaxis and degranulation of neutrophils (11). CXCL8 markers can assist early diagnosis, reduce mortality rate, and is abnormally expressed in many tumors and is involved

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(2):1053-1062 | http://dx.doi.org/10.21037/tcr.2019.12.52 1054 Qi et al. Clinical value of CXCL8 expression in GC in tumor development (12). In addition to chemotaxis and genes (DEGs). Results were visualized using Cytoscape activation of immune cells, CXCL8 expression is closely software (19). A confidence score >0.4 was defined as related to the occurrence, development, and metastasis of significant. The hub genes were those that had a connectivity cancer cells in many tumors (13). However, the diagnostic degree over 20 in the PPI network. and prognostic values of CXCL8 in GC have not been extensively studied. Prognostic value of each hub gene In the current study, we explored potential prognostic predictors and therapeutic targets by systematically Based on the TCGA database, the RStudio-1.1.419 software analyzing the gene expression profiles of GC and control was used to investigate the impacts of the expression levels samples using the Cancer Genome Atlas (TCGA) database, of each hub gene on OS or DFS of GC patients. The log- and found that CXCL8 is up-regulated in GC. We further rank test and the Mantel-Cox test were used as hypothesis validated the CXCL8 expression pattern in the Oncomine tests. The cohort thresholds were adjusted and gene-pairs database, a cancer microarray database, and a web-based were used. data-mining platform (14). Our subsequent survival analysis suggests that CXCL8 is a potential biomarker for predicting Survival analyses progress of GC. The Kaplan plot univariate and Cox multivariate survival analyses were used to verify the correlation between gene Methods expression and clinicopathological parameters in GC Databases patients, as well as their clinical prognostic significance. The survival plots also contained the cox proportional hazard Data for the GC tissue (375 cases, GC group) and ratio and P=0.05 was set as the significance level. neighboring noncancerous adrenal tissues (32 cases, Control group) with complete clinical and survival information were downloaded from the TCGA database (https:// Immunohistochemistry (IHC) assay cancergenome.nih.gov/). The RStudio-1.1.419 software was The IHC assay data from GC and normal gastric tissues employed to analyze the differential expression levels between were downloaded from The Human Atlas (https:// the two groups. The Oncomine database (https://www. www.proteinatlas.org/ENSG00000169429-CXCL8/tissue/ oncomine.org) (14) was also selected to search the differential stomach). A polyclonal antibody against human CXCL8 expression levels of target genes between groups. Additionally, (HPA057179) was used for the IHC assay. the Kaplan-Meier Plotter database (http://kmplot. com/analysis/in­dex.php?p=service&cancer=gastric) (15) was used to generate the overall survival (OS) and disease- Statistical analysis free survival (DFS) curves. SPSS 22.0 software (IBM, Armonk, NY, USA) was used for statistical analysis. The Chi-square test and Fisher exact Functional and pathway enrichment analysis probability method were used to analyze the correlations between the expression levels of biomarkers and the The Kyoto Encyclopedia of Genes and Genomes (KEGG) clinicopathological characteristics of patients. Statistical Orthology Based Annotation System (https://david.ncifcrf. significance was defined as P<0.05. gov/), based on multiple databases including pathways, diseases, and (GO), was used to execute KEGG pathway enrichment analysis and GO enrichment Results analysis (16,17). Identification of DEGs between GC and healthy control samples Protein-protein interaction (PPI) network construction First, we performed gene expression profiling for 375 GC The RStudio Tool was used to generate PPI data (18). tissues and 32 normal gastric tissues, whose information was In addition, the database was employed to quantify the deposited in the TCGA database. After quality assessment interrelationships of analyzed differentially expressed and data preprocessing, such as a rank aggregation analysis

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(2):1053-1062 | http://dx.doi.org/10.21037/tcr.2019.12.52 Translational Cancer Research, Vol 9, No 2 February 2020 1055 of the expression matrices, we identified 1,313 statistically CXCL8 in the GC data in the Oncomine database. Twenty significantly altered genes, including 781 up-regulated and genes—AREG, HBEGF, IL6, IL1B, PTGS2, KLF6, FOS, 532 down-regulated genes, in GC samples compared to FOSB, MAP3KB, ETS2, FOSL2, LIF, PIM1, OSM, IL1RN, control tissues. The heatmap (Figure 1A) and Volcano plot CSF3, WEE1, CTGF, SERPINE1, and MLF1—correlated (Figure 1B) show the up-regulated DEGs (red) and down- with CXCL8 in GC (Figure 6). regulated DEGs (green).

Higher expression of CXCL8 in GC tissues predicts worse Functional and pathway DEG enrichment analysis survival

To gain further insight about the function of the identified We further validated CXCL8 protein expression in clinical DEGs, all DEGs were subjected to pathway enrichment samples from the tumor tissues of GC patients. As shown analysis (https://david.ncifcrf.gov/). The GO analysis in Figure 7, IHC analysis suggested medium expression of indicated that five biological processes were enriched: Go: both cytoplasmic and membranous CXCL8 in GC tissues. 0001580, Go: 0006508, Go: 0030216, Go: 0018149, and To reveal the clinical prognostic significance of CXCL8 Go: 0007586 (all P value <0.05) (Figure 2A). Furthermore, expression, we used the Kaplan plot univariate and Cox 19 significantly enriched KEGG pathways (hsa05204, multivariate survival analyses to verify the correlation hsa05033, hsa04975, hsa04974, hsa04972, hsa04970, between CXCL8 mRNA levels and clinicopathological hsa04911, hsa04727, hsa04530, sha04080, hsa04002, parameters in GC patients. The patients with lower CXCL8 sha00983, hsa00982, hsa00980, hsa00860, hsa00830, mRNA levels in the tumor tissues displayed significantly hsa00140, hsa000530, and hsa00040) were identified (all P (P<0.001) prolonged relapse-free survival (RFS) compared value <0.05). The top 5 most significantly enriched KEGG to the patients with higher CXCL8 mRNA levels in tumor pathways were as follows: hsa04972, protein digestion tissues (Figure 8). Therefore, CXCL8 is a potential biomarker and absorption; hsa00980, metabolism of xenobiotics by for predicting disease progress and prognosis in GC. cytochrome P450; hsa05204, chemical carcinogenesis; hsa00982, drug metabolism-cytochrome P450; and Discussion hsa00830, retinol metabolism (Figure 2B). There are more than ten immunohistochemical markers that can be used to clinically diagnose GC and predict the Identification of hub genes using PPI network analysis of prognosis of GC patients. However, markers with high DEGs specificity and sensitivity are still lacking (20). In this study, We constructed a PPI network using a cutoff confidence we conducted integrated bioinformatics analysis of gene >0.4 and degree of connectivity >20. We found 20 genes expression datasets in GC and control samples using the that were regarded as hub genes among the 1,313 DEGs TCGA and Oncomine databases, and screened hub genes (Figure 3). The top 10 hub genes—GNGT1, KNG1, GNG7, that might be involved in GC pathogenesis. We found ANXA1, GPR17, LPAR3, PPBP, CXCL8, NPY, and SST— that CXCL8 mRNA levels were increased in GC tissues were chosen for further analysis. compared to normal gastric tissues, and that GC patients with higher CXCL8 tumor expression had significantly shortened RFS. Thus, we conclude that CXCL8 is a potential Assessment of hub genes in the TCGA and Oncomine database novel biomarker that could be used for the early diagnosis of To assess the roles of the ten hub genes in GC, we validated GC and prognosis prediction of patients with GC. the gene expression using both the TCGA and Oncomine CXCL8 can contribute to the tumorigenesis and databases. CXCL8 was the only gene that was up-regulated pathogenesis of GC in many aspects. CXCL8 is in tumor tissues in both the TCGA and Oncomine predominantly secreted by macrophages and contributes to databases. In the comparison analysis, the mRNA levels of the immunosuppressive microenvironment by inducing PD- CXCL8 were also up-regulated in multiple cancer types L1+ macrophages in GC (10). Consistent with our conclusion, (Figure 4) and in cancer tissues from different cohorts CXCL8 inhibitors have been reported to promote an anti- of patients with GC (Figure 5) compared to controls. In tumor response, providing potential therapeutic effects addition, we analyzed genes that potentially correlated with for patients with GC (10). In addition, CXCL8 derived

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(2):1053-1062 | http://dx.doi.org/10.21037/tcr.2019.12.52 1056 Qi et al. Clinical value of CXCL8 expression in GC

A

Volcano plot B

5

Sig Down 0 Not

Log2FC Up

–5

0 40 80 120 –Log10 (FDR)

Figure 1 Identification of the DEGs between GC and healthy control samples. (A,B) Gene expression profiling was performed in the TCGA database-derived 375 GC tissues and 32 normal gastric tissues: (A) a hierarchical clustering heatmap of the DEGs screened on the basis of |FC| >2.0 and a corrected P value <0.05. Red indicates that expression of the indicated gene is relatively up-regulated; green indicates that expression of the indicated gene is relatively down-regulated; black indicates no significant changes in gene expression; gray indicates that the signal strength of the indicated gene is not strong enough to be detected; (B) DEG data between two sets of samples. The red points represent up-regulated genes defined on the basis of |FC| >2.0 and a corrected P value <0.05. The green points represent down-regulated genes defined on the basis of |FC| >2.0 and a corrected P value <0.05. The black points represent genes with no significant difference in expression levels. DEG, differentially expressed gene; GC, gastric cancer; FC, fold change.

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(2):1053-1062 | http://dx.doi.org/10.21037/tcr.2019.12.52 Translational Cancer Research, Vol 9, No 2 February 2020 1057

A

GO: 0001580

GO: 0006508

GO: 0030216 Term

GO: 0018149

GO: 0007586

0 20 40 60 Count

B hsa05204: Chemical carcinogenesis

hsa05033: Nicotine addiction

hsa04975: Fat digestion and absorption

hsa04974: Protein digestion and absorption

hsa04972: Pancreatic secretion

hsa04970: Salivary secretion Percent hsa04911: Insulin secretion 1.0

hsa04727: GABAergic synapse 1.5 2.0 hsa04530: Tight junction

hsa04080: Neuroactive ligand-receptor interaction –log 10 (P value) Term 7 hsa04022: cGMP-PKG signaling pathway 6 5 hsa00983: Drug metabolism - other enzymes 4 hsa00982: Drug metabolism - cytochrome P450 3 2 hsa00980: Metabolism of xenobiotics by cytochrome P450

hsa00860: Porphyrin and chlorophyll metabolism

hsa00830: Retinol metabolism

hsa00140: hormone biosynthesis

hsa00053: Ascorbate and aldarate metabolism

hsa00040: Pentose and glucuronate interconversions

10 15 20 25 30 Count Figure 2 GO and KEGG analysis of the DEGs in GC. (A) The top 5 significantly enriched terms using GO enrichment analysis of the DEGs in GC; (B) the top 10 significantly enriched pathways using KEGG pathway analysis of the DEGs in GC. GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEG, differentially expressed gene; GC, gastric cancer.

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(2):1053-1062 | http://dx.doi.org/10.21037/tcr.2019.12.52 1058 Qi et al. 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interacts with interacts with LPAR3 interacts with interacts with MATN3 interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with AFP interacts with interacts with TF interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with TAS2R43 interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with TAS2R40 interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with CXCL11 interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with DAB2 interacts with interacts with interacts with interacts with P2RY14 MET interacts with interacts with interacts with interacts with interacts with interacts with SLC18A3 interacts with interacts with interacts with interacts with interacts with CXCL5 interacts with interacts with interacts with TAS2R39 interacts with SST interacts with MTTP interacts with interacts with interacts with LIPC interacts with SH3GL2 IGF2 SUCNR1 MFI2 KLK3 CBX2 RBP2 APOA2 CXCL8 TAS2R30 interacts with BAMBI interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with DAB1 interacts with NRXN3 TRIB3 NRXN1 Analysis of the PPI network reveals the hub genes among the DEGs in gastric cancer. Under the cutoff confidence >0.4 and degree of connectivity >20, a PPI interacts with CREB3L3 MBTPS2 ATP6V0A4 interacts with SLC32A1 LIPF AP4B1 EPHB2 RBP4 interacts with RDH12 interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with interacts with GAD1 IRS4 ALPI interacts with PSCA LY6H interacts with PCGF1 interacts with KLK13 AKR1B10 interacts with AKR1C1 LYPD8 interacts with NEGR1 Figure 3 network was constructed; 20 genes were regarded as hub genes among the 1,313 DEGs in GC. PPI, protein-protein interaction; DEGs, differentially expressed genes; GC, gastric cancer. interacts with ALPPL2 PRSS21 XPNPEP2 interacts with EPHA7 BAAT PAH CES5A CYP3A4 interacts with ADH4 AKR1C2 interacts with ALDH3A1

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(2):1053-1062 | http://dx.doi.org/10.21037/tcr.2019.12.52 Translational Cancer Research, Vol 9, No 2 February 2020 1059

Cancer Cancer vs. Cancer vs. Normal Analysis type by cancer Cancer histology Multi-cancer Bladder cancer Brain and CNS cancer Breast cancer Cervical cancer Colorectal cancer Esophageal cancer Gastric cancer Head and neck cancer Kidney cancer Leukemia Liver cancer Lung cancer Lymphoma Melanoma Myeloma Other cancer Ovarian cancer Pancreatic cancer Sarcoma Significant unique analyses Total unique analyses

Figure 4 The mRNA levels of CXCL8 in different tumor types. This graphic shows the numbers of datasets with statistically significant mRNA over-expression (red) or down-regulation (blue) of the target gene (cancer vs. normal tissue). The P value threshold is 0.01. The number in each cell represents the number of analyses that meet the threshold within those analyses and cancer types. The gene rank was analyzed by calculating the percentile of target genes in the top of all genes measured in each study. Cell color is determined by the best gene rank percentile for the analyses within the cell. from tumors can activate endothelial cells in tumor vessels, than that of parental cell lines (26). One study demonstrated promote angiogenesis, and induce neutrophil chemotaxis to that the proliferation rate of ovarian cancer epithelial cell allow immune cell infiltration into the tumor site (21,22). lines with high expression of CXCL8 was significantly faster Therefore, CXCL8 can both promote the invasion and compared to cell lines with low CXCL8 expression (26). migration of cancer cells and induce macrophages to secrete Furthermore, the expression level of CXCL8 in the PC- additional growth factors, both of which further increase the 3M-LN4 cell line with high metastasis capability was proliferation and invasion rate of cancer cells. Therefore, significantly higher compared to the PC-3P cell line with inhibition of CXCL8 in the tumor microenvironment has low metastasis capability. Overexpression of CXCL8 could significant potential for tumor treatment( 23). significantly promote the tumorigenicity, metastasis, and CXCL8 is a potential biomarker for predicting tumor angiogenesis of PC-3P cells in nude mice (27). Taken progression in other malignancies. For example, CXCL8 together, these studies indicate a pervasive role of CXCL8 is continuously expressed in melanoma and is up-regulated in promoting tumorigenesis, metastasis, and tumor survival. in melanoma cells with high metastasis potential (24). In This study has some limitations. First, the TCGA breast cancer, CXCL8 has been identified as a key factor and Oncomine databases provide data at the RNA level, involved in metastasis (25). In human ovarian cancer cell which may not accurately reflect protein expression lines with high metastasis, CXCL8 expression was higher demonstrated by traditionally pathological IHC. Therefore,

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Oncomine dataset 10.0 6.5 6.0 5.5 5.0 5.0 4.5 0.0 4.0 3.5 3.0 –5.0 2.5 2.0 1.5 –10.0 Log2 median-centered intensity Log2 median-centered Log2 median-centered intensity Log2 median-centered 1.0 0.5 –15.0 0.0

Control Gastric cancer Control Gastric cancer

6.5 6.5 6.0 6.0 5.5 5.5 5.0 5.0 4.5 4.5 4.0 4.0 3.5 3.5 3.0 3.0 2.5 2.5 2.0 2.0 1.5 1.5

Log2 median-centered intensity Log2 median-centered 1.0 intensity Log2 median-centered 1.0 0.5 0.5 0.0 0.0

Control Gastric cancer Control Gastric cancer

Median rank P value Gene 182.0 1.12E-6 IL8

Figure 5 Analyses on the mRNA levels of CXCL8 in Oncomine database-derived GC and control samples. Box plots derived from gene expression data in the Oncomine database compare the expression of CXCL8 mRNA in normal and GC tissues from different cohorts. The P value was set at 0.01 and fold change was defined as 2. GC, gastric cancer.

Figure 6 Correlation analysis of CXCL8 gene expression in Oncomine database-derived GC samples. The name of CXCL8 correlated genes and the correlation index are shown. GC, gastric cancer.

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Figure 7 The protein level of the selected hub gene, CXCL8, in gastric tissues from The Human Protein Atlas. Representative images show the immunohistochemistry analysis for CXCL8 protein expression in gastric tissues. Data were down-loaded from The Human Protein Atlas (https://www.proteinatlas.org/ENSG00000169429-CXCL8/tissue/stomach).

100 Low High of CXCL8 for diagnosis and prognosis prediction of GC N=261 N=261 patients, as well as targeting CXCL8 for GC therapy. 80

60 Acknowledgments Funding: None. 40 % Surviving

20 Footnote

0 Conflicts of Interest: All authors have completed the ICMJE 0 1000 2000 3000 4000 5000 uniform disclosure form (available at http://dx.doi. Day org/10.21037/tcr.2019.12.52). The authors have no conflicts Figure 8 Higher mRNA levels of CXCL8 in GC tissues predicts of interest to declare. worse survival in GC patients. Based on the mRNA levels of CXCL8 in GC tissues, patients with GC were categorized into Ethical Statement: The authors are accountable for all two groups: CXCL8-high (n=261) and CXCL8-low (n=261). Low aspects of the work in ensuring that questions related mRNA levels of CXCL8 were associated with longer relapse-free to the accuracy or integrity of any part of the work are survival (RFS) in all patients with GC. GC, gastric cancer. appropriately investigated and resolved.

Open Access Statement: This is an Open Access article the bioinformatics data shown in this study cannot fully distributed in accordance with the Creative Commons represent the actual protein expression of CXCL8. The Attribution-NonCommercial-NoDerivs 4.0 International clinical value of CXCL8 in GC should be analyzed using License (CC BY-NC-ND 4.0), which permits the non- IHC in future studies. Secondly, this study mainly excavates commercial replication and distribution of the article with the related data and analysis based on the information the strict proviso that no changes or edits are made and provided by online databases; thus, our conclusion needs to the original work is properly cited (including links to both be further validated using relevant, actual clinical research the formal publication through the relevant DOI and the results. Despite these limitations, we believe our work license). See: https://creativecommons.org/licenses/by-nc- provides complementary evidence on the potential use nd/4.0/.

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© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(2):1053-1062 | http://dx.doi.org/10.21037/tcr.2019.12.52