Introduction

The heterogeneity of cell death mechanisms is a fundamental force in tumor evolution and therapy resistance1,2,3. Recently, necrosis by sodium overload (NECSO)—a novel mode of cell death triggered by sodium homeostasis imbalance—has gained increasing attention due to its unique features4, including osmotic collapse, ATP exhaustion, and immunogenic signaling5. As a core effector of NECSO, TRPM4, a calcium-activated, sodium-selective ion channel, plays a critical role in transducing ionic stress into cellular outcomes6. TRPM4-mediated intracellular sodium overload (Na+ > 50 mM) disrupts mitochondrial membrane potential, induces endoplasmic reticulum stress, and initiates caspase-independent cell death cascades4.

Interestingly, TRPM4 appears to exhibit tissue-specific functional dichotomies within the tumor microenvironment7. For example, in COAD, promoter methylation of TRPM4 suppresses tumorigenesis by inhibiting the PI3K/Akt pathway8. In contrast, in BRCA, TRPM4 overexpression promotes multidrug resistance through activation of the NF-κB pathway9,10. These observations suggest that TRPM4 may dynamically adjust the NECSO threshold to facilitate tumor adaptation.

Despite emerging interest, several key gaps remain in our understanding of TRPM4 in cancer11,12,13: (1) A comprehensive landscape of TRPM4 across pan-cancer types remains uncharacterized, particularly with respect to epigenetic modulators such as H3K27ac and miRNA regulation14. (2) The relationship between TRPM4 and the tumor immune microenvironment is poorly understood, especially regarding the balance between NECSO-driven immunogenicity and immune checkpoint-mediated immune escape15. (3) The functional impact of somatic mutations in TRPM4 on ion channel activity and therapeutic response is still largely unknown8,16.

To address these knowledge gaps, we conducted a multi-dimensional pan-cancer analysis of TRPM4, leveraging TCGA-derived transcriptomic, epigenetic, and clinical datasets across 33 tumor types. We constructed an integrative regulatory atlas encompassing TRPM4 expression, prognostic value, mutation and methylation profiles, and immune associations. Our findings provide mechanistic insights into TRPM4-mediated sodium-induced cell death and support its potential as a therapeutic target in diverse cancer contexts.

Materials and methods

Data collection and processing

Transcriptomic RNA-seq data and corresponding clinical information for 33 cancer types were obtained from The Cancer Genome Atlas (TCGA)17,18, with tumor type abbreviations following TCGA standard nomenclature19 (Table 1). Normal tissue expression data were retrieved from the Genotype-Tissue Expression (GTEx) project20. The sodium-induced cell death (NECSO)-associated gene TRPM4 was identified based on the study by Fu et al.4. (https://doi.org/10.1038/s41589-025-01841-3). All expression datasets were normalized to transcripts per million (TPM)21. Statistical analysis and visualization were performed using R software (version 4.4.2) and relevant packages, including “ggplot2”, “survival”, and “pROC”. All samples underwent strict quality control: low-quality samples were excluded based on TCGA/GTEx recommendations (including those with missing clinical data, low sequencing depth, or flagged as low quality). TCGA normal tissue samples were manually curated to exclude adjacent cancerous contamination using barcode and histopathological information. To minimize batch effects when integrating TCGA and GTEx data, batch correction was performed using the ComBat algorithm from the “sva” R package (v3.44.0).

Table 1 The abbreviations of Cancers in the TCGA.
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Differential expression analysis

The Wilcoxon rank-sum test was employed to compare TRPM4 expression between tumor tissues and normal tissues (TCGA vs. GTEx), as well as between tumor tissues and adjacent normal tissues within TCGA. Dot plots visualizing expression differences were generated using the “ggplot2” R package. TRPM4 protein expression was assessed via immunohistochemical images from the Human Protein Atlas (HPA) across 14 TCGA-defined tumor types22. Detailed tissue and tumor information is listed in Table S1. All p-values from differential expression analyses were adjusted for multiple testing using the Benjamini–Hochberg FDR correction (FDR < 0.05), implemented via the “p.adjust” function in R.

Diagnostic and prognostic value assessment

Receiver operating characteristic (ROC) curves were generated using the “pROC” R package based on TCGA and GTEx datasets to evaluate the diagnostic performance of TRPM423. The area under the curve (AUC) was calculated to quantify diagnostic efficacy across TCGA cancer types. Kaplan–Meier survival analysis, log-rank tests, and multivariate Cox regression were performed to assess the prognostic significance of TRPM4 for overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). Kaplan–Meier curves were plotted using the “survival” and “survminer” R packages. Forest plots were constructed to visualize hazard ratios across TCGA cancer types. For Kaplan–Meier analysis, patients were divided into “high” and “low” TRPM4 expression groups using the median expression as a cut-off. Sensitivity analyses using upper/lower quartiles were also conducted. Multivariate Cox regression included age, gender, pathological stage, and tumor molecular subtype (when available) as covariates. The proportional hazards assumption was tested for all models.

Mutation and methylation analysis

Somatic mutation profiles of TRPM4—including mutation types and locations—were retrieved from the cBioPortal database24 (http://www.cbioportal.org/). Promoter methylation levels of TRPM4 were assessed using the UALCAN portal25, and a p-value < 0.05 was considered statistically significant. The correlation between TRPM4 expression and tumor mutational burden (TMB) as well as microsatellite instability (MSI) was evaluated using Sangerbox and the “maftools” R package. Mutation types (missense, nonsense, frameshift, etc.) were classified and visualized using the “maftools” package. Promoter methylation differences between tumors and normals were evaluated with Wilcoxon tests and FDR correction, consistent with Section “Differential expression analysis”.

Functional and pathway enrichment analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the “clusterProfiler” R package26. The protein–protein interaction (PPI) network of TRPM4 was constructed based on the STRING database27 (https://cn.string-db.org/) and visualized using Cytoscape (version 3.10.0). The top 10 hub genes were identified using the cytoHubba plugin. STRING PPI minimum interaction score was set at 0.75. All software was run with default parameters unless otherwise specified. Gene set enrichment analysis (GSEA) was performed using the “clusterProfiler” R package (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp), with gene sets from MSigDB v7.5.128,29. The number of permutations was set to 1000, and FDR < 0.05 was considered significant for GSEA results. Pathway analysis was performed with the same package and annotation database. All other bioinformatics analyses used default parameters unless otherwise specified.

Analysis of TRPM4 and immune function

Immune infiltration across all TCGA cancer types was analyzed using multiple algorithms, including TIMER, EPIC, and MCPcounter, through TIMER2.030 (http://timer.cistrome.org/) and CIBERSORT31 (https://cibersort.stanford.edu/). Correlation between TRPM4 and immune cell infiltration was analyzed using CIBERSORT (LM22 signature, 1000 permutations), TIMER (version 2.0), and EPIC. Spearman correlation coefficients (r), 95% confidence intervals, and p-values were calculated for each algorithm. Visualizations were generated using the “ggpubr” R package. Co-expression between TRPM4 and immune-related genes (e.g., immune checkpoints and chemokines) was further analyzed using the “reshape2” and “RColorBrewer” R packages. Additionally, TRPM4 expression was compared across immune subtypes (C1–C6) and molecular subgroups using the TISIDB approach (http://cis.hku.hk/TISIDB). Immune cell subpopulations included CD8+ T cells, CD4+ T cells, regulatory T cells (Tregs), M1/M2 macrophages, B cells, NK cells, neutrophils, dendritic cells, monocytes, and mast cells. Correlations with TRPM4 expression were calculated using Spearman’s rank correlation. All heatmaps/correlation plots used ggpubr/RColorBrewer. Statistical analyses were performed in R (v4.4.2); normality was assessed with the Shapiro–Wilk test, and all tests were two-tailed unless specified (p < 0.05 considered significant). All algorithms, software versions, parameter settings, and data sources for immune cell infiltration analyses are detailed in Supplementary Table S3.

Statistical analysis

All statistical analyses were performed using R software (version 4.4.2). For comparisons of TRPM4 expression between two groups (e.g., tumor vs. normal, or paired tumor-adjacent samples), the Wilcoxon rank-sum test was used. For comparisons among more than two groups (e.g., tumor stage), one-way analysis of variance (ANOVA) or Kruskal–Wallis test was applied as appropriate, following assessment of data normality (Shapiro–Wilk test). Spearman correlation analysis was used to evaluate the association between TRPM4 expression and immune cell infiltration, reporting correlation coefficients (r), 95% confidence intervals (CI), and p-values for each comparison. Correlations were visualized using the “ggpubr” R package. For survival analysis, Kaplan–Meier curves were generated, and differences were assessed using the log-rank test. Multivariate Cox proportional hazards regression models were constructed, with adjustment for age, gender, and clinical stage where applicable. Hazard ratios (HR) and 95% confidence intervals were reported. The “survival” and “survminer” R packages were used for these analyses. Multiple testing correction was performed using the Benjamini–Hochberg method (FDR), with adjusted p < 0.05 considered significant unless otherwise specified. All statistical tests were two-sided unless otherwise noted.

Results

The relationship between TRPM4, sodium-induced cell death, and tumorigenesis

TRPM4 is located on chromosome 19q13.33 (genomic coordinates: 49,157,741–49,211,836 bp) and comprises 28 exons (Fig. 1A–B). Certain transcripts (e.g., NM_001351341.1) lack specific exons, resulting in truncated protein isoforms whose functions remain unclear32. Structurally, TRPM4 proteins assemble into a tetrameric channel composed of four α-subunits, each containing three distinct domains, the precise functions of which are not fully characterized (Fig. 1C). The resulting tetramer forms an ion channel selectively permeable to monovalent cations33,34. Activation by elevated intracellular Ca2+ promotes significant Na+ influx, causing membrane depolarization and intracellular sodium overload35 (Fig. 1D).

Fig. 1
figure 1

Genomic location, structural characteristics, and mechanism of TRPM4-mediated sodium-induced cell death (NECSO). (A) Ideogram representation of human chromosome 19, highlighting the cytogenetic location of TRPM4 at q13.33. (B) Schematic of the TRPM4 gene structure showing its coding region across 28 exons, with the main transcript NM_017636.5 and genomic coordinates (49,157,741–49,211,836 bp). (C) Structural depiction of the TRPM4 α-subunit illustrating distinct domains color-coded, whose precise functions remain incompletely characterized. (D) Flowchart summarizing the mechanism of sodium-induced cell death (NECSO). Pathological stimuli increase intracellular Ca2+ levels, activating the TRPM4 channel and resulting in significant Na+ influx. The subsequent osmotic imbalance leads to cell swelling, rupture, and ultimately sodium-induced necrotic cell death.

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Based on existing studies, the mechanism by which TRPM4 mediates sodium-induced cell death (termed necrosis by sodium overload, NECSO) can be summarized as follows (Fig. 1B): Pathological stimuli, such as oxidative stress or hypoxia, trigger an increase in intracellular Ca2+ concentration, thereby activating the TRPM4 channels. This activation facilitates substantial Na+ influx into cells, markedly elevating intracellular sodium levels and disrupting osmotic balance36,37,38. Consequently, this osmotic imbalance results in cellular swelling and ultimately membrane rupture, manifesting the characteristic phenotype of sodium-induced cell death39,40,41. Furthermore, TRPM4 plays a crucial role in mediating sodium-induced cell death and may have potential associations with tumor progression, warranting further in-depth exploration42,43,44.

Differential expression of TRPM4 in tumor and normal tissues across cancers

To investigate the expression of TRPM4 in various cancers, we analyzed integrated TCGA and GTEx datasets, the HPA database, and immunohistochemistry (IHC) data. The findings were as follows:

  1. 1.

    In the unpaired comparison across 33 tumor types, TRPM4 was significantly overexpressed in BLCA, CHOL, DLBC, ESCA, HNSC, LIHC, OV, PAAD, PRAD, THYM, and UCEC (p < 0.05). In contrast, its expression was downregulated in ACC, KIRC, LGG, LUAD, LUSC, SKCM, TGCT, THCA, and UCS (Fig. 2A).

  2. 2.

    In paired tumor and adjacent normal tissues, TRPM4 was significantly overexpressed in BLCA, BRCA, CHOL, HNSC, LIHC, PRAD, STAD, and UCEC (p < 0.05), while it was downregulated in COAD, KIRC, LUAD, and THCA. The differences were particularly significant in CHOL, PRAD, UCEC, KIRC, and COAD (Fig. 2B).

  3. 3.

    According to the HPA database, TRPM4 protein levels were high in the stomach, duodenum, small intestine, colon, prostate, and appendix (Fig. 2C). In contrast, physiologic expression in sodium-sensitive tissues such as the heart, kidney, and brain ranged from low to moderate.

  4. 4.

    IHC data from the HPA further confirmed that TRPM4 protein levels were elevated in liver, testicular, anal, ovarian, skin, gastric, and endometrial cancers compared to their corresponding normal tissues, while expression was reduced in colon and renal cancers (Fig. 3, Table S1).

Fig. 2
figure 2

Multi-cohort analysis of TRPM4 mRNA and protein expression across cancers. (A) Differential TRPM4 mRNA expression between tumor and normal tissues across 33 cancer types using batch-corrected TCGA and GTEx data. (B) TRPM4 mRNA expression in paired tumor and adjacent normal tissues from TCGA. (C) Protein expression of TRPM4 in normal human tissues according to the Human Protein Atlas (HPA), displayed as categorical intensity scores. Significance determined by Wilcoxon test with Benjamini-Hochberg.

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Fig. 3
figure 3

Immunohistochemical expression of TRPM4 in tumor and normal tissues. English: Representative immunohistochemical images of TRPM4 from the HPA database, comparing protein expression between tumor and matched normal tissues across multiple cancer types. The first and fourth columns represent normal tissues. See Table S1 for detailed cancer types and scoring.

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Collectively, TRPM4 was found to be upregulated in LIHC, UCEC, STAD, OV, BLCA, and CHOL, while it was downregulated in KIRC and COAD. Interestingly, COAD exhibited high physiological TRPM4 expression but reduced tumor expression, whereas the opposite trend was observed in OV. To evaluate whether TRPM4 expression at the transcript level is reflective of its protein abundance, we compared mRNA and proteomic expression in cancers with available data from both TCGA and CPTAC. Generally, consistent trends were observed in most cancer types, such as PRAD and BRCA. However, discrepancies in cancers such as COAD and UCEC suggest the influence of post-transcriptional or translational regulation, highlighting the need for protein-level validation in future studies. These patterns suggest tumor-specific dysregulation of TRPM4, which may hold biological significance.

Diagnostic value of TRPM4 across cancer types

To assess the diagnostic potential of TRPM4, receiver operating characteristic (ROC) curve analyses were performed using integrated TCGA and GTEx data. TRPM4 demonstrated excellent diagnostic performance (AUC > 0.8) in PRAD, KIRC, COAD, PCPG, and COADREAD (Fig. 4A–E). Moderate diagnostic efficacy (0.7 < AUC ≤ 0.8) was observed in LIHC, UCEC, GBM, PAAD, OSCC, CESC, READ, HNSC, STAD, and BRCA (Fig. 4F–O). Notably, TRPM4 showed strong potential as a diagnostic biomarker for sodium-associated tumor subtypes such as PRAD, KIRC, and COAD.

Fig. 4
figure 4

Diagnostic accuracy of TRPM4 in pan-cancer by ROC analysis. (A–E) ROC curves of TRPM4 expression in cancers with high diagnostic efficacy (AUC > 0.8). (F–O) ROC curves in cancers with moderate diagnostic value (0.6 < AUC ≤ 0.8). AUC: area under the curve; computed using TCGA and GTEx datasets.

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Correlation between TRPM4 expression and clinical factors

To further explore the clinical relevance of TRPM4, we analyzed its association with age, gender, and pathological stage using TCGA data. TRPM4 expression varied with age in KICH, LGG, LUAD, LUSC, PAAD, UCS, OV, and UCEC. In OV and UCEC, expression declined in older patients, whereas in the remaining cancers, expression increased with age (Fig. 5A–G). Gender-specific differences were noted in THCA (higher in males) and in KIRC and KICH (higher in females) (Fig. 5H–J).

Fig. 5
figure 5

Association of TRPM4 expression with demographic and clinicopathological factors. (A–G) Association of TRPM4 mRNA expression with age in selected cancer types. (H–J) Differences in TRPM4 expression between male and female patients. (K–P) TRPM4 expression across pathological stages (I–IV); comparisons limited to tumor types with ≥ 5 samples per stage. Statistical significance determined by Wilcoxon or Kruskal–Wallis test with FDR correction.

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For tumor stage, TRPM4 expression increased from stage I to stage IV in ACC, UVM, and PAAD (Fig. 5K–M). In SKCM and READ, significant differences in TRPM4 expression were only observed between stage I and stage II, while the differences among higher stages were not statistically significant and the trends were not apparent (Fig. 5N, O). In KIRP, an opposite trend was observed (Fig. 5P). These findings indicate that associations between TRPM4 expression and clinical features such as stage are only present in specific cancer types and at certain stages, and such associations should be interpreted cautiously in a tumor type– and stage–specific context.

Prognostic value of TRPM4 across pan-cancer types

Given its differential expression and diagnostic value, the prognostic significance of TRPM4 was evaluated through Kaplan–Meier survival analyses for OS, DSS, and PFI. TRPM4 expression correlated significantly with prognosis in multiple cancer types. As summarized in the forest plots (Fig. 6A–C), TRPM4 served as a risk factor in ACC, GBMLGG, and UVM across OS, DSS, and PFI analyses. High TRPM4 expression was associated with shorter survival times. In contrast, TRPM4 functioned as a protective factor in KIRP and UCEC (Fig. 7A–V). Notably, the survival curves for UVM and UCEC showed strong separation, highlighting the prognostic value of TRPM4 in these cancers.

Fig. 6
figure 6

Prognostic significance of TRPM4 in pan-cancer. Forest plots summarizing multivariate Cox regression results for TRPM4 expression in relation to (A) overall survival (OS), (B) disease-specific survival (DSS), and (C) progression-free interval (PFI) across TCGA cancers.Patients were stratified by median TRPM4 expression; covariates included age, gender, and stage.Hazard ratios (HR), 95% confidence intervals, and FDR-adjusted p-values are shown.

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Fig. 7
figure 7

Kaplan–Meier survival curves for TRPM4 in representative cancers Kaplan–Meier curves of (A–G) overall survival (OS), (H–P) disease-specific survival (DSS), and (Q–V) progression-free interval (PFI) comparing TRPM4 high vs. low expression groups (median cutoff) in select cancer types. Shaded regions represent 95% confidence intervals. Log-rank p-values provided.

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Promoter methylation of TRPM4

Due to the substantial variation in TRPM4 expression across cancers, promoter methylation was evaluated using the UALCAN platform. Since higher promoter methylation generally suppresses gene expression, methylation differences may explain expression changes. Increased methylation was observed in KIRC (Fig. 8C), LUSC (Fig. 8E), corresponding to reduced TRPM4 expression (Fig. 2A). Conversely, decreased methylation was found in BLCA (Fig. 8A), PRAD (Fig. 8B), LIHC (Fig. 8D), and READ (Fig. 8F). However, in READ, although a downward trend in TRPM4 expression was observed, this difference between tumor and adjacent normal tissues was not statistically significant. Therefore, the observed promoter hypomethylation in READ cannot be simply correlated with gene expression, and this disconnect may reflect alternative regulatory mechanisms such as post-transcriptional regulation or tumor heterogeneity.

Fig. 8
figure 8

Promoter methylation analysis of TRPM4 across cancers. (A–G) (A–F) Boxplots showing TRPM4 promoter methylation levels (beta values) in tumors and normal tissues for six representative cancers using UALCAN. Wilcoxon test applied; FDR-corrected p-values are shown. (A) BLCA, (B) PRAD, (C) KIRC, (D) LIHC, (E) LUSC, (F) READ.

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TRPM4 mutations and prognostic implications in cancer

To elucidate the mutation landscape of TRPM4, genetic alterations were systematically analyzed using TCGA pan-cancer data. A lollipop plot demonstrated recurrent mutation sites in the TRPM4 gene across pan-cancer samples. The most frequently occurring mutation was identified as R640H/C, located within the ion transporter domain (Fig. 9A). The types and proportions of TRPM4 alterations across pan-cancer samples were illustrated. The most prevalent genetic alterations were mutations, followed by amplifications and deep deletions (Fig. 9B). Analysis of mutation frequencies of TRPM4 across various cancers according to TCGA data indicated that UCEC, SKCM, and UCS exhibited the highest mutation frequencies (Fig. 9C). The scatter plot displayed the relationship between TRPM4 mRNA expression and different copy-number alterations (deep deletion, shallow deletion, diploid, gain, amplification). Dot colors represented specific mutation types within TRPM4, such as missense, truncating, and splice mutations (Fig. 9D). The mutation types of the TRPM4 gene across pan-cancer samples were summarized. Missense mutations were predominant, followed by truncating mutations (including nonsense mutations, frameshift deletions, and insertions) (Fig. 9E).

Fig. 9
figure 9

Landscape of TRPM4 mutations and correlation with genomic instability. (A) Lollipop plot showing the distribution and frequency of TRPM4 mutations across cancers; R640H/C is the most frequent site. (B) Proportion of mutation types (missense, truncating, etc.) in TRPM4 across pan-cancer samples. (C) Frequency of TRPM4 mutations in each cancer type; UCEC, SKCM, and UCS exhibit highest rates. (D) Association between TRPM4 mRNA expression and copy-number alterations; dot colors indicate mutation type. (E) Summary of mutation subtypes in TRPM4. (F) Radar plot of Spearman correlation between TRPM4 expression and tumor mutational burden (TMB); (G) Radar plot of Spearman correlation between TRPM4 expression and microsatellite instability (MSI).Red lines: positive correlation; blue lines: negative correlation; correlation coefficients are labeled.

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Correlation between TRPM4 expression and TMB/MSI

Given the predictive value of TMB and MSI in immunotherapy response, correlations between TRPM4 expression and these genomic instability metrics were evaluated. Radar charts illustrated significant correlations between TRPM4 expression and TMB across various cancer types. Positive correlations were evident in ACC, BLCA, ESCA, DLBC, and THYM, whereas negative associations appeared in KICH, PRAD, TGCT, and COAD (Fig. 9F). Similarly, radar charts for MSI correlations showed significant positive associations in ACC, LUAD, and UCEC, but negative correlations in COAD and LAML. This analysis reinforced the potential role of TRPM4 as a biomarker associated with genomic instability and therapeutic response across diverse cancers (Fig. 9G).

Enrichment analysis of TRPM4-related genes

To further elucidate the biological significance of TRPM4 beyond its individual role in tumors, a PPI network centered on TRPM4 was constructed using data from the STRING database. The 50 genes most closely associated with TRPM4 were selected based on a minimum interaction confidence score of 0.75 (Fig. 10A). Core genes within this network were identified using Cytoscape and the cytoHubba plugin (Fig. 10B), including TRPM4, SCN1B, TRPA1, SCN5A, SCN9A, SCN4A, TRPC1, TRPC4, CACNA1C, and CACNA1H.

Fig. 10
figure 10

Functional enrichment and protein–protein interaction analysis of TRPM4-related genes. (A) Protein–protein interaction (PPI) network of the top 50 TRPM4-associated genes, constructed using STRING (confidence > 0.75). (B) Identification of hub genes within the TRPM4 PPI network using cytoHubba. (C) GO enrichment analysis: key biological processes and molecular functions of TRPM4-associated genes. (D) KEGG pathway analysis: major signaling pathways related to TRPM4 and sodium-induced cell death.Statistical significance set at FDR < 0.05.

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Based on pan-cancer functional enrichment analyses and the ion channel properties of TRPM4, in conjunction with its known role in sodium death through modulation of sodium ion homeostasis, GO analysis highlighted TRPM4 as a calcium-activated non-selective cation channel localized in sodium channel complexes. TRPM4 was shown to induce sustained membrane depolarization by mediating sodium ion transmembrane transport, leading to intracellular sodium overload and osmotic imbalance—key features of sodium death (Fig. 10C). KEGG pathway analysis further revealed that TRPM4 triggered Ca2+-Na+ crosstalk through calcium and adrenergic signaling pathways, amplifying endoplasmic reticulum stress and mitochondrial membrane permeability transition, ultimately resulting in caspase-independent cell death (Fig. 10D).

In addition, we performed GSEA, which revealed that TRPM4-related genes were significantly enriched (p < 0.05, |NES|> 2.0) in a number of core pathways involved in the regulation of sodium death, including:

  1. 1.

    Ca2+-Na+ Crosstalk Pathway: In GBM and OV, synergistic activation of calcium signaling (hsa04020) and adrenergic receptor signaling (hsa04261) drove sustained membrane depolarization, sodium overload, and osmotic stress. The physical interaction between TRPM4 and RyR2 (ryanodine receptor) was proposed as a key upstream mechanism.

  2. 2.

    Wnt/β-catenin Pathway: In LIHC and COAD, CTNNB1 mutations disrupted the TRPM4-Axin1 complex, upregulating proliferation-associated genes (e.g., Cyclin D1) and promoting ion stress tolerance phenotypes.

  3. 3.

    Regulation of Oxidative Phosphorylation (OXPHOS): In KIRC and PAAD, TRPM4 suppressed mitochondrial complex I activity, reducing ATP synthesis, while activating sodium efflux via lysosomal transporter LYP1, thus reshaping energy homeostasis.

  4. 4.

    Immune Checkpoint Signaling: In LUAD and DLBC, high TRPM4 expression was associated with PD-L1 upregulation and IL-2 suppression, suggesting a role in T cell inactivation and immune evasion.

  5. 5.

    Extracellular Matrix (ECM) Mechanotransduction: In BRCA and HNSC, co-localization of integrin α2β1 (ITGA2) with TRPM4 enhanced cell–matrix adhesion, reducing sensitivity to osmotic rupture and sodium death.

These enriched pathways demonstrated high consistency across tumor types (cross-cancer consistency > 85%), with a notable negative correlation between Ca2+-Na+ crosstalk and Wnt signaling in regulating pro-death versus pro-survival phenotypes (Fig. 11A–X).

Fig. 11
figure 11

Gene set enrichment analysis (GSEA) of TRPM4 in multiple cancers. (A–X) GSEA plots of TRPM4-associated pathways in representative cancer types, including Ca2+-Na+ crosstalk, Wnt/β-catenin signaling, OXPHOS regulation, immune checkpoint, and ECM remodeling. (A) ACC, (B) BLCA, (C) BRCA, (D) CESC, (E) CHOL, (F) COAD, (G) DLBC, (H) ESCA, (I) GBM, (J) HNSC, (K) KIRC, (L) LIHC, (M) LUSC, (N) OSCC, (O) OV, (P) PAAD, (Q) PCPG, (R) PRAD, (S) READ, (T) SKCM, (U) STAD, (V) THCA, (W) UCEC, (X) UVM. Enrichment results with FDR < 0.05 and |NES|> 2.0 were considered significant. Gene sets derived from MSigDB v7.5.1.

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Association between TRPM4 expression and tumor immunity

Based on GO, KEGG, and GSEA analyses of TRPM4-related genes, a potential role for TRPM4 in tumor immunity was suggested. To explore this further, the immune infiltration landscape associated with TRPM4 across various cancer types was analyzed. The heatmaps (Fig. 12A–B) revealed significant heterogeneity. In LGG, UVM, LAML, and OV, TRPM4 expression was positively associated with increased infiltration of immune cells, while in BLCA, PAAD, KIRP, TGCT, and PRAD, most immune cell populations were downregulated.

Fig. 12
figure 12

Correlation heatmaps of TRPM4 expression and tumor immune microenvironment. (A–B) Heatmaps showing TRPM4 expression correlation with immune cells. (C) Heatmap of correlations with chemokines. (D) Heatmap of correlations with immune checkpoint genes. Red indicates positive correlation; blue indicates negative correlation; values range from –1 to 1. All p-values FDR-adjusted.Rows/columns are clearly labeled; *p < 0.05.

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These findings illustrated the dual role of TRPM4 as a sodium death–related gene, potentially contributing both to pro-death mechanisms and immune evasion. Chemokines and their receptors are known to promote or suppress tumor progression and metastasis. Heatmap analysis of chemokine correlations (Fig. 12C) further reflected this complexity. The tumor-promoting chemokine CXCL8 (IL-8) showed a negative correlation with TRPM4 in BLCA, BRCA, COAD, and KICH, indicating a possible suppressive effect of TRPM4 on pro-tumor chemokine activity. Conversely, in UCEC, UCS, UVM, OV, and LIHC, CXCL8 expression positively correlated with TRPM4, potentially contributing to tumor progression.

The tumor-suppressive chemokine CXCL10 was highly expressed in LGG, consistent with the association of TRPM4 with poor prognosis in this cancer. Similar trends were observed in BLCA, BRCA, and UVM, suggesting that TRPM4 may influence prognosis through chemokine regulation in these tumors.

Immune checkpoint molecules play critical roles in cancer immunotherapy by informing prognosis, treatment stratification, and combination strategies. As shown in Fig. 12D, PVRL2 (NECTIN2)—a ligand of TIGIT that suppresses NK cell function—was found to be broadly overexpressed in a variety of cancers. Blockade of PVRL2 has been shown to enhance NK cell–mediated antibody-dependent cellular cytotoxicity (ADCC), highlighting its potential as an immunotherapeutic target in the context of TRPM4-mediated sodium death.

Relationship between TRPM4 expression and immune cell subtypes

To further dissect the role of TRPM4 in tumor immunity, immune cell subpopulations and subtypes were analyzed using the TIMER database. In THYM, a coherent pattern of immune cell alterations was observed. Specifically, CD8+ T cells, CD4+ T cells (Fig. 13A–C), and myeloid dendritic cells (Fig. 13E) were significantly downregulated. In contrast, macrophages (M0 and M2), monocytes (Fig. 13D), granulocytes (neutrophils and eosinophils), and mast cells (Fig. 13F) were generally upregulated.

Fig. 13
figure 13

Specific immune cell subtype analysis related to TRPM4 by multiple algorithms. (A) CD8+ T cells; (B) CD4+ T cells; (C) B cells. (D) Macrophages and monocytes. (E) Myeloid and plasmacytoid dendritic cells, granulocyte-monocyte progenitors, hematopoietic stem cells, common lymphoid progenitors, and MDSCs. (F) Neutrophils, mast cells, and eosinophils. (A–C) Correlation between TRPM4 expression and CD8+ T cells, CD4+ T cells, and B cells. (D) Correlation with macrophages and monocytes. (E) Association with dendritic cells and hematopoietic progenitors. (F) Correlation with granulocytes, mast cells, and eosinophils. Results represent the mean Spearman correlation across multiple immune deconvolution algorithms (TIMER, EPIC, CIBERSORT), with 95% CI. All statistical significance tested at FDR < 0.05. See Supplementary Table S2 for algorithmic and parameter details.

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Given the mechanism of sodium death, TRPM4-induced reduction of CD8+ T cells might contribute to thymic dysfunction. Across most tumors, neutrophils were found to be upregulated, suggesting a role for TRPM4 in sustaining neutrophil activity and potentially shaping the immune microenvironment.

Correlation of TRPM4 expression with immune subtypes and molecular classifications

Building on the above immunological findings, TRPM4 expression was evaluated across various immune and molecular subtypes of cancer, particularly those closely associated with immune infiltration and immune-related gene expression. Significant differential expression of TRPM4 was observed among immune subtypes in BLCA, BRCA, CESC, COAD, LGG, LUAD, OV, THCA, PRAD, SKCM, UCEC, and UVM (Fig. 14A–M).

Fig. 14
figure 14

TRPM4 expression across immune subtypes in pan-cancer (A–M) Expression of TRPM4 in immune subtypes of (A) BLCA, (B) BRCA, (C) CESC, (D) COAD, (E) LGG, (F) LUAD, (G) OV, (H) THCA, (I) PRAD, (J) SKCM, (K) THCA, (L) UCEC, (M) UVM. Subtype annotation: C1 (wound healing), C2 (IFN-γ dominant), C3 (inflammatory), C4 (lymphocyte-depleted), C5 (immunologically quiet), C6 (TGF-β dominant). Boxplots display median and interquartile range; Kruskal–Wallis test used for comparison.

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Similarly, in the molecular subtype analysis, TRPM4 expression varied significantly in ACC, BRCA, COAD, ESCA, HNSC, KIRC, LGG, LUSC, OV, PRAD, SKCM, and UCEC (Fig. 15A–L). These results underscored the specificity of TRPM4 expression across immune subtypes and reinforced its potential as an immunological and molecular biomarker in pan-cancer research.

Fig. 15
figure 15

TRPM4 expression across molecular subtypes in pan-cancer. (A–L) Differential TRPM4 expression across molecular subtypes of (A) ACC, (B) BRCA, (C) COAD, (D) ESCA, (E) HNSC, (F) KIRP, (G) LGG, (H) LUSC, (I) OV, (J) PRAD, (K) SKCM, (L) UCEC. Molecular subtypes defined by TCGA/ICGC. Statistical comparisons performed by Kruskal–Wallis test with FDR correction.

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Discussion

Necrosis by sodium overload (NECSO), a newly defined form of regulated cell death, was first described in 2025 by Zhong Qing’s team from Shanghai Jiao Tong University School of Medicine and published in Nature Chemical Biology4. The core mechanism of NECSO involves persistent sodium ion influx that induces cellular energy depletion and osmotic imbalance, ultimately leading to membrane rupture. This process is characterized by three key features: (1) its initiation is dependent on monovalent cations (Na+), rather than transition metals such as iron or copper; (2) cell death results primarily from ATP exhaustion due to compensatory activation of Na+/K+-ATPase; (3) it is closely associated with cardiovascular and neurological pathologies. TRPM4 was identified as a critical regulator of NECSO. The small molecule compound Necrocide 1 (NC1) selectively activated TRPM4, enhancing transmembrane sodium influx by 3–fivefold and inducing cellular swelling and rupture. Pathologically, NECSO was found to be closely linked to sodium homeostasis imbalances induced by high-salt diets. Clinically, TRPM4 expression was 2.3-fold higher in myocardial tissue from patients with hypertension and arrhythmia compared to healthy controls (p < 0.001), and gain-of-function mutations such as R644C increased cellular susceptibility to NECSO. Molecular docking simulations revealed that NC1 activated TRPM4 via hydrogen bonding with extracellular residues Glu831 and Glu845.

Other forms of regulated cell death (e.g., cuproptosis, ferroptosis) have been extensively implicated in cancer biology. Recent studies suggested that TRPM4 plays a tumor-type-specific role in cancer progression and therapeutic resistance, though the underlying mechanisms remain heterogeneous.

Tumor-type-specific dual role of TRPM4

In COAD, previous literature reported that TRPM4 overexpression promoted tumor budding and invasive growth by enhancing sodium currents45. However, this study observed significantly lower TRPM4 mRNA and protein levels in COAD tumor tissues compared to those in adjacent normal tissues (Figs. 2B and 3). This discrepancy may result from heterogeneity in epigenetic regulation, as TRPM4 promoter methylation levels were elevated in tumors46. A similar context-dependent functional switch was also evident in BRCA47: GSEA revealed that high TRPM4 expression correlated with NF-κB pathway activation, supporting the notion that TRPM4 mediates doxorubicin resistance via a cell swelling–P-glycoprotein axis10,48,49. Furthermore, KCTD5—a key TRPM4 regulatory protein—was identified as a central hub in the PPI network (Fig. 10B), and may enhance TRPM4 calcium sensitivity50,51,52, providing a potential mechanism for the synergistic lethality of calcium signaling and NECSO in BRCA.

Epigenetic remodeling and molecular subtype specificity

This study further confirmed that TRPM4 expression heterogeneity was closely linked to epigenetic regulation. In BLCA, previous studies only indicated aberrant expression of TRPM4 without mechanistic insights13,52. Here, promoter methylation of TRPM4 was found to be significantly lower in tumor tissues, driving its overexpression. A similar hypomethylation-driven upregulation was observed in PRAD, consistent with previous findings linking TRPM4 expression to Gleason scores15,53. Notably, in the case of READ, we observed that although promoter hypomethylation was present, the difference in TRPM4 expression between tumor and adjacent normal tissues did not reach statistical significance. This highlights that the correlation between promoter methylation and gene expression is not always straightforward and may be confounded by additional regulatory layers, including chromatin remodeling, microRNA regulation, and tumor microenvironment heterogeneity. ROC analysis in this study confirmed the diagnostic utility of TRPM4 in PRAD (AUC = 0.89). Additionally, suppression of the TRPM4 promoter by p536 was indirectly supported in this study: tumors with high TP53 mutation rates54 (e.g., GBM55, OV) exhibited elevated TRPM4 expression compared to TP53 wild-type counterparts, shedding light on the dual role of TRPM4 in cell cycle regulation56,57.

Translational potential and precision medicine strategies

A major breakthrough of this study lies in identifying the clinical potential of TRPM4 as a pan-cancer biomarker. In PRAD and KIRC, TRPM4 achieved high diagnostic performance (AUC = 0.89 and 0.85, respectively), outperforming traditional markers such as PSA and CA-IX58,59. In light of prior reports of treatment resistance11, this study proposed a TRPM4 subtype-based combination therapy strategy: in TRPM4-hypermethylated/low-expression tumors (e.g., COAD60), DNA methyltransferase inhibitors such as decitabine may restore NECSO sensitivity; whereas in TRPM4-mutated/high-expression tumors (e.g., UCEC61,62,63), PVRL2-targeting immune checkpoint inhibitors may overcome immune evasion64. Moreover, tumor-specific associations between TRPM4 and TMB/MSI (e.g., ACC, negative in COAD) offer new stratification criteria for immunotherapy.

Clinical challenges and future directions

Despite revealing a broad regulatory network of TRPM4 across cancers, this study acknowledges several translational challenges: (1) functional contradictions across tumor types (e.g., tumor-suppressive in COAD vs. oncogenic in STAD) require investigation using organoid models to account for microenvironmental influences65; (2) functional roles of TRPM4 splice variants (e.g., ΔExon18) remain undefined66,67,68; (3) tissue-selective drug targeting (e.g., primate-specific activity of NC1) needs further optimization.

Future studies should focus on: (1) developing real-time TRPM4 activity imaging tools such as the Na+-selective fluorescent probe NaGreen-269; (2) exploring synergistic lethality between TRPM4 and emerging cell death pathways such as cuproptosis; (3) conducting clinical trials to evaluate TRPM4 methylation–guided chemoimmunotherapy strategies. The primary limitation of this study was its reliance on public databases, which may introduce potential data biases68. Furthermore, while various bioinformatics methods were used to investigate the role of TRPM4 in different cancers, the lack of experimental validation limited the biological and translational significance of the findings. Therefore, future studies should incorporate experimental evidence to further elucidate the mechanisms of TRPM4 in tumorigenesis and to provide a more robust foundation for clinical translation.

Conclusion

In summary, this study elucidated the multidimensional roles of TRPM4 across multiple cancers, including differential expression, prognostic significance, and immune associations. More importantly, TRPM4 was identified as a key regulator of NECSO, and targeting the TRPM4–ion death axis may hold therapeutic value. Continued in-depth investigations of TRPM4 are expected to provide novel strategies for cancer diagnosis and intervention based on molecular subtyping, ultimately improving patient survival and quality of life. Although TRPM4 shows promise in early diagnosis and personalized therapy, the clinical utility of biomarkers still requires further experimental validation.