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. 2024 Sep 27;22(4):100426. doi: 10.1016/j.jgeb.2024.100426

Bioinformatics analysis identifies WNK1 gene as a potential biomarker for cholangiocarcinoma diagnosis and immune infiltration

Qi Sun a,1, Xianli Lei b,1, Xiangrong Meng c, Caijun Zha d, Lei Yan e, Wenjing Zhang c,⁎
PMCID: PMC11465147  PMID: 39674642

Abstract

Background

Cholangiocarcinoma (CHOL) is a malignant epithelial carcinoma of the digestive system with poor prognosis and high mortality. WNK lysine deficient protein kinase 1 (WNK1) is known to be associated with tumorigenesis in various cancers. However, the relationship between WNK1 and CHOL development, as well as the potential mechanisms involved, remains poorly understood.

Methods

Microarray datasets of CHOL (GSE22633 and GSE32879) were retrieved from the Gene Expression Omnibus (GEO) database. Functional enrichment and immunoinfiltration analyses were performed for genes co-expressed with WNK1. GraphPad Prism 9 was utilized for statistical data analysis and the construction of receiver operating characteristic (ROC) curves. The impact of WNK1 on the CHOL tumor microenvironment was analyzed using Tumor Immune Estimation Resource (TIMER), Venn diagrams, STRING, and TISIDB database for information on WNK1-related chemokines and chemokine receptors. Protein-protein interaction (PPI) networks were used to predict transcription factors and microRNAs interacting with WNK1 and the associated hub genes.

Results

Differential expression of WNK1 was observed between CHOL and normal samples, suggesting its diagnostic value. Functional analysis showed that WNK1 and its associated genes were primarily enriched in pathways such as leukocyte transendothelial migration and chemokine signaling. Neutrophils were the only type of infiltrating immune cells associated with WNK1 in the CHOL tumor microenvironment (TME). VEGFA and ALB were identified as hub genes, and X-C motif chemokine receptor 1 (XCR1) and C-X-C motif chemokine ligand 5 (CXCL5) were identified as core chemokines and chemokine receptors related to WNK1 and neutrophil infiltration in CHOL.

Conclusions

Based on network analysis and the summary of previous studies, it was proposed that CHOL tumor cells secrete CXCL5, leading to neutrophil recruitment to the tumor microenvironment. Vascular endothelial growth factor A (VEGFA) released by the infiltrating neutrophils is suggested to promote overexpression of WNK1 by tumor cells, activating the VEGFA downstream pathway to promote angiogenesis and tumor progression.

Keywords: Cholangiocarcinoma, WNK1, VEGFA, CXCL5, Immune cell infiltration

1. Introduction

Cholangiocarcinoma (CHOL), originating in the biliary tract and hepatic parenchyma, is a heterogeneous malignancy of the hepatobiliary system.1 It represents the second most common primary malignancy of the hepatobiliary system and has become increasingly prevalent in recent decades throughout the world, although most commonly seen in Southeast Asia.2, 3 Data indicate a 5-year relative survival rate of 7–20 % and an overall survival rate of approximately 30 % in CHOL.4, 5 Although carbohydrate antigen 19-9 (CA19-9) and serum carcinoembryonic antigen (CEA) are frequently utilized as diagnostic tools in the detection of CHOL, their relatively low sensitivity and specificity render them unreliable. Currently, histology biopsies remain the standard method for CHOL diagnosis but are limited by the risk of operation.6, 7, 8 Therefore, the identification of new target biomarkers remains challenging for the management of CHOL.

WNK lysine deficient protein kinase 1 (WNK1) proteins are soluble intracellular serine/threonine kinases that play key roles in the regulation of ion transport in epithelia.9 The WNK signaling pathway is critical for the maintenance of blood pressure and renal homeostasis, and WNK1 gain-of-function resulting from genetic mutation leads to pseudohypoaldosteronism type 2 (PHA2), an autosomal-dominant genetic disorder characterized by hypertension, hyperkalemia, and hyperchloric metabolic acidosis.10, 11 An emerging research area is the association between WNK1 and cancer development and progression.12 Earlier studies have demonstrated that increased WNK1 expression enhances disease progression in retinoblastoma and that the Akt-WNK1 axis promotes cell migration and invasion in hepatocellular carcinoma.13, 14 However, the underlying mechanism associated with WNK1 in the CHOL immune response remains to be explored. An in-depth understanding of the regulatory pathways and molecular functions of WNK1 may offer potential avenues for the diagnosis and therapeutic intervention of CHOL.

In recent years, the application of bioinformatics methods has enabled the identification of biomarkers crucial for tumorigenesis, enhancing diagnostic precision, and refining prognostic assessments.15 However, a review of the current literature reveals a paucity of bioinformatics data supporting the predictive role of WNK1 in cholangiocarcinoma. This research aimed to conduct a comprehensive analysis, using multiple bioinformatics methods to explore the possible role of WNK1 in CHOL. The expression of WNK1 was analyzed in data from the Gene Expression Omnibus (GEO) and The Cancer Gene Atlas (TCGA) databases. Analysis of the role of WNK1 in CHOL diagnosis and the immune microenvironment was undertaken using protein–protein interaction (PPI) networks, gene set enrichment analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses, and receiver operating characteristic (ROC) curves. The findings suggested that WNK1 may be a novel oncogene promoting CHOL tumorigenesis and provide new insight into the genesis and progression of CHOL.

2. Materials and methods

2.1. Databases

The Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo) is a publicly available database containing genome-wide transcriptomic data. Two CHOL expression profile datasets, GSE32879 and GSE32633, were obtained. The GSE32879 dataset contained 16 CHOL tissue samples and 7 samples of normal bile duct tissue,16 while the GSE22633 dataset contained data from 17 CHOL cell lines and 5 normal bile duct epithelial cells.17 The criteria used to verify WNK1 expression in GSE32879 were an adjusted P value < 0.05 and | log2FoldChange | > 1.

2.2. Analysis of WNK1 expression

The expression profile of WNK1 in CHOL was visualized using the “Diff Exp” module in Tumor Immune Estimation Resource (TIMER, https://timer.cistrome.org/). The results were confirmed using Gene Expression Profiling Interactive Analysis (GEPIA, https://gepia.cancer-pku.cn/detail.php) which uses interactive and customizable functions to analyze RNA sequencing data based on the Genotype-Tissue Expression (GTEx) and TCGA data with the normalized processing pipeline.18, 19

2.3. Analysis of clinical subtypes and co-expressed genes

The expression of WNK1 in different clinical subtypes in relation to clinical features such as weight, age, sex, tumor stage, and metastasis status were performed using the UALCAN data portal (Ualcan.path.uab.edu/analysis) which is known for the evaluation and visualization of cancer omics data.20 In addition, 24 genes that were positively correlated with WNK1 were detected in CHOL.

2.4. Analysis of immune cell infiltration

TIMER is used for comprehensive characterizations of the associations between immune cells and tumors.21 The associations between WNK1 expression and six commonly analyzed immune cells (B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells) were analyzed using the “gene” module of TIMER.

2.5. Gene Ontology and pathway enrichment analyses

Using the “clusterProfiler” package in R (version 3.14.3), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of CHOL-specific differentially expressed genes（DEGs） were performed. Genes with minimum count = 5000 and p < 0.05 were considered significantly enriched.

2.6. GSEA functional enrichment of WNK1

Gene set enrichment analysis (GSEA) was performed to identify the WNK1-related signaling pathways of c2 (c2.cp.KEGG.V7.4.symbols.gmt) in the molecular signature database. GO, Reactom, and WikiPathway sets were obtained through the same method. The gene set used was between 5 and 5000, with 1000-fold re-sampling. A nominal p-value < 0.05 and false discovery rate (FDR) < 0.25 were considered statistically significant.

2.7. Analysis of WNK1-related chemokines and chemokine receptors

TISIDB (https://cis.hku.hk/TISIDB/index.php) is an integrated repository portal for analysis of tumor-immune system interactions.22 Genes encoding chemokines and chemokine receptors associated with 28 tumor-infiltrating lymphocytes (TILs) were downloaded from the TISIDB website. The relationships between chemokines, chemokine receptors, and WNK1 expression in CHOL were visualized using a heatmap.

2.8. Protein–protein interaction analysis

The STRING platform (https://string-db.org), a user-friendly tool, was used to predict interactions between proteins of interest by compiling protein–protein interaction (PPI) network.23, 24 The network was visualized using Cytoscape (https://apps.cytoscape) to identify the top 5 genes representing the hub genes.

2.9. Co-regulatory networks of transcription factor (TF), miRNAs, and WNK1

Networkanalysis (https://www.networkanalyst.ca/), a open-access tool for analysis of gene-related functional and regulatory networks, was used to explore the relationships between TFs and miRNAs associated with WNK1 and the hub genes.25 The “Gene Regulatory Networks (GRN)” module in Networkanalysis was used to construct the TF-miRNA co-regulatory network.

2.10. Analysis of correlations between WNK1 and target genes

The correlations between WNK1, the hub genes, and chemokines in CHOL were determined by GEPIA. Spearman’s ρ correlation coefficient was calculated and P < 0.05 was considered statistically significant.

2.11. Statistical analysis

Receiver operating characteristic (ROC) curves were drawn using data from the GSE32879 dataset and was visualized using GraphPad Prism 9.0.

3. Results

3.1. Overexpression of WNK1 in CHOL patients

The TIMER platform was used to analyze WNK1 expression in CHOL and normal tissue samples in the GSE32879 dataset. Fig. 1A shows that WNK1 was up-regulated in CHOL. The WNK1 expression patterns were validated using CHOL TCGA data. Gene expression trends were confirmed in the GEPIA and UALCAN databases (Fig. 1B). As illustrated in Fig. 1, expression of WNK1 was associated with the age, weight, and sex of CHOL patients, with younger (21–40 years), obese, or male patients tending to have higher expression of WNK1 (Fig. 1C-E). This trend was especially apparent in cancer staging, with patients in stages 1 and 2 having markedly increased WNK1 expression. The highest levels of WNK1 expression were found in stage 4. Due to the small number of patients with stage 3 disease, variations in WNK1 expression levels could not be analyzed accurately (Fig. 1F). Elevated expression levels of WNK1 are correlated with the presence of nodal metastasis (Fig. 1G). The over-expression of WNK1 in CHOL was further validated using the GSE32879 (normal tissue: n = 7, tumor tissue: n = 16) and GSE22633 (normal cell line: n = 5, tumor cell line: n = 17) datasets (Fig. 1H-I). ROC curves were established based on the GSE32879 dataset. The ROC area under the curve (AUC) was 0.8661, with a 95 % confidence interval (CI) between 0.7187 and 1.013, P = 0.006176 (Fig. 1J). This result indicated that WNK1 has predictive value for CHOL diagnosis.

Fig. 1.

Fig. 1

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The differential expression of WNK1. (A) WNK1 was over-expression in CHOL analysis by TIMER (***P < 0.001); (B) High level of WNK1 expression was analyzed by GEPIA (*P < 0.05); WNK1 displays different expression levels based on (C) age, (D) weight, (E) gender, (F) cancer stage, and (G) nodal metastasis status in CHOL patients (*P < 0.05, ***P < 0.001). WNK1 over-expression in CHOL of (H) GSE22633 and (I) GSE32879 datasets. (J) ROC curve analysis of WNK1 based on GSE32879 (AUC = 0.8661, 95 %CI: 0.7187–1.013).

3.2. GO annotation and KEGG pathway analysis of WNK1-associated genes

The top 24 genes showing the same expression trend as WNK1 (Fig. 2A). In addition, GO and KEGG enrichment analyses were performed on WNK1 and its similarly expressed genes (Fig. 2B-E). These significant genes were primarily enriched in transcription regulator activity (GO cellular component [CC] category), transcription repressor activity (GO molecular function [MF] category), nuclear protein containing complex, negative regulation of the metabolic process of nucleobase-containing compounds, negative regulation of the biosynthetic process, RNA splicing, and mRNA metabolism (GO biological process [BP] category). The KEGG enrichment results demonstrated that WNK1-related genes were primarily involved in the RNA polymerase pathway, microRNA in cancer, and viral carcinogenesis, as well as in leukocyte transendothelial migration and chemokine signaling pathway.

Fig. 2.

Fig. 2

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GO, KEGG analysis of WNK1-correlated genes. (A) Top 24 genes with a positive correlation to WNK1 were presented as heat maps. The bubble diagram exhibited (B) GO-BP, (C) GO-CC, (D) GO-MF, and (E) KEGG pathway fuctional enrichment analysis of WNK1-correlated genes (P value < 0.05).

3.3. GSEA of WNK1

The GSE32879 dataset was used for the GSEA of WNK1. The GO term enrichment analysis included regulation of leukocyte degranulation, store-operated calcium entry, positive regulation of cell death, Golgi-associated vesicle, cytoplasmic microtubule, azurophil granule, phosphatidylinositol phosphate binding, small molecule sensor activity, kinase regulatory activity, and other functions (Fig. 3A-C). KEGG pathways were found to be enriched in circadian rhythm, glycosphingolipid biosynthesis GLOBO series, lysosome, and Toll-like receptor signaling pathway, amongst others (Fig. 3D). Enrichment was also seen in cell-extracellular matrix interactions, formyl peptide receptors binding formyl peptides and many other ligands, neutrophil degranulation, signaling by EGFR, and TCF-dependent signaling in response to Wnt (Fig. 3E). Leukocyte intrinsic hippo pathway information and cancer treatment by PD1 blockage were both enriched in the WNK1 pathway (Fig. 3F). WNK1 may thus significantly regulate leukocyte activity, tumor immunity, ion transport, and cell death.

Fig. 3.

Fig. 3

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GSEA analysis of WNK1 in CHOL of GSE32879 dataset. Based on the median expression level of WNK1, samples were divided into high expression group and low expression group. The GSEA results were showed in the bubble diagram. (A) GO-BP (B) GO-CC (C) GO-MF and (D) KEGG pathway (E) Reactome (F) WikiPathway analysis (NP value < 0.05).

3.4. Immune cell infiltration and functional enrichment analysis in CHOL

The TIMER findings showed that the only infiltrating immune cell type significantly associated with WNK1 were neutrophils (p = 3.93e-03, cor = 0.475) (Fig. 4). We obtained 778 neutrophil-associated genes in the gene module of the NCBI website, which was used to perform intersection analysis with 6618 differentially expressed genes (DEGs) in the GSE32879 dataset (adj P < 0.05), leading to the identification of 259 common genes (Fig. 5A) (Supplementary table 1). The interactions of WNK1-related genes were examined using a PPI network (Fig. 5B). The functional enrichment analysis of the 259 common genes provided some noteworthy points. The GO annotation analysis indicated that cell migration, cell activation, immune effector process (GO-BP), secretory vesicle, secretory granule, cell surface (GO-CC), signaling receptor binding, enzyme binding, and identical protein binding (GO-MF) were the most significant pathways (Fig. 5C-E). WNK1 was found to be mainly involved in chemokine signaling, proteoglycans, and cancer pathways. Specifically, WKN1 and associated immune cell infiltration are involved in CHOL progression, and WNK1-associated genes were enriched in these processes (Fig. 5F).

Fig. 4.

Fig. 4

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Immune cell infiltration analysis of WNK1 in CHOL.

Fig. 5.

Fig. 5

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GO, KEGG analysis of common genes between DEGs of GSE32879 and neutrophil-related genes. (A) 259 common genes between DEGs of GSE32879 and neutrophil-related genes were detected by using Venn method. (B) PPIs network analysis of 259 common genes. (C) GO-BP (D) GO-CC (E) GO-MF and (F) KEGG pathway functional enrichment analysis of the common genes (P value < 0.05).

3.5. Analysis of hub genes and transcription factors (TFs)

Based on the Degree, Maximal Clique Centrality (MCC) and Edge Percolated Component (EPC) algorithms, PPI networks were used to identify the top 5 most significant genes, or hub genes, associated with CHOL. ALB and VEGFA were identified as the essential hub genes (Fig. 6A-C). GEPIA analysis showed that WNK1 was associated with VEGFA (P = 0.015, cor = 0.36) and ALB (P = 0.0013, cor = -0.46) (Fig. 6D-E). The Networkanalysis platform was used to identify TFs and microRNAs (miRNAs) associated with WNK1. SP1 was found to be involved in the transcription of WNK1, VEGFA, and ALB while miR-15a, miR-497, and miR15b are common VEGFA and ALB target microRNAs. MiR543 is a common target microRNA of both VEGFA and WNK1 (Fig. 6F).

Fig. 6.

Fig. 6

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Hub genes identification and TF-miRNA regulatory network analysis. The Hub genes identificated by (A) degree, (B) MCC and (C) EPC algorithms. (D) The correlation between VEGFA and WNK1 (cor = 0.36, p = 0.015). (E) The correlation between ALB and WNK1 (cor = -0.46, p = 0.0013). (F) The TF-miRNA regulatory network of ALB, VEGFA, and WNK1.

3.6. Identification of WNK1-related chemokines and chemokine receptors

Chemokines and chemokine receptors are critical for immune cell infiltration. The correlations between between WNK1, chemokines, and chemokine receptors in CHOL are displayed in a heatmap (Fig. 7A). GEPIA analysis further confirmed that CCL25 (P < 0.05, Cor = 0.334), CCL28 (P < 0.05, Cor = 0.354), CXCL5 (P < 0.05, Cor = 0.337), CCL11 (P < 0.05, Cor = 0.45), and XCR1 (P < 0.05, Cor = 0.549) were correlated with WNK1 (Fig. 7B-F). The intersection analysis of WNK1-related chemokines and receptors with the DEGs of the GSE32879 dataset confirmed that CXCL5 and XCR1 were significantly associated with WNK1 (Fig. 7G). The PPI networks for CXCL5, XCR1, SP1, VEGFRA, ALB, and WNK1 are illustrated in Fig. 7H.

Fig. 7.

Fig. 7

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Correlation of chemokines and chemokine receptors with WNK1. (A) The heat maps of WNK1-related chemokines and chemokine receptors in CHOL. (B) CCL26 (cor = 0.334, p = 0.0473), (C) CCL11 (cor = 0.45, p = 0.00588), (D) CCL28 (cor = 0.354, p = 0.0347), (E) XCR1 (cor = 0.549, p = 0.0005), and (F) CXCL5 (cor = 0.337, p = 0.0452) correlated with WNK1 were verified by TIMER. (G) Common gene identification of WNK1-related chemokines and chemokine receptors with DEGs of GSE32879. (H) PPIs network analysis of WNK1, CXCL5, XCR1, ALB, VEGFRA, and SP1.

4. Discussion

Previous studies have shown the involvement of WNK1 with many different cancer types, including glioma, primary hepatocellular carcinoma, non-small cell lung cancer, and thyroid cancer, where it appears to play a crucial role in promoting tumor growth and cell migration.26, 27, 28, 29 Based on bioinformatics analysis, this study suggests the importance of WNK1 in CHOL. WNK1 was found to be highly expressed in CHOL and was significantly associated with the diagnosis of CHOL. Additionally, WNK1 appears to be significantly involved in the tumor microenvironment (TME) and the biological process of neutrophil infiltration into tumor tissue, which could affect CHOL development. WNK1 thus represents a putative immune-related biomarker of CHOL. These findings add to our understanding of how WNK1 functions in the genesis and progression of CHOL.

We observed elevated WNK1 expression levels in CHOL tumor tissues using two separate datasets in the TIMER and GEPIA platforms, which was supported by the genome sequencing findings of two datasets, GSE22633 and GSE32879. The results suggested that WNK1 was associated with clinicopathological and typical variables in CHOL patients, including obesity, metastasis, age, sex, and clinical stage. Analysis of the GSE32879 dataset further suggested the predictive value of WNK1 in CHOL diagnosis (AUC: 0.8661; P < 0.05). WNK1-correlated genes in CHOL were identified by UALCAN, which is a portal for the analysis of tumor subgroup gene expression. Functional and pathway enrichments, as well as immune cell infiltration, associated with WNK1 and its related genes were investigated. The GSE32879 dataset was used for further validation of the possible role of WNK1 in CHOL using GSEA. Three key shared characteristics between leukocyte transendothelial migration, transcription factor binding, and chemokine pathways drew our attention to these enrichment data, suggesting that WNK1 may influence immune activity in CHOL through various pathways.

In particular, the immune microenvironment of tumors has a significant impact on the progression of the cancer.30, 31 Nevertheless, only a few publications have focused on the role of WNK1 in the TME. Using the TIMER database, we discovered a correlation between WNK1 over-expression and neutrophil infiltration in CHOL. Neutrophils can regulate inflammation associated with the adaptive immune response via interaction with antigen-presenting cells and lymphocytes.32, 33 Correlations between inflammation and cancer pathogenesis have been observed since the 19th century.34 Cancer-related inflammation is mediated by both stromal cells and inflammatory effector cells in the TME.35, 36 Contrary to other immune cells such as macrophages, neutrophils are not thought to have a substantial role in the TME.

However, earlier research has shown that neutrophils play a crucial role in TME, particularly in tumor progression.37, 38 Neutrophils can release various cytoplasmic and nuclear components that regulate gene expression.39, 40 A Total of 787 genes associated with neutrophils were selected in the “gene“ module on the NCBI website, and 259 common genes were discovered through cross-analysis of genes that differ in expression between CHOL patients and healthy individuals in the GSE32879 dataset. Functional enrichment showed a strong correlation between these overlapping genes and chemotaxis, including granulocyte chemotaxis, chemokine receptor binding, chemokine signaling pathways, and other functions. VEGFA and ALB were selected as hub genes according to the Degree, MCC, and EPC calculations in Cytoscape. In CHOL, vascular endothelial growth factor A (VEGFA) and WNK1 were positively associated, whereas ALB and WNK1 were negatively correlated. VEGF plays an important part in the control of angiogenesis in cancer, stimulating the proliferation, migration, and survival of vascular endothelial cells.41 During the menstrual cycle, the source of VEGF in endometrial tissues with increased angiogenesis was found to be neutrophils.42 Studies have shown that xenotransplanted tumors displayed higher expression levels of WNK1, derived from tumor cells and regulated by VEGF. The binding of VEGF to VEGFR2 phosphorylates WNK1 through the PI3K-Akt kinase cascade, and inhibition of the WNK1 signaling cascade reduces angiogenesis in colorectal cancer and hepatocellular tumors.43, 44 The Networkanalyst tool found that the transcription activator-specific protein 1 (SP1) could be a transcription factor associated with WNK1 and VEGFA. Through binding to the P1/P2 region of the WNK1 proximal promoter, SP1 plays an essential role.45 On the other hand, SP1 can also interact with the promoter of VEGFA to upregulate its expression, which promotes vascular endothelial proliferation, angiogenesis, and vascular permeability, and then promotes tumor growth and metastasis by binding to VEGF receptor 2.46

A complex network of chemokine receptor signaling cascades mediates neutrophil-directed migration to injury or infection sites. The most recent finding that neutrophils play an active role in tumor development and spread has prompted concerns about the chemokine systems that may promote neutrophil recruitment to cancers. We detected 5 chemokines associated with WNK1 in CHOL, which showed a consistent positive correlation with WNK1. Finally, the chemokines XCR1 and CXCL5 were among the identified DEGs, found by cross-analyzing the identified chemokines with the GSE32879 dataset. As a result of its high expression and significant association with neutrophil infiltration in cancers, CXCL5 is an effective neutrophil chemoattractant.47, 48 Studies have shown that CXCL5 production in the TME of intrahepatic cholangiocarcinoma is mainly induced by interleukin-17A (IL-17A) or interleukin-1 β (IL-1β) and secreted from tumor cells.49 This could explain why elevated WNK1 expression is linked to neutrophil infiltration in CHOL.

Similarly, WNK1 and CXCL5 are secreted by CHOL tumor cells. CXCL5 effectively recruits neutrophils to infiltrate the TME, leading to the secretion of VEGFA. In the CHOL TME, VEGFA promotes WNK1 expression, forming a positive feedback loop, and phosphorylates WNK1 to promote tumor angiogenesis.

Interestingly, WNK1 inhibitors showed stronger antitumor activity than PTK7787, a VEGFR inhibitor, suggesting that WNK1 has other tumor-promoting effects besides angiogenesis, including the direct promotion of tumor proliferation.45, 50 While the prevention of angiogenesis by targeting VEGF is an integral part of anticancer therapy, WNK1 seems to be more promising as a biomarker for CHOL-targeted therapy. Many questions remain regarding the chemokine signaling pathways that govern the tumor neutrophil microenvironment, including whether chemokines offer molecular guidance depending on the stage of tumor development and how this may enhance or inhibit neutrophil recruitment activity.

5. Conclusions

In summary, employing integrated bioinformatics analysis, this is the first study that describes the possible roles of WNK1 and its interaction with tumor-infiltrating immune cells in CHOL. The mechanism by which WNK1 is activated and its impact on the clinical outcome of CHOL warrants further molecular analysis and follow-up studies. The relationship between WNK1 and chemokines must also be integrated and clarified for us to fully comprehend the TME, particularly the immunological microenvironment of neutrophil infiltration in tumors.

Consent for publication

All authors have contributed to, read, and approved the content and agree to submit for consideration for publication in the journal.

Availability of data and materials

In this study, two public datasets were used, GSE32879 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32879) and GSE22633 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE22633).

Statements and declarations

The authors declare that they have no competing financial interests or personal relationships that may have an impact on the work reported in this paper.

Funding Source

This work was supported by National Natural Science Foundation of China (Grant Nos. 82001715).

CRediT authorship contribution statement

Qi Sun: Writing – original draft. Xianli Lei: Writing – original draft. Xiangrong Meng: Resources, Data curation. Caijun Zha: Data curation. Lei Yan: Validation. Wenjing Zhang: Writing – review & editing, Project administration, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jgeb.2024.100426.

Appendix A. Supplementary material

The following are the Supplementary data to this article:

Supplementary Data 1
mmc1.xlsx (11.1KB, xlsx)

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