Identification of mechanosensitive ion channel-related molecular subtypes and key genes for ovarian cancer

Introduction

Ovarian cancer (OC) is a major health issue due to its complicated causes, challenges in early diagnosis, and low survival rate after 5 years (1). The survival rate for early-stage epithelial OC exceeds 90%, but most diagnoses occur at a later stage, decreasing the rate to approximately 49% (2). The intricate causes of OC, which include genetic, environmental, and hormonal elements, and ambiguous early symptoms frequently lead to a delayed diagnosis, resulting in poorer outcomes (1,2). A significant number of patients become resistant to therapies such as platinum-based medications, reducing their efficacy and adversely affecting prognosis (1,2). Genetic mutations, epigenetic alterations, and disrupted signaling pathways drive its progression, leading to tumor growth, metastasis, and resistance to drugs (1,2). This highlights a pressing need for efficient early detection and the innovation of diagnostic tools.

Mechanosensitive ion channels (MICs) play a crucial role in various cellular processes by responding to mechanical stimuli and converting them into biochemical signals. In the context of OC, these channels are involved in the mechanotransduction pathways that allow cancer cells to sense and respond to mechanical stimuli in their microenvironment. This ability to perceive and react to mechanical cues is essential for cancer cell migration, invasion, and metastasis, which are critical steps in cancer progression. The TRPV4 and P2X7 MICs have been shown to significantly affect tumor cell dissemination. These channels help form tumor neovasculature, promote transendothelial migration, and increase cell motility, which are all vital for the metastatic spread of OC cells. However, their activation can also lead to forms of cancer cell death, indicating a complex functionality that could be targeted for therapeutic purposes (3).

Furthermore, MICs are involved in the regulation of calcium signaling within cancer cells. Channels such as Piezo1 are known to mediate mechanical transduction, which is crucial for maintaining the electrochemical gradients necessary for calcium influx. This calcium signaling is pivotal for various cellular functions, including proliferation and migration, which are often dysregulated in cancer cells. The role of Piezo channels in cancer progression, particularly in altering calcium signaling, point to their potential to be therapeutic targets in OC (4). In addition to their role in calcium signaling, MICs may also be involved in the response to changes in the extracellular matrix (ECM) stiffness, a common feature in the tumor microenvironment. The Piezo1 channel, for instance, is activated by matrix stiffness and regulates oxidative stress-induced senescence and apoptosis. This suggests that targeting Piezo1 can modulate the mechanical stress responses in OC cells, potentially reducing their metastatic potential (5). Overall, the involvement of MICs in OC suggests that they may serve as therapeutic targets.

This study developed a prognostic model by combining gene expression and clinical data from patients with OC, focusing on genes related to MICs, which may be useful for predicting outcomes in OC. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1219/rc).

Methods

Identification of MICs

The GeneCards database (https://www.genecards.org/) was queried to identify genes linked to MICs, and the top 20 genes based on relevance score were selected. The 20 most significant genes were recognized, including PIEZO1, SCN5A, KCNQ1, CFTR, PIEZO2, KCNMA1, ASIC2, CACNA1C, ASIC3, SCN1A, TRPV4, TRPV1, KCNN4, SCNN1B, SCNN1A, CACNA1B, SCNN1G, TRPM7, KCNK2, and TRPA1.

Data collection and preprocessing

RNA-sequencing (RNA-seq) data and related clinical details for this study were sourced from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov), encompassing 376 OC patient tissue samples. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

OC subtypes based on MIC-related genes

Using the “ConsensusClusterPlus package” (version 1.54.0), we carried out consensus clustering on the TCGA-OC dataset to study the association between MIC-related genes and OC subtypes, with the number of clusters (k) set between 2 and 6. The “pheatmap” package (version 1.0.12) was used to visualize gene expression profiles, and Kaplan-Meier survival analysis was employed to assess survival differences between subgroups.

Identification of differentially expressed genes (DEGs) and functional enrichment analysis between subgroups

DEGs between OC clusters were identified via the “DEseq2” package, with a significance threshold set at P<0.05 and an absolute log₂ fold change greater than 0.75. Visualization of these DEGs was accomplished via the “ggplot2” package. Subsequently, a heatmap was constructed using the “pheatmap” package (version 1.0.12). Functional enrichment analysis was then performed with the “clusterProfiler” package to explore Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.

Immune score estimation differences between subgroups

This study employed immuno-economics to assess immune function regarding genes related to MIC in different OC subtypes. The expression levels of ten immune checkpoint genes, including CD274, CTLA4, HAVCR2, IGSF8, ITPRIPL1, LAG3, PDCD1, PDCD1LG2, SIGLEC15, and TIGIT, were assessed to compare immune activity between two OC subtypes. To evaluate immune cell infiltration in the two groups, the Wilcoxon test was conducted, with a P value under 0.05 being considered significant.

Construction of a prognostic risk model

The analysis of least absolute shrinkage and selection operator (LASSO) regression was used to identify genes connected to the survival of patients with OC. Subsequently, the regression coefficients for these genes were determined via multiple Cox regression analysis. A risk score model was then developed based on gene expression levels. The differences in overall survival (OS) between subgroups were evaluated with the “survival” package, with hazard ratios (HRs) and 95% confidence intervals (CIs) estimated via Cox proportional hazards analysis.

Single-cell sequencing analysis of OC

We used the Tumor Immune Single-cell Hub 2 (TISCH2) pipeline (http://tisch.comp-genomics.org/) to analyze genes related to MICs at the single-cell level. We identified cell types by searching the Gene Expression Omnibus (GEO) database for information from the GSE130000 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE130000). The differential gene expression of each cluster was represented in a heatmap.

Statistical analysis

All statistical evaluations were conducted using R software version 3.6.2. The main emphasis of this research was on OS, defined as the duration from the date of diagnosis until the date of death. Receiver operating characteristic (ROC) curves were created utilizing the “survivalROC” R package, while the nomogram was constructed with the assistance of the “rms” R package. All statistical assessments were two-tailed, and a P value of less than 0.05 was deemed statistically significant, unless stated otherwise.

Results

OC subtypes based on MIC-related genes

We examined MICs and investigated their functional significance in the TCGA-OC cohort. First, the top 20 genes were identified based on relevance score and included PIEZO1, SCN5A, KCNQ1, CFTR, PIEZO2, KCNMA1, ASIC2, CACNA1C, ASIC3, SCN1A, TRPV4, TRPV1, KCNN4, SCNN1B, SCNN1A, CACNA1B, SCNN1G, TRPM7, KCNK2, and TRPA1 (Figure 1A). Patients were distinctly separated into two clusters at k=2 by both principal component analysis and consistency clustering (Figure 1A,1B). The heatmap illustrated the different expression patterns of genes associated with MICs between the groups (Figure 1C). The survival analysis indicated that the low-risk group (cluster 2) had a notably longer OS than did the high-risk group (cluster 1) (Figure 1D).

Figure 1 Ovarian cancer subtypes based on mechanosensitive ion channel-related genes. (A) PCA conducted on the two identified clusters. (B) Consensus clustering matrix illustrating the subtypes of OC. (C) Heatmap depicting gene expression patterns distinguishing the two groups. (D) Kaplan-Meier curves illustrating the OS for the two groups. CI, confidence interval; C1, cluster 1; C2, cluster 2; HR, hazard ratio; OC, ovarian cancer; OS, overall survival; PCA, principal component analysis; PC, principal component.

Identification of DEGs between the subgroups and functional enrichment analysis

Analysis of MIC-related clusters 1 and 2 showed 2,336 DEGs, with 2,295 genes being upregulated and 41 being downregulated. GO analysis of the upregulated DEGs in cluster 1 revealed that the biological pathways were significantly enriched in ECM organization, extracellular structure organization, cell-substrate adhesion, axonogenesis, axon guidance, neuron projection guidance, and cell junction assembly (Figure 2A), while the downregulated DEGs in cluster 1 were significantly enriched in oxidative phosphorylation, adenosine triphosphate (ATP) metabolic process, mitochondrial ATP synthesis coupled electron transport, and cellular respiration (Figure 2B).

Figure 2 Identification of DEGs between subgroups and functional enrichment analysis. (A) GO biological processes of upregulated DEGs between C1 and C2 samples. (B) GO biological process of downregulated DEGs between C1 and C2 samples. (C) KEGG pathway of upregulated DEGs. (D) KEGG pathway of downregulated DEGs. C1, cluster 1; C2, cluster 2; DEG, differentially expressed gene; ECM, extracellular matrix; EGFR, epidermal growth factor receptor; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

According to functional analysis, the upregulated DEGs in cluster 1 were found to be notably enriched in pathways, including focal adhesion, axon guidance, proteoglycans in cancer, ECM-receptor interaction, Wnt signaling pathway, hippo signaling pathway, and thyroid hormone signaling pathway (Figure 2C), while the downregulated DEGs in cluster 1 were enriched in pathways of oxidative phosphorylation, thermogenesis, diabetic cardiomyopathy, chemical carcinogenesis-reactive oxygen species, cardiac muscle contraction, and nonalcoholic fatty liver disease (Figure 2D). Collectively, these results suggest that the G1 OC subtype exhibits enhanced migratory and proliferative potential as compared to the G2 subtype.

Immune score estimation differences between the high- and low-risk groups

We performed an evaluation of the immune response across two clusters related to the MICs in OC. The box plots revealed a significant difference in the populations of immune cells, particularly CD4⁺ T cells, neutrophils, and macrophages, in the comparison between OC samples from clusters 1 and 2 (Figure 3A).

Figure 3 The infiltration of immune cells was examined via a comparison of the high- and low-risk OC clusters. (A) Enrichment scores for six immune cell types were compared between high- and low-risk OC clusters. (B) The analysis also included the enrichment scores of ICIs between high- and low-risk OC clusters. ns, non-significant results; *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. C1, cluster 1; C2, cluster 2; ICI, immune checkpoint inhibitor; OC, ovarian cancer; TIMER, Tumor Immune Microenvironment Estimation Resource.

Furthermore, the box plots demonstrated that 7 out of 10 genes linked to immune checkpoint inhibitors (ICIs)—CD274, HAVCR2, IGSF8, ITPRIPL1, PDCD1, PDCD1LG2, and SIGLEC15—showed increased expression levels in the OC samples of cluster 1 as compared to those in cluster 2 (Figure 3B). These results indicate there being a strong correlation between MICs and immune function in OC.

Prognostic model based on MIC-related genes

Through the application of LASSO and Cox regression analyses, we identified a set of 20 genes associated with MICs in OC. This set was further refined to a prognostic signature comprising three genes, determined based on the optimal λ value (Figure 4A). The risk score was computed using the following formula: risk score = (0.0398) × PIEZO1 + (0.1996) × CACNA1C + (0.0604) × TRPV4.

Figure 4 Prognostic model based on mechanosensitive ion channel-related genes. (A) The expression levels of genes associated with mechanosensitive ion channels are shown for TCGA-OC patients, who were divided into low-risk (blue) and high-risk (red) categories. (B) Kaplan-Meier survival analysis comparing high- and low-risk groups. (C) ROC curve analysis showing AUC values for risk group stratification. AUC, area under the curve; CI, confidence interval; HR, hazard ratio; OC, ovarian cancer; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas.

Patients from the TCGA-OC cohort were categorized into low- and high-risk groups according to this signature. The odds ratios with their corresponding 95% CIs and P values for all variables were analyzed (Figure 4B). The low-risk group demonstrated a significantly improved OS compared to the high-risk group (HR: 1.635, 95% CI: 1.26–2.121; P<0.001; Figure 4B). The prognostic efficacy of the model was confirmed through ROC curve analysis, yielding area under the curve (AUC) values of 0.631, 0.616, and 0.621 for 1-, 3-, and 5-year survival, respectively (Figure 4C), thereby underscoring its predictive accuracy.

Cell types and distributions of MIC-related genes in OC

In this study, the single-cell RNA-sequencing (scRNA-seq) dataset from GSE130000 was used. Through unsupervised hierarchical clustering analysis of the scRNA-seq data, six distinct cell subtypes within OC were identified, namely CD8⁺ T cells, fibroblasts, malignant cells, monocytes/macrophages, and myofibroblasts (Figure 5A). The expression levels of three genes—CACNA1C, PIEZO1, and TRPV4—were examined across five distinct cell subtypes in OC. It was observed that CACNA1C was expressed in fibroblasts and myofibroblasts (Figure 5B), PIEZO1 was expressed across all five cell subtypes (Figure 5C), and TRPV4 was expressed in fibroblasts and monocytes/macrophages (Figure 5D). The box plots further confirmed these results (Figure 5E).

Figure 5 Cell types and distributions of mechanosensitive ion channel-related genes in OC. (A) Cell types and distributions in OC. (B) The expression levels of CACNA1C were examined across five distinct cell subtypes in OC. (C) The expression levels of PIEZO1 were examined across five distinct cell subtypes in OC. (D) The expression levels of TRPV4 were examined across five distinct cell subtypes in OC. (E) The expression levels of CACNA1C, PIEZO1, and TRPV4 were examined across five distinct cell subtypes. OC, ovarian cancer.

Discussion

In this study, top 20 genes in terms of relevance score were PIEZO1, SCN5A, KCNQ1, CFTR, PIEZO2, KCNMA1, ASIC2, CACNA1C, ASIC3, SCN1A, TRPV4, TRPV1, KCNN4, SCNN1B, SCNN1A, CACNA1B, SCNN1G, TRPM7, KCNK2, and TRPA1 (Figure 1A). Patients were separated into a high-risk group (cluster 1) and low-risk group (cluster 2) based on these MIC-related genes. The upregulated DEGs in cluster 1 were found to be notably enriched in pathways according to functional analysis, including focal adhesion, axon guidance, proteoglycans in cancer, ECM-receptor interaction, Wnt signaling pathway, hippo signaling pathway, and thyroid hormone signaling pathway, while the downregulated DEGs in cluster 1 were enriched in pathways of oxidative phosphorylation, chemical carcinogenesis-reactive oxygen species, and nonalcoholic fatty liver disease. GO analysis of the upregulated DEGs in cluster 1 revealed that the biological pathways were significantly enriched in ECM organization, cell-substrate adhesion, and cell junction assembly, while the downregulated DEGs in cluster 1 were significantly enriched in oxidative phosphorylation, ATP metabolic process, and cellular respiration. Immune score estimation was different between the high- and low-risk groups. Through the application of LASSO and Cox regression analyses, we identified a set of 20 genes associated with MICs in OC, among which three genes were further selected: PIEZO1, CACNA1C, and TRPV4. The scRNAseq revealed that CACNA1C was expressed in fibroblasts and myofibroblasts, PIEZO1 was expressed across all five cell subtypes, and TRPV4 was expressed in fibroblasts and monocytes/macrophages.

MICs are critical to converting mechanical stimuli into biochemical signals, which is crucial for OC progression. In cancer, they facilitate mechanotransduction, aiding cell migration, invasion, and metastasis (3-5). The TRPV4 and P2X7 channels significantly influence tumor spread by promoting neovasculature, transendothelial migration, and cell motility (3). Despite their role in metastasis, their activation can also induce cancer cell death, suggesting potential therapeutic targets. MICs such as Piezo, regulate calcium signaling in cancer cells, influencing functions of proliferation and migration. These channels are crucial in cancer progression and serve as potential therapeutic targets in OC (4,5). Piezo1, activated by ECM stiffness, also regulates oxidative stress responses, suggesting that targeting it could reduce metastatic potential in OC cells (5). Using LASSO and Cox regression analyses, we identified 20 genes linked to MICs in OC, from which we selected three key genes: PIEZO1, CACNA1C, and TRPV4.

Research on MICs such as PIEZO1 and TRPV4 and voltage-dependent calcium channels such as CACNA1C is becoming increasingly important to OC studies (6-10). These channels are key to processes that drive cancer progression such as cell proliferation, migration, and metastasis. PIEZO1, in particular, is linked to OC metastasis through the Hippo/YAP signaling pathway. It is overexpressed in OC tissues, promoting tumor growth and spread (6). Mechanical activation of PIEZO1 triggers the Hippo/YAP pathway, enhancing cell proliferation and migration, thus aiding metastasis (6). PIEZO1 is also recognized as an oncogenic mediator in various cancers, suggesting its potential as a biomarker and therapeutic target (7). TRPV4, an MIC, boosts fatty-acid synthesis and advances OC via the calcium-mTORC1/SREBP1 pathway. Its high expression correlates with poor prognosis by promoting cancer cell proliferation and migration through altered calcium levels affecting key signaling pathways (8). Additionally, in endometrial cancer, TRPV4 influences the cytoskeleton via the RhoA/ROCK1 pathway, highlighting its role in cancer cell migration and metastasis (9). CACNA1C, a voltage-dependent calcium channel, is a prognostic marker for OC, linked to patient survival and immune interactions. Its role in tumor development suggests it could be a therapeutic target (10). Alongside MICs PIEZO1 and TRPV4, CACNA1C significantly influences OC pathophysiology, impacting cell proliferation, migration, and metastasis, suggesting it as a promising target for future research and treatment.

There are several limitations to this study which should be acknowledged. The current study does have some inherent constraints, notably related to the sample size. The results need to be confirmed with in vitro and in vivo experiments that focus on the three genes related to MICs, and further investigation into their molecular mechanisms is required.

Conclusions

This study was the first to identify specific molecular subtypes and important genes in OC related to MICs. The findings of our research offer significant understanding regarding the forecasting of prognosis and the possible advantages of immunotherapy in patients with OC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1219/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1219/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1219/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/.

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(English Language Editor: J. Gray)