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Abstract

BACKGROUD:

Hepatocellular carcinoma (HCC) is characterized by occult onset, rapid progression and poor prognosis. CXC chemokines play an important role in tumor microenvironment and development.

OBJECTIVE:

The potential mechanistic values of CXC chemokines as clinical biomarkers and therapeutic targets in HCC have not been fully clarified.

METHODS:

ONCOMINE, UALCAN, GEPIA, cBioPortal, SurvExpress, MethSurv, SurvivalMeth, String, GeneMANIA, DAVID, Metascape, TRRUST, LinkedOmics, and Timer were applied in this study.

RESULTS:

The transcriptional levels of CXCL9/16/17 in HCC tissues were significantly elevated while CXCL1/2/5/6/7/12/14 were significantly reduced. Significant correlation was found between the expression of CXC3/5 and the pathological stage of HCC patients. High level of CXCL4 was associated with a longer disease-free survival. For overall survival, lower expressions of CXCL1/3/5/8 and higher expressions of CXCL2 were associated with a better outcome. In addition, the prognostic values of CXC chemokines signature in HCC were explored in four independent cohorts, the high-risk group displayed unfavorable survival outcome compared with the low-risk group. And for the prognostic value of the DNA methylation of CXC chemokines, we identified the CpGs which were significantly associated with prognosis in HCC patients. DNA methylation signature analysis also showed a statistically significant association between the high- and low-risk groups. For potential mechanism, the neighbor gene networks, interaction analyses, functional enrichment analyses of CC chemokine receptors in HCC were performed, the transcription factor targets, kinase targets, and miRNA targets of CXC chemokines were also identified in HCC. We also found significant correlations among CXC chemokines expression and the infiltration of immune cells, the tumor infiltration levels among HCC with different somatic copy number alterations of these chemokine receptors were also assessed. Moreover, the Cox proportional hazard model showed that CCR2/6/8/12, B cell, macrophage and dendritic cell were significantly related to the clinical outcome of HCC patients.

CONCLUSION:

CXC chemokines might serve as therapeutic targets and prognostic biomarkers in HCC.

Keywords

CXC chemokines HCC prognostic biomarker therapeutic target

1. Introduction

Inflammation is considered to be the seventh major feature of tumor [1]. Tumor-associated inflammation is closely related to the occurrence and development of tumors. Hepatocellular carcinoma (HCC) is one of the malignant tumors with high incidence rate and mortality rate [2, 3]. According to the Surveillance, Epidemiology, and End Results (SEER) Program provided information on cancer statistics, the rate of new cases of liver and intrahepatic bile duct cancer was 9.0 per 100,000 men and women per year. The death rate was 6.6 per 100,000 men and women per year [4]. There is an estimated 42,230 new cases, and 30,230 deaths from U.S. mortality in 2021. Tumor invasion and metastasis is a series of complex, multi-step and multi-factor interaction between tumor cells, host and tumor microenvironment [5]. The interaction between tumor cells and different immune/inflammatory cells and stroma in tumor microenvironment has an important influence on the metastasis and recurrence of HCC [6]. Further study of the inflammatory microenvironment is of great value for the diagnosis and treatment of HCC.

As a small secretory protein, chemokines can regulate the migration and localization of immune cells in tissues. Recent studies have shown that chemokines play an important role in tumor progression and metastasis. For us humans, a total of 16 CXC chemokines (CXCL1-17, CXCL15 is specific to mice) were found to play key roles in physiological and pathological processes [7, 8]. Previously, we have investigated the effects of macrophages on HCC proliferation and metastasis through CXCL8, and found that cancer cells and macrophages interaction promoted cancer cell proliferation and metastasis through the up-regulation of CXCL8/miR-17 cluster [9]. Recently, Yan et al. revealed that the interferon regulatory factor 1 contributes to the anti-tumor microenvironment in HCC through CXCL10/CXCR3 axis [10]. Nie et al. have demonstrated that CXCL5 is a potential prognostic marker of HCC and provided clues about immune infiltration [11]. Liu et al.’s research indicated that cancer associated fibroblast derived CXCL11 regulates HCC cell migration and metastasis through the circUBAP2/miR-4756/IFIT1/3 axis [12]. Wang et al. provided new evidence that CXCL17 enhanced malignant invasion and inhibited autophagy through the LKB1-AMPK pathway in HCC [13]. Thus in HCC microenvironment, a variety of chemokines and their receptors shape a unique cross-talk mechanism between immune and tumor cells.

There is a lot of evidence that chemokines play an anti-tumor role in guiding immune cells to gather into tumor microenvironment. At the same time, there are many conclusions that chemokines can also induce immune cells to reshape the tumor microenvironment and promote the progress of cancer [14, 15]. In this study, based on several large public bioinformatics databases, an in-depth and comprehensive analysis of the potential values of CXC chemokines as clinical markers and immunotherapeutic targets in HCC was performed, thus to provide additional data to help clinicians choose appropriate treatment drugs and predict the long-term prognosis of patients with HCC more accurately.

2. Materials and methods

HCC samples of all online platform databases are: GTEx normal (110 cases), TCGA adjacent to cancer (50 cases) and TCGA tumor (371 cases).

2.1 GEPIA

GEPIA (Gene Expression Profiling Interactive Analysis), a web server for cancer and normal gene expression profiling and interactive analyses developed at Peking University (http://gepia.cancer-pku.cn/index.html) [16]. In our study, “Single Gene Analysis” module of GEPIA was used to analyze the differential mRNA expression of both tumor and para-tumor/normal tissues, pathological stage and corresponding prognostics associated with CXC chemokines. Based on the “LIHC” dataset, “Multiple Gene Comparison” module was applied to compare gene association in CXC chemokine. R software was used in the background server. $P$ value of 0.05 was set as the significance threshold. Student’s $t$ test was used for gene expression or pathological staging analysis. Kaplan-Meier curve was used to analyze the prognosis.

2.2 Oncomine

The Oncomine ${}^{\text{TM}}$ Platform (www.oncomine.org) is a web application as well as a translational bioinformatics service, RNA/DNA-seq data from the Gene Expression Omnibus (GEO), the Tumor Cancer Genome Atlas (TCGA) and published literatures are integrated [17]. Oncomine provides solutions and analyses based on the gene expression signatures, clusters and gene-set modules. In this study, we extracted relevant data to evaluate the expression of CXC chemokines in HCC. Among them, the significant difference threshold was set at $P=$ 0.05, multiple change was 2, and the gene ranked in the top 10%. Student’s $t$ test was used to analyze the difference of CXC chemokines expression in HCC.

2.3 UALCAN

UALCAN (http://ualcan.path.uab.edu/analysis.html) is a comprehensive, user-friendly, and interactive web resource for analyzing cancer OMICS data, allows researchers to gather valuable information and data about the genes/targets of interest [18]. In this study, based on the “LIHC” dataset and “Expression Analysis” module, CXC chemokine expression data were extracted. Student’s $t$ test was applicated and $P$ value was set at 0.05.

2.4 cBioPortal

cBioPortal (www.cbioportal.org) integrates the data of 126 tumor genome studies, including large-scale tumor research projects such as TCGA and ICGC, covering 28,000 samples [19]. cBioPortal is widely used to explore, visualize and analyze multidimensional cancer genome data. 184 PDAC samples (TGCA, PanCancer Atlas), genetic alteration, and co-expression module of CXC chemokine were achieved and analyzed. mRNA expression $z$ score (RNA Seq V2 RSEM) and protein expression $z$ score (RPPA) were generated by a significant threshold. $z$ was set at $\pm$ 2.0.

2.5 GeneMANIA

GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays [20]. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity.

2.6 STRING

STRING (https://string-db.org/) is a database of known and predicted protein-protein interactions (PPI) [21]. We performed the CXC chemokine differential expression PPI network analysis based on the computational prediction, the knowledge transfer between organisms, and the interactions aggregated from other (primary) databases.

2.7 SurvExpress

SurvExpress (http://bioinformatica.mty.itesm.mx/SurvExpress) is a friendly and versatile free web tool, it could provide a comprehensive analysis of multi-gene biomarkers for gene expression and clinical outcomes in human tumors [22]. In this study, three training cohorts (GSE10143, 162 patients; GSE17856, 95 patients; GSE10186, 118 patients) and one validation cohort (TCGA, 422 patients) were used to explore the prognostic value of CXC chemokines signature in HCC patients.

2.8 MethSurv

MethSurv (https://biit.cs.ut.ee/methsurv/), a web tool for survival analysis based on CpG methylation patterns, was applied to explore the prognostic value of single CpG methylation of the CXC chemokines in HCC patients [23].

2.9 SurvivalMeth

SurvivalMeth (http://bio-bigdata.hrbmu.edu.cn/survivalmeth/) [24] was used to analyze the DNA methylation of CXC chemokines signature on HCC prognosis.

2.10 LinkedOmics

LinkedOmics (http://www.linkedomics.org/) provides a unique platform to access, analyze and compare cancer multi-omics data within and across all 32 TCGA cancer types [25]. “LinkInterpreter” module was used to perform enrichment analysis based on Gene Ontology, biological pathways, network modules, among other functional categories in this study. Within the LIHC dataset, Gene Set Enrichment Analysis (GSEA) was used to perform analyses with a minimum number of genes (size) of 3 and a simulation of 1000. Spearman’s correlation test was used with the $P$ value set at 0.05.

2.11 TRRUST

Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST) database (https://www.grnpedia.org/trrust/), a manually curated database of human and mouse transcriptional regulatory networks [26]. Also, it provides information of mode of regulation (activation or repression).

Figure 1.

mRNA levels of CXC chemokines in HCC (ONCOMINE). The figure shows the numbers of datasets with statistically significant mRNA over-expression (red) or downregulated expression (blue) of CXC chemokines.

Figure 2.

The transcription of CXC chemokines in HCC (UALCAN). The transcriptional levels of (A) CXCL1, (B) CXCL2, (C) CXCL3, (D) CXCL4, (E) CXCL5, (F) CXCL6, (G) CXCL7, (H) CXCL8, (I) CXCL9, (J) CXCL10, (K) CXCL11, (L) CXCL12, (M) CXCL13, (N) CXCL14, (O) CXCL16, (P) CXCL17 displayed. (Q) Heatmap of the expression pattern of the input genes in HCC were displayed. The $p$ value was set at 0.05. was significantly elevated.

Figure 3.

The relative level of CXC chemokines in HCC.

2.12 Timer

Timer (Tumor Immune Estimation Resource, https://cistrome.shinyapps.io/timer/) used RNA-seq expression profile data to detect the infiltration of immune cells in tumor tissues as well as their clinical impact [27]. The association of CXC chemokine and the infiltrated B cell, CD8 $+$ /CD4 $+$ T cell, macrophage, neutrophil and dendritic cell was evaluated by the “Gene” and “Survival” module.

3. Results

3.1 CXC chemokine expression in HCC

There are 16 chemokines of CXC ligand family in human, including CXCL1-17 (CXCL15 is specific to mice). The Oncomine ${}^{\text{TM}}$ Platform as well as the Oncomine ${}^{\text{TM}}$ software applications were used to display the mRNA differentiation levels of CXC chemokines between HCC and normal tissues. As presented in Fig. 1, gene summary view of each CXC chemokine was demonstrated by the numbers of datasets with statistically significant mRNA over-/down-expression. Based on the data from ONCOMINE, the transcriptional levels of CXCL6, CXCL10, and CXCL11 in HCC tissues were significantly elevated while the transcriptional levels of CXCL2 and CXCL14 were significantly reduced in HCC vs. normal liver tissue. In Supplementary Table 1, the mRNA level of CXC chemokines in different types of HCC tissues and normal liver tissues at transcriptome level was shown in detail. In Mas et al.’s [28] study, the fold change of CXCL6 was as high as 3.749 in HCC as to normal tissue ( $P=$ 3.15E-7). Two datasets showed a significantly higher expression of CXCL10 by Mas et al.’s [28] (fold change $=$ 9.727) and Wurmbach et al.’s [29] (fold change $=$ 5.928) series. Similarly, CXCL8, CXCL9, and CXCL11 were all found an increased level expression in HCC compared with that in normal tissues, 28 with the fold changes were 3.323, 5.334 and 2.188 respectively.

Furthermore, the transcription of CXC chemokine in HCC was explored by UALCAN. Figure 2: As expected, the transcriptional levels of CXCL9 ( $P=$ 1.07e-02), CXCL16 ( $P=$ 3.16e-07), CXCL17 ( $P=$ 1.23e-04) in HCC tissues were significantly elevated while the transcriptional levels of CXCL1 ( $P=$ 1.56e-04), CXCL2 ( $P=$ 9.99e-10), CXCL5 ( $P=$ 6.75e-04), CXCL6 ( $P=$ 4.44e-02), CXCL7 ( $P=$ 6.16e-03), CXCL12 ( $P<$ 1.00e-12), and CXCL14 ( $P=$ 1.62e-12) were significantly reduced. Figure 3 shows that, among all types of CXC chemokines, the relative expression of CXCL16 was the highest in HCC. Next, the correlation between the differential expression of CXC chemokines and the pathological stage of HCC was evaluated by GEPIA, and we detected a significant correlation between CXCL3 ( $P=$ 0.0381), CXCL5 ( $P=$ 0.0139) and the pathological stage of HCC (Fig. 4). Higher expression of CXCL3 and CXCL5 promotes tumor progression. It revealed that CXC chemokines are extremely closely connected with the pathological progress of HCC and play a significant role in the tumorigenesis.

Figure 4.

Correlation between different expressed CXC chemokines and the pathological stage of HCC patients (GEPIA). ${}^{*}P<$ 0.05. (A) CXCL1, (B) CXCL2, (C) CXCL3, (D) CXCL4, (E) CXCL5, (F) CXCL6, (G) CXCL7, (H) CXCL8, (I) CXCL9, (J) CXCL10, (K) CXCL11, (L) CXCL12, (M) CXCL13, (N) CXCL14, (O) CXCL16, (P) CXCL17.

Figure 5.

The prognostic value of different expressed CXC chemokines in HCC patients in the disease-free survival curve (GEPIA). The disease-free survival curve of (A) CXCL1, (B) CXCL2, (C) CXCL3, (D) CXCL4, (E) CXCL5, (F) CXCL6, (G) CXCL7, (H) CXCL8, (I) CXCL9, (J) CXCL10, (K) CXCL11, (L) CXCL12, (M) CXCL13, (N) CXCL14, (O) CXCL16, (P) CXCL17 in HCC.

3.2 Prognostic value of CXC chemokine in HCC

The correlation of differentially expressed CXC chemokines and clinical outcomes were explored by GEPIA. Figure 5, a notably longer disease-free survival was demonstrated with the high transcriptional level of CXCL4 ( $P=$ 0.015). As for overall survival, lower expressions of CXCL1 ( $P=$ 0.0037), CXCL3 ( $P=$ 0.011), CXCL5 ( $P=$ 0.013), CXCL8 ( $P=$ 0.012), and higher expressions of CXCL2 ( $P=$ 0.0046), were associated with a notably longer survival time in HCC (Fig. 6).

Figure 6.

The prognostic value of different expressed CXC chemokines in HCC patients in the overall survival curve (GEPIA). The disease-free survival curve of (A) CXCL1, (B) CXCL2, (C) CXCL3, (D) CXCL4, (E) CXCL5, (F) CXCL6, (G) CXCL7, (H) CXCL8, (I) CXCL9, (J) CXCL10, (K) CXCL11, (L) CXCL12, (M) CXCL13, (N) CXCL14, (O) CXCL16, (P) CXCL17 in HCC.

3.3 Prognostic values of CXC chemokines signature in patients with HCC

The CXC chemokines signature was input for prognostic analysis in SurvExpress. For overall survival, in the training cohort 1 (GSE10143, 162 patients), the mRNA expression of CXCL1, CXCL14 was higher in the low-risk group than that in the high-risk group, while CXCL4, CXCL5, CXCL6, and CXCL11 were lower expressed in the low-risk group than that in the high-risk group (Fig. 7A and B), the high-risk group displayed an unfavorable overall survival compared with the low-risk group (Fig. 7C). For relapse-free survival, in the training cohort 2 (GSE17856, 95 patients), the mRNA expression of CXCL9, CXCL10 and CXCL16 was higher in the low-risk group than that in the high-risk group. However, a comparable relapse-free survival was observed of the low- and high-risk groups (Fig. 7D–F). Similarly, these results were also displayed in the training cohort 3 (GSE10186, 118 patients), the high-risk group had a worse OS compared with the low-risk group (Fig. 7G–I). And the prognostic value of CXC chemokines signature in HCC was also confirmed in the validation cohort (TCGA, 361 patients) (Fig. 7J–L).

Figure 7.

The prognostic values of CXC chemokines signature for survival analysis in HCC cases from the training cohort 1 (GSE10143, 162 patients), training cohort 2 (GSE17856, 95 patients), training cohort 3 (GSE10186, 118 patients), and validation cohort (TCGA, 361 patients) via SurvExpress platform. Notes: (A, D, G and J) the heat maps of mRNA expression of CXC chemokines; (B, E, H and K) the mRNA expressions of CXC chemokines between high-and low-risk groups; (C, F, I and L). Survival curves of CXC chemokines between high- and low-risk groups.

Figure 8.

The heat map of DNA methylation clustered expression level of CXC chemokines (MethSurv). Notes: cg10350689 of CXCL1, cg26013975 of CXCL2, cg13468041 of CXCL3, cg14882398 of CXCL4, cg15478045 of CXCL5, cg22670329 of CXCL6, cg26523478 of CXCL7, cg125799590 of CXCL10, cg08046471 of CXCL11, cg06671614 of CXCL12, cg12020230 of CXCL13, cg23510026 of CXCL14, cg08313880 of CXCL16, cg22276896 of CXCL17 showed the highest DNA methylation level in HCC.

3.4 Prognostic value of single CpG and DNA methylation of CXC chemokines

The prognostic value of DNA methylation of CXC chemokines in HCC was analyzed by MethSurv. The heat maps of DNA methylation of the CXC chemokines are displayed in Fig. 8. Among them, cg10350689 of CXCL1, cg26013975 of CXCL2, cg13468041 of CXCL3, cg14882398 of CXCL4, cg15478045 of CXCL5, cg22670329 of CXCL6, cg26523478 of CXCL7, cg125799590 of CXCL10, cg08046471 of CXCL11, cg06671614 of CXCL12, cg12020230 of CXCL13, cg23510026 of CXCL14, cg08313880 of CXCL16, cg22276896 of CXCL17 showed the highest DNA methylation level. And overall, we found that 7 CpGs of CXCL1, 5 CpGs of CXCL2, 4 CpGs of CXCL3, 5 CpGs of CXCL4, 10 CpGs of CXCL5, 1 CpG of CXCL6, 1 CpG of CXCL7, 2 CpGs of CXCL10, 3 CpGs of CXCL12, 3 CpGs of CXCL14, and 5 CpGs of CXCL17 were significantly associated with prognosis in HCC patients (Supplementary Table 2).

Figure 9.

The prognostic value of the DNA methylation of CXC chemokine signature in HCC via SurvivalMeth. Notes: (A) The methylation level of CpGs in high- and low-risk group, (B) The survival curve of Kaplan-Meier plot, (C) The heatmap of CpG methylation level.

The gene symbols of CXC chemokines were input for prognostic analysis in SurvivalMeth. As shown in Figs 9A–C, the significant expression patterns were found in CXCL4/5/7/8/9/10/12/13 between low- and high-risk groups. A significantly better survival outcome was observed for the low-risk group as compared to the high-risk group ( $P=$ 0.0054). And the heatmap showed that the DNA methylation level of CXCL13 was the highest, while CXCL3 was the lowest.

Figure 10.

Genetic alteration, neighbor gene network, and interaction analyses of different expressed CXC chemokines in HCC patients. (A) Summary of alterations in different expressed CXC chemokines in HCC. (B) Correlation heat map of CXC chemokines in HCC. (C and D) Protein-protein interaction network of different expressed CXC chemokines.

3.5 CXC chemokine genetic alteration, co-expression, neighbor gene network, and interaction analyses in HCC

A comprehensive molecular characterization of differentially expressed CXC chemokines was performed. The genetic alteration of differentially expressed CXC chemokines was analyzed by the provisional datasets of TCGA. Fig. 10A, the analytical results showed that CXCL-17 were altered in 3, 5, 4, 3, 6, 2.6, 4, 4, 6, 5, 7, 3, 4, 5, 7, and 4% of the queried HCC samples, respectively. Gene amplification, mutation, deep deletion and multiple alterations were the main genetic alteration types in HCC. Enhanced mRNA expression was the most common change in these samples. Next, the potential co-expression of the differentially expressed CXC chemokines was explored. There was a moderate to high correlation among the expression of CXCL1, CXCL2, CXCL3, and CXCL17, as showed by the heatmap of the co-expression of the differentially expressed CXC chemokines (Fig. 10B). Figure 10C: A PPI network analysis of differentially expressed CXC chemokines with STRING was conducted to explore their possible interactions. Combined with GeneMANIA analysis (Fig. 10D), the effect of 16 CXC chemokines was mainly associated with chemokine receptor binding and the inflammatory response. Besides, the top 50 most regularly interacting neighboring genes related to 16 CXC chemokines were analyzed by STRING. These data suggested that ACKR1, ACKR3, CCL1, CCL11, CCL19, CCL2, CCL20, CCL21, CCL25, CCL3, CCL4, CCL5, CCR1, CCR2, CCR3, CCR4, CCR5, CCR7, CD4, CD80, CD86, CSF1, CSF2, CSF3, CXCR1, CXCR2, CXCR3, CXCR4, CXCR5, CXCR6, ICAM1, IL10, IL10RA, IL10RB, IL13, IL18, IL1B, IL4, IL6, ITCH, JAK1, MMP9, PTK2, PTPRC, RELA, STAT3, STAT4, TIMP1, TNF, TNFRSF1B are mainly related to the regulation and capability of CXC chemokines.

3.6 CXC chemokine functional enrichment analysis in HCC

The enrichment analysis of CXC chemokines and their closely interacting neighboring genes were performed by DAVID 6.8 and Metascape. Figure 11A–C shows that among the three most highly enriched functions in the BP category, cellular response to chemokine, response to chemokine, and chemokine-mediated signaling pathway were associated with the tumorigenesis and progression of HCC. Platelet alpha granule lumen, tertiary granule lumen and external side of plasma membrane were the three most highly enriched items in the CC category. In the molecular function (MF) category, they were cytokine activity, cytokine receptor binding, and chemokine receptor binding. Figure 11D: KEGG pathway analyses were also performed. As expected, among the top three KEGG pathways, chemokine signaling pathway, cytokine-cytokine receptor interaction, and viral protein interaction with cytokine and cytokine receptor were significantly associated with the tumorigenesis and progression of HCC.

3.7 Transcription factor targets, kinase targets and miRNA targets of CXC chemokine in HCC

Key regulated factor of CXC chemokines in HCC was performed by TRRUST. Three transcription factors, RELA, NFKB1 and SP1 were found associated with CXC chemokine regulation (Table 1). Table 2, the top two kinase targets of CXC chemokines were identified from LinkedOmics ( $P<$ 0.05 and FDR $<$ 0.05). The most common seen targets were Kinase_LCK, Kinase_LYN, and Kinase_SYK. The top two miRNA targets of CXC chemokines were also identified in HCC, which is presented in Supplementary Table 3 and Supplementary Fig. 1.

Table 1
Key regulated factor of CXC chemokines in HCC (TRRUST)

Key TF	Description	List of overlapped genes	$P$ -value	$Q$ -value
RELA	v-rel reticuloendotheliosis viral oncogene homolog A (avian)	CXCL10, CXCL8, CXCL12, CXCL2, CXCL5, CXCL1	1.09E-07	1.71E-07
NFKB1	Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1	CXCL12, CXCL8, CXCL10, CXCL5, CXCL1, CXCL2	1.14E-07	1.71E-07
SP1	Sp1 transcription factor	CXCL5, CXCL1, CXCL14	0.00683	0.00683

Table 2

The Kinase target networks of CXC chemokines in HCC dataset (LinkedOmics)

CXCL chemokine	Enriched kinase target	Description	Number of leading edge IDs	FDR	Normalized enrichment score
CXCL2	Kinase_LCK	LCK proto-oncogene, Src family tyrosine kinase	24	0.0033758	1.9492
	Kinase_WEE1	WEE1 G2 checkpoint kinase	4	0.042082	$-$ 1.7133
CXCL3	Kinase_LCK	LCK proto-oncogene, Src family tyrosine kinase	24	0.0021859	1.7531
	Kinase_SYK	spleen associated tyrosine kinase	19	0.0043718	1.7306
CXCL4	Kinase_LYN	LYN proto-oncogene, Src family tyrosine kinase	25	0.0018869	1.8952
	Kinase_SYK	spleen associated tyrosine kinase	16	0.00094344	1.8185
CXCL8	Kinase_CDK1	cyclin dependent kinase 1	104	0	$-$ 2.6376
	Kinase_ATR	ATR serine/threonine kinase	24	0	$-$ 2.5482
CXCL9	Kinase_LCK	LCK proto-oncogene, Src family tyrosine kinase	22	0.0021455	1.6362
	Kinase_SYK	spleen associated tyrosine kinase	17	0.023600	1.5752
CXCL10	Kinase_LCK	LCK proto-oncogene, Src family tyrosine kinase	24	0	1.8117
	Kinase_LYN	LYN proto-oncogene, Src family tyrosine kinase	22	0.0076739	1.6955
CXCL11	Kinase_LCK	LCK proto-oncogene, Src family tyrosine kinase	23	0	1.7883
	Kinase_SYK	spleen associated tyrosine kinase	18	0.0057639	1.6946
CXCL12	Kinase_ATR	ATR serine/threonine kinase	22	0	$-$ 2.1713
	Kinase_LCK	LCK proto-oncogene, Src family tyrosine kinase	22	0.015982	1.5675
CXCL13	Kinase_LCK	LCK proto-oncogene, Src family tyrosine kinase	22	0	1.8565
	Kinase_LYN	LYN proto-oncogene, Src family tyrosine kinase	21	0.0010142	1.7291

Figure 11.

The enrichment analysis of different expressed CXC chemokines and 50 most frequently altered neighboring genes in HCC (David 6.8). GO enrichment in cellular component terms, biological process terms, molecular function terms and KEGG enriched terms.

Figure 12.

The correlation between different expressed CXC chemokines and immune cell infiltration (TIMER). The correlation between the abundance of immune cell and the expression of (A) CXCL1, (B) CXCL2, (C) CXCL3, (D) CXCL4, (E) CXCL5, (F) CXCL6, (G) CXCL7, (H) CXCL8.

Figure 12.

Continued. (I) CXCL9, (J) CXCL10, (K) CXCL11, (L) CXCL12, (M) CXCL13, (N) CXCL14, (O) CXCL16, (P) CXCL17 in HCC.

3.8 Immune cell infiltration of CXC chemokine in HCC

TIMER database was applied to display a comprehensive exploration of the correlation between differentially expressed CXC chemokines and immune cell infiltration (Fig. 12). Moreover, the Cox proportional hazard model was applied for CXC chemokines and six tumor-infiltrating immune cells in HCC (Supplementary Fig. 2). As shown in Table 3, CXCL2 ( $P=$ 0.001), CXCL6 ( $P=$ 0.013), CXCL8 ( $P=$ 0.001), CXCL12 ( $P=$ 0.013), macrophage ( $P=$ 0.011) and dendritic cell ( $P=$ 0.001) were found to be significantly related to the clinical outcome of HCC patients.

Table 3
The cox proportional hazard model of CXC chemokines and six tumor-infiltrating immune cells in HCC (TIMER)

	Coef	HR	95%CI_l	95%CI_u	$P$ -value	Sig
CXCL1	0.130	1.139	0.928	1.397	0.213
CXCL2	$-$ 0.266	0.798	0.699	0.911	0.001	**
CXCL3	$-$ 0.393	0.675	0.434	1.050	0.081
CXCL4	$-$ 0.058	0.944	0.631	1.412	0.778
CXCL5	0.068	1.071	0.880	1.302	0.493
CXCL6	$-$ 0.219	0.803	0.676	0.954	0.013	*
CXCL7	$-$ 0.095	0.909	0.484	1.710	0.768
CXCL8	0.292	1.340	1.126	1.594	0.001	**
CXCL9	$-$ 0.174	0.840	0.671	1.051	0.127
CXCL10	$-$ 0.002	0.998	0.797	1.249	0.984
CXCL11	0.039	1.040	0.758	1.427	0.810
CXCL12	$-$ 0.173	0.841	0.725	0.975	0.022	*
CXCL13	0.080	1.083	0.960	1.223	0.195
CXCL14	$-$ 0.003	0.997	0.822	1.210	0.978
CXCL16	$-$ 0.058	0.943	0.790	1.126	0.517
CXCL17	0.142	1.153	0.994	1.336	0.059
B cell	$-$ 9.198	0.000	0.000	0.146	0.013	*
CD8 $+$ T cell	$-$ 5.388	0.005	0.000	1.095	0.054
CD4 $+$ T cell	$-$ 3.968	0.019	0.000	15.100	0.245
Macrophage	7.093	1203.394	5.072	285492.940	0.011	*
Neutrophil	$-$ 4.923	0.007	0.000	5375.505	0.475
Dendritic	6.610	742.556	16.357	33710.562	0.001	**

TIMER in LIHC (362 patients with 127 dying).

4. Discussion

In HCC, immunosuppressive cells including MDSCs, TAMs, TANs, Tregs, CAFs and TILs are important components of tumor microenvironment. They interact with each other to produce growth factors, cytokines and chemokines to participate in immunosuppression, thus promoting the progress of HCC [30]. The differentiation, maturation and function of these immune cells are also regulated by cytokines and other chemokines. By regulating a variety of signaling pathways, chemokines and their receptors directly or indirectly shape the microenvironment of tumor cells and regulate the biological behavior of tumor, and therefore, involved in inflammatory response, tumor immune response, proliferation, invasion and metastasis. Recent studies have shown that chemokines in HCC microenvironment play an important role in the occurrence and development of tumor. Through the recruitment of inflammatory cells, inhibition of anti-tumor immune response, promotion of angiogenesis/tumor resistance, it eventually leads to the occurrence of HCC, and leads to invasion and metastasis [31, 32].

Previously, Wang et al. explored the mRNA transcriptional and survival analysis of CXCLs in patients with HCC from the databases involving ONCOMINE, GEPIA, and cBioPortal [43]. In this study, we first discussed the expression difference of CXC chemokines in HCC and its normal liver tissues, and showed the correlation between CXC chemokines and the staging of HCC. In addition, the differences of CXC chemokines expression and the survival and prognosis of HCC were also explored. We also revealed the role of epigenetic factors from methylation analysis of CXC chemokines related genes. After that, the molecular characteristics and functional enrichment of CXC chemokines have also been explored. Then we comprehensively analyzed and predicted CXC chemokines transcription factors, kinases and miRNA targets. Finally, we also introduced the correlation analysis of immune infiltration to demonstrate the influence of various CXC chemokines on immune cells and microenvironment in HCC.

In the present study, we focused on the CXC subfamily chemokines with the purpose providing novel insights as well as prognostic biomarkers for the immunotherapy strategy selection in HCC. In line with our work, a recently published meta-analysis explored the correlation of chemokine concentrations and HCC prognosis. They found that higher levels of CCL20, CXCL8 and CXCR4 could be served as HCC prediction biomarkers [33]. In consist with that study, CXCL8 was also found an increased level expression in HCC compared with that in normal tissues with the fold changes of 3.323, and a higher concentration of CXCL8 indicated a worse survival by our analysis. It has been reported that the AKT/mTOR/STAT3 signaling pathway was involved in the progression and metastasis of CXCL8 [34]. What’s more, CXCL8 was responsible for the Tregs infiltration in HCC microenvironment [35].

A significantly higher expression of CXCL10 was found in HCC tumor tissues as compared with the normal tissues. Of note, there was research showed that a positive correlation existed between CXCL10 and AFP, the size and number of tumor as well as the TNM staging in HCC, and CXCL10 could be regarded as an independent prognostic factor for HCC from the multivariate analysis [36]. Another CXC subfamily chemokine, CXCL12, is found notably reduced in HCC tissue ( $P<$ 1.00e-12), however, its role in HCC development is important. It has been considered that CXCL12/CXCR4 serves a key role in cell migrations of organs, including the liver, which could promote EMT and cancer cell self-renewal [37]. Also, hepatitis B virus X protein (HBx) could enhance CXCL12 and CXCR4 transcription activity and the Wnt signaling pathway in turn [38]. Furthermore, MDSCs can mediate cell migration through autocrine or paracrine chemokines, thereby modulating immunosuppressive networks and participating in all aspects of tumor progression. Animal experiments have demonstrated that CXCL1, CXCL5 and CXCL12 could be secreted by hematopoietic stem cells and then bind to MDSC surface to mediate cell migration [39]. This highlights a potential strategy to alter the tumor microenvironment by regulating the secretion of hepatic mesenchymal chemokines in the future.

Reviewing the history of systemic treatment of HCC, in 2008, sorafenib became the first-line therapy to achieve a significant survival benefit in advanced HCC [40]. Since then, sorafenib has remained alone in the first-line, and no other first-line therapy has been shown to be more effective than it. Ten years later, a study showed that the overall survival improvement of lenvatinib was no worse than that of sorafenib, and progression-free survival and time to progression were better than that of sorafenib [41]. In the NCCN Guidelines for Hepatobiliary Cancer of the United States of America, Version 2021, the combination therapy of atelizumab and bevacizumab is the only first-line treatment option recommended in the Guidelines [42]. This also completely elevates the status of “T $+$ A” therapy regimen to the forefront of systemic treatment of HCC, which means that systemic treatment of HCC overturns the previous single-drug treatment mode, and targeted combined immunotherapy regimen becomes the optimal choice. Based on more and more in-depth exploration of the dialogue mechanism between tumor cells and immune infiltrating cells in HCC microenvironment, the unique malignant biological behavior of HCC will be more fully explained. Preventing immune tolerance and altering the tumor microenvironment are the goals of novel immune-based methods. This includes the biological process of chemokines mediated immune cell migration and subsequent tumor cell remodeling. In the future, there will be more chemokine based targeted drugs, which will undoubtedly add new intervention strategies to the combined immunotherapy mode of HCC.

5. Conclusion

In conclusion, CXC chemokines in HCC could be regarded as prognostic indicators. Targeting the CXC chemokines may result in a valuable prognostic impact and assist novel immunity-based treatment identification in HCC. It can be predicted that HCC patients may benefit from the treatment strategies targeting CXC chemokines in the future. This study will also provide important explanations and innovation ideas for oncologists in the future.

Statement of ethics

This study is a bibliometric analysis of the articles related to management of hepatocellular carcinoma, and the data were obtained from the Clarivate Analytics Web of Science Core Collection database, so ethical approval and consent to participate were not necessary for this paper.

Funding

Natural Science Foundation of Guangdong (No. 2021A1515012234, Zi Yin); Science and Technology Program of Guangzhou (No. 201904010043, Zi Yin); National Key Clinical Specialty Construction Project (2021–2024, No. 2022YW030009).

Author contributions

Conception: Zi Yin.

Interpretation or analysis of data: Zi Yin, Tingrting Ma.

Preparation of the manuscript: Zi Yin, Min Yu.

Revision for important intellectual content: Sheng Chen.

Supervision: Zi Yin.

Supplementary data

The supplementary files are available to download from http://dx.doi.org/10.3233/CBM-210300.

sj-docx-1-cbm-10.3233_CBM-210300.docx - Supplemental material

Supplemental material, sj-docx-1-cbm-10.3233_CBM-210300.docx

Footnotes

Conflict of interest

The authors declare no potential conflicts of interest.

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Identification of therapeutic targets and prognostic biomarkers among CXC chemokines in hepatocellular carcinoma microenvironment

Abstract

BACKGROUD:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Materials and methods

2.1 GEPIA

2.2 Oncomine

2.3 UALCAN

2.4 cBioPortal

2.5 GeneMANIA

2.6 STRING

2.7 SurvExpress

2.8 MethSurv

2.9 SurvivalMeth

2.10 LinkedOmics

2.11 TRRUST

3. Results

3.1 CXC chemokine expression in HCC

3.6 CXC chemokine functional enrichment analysis in HCC

3.7 Transcription factor targets, kinase targets and miRNA targets of CXC chemokine in HCC

Table 1 Key regulated factor of CXC chemokines in HCC (TRRUST)

Table 3 The cox proportional hazard model of CXC chemokines and six tumor-infiltrating immune cells in HCC (TIMER)

5. Conclusion

Statement of ethics

Funding

Author contributions

Supplementary data

sj-docx-1-cbm-10.3233_CBM-210300.docx - Supplemental material

Footnotes

Conflict of interest

References

Table 1
Key regulated factor of CXC chemokines in HCC (TRRUST)

Table 3
The cox proportional hazard model of CXC chemokines and six tumor-infiltrating immune cells in HCC (TIMER)