Introduction
The third most common cause of mortality from cancer worldwide is colorectal malignancies. It is startling to learn that 20% of colorectal cancer (CRC) patients who receive a new diagnosis have already experienced metastatic disease [1]. According to Fabregas et al. [2], there has been a rise in the occurrence of CRC among young adults. This highlights the need for improved screening and treatment measures. Even though immunotherapy has shown clinical success in treating CRC, there are still potential immune-related adverse events that need to be considered [3]. The effectiveness of immune checkpoint inhibitors (ICIs) in treating cancer, especially anti-PD-1 therapies, has demonstrated encouraging results. Nevertheless, the efficacy of these treatments is restricted in individuals diagnosed with CRC and having microsatellite stable CRC, as highlighted in a study conducted by Liu et al. in 2021 [4]. This indicates that there is still much to be learned about the immune microenvironment of CRC.
Calcium-activated chloride channel (CLCA4) has been shown to play a significant role in both carcinogenesis and development through a growing body of research. Wei et al. established a correlation between the down-regulation of CLCA4 and the initiation and progression of CRC [5]. This suggests that CLCA4 may be a useful molecular biomarker for the identification of CRC in its basic stages [6]. As a tumour suppressor, CLCA4 plays a role in the growth of a variety of cancerous tumour types. Through PI3K/AKT signalling, it prevents the epithelial-mesenchymal transition and restrains the invasive and migratory abilities of bladder, hepatocellular, and colorectal cancer [7–9]. Meanwhile, a worse prognosis for HNSCC patients has been linked to CLCA4, a crucial regulator influencing the biological behaviour of HNSCC cells [10]. According to recent research [11], miR-19a targets CLCA4, and miR-19a overexpression raises the risk of CRC by lowering CLCA4 levels. Nevertheless, a clear understanding of the molecular mechanism underlying the connection between tumour immune infiltration and CLCA4 remains elusive.
Using many databases, we investigated CLCA4 levels in colon adenocarcinoma (COAD) in this work, as well as CLCA4’s potential involvement in colon adenocarcinogenesis, clinicopathological features, and the relationship between CLCA4 and tumour immune infiltration. The purpose of this study is to gather more data regarding CLCA4 as a possible biomarker, which is particularly relevant for COAD patients to enable prompt and informed diagnosis and treatment decisions.
Material and methods
Collection of data sets
Following a search across multiple publicly accessible databases, information regarding COAD from The Cancer Genome Atlas (TCGA) database was located, and the CLCA4 gene was obtained and examined.
Calcium-activated chloride channel expression analysis
We looked at the levels of CLCA4 expression in both normal human tissues and various cancer types using 2 independent datasets: the human protein atlas (HPA) dataset (https://www.proteinatlas.org) and TIMER https://cistrome.shinyapps.io/timer/). We performed differential analysis of CLCA4 mRNA and protein expression as well as patient survival prognosis analysis in COAD using several online tools, including the Assistant for Clinical Bioinformatics (https://www.aclb.com/), UALCAN (http://ualcan.path.uab.edu), TISIDB (http://cis.hku.hk/TISIDB), and GEPIA (http://gepia.cancer-pku.cn/). Validation by immunohistochemistry: the information came from the HPA database, and the antibody utilised was HPA 017045. Furthermore, the location of CLCA4 in the cell lines HeLa, RT-4, and U2OS was evaluated using the antibody HPA064770.
Relationship between calcium-activated chloride channel and copy number variation
Using the GSCA dataset (http://bioinfo.life.hust.edu.cn/GSCA/#/), we looked at the relationship between CLCA4 and copy number variation (CNV). Next, the TIMER dataset was utilised to investigate the connection between its CNV and immune infiltration to examine the effect of different copy states of CLCA4, including arm-level deletion (–1), diploid/normal (0), and arm-level gain (1), on the immune infiltration as compared to normal tissues. Additionally, we looked at the connection between COAD patients’ ESTIMATE scores and CLCA4 expression using the SangerBox database (http://www.sangerbox.com).
An in-depth examination of tumour-infiltrating immune cells
Utilising the gene module provided by TIMER, our initial focus was on exploring the correlation between CLCA4 and the abundance of immune infiltration as well as immunological markers. Afterwards, we examined the link between CLCA4 and tumour-infiltrating lymphocytes (TILs) using the TISIDE dataset as a way to understand the types of TILs that may be regulated by CLCA4 in COAD.
Immune checkpoint analysis
The study assessed immune checkpoint gene expression in COAD tissues and normal tissues using the Assistant for Clinical Bioinformatics dataset. These genes included SIGLEC15, CTLA4, LAG3, PDCD1LG2, CD274, HAVCR2, TIGIT, and PDCD1. The association between CLCA4 and these genes was also examined, and the TIDE algorithm was used to forecast how ICIs will affect CLCA4 high and low expression samples.
Association between calcium-activated chloride channel and clinical features
The connection between CLCA4 expression and various clinicopathological factors, including gender, age, race, and tumour-node-metastasis stage, was examined using the TCGA data from the Assistant for Clinical Bioinformatics.
Analysis of calcium-activated chloride channel co-expressed proteins and their functional enrichment
CLCA4-related genes were identified by GeneMANIA (http://genemania.org/) and subsequently subjected to functional enrichment analysis with the SangerBox dataset. Furthermore, we used the gene set enrichment analysis (GSEA) from the LinkedOmics (https://www.linkedomics.org/) dataset to confirm the possible function of CLCA4.
Statistical methods
Significance was tested by the Wilcox test for 2 samples and the Kruskal-Wallis test for 3 samples (*
p < 0.05,**
p < 0.01,***
p < 0.001).
Results
Calcium-activated chloride channel levels in normal tissues and pan-cancer tissues
Using the HPA dataset, the amounts of CLCA4 mRNA and protein in healthy human tissues were examined. The findings are presented in Figure 1A, indicating that CLCA4 mRNA expression is tissue-specific, with particularly strong expression observed in the oesophagus, colon, and rectum. However, the concordance between CLCA4 protein level expression (Fig. 1B) and its mRNA expression data was low, which may also be related to CLCA4 antibody specificity and needs further confirmation. In the following study, we conducted an analysis of CLCA4 expression across various cancer types using TIEMR. CLCA4 was expressed at low levels in BRCA, COAD, BLCA, HNSC, KICH, KIRC, KIRP, LUAD, LUSC, READ, PRAD, SKCM, and UCEC (Fig. 1C).
Expression and prognostic analysis of calcium-activated chloride channel in colon adenocarcinoma
We further validated the low expression of CLCA4 mRNA in COAD by utilising the GEPIA, UALCAN, and Assistant for Clinical Bioinformatics (
p < 0.05) datasets (Figs. 2A–C). The study of CPTAC data in UALCAN revealed that COAD had lower CLCA4 protein expression than normal tissues (
p < 0.05) (Fig. 2D). Poorer overall survival (OS) in COAD patients was found to be associated with decreased CLCA4 expression, according to prognostic analysis using UALCAN (Fig. 2J) and TISIDB (Fig. 2K). Additionally, there were differences in the prognosis survival rates of men and women in the groups with low and high CLCA4 expression (
p < 0.05) (Fig. 2L). Immunohistochemistry results showed moderate or weak CLCA4 staining in normal colon tissues (Fig. 2E), whereas no CLCA4 staining was detected in COAD tissues (Fig. 2F). Furthermore, CLCA4 expression was mostly seen in the plasma membrane, as well as in the nucleoplasm and aggregates, in HeLa, RT-4, and U2OS cell lines (Figs. 2G–I). These results further demonstrate that CLCA4 mRNA expression shows low concordance with protein expression, in approximate agreement with the above results.
Calcium-activated chloride channel mRNA expression and prognosis are correlated with copy number variation expression
Using the GSCA database, we closely examined the relationship between CLCA4 CNV and CLCA4 to comprehend the mechanism of CLCA4 aberrant expression. The study’s findings indicate a positive correlation between CLCA4 and CNV in COAD patients (Cor = 0.15, FDR = 0.032) (Fig. 3A). Additionally, the study found that patients in different CLCA4 CNV groups had varying survival prognoses (OS, DSS, PFS, and DFI) (Figs. 3B–E).
We examined CLCA4 somatic-based CNV to better comprehend the possible function of the CLCA4 gene and its effect on immune cell infiltration. Our results showed that the CNV of CLCA4 signatures, such as arm-level deletion, diploid/normal, and arm-level gain, had a significant impact on the quantities of B-cells, CD8+ T-cells, neutrophils, and dendritic cell infiltration in COAD (Fig. 3F). SangerBox evaluation in COAD patients showed a statistically significant positive correlation (
r = 0.14,
p = 0.02) between immune infiltration and CLCA4 (Fig. 3G). This study reveals that the tumour immune microenvironment (TIME) of COAD patients may be significantly influenced by CLCA4.
Immune infiltration analysis
To evaluate the impact of CLCA4 on the immune environment of COAD, we utilised the TIMER dataset to examine the association between CLCA4 and 6 different levels of immune cell infiltration.
As shown in Figure 4A, our results show a significant positive connection between CLCA4 and the degree of B- cell infiltration (
p = 1.47e–04). Further evaluating the correlation between CLCA4 and immune cell subtypes based on CIBERSOR algorithm, the results showed that CLCA4 was positively correlated with neutrophil (rho = 0.121,
p = 4.52e–02), memory resting CD4+ T-cell (rho = 0.181,
p = 2.63e–03), eosinophil (rho = 0.14,
p = 1.98e–02), and resting mast cell (rho = 0.129,
p = 3.29e–02), γ d T-cell (rho = –0.126,
p = 3.73e–02) and negatively correlated with macrophage M0 (rho = –0.201,
p = 8.17e–04) and activated myeloid dendritic cell (rho = 0.242,
p = 4.92e–05) (Fig. 4B).
TISIDB analysis showed a substantial connection between CLCA4 and 28 tumour-infiltrating cells in pan-cancer, with the exception of Act CD8, Tcm CD8, Tcm CD4, Tem CD4, Tgd, Treg, Mem B, NK, CD56bright, CD56dim, and NKT (Fig. 5A). Specifically, Act B (
p = 1.91e–07), Act CD4 (
p = 0.00788), Act DC (
p = 1.05e–06), Eosinophil (
p = 9.09e–05), iDC (
p = 1.27e–05), Imm B (
p = 4.42e–06), Macrophage (
p = 0.0433), Mast (
p = 1.16e–05), MDSC (
p = 0.0414), Monocyte (
p = 2.54e–05), Neutrophil (
p = 3.25e–18), pDC (
p = 0.0188), Tem CD8 (
p = 0.00493), Tfh (
p = 0.0182), Th1 (
p = 0.000453), Th2 (
p = 0.0367), and Th17 (
p < 2.2e–16) were positively correlated with CLCA4 (Figs. 5B–R). This shows that the modulation of various TIL types in COAD tissues may be influenced by CLCA4 gene regulation.
Calcium-activated chloride channel and immune cell marker expression levels are related
The research discovered a positive correlation between CLCA4 and several immune cell markers in COAD, as Table I illustrates. These markers included neutrophils (CCR7, CEACAM8), B-cells (CD19, CD79A), CD8+ T-cells (CD8A), CD4+ T-cells (CD4), dendritic cells (HLA-DPB1, HLA-DQB1, HLA-DPA1, CD1C), monocytes (CD86), T-cell exhaustion (CTLA4, LAG3), M1 macrophages (NOS2, PTGS2), and M2 macrophages (MS4A4A).
Analysis of immune checkpoints and the connection with calcium-activated chloride channel
The expression of immunological checkpoints, such as CD274, HAVCR2, TIGIT, SIGLEC15, CTLA4, LAG3, and PDCD1LG2, in COAD tissues and healthy tissues was also assessed using the Assistant for Clinical Bioinformatics. The results showed that only SIGLEC15 (
p = 5.04e–12), CTLA4 (
p = 5.64e–09), LAG3 (
p = 1.34e–08), and PDCD1LG2 (
p = 5.13e–03) (Figs. 6A, B). A favourable correlation between CLCA4 and CTLA4, CD274, LAG3, and PDCD1LG2 was found (
p = 0.024, 0.011, 0.029, and 0.033, respectively) when we additionally examined the association between CLCA4 and immunological checkpoints (Figs. 6C–J). Additionally, TIDE scores were higher in COAD patients who had low CLCA4 expression (Fig. 6K), which suggests that immune checkpoint blockade therapy (ICB) was ineffective and that these patients had short survival times after starting ICB therapy.
Calcium-activated chloride channel levels and clinicopathological traits in colon adenocarcinoma patients
Using the Assistant for Clinical Bioinformatics, we investigated the relationship between clinicopathological features and CLCA4. The findings demonstrated that CLCA4 expression substantially predicted the M stage (
p < 0.05). Other pathological variables, such as gender, age, race, T and N stages, and clinical stage, showed no statistically significant connection. The difference between T3 and T4 was, nevertheless, significant (
p < 0.05) (Figs. 7A–G). The overall survival and disease-free survival of M0 patients were considerably better than those of M1 patients, according to further study of the prognosis between M0 and M1 groups (Figs. 7H–I).
Functional enrichment analysis
Twenty CLCA4-related genes are shown in the GeneMANIA network, which has 21 genes overall (plus CLCA4) and 179 total links (Fig. 8A). The biological functions of these genes were explored by enriching them for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. According to what we discovered, the GO functional enrichment analysis showed enrichment mostly in the following areas: plasma membrane part, ion transmembrane transporter activity, transmembrane transporter activity, and ion channel activity. There are other biological processes, such as passive transmembrane transporter activity and substrate-specific channel activity (Fig. 8B). These genes are primarily abundant in the metabolism-related pathways: renin secretion, pancreatic secretion, cGMP-PKG, NOD-like receptor, cAMP, and other signalling pathways (Fig. 8C).
We utilised the LinkedOmics dataset to validate CLCA4’s potential role. A volcano map (Fig. 9A) illustrates the co-expression of CLCA4 genes in COAD. We also created a heat map to visualise the top 50 genes associated with CLCA4 (Figs. 9B, C). According to the GSEA analysis, the biological processes that are enriched are related to mitochondrial gene expression, mitochondrial respiratory chain complex assembly, translational elongation, nicotinamide adenine dinucleotide (NADH) dehydrogenase complex assembly, digestion, and coronary vasculature development. As demonstrated in Figure 9D, the KEGG pathway enrichment analysis identified enrichment in multiple pathways, including oxidative phosphorylation, Parkinson’s disease, Alzheimer’s disease, ribosome, and non-alcoholic fatty liver disease, as well as alcoholic fatty liver disease (NAFLD), drug metabolism, retinol metabolism, chemical carcinogenesis, fatty acid degradation, pancreatic secretion, extracellular matrix (ECM)-receptor interaction, and other types of O-glycan biosynthesis (Figs. 9E–I). These discoveries illuminate the possible importance of CLCA4 in COAD and give information on the disease’s underlying molecular pathways.
Discussion
Clinical trials have shown that the molecular and pathological characteristics of the tumour determine the response to treatment and can improve OS [1]. Further investigation into the molecular mechanisms of colon carcinogenesis could offer novel avenues for the treatment of colon cancer. In COAD tissues, we observed low expression of CLCA4 in this investigation. This finding was based on information obtained from various datasets, including the Assistant for Clinical Bioinformatics, TIMER, TISIDE, and HPA. Furthermore, we found that the downregulation of CLCA4 was linked to a poorer prognosis in COAD patients. Consequently, CLCA4 mRNA and protein expression were found to vary between normal human tissues, with oesophageal and colonic tissues expressing CLCA4 mRNA most highly, followed by the rectum and bladder, with the nervous system expressing CLCA4 mRNA weakly, which is in disagreement with what Agnel et al. previously reported [12]. They concluded that CLCA4 is widely expressed in the human nervous system, but the strongest signal is from the colon, which is twice as strong as the neural signal. Furthermore, our findings indicate that the CLCA4 protein is abundantly expressed in various parts of the body, including the gastrointestinal tract and several glands, such as the thyroid, parathyroid, adrenal, testis, prostate, seminal vesicles, and breast. However, we observed a disparity between CLCA4 RNA and protein expression in some of these regions, which we speculate may be brought on by variations in antibody specificity.
Genomic analysis plays a crucial role in detecting somatic variants because it can help identify potentially effective treatments [1]. In particular, analysis revealed that for COAD, CNV is positively correlated with CLCA4 and is associated with patient survival. Subsequently, we found that these variations significantly affected the counts of B-cells, CD8+ T-cells, neutrophils, and dendritic cells infiltrating COAD. More investigation was done on the possible connection between immune cell infiltration and CLCA4 in COAD.
There is emerging evidence that immune cells that penetrate tumours influence tumour growth as well as patient prognosis [13]. In this work, we discovered that CLCA4 is linked to B-cell infiltration and affects several other immune cell types, such as M0 macrophages, activated myeloid dendritic cells, neutrophils, resting memory CD4+ T-cells, eosinophils, resting mast cells, and g T-cells. B-cells are known to express many MHC II molecules and play a substantial role in humoral immunity. These cells are also recognised as key antigen-presenting cells that contribute to tumour immunity. Surprisingly, research has revealed that a high amount of B-cell infiltration in tumours might forecast a good prognosis for cancer patients [13]. Not surprisingly, CLCA4 has been shown to positively correlate with several other subsets of lymphocytes, including Act B, Act CD4, Act DC, Eosinophil, iDC, Imm B, Macrophage, Mast, MDSC, Monocyte, Neutrophil, pDC, Th1, Th2, and Th17. As part of our adaptive immune response, CD4+ T-cells play a significant role. As soon as they are activated, they divide into a variety of subpopulations, such as Th1 and Th2 cells, Tfh cells, Th17 cells, and Treg [14] – Th17 in particular, the presence of which in the mouse gut has been linked in the literature to the development of intestinal adenomas in mice [15]. There is mounting evidence indicating that IL-17 and its primary source, Th17, are highly concentrated in CRC and have a negative correlation with patient prognosis. Furthermore, IL-17A was previously demonstrated to enhance the levels of PD-L1, reducing the efficacy of immunotherapy. IL-17 may be a potential target for sensitizing tumour cells to ICI [16]. Furthermore, we found several immune cell markers, including STAT3 and STAT4, to have a favourable correlation with CLCA4 expression. STAT3 affects the clinical outcome of CRC patients and is implicated in the invasion of many immune cell types, such as B- cells, CD8+ T-cells, CD4+ T-cells, and macrophages [17]. Based on this, we propose that CLCA4 may be involved in CD4+ T-cell-mediated anti-tumour immunity within the COAD tumour microenvironment, but further study is needed to fully grasp the underlying process.
Immune checkpoint molecules that are produced on immune cells may impede their ability to perform their functions, which could lead to an insufficient anti-tumour immunological response and let the tumour evade the immune system. Our research indicates a positive correlation between CLCA4 and immune checkpoints CTLA4, CD274, LAG3, and PDCD1LG2. Furthermore, COAD patients with reduced CLCA4 expression had higher TIDE scores, indicating that immune checkpoint blockade treatment was possibly ineffective for them. Metastasis, which frequently affects the liver, is the primary factor in deaths due to cancer in persons with CRC [18]. Subsequent analysis of the relationship between CLCA4 and clinicopathological features showed that it was significantly correlated with the M stage. Conversely, no statistically significant link was discovered between CLCA4 and age, gender, race, or clinical stage.
GeneMANIA produced a network of 21 genes that interact with each other. The biological functions associated with the CLCA4 genes were analysed through GO functional enrichment. According to the GO enrichment study, they were highly enriched for ion channel activity and transmembrane transporter activity. Additionally, the evaluation of KEGG signalling pathways showed that they were enriched in certain signalling pathways, such as renin secretion, pancreatic secretion, and the cAMP signalling pathway. Furthermore, the GSEA’s findings demonstrated that the GO’s biological activities were mostly related to digestion, coronary vascular development, mitochondrial respiratory chain complex assembly, translation extension, NADH dehydrogenase complex assembly, and structural organisation. During the analysis of signalling pathways, the focus was primarily on various pathways such as oxidative phosphorylation, Parkinson’s disease, Alzheimer’s disease, NAFLD, drug metabolism, retinol metabolism, chemical carcinogenesis, fatty acid degradation, pancreatic secretion, and other pathways that were deemed relevant. According to Moreno-Sánchez et al. [19], most cancer cells do not experience impairment in their mitochondrial function. Cancer cells experience aerobic glycolysis, a metabolic process that converts glucose into carbon dioxide. This occurs through the oxidation of glycolytic pyruvate in the mitochondrial tricarboxylic acid cycle. As a result of this process, NADH is created, which encourages oxidative phosphorylation to increase adenosine triphosphate synthesis while reducing lactate formation [20]. Recent research suggests that cancers may have diverse cellular metabolisms. Based on the research conducted by Sonveaux et al. [21], particular cells might utilise the additional lactate produced as an energy source for the process of mitochondrial oxidative phosphorylation. Interestingly, a growing body of research indicates that insulin plays a major role in the development of tumours via regulating signalling networks [22]. Numerous studies have linked the renin-angiotensin system (RAS) to the development of CRC. In particular, research indicates that dysregulation of the RAS may contribute to the pathophysiology of CRC [23, 24]. A growing number of epidemiological studies indicate a remarkable inverse correlation between Alzheimer’s and Parkinson’s disease. According to Hang et al., this correlation may be influenced by the metabolites of gut bacteria, which accelerate the development of Alzheimer’s disease/Parkinson’s disease and cancer [25]. On the other hand, when there is gut microbial dysbiosis, it causes excessive activation of CD8+ T-cells and triggers chronic inflammation and premature T-cell failure. This ultimately results in a higher susceptibility to colon tumours and a subsequent decrease in anti-tumour immunity [26]. Thus, it appears that the functional annotation of CLCA4 co-expressed genes suggests that they are involved in intestinal digestion, ion transport, and metabolism, which in turn affect colorectal carcinogenesis and progression by altering the intestinal microenvironment.
Conclusions
In conclusion, the expression of CLCA4 may impact the immune microenvironment, ultimately influencing the occurrence and progression of COAD and subsequently affecting the prognosis of COAD patients. As such, CLCA4 may function as a diagnostic predictor for COAD. A constraint of this research is the requirement for a substantial quantity of clinical specimens to verify the association between immune cell counts and CLCA4 expression. Additionally, it is critical to do additional research to better comprehend the immunological systems involved in the emergence of COAD and to pinpoint prospective therapeutic targets. This information could aid in defining the factors that influence immune therapy response.
Disclosures
- The database used in this study can be found on the internet. Our study did not require ethical board approval because it did not contain human or animal trials.
- We appreciate the platform provided by the Cancer Genome Atlas database, datasets like HPA, and contributors who uploaded valuable datasets.
- The study was supported by the Anhui Provincial Education Department’s Key project (2022AH051530) and the Bengbu Medical University’s Natural Science Foundation’s Key project (2021byzd031), and Anhui Key Laboratory of Computational Medicine and Intelligent Health (Bengbu Medical University) (AHCM2023Z005).
- Conflicts of interest: None.
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