Systematic analysis of CXC chemokine–vascular endothelial growth factor A network in colonic adenocarcinoma from the perspective of angiogenesis

doi:10.21203/rs.3.rs-1346159/v1

PDF

Research Article

Systematic analysis of CXC chemokine–vascular endothelial growth factor A network in colonic adenocarcinoma from the perspective of angiogenesis

https://doi.org/10.21203/rs.3.rs-1346159/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted 17 Mar, 2022

You are reading this latest preprint version

Background

Tumor angiogenesis plays a vital role in tumorigenesis, proliferation, and metastasis. Recently, it has been gradually recognized that VEGFA and CXC chemokines are important players in angiogenesis. It is interesting to explore the expression level, gene regulatory network, prognostic value, and target prediction of CXC chemokine-VEGFA network in colon adenocarcinoma (COAD) from the perspective of tumor angiogenesis.

Methods

In this study, we analyzed gene expression and regulation, prognostic value, target prediction, and immune infiltrates related to CXC chemokine-VEGFA network in patients with COAD using multiple databases (cBioPortal, UALCAN, Human Protein Atlas, GeneMANIA, GEPIA, TIMER, TRRUST, LinkedOmics, and Metascape).

Results

Our results showed that CXCL1/2/3/5/6/8/11/16/17 and VEGFA were significantly overexpressed, while CXCL12/13/14 was underexpressed in patients with COAD. Moreover, genetic alterations in the CXC chemokine-VEGFA network found at varying rates in patients with COAD were as follows: CXCL1/2/17 (2.1%), CXCL3/16 (2.6%), CXCL5/14 (2.4%), CXCL6 (3%), CXCL8 (0.8%), CXCL11/13 (1.9%), CXCL12 (0.6%), and VEGFA (1.3%). Promoter methylation of CXCL1/2/3/11/13/17 was significantly lower in patients with COAD, while methylation of CXCL5/6/12/14 and VEGFA was significantly higher. Furthermore, CXCL9/10/11 and VEGFA expression was significantly correlated with the pathological stages of COAD. In addition, patients with COAD with high CXCL8/11/14 or low VEGFA expression levels survived longer than patients without these characteristics. CXC chemokines and VEGFA form a complex regulatory network through co-expression, co-localization, and genetic interactions. Moreover, many transcription factor targets of the CXC chemokine-VEGFA network in patients with COAD were found: RELA, NFKB1, ZFP36, XBP1, HDAC2, SP1, ATF4, EP300, BRCA1, ESR1, HIF1A, EGR1, STAT3, and JUN. We further identified the top three miRNAs involved in regulating each CXC chemokine within the network: miR-518C, miR-369-3P, and miR-448 regulated CXCL1; miR-518C, miR-218, and miR-493 regulated CXCL2; miR-448, miR-369-3P, and miR-221 regulated CXCL3; miR-423 regulated CXCL13; miR-378, miR-381, and miR-210 regulated CXCL14; miR-369-3P, miR-382, and miR-208 regulated CXCL17; miR-486 and miR-199A regulated VEGFA. Furthermore, the CXC chemokine-VEGFA network in patients with COAD was significantly associated with immune infiltration.

Conclusions

This study revealed that the CXC chemokine-VEGFA network is a prognostic biomarker for patients with COAD. Moreover, our study provides new therapeutic targets for COAD which will act as a reference for further research in the future.

CXC chemokines

vascular endothelial growth factor A

colon adenocarcinoma

gene regulation network

target prediction

Colon cancer is a common malignant tumor of the digestive tract. The incidence and mortality of colon adenocarcinoma (COAD) is the third highest of all cancer types ¹. Since early diagnosis of COAD remains difficult, its mortality is increasing significantly every year ². Approximately half of the patients relapsed or died within 5 years ³. Therefore, finding new biomarkers and therapeutic targets for early diagnosis is the most important first step in the prevention and treatment of COAD.

Chemokines are a family of small heparin-binding proteins 8–10 kDa in size. Within the chemokine family, there are four subgroups (CXC, CC, CX3C, and C). The CXC subgroup has been shown to play a key role in angiogenesis in both physiological and pathological settings ⁴. Recently, the role of CXCL in the regulation of tumor angiogenesis has attracted increasing interest ⁵. Different members of the CXC chemokines subgroup can promote or inhibit angiogenesis, thus promoting or inhibiting tumor growth ⁶. Multiple factors have been identified as regulators of angiogenesis. However, CXC chemokines are a unique family of cytokines that regulate angiogenesis in many different ways ⁷. Vascular endothelial growth factor A (VEGFA) is a vital factor that plays an essential role in tumor angiogenesis and development ⁸. Sunitinib has been used for treatment of advanced renal cell carcinoma as a VEGFA inhibitor. However, the side effects of sunitinib can be quite severe, such as kidney damage and cardiovascular damage ⁹. CXC chemokines and VEGFA are heavily regulated during tumor angiogenesis. It has been shown that CXCL12 can promote a malignant phenotype by promoting the clonal growth of colorectal cancer cells and regulating the expression of VEGF and ICAM-1 ¹⁰.

Multiple online databases were used to explore the expression level, gene regulation network, prognostic value, and regulation targets of the CXC chemokine-VEGFA network in patients with COAD from an angiogenic perspective in this study. In addition, we want to identify the relationship between CXC chemokine and VEGFA expression and the development and prognosis of COAD, and to provide new insights into targeted therapies for patients with COAD.

2.1 UALCAN analysis

UALCAN (http://ualcan.path.uab.edu/analysis.html) is a free online database that provides analysis based on The Cancer Genome Atlas (TCGA) and MET500 cohort data ¹¹. The UALCAN database's "Expression Analysis" module was utilized to examine TCGA gene expression data, and the following screening criteria were applied: (1) gene: CXC chemokines and VEGFA, (2) dataset: COAD, and (3) threshold setting conditions: P-value cutoff = 0.05. A Student’s t-test was used for the comparative analysis ¹².

2.2 Human Protein Atlas analysis

The Human Protein Atlas (https://www.proteinatlas.org/), an open access resource, provides analyses for specific human genes and proteins ¹³. Screening condition: (1) gene: CXC chemokines and VEGFA, (2) choose section: tissue and pathology, (3) choose tissue: colon and COAD, and (4) choose a picture of tissue types: normal colon tissue and COAD.

2.3 GEPIA

GEPIA (http://gepia.cancer-pku.cn/index.html) is an analysis tool that delivers RNA sequencing expression data from 9,736 cancers and 8,587 non-cancerous samples ¹⁴. Gene (CXC chemokines and VEGFA), dataset (COAD), and threshold conditions (P-value cutoff = 0.05) were set as screening criteria. The expression of CXC chemokines and VEGFA, as well as the pathological stage of COAD, were analyzed using a Student's t-test. The prognosis of patients with COAD was analyzed using the Kaplan–Meier curve ¹².

2.4 cBioPortal analysis

cBioPortal (http://cbioportal.org) is a free online database for visualizing, studying, and analyzing cancer genomic data ¹⁵. The analysis of genetic alterations in the CXC chemokine-VEGFA network was conducted using cBioPortal in this study. 636 samples of COAD were analyzed. A z-score threshold of ± 2.0 was used to calculate mRNA expression z-scores for all samples (log RNA Seq V2 RSEM). CXC chemokines and VEGFA were the chosen gene ¹².

2.5 STRING analysis

STRING (https://string-db.org/cgi/input.pl) is a free online database that helps researchers to analyses all publicly available sources of protein–protein interaction (PPI) data ¹⁶. We created the PPI network interaction using STRING in this study. The screening criteria were set as follows: (1) confidence: 0.400, and species: Homo sapiens ¹².

2.6 GeneMANIA analysis

GeneMANIA (http://www.genemania.org) is a free online database that used to create PPI networks, and analyze gene function ¹⁷. The interaction networks were built using this database to explore the roles of CXC chemokines and VEGFA ¹².

2.7 Metascape analysis

Metascape (https://metascape.org) is a free online gene function analysis tool that assists users in using current common bioinformatics analysis approaches to batch gene and protein analysis in order to predict function ¹⁸. We conducted Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the CXC chemokine-VEGFA network in COAD using Metascape ¹².

2.8 TRRUST analysis

TRRUST (https://www.grnpedia.org/trrust/) is a free online database for human transcriptional regulatory networks ¹⁹. We sought to discover key factors regulating the expression of the CXC chemokine-VEGFA network in COAD patients using TRRUST. The “Find key regulators for query genesa” module of TRRUST, species (human), and gene (CXC chemokines and VEGFA) were chosen in this study ¹².

2.9 LinkedOmics analysis

LinkedOmics (http://www.linkedomics.org/) is a free database that provides methods for analyzing and comparing cancer multiomics data ²⁰. The “LinkInterpreter” module of LinkedOmics was used to derive biological insights into miRNA target enrichment, and transcription factor target enrichment of the CXC chemokine-VEGFA network. A minimum number of three genes (size), cancer type (COAD), a simulation of 500, gene (CXC chemokines and VEGFA), and target dataset (RNA-seq) were chosen in this study ¹².

2.10 Timer analysis

TIMER (https://cistrome.shinyapps.io/timer/) is a free online platform for systematic analysis of tumor-infiltrating immune cells ²¹. The “Gene module” of TIMER was used to assess the correlation between the expression level of CXC chemokine-VEGFA network and tumor-infiltrating immune cells ¹².

3.1 Aberrant expression of CXC chemokine-VEGFA network

The expression levels of the CXC chemokine-VEGFA network in patients with COAD to those without COAD were analyzed. We found that the transcriptional levels of CXCL1/2/3/5/6/8/11/16/17 and VEGFA were remarkably upregulated in sex, pathological stage, and sample type (P < 0.05) (Fig. 1A1–G3, K1–M3). CXCL12/13/14 expression level in patients with COAD was downregulated in sex, pathological stage, and sample type (P < 0.05) (Fig. 1H1–J3). In addition, immunohistochemical results validated the differential expression of CXC chemokine-VEGFA network between patient with COAD and those without COAD (Supplementary Fig. 1). The pathological stage of COAD and the differential expression of the CXC chemokine-VEGFA network were assessed in this study. The pathological stage in patients with COAD and the expression of CXCL9/10/11 and VEGFA was found a significant correlation (P < 0.05) (Fig. 2). Next, the prognostic ability of the CXC chemokine-VEGFA network expression in COAD patients was evaluated. The overall survival was longer in COAD patients when levels of CXCL8/11/14 expression were higher (P ≤ 0.05) (Fig. 3A, 3B, and 3C) or when levels of VEGFA expression were lower (P < 0.05) (Fig. 3D).

3.2 Promoter methylation and genetic alteration analyses of CXC chemokine-VEGFA network

TCGA were utilized to analyze the genetic alterations of the CXC chemokine-VEGFA network in patients with COAD. As a result, the expression of VEGFA was altered by 1.3% in COAD patients (Supplementary Fig. 2). COAD patients had higher promoter methylation level of VEGFA than that in individuals without COAD (Supplementary Fig. 3). However, differences in chemokine expression levels in patients with COAD: CXC1/2/17 (2.1%), CXCL3/16 (2.6%), CXCL5/14 (2.4%), CXCL6 (3%), CXCL8 (0.8%), CXCL11/13 (1.9%), and CXCL12 (0.6%) were found (Supplementary Fig. 2). Similarly, the promoter methylation level of VEGFA and CXCL5/6/12/14 was higher in COAD patients than that in healthy people (Supplementary Fig. 3). Conversely, healthy people had higher promoter methylation level of CXCL1/2/3/11/13/17 expression than in with COAD patients (Supplementary Fig. 3).

3.3 CXC chemokines and VEGFA interaction network

The potential interactions between CXC chemokines and VEGFA in patients with COAD were explored. 13 nodes and 68 edges were obtained in PPI network using STRING software. (Supplementary Fig. 4A). Moreover, cell chemotaxis, chemokine and cytokine receptor binding, chemokine and cytokine activity, leukocyte chemotaxis, and migration were the major functions of the CXC chemokine-VEGFA network in COAD patients (Supplementary Fig. 4B). In brief, CXC chemokines are connected to and interact with VEGFA in a complex network.

3.4 GO and KEGG pathway enrichment analysis

Metascape were utilized to analyze the functions of the CXC chemokine-VEGFA network in patients with COAD. We found that the biological processes connected with CXC chemokines and VEGFA were mainly related to leukocyte chemotaxis, myeloid leukocyte migration, positive regulation of leukocyte chemotaxis, lymphocyte migration, and regulation of multi-organism processes (Supplementary Fig. 5A). Moreover, chemokine activity, cytokine activity, heparin binding, and growth factor activity were the main molecular functions of CXC chemokine-VEGFA network expression (Supplementary Fig. 5B). The KEGG pathway of the CXC chemokine-VEGFA network in COAD was mainly involved in cytokine-cytokine receptor interaction, rheumatoid arthritis, interleukin (IL)-17 signaling pathway, and nuclear factor kappa B (NF-κB) signaling pathway (Supplementary Fig. 5C).

3.5 Transcription factor targets involved with the CXC chemokine-VEGFA network

Potential transcription factors involved with the CXC chemokine-VEGFA network in COAD patients were identified (Table 1). v-rel reticuloendotheliosis viral oncogene homolog A (RELA) and nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (NFKB1) were the key transcription factors involved with CXCL1/2/5/8/12 and VEGFA in COAD patients (P < 0.001). In addition, CXCL8 and VEGFA were found to be regulated by ZFP36 ring finger protein (ZFP36), X-box binding protein 1 (XBP1), histone deacetylase 2 (HDAC2), activating transcription factor 4 (ATF4), E1A binding protein p300 (EP300), early growth response 1 (EGR1), signal transducer and activator of transcription 3 (STAT3), and Jun proto-oncogene (JUN) (P < 0. 01). Furthermore, CXCL1/5/14 and VEGFA were found to be regulated by Sp1 transcription factor (SP1) (P < 0.001). Breast cancer 1 (BRCA1) is the key transcription factor involved with CXCL1 and VEGFA in COAD patients (P < 0. 01). Finally, estrogen receptor 1 (ESR1) and hypoxia inducible factor 1 alpha subunit (HIF1A) regulated the functions of CXCL12 and VEGFA (P < 0.01).

Table 1

Key regulated factor of CXCL and *VEGFA* in COAD (TRRUST).
Key TF	Description	Regulated gene	P-value	FDR
RELA	v-rel reticuloendotheliosis viral oncogene homolog A (avian)	CXCL1, CXCL2, CXCL5,CXCL8, CXCL12,VEGFA	2.44e-08	1.78e-07
NFKB1	nuclear factor of kappa light polypeptide gene enhancer in B-cells 1	CXCL1, CXCL2, CXCL5,CXCL8, CXCL12,VEGFA	2.54e-08	11.78e-07
ZFP36	ZFP36 ring finger protein	CXCL8,VEGFA	1.22e-05	5.71e-05
XBP1	X-box binding protein 1	CXCL8,VEGFA	7.44e-05	0.000261
HDAC2	histone deacetylase 2	CXCL8,VEGFA	0.000152	0.000426
SP1	Sp1 transcription factor	CXCL1,CXCL5, CXCL14,VEGFA	0.000231	0.000515
ATF4	activating transcription factor 4 (tax-responsive enhancer element B67)	CXCL8,VEGFA	0.000257	0.000515
EP300	E1A binding protein p300	CXCL8,VEGFA	0.000661	0.00106
BRCA1	breast cancer 1, early onset	CXCL1,VEGFA	0.000685	0.00106
ESR1	estrogen receptor 1	CXCL12,VEGFA	0.00121	0.0017
HIF1A	hypoxia inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor)	CXCL12,VEGFA	0.00144	0.00184
EGR1	early growth response 1	CXCL8,VEGFA	0.00162	0.00189
STAT3	signal transducer and activator of transcription 3 (acute-phase response factor)	CXCL8,VEGFA	0.00415	0.00447
JUN	jun proto-oncogene	CXCL8,VEGFA	0.00456	0.00456

3.6 miRNA targets of CXC chemokine-VEGFA network

The top three miRNA targets of the CXC chemokine-VEGFA network were obtained (Table 2). The miRNA targets of CXCL1 were miR-518C, miR-369-3P, and miR-44. In addition, miR-518C, miR-218, and miR-493 were identified as potential miRNA targets that regulate CXCL2. Furthermore, we found that CXCL3 was regulated by miR-448, miR-369-3P, and miR-221. An miRNA target of CXCL13 is miR-423. Moreover, miR-378, miR-381, and miR-210 were identified as potential miRNA targets that regulate CXCL14. CXCL17 is regulated by miR-369-3P, miR-382, and miR-208. Furthermore, our results showed that miR-486 and miR-199A are potential miRNA targets that regulate VEGFA.

Table 2

The miRNA target of CXCL and *VEGFA* in COAD (LinkedOmics).
Cancer type	Gene	Gene set	Leading Edge Number	P-value	FDR
COAD	CXCL1	TCCAGAG,MIR-518C	47	0	0.013584
		GTATTAT,MIR-369-3P	86	0	0.018113
		ATATGCA,MIR-448	64	0	0.024150
	CXCL2	TCCAGAG.MIR-518C	51	0	0
		AAGCACA,MIR-218	143	0	0
		ATGTACA,MIR-493	130	0	0
	CXCL3	ATATGCA,MIR-448	92	0	0
		GTATTAT,MIR-369-3P	100	0	0
		ATGTAGC, MIR-221, MIR-222	49	0	0.00052445
	CXCL13	ACCGAGC, MIR-423	3	0.015625	0.028853
	CXCL14	GTCAGGA,MIR-378	18	0.0051151	0.02937
		CTTGTAT,MIR-381	55	0	0.031719
		ACGCACA,MIR-210	3	0.0031348	0.037446
	CXCL17	GTATTAT,MIR-369-3P	97	0	0.0039402
		ACAACTT,MIR-382	18	0	0.023641
		CGTCTTA,MIR-208	5	0	0.029552
	VEGFA	GTACAGG,MIR-486	16	0.0030120	0.053189
		CTACTGT,MIR-199A	52	0	0.054125

3.7 Correlation of CXC chemokine-VEGFA network expression and differentially expressed genes

mRNA sequencing data of 379 patients with COAD were obtained from the TCGA database of LinkedOmics. 19,828 genes were closely related to CXCL1/2/3/5/6/8/11/12/13/14/16/17 and VEGFA (Supplementary Fig. 6). Among them, we found that 11,701 and 8,127 genes were negatively and positively correlated with CXCL1 expression, respectively (Supplementary Fig. 6A1). Moreover, 50 genes had a notable positive or negative correlation with CXCL1 expression in COAD patients (P < 0.05) (Supplementary Fig. 6A2 and 6A3). CXCL1 expression was strongly positively associated with expression of CXCL3 (Pearson correlation coefficient (PCO) = 0.8921, P = 4.226e–132) (Supplementary Fig. 7A1), CXCL2 (PCO = 0.8121, P = 3.304e–90) (Supplementary Fig. 7A2), and ZC3H12A (Pearson correlation = 0.6531, P = 1.882e–47) (Supplementary Fig. 7A3).

Furthermore, we found that 11,137 and 8,691 genes were negatively and positively correlated with CXCL2 expression, respectively (Supplementary Fig. 6B1). 50 genes had a marked positive or negative correlation with CXCL2 expression in COAD patients (Supplementary Fig. 6B2 and 6B3). Moreover, the expression of CXCL2 was positively associated with the expression of CXCL3 (PCO = 0.8728, P = 1.601e–119) (Supplementary Fig. 7B1), CXCL1 (Pearson correlation = 0.8121, P = 3.304e–90) (Supplementary Fig. 7B2), and ZC3H12A (PCO = 0.6447, P = 6.735e–46) (Supplementary Fig. 7B3). Furthermore, 12,096 and 7,732 genes were negatively and positively correlated with CXCL3 expression, respectively (Supplementary Fig. 6C1). 50 genes had a notable positive or negative correlation with CXCL3 expression in COAD patients (Supplementary Fig. 6C2 and 6C3). Expression of CXCL3 was positively associated with the expression of CXCL1 (PCO = 0.8921, P = 4.226e–132) (Supplementary Fig. 7C1), CXCL2 (PCO = 0.8728, P = 1.601e–119) (Supplementary Fig. 7C2), and ZC3H12A (PCO = 0.6707, P = 7.446e–51) (Supplementary Fig. 7C3). Our results showed that 8,680 and 11,148 genes were negatively and positively correlated with CXCL5 expression, respectively (Supplementary Fig. 6D1). 50 genes had a notable positive or negative correlation with CXCL5 expression in COAD patients (Supplementary Fig. 6D2 and 6D3). CXCL5 expression was positively associated with the expression of IL24 (PCO = 0.7438, P = 5.884e–68) (Supplementary Fig. 7D1), IL8 (PCO = 0.7269, P = 1.632e–63) (Supplementary Fig. 7D2), and MMP3 (PCO = 0.7213, P = 4.269e–62) (Supplementary Fig. 7D3). Our results showed that 8,605 and 11,223 genes were negatively and positively correlated with CXCL6 expression, respectively (Supplementary Fig. 6E1). 50 genes had a marked positive or negative correlation with CXCL6 expression in COAD patients (Supplementary Fig. 6E2 and 6E3). CXCL6 expression was positively associated with the expression of CXCL5 (PCO = 0.7105, P = 1.689e–59) (Supplementary Fig. 7E1), MMP3 (PCO = 0.6904, P = 5.921e–55) (Supplementary Fig. 7E2), and IL8 (PCO = 0.6833, P = 1.935e–53) (Supplementary Fig. 7E3). In addition, 9,079 and 10,749 genes were negatively and positively correlated with CXCL8 expression, respectively (Supplementary Fig. 6F1). 50 genes had a significant positive or negative correlation with CXCL8 expression in COAD patients (Supplementary Fig. 6F2 and 6F3). CXCL8 expression was positively associated with GPR109B (PCO = 0.7712, P = 5.939e–76) (Supplementary Fig. 7F1), IL1B (PCO = 0.7623, P = 3.25e–73) (Supplementary Fig. 7F2), and OSM (PCO = 0.7593, P = 2.368e–72) (Supplementary Fig. 7F3). Furthermore, 9,517 and 10,311 genes were negatively and positively correlated with CXCL11 expression, respectively (Supplementary Fig. 6G1). 50 genes had a significant positive or negative correlation with CXCL11 expression in COAD patients (Supplementary Fig. 6G2 and 6G3). CXCL11 expression was positively associated with CXCL10 (PCO = 0.8389, P = 1.299e–101) (Supplementary Fig. 7G1), UBD (PCO = 0.7214, P = 3.935e–62) (Supplementary Fig. 7G2), and IDO1 (PCO = 0.7116, P = 9.137e–60) (Supplementary Fig. 7G3). Moreover, 8,017 and 11,811 genes were negatively and positively correlated with CXCL12 expression, respectively (Supplementary Fig. 6H1). 50 genes had a significant positive or negative correlation with CXCL12 expression in COAD patients (Supplementary Fig. 6H2 and 6H3). CXCL12 expression was positively associated with NPR1 (PCO = 0.804, P = 3.835e–87) (Supplementary Fig. 7H1), SLIT3 (PCO = 0.8013, P = 3.915e–86) (Supplementary Fig. 7H2), and SHE (PCO = 0.7966, P = 1.928e–84) (Supplementary Fig. 7H3). Our results showed that 8,779 and 11,049 genes were negatively and positively correlated with CXCL13 expression, respectively (Supplementary Fig. 6I1). 50 genes had a significant positive or negative correlation with CXCL13 expression in COAD patients (Supplementary Fig. 6I2 and 6I3). CXCL13 expression was positively associated with expression of TIGIT (PCO = 0.8089, P = 5.598e–89) (Supplementary Fig. 7I1), SH2D1A (PCO = 0.7857, P = 1.229e–80) (Supplementary Fig. 7I2), and SIRPG (PCO = 0.7854, P = 1.508e–80) (Supplementary Fig. 7I3). In addition, 8,724 and 11,104 genes were negatively and positively correlated with CXCL14 expression, respectively (Supplementary Fig. 6J1). 50 genes had a significant positive or negative correlation with CXCL14 expression in COAD patients (Supplementary Fig. 6J2 and 6J3). CXCL14 expression was positively associated with the expression of D4S234E (PCO = 0.7057, P = 2.24e–58) (Supplementary Fig. 7J1), TNFSF11 (PCO = 0.6172, P = 3.643e–41) (Supplementary Fig. 7J2), and COL9A1 (PCO = 0.6154, P = 7.338e–41) (Supplementary Fig. 7J3). Furthermore, 9,737 and 10,091 genes were negatively and positively correlated with CXCL16 expression, respectively (Supplementary Fig. 6K1). 50 genes had a significant positive or negative correlation with CXCL16 expression in COAD patients (Supplementary Fig. 6K2 and 6K3). CXCL16 expression was positively associated with the expression of ZMYND15 (PCO = 0.7944, P = 1.175e–83) (Supplementary Fig. 7K1), FLII (PCO = 0.6123, P = 2.248e–40) (Supplementary Fig. 7K2), and NDEL1 (PCO = 0.6072, P = 1.486e–39) (Supplementary Fig. 7K3). We also found that 10,483 and 9,345 genes were negatively and positively correlated with CXCL17 expression, respectively (Supplementary Fig. 6L1). 50 genes had a significant positive or negative correlation with CXCL17 expression in COAD patients (Supplementary Fig. 6L2 and 6L3). CXCL17 expression was positively associated with the expression of FAM83A (PCO = 0.4586, P = 4.148e–21) (Supplementary Fig. 7L1), GPR110 (PCO = 0.435, P = 6.333e–19) (Supplementary Fig. 7L2), and SEMG1 (PCO = 0.402, P = 3.753e–16) (Supplementary Fig. 7L3). Finally, we found that 10,446 and 9,382 genes were negatively and positively correlated with VEGFA expression, respectively (Supplementary Fig. 6M1). 50 genes had a significant positive or negative correlation with VEGFA expression in COAD patients (Supplementary Fig. 6M2 and 6M3). VEGFA expression was positively associated with the expression of GTPBP2 (PCO = 0.5773, P = 4.639e–35) (Supplementary Fig. 7M1), CCNL1 (PCO = 0.5422, P = 2.411e–30) (Supplementary Fig. 7M2), and CREBZF (PCO = 0.516, P = 3.606e–27) (Supplementary Fig. 7M3).

3.8 Immune cell infiltration and CXC chemokine-VEGFA network expression

CXCL1 expression in COAD patients was positively associated with CD8 + T cells infiltration, neutrophils, and dendritic cells (P < 0.05) (Supplementary Fig. 8A). However, macrophages were negatively associated with CXCL1 expression (P < 0.01) (Supplementary Fig. 8A). In addition, neutrophil infiltration was positively associated with the expression of CXCL2 and CXCL3 (P < 0.001) (Supplementary Fig. 8B and 8C). However, macrophages were negatively associated with CXCL2 and CXCL3 expression (P < 0.001) (Supplementary Fig. 8B and 8C). Furthermore, expression levels of CXCL5/6/8 in patients with COAD were positively associated with the infiltration of CD8 + T cells, macrophages, neutrophils, and dendritic cells (P < 0.01) (Supplementary Fig. 8D, 8E, and 8F). B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells were positively associated with CXCL11/12/13/16 expression (P < 0.01) (Supplementary Fig. 8G, 8H, 8I, and 8K). The expression level of CXCL14 in patients with COAD was positively associated with the infiltration of CD8 + T cells, CD4 + T cells, neutrophils, and dendritic cells (P < 0.05) (Supplementary Fig. 8J). B cells were positively associated with CXCL17 expression (P < 0.001) (Supplementary Fig. 8L). CD4 + T cells were positively associated with VEGFA expression (P < 0.01) (Supplementary Fig. 8M).

Tumor angiogenesis plays a vital role in tumorigenesis, proliferation, and metastasis. In recent years, studies have identified VEGFA and CXC chemokines as important participants in angiogenesis, particularly tumor angiogenesis ^{12, 22, 23}. The expression levels of CXC chemokines and VEGFA have been studied in a range of tumor types; however, findings are contradictory with regard to colonic adenocarcinomas ^{24, 25}. This study investigated expression level, gene regulatory network, prognostic value, and target prediction of the CXC chemokine-VEGFA network for COAD from the perspective of tumor angiogenesis.

In this study, we also examined whether there was correlation between pathological stage and differential expression of COAD. The expression of CXCL1/2/3/5/6/8/11/16/17 and VEGFA was upregulated in patients with COAD compared to individuals without COAD. Patients with COAD also showed downregulated CXCL12/13/14 expression. The results were same with those in a previous study in patients with COAD ²⁵, and contradict previous findings in patients with colorectal cancer ²⁴. This may be due to the small sample size and the many different types of colorectal cancer. We further attempted to explain the different expression levels by investigating promoter methylation and gene alteration in patients with COAD, as these are the factors that affect tumor cell proliferation, angiogenesis, and metastasis. We found that patients with COAD had different rates of genetic alteration in their genes. Moreover, the promoter methylation levels of CXCL5/6/12/14 and VEGFA were higher in patients with COAD than those in normal people. Conversely, the promoter methylation levels of CXCL1/2/3/11/13/17 were lower in patients with COAD. So, we hypothesized that genetic methylation and alteration within the CXC chemokine-VEGFA network may be the leading cause of abnormal gene expression levels in patients with COAD.

We also found a notable correlation between the CXCL9/10/11 and VEGFA expression, and the pathological stage of COAD. Furthermore, the survival time of patients with COAD was longer with low VEGFA expression levels or high CXCL8/11/14 expression levels. Therefore, the expression levels of CXCL8/11/14 and VEGFA may be potential prognostic indicators for COAD patients. CXCL8/11/14 and VEGFA promote tumor angiogenesis in different ways ^26–28. Thus, they may affect the prognosis of patients with COAD through multiple biological functions.

The potential functions and interactions of the CXC chemokine-VEGFA network were explored in this study. They were found to be complex and tightly connected. Genes in the network were mainly involved in cytokine receptor binding, chemokine and cytokine activity, leukocyte chemotaxis, and migration. All were closely related to angiogenesis. For example, IL-8 (CXCL8) promotes tumor angiogenesis by binding to CXCR1 and CXCR2 receptors ²⁹. In addition, increasing the anti-tumor activity of cytokine-induced killer cells could reduce tumor proliferation and angiogenesis ³⁰. Taken together, these results suggest that the CXC chemokine-VEGFA network may influence the development of COAD by increasing tumor angiogenesis.

Furthermore, the functions of the CXC chemokine-VEGFA network in patients with COAD were mainly related to chemokine activity, cytokine activity, and growth factor activity with GO enrichment analysis, all functions closely related to tumor angiogenesis. More studies are needed to confirm the mechanism by which this happens. In this study, we further found through KEGG pathway analysis that the cytokine–cytokine receptor interaction signaling pathway, IL-17 signaling pathway, and NF-κB signaling pathway were highly involved in the CXC chemokine-VEGFA network in COAD patients. All of them are highly related to tumor angiogenesis ^{31, 32}. Therefore, regulation of them may be a potential treatment strategy for patients with COAD.

Mutated or altered transcription factors represent a unique class of drug targets that mediate aberrant gene expression, and the development of these drugs may impact future cancer treatments. Thus, the targets and regulators of the CXC chemokine-VEGFA network in COAD patients were further analyzed. The transcription factor targets of the CXC chemokine-VEGFA network in patients with COAD were identify. RELA, NFKB1, ZFP36, XBP1, HDAC2, SP1, ATF4, EP300, BRCA1, ESR1, HIF1A, EGR1, STAT3, and JUN are crucial regulatory factors. Our results showed that these factors have potential functions in regulating tumor angiogenesis by targeting VEGFA. Studies have shown that RELA, NFKB1, HDAC2, SP1, ATF4, EP300, BRCA1, ESR1, HIF1A, EGR1, STAT3, and JUN regulate tumor angiogenesis, thus affecting tumor growth and prognosis ^33–44. However, the role of ZFP36 and XBP1 in tumor angiogenesis has not yet been reported. miRNAs also play a crucial role in regulating gene expression. miRNAs suppress target genes expression by targeting at their 3′-untranslated regions. miRNA target discovery may ultimately help elucidate the underlying mechanisms of tumorigenesis. Thus, CXC chemokine-VEGFA network-associated miRNA targets in patients with COAD were further explored. Most of them (miR-218, miR-493, miR-221, miR-222, miR-423, miR-378, miR-381, miR-210, miR-382, and miR-199A) have been shown to regulate tumor angiogenesis ^45–48. In short, our study provides potential therapeutic strategies for the treatment of COAD through the prediction of regulated factors and miRNA targets.

The correlation between CXC chemokine-VEGFA network expression and differentially expressed genes in COAD patients was explored in this study. We found that in patients with COAD, close to 20,000 genes were negatively or positively correlated with CXC chemokine-VEGFA network expression. From these, we screened for genes with the highest correlation with CXC chemokines and VEGFA. Some of the genes with the highest correlation (ZC3H12A, IL24, MMP3, IL1B, OSM, IDO1, NPR1, and TIGIT) were positively associated with tumor angiogenesis ^{49, 50}. Regulation of these cancer-related genes may offer an alternative therapeutic strategy for the treatment of patients with COAD. Immune infiltration is highly related to the clinical prognosis of tumors. Immune cells reach the tumor site through vascular transport, and vascularization of tumors is a process mediated by angiogenesis. We found that CXC chemokine-VEGFA network expression, which regulates angiogenesis, is correlated with the infiltration of immune cell. This infiltration involved CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells. Improving immune cell infiltration in COAD by developing drugs that act on the CXC chemokine-VEGFA network or CXC chemokines and VEGFA-related regulatory targets is a viable therapeutic oncology strategy.

In this study, we determined the expression level and gene regulatory network of the CXC chemokine-VEGFA network, which plays an important role in angiogenesis in COAD. We also identified new prognostic biomarkers and therapeutic targets. These findings will provide insight for the study and treatment of COAD.

COAD

Colon adenocarcinoma

VEGFA

Vascular endothelial growth factor A

GEPIA

Gene expression profiling analysis

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

TCGA

The Cancer Genome Atlas

RELA

v-rel reticuloendotheliosis viral oncogene homolog A

NFKB1

Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1

ZFP36

ZFP36 ring finger protein

XBP1

X-box binding protein 1

HDAC2

Histone deacetylase 2

ATF4

Activating transcription factor 4

EP300

E1A binding protein p300

EGR1

Early growth response 1

STAT3

Signal transducer and activator of transcription 3

JUN

Jun proto-oncogene

SP1

Sp1 transcription factor

BRCA1

Breast cancer 1

ESR1

Estrogen receptor 1

HIF1A

Hypoxia inducible factor 1 alpha subunit

Ethics approval and consent to participate

Not applicable. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles.

Consent for publication

Not applicable.

Availability of data and material

The datasets generated and analyzed during the current study are available in the UALCAN (http://ualcan.path.uab.edu/analysis.html), The Human Protein Atlas (https://www.proteinatlas.org/), GEPIA (http://gepia.cancer-pku.cn/index.html), cBioPortal (http://cbioportal.org), STRING (https://string-db.org/cgi/input.pl), GeneMANIA (http://www.genemania.org), and Metascape (https://metascape.org).

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the project of financial fund science and technology special competitive allocation of Zhanjiang (Zhanke[2010]174), Guangdong Medical University "punching and reinforcing" 2021 funding project (4SG21202G), and Science and Technology Development Center of Chinese Pharmaceutical Society (CMEI2021KPYJ00310).

Authors' contributions

ZS and JC performed data analysis work and aided in writing the manuscript. YLST designed the study and assisted in writing the manuscript. QYX, LD, XYL, YSC, and HL edited the manuscript. All authors read and approved the final manuscript.

Acknowledgments

Not applicable.

Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69(1):7–34.
Aran V, Victorino AP, Thuler LC, et al. Colorectal cancer: epidemiology, disease mechanisms and interventions to reduce onset and mortality. Clin Colorectal Cancer. 2016;15(3):195–203.
Parkin DM, Bray F, Ferlay J, et al. Global cancer statistics, 2002. CA Cancer J Clin. 2005;55(2):74–108.
Keeley EC, Mehrad B, Strieter RM. CXC chemokines in cancer angiogenesis and metastases. Adv Cancer Res. 2010;106:91–111.
Zhang W, Wu Q, Wang C, et al. AKIP1 promotes angiogenesis and tumor growth by upregulating CXC-chemokines in cervical cancer cells. Mol Cell Biochem. 2018;448(1–2):311–20.
Strieter RM, Belperio JA, Phillips RJ, et al. CXC chemokines in angiogenesis of cancer. Semin Cancer Biol. 2004;14(3):195–200.
Strieter RM, Burdick MD, Gomperts BN, et al. CXC chemokines in angiogenesis. Cytokine Growth Factor Rev. 2005;16(6):593–609.
Xu S, Zhang H, Chong Y, et al. YAP promotes VEGFA expression and tumor angiogenesis though Gli2 in human renal cell carcinoma. Arch Med Res. 2019;50:225–33.
Lai S, Molfino A, Seminara P, et al. Vascular endothelial growth factor inhibitor therapy and cardiovascular and renal damage in renal cell carcinoma. Curr Vasc Pharmacol. 2018;16:190–6.
Ottaiano A, Franco R, Aiello Talamanca A, et al. Overexpression of both CXC chemokine receptor 4 and vascular endothelial growth factor proteins predicts early distant relapse in stage II-III colorectal cancer patients. Clin Cancer Res. 2006;12(9):2795–803.
Chandrashekar DS, Bashel B, Balasubramanya SAH, et al. UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 2017;19:649–58.
Situ Y, Xu Q, Deng L, Zhu Y, et al. System analysis of VEGFA in renal cell carcinoma: The expression, prognosis, gene regulation network and regulation targets. Int J Biol Markers. 2021;6:17246008211063501.
Navani S. The human protein atlas. J Obstet Gynecol India. 2011;61:27–31.
Tang Z, Li C, Kang B, et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45:W98–102.
Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6:pl1.
Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–13.
Warde-Farley D, Donaldson SL, Comes O, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010;38:W214-20.
Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523.
Han H, Cho JW, Lee S, et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 2018;46:D380–6.
Vasaikar SV, Straub P, Wang J, et al. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res. 2018;46:D956–63.
Li T, Fan J, Wang B, et al. TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77:e108–10.
Li W, Ma JA, Sheng X, et al. Screening of CXC chemokines in the microenvironment of ovarian cancer and the biological function of CXCL10. World J Surg Oncol. 2021;19(1):329.
Kumar A, Cherukumilli M, Mahmoudpour SH, et al. ShRNA-mediated knock-down of CXCL8 inhibits tumor growth in colorectal liver metastasis. Biochem Biophys Res Commun. 2018;500(3):731–7.
Yang X, Wei Y, Sheng F, et al. Comprehensive analysis of the prognosis and immune infiltration for CXC chemokines in colorectal cancer. Aging. 2021;13(13):17548–67.
Zhao QQ, Jiang C, Gao Q, et al. Gene expression and methylation profiles identified CXCL3 and CXCL8 as key genes for diagnosis and prognosis of colon adenocarcinoma. J Cell Physiol. 2020;235(5):4902–12.
Song YS, Kim MJ, Sun HJ, et al. Aberrant thyroid-stimulating hormone receptor signaling increases VEGF-A and CXCL8 secretion of thyroid cancer cells, contributing to angiogenesis and tumor growth. Clin Cancer Res. 2019;25(1):414–25.
Rupertus K, Sinistra J, Scheuer C, et al. Interaction of the chemokines I-TAC (CXCL11) and SDF-1 (CXCL12) in the regulation of tumor angiogenesis of colorectal cancer. Clin Exp Metastasis. 2014;31(4):447–59.
Augsten M, Hägglöf C, Olsson E, et al. CXCL14 is an autocrine growth factor for fibroblasts and acts as a multi-modal stimulator of prostate tumor growth. Proc Natl Acad Sci U S A. 2009;106(9):3414–9.
Ewington L, Taylor A, Sriraksa R, et al. The expression of interleukin-8 and interleukin-8 receptors in endometrial carcinoma. Cytokine. 2012;59(2):417–22.
Lin X, Li H, Li X, et al. A single-chain variable fragment antibody/chemokine fusion protein targeting human endoglin to enhance the anti-tumor activity of cytokine-induced killer cells. J Biomed Nanotechnol. 2021;17(8):1574–83.
Pan B, Shen J, Cao J, et al. Interleukin-17 promotes angiogenesis by stimulating VEGF production of cancer cells via the STAT3/GIV signaling pathway in non-small-cell lung cancer. Sci Rep. 2015;5:16053.
Luo LH, Rao L, Luo LF, et al. Long non-coding RNA NKILA inhibited angiogenesis of breast cancer through NF-κB/IL-6 signaling pathway. Microvasc Res. 2020;129:103968.
Ding X, Xu J, Wang C, et al. Suppression of the SAP18/HDAC1 complex by targeting TRIM56 and Nanog is essential for oncogenic viral FLICE-inhibitory protein-induced acetylation of p65/RelA, NF-κB activation, and promotion of cell invasion and angiogenesis. Cell Death Differ. 2019;26(10):1970–86.
Li H, Yan R, Chen W, et al. Long non coding RNA SLC26A4-AS1 exerts antiangiogenic effects in human glioma by upregulating NPTX1 via NFKB1 transcriptional factor. FEBS J. 2021;288(1):212–28.
Hulsurkar M, Li Z, Zhang Y, et al. Beta-adrenergic signaling promotes tumor angiogenesis and prostate cancer progression through HDAC2-mediated suppression of thrombospondin-1. Oncogene. 2017;36(11):1525–36.
Li X, Zou ZZ, Wen M, et al. ZLM-7 inhibits the occurrence and angiogenesis of breast cancer through miR-212-3p/Sp1/VEGFA signal axis. Mol Med. 2020;26(1):109.
Wang Y, Ning Y, Alam GN, et al. Amino acid deprivation promotes tumor angiogenesis through the GCN2/ATF4 pathway. Neoplasia. 2013;15(8):989–97.
Bi Y, Kong P, Zhang L, Cui H, Xu X, Chang F, Yan T, Li J, Cheng C, Song B, Niu X,. Liu X, Liu X, Xu E, et al. EP300 as an oncogene correlates with poor prognosis in esophageal squamous carcinoma. J Cancer. 2019;10(22):38. 5413–5426.
Grillo J, DelloRusso C, Lynch RC, et al. Regulation of the angiogenesis inhibitor thrombospondin-1 by the breast cancer susceptibility gene-1 (BRCA1). Breast J. 2011;17(4):434–5.
Wu Y, Zhang M, Bi X, et al. ESR1 mediated circ_0004018 suppresses angiogenesis in hepatocellular carcinoma via recruiting FUS and stabilizing TIMP2 expression. Exp Cell Res. 2021;408(2):112804.
Maruggi M, Layng FI, Lemos R Jr, et al. Absence of HIF1A leads to glycogen accumulation and an inflammatory response that enables pancreatic tumor growth. Cancer Res. 2019;79(22):5839–48.
Zhang S, Tao X, Cao Q, et al. lnc003875/miR-363/EGR1 regulatory network in the carcinoma -associated fibroblasts controls the angiogenesis of human placental site trophoblastic tumor (PSTT). Exp Cell Res. 2020;387(2):111783.
Fang K, Zhan Y, Zhu R, et al. Bufalin suppresses tumour microenvironment-mediated angiogenesis by inhibiting the STAT3 signalling pathway. J Transl Med. 2021;19(1):383.
Lin ZY, Chen G, Zhang YQ, et al. MicroRNA-30d promotes angiogenesis and tumor growth via MYPT1/c-JUN/VEGFA pathway and predicts aggressive outcome in prostate cancer. Mol Cancer. 2017 Feb 27;16(1):48.
Zhang X, Dong J, He Y, et al. miR-218 inhibited tumor angiogenesis by targeting ROBO1 in gastric cancer. Gene. 2017;615:42–9.
Li Q, He Q, Baral S, et al. MicroRNA-493 regulates angiogenesis in a rat model of ischemic stroke by targeting MIF. FEBS J. 2016;283(9):1720–33.
Yang F, Wang W, Zhou C, et al. MiR-221/222 promote human glioma cell invasion and angiogenesis by targeting TIMP2. Tumour Biol. 2015;36(5):3763–73.
Sun X, Huang T, Zhang C, et al. Long non-coding RNA LINC00968 reduces cell proliferation and migration and angiogenesis in breast cancer through up-regulation of PROX1 by reducing hsa-miR-423-5p. Cell Cycle. 2019;18(16):1908–24.
Frieling JS, Li T, Tauro M, et al. Prostate cancer-derived MMP-3 controls intrinsic cell growth and extrinsic angiogenesis. Neoplasia. 2020;22(10):511–21.
Dey S, Mondal A, DuHadaway JB, et al. IDO1 Signaling through GCN2 in a Subpopulation of Gr-1 + Cells Shifts the IFNγ/IL6 Balance to Promote Neovascularization. Cancer Immunol Res. 2021;9(5):514–28.

SupplementaryFigure1.tif
The protein expression of CXC chemokines-VEGFA network in COAD (The Human Protein Atlas). (A1-H1) The protein expression of CXCL5/8/11/12/13/14/16 and VEGFA in normal colon tissue, respectively; (B) (A2-H2) The protein expression of CXCL5/8/11/12/13/14/16 and VEGFA in COAD tissue, respectively.
Supplementaryfigure2.tif
Genetic alteration of CXC chemokines-VEGFA network in COAD.
Supplementaryfigure3.tif
Promoter methylation of CXC chemokines-VEGFA network in COAD. (A) The promoter methylation level of CXCL1 in health people and COAD patients; (B) The promoter methylation level of CXCL2 in health people and COAD patients; (C) The promoter methylation level of CXCL3 in health people and COAD patients; (D) The promoter methylation level of CXCL5 in health people and COAD patients; (E) The promoter methylation level of CXCL6 in health people and COAD patients; (F) The promoter methylation level of CXCL11 in health people and COAD patients; (G) The promoter methylation level of CXCL12 in health people and COAD patients; (H) The promoter methylation level of CXCL13 in health people and COAD patients; (I) The promoter methylation level of CXCL14 in health people and COAD patients; (J) The promoter methylation level of CXCL17 in health people and COAD patients; (K) The promoter methylation level of VEGFA in health people and COAD patients.
Supplementaryfigure4.tif
Interaction analyses of CXC chemokines-VEGFA network in COAD. (A) PPI network of CXC chemokines-VEGFA network in COAD; (B) Network and function analyses of CXC chemokines-VEGFA network in COAD.
Supplementaryfigure5.tif
GO function and KEGG pathways enrichment analysis of CXC chemokines-VEGFA network in COAD (metascape). (A) Biological processes in COAD; (B) Molecular functions in COAD; (C) KEGG pathway analysis in COAD.
Supplementaryfigure6.tif
Genes differentially expressed in correlation with CXC chemokines-VEGFA network in COAD (LinkedOmics). (A1-M1) A pearson test was used to analyze correlations between CXCL1/2/3/5/6/8/11/12/13/14/16/17, VEGFA and genes differentially expressed in COAD, respectively. (B2-M2 and C3-M3) Heat maps showing genes positively and negatively correlated with CXCL1/2/3/5/6/8/11/12/13/14/16/17 and VEGFA in COAD, respectively (Top 50 genes).
Supplementaryfigure7.tif
Gene expression correlation analysis of CXC chemokines-VEGFA network in COAD (LinkedOmics). The scatter plot shows pearson correlation of CXCL1 expression with expression of CXCL3 (A1), CXCL2 (A2), and ZC3H12A (A3) in COAD; The scatter plot shows pearson correlation of CXCL2 expression with expression of CXCL3 (B1), CXCL1 (B2), and ZC3H12A (B3) in COAD; The scatter plot shows pearson correlation of CXCL3 expression with expression of CXCL1 (C1), CXCL2 (C2), and ZC3H12A (C3) in COAD; The scatter plot shows pearson correlation of CXCL5 expression with expression of IL24 (D1), IL8 (D2), and MMP3 (D3) in COAD; The scatter plot shows pearson correlation of CXCL6 expression with expression of CXCL5 (E1), MMP3 (E2), and IL8 (E3) in COAD; The scatter plot shows pearson correlation of CXCL8 expression with expression of GPR109B (F1), IL1B (F2), and OSM (F3) in COAD; The scatter plot shows pearson correlation of CXCL11 expression with expression of CXCL10 (G1), UBD (G2), and IDO1 (G3) in COAD; The scatter plot shows pearson correlation of CXCL12 expression with expression of NPR1 (H1), SLIT3 (H2), and SHE (H3) in COAD; The scatter plot shows pearson correlation of CXCL13 expression with expression of TIGIT (I1), SH2D1A (I2), and SIRPG (I3) in COAD; The scatter plot shows pearson correlation of CXCL14 expression with expression of D4S234E (J1), TNFSF11 (J2), and COL9A1 (J3) in COAD; The scatter plot shows pearson correlation of CXCL16 expression with expression of ZMYND15 (K1), FLII (K2), and NDEL1 (K3) in COAD; The scatter plot shows pearson correlation of CXCL17 expression with expression of FAM83A (L1), GPR110 (L2), and SEMG1 (L3) in COAD; The scatter plot shows pearson correlation of VEGFA expression with expression of GTPBP2 (M1), CCNL1 (M2), and CREBZF (M3) in COAD.
Supplementaryfigure8.tif
The correlation between CXC chemokines-VEGFA network and immune cell infiltration in COAD (TIMER). (A) CXCL1; (B) CXCL2; (C) CXCL3; (D) CXCL5; (E) CXCL6; (F) CXCL8; (G) CXCL11; (H) CXCL12; (I) CXCL13; (J) CXCL14; (K) CXCL16; (L) CXCL17; (M) VEGFA.

PDF

Version 1

posted 17 Mar, 2022

You are reading this latest preprint version

Systematic analysis of CXC chemokine–vascular endothelial growth factor A network in colonic adenocarcinoma from the perspective of angiogenesis

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

1. Background

2. Methods

2.1 UALCAN analysis

2.2 Human Protein Atlas analysis

2.3 GEPIA

2.4 cBioPortal analysis

2.5 STRING analysis

2.6 GeneMANIA analysis

2.7 Metascape analysis

2.8 TRRUST analysis

2.9 LinkedOmics analysis

2.10 Timer analysis

3. Results

3.1 Aberrant expression of CXC chemokine-VEGFA network

3.2 Promoter methylation and genetic alteration analyses of CXC chemokine-VEGFA network

3.3 CXC chemokines and VEGFA interaction network

3.4 GO and KEGG pathway enrichment analysis

3.5 Transcription factor targets involved with the CXC chemokine-VEGFA network

3.6 miRNA targets of CXC chemokine-VEGFA network

3.7 Correlation of CXC chemokine-VEGFA network expression and differentially expressed genes

3.8 Immune cell infiltration and CXC chemokine-VEGFA network expression

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Supplementary Files

Status:

Version 1