Genes associated with calcium signaling have promising diagnostic potential for gastric cancer

Introduction

Gastric cancer (GC) poses a significant health threat, and has a poor 5-year survival rate of approximately less than 15–20%. The complexity of its etiology, coupled with challenges in early diagnosis, often leads to delayed treatment and poor patient outcomes. Factors contributing to the development of GC include genetic predispositions, environmental influences such as gastroesophageal reflux disease, and lifestyle choices, which together create a multifaceted landscape that complicates early detection (1-3). Additionally, patients with GC have limited systemic treatment options and frequently develop resistance to chemotherapeutic agents, resulting in suboptimal therapeutic efficacy. This resistance is attributed to various mechanisms, including alterations in drug transport, enhanced DNA repair capabilities, and the activation of survival pathways that allow cancer cells to evade the effects of chemotherapy (3). The progression of GC is also governed by intricate regulatory networks involving signaling pathways, tumor microenvironment interactions, and epigenetic modifications (4). Despite advances in our understanding of these mechanisms, the precise pathways that drive the onset and progression of GC, as well as the underlying causes of chemotherapy resistance, remain poorly understood (1-4). Consequently, further research needs to be conducted to unravel the complexities of GC pathogenesis and to develop more effective therapeutic strategies. Addressing these gaps in knowledge is crucial for improving early detection methods and enhancing the treatment outcomes of patients suffering from this aggressive malignancy.

Intracellular calcium signaling is fundamental to nearly every aspect of cellular physiology, including secretion, muscle contraction, fertilization, synaptic transmission, cell division, and gene expression (5). The meticulous regulation of the mechanisms that maintain Ca²⁺ homeostasis is vital for preventing dysfunctions that can result in various pathological conditions. In cancer, significant abnormalities arise in the mechanisms that control cell proliferation, apoptosis, and migration (6). Calcium ions serve as essential signaling molecules in these processes, where the spatial distribution, intensity, and temporal characteristics of Ca²⁺ signals critically determine cellular outcomes. In the context of cancer, mutations and anomalies in the expression, regulation, and/or subcellular localization of proteins responsible for Ca²⁺ handling and transport disrupt the normal interactions among extracellular, cytosolic, endoplasmic reticulum (ER), and mitochondrial Ca²⁺ concentrations, and the spatiotemporal dynamics of Ca²⁺ signaling. This disruption results in the dysregulation of Ca²⁺-dependent effectors that modulate signaling pathways, consequently affecting cellular behavior in ways that promote pathophysiological traits of cancer, such as heightened proliferation, improved survival, and increased invasive capacity (5,6). Additionally, histidine-rich calcium binding protein (HRC) has emerged as a significant regulator of intracellular calcium homeostasis in GC. HRC expression is upregulated in GC samples and correlates with the overall survival rate of patients. It promotes GC cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) through the Raf/MEK/ERK signaling pathway. The knockdown of HRC has been shown to reduce intracellular calcium ion levels, thereby inhibiting these malignant processes. This indicates that HRC represents a potential target for GC treatment, as it plays a critical role in regulating calcium signaling pathways that contribute to cancer progression (7). Therefore, more comprehensive studies need to be conducted to elucidate the function of calcium signaling in GC.

Consequently, we conducted an extensive analysis to assess the expression levels of calcium signaling in GC, explored the relationship between calcium signaling and the tumor immune microenvironment, and evaluated the prognostic significance. We present this article in accordance with the TRIPOD reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-219/rc).

Methods

Data sources from The Cancer Genome Atlas (TCGA)-GC cohort

The RNA sequencing data and clinical information of GC patients were collected from TCGA database (https://portal.gdc.cancer.gov). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Prognostic gene identification in GC

A univariate Cox analysis was conducted to identify the genes associated with prognosis in the GC patients, of which the top 20 genes were selected based on their P values. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted using the “ClusterProfiler” R package to identify the pathways associated with these prognostic genes.

Tumor subtype identification via the calcium signaling pathway in GC

To investigate the relationship between the expression of the calcium signaling-related genes and subtypes of GC, a consistent cluster analysis of TCGA-GC dataset was performed using the “ConsensusClusterPlus” R package (version 1.54.0). The number of clusters (k) ranged from 2 to 6. A heatmap was generated using the “pheatmap” R package (version 1.0.12). Further, the overall survival (OS) of the various subgroups was evaluated using a Kaplan-Meier analysis.

Identification of calcium signaling-related differentially expressed genes (DEGs) in GC

The DEGs among the GC clusters were identified using the “DEseq2” R package based on the following criteria: P<0.05, and |log₂ fold change| >1. To visually represent these DEGs, volcano plots were constructed using the “ggplot2” package, while a heatmap was generated using the “pheatmap” R package (version 1.0.12). For the purpose of the functional enrichment analysis, Gene Ontology (GO) and KEGG pathway analyses were conducted using the “ClusterProfiler” package.

Immune activity in calcium signaling-related clusters in GC

To evaluate the immune activity of the two clusters associated with calcium signaling, the focal adhesion genes were analyzed using the CIBERSORT algorithm from “immunoeconomics”. To compare the immune responses, the following 10 immune checkpoint genes were examined: CD274, PDCD1, PDCD1LG2, CTLA4, LAG3, HAVCR2, TIGIT, and SIGLEC15. A heatmap was generated using the “pheatmap” package, while a boxplot was generated using the “ggplot2” package. The Wilcoxon test was used to determine the differences in immune cell infiltration and pathway activation across the groups, and the significance threshold was set at P<0.05.

Establishment of a calcium signaling-related gene prognostic model

To assess the prognostic relevance of the calcium signaling-related genes, a Cox regression analysis was conducted to establish a model in TCGA-GC cohort using the “glmnet” package in R. Non-zero coefficients were identified according to the minimum lambda criterion. The risk score was computed using the following formula: risk score = sum (gene expression × coefficient). Patients in TCGA-GC cohort were stratified into low- and high-risk groups based on the median risk score. The OS outcomes were examined between these groups by a Kaplan-Meier analysis using the “survival” package in R, and hazard ratios (HRs) along with 95% confidence intervals (CIs) were obtained through the Cox proportional hazards analysis.

Statistical analysis

Differences were deemed statistically significant with a P value <0.05 and |log₂ fold change| >1. The chi-square test assessed the link between clinical characteristics and GC subtypes, using P<0.05 as the significance threshold. A univariate Cox regression identified gene expressions significantly linked to survival, also using P<0.05 as the threshold.

Results

Identification of the prognostic genes in GC and the related KEGG pathways

A univariate Cox regression analysis was conducted to identify the prognostic genes associated with GC, with a particular focus on those influencing patient outcomes. A total of 829 prognostic genes were identified, of which the top 20 were selected based on their P values (Figure 1A). Subsequently, a KEGG enrichment analysis of these genes was performed to identify the critical pathways, including the calcium signaling pathway, focal adhesion, extracellular matrix (ECM)-receptor interaction, and cancer-related pathways that may play a significant role in GC (Figure 1B). Given the robust correlation between calcium signaling and cancer, the present study sought to assess the functional consequences of calcium signaling status in patients diagnosed with GC.

Figure 1 Identification and KEGG pathways of prognostic genes in GC. (A) The top 20 prognostic genes were identified based on their P values. (B) The enriched KEGG pathways for the prognostic genes in GC. CI, confidence interval; ECM, extracellular matrix; GC, gastric cancer; HR, hazard ratio; KEGG, Kyoto Encyclopedia of Genes and Genomes; Se, standard error.

Consensus clustering identified two molecular subtypes of GC

To further investigate these associations, the ConsensusClusterPlus tool was used to perform consensus clustering based on the calcium signaling-related genes. The results indicated that the empirical cumulative distribution function (CDF) curve achieved optimal stability when k was designated as 2 (Figure 2A,2B). As a result, the patient cohort was divided into two distinct clusters: Cluster 1 (C1) and Cluster 2 (C2) (Figure 2C,2D). The heatmap analysis corroborated the expression profiles of the calcium signaling-related genes across samples from both C1 and C2 (Figure 2E). Additionally, a survival analysis was performed to assess the association between the identified clusters (C1/C2) and OS. The findings revealed significant differences in OS between the C1 and C2 subtypes, such that the C1 subtype showed significantly better survival than the C2 subtype (Figure 2F). These results suggest that the distinct molecular subtypes of GC may be correlated with differing clinical prognoses.

Figure 2 Consensus clustering identified two molecular subtypes of GC. (A) The CDF associated with consensus clustering is illustrated for values of k ranging from 2 to 6. (B) The variation in the AUCs of the CDF is represented for k values ranging from 2 to 6. (C) A principal component analysis was conducted that revealed two distinct clusters among the patients from TCGA-GC dataset. (D) A consensus clustering matrix of two clusters in GC. (E) A heatmap representing the two identified clusters in GC. (F) Kaplan-Meier survival curves were used to evaluate the differences in OS between the two identified clusters. AUC, area under the curve; C1, cluster 1; C2, cluster 2; CDF, cumulative distribution function; CI, confidence interval; GC, gastric cancer; HR, hazard ratio; OS, overall survival; PC, principal component; TCGA, The Cancer Genome Atlas.

Differences in clinical features between the two GC subtypes

A study was performed to examine the expression profiles of the essential genes associated with calcium signaling in relation to diverse clinical characteristics. The analysis revealed that the distribution of C2 was significantly associated with the variables of sex, ethnicity, and grading stages (G1, G2, and G3) (Figure 3A-3H). These results suggest that C2 can function as a prognostic marker for risk, and may be indicative of the advancement of clinical manifestations.

Figure 3 Variations in the distribution of the clinical characteristics between C1 and C2 were evident. Significant differences were observed in the distribution proportions of C1 and C2 in terms of (A) gender, (B) race, (C) T stage, (D) N stage, (E) M stage, (F) stage, (G) grade staging, and (H) radiation. BF, bayes factor; C1, cluster 1; C2, cluster 2; CI, confidence interval; HDI, highest density interval.

Investigation of the mechanisms differentiating the two GC groups

To investigate the underlying mechanisms that distinguished the two groups, we identified a total of 980 DEGs that met the following criteria: P<0.05 and |log₂ fold change| >1. This gene set included seven upregulated genes, and 973 downregulated genes, as shown in the volcano plot (G1 vs. G2; Figure 4A). The accompanying heatmap (Figure 4B) showed the distinct expression patterns of the prominent DEGs across the two clusters.

Figure 4 The molecular regulatory mechanisms based on the DEGs between the two GC subtypes. (A) A volcano plot revealed seven upregulated DEGs, and 973 downregulated DEGs based on an adjusted P value of <0.05 and a |log2 fold change| >1. (B) A heatmap showing the expression patterns of DEGs across C1 and C2. (C) The enriched gene ontology for the DEGs. (D) The KEGG pathways enriched for the DEGs. C1, cluster 1; C2, cluster 2; DEGs, differentially expressed genes; ECM, extracellular matrix; GC, gastric cancer; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; NS, non-significant; TGF, transforming growth factor.

To gain further insights into the biological processes associated with these 980 DEGs, GO and KEGG enrichment analyses were conducted. The GO analysis of biological processes revealed that these genes were significantly enhanced in cell-matrix adhesion, calcium ion homeostasis, and cell-substrate adhesion (Figure 4C). Similarly, the KEGG analysis revealed that the DEGs were significantly enriched with pathways involved in ECM-receptor interactions, focal adhesion, vascular smooth muscle contraction, cancer-related proteoglycans, and calcium signaling pathways (Figure 4D). Previous studies have indicated that ECM-receptor interactions, focal adhesion, and calcium ion homeostasis associated with cancer are critical tumor markers (5-7). These findings suggest that tumor cells in the G2 GC subtypes may have enhanced migration and proliferation capabilities.

Immune activity between the two-calcium signaling-related groups in GC

Numerous research studies have established a robust association between calcium signaling pathways and immune functions in a variety of cancer types (6-8). In the present study, we focused on the immune responses associated with two distinct clusters related to calcium signaling in patients diagnosed with GC. The boxplot analyses revealed notable differences in the populations of immune cells, including B cells, endothelial cells, macrophages, and cluster of differentiation (CD)4⁺ T cells, when contrasting samples from the C1 and C2 GC patients (Figure 5A). Further, our findings indicated that nine genes associated with immune checkpoint inhibitors (i.e., CD274, CTLA4, HAVCR2, ITPRIPL1, LAG3, PDCD1, PDCD1LG2, TIGIT, and SIGLEC15) had reduced expression levels in the C1 GC samples compared to the C2 CG samples, with the exception of IGSF8 (Figure 5B). Collectively, these results suggest a significant relationship between calcium signaling mechanisms and immune system activity.

Figure 5 The immune activity in two distinct calcium signaling-associated groups in GC. (A) A comparative analysis of the enrichment scores for six different immune cell types was conducted across the two calcium signaling-related clusters in GC. (B) The expression levels of the genes that encode immune checkpoint inhibitors were assessed between the two GC clusters. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. ns, non-significant. EPIC, the proportions of immune and cancer cell; GC, gastric cancer.

Development of a prognostic model using calcium signaling-related genes

A least absolute shrinkage and selection operator (LASSO) regression analysis was performed, and 10 genes associated with calcium signaling in GC were identified. A gene signature comprising these 10 genes was established based on the most favorable λ value. The risk score was computed using the following formula: risk score = (0.1184) * PDE1B + (0.0172) * NGF + (0.1205) * FGF1 + (0.1257) * ADRA1B + (0.0302) * TACR1 + (0.008) * CXCR4 + (0.1563) * GNAS + (0.0181) * EDNRB + (0.2336) * EGF + (0.1195) * ERBB4 (Figure 6A). Using this gene signature, the patients from TCGA-GC cohort were stratified into the low- and high-risk groups (Figure 6A). The survival analysis revealed that the patients in the low-risk group had significantly better survival outcomes than those in the high-risk group (HR: 1.95, P<0.001; Figure 6B). The accuracy of the prognostic model was evaluated by a receiver operating characteristic (ROC) curve analysis, which produced areas under the curve (AUC) of 0.639 for 1 year, 0.707 for 3 years, and 0.674 for 5 years (Figure 6C), which suggest that the model has considerable prognostic capability.

Figure 6 Development of a prognostic model using calcium signaling-related genes. (A) An evaluation of the prognostic gene signature was conducted using TCGA cohort, a heatmap illustrating the expression profiles of these prognostic genes was generated, and the patients were categorized into low- and high-risk groups. The x-axis represents samples ordered by ascending risk score. (B) A Kaplan-Meier survival analysis was conducted to examine the prognostic signature. (C) A time-dependent ROC curve analysis was also performed to assess the performance of the gene signature over time. AUC, area under the curve; CI, confidence interval; HR, hazard ratio; OS, overall survival; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas.

Discussion

In this study, a univariate Cox regression analysis identified 829 prognostic genes, which were predominantly associated with the calcium signaling pathway, focal adhesion, ECM-receptor interaction, and cancer-related pathways that may play a significant role in GC. Consensus clustering identified two molecular subtypes (C1 and C2) of GC based on the calcium signaling-related genes. C2 could function as a prognostic marker for risk and may be indicative of the advancement of clinical manifestations. The GO analysis of biological processes highlighted that these genes were significantly enriched in cell-matrix adhesion, calcium ion homeostasis, and cell-substrate adhesion in the high-risk C1 group. Similarly, the KEGG analysis revealed that the DEGs were predominantly associated with pathways involved in ECM-receptor interactions, focal adhesion, vascular smooth muscle contraction, cancer-related proteoglycans, and calcium signaling pathways in the high-risk C2 group. The immune activity between the two-calcium signaling-related GC groups differed. The LASSO regression analysis identified 10 genes associated with calcium signaling in GC (i.e., PDE1B, NGF, FGF1, ADRA1B, TACR1, CXCR4, GNAS, EDNRB, EGF, and ERBB4). The accuracy of the prognostic model was evaluated by a ROC curve analysis, which produced AUCs of 0.639 for 1 year, 0.707 for three years, and 0.674 for 5 years.

The precise regulation of the processes responsible for maintaining Ca²⁺ homeostasis is crucial for averting dysfunctions that may lead to a range of pathological conditions (5-7). In the context of cancer, notable irregularities emerge in the systems that govern cellular proliferation, apoptosis, and migration. Calcium ions act as critical signaling entities in these biological processes, wherein the spatial distribution, intensity, and temporal dynamics of Ca²⁺ signals play a pivotal role in determining cellular responses (5-7). Such disruptions lead to the misregulation of Ca²⁺-dependent effectors that influence signaling pathways, thereby affecting cellular behavior in ways that foster the pathophysiological characteristics of cancer, including increased proliferation, enhanced survival, and greater invasive potential (5-7). Consequently, there is a pressing need for further extensive research to clarify the role of calcium signaling in GC.

A prognostic model comprising 10 genes was developed to predict outcomes in patients diagnosed with GC. This model included the following genes: PDE1B, NGF, FGF1, ADRA1B, TACR1, CXCR4, GNAS, EDNRB, EGF, and ERBB4. Phosphodiesterase 1B (PDE1B) is part of the phosphodiesterase (PDE) family, specifically the PDE1 subfamily. It has emerged as a potential biomarker linked to the tumor microenvironment and holds clinical prognostic relevance in osteosarcoma (8). The autophagic activity of Schwann cells facilitates perineural invasion in pancreatic cancer via the NGF/ATG7 paracrine signaling pathway (9). Fibroblast growth factor 1 (FGF1) facilitates glycolytic metabolism via the estrogen receptor in breast cancer cases that are resistant to endocrine therapy (10). The recurrent downregulation of the alpha-1B-adrenergic receptor (ADRA1B) is attributed to abnormal promoter methylation events in GC (11). The silencing of long non-coding RNA inhibits the biological processes associated with hepatocellular carcinoma through the modulation of miRNA-206 and TACR1 (12). CXCR4 has emerged as a significant prognostic biomarker in gastrointestinal cancer (13). The protein GPR176 contributes to the advancement of cancer by binding to the G protein GNAS, thereby inhibiting mitophagy in colorectal cancer cells (14). The influence of miR-124-3p on cellular growth and programmed cell death in bladder carcinoma via the modulation of EDNRB has been investigated (15). Research has also been conducted on the role of ErbB4 in modulating the vasculogenic mimicry potential of breast cancer cells (16). Through transcriptomic analysis, CTSK, C3, and IFITM1 have been identified as key immune-related genes for diagnosing GC linked to Helicobacter pylori infection (17). In-depth examination of the role of the helicobacter-linked ferroptosis gene YWHAE in GC using multi-omics integration (18).

Calcium signaling plays a crucial role in various cellular processes, and its dysregulation is often implicated in cancer development and progression (19). In the context of GC, calcium-sensing receptor (CaSR) expression has been studied for its potential diagnostic and prognostic values. The expression levels of CaSR can vary significantly between GC tissues and non-tumor gastric tissues, which may provide insights into the tumor’s behavior and potential progression (19). A study investigating CaSR expression in GC found that downregulation of CaSR mRNA was observed in a significant proportion of GC tissues compared to matched normal tissues (19). This downregulation was associated with more advanced stages of cancer and deeper invasion into the gastric wall, suggesting that CaSR could serve as a marker for tumor aggressiveness and progression (19). The differential expression of CaSR in GC versus normal tissues highlights its potential as a diagnostic biomarker, helping to distinguish between malignant and non-malignant gastric tissues.

The present study had a number of limitations. Notably, additional investigations, both in vitro and in vivo, need to be conducted to elucidate the precise function of calcium signaling in GC. Future research should seek to identify the mechanisms by which the genes associated with calcium signaling affect the progression of GC, thereby offering novel therapeutic strategies for its management.

Conclusions

A significant correlation was found between calcium signaling-related genes and tumor classification in patients with GC. An innovative signature associated with calcium signaling was established that can serve as a reliable prognostic indicator for GC. Our findings may pave the way for enhanced diagnostic and therapeutic approaches in the context of GC.

Acknowledgments

The authors would like to express their appreciation to the TCGA for the unrestricted use of TCGA data.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-219/rc

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-219/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-219/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Smyth EC, Nilsson M, Grabsch HI, et al. Gastric cancer. Lancet 2020;396:635-48. [Crossref] [PubMed]
2. Röcken C. Predictive biomarkers in gastric cancer. J Cancer Res Clin Oncol 2023;149:467-81. [Crossref] [PubMed]
3. López MJ, Carbajal J, Alfaro AL, et al. Characteristics of gastric cancer around the world. Crit Rev Oncol Hematol 2023;181:103841. [Crossref] [PubMed]
4. Salvatori S, Marafini I, Laudisi F, et al. Helicobacter pylori and Gastric Cancer: Pathogenetic Mechanisms. Int J Mol Sci 2023;24:2895. [Crossref] [PubMed]
5. Zheng S, Wang X, Zhao D, et al. Calcium homeostasis and cancer: insights from endoplasmic reticulum-centered organelle communications. Trends Cell Biol 2023;33:312-23. [Crossref] [PubMed]
6. Moon DO. Calcium's Role in Orchestrating Cancer Apoptosis: Mitochondrial-Centric Perspective. Int J Mol Sci 2023;24:8982. [Crossref] [PubMed]
7. Wang C, Ren C, Hu Q, et al. Histidine-rich calcium binding protein promotes gastric cancer cell proliferation, migration, invasion and epithelial-mesenchymal transition through Raf/MEK/ERK signaling. J Cancer 2022;13:1073-85. [Crossref] [PubMed]
8. Chen Q, Xing C, Zhang Q, et al. PDE1B, a potential biomarker associated with tumor microenvironment and clinical prognostic significance in osteosarcoma. Sci Rep 2024;14:13790. [Crossref] [PubMed]
9. Zhang W, He R, Yang W, et al. Autophagic Schwann cells promote perineural invasion mediated by the NGF/ATG7 paracrine pathway in pancreatic cancer. J Exp Clin Cancer Res 2022;41:48. [Crossref] [PubMed]
10. Castillo-Castrejon M, Sankofi BM, Murguia SJ, et al. FGF1 supports glycolytic metabolism through the estrogen receptor in endocrine-resistant and obesity-associated breast cancer. Breast Cancer Res 2023;25:99. [Crossref] [PubMed]
11. Noda H, Miyaji Y, Nakanishi A, et al. Frequent reduced expression of alpha-1B-adrenergic receptor caused by aberrant promoter methylation in gastric cancers. Br J Cancer 2007;96:383-90. [Crossref] [PubMed]
12. Hongfeng Z, Andong J, Liwen S, et al. lncRNA RMRP knockdown suppress hepatocellular carcinoma biological activities via regulation miRNA-206/TACR1. J Cell Biochem 2020;121:1690-702. [Crossref] [PubMed]
13. Jiang Q, Sun Y, Liu X. CXCR4 as a prognostic biomarker in gastrointestinal cancer: a meta-analysis. Biomarkers 2019;24:510-6. [Crossref] [PubMed]
14. Tang J, Peng W, Ji J, et al. GPR176 Promotes Cancer Progression by Interacting with G Protein GNAS to Restrain Cell Mitophagy in Colorectal Cancer. Adv Sci (Weinh) 2023;10:e2205627. [Crossref] [PubMed]
15. Fu W, Wu X, Yang Z, et al. The effect of miR-124-3p on cell proliferation and apoptosis in bladder cancer by targeting EDNRB. Arch Med Sci 2019;15:1154-62. [Crossref] [PubMed]
16. Kawahara R, Simizu S. ErbB4-mediated regulation of vasculogenic mimicry capability in breast cancer cells. Cancer Sci 2022;113:950-9. [Crossref] [PubMed]
17. Chen Y, Tang Z, Tang Z, et al. Identification of core immune-related genes CTSK, C3, and IFITM1 for diagnosing Helicobacter pylori infection-associated gastric cancer through transcriptomic analysis. Int J Biol Macromol. 2025;287:138645. [Crossref] [PubMed]
18. Liu D, Peng J, Xie J, et al. Comprehensive analysis of the function of helicobacter-associated ferroptosis gene YWHAE in gastric cancer through multi-omics integration, molecular docking, and machine learning. Apoptosis 2024;29:439-56. [Crossref] [PubMed]
19. Tae CH, Shim KN, Kim HI, et al. Significance of calcium-sensing receptor expression in gastric cancer. Scand J Gastroenterol 2016;51:67-72. [Crossref] [PubMed]