[Retracted] Bioinformatics Analyses Reveals a Comprehensive Landscape of CXC Chemokine Family Functions in Non-Small Cell Lung Cancer

Tian, He; Wang, Liyu; Liu, Yu; Wang, Yalong; Zheng, Yujia; Fan, Tao; Zheng, Bo; Tan, Fengwei; Xue, Qi; Gao, Shugeng; Li, Chunxiang; He, Jie

doi:https://doi.org/10.1155/2021/6686158

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Volume 2021 | Article ID 6686158 | https://doi.org/10.1155/2021/6686158

[Retracted] Bioinformatics Analyses Reveals a Comprehensive Landscape of CXC Chemokine Family Functions in Non-Small Cell Lung Cancer

He Tian,¹Liyu Wang,¹Yu Liu,¹Yalong Wang,²Yujia Zheng,¹Tao Fan,¹Bo Zheng,³Fengwei Tan,¹Qi Xue,¹Shugeng Gao,¹Chunxiang Li,¹and Jie He¹

Academic Editor: Jialiang Yang

Received20 Oct 2020

Revised08 Dec 2020

Accepted02 Jan 2021

Published27 Jan 2021

Abstract

Backgrounds. Lung cancer is a major source of tumor-related death each year with non-small cell lung cancer (NSCLC) being a prevalent subtype. The metastasis from NSCLC to the brain usually imposes many neuron disorders. Previous studies have suggested that communications among cancer cells and interstitial cells are essential in tumorigenesis and are influenced by chemokines. In the tumor microenvironment, CXC chemokines can participate in the shifting of immune cells and manage tumor cell condition, thus affecting the progression of cancer and patient destinies. However, the expression and values of CXC chemokine family in NSCLC have not been systematically illustrated using public databases. Methods. UALCAN, STRING, ONCOMINE, GeneMANIA, cBioPortal, GEPIA, TISIDB, TRRUST, TIMER, Kaplan-Meier Plotter, and R software were utilized in this study. Results. Based on the TIMER and UACLCAN databases, in LUAD patients, the expression levels of CXCL10, CXCL13, and CXCL14 were significantly elevated while the transcriptional levels of CXCL2/3/4/7/12/16 were significantly reduced; in LUSC patients, the expression levels of CXCL6/10/13/14 were significantly elevated while the expression levels of CXCL2/3/4/5/7/11/12/16/17 were significantly reduced. We found remarkable relevance between the pathological stages of LUAD patients and the expressions of CXCL8 (positive) and CXCL17 (negative). Similarly, there are significant correlations between the pathological stages of LUSC patients and the expressions of CXCL1/2/6/17. In LUAD, patients with low expression levels of CXCL1/4/7/8 and patients with high expression levels of CXCL12/14/16 were associated with a significantly better prognosis. But in LUSC, all correlations between chemokines and prognosis are statistically insignificant. Pairwise expression correlation analysis among CXC chemokines shows that there are 7 significant correlations (between CXCL1 and CXCL2, between CXCL1 and CXCL3, between CXCL1 and CXCL8, between CXCL2 and CXCL3, between CXCL4 and CXCL7, between CXCL9 and CXCL10, and between CXCL9 and CXCL11) in LUAD and 4 significant correlations (between CXCL1 and CXCL8, between CXCL2 and CXCL3, between CXCL4 and CXCL7, and between CXCL10 and CXCL11) in LUSC. Significant correlations between the expressions of CXC chemokines and the infiltration of six common types of immune cells were also discovered in both LUAD and LUSC. Conclusions. We provided a comprehensive landscape of the CXC chemokine family in LUAD and LUSC using the bioinformatics method and found differences between LUSC and LUAD in the field of CXC chemokines. Our study may help validate and identify known novel immunotherapeutic targets and prognostic biomarkers.

1. Introduction

Annually, about 1.8 million people develop lung cancer, and 1.6 million people die as a result of this disease [1]. Approximately 85% of patients are classified as NSCLC (non-small cell lung cancer), of which the main histological types are adenocarcinoma and squamous cell carcinoma. There are enormous genomic and clinical heterogeneities between LUAD and LUSC [2]. Squamous-cell lung cancer, constituting 25%-30% of NSCLC cases, is usually centrally located and more likely to invade large blood vessels and has rare mutations/alterations which can be targeted from afar, which lead to many challenges in the treatment [3, 4]. Adenocarcinoma of the lung comprises around 40% of all lung cancer and tends to occur in the periphery of the lung. It is easier to identify before it has spread outside of the lungs, so it has better prognosis than LUSC [5]. Many researchers have focused on the therapeutic targets of NSCLC, especially immune checkpoint suppressor and oncogene mutations, and many advancements have been made [6]; however, it is far from sufficient; more therapeutic targets and prognostic biomarkers need to be identified.

Endogenous chemokine ligands and G protein-coupled seven-transmembrane spanning signaling receptors are the main members of the chemokine superfamily; they are chemotactic cytokines that control the shifting and communication cells and tissues [7]. In cancer microenvironment, chemokines are mainly produced by tumor cells and immune cells and play important role in mediating immune cell trafficking and lymphoid tissue development; thus, they are involved in antitumor immunological responses [8]. It has been convinced that chemokines can affect many cancer-related biological processes including tumor angiogenesis, tumorigenesis, progression, and metastasis and even influence patients’ clinical outcomes [9–11].

CXC chemokines participate in many biological progresses of malignant disease in several different organs, including the lung [12], breast [13], colorectal [14], and kidney [15]. In lung cancer, CXC chemokines are mainly involved in angiogenesis, immunoangiostasis, and metastases [16, 17]. CXCL1 [18], CXCL5 [19], CXCL8 [20], and CXCL12 [21] are CXC chemokines that have attracted much attention, since they are considered to facilitate lung cancer initiation, development, and metastasis by different mechanisms; however, for some CXC chemokines like CXCL4 [22, 23], CXCL14 [24, 25], and CXCL16 [26, 27], researchers have opposite opinions towards their roles in tumor development.

Generally, previous studies have sporadically characterized the function of some CXC chemokines in NSCLC, but our study is the first to give a comprehensive landscape of CXC chemokines in LUAD and LUSC using public database and bioinformatics skills. Technology development of second-generation gene sequencing and springing up of various databases will accelerate the macrolevel research of the CXC chemokine family in tumors.

Our study conducted a comprehensive and profound bioinformatics analysis of the expression of CXC chemokines in LUAD and LUSC and explored their roles as therapeutic targets and prognostic biomarkers on several credible public databases and R packages, thus offering more evidence to facilitate the clinical routine in selecting new drugs and investigating NSCLC patients’ long-term outcome more accurately.

2. Materials and Methods

2.1. ONCOMINE

ONCOMINE (http://www.oncomine.org) is a translational bioinformatics service that provides powerful, genome-wide expression analysis. Datasets in ONCOMINE were collected from public repositories such as Gene Expression Omnibus (GEO) and Array Express by Compendia Bioscience (a prominent cancer bioinformatics company widely used by the pharmaceutical industry to identify novel gene targets for drug discovery and development), and the datasets are composed of microarray data of primary tumors, cell lines, or xenografts [28]. Data were extracted to evaluate the expression of CXC chemokines in lung cancer. In our study, a value of 0.05, a fold change of 2, and a gene rank in the top 10% were set as the significance thresholds. Student’s test was used to analyze the difference in the expression of CXC chemokines in lung cancer.

2.2. GEPIA

GEPIA (http://gepia.cancer-pku.cn/index.html) is an analysis tool containing RNA sequence expression data of 9736 tumors and 8587 normal tissue samples, which was developed at Peking University. Data in GEPIA are extracted from TCGA and GETx projects using a standard pipeline, and the RNA-Seq datasets GEPIA used are based on the UCSC Xena project (http://xena.ucsc.edu) [29]. In this study, we performed pathological stage analysis with the “Single Gene Analysis” module and correlation analysis with the “Correlation Analysis” module. Multiple gene comparison analysis of CXC chemokines was performed with the “Multiple Gene Comparison” module, using the “LUAD” and “LUSC” datasets. 3D principle component analysis and survival map were performed with the “Dimensionality Reduction” module and “Survival Analysis” module of GEPIA2 (test), respectively. Student’s test was used to generate a value for expression or pathological stage analysis, and we take 0.05 as the cutoff of the value.

2.3. UALCAN

UALCAN (http://ualcan.path.uab.edu/) is a comprehensive web resource, providing analyses based on The Cancer Genome Atlas (TCGA) and MET500 cohort data [30]. In our study, expression data for CXC chemokines was obtained using the “Expression Analysis” module and “Methylation Analysis” module of UALCAN and the “LUAD” and “LUSC” datasets. Student’s test was used to generate a value. We set 0.05 as the value cutoff.

2.4. cBioPortal

cBioPortal (http://www.cbioportal.org) is a comprehensive web resource, which can visualize and analyze multidimensional cancer genomics data; it stores nonsynonymous mutations, DNA copy-number data, mRNA and microRNA expression data, protein-level and phosphoprotein-level data, DNA methylation data, and deidentified clinical data. The data in cBioPortal was based on TCGA database and a large number of published articles [31]. Genetic change, coexpression, and the network module of CXC chemokines based on TCGA database were obtained from cBioPortal. A total of 556 LUAD samples (TCGA, Pan-Cancer Atlas) and 487 LUSC (TCGA, Pan-Cancer Atlas) were included. mRNA expression scores (RNA Seq V2 RSEM) were obtained using a score threshold of ±2.0.

2.5. GeneMANIA

GeneMANIA (http://www.genemania.org) is a website providing information for genetic interactions and proteins, pathways, coexpression, colocalization, and protein domain similarity of genes; it can find other genes that are related to input genes with a very large set of functional association data, and it relies on several data sources including GEO, BioGRID, EMBL-EBI, Pfam, Ensembl, NCBI, MGI, I2D, InParanoid, and Pathway Commons [32].

2.6. STRING

STRING (https://string-db.org/) is aimed at collecting, scoring, and integrating all publicly available sources of protein-protein interaction (PPI) data and at complementing these with computational predictions of potential functions; it relies on many credible resources including COG, Ensembl, Intact, RefSeq, PubMed, Reactome, DIP, BioGRID, MINT, KEGG, SGD, FlyBase, Swiss-Prot/UniProt, SWISS-MODEL, HUGO, OMIM, NCI/NaturePID, PDB, The Interactive Fly, BioCyc, Gene Ontology, and SIMAP [33]. We conducted a PPI network analysis of differentially expressed CXC chemokines to explore the interactions among them with STRING in July 2020.

2.7. TRRUST

TRRUST (https://www.grnpedia.org/trrust/) is a reliable, intuitive tool for transcriptional regulatory networks based on 11237 articles describing small-scale experimental studies of transcriptional regulations in PubMed. Containing 8444 transcription factor- (TF-) target regulatory relationships of 800 human TFs, the TRRUST database can provide information on how these interactions are regulated [34].

2.8. TIMER

TIMER (https://cistrome.shinyapps.io/timer/) is an online tool that evaluated the infiltration of different immune cells and their clinical impact, and its data is based on samples from TCGA [35]. In our study, the correlation between immune cell and CXC chemokine level was evaluated with “Gene module”; the “Diff Exp” module was applied to show the expression of CXC chemokines among different cancer types; somatic copy number alteration of CXC chemokines was performed with “SCNA” module; and “Survival module” was used to evaluate the correlation among clinical outcome and the infiltration of immune cells and CXC chemokine expression.

2.9. TISIDB

TISIDB (http://cis.hku.hk/TISIDB/) is a gene-based resource exploring interactions between tumor and immune contexture. It is based on several credible resources, including PubMed database, high throughput screening data, RNA, and exome sequencing datasets of patient cohorts with immunotherapy, TCGA, UniProt, GO, DrugBank, etc. TISIDB allows users to interrogate the function of a specific gene in tumor-immune interplay through literature mining and the data analysis of genome-wide screening and high-throughput profiling [36]. In our study, we used the “Chemokine” module of TISIDB to make Spearman expression correlations between 16 CXC chemokines in our research and several common chemokines (including the 16 CXC chemokines themselves in our study) among different types of cancer.

2.10. Kaplan-Meier Plotter

The Kaplan-Meier Plotter-Lung Cancer (https://kmplot.com/analysis/index.php?p=service&cancer=lung) is an online tool that can show how genes influence survival in lung cancer (). EGA, GEO, and TCGA are included as sources in this database. Initially, the main function of this website is to identify survival biomarkers based on meta-analysis [37]. In our study, 719 lung adenocarcinoma samples and 524 lung squamous-cell carcinoma samples were used to draw the OS survival curve of CXC chemokines in LUAD and LUSC, respectively.

2.11. Statistical Analysis

R software with package “clusterProfiler” was used for GO and KEGG analysis and plotting.

3. Results

3.1. Aberrant Transcription and Methylation Level of CXC Chemokines in LUAD and LUSC Patients

Sixteen CXC chemokines (not including CXCL15) were retrieved from the ONCOMINE database. We first explored the mRNA expression levels of CXC chemokines in lung cancer and normal lung tissues with ONCOMINE (where LUAD and LUSC are not discriminated). Results are displayed in Figure 1(a). Based on the data from ONCOMINE, the expressions of CXCL14, CXCL13, and CXCL9 in lung cancer tissues were elevated with statistical significance while the transcriptional levels of CXCL1, CXCL2, CXCL3, CXCL4, CXCL5, CXCL7, CXCL12, CXCL16, and CXCL17 were significantly reduced in lung cancer vs. normal renal tissue. We also assessed the expression levels of CXC chemokines in cancer vs. normal tissue with TIMER where LUAD and LUSC are divided in Figure 1(b). In LUAD, CXCL9, CXCL10, CXCL13, and CXCL14 were significantly elevated and CXCL2, CXCL3, CXCL4, CXCL5, CXCL7, CXCL12, and CXCL16 were significantly reduced, while in LUSC, CXCL6, CXCL10, CXCL13, and CXCL14 were significantly elevated and CXCL2, CXCL3, CXCL4, CXCL5, CXCL7, CXCL12, CXCL16, and CXCL17 were significantly reduced. For triple verification, we visited the UALCAN database, using its “Expression analysis” module to explore the CXC chemokine transcriptional level in cancer vs. normal tissue in Figure 1(c), and the results are as follows: the transcriptional levels of CXCL2, CXCL3, CXCL4/PF4, CXCL7/PPBP, CXCL11, CXCL12, CXCL16, and CXCL17 were significantly reduced in both LUAD and LUSC tissues; the transcriptional levels of CXCL6, CXCL9, CXCL10, CXCL13, and CXCL14 were significantly elevated in both LUAD and LUSC tissues. For CXCL5, it was significantly reduced in LUSC tissues and reduced in LUAD tissues without statistical significance. From the TIMER and UALCAN databases, CXCL9/10/13/14 were elevated and CXCL2/3/4/7/12/16 were decreased in LUAD with significance and CXCL6/10/13/14 were elevated and CXCL2/3/4/5/7/12/16/17 were reduced in LUSC with significance.

(a) mRNA levels of CXC chemokines in lung cancer (ONCOMINE). The figure shows the numbers of datasets with statistically significant mRNA overexpression (red) or downregulated expression (blue) of CXC chemokines (reproduced from Zeng et al. 2020 [under the Creative Commons Attribution License/public domain])

(b) Differential expression between tumor and adjacent normal tissues for CXC chemokines across all TCGA tumors types (TIMER); statistical significance was evaluated using the Wilcoxon test. Columns are gray when normal data are available

(c) The transcription of CXC chemokines in LUAD and LUSC (UALCAN): (A) CXCL2, (B) CXCL3, (C) CXCL4, (D) CXCL5, (E) CXCL6, (F) CXCL7, (G) CXCL9, (H) CXCL10, (I) CXCL11, (J) CXCL12, (K) CXCL13, (L) CXCL14, (M) CXCL16, and (N) CXCL17

(d) The methylation level of CXC chemokines in LUAD and LUSC (UALCAN). (A) CXCL1 (B) CXCL2 (C) CXCL3 (D) CXCL4 (E) CXCL5 (F) CXCL6 (G) CXCL7 (H) CXCL10 (I) CXCL11 (J) CXCL12 (K) CXCL13 (L) CXCL16 (M) CXCL17

(e) The relative level of CXC chemokines in LUAD and LUSC

In the UALCAN database, except for the expression level, we also explored the methylation level of CXC chemokines in LUAD and LUSC since epigenetic modifications are on the spotlight of tumor research; the results are presented in Figure 1(d). The methylation levels of CXCL7/PPBP, CXCL11, CXCL16, and CXCL17 were significantly reduced in both LUAD and LUSC tissues; the methylation levels of CXCL1, CXCL3, CXCL5, CXCL6, CXCL10, and CXCL12 were significantly elevated in both LUAD and LUSC tissues; the methylation level of CXCL2 was significantly decreased in LUAD, but its data is not significant in LUSC; the methylation level of CXCL4/PF4 was only significantly higher in LUSC. For CXCL13, its methylation level is significantly elevated in LUAD, but is significantly decreased in LUSC, which is the opposite trend in LUAD and LUSC.

We also compared the relative expression levels of CXC chemokines in LUAD and LUSC tissues using GEPIA in Figure 1(e) and found that among all CXC chemokines we evaluated, the relative level of CXCL17 was the highest in LUAD; those of CXCL1, CXCL8, CXCL10, CXCL14, CXCL16, and CXCL17 were higher in LUSC. We evaluated all the 16 CXC chemokines so that we could identify additional CXC chemokines associated with tumorigenesis, development, and clinical outcome in LUAD and LUSC.

3.2. The Prognostic Value of CXC Chemokines in Patients with LUAD and LUSC

We firstly assessed the correlation between the CXC chemokines and the pathological stage of LUAD and LUSC patients in Figure 2(a). In LUAD, significant associations were found between the expressions of CXCL8 () and CXCL17 (). As the tumor progressed, the expression of CXCL8 increased and the expression of CXCL17 decreased. In LUSC, we found a significant correlation between the expressions of CXCL1 (), CXCL2 (), CXCL6 (), and CXCL17 (), and as the tumor progressed to stage IV, the expression levels of CXCL1 and CXCL6 increased positively.

(a) Correlation between different expressed CXC chemokines and the pathological stage of LUAD (red color) and LUSC (green color) patients (GEPIA);

(b) The overall survival curve of different expressed CXC chemokines in LUAD patients. Upper from left to right are CXCL1, CXCL4, CXCL7, and CXCL8; lower from left to right are CXCL12, CXCL14, and CXCL16;

(c) The overall survival curve of different expressed CXC chemokines in LUSC patients. Upper from left to right are CXCL1, CXCL4, CXCL7, and CXCL8; lower from left to right are CXCL12, CXCL14, and CXCL16

(d) Survival map, which showed the survival contribution of different CXC chemokines in multiple cancer types, estimated using the Mantel-Cox test (GEPIA)

To evaluate the value of CXC chemokines in the progression of LUAD and LUSC, we assessed the correlation between CXC chemokines and clinical outcome using Kaplan-Meier Plotter-Lung Cancer. OS (overall survival) curves are presented. In LUAD (Figure 2(b)), patients with low expression levels of CXCL1 (), CXCL4 (), CXCL7 (), and CXCL8 () were significantly associated with longer OS; patients with high expression levels of CXCL12 (), CXCL14 (), and CXCL16 () were significantly associated with longer OS. In LUSC (Figure 2(c)), all of the 16 CXC chemokines in our study have no statistical significance correlation with clinical outcome, so we displayed the OS curve of the CXC chemokines that are significant in LUAD to make a comparison.

We also conducted a survival map using GEPIA2.0, which displayed the survival contribution of different CXC chemokines in multiple cancer types, including LUAD and LUSC (Figure 2(d).

We conducted a comprehensive analysis of the molecular characteristics of CXC chemokines. Using the cBioPortal database, CXCL1, CXCL2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7, CXCL8, CXCL9, CXCL10, CXCL11, CXCL12, CXCL13, CXCL14, CXCL16, and CXCL17 were mutated 4, 5, 5, 4, 4, 6, 5, 4, 6, 5, 5, 5, 6, 6, 5, and 6% of the queried LUAD samples, respectively (Figure 3(a)), and they were mutated in 7, 6, 7, 6, 7, 9, 7, 5, 7, 6, 6, 4, 6, 5, 6, and 7% of the queried LUSC samples, respectively (Figure 3(a)). Enhanced/declined mRNA expression was the most common change in these samples. Next, we conducted a correlation heat map of CXC chemokines in different types of cancer, including LUAD and LUSC using the “Chemokine” module of the TISIDB database (Figure 3(b)); blank areas in CXCL4 and CXCL17 were due to the data vacancy of these two CXC chemokines in TISIDB. There was a moderate to high correlation among CXCL1, CXCL2, CXCL3, CXCL4, CXCL5, CXCL6, and CXCL8 and another moderate to high correlation among CXCL9, CXCL10, CXCL11, and CXCL13. Using the “Correlation Analysis” module of GEPIA, we conducted gene-to-gene correlation analysis among CXC chemokines (Figure 3(c)). Under the standard “ value < 0.05 and value ≥ 0.6,” in LUAD, we found significant expression correlation between these pairs: CXCL1 and CXCL2 (), CXC1 and CXCL3 (), CXCL1 and CXCL8 (), CXCL2 and CXCL3 (), CXCL4 and CXCL7 (), CXCL9 and CXCL10 (), CXCL9 and CXCL11 (), CXCL10 and CXCL11 (; ); in LUSC, we found significant expression correlation between these couples: CXCL1 and CXCL8 (), CXC2 and CXCL3 (), CXCL4 and CXCL7 (), and CXCL10 and CXCL11 (). Moreover, we made a PPI network analysis of CXC chemokines in humans with STRING in July 2020 to explore the potential interactions among them. As expected, several nodes (16) and several edges (111) were obtained in the PPI network (Figure 3(d)), and the PPI enrichment value <1.0e-16. The function of these CXC chemokines was associated with the chemokine signaling pathway and the inflammatory reaction. Outcomes of GeneMANIA also showed that the functions of these CXC chemokines were mainly related to chemotaxis and several chemokine activities such as chemokine receptor binding process; it showed 20 related genes and 1512 total links (Figure 3(e)).

(a) Summary of alterations in CXC chemokines in LUAD (upper) and LUSC (lower) (cBioPortal)

(b) Correlation heat map of different expressed CXC chemokines in different types of cancer, including LUAD and LUSC (TISIDB)

(c) Correlation analysis between different CXC chemokines with statistical significance () in LUAD and LUSC (GEPIA). (1) to (8) were analyses in LUAD patients; (9) to (12) were analyses in LUSC patients

(d) Protein-protein interaction network of different expressed CXC chemokines (E-STRING, F-GENE MANIA)

(e) Protein-protein interaction network of different expressed CXC chemokines (E-STRING, F-GENE MANIA)

3.3. Function Enrichment and Pathway Analysis of CXC Chemokines in Patients with LUAD and LUSC

R software with package “clusterProfiler” was applied to analyze the functions of differentially expressed CXC chemokines and their neighboring genes in LUAD and LUSC, respectively (Figure 4). Figure 4(a) shows the top 10 most highly enriched GO items in LUAD. Among the 10 most highly enriched functions in the BP category, chemokine-mediated signaling pathway, response to chemokine, cellar response to chemokine, and cell chemotaxis were associated with the tumorigenesis and progression of LUAD. The external side of the plasma membrane, focal adhesion, and cell-substrate junction were the 3 most highly enriched items in the CC category. In the molecular function part, the CXC chemokines and their neighboring genes were mainly enriched in chemokine receptor binding, chemokine activity, G protein-coupled receptor binding, cytokine receptor binding, and cytokine activity.

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Figure 4(b) showed the KEGG pathway analyses of the CXC chemokine in LUAD. As expected, among the top 30 KEGG pathways, CXC chemokines were mainly enriched in the chemokine signaling pathway, viral protein interaction with cytokine and cytokine receptor, cytokine-cytokine receptor interaction, Kaposi sarcoma-associated herpesvirus infection, and human cytomegalovirus infection. Figure 4(c) displayed the GO enrichment analysis in LUSC. The top 10 items are cell chemotaxis, chemokine-mediated signaling pathway, response to chemokine, cellular response to chemokine, leukocyte migration, myeloid leukocyte migration, positive regulation of chemotaxis, granulocyte chemotaxis, and positive regulation for leukocyte chemotaxis. The external side of the plasma membrane, focal adhesion, and cell-substrate junction were the 3 most highly enriched items in the CC category. In the molecular function category, the CXC chemokines and their neighboring genes were mainly enriched in G protein-coupled receptor binding, cytokine receptor binding, and chemokine receptor binding. Figure 4(d) is the KEGG pathway analyses of the CXC chemokine in LUSC where CXC chemokines were mainly enriched in the chemokine signaling pathway, cytokine-cytokine receptor interaction, and viral protein interaction with the cytokine and cytokine receptor.

3.4. Transcription Factor Targets and Principal Component Analysis of CXC Chemokines in Patients with LUAD and LUSC

Since there is significant difference in the expressions of CXC chemokines in LUAD/LUSC vs. normal tissue, we explored possible transcription factor targets of the CXC chemokines using the TRRUST database in Table 1. CXCL1, CXCL2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7, CXCL8, CXCL9, CXCL10, CXCL12, CXCL13, CXCL14, CXCL16, and CXCL17 were included in TRRUST. Three transcription factors (RELA, NFKB1, and SP1) were found to be related to the regulation of CXC chemokines. RELA and NFKB1 were the key transcription factors for CXCL1, CXCL2, CXCL5, CXCL8, CXCL10, and CXCL12. SP1 was the key transcription factor for CXCL1, CXCL5, and CXCL14. Figure 5 showed the 3D PCA (principal component analysis) on samples from the TCGA tumor and TCGA normal, including LUAD, LUAD normal, LUSC, and LUSC normal, based on their expression of CXC chemokines using the GEPIA2.0 database.

3.5. Immune Cell Infiltration Levels among Tumors with Different SCNA of CXC Chemokines in Patients with LUAD and LUSC

CXC chemokines are part of immune cell infiltration and inflammatory responses and so can affect the clinical outcome of LUAD and LUSC patients. Therefore, we explored the correlation between CXC chemokines and immune cell infiltration with the TIMER database. The correlation maps are displayed from CXCL1, CXCL2 to CXCL17 in Figure 6. To extract the key information more easily, we made summaries for Figure 6 in Tables 2 and 3, which are for LUAD and LUSC, respectively. CXCL9, CXCL10, CXCL11, CXCL12, and CXCL16 were chemokines that were positively correlated with all 6 types of immune cells in both LUAD and LUSC. In LUAD, expressions of CXCL4, CXCL5, CXCL6, CXCL7, and CXCL8 were negatively correlated with CD4⁺ T cells. CXCL5, CXCL7, CXCL8, and CXCL17 were chemokines that had both positive and negative associations among different immune cells. In LUSC, all 16 CXC chemokines (except CXCL17) had positive or negative correlation with neutrophil cells, and CXCL13 was positively associated with all 6 types of immune cell. We also evaluated the correlation of CXC chemokines and immune cell infiltration (Tables 4(a) and 4(b)). The Cox proportional hazard model was adopted. B cells () was significantly associated with the clinical outcome of LUAD patients (Table 4(a)). In LUSC patients, CXCL2 (), CXCL8 (), and CXCL13 () were significantly associated with the clinical outcome.

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At last, we conducted the comparison of tumor infiltration levels among tumors with different SCNAs (somatic copy number alterations) for CXC chemokines in LUAD and LUSC samples (Figure 7) using the TIMER database. From the results, we found that in both LUAD and LUSC patients, all of the SCNA types that are significantly correlated with immune cells are (1) arm-level deletion, (2) arm-level gain, and (3) high amplification. In LUAD, all 16 CXC chemokines have a significant part in CD4⁺ T cells (in which SCNA types are mainly arm-level depletion) and neutrophil cells (in which SCNA types are mainly high amplification). In LUSC, all 16 CXC chemokines have a significant part in CD4⁺ T cells, macrophage cells, and neutrophil cells; predominant SCNA types of the three types are the same—arm-level alterations. In LUAD samples, CXC14 are only chemokines of which SCNA is significantly correlated with CD8⁺ T cells among 16 CXC chemokines.

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Figure 7

Comparison of tumor infiltration levels among tumors with different SCNAs (somatic copy number alterations) for CXC chemokines in LUAD and LUSC samples. SCNAs are defined by GISTIC2.0. Box plots are presented to show the distributions of each immune subset at each copy number status in LUAD and LUSC patients. The infiltration level for each SCNA category is compared with the normal using a two-sided Wilcoxon rank-sum test: (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, and (p) CXCL7.

4. Discussion

CXC chemokines and their receptors were initially identified as important regulators in inflammatory response [10]. In cancer, accumulative evidence has proven that CXC chemokines are critical in tumor initiation, angiogenesis, and progression [7], and with the rapid development of tumor immunology, the correlation among CXC chemokines, tumor microenvironment, and cancer immunotherapy have been reported [8, 11].

In NSCLC, much attention has been paid to the functions of some CXC chemokines. For example, the CXCL1 paracrine network was identified to be linked with cancer chemoresistance and metastasis in 2012 [18], and in 2020, researchers found that CXCL1 was an unfavorable prognosis factor negatively regulated by DACH1 in NSCLC using immunohistochemistry staining [38]. In 2004, researchers found that COX-2 (cyclooxygenase-2) contributes to the progression of NSCLC tumorigenesis by enhancing the expression of angiogenic chemokines CXCL8 and CXCL5 [39], both of which can contribute to lung cancer progression. As for the cancer treatment, combination of CXCL9 or CXCL10 with cisplatin improves therapeutic efficacy in solid tumors [40, 41]. Changes in the serum level of CXCL8 can reflect and predict the response to anti-PD-1 therapy in NSCLC [42]. Although the CXC chemokine family in lung cancer has been studied for years, there are still unknown corners and even controversies about several CXC chemokines. For instance, researchers have opposite views about the roles of CXCL4 [22, 43], CXCL14 [24, 44], and CXCL16 [27, 45], and we know little about the positions of CXCL2, CXCL6, and CXCL13 in NSCLC. Generally, although many researchers have studied the function of some CXC chemokines singly and have put up solid evidence, the comprehensive landscape of CXC chemokines and their behavior in LUAD and LUSC distinctly have not been well-portrayed yet.

Firstly, we explored the expression and methylation of CXC chemokines in 3 database and their correlation with pathological stages in LUAD and LUSC. In the ONCOMINE database where lung cancer types are not discriminated, we found that 12 genes were differentially expressed in lung cancer compared with normal tissue (upregulation of CXCL9/13/14; downregulation of CXCL1/2/3/4/5/7/12/16/17). And in the TIMER and UALCAN databases, where LUAD and LUSC are distinguished, we found that 9 genes were differentially expressed in LUAD compared with normal tissue (upregulation of CXCL10/13/14; downregulation of CXCL2/3/4/7/12/16), and in LUSC, 13 genes were differentially expressed compared with normal tissue (upregulation of CXCL6/10/13/14; downregulation of CXCL2/3/4/5/7/11/12/16/17). That is the whole expression condition of CXC chemokines in lung cancer. In the UALCAN database where we conducted the methylation analysis of CXC chemokines, we found that methylation levels of CXC1/3/5/6/10/12 were significantly elevated, and methylation levels of CXCL7/11/16/17 were significantly reduced in both LUAD and LUSC. Suzuki et al. reported that aberrant methylation of CXCL12 in NSCLC is associated with poor prognosis [46], which agrees with our research to some degree and inspires us that the methylation level of other CXC chemokines may be involved with tumor prognosis. Then, we explore the association between CXC chemokines and pathological stages and the clinical prognosis. We found that in LUAD, expression of CXCL8 increased and expression of CXCL17 decreased as the tumor progressed, and in LUSC, the expressions of CXCL1 and CXCL6 increased considerably when the tumor progressed to stage IV. These results imply that CXCL1/6/8/17 may predict the stage or tendency of tumors. In the UALCAN database, we found that LUAD patients with low expression of CXCL1/4/7/8 and LUAD patients with high expression of CXCL12/14/16 were significantly associated with better OS (overall survival), while no CXC chemokines in LUSC have statistical significance. These data demonstrate that differently expressed CXC chemokines may be important in LUAD. Yu et al. has identified CXCL1 as an unfavorable prognosis factor in NSCLC [38], which highly agrees with our results, and many researchers have believed that CXCL8 was associated with tumor progression, angiogenesis, and relapse for a long time [47]; therefore, our results may pave the way for CXCL8 to be an adverse prognosis factor in LUAD, even NSCLC. These data also suggest that in LUAD, CXCL4/7 may be adverse prognosis factors while CXCL12/14/16 may be favorable prognosis factors. As mentioned before, there are opposite views about the roles of CXCL4, CXCL14, and CXCL16 in NSCLC; our data gives more evidence on these controversies. So far, we do not know enough about CXCL7, and these data offer new information. For CXCL12, it has been believed that CXCL12 and its receptor, CXCR4, formed the CXC12/CXCR4 axis, and the axis was believed to contribute to tumor progression and metastasis in LUAD [21, 48], but in our study, LUAD patients with high CXCL12 expression were associated with better OS significantly; the reasons that lie behind these two contrary findings need further exploration. From the survival map, we found that CXCL2/7/12/16 made significant contribution to the survival in LUSC, and no CXC chemokines were significant in LUAD, which suggest that we expect more research on the roles of CXC chemokines in LUSC.

In lung cancer, since several chemokines were differentially expressed, we explored their molecular traits in LUAD and LUSC. Frequent genetic alterations are found in CXC chemokines, and the elevated/reduced mRNA expression was the type that had the most alteration in both LUAD and LUSC. Tumor initiation progression of lung cancer is complex and intricate, and genetic alteration plays a significant role in this story [49]. From correlation heat maps and gene-to-gene correlation analysis, we found tight corrections among these 16 CXC chemokines in both LUAD and LUSC, implying that these cytokines work synergistically in tumor initiation and progression. In the past 5 years, researchers have identified that CXCL1, CXCL5, and CXCL8 acted as tumor promoters in LUAD or NSCLC, and all of them could be antagonized by DACH1 (the human Dachshund homologue 1) [20, 38, 50], so we speculated if there were any correlations among them. This speculation was partly validated in our study, since we found significant correlations between CXCL1 and CXC8 in both LUAD and LUSC patients. Tight association between CXCL9 and CXCL10 in LUAD was found, and as we mentioned above, combination of CXCL9 or CXCL10 with cisplatin improves therapeutic efficacy in solid tumors [40, 41]; we have reason to believe that there may be a close relation between the CXCL9 and CXCL10. Similarly, Wang et al. found that combination of CXCL10 and CXCL11 led to synergistic antitumor effects [51], and correlations with statistical significance were found between CXCL10 and CXCL11 in both LUAD and LUSC; therefore, we can also speculate the unusual correlation between these two chemokines. As for the driver gene of NSCLC, Luppi et al. and Tsai et al. identified CXCL8 [52] and CXCL12 [53] which worked together with the EGFR gene in the progression of NSCLC, respectively; however, no significant association was12 and EGFR/12 and EGFR (Supplementary Figure 1).

Next, we focused on the function of CXC chemokines using GO enrichment analysis and KEGG pathway enrichment analysis. Not surprisingly, we found that the functions of these genes are mainly related to the chemokine signaling pathway, cytokine-cytokine receptor interactions, and viral protein interaction with cytokine and cytokine receptor in both LUAD and LUSC samples. It has been identified that chemokine signaling pathways are important in the progression, metastasis angiogenesis, senescence, epithelial-mesenchymal transition, and immune evasion of various cancers [54–56]. The roles of cytokine-cytokine receptor interactions are also pivotal in several tumor-associated biological processes [21, 57]. Viral protein can interact with the cytokine and cytokine receptor to affect and even subvert the function of the cytokine network and to regulate the immune response, which is important in cancer [58–60]. Generally, these GO and KEGG results suggest that the CXC chemokines are potential drug therapeutic targets in LUAD and LUSC.

The PCA analysis on samples from LUAD and LUSC samples in TCGA based on their expression of CXC chemokines reflects the ability of all the 16 CXC chemokines in differentiating the LUAD tumor/normal and LUSC tumor/normal patients; from the 3D figure, we found that CXC chemokines did enable to make these discriminations. We also characterized the transcription factor targets for the 16 CXC chemokines and found that RELA, NFKB1, and SP1 may be the key transcription factors of CXC chemokines. Zeng et al. made the same conclusion without study about this [15], but we included more CXC chemokines in our study. RELA plays a pivotal role in regulating oncogene-induced senescence in preneoplastic lesions [61], is essential to link smoke-induced inflammation with lung cancer growth, and participates in the activation of Wnt/beta-signaling in tumors [62]. NFKB1 is identified as an inhibitor of tumors and inflammatory response; by reducing the aberrant activation of the NF-κB signaling pathway, it negatively regulated the tumorigenesis and progression of several types of cancers [63]. NKKB1 also interfered with diverse complex immunological progresses, associated with many autoinflammatory disorders [64]. SP1 is overexpressed in many cancers and implicated in inflammation, genomic instability, and epigenetic silencing [65]; it has also been reported to be a target in cancer chemotherapy [66]. In NSCL, SP1 could promote cancer progression by interacting with lncRNA LINCo1234 and OTUB1 [67].

The chemokine system can orchestrate the immune cell migration and position them properly in a spatiotemporal manner [68]. In NSCLC, accumulating evidence shows that immune cell infiltration could affect tumor initiation, progression, and prognosis and could be important determinants of response to immunotherapies [69–71]. In our study, we found a significant correlation between the expression of CXC chemokines and the infiltration of the six immune cell types, B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells, indicating that CXC chemokines not only are prognostic indicators but may also reflect the immune status. Gao et al. reported that CXCL11 could promote CD8⁺ T cell infiltration in NSCLC [72], which is consistent with our findings in this study in that we found that CXCL11 was significantly correlated with CD8⁺ T cell infiltration in both LUAD and LUSC.

Moreover, we explored the correlation between aneuploidy and the infiltration of the six immune cell types. In cancer, chromosomal instability underpins much of intratumor heterogeneity and can drive phenotypic adaptation during tumor evolution [73]; meanwhile, tumor aneuploidy is correlated with markers in immune evasion and indicates reduced response to immunotherapy [74]. Copy number alterations have been reported as risk factors, predictors, and even drivers of tumor progress and poor clinical outcome in various cancer types [75–78]. In our study, we found a significant association between the SCNA of CXC chemokines and the infiltration of the six immune cell types and therefore offer more information for the exploration of relationship between SCNA and CXC chemokines in lung cancer.

Generally, differences existed in many aspects between LUAD and LUSC; they have a distinct expression pattern in several CXC chemokines, but it is hard to find a clear law to distinguish them, which might be due to the fact that our study was purely based on bioinformatics analysis; therefore, it was not effective enough to do the distinction, which also might be due to the fact that both LUAD and LUSC belong to non-small cell lung cancer; therefore, the disparity between them is just not so obvious.

Our study is the first to portray the overall view of CXC chemokines in non-small lung cancer using several public databases and bioinformatics analysis, which is our biggest highlight and main limitation. Therefore, independent cohort and in vitro or in vivo research should be performed to validate our results to a large extent. In conclusion, we hope that our results offer a comprehensive landscape and new insight of CXC chemokines in LUAD and LUSC, thus provide more information for the development of new immunotherapy medicine, facilitate the clinical routine of selecting drugs, and help the clinicians predict the prognosis of patients more accurately.

Data Availability

The datasets analyzed for this study can be found in the ONCOMINE, GEPIA, UALCAN, Kaplan-Meier Plotter, and cBioPortal web resources, and requests to further access to datasets can be directed to [email protected].

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Authors’ Contributions

JH and CL designed the study and revised the manuscript. HT, LW, YL, FT, and QX performed data analysis work and aided in writing the manuscript. YW, YZ, TF, SG, and BZ designed the study and assisted in writing the manuscript. All authors read and approved the final manuscript.

Acknowledgments

This work was supported by the Chinese National Natural Science Foundation (81972196), the National Key Basic Research Development Plan (2018YFC1312100, 2016YFC1303200), and the CAMS Innovation Fund for Medical Sciences (CIFMS) (2017-I2M-1-005).

Supplementary Materials

Figure S1: correlation analysis between CXC8/12 and EGFR expression chemokines: LUAD (upper); LUSC (lower). (Supplementary Materials)

References

F. R. Hirsch, G. V. Scagliotti, J. L. Mulshine et al., “Lung cancer: current therapies and new targeted treatments,” Lancet, vol. 389, no. 10066, pp. 299–311, 2017.
View at: Publisher Site | Google Scholar
R. S. Herbst, D. Morgensztern, and C. Boshoff, “The biology and management of non-small cell lung cancer,” Nature, vol. 553, no. 7689, pp. 446–454, 2018.
View at: Publisher Site | Google Scholar
M. A. Socinski, C. Obasaju, D. Gandara et al., “Current and emergent therapy options for advanced squamous cell lung cancer,” Journal of Thoracic Oncology, vol. 13, no. 2, pp. 165–183, 2018.
View at: Publisher Site | Google Scholar
The Cancer Genome Atlas Research Network, “Comprehensive genomic characterization of squamous cell lung cancers,” Nature, vol. 489, no. 7417, pp. 519–525, 2012.
View at: Publisher Site | Google Scholar
C. Zappa and S. A. Mousa, “Non-small cell lung cancer: current treatment and future advances,” Translational Lung Cancer Research, vol. 5, no. 3, pp. 288–300, 2016.
View at: Publisher Site | Google Scholar
K. C. Arbour and G. J. Riely, “Systemic therapy for locally advanced and metastatic non-small cell lung cancer: a review,” JAMA, vol. 322, no. 8, pp. 764–774, 2019.
View at: Publisher Site | Google Scholar
M. T. Chow and A. D. Luster, “Chemokines in cancer,” Cancer Immunology Research, vol. 2, no. 12, pp. 1125–1131, 2014.
View at: Publisher Site | Google Scholar
N. Nagarsheth, M. S. Wicha, and W. Zou, “Chemokines in the cancer microenvironment and their relevance in cancer immunotherapy,” Nature Reviews. Immunology, vol. 17, no. 9, pp. 559–572, 2017.
View at: Publisher Site | Google Scholar
F. Balkwill, “Cancer and the chemokine network,” Nature Reviews. Cancer, vol. 4, no. 7, pp. 540–550, 2004.
View at: Publisher Site | Google Scholar
J. Vandercappellen, J. Van Damme, and S. Struyf, “The role of CXC chemokines and their receptors in cancer,” Cancer Letters, vol. 267, no. 2, pp. 226–244, 2008.
View at: Publisher Site | Google Scholar
V. Mollica Poeta, M. Massara, A. Capucetti, and R. Bonecchi, “Chemokines and chemokine receptors: new targets for cancer immunotherapy,” Frontiers in Immunology, vol. 10, p. 379, 2019.
View at: Publisher Site | Google Scholar
A. Spaks, “Role of CXC group chemokines in lung cancer development and progression,” Journal of Thoracic Disease, vol. 9, Suppl 3, pp. S164–s171, 2017.
View at: Publisher Site | Google Scholar
E. Chen, X. Qin, K. Peng et al., “Identification of potential therapeutic targets among CXC chemokines in breast tumor microenvironment using integrative bioinformatics analysis,” Cellular Physiology and Biochemistry, vol. 45, no. 5, pp. 1731–1746, 2018.
View at: Publisher Site | Google Scholar
Y. S. Lee, I. Choi, Y. Ning et al., “Interleukin-8 and its receptor CXCR2 in the tumour microenvironment promote colon cancer growth, progression and metastasis,” British Journal of Cancer, vol. 106, no. 11, pp. 1833–1841, 2012.
View at: Publisher Site | Google Scholar
Q. Zeng, S. Sun, Y. Li, X. Li, Z. Li, and H. Liang, “Identification of therapeutic targets and prognostic biomarkers among CXC chemokines in the renal cell carcinoma microenvironment,” Frontiers in Oncology, vol. 9, p. 1555, 2020.
View at: Publisher Site | Google Scholar
R. M. Strieter, P. J. Polverini, D. A. Arenberg et al., “Role of C-X-C chemokines as regulators of angiogenesis in lung cancer,” Journal of Leukocyte Biology, vol. 57, no. 5, pp. 752–762, 1995.
View at: Publisher Site | Google Scholar
R. M. Strieter et al., “CXC chemokines: angiogenesis, immunoangiostasis, and metastases in lung cancer,” Annals of the New York Academy of Sciences, vol. 1028, no. 1, pp. 351–360, 2004.
View at: Publisher Site | Google Scholar
S. Acharyya, T. Oskarsson, S. Vanharanta et al., “A CXCL1 paracrine network links cancer chemoresistance and metastasis,” Cell, vol. 150, no. 1, pp. 165–178, 2012.
View at: Publisher Site | Google Scholar
K. Wu, S. Yu, Q. Liu, X. Bai, X. Zheng, and K. Wu, “The clinical significance of CXCL5 in non-small cell lung cancer,” Oncotargets and Therapy, vol. 10, pp. 5561–5573, 2017.
View at: Publisher Site | Google Scholar
Q. Liu, A. Li, S. Yu et al., “DACH1 antagonizes CXCL8 to repress tumorigenesis of lung adenocarcinoma and improve prognosis,” Journal of Hematology & Oncology, vol. 11, no. 1, p. 53, 2018.
View at: Publisher Site | Google Scholar
O. Wald, O. M. Shapira, and U. Izhar, “CXCR4/CXCL12 axis in non small cell lung cancer (NSCLC) pathologic roles and therapeutic potential,” Theranostics, vol. 3, no. 1, pp. 26–33, 2013.
View at: Publisher Site | Google Scholar
F. Pucci, S. Rickelt, A. P. Newton et al., “PF4 promotes platelet production and lung cancer growth,” Cell Reports, vol. 17, no. 7, pp. 1764–1772, 2016.
View at: Publisher Site | Google Scholar
J. Jian, Y. Pang, H. H. Yan et al., “Platelet factor 4 is produced by subsets of myeloid cells in premetastatic lung and inhibits tumor metastasis,” Oncotarget, vol. 8, no. 17, pp. 27725–27739, 2017.
View at: Publisher Site | Google Scholar
C. R. Park, D. J. You, D. K. Kim et al., “CXCL14 enhances proliferation and migration of NCI-H460 human lung cancer cells overexpressing the glycoproteins containing heparan sulfate or sialic acid,” Journal of Cellular Biochemistry, vol. 114, no. 5, pp. 1084–1096, 2013.
View at: Publisher Site | Google Scholar
R. Hata, K. Izukuri, Y. Kato et al., “Suppressed rate of carcinogenesis and decreases in tumour volume and lung metastasis in CXCL14/BRAK transgenic mice,” Scientific Reports, vol. 5, no. 1, pp. 1–10, 2015.
View at: Publisher Site | Google Scholar
W. Hu, Y. Liu, W. Zhou, L. Si, and L. Ren, “CXCL16 and CXCR6 are coexpressed in human lung cancer in vivo and mediate the invasion of lung cancer cell lines in vitro,” PLoS One, vol. 9, no. 6, p. e99056, 2014.
View at: Publisher Site | Google Scholar
S. M. Hald, Y. Kiselev, S. al-Saad et al., “Prognostic impact of CXCL16 and CXCR6 in non-small cell lung cancer: combined high CXCL16 expression in tumor stroma and cancer cells yields improved survival,” BMC Cancer, vol. 15, no. 1, p. 441, 2015.
View at: Publisher Site | Google Scholar
D. R. Rhodes, J. Yu, K. Shanker et al., “ONCOMINE: a cancer microarray database and integrated data-mining platform,” Neoplasia, vol. 6, no. 1, pp. 1–6, 2004.
View at: Publisher Site | Google Scholar
Z. Tang, C. Li, B. Kang, G. Gao, C. Li, and Z. Zhang, “GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses,” Nucleic Acids Research, vol. 45, no. W1, pp. W98–w102, 2017.
View at: Publisher Site | Google Scholar
D. S. Chandrashekar, B. Bashel, S. A. H. Balasubramanya et al., “UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses,” Neoplasia, vol. 19, no. 8, pp. 649–658, 2017.
View at: Publisher Site | Google Scholar
J. Gao, B. A. Aksoy, U. Dogrusoz et al., “Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal,” Science Signaling, vol. 6, no. 269, p. pl1, 2013.
View at: Publisher Site | Google Scholar
D. Warde-Farley, S. L. Donaldson, O. Comes et al., “The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function,” Nucleic Acids Research, vol. 38, suppl_2, pp. W214–W220, 2010.
View at: Publisher Site | Google Scholar
D. Szklarczyk, A. L. Gable, D. Lyon et al., “STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets,” Nucleic Acids Research, vol. 47, no. D1, pp. D607–d613, 2019.
View at: Publisher Site | Google Scholar
H. Han, J. W. Cho, S. Lee et al., “TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions,” Nucleic Acids Research, vol. 46, no. D1, pp. D380–d386, 2018.
View at: Publisher Site | Google Scholar
T. Li, J. Fan, B. Wang et al., “TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells,” Cancer Research, vol. 77, no. 21, pp. e108–e110, 2017.
View at: Publisher Site | Google Scholar
B. Ru, C. N. Wong, Y. Tong et al., “TISIDB: an integrated repository portal for tumor-immune system interactions,” Bioinformatics, vol. 35, no. 20, pp. 4200–4202, 2019.
View at: Publisher Site | Google Scholar
B. Győrffy, P. Surowiak, J. Budczies, and A. Lánczky, “Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer,” PLoS One, vol. 8, no. 12, p. e82241, 2013.
View at: Publisher Site | Google Scholar
S. Yu, M. Yi, L. Xu, S. Qin, A. Li, and K. Wu, “CXCL1 as an unfavorable prognosis factor negatively regulated by DACH1 in non-small cell lung cancer,” Frontiers in Oncology, vol. 9, p. 1515, 2019.
View at: Publisher Site | Google Scholar
M. Põld, L. X. Zhu, S. Sharma et al., “Cyclooxygenase-2-dependent expression of angiogenic CXC chemokines ENA-78/CXC ligand (CXCL) 5 and interleukin-8/CXCL8 in human non-small cell lung cancer,” Cancer Research, vol. 64, no. 5, pp. 1853–1860, 2004.
View at: Publisher Site | Google Scholar
G. Li, L. Tian, J. M. Hou et al., “Improved therapeutic effectiveness by combining recombinant CXC chemokine ligand 10 with cisplatin in solid tumors,” Clinical Cancer Research, vol. 11, no. 11, pp. 4217–4224, 2005.
View at: Publisher Site | Google Scholar
R. Zhang, L. Tian, L. J. Chen et al., “Combination of MIG (CXCL9) chemokine gene therapy with low-dose cisplatin improves therapeutic efficacy against murine carcinoma,” Gene Therapy, vol. 13, no. 17, pp. 1263–1271, 2006.
View at: Publisher Site | Google Scholar
M. F. Sanmamed, J. L. Perez-Gracia, K. A. Schalper et al., “Changes in serum interleukin-8 (IL-8) levels reflect and predict response to anti-PD-1 treatment in melanoma and non-small-cell lung cancer patients,” Annals of Oncology, vol. 28, no. 8, pp. 1988–1995, 2017.
View at: Publisher Site | Google Scholar
K. Yamaguchi, K. Ogawa, T. Katsube et al., “Platelet factor 4 gene transfection into tumor cells inhibits angiogenesis, tumor growth and metastasis,” Anticancer Research, vol. 25, no. 2a, pp. 847–851, 2005.
View at: Google Scholar
M. Tessema, D. M. Klinge, C. M. Yingling, K. Do, L. van Neste, and S. A. Belinsky, “Re-expression of CXCL14, a common target for epigenetic silencing in lung cancer, induces tumor necrosis,” Oncogene, vol. 29, no. 37, pp. 5159–5170, 2010.
View at: Publisher Site | Google Scholar
Y. Shibata, N. Kobayashi, T. Sato, K. Nakashima, and T. Kaneko, “The clinical significance of CXCL16 in the treatment of advanced non-small cell lung cancer,” Thoracic Cancer, vol. 11, no. 5, pp. 1258–1264, 2020.
View at: Publisher Site | Google Scholar
M. Suzuki, S. Mohamed, T. Nakajima et al., “Aberrant methylation of CXCL12 in non-small cell lung cancer is associated with an unfavorable prognosis,” International Journal of Oncology, vol. 33, no. 1, pp. 113–119, 2008.
View at: Google Scholar
A. YUAN, P. C. YANG, C. J. YU et al., “Interleukin-8 messenger ribonucleic acid expression correlates with tumor progression, tumor angiogenesis, patient survival, and timing of relapse in non-small-cell lung cancer,” American Journal of Respiratory and Critical Care Medicine, vol. 162, no. 5, pp. 1957–1963, 2000.
View at: Publisher Site | Google Scholar
P. L. Wagner, E. Hyjek, M. F. Vazquez et al., “CXCL12 and CXCR4 in adenocarcinoma of the lung: association with metastasis and survival,” The Journal of Thoracic and Cardiovascular Surgery, vol. 137, no. 3, pp. 615–621, 2009.
View at: Publisher Site | Google Scholar
M. T. Agulló-Ortuño, F. López-Ríos, and L. Paz-Ares, “Lung cancer genomic signatures,” Journal of Thoracic Oncology, vol. 5, no. 10, pp. 1673–1691, 2010.
View at: Publisher Site | Google Scholar
N. Han, X. Yuan, H. Wu et al., “DACH1 inhibits lung adenocarcinoma invasion and tumor growth by repressing CXCL5 signaling,” Oncotarget, vol. 6, no. 8, pp. 5877–5888, 2015.
View at: Publisher Site | Google Scholar
P. Wang, X. Yang, W. Xu, K. Li, Y. Chu, and S. Xiong, “Integrating individual functional moieties of CXCL10 and CXCL11 into a novel chimeric chemokine leads to synergistic antitumor effects: a strategy for chemokine-based multi-target-directed cancer therapy,” Cancer Immunology, Immunotherapy, vol. 59, no. 11, pp. 1715–1726, 2010.
View at: Publisher Site | Google Scholar
F. Luppi, A. M. Longo, W. I. de Boer, K. F. Rabe, and P. S. Hiemstra, “Interleukin-8 stimulates cell proliferation in non-small cell lung cancer through epidermal growth factor receptor transactivation,” Lung Cancer, vol. 56, no. 1, pp. 25–33, 2007.
View at: Publisher Site | Google Scholar
M. F. Tsai, T. H. Chang, S. G. Wu et al., “EGFR-L858R mutant enhances lung adenocarcinoma cell invasive ability and promotes malignant pleural effusion formation through activation of the CXCL12-CXCR4 pathway,” Scientific Reports, vol. 5, no. 1, p. 13574, 2015.
View at: Publisher Site | Google Scholar
J. Ehling and F. Tacke, “Role of chemokine pathways in hepatobiliary cancer,” Cancer Letters, vol. 379, no. 2, pp. 173–183, 2016.
View at: Publisher Site | Google Scholar
D. J. Waugh and C. Wilson, “The interleukin-8 pathway in cancer,” Clinical Cancer Research, vol. 14, no. 21, pp. 6735–6741, 2008.
View at: Publisher Site | Google Scholar
H. van Sweringen, N. Sakai, A. D. Tevar, J. M. Burns, M. J. Edwards, and A. B. Lentsch, “CXC chemokine signaling in the liver: impact on repair and regeneration,” Hepatology, vol. 54, no. 4, pp. 1445–1453, 2011.
View at: Publisher Site | Google Scholar
J. B. Spangler, I. Moraga, J. L. Mendoza, and K. C. Garcia, “Insights into cytokine-receptor interactions from cytokine engineering,” Annual Review of Immunology, vol. 33, no. 1, pp. 139–167, 2015.
View at: Publisher Site | Google Scholar
M. L. Epperson, C. A. Lee, and D. H. Fremont, “Subversion of cytokine networks by virally encoded decoy receptors,” Immunological Reviews, vol. 250, no. 1, pp. 199–215, 2012.
View at: Publisher Site | Google Scholar
S. Lee, C. B. Wilen, A. Orvedahl et al., “Norovirus Cell Tropism Is Determined by Combinatorial Action of a Viral Non- structural Protein and Host Cytokine,” Cell Host Microbe, vol. 22, no. 4, pp. 449–459.e4, 2017.
View at: Publisher Site | Google Scholar
G. Mosialos, “Cytokine signaling and Epstein-Barr virus-mediated cell transformation,” Cytokine & Growth Factor Reviews, vol. 12, no. 2-3, pp. 259–270, 2001.
View at: Publisher Site | Google Scholar
M. Lesina, S. M. Wörmann, J. Morton et al., “RelA regulates CXCL1/CXCR2-dependent oncogene-induced senescence in murine Kras-driven pancreatic carcinogenesis,” The Journal of Clinical Investigation, vol. 126, no. 8, pp. 2919–2932, 2016.
View at: Publisher Site | Google Scholar
D. Li, C. Beisswenger, C. Herr et al., “Myeloid cell RelA/p65 promotes lung cancer proliferation through Wnt/β-catenin signaling in murine and human tumor cells,” Oncogene, vol. 33, no. 10, pp. 1239–1248, 2014.
View at: Publisher Site | Google Scholar
J. Concetti and C. L. Wilson, “NFKB1 and cancer: friend or foe?” Cells, vol. 7, no. 9, p. 133, 2018.
View at: Publisher Site | Google Scholar
M. Kaustio, E. Haapaniemi, H. Göös et al., “Damaging heterozygous mutations in NFKB1 lead to diverse immunologic phenotypes,” The Journal of Allergy and Clinical Immunology, vol. 140, no. 3, pp. 782–796, 2017.
View at: Publisher Site | Google Scholar
K. Beishline and J. Azizkhan-Clifford, “Sp1 and the 'hallmarks of cancer',” The FEBS Journal, vol. 282, no. 2, pp. 224–258, 2015.
View at: Publisher Site | Google Scholar
C. Vizcaíno, S. Mansilla, and J. Portugal, “Sp1 transcription factor: a long-standing target in cancer chemotherapy,” Pharmacology & Therapeutics, vol. 152, pp. 111–124, 2015.
View at: Publisher Site | Google Scholar
J. J. Xie, Q. Y. Guo, J. Y. Jin, and D. Jin, “SP1-mediated overexpression of lncRNA LINC01234 as a ceRNA facilitates non-small-cell lung cancer progression via regulating OTUB1,” Journal of Cellular Physiology, vol. 234, no. 12, pp. 22845–22856, 2019.
View at: Publisher Site | Google Scholar
J. W. Griffith, C. L. Sokol, and A. D. Luster, “Chemokines and chemokine receptors: positioning cells for host defense and immunity,” Annual Review of Immunology, vol. 32, no. 1, pp. 659–702, 2014.
View at: Publisher Site | Google Scholar
R. M. Bremnes, K. al-Shibli, T. Donnem et al., “The role of tumor-infiltrating immune cells and chronic inflammation at the tumor site on cancer development, progression, and prognosis: emphasis on non-small cell lung cancer,” Journal of Thoracic Oncology, vol. 6, no. 4, pp. 824–833, 2011.
View at: Publisher Site | Google Scholar
R. M. Bremnes, L. T. Busund, T. L. Kilvær et al., “The role of tumor-infiltrating lymphocytes in development, progression, and prognosis of non-small cell lung cancer,” Journal of Thoracic Oncology, vol. 11, no. 6, pp. 789–800, 2016.
View at: Publisher Site | Google Scholar
X. Liu, S. Wu, Y. Yang, M. Zhao, G. Zhu, and Z. Hou, “The prognostic landscape of tumor-infiltrating immune cell and immunomodulators in lung cancer,” Biomedicine & Pharmacotherapy, vol. 95, pp. 55–61, 2017.
View at: Publisher Site | Google Scholar
Q. Gao, S. Wang, X. Chen et al., “Cancer-cell-secreted CXCL11 promoted CD8(+) T cells infiltration through docetaxel-induced-release of HMGB1 in NSCLC,” Journal for Immunotherapy of Cancer, vol. 7, no. 1, p. 42, 2019.
View at: Publisher Site | Google Scholar
L. Sansregret, B. Vanhaesebroeck, and C. Swanton, “Determinants and clinical implications of chromosomal instability in cancer,” Nature Reviews. Clinical Oncology, vol. 15, no. 3, pp. 139–150, 2018.
View at: Publisher Site | Google Scholar
T. Davoli, H. Uno, E. C. Wooten, and S. J. Elledge, “Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy,” Science, vol. 355, no. 6322, p. eaaf8399, 2017.
View at: Publisher Site | Google Scholar
R. J. Clifford, J. Zhang, D. M. Meerzaman et al., “Genetic variations at loci involved in the immune response are risk factors for hepatocellular carcinoma,” Hepatology, vol. 52, no. 6, pp. 2034–2043, 2010.
View at: Publisher Site | Google Scholar
M. Linch, G. Goh, C. Hiley et al., “Intratumoural evolutionary landscape of high-risk prostate cancer: the PROGENY study of genomic and immune parameters,” Annals of Oncology, vol. 28, no. 10, pp. 2472–2480, 2017.
View at: Publisher Site | Google Scholar
Y. Huang, J. Wang, P. Jia et al., “Clonal architectures predict clinical outcome in clear cell renal cell carcinoma,” Nature Communications, vol. 10, no. 1, p. 1245, 2019.
View at: Publisher Site | Google Scholar
J. Marcon, R. G. DiNatale, A. Sanchez et al., “Comprehensive genomic analysis of translocation renal cell carcinoma reveals copy-number variations as drivers of disease progression,” Clinical Cancer Research, vol. 26, no. 14, pp. 3629–3640, 2020.
View at: Publisher Site | Google Scholar

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Copyright © 2021 He Tian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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