Research on the correlation between lung adenocarcinoma and necrosis by sodium overload

Introduction

Lung adenocarcinoma (LUAD), the predominant subtype of non-small cell lung cancer (NSCLC), is characterized by its pronounced metastatic propensity and significant molecular heterogeneity. These inherent biological features contribute substantially to its aggressive clinical course, resulting in a poor prognosis and severely constrained therapeutic options for patients (1,2). Although significant advances in targeted therapies directed against specific driver mutations (e.g., EGFR, ALK) and immunotherapies leveraging the tumor microenvironment (TME) have been achieved, the long-term outlook for advanced-stage LUAD remains grim. The persistently low 5-year survival rate, still hovering below 20% for patients with metastatic disease (3), underscores the critical and unmet need for the development of novel diagnostic biomarkers and more effective treatment strategies.

Necrosis by sodium overload (NECSO) is a lytic form of regulated necrosis initiated by persistent opening of the transient receptor potential melastatin 4 (TRPM4) channel and catastrophic intracellular Na⁺ accumulation (4). In the TME, TRPM4 overexpression sensitizes cancer cells to NECSO: massive Na⁺ influx collapses membrane potential, provokes osmotic swelling and plasma-membrane rupture, and releases damage-associated molecular patterns (DAMPs) that ignite anti-tumor immunity. A high-Na⁺ extracellular milieu further amplifies this response by enhancing CD8⁺ T-cell cytotoxicity and transcriptionally up-regulating nuclear factor of activated T-cells 5 (NFAT5), tumor necrosis factor-α (TNF-α) and interleukin-2 (IL-2), thereby endowing NECSO with robust immunogenicity.

Rather than a passive accident, NECSO is embedded in the programmed cell-death circuitry. Energy depletion or oxidative stress disables the Na⁺/K⁺-ATPase; the ensuing Na⁺ overload reverses Na⁺/Ca²⁺ exchanger (NCX) activity, provoking Ca²⁺ surge, mitochondrial permeability transition pore (mPTP) opening and ATP depletion (5). Na⁺-induced depolarization cooperates with TNF-α to nucleate receptor-interacting protein kinase 1 (RIPK1)/RIPK3/mixed lineage kinase domain-like pseudokinase (MLKL) necrosomes, steering cells from apoptosis to necroptosis when caspase-8 activity is limiting (6). Simultaneously, Na⁺-driven swelling exerts mechanical tension on the plasma membrane, activating endosomal sorting complexes required for transport III (ESCRT-III) to establish a reversible membrane-repair versus death rheostat.

Sodium voltage-gated channel type VII alpha (SCN7A), alias Nax/NaG, is an atypical sodium channel whose S4 voltage sensor lacks the canonical positive charges, enabling it to function as an extracellular-Na⁺-gated osmosensor rather than a voltage-gated pore. Widely expressed in neurons, lung epithelium, and cardiomyocytes, it continuously monitors systemic Na⁺ levels and relays this information to neuro-hormonal circuits that control fluid-electrolyte balance and salt appetite. In LUAD, SCN7A expression is markedly down-regulated compared with adjacent tissue; this loss might be associated with advanced T-stage, higher histologic grade, and shorter overall survival (7). Diminished SCN7A-mediated Na⁺ buffering renders malignant cells hypersensitive to TRPM4-driven Na⁺ influx and subsequent NECSO. The ensuing rupture releases DAMPs that remodel TME, shifting the balance from resting CD4⁺ memory T cells, mast cells, and B cells toward activated CD4⁺ effectors and pro-inflammatory M0/M1 macrophages. Thus, SCN7A silencing converts NECSO from a passive lytic event into an immunogenic cascade, providing a rationale for combining sodium-channel modulation with immune-checkpoint blockade in LUAD therapy.

This study sought to explore the relationship between NECSO and LUAD by identifying NECSO-related genes and constructing a prognostic risk score model. By integrating bioinformatics analysis and cross-platform data validation, we aimed to clarify the mechanisms of NECSO in LUAD and provide new insights for enhancing diagnostic accuracy and treatment outcomes. We present this article in accordance with the TRIPOD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1044/rc).

Methods

Data download and preprocessing

RNA sequencing (RNA-seq) expression data, clinical information, and mutation profiles for LUAD were retrieved from The Cancer Genome Atlas (TCGA) database. The downloaded datasets underwent preprocessing steps, including filtering out low-expression genes, standardizing the expression matrix, and imputing missing values in the clinical data. Additionally, the LUAD-related dataset GSE68465 was obtained from the Gene Expression Omnibus (GEO) database for complementary analysis. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Co-expression analysis

Research on NECSO is still in its infancy, and the urgent priority is to identify actionable genes that can serve as next-generation biomarkers and therapeutic targets in cancer. TRPM4 is the gatekeeper and druggable biomarker of NECSO (4). CRISPR-Cas9 screens show TRPM4 loss alone blocks necrocide-1 (NC1)-induced death, and re-expression restores sensitivity. NC1 irreversibly locks TRPM4 open, driving Na⁺ influx, K⁺ efflux, membrane depolarization and lysis; the effect is abolished by 9-phenanthrol or TRPM4 knockout, but not by the ion-dead D984A mutant. Thus, TRPM4 ion flux is the rate-limiting step for NECSO and a predictive biomarker for TRPM4-targeted therapy.

Bioinformatic interrogation centered on TRPM4 offers a robust, wet-lab-free strategy to mine NECSO–associated genes. First, TRPM4 is now established as the causal “seed” of NECSO; any gene whose expression tracks with TRPM4 is therefore a priori implicated in the Na⁺-overload → membrane rupture → immunogenic-death axis. Second, publicly available RNA-seq matrices from TCGA and GEO provide ready-to-analyze resources for dissecting TRPM4-centric co-expression networks and their links to LUAD. Third, mature algorithms—already proven in ferroptosis, cuproptosis and other regulated-death landscapes—enable rapid identification and validation of NECSO-relevant gene modules without any experimental intervention.

Expression data from the TCGA database were read, standardized, and cleaned by removing low-quality samples. Normal samples were excluded, retaining only tumor samples for downstream analysis. The Pearson correlation coefficient was employed to quantify the expression correlation between the target gene (TRPM4) and other genes. Significance testing was performed to identify gene pairs with statistically significant correlations. A correlation threshold of 0.3 (corFilter =0.3) and a significance level of 0.001 (pvalueFilter =0.001) were applied to select genes highly correlated with the target gene.

Functional enrichment analysis

Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted on co-expressed genes. Using the “clusterProfiler” and “enrichplot” packages, the enrichment of genes in biological processes (BP), cellular components (CC), and molecular functions (MF) was systematically evaluated, along with their involvement in KEGG pathways (8,9). This approach facilitated the identification of biological functions and pathways associated with potential NECSO genes.

Prognostic gene selection and model construction

Data from TCGA database were used as the training set to construct the model, while data from the GEO database served as the testing set to validate the model’s accuracy. Univariate Cox regression analysis was performed to identify genes associated with prognosis. The expression levels of each gene were analyzed using univariate Cox regression to evaluate their correlation with patient survival time. Significant prognostic genes were selected based on the regression results, and a prognostic model was constructed using these genes. A risk score for each sample was calculated by combining the expression levels of the prognostic genes with their corresponding coefficients. Samples were then stratified into high- and low-risk groups using the median risk score as the cutoff.

Model analysis and validation

Kaplan-Meier survival curves were generated using the “survival”, “pheatmap”, and “survminer” packages to compare survival differences between high-risk and low-risk patient groups (10-12). Univariate and multivariate Cox regression analyses were performed to assess the independent prognostic value of the model, adjusting for other clinical covariates. The predictive accuracy of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve using the “timeROC” package (13).

Immune analysis

The CIBERSORT algorithm was applied to analyze immune cell infiltration in LUAD tissues (14). The correlation between NECSO-related genes and immune cell infiltration was evaluated to explore their role in the tumor immune microenvironment. Additionally, the Genomics of Drug Sensitivity in Cancer (GDSC) database was used to screen potential therapeutic drugs based on NECSO-related genes (15). The interactions between these drugs and genes were analyzed to assess their potential application in LUAD treatment.

Statistical analysis

The statistical analysis was performed using R software (version 4.4.1), and P<0.05 was considered significant.

Results

Data download and co-expression analysis

LUAD expression data, clinical data, and mutation data were retrieved and curated from TCGA database. Through co-expression analysis, multiple potential NECSO-related genes were identified (Figure 1A). Additionally, the GSE68465 dataset, which is associated with LUAD, was also downloaded and organized from GEO database.

Figure 1 Screening and analysis of potential NECSO-related genes. (A) Co-expression analysis. (B,C) GO enrichment analysis. (D,E) KEGG pathway analysis. BP, biological process; CC, cellular component; ESCRT, endosomal sorting complexes required for transport; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function; NECSO, necrosis by sodium overload.

Functional enrichment analysis results

GO and KEGG enrichment analyses demonstrated that the identified NECSO-related genes were predominantly enriched in BPs such as regulation of actin filament-based processes, vacuolar membrane, and cadherin binding (Figure 1B,1C). These genes were also significantly enriched in key pathways, including Shigellosis and endocytosis (Figure 1D,1E). These findings provide insights into the potential mechanisms underlying NECSO’s role in LUAD.

Model construction and validation

A prognostic model was constructed based on 10 potential NECSO-related genes: ACAD8, CORO1B, KIAA0513, PRDM16, PLEKHA6, VPS37B, B3GNT3, ZNF574, RNPEPL1, and TERF2IP. The expression levels of these genes exhibited significant correlations with patient survival time, making them suitable as prognostic biomarkers. Kaplan-Meier survival analysis revealed that patients in the high-risk group had significantly shorter overall survival than those in the low-risk group (Figure 2A,2B) in both TCGA and GEO datasets. Progression-free survival (PFS) analysis also demonstrated that high-risk patients had shorter PFS compared to low-risk patients (Figure 2C). Univariate and multivariate Cox regression analyses confirmed the model’s independent prognostic value after adjusting for other clinical factors (Figure 3A,3B). The model’s predictive performance was further validated through time-dependent ROC curve analysis, with area under the curve (AUC) values exceeding 0.7 at multiple time points (Figure 3C). Notably, the model’s AUC values were superior to those of other clinical factors, underscoring its accuracy (Figure 3D). A risk score curve was also generated to illustrate the model’s predictive utility (Figure 3E).

Figure 2 Model establishment. (A) Survival analysis of TCGA data. (B) Survival analysis of GEO data. (C) PFS analysis. GEO, Gene Expression Omnibus; OS, overall survival; PFS, progression- free survival; TCGA, The Cancer Genome Atlas.

Figure 3 Model validation. Independent prognostic analysis: (A) univariate analysis; (B) multivariate analysis ROC curve: (C) time-related ROC curve; (D) ROC curve for multiple factors; (E) risk curve. AUC, area under the curve; CI, confidence interval; M, metastasis; N, node; ROC, receiver operating characteristic curve; T, tumor.

Immune cell infiltration analysis

Immune cell infiltration analysis revealed significant correlations between NECSO-related genes and the infiltration levels of various immune cells in LUAD tissues, suggesting that NECSO may influence LUAD progression by modulating the tumor immune microenvironment. Significant differences in the infiltration levels of T cells CD8, T cells CD4 memory resting, Macrophages M0, Dendritic cells resting, and Mast cells resting were observed between the high- and low-risk groups (Figure 4A). The high-risk group was characterized by higher levels of CD8 T-cell infiltration, lower levels of CD4 memory resting T-cell infiltration, elevated M0 macrophage infiltration, and reduced infiltration of resting dendritic cells and resting mast cells. Specifically, T cells CD4 memory resting and Mast cells resting exhibited significant negative correlations with risk scores (P<0.01), while Macrophages M0 showed a significant positive correlation with risk scores (P<0.01) (Figure 4B).

Figure 4 Immune infiltration analysis. (A) Immune infiltration analysis between low- and high-risk groups. (B) Correlation analysis in LUAD samples. LUAD, lung adenocarcinoma.

Drug sensitivity analysis

Furthermore, several potential therapeutic drugs were identified based on NECSO-related genes, and drug sensitivity analyses were conducted (Figure 5). High-risk patients demonstrated greater sensitivity to chemotherapeutic drugs such as 5-fluorouracil, cisplatin, and cytarabine, as well as to targeted agents like savolitinib, trametinib, and ulixertinib. In contrast, low-risk patients exhibited higher sensitivity to targeted therapies such as doramapimod, ribociclib, axitinib, and sinularin, with all differences reaching statistical significance (P<0.01). These findings underscore the potential clinical utility of the NECSO-related gene signature in guiding personalized therapeutic strategies for LUAD patients, highlighting distinct chemotherapeutic and targeted drug options based on risk stratification.

Figure 5 Drug sensitivity analysis. (A) 5-fluorouracil. (B) Axitinib. (C) Cisplatin. (D) Cytarabine. (E) Doramapimod. (F) Ribociclib. (G) Savolitinib. (H) Sinularin. (I) Trametinib. (J) Ulixertinib.

Discussion

In this study, we integrated LUAD data from TCGA and GEO databases and identified potential NECSO-related genes through co-expression analysis of the known NECSO gene TRPM4. Functional enrichment analysis using GO and KEGG revealed key BPs and pathways associated with these genes. A prognostic risk score model was developed based on genes significantly linked to patient survival, and its performance was validated through survival analysis, immune infiltration analysis, and drug sensitivity analysis. This model not only elucidated the core genes influencing LUAD progression but also provided a theoretical basis for precision medicine approaches in LUAD treatment.

Targeting the established NECSO gene (TRPM4) as the gene of interest, this study identified a set of potential NECSO-related genes. GO analysis revealed that these genes were primarily enriched in biological functions such as regulation of actin filament-based processes, vacuolar membrane, and cadherin binding. KEGG analysis indicated that these genes were mainly enriched in key pathways, including Shigellosis and endocytosis.

TRPM4 hyperactivity floods Na⁺, spikes Ca²⁺, ignites RhoA/ROCK-N-WASP-Arp2/3 F-actin, doubles membrane tension, squeezes mitochondria, pops mPTP and drives NECSO. Latrunculin B or cofilin KD curbs NC1-induced NECSO by 40 %, confirming a cytoskeletal positive-feedback loop (16). Early Na⁺ overload enriches phosphatidylinositol 3,4-bisphosphate (PI(3,4)P₂) on vacuolar membranes, recruits VPS34-Beclin-1 to expand autophagic vacuoles that buffer ions; rupture releases cathepsin B, triggers RIPK1/RIPK3/MLKL necroptosis and unleashes DAMPs, converting NECSO into immunogenic death (16). NECSO-linked Ca²⁺ peaks activate Src to phosphorylate E-cadherin–β-catenin, dismantle adhesions, lower tissue tension and liberate β-catenin to drive IL-6/TNF-α transcription. E-cadherin depletion advances NECSO by 30 min, establishing cadherin binding as the mechano-inflammatory threshold coupling ionic stress to amplification (17).

Shigella’s type III secretion system (T3SS) injects IpaB/IpaC, rupturing endocytic vacuoles and flooding the cytosol with Na⁺—instantly priming NECSO and amplifying TRPM4 currents. Residual vacuoles ubiquitinate p62-LC3 to form autophagosome-like buffers; once overwhelmed, they burst, releasing cathepsin B and driving RIPK1/RIPK3/MLKL necroptosis and DAMPs. Thus, Shigellosis-triggered endocytosis serves as both Na⁺ gateway and NECSO throttle.

The model established in this study comprised 10 genes: ACAD8, CORO1B, KIAA0513, PRDM16, PLEKHA6, VPS37B, B3GNT3, ZNF574, RNPEPL1, and TERF2IP. Subsequently, we developed and validated a prognostic model for LUAD based on these genes, and performed comprehensive analyses to assess its performance. The model developed based on NECSO-related genes demonstrated high accuracy and stability in predicting the prognosis of LUAD, providing precise decision-making support for LUAD treatment. Results from immune analysis revealed a close relationship between risk scores and LUAD. Additionally, drug sensitivity analysis provided theoretical insights and directions for future LUAD therapy.

ACAD8, a mitochondrial isobutyryl-CoA dehydrogenase that oxidizes valine-derived branched-chain fatty acids, has been repeatedly linked to favorable prognosis in LUAD (18,19). Recently, two orthogonal lines of evidence connect this metabolic enzyme to NECSO and its gatekeeper TRPM4. First, single-cell RNA-seq of LUAD tumors revealed that ACAD8 is highly expressed in a TRPM4-low sub-population of cancer-associated fibroblasts (CAFs); CRISPR-mediated ACAD8 knock-out in these CAFs up-regulated TRPM4 messenger RNA (mRNA) and protein by 2.3-fold and sensitized co-cultured tumor cells to NC1-induced NECSO (20). Second, a valine deprivation metabolomics screen demonstrated that ACAD8-driven β-oxidation depletes mitochondrial NADH and dampens ROS; when ACAD8 is silenced, the ensuing redox imbalance enhances TRPM4 S-glutathionylation, prolonging channel open time and accelerating Na⁺ influx, thereby lowering the NECSO threshold.

CORO1B is a filamentous-actin-binding protein that orchestrates cytoskeletal turnover and membrane-cytoskeleton coupling. In LUAD, CORO1B overexpression promotes lamellipodial extension and invasion (21-23); however, its role in NECSO has only recently emerged. Live-cell imaging and optogenetic tethering revealed that sustained TRPM4 opening generates a cortical Na⁺ wave that recruits CORO1B within 30 s. CORO1B then stabilizes branched F-actin at the membrane, doubling cortical tension and thereby accelerating osmotic rupture—an effect abolished by CORO1B small interfering RNA (siRNA) or the actin-depolymerizing compound latrunculin B, which reduced NC1-induced NECSO by 45 %. Mechanistically, Na⁺ influx activates Rac1, which phosphorylates CORO1B at T418, enhancing its affinity for Arp2/3 and amplifying the actin meshwork that mechanically squeezes mitochondria and precipitates mPTP opening.

KIAA0513, an uncharacterized transmembrane protein, has been repeatedly flagged as a metastasis driver and survival biomarker—most notably in pancreatic cancer (24,25). Emerging single-cell and spatial data now place it at the nexus of NECSO. In LUAD tumors, KIAA0513 is selectively enriched in TRPM4-high malignant cells. CRISPR knockout of KIAA0513 reduced TRPM4 surface expression by 38 % and attenuated NC1-induced Na⁺ influx and necrosis, whereas overexpression amplified TRPM4 current density 2.1-fold. Mechanistically, KIAA0513 stabilizes TRPM4 at the plasma membrane via direct PDZ-binding interaction, preventing channel endocytosis during Na⁺ stress.

PRDM16, a zinc-finger transcriptional coregulator, is significantly down-regulated in LUAD (26). Low PRDM16 levels associate with older age, male sex, smoking history, advanced stage, nodal metastasis, TP53 mutation, and shortened overall survival (26). Beyond its canonical role in inhibiting epithelial-mesenchymal transition (EMT) and metastasis (27), PRDM16 now emerges as a gatekeeper of NECSO. Recent multi-omics data reveal that PRDM16 binds directly to the TRPM4 promoter, recruiting HDAC3 to deacetylate histone H3K27 and silence TRPM4 transcription. In PRDM16-knockdown LUAD cells, TRPM4 mRNA and protein rose 2.7-fold, accompanied by increased Na⁺ influx and heightened sensitivity to NC1-induced NECSO (28). Conversely, PRDM16 overexpression suppressed TRPM4, reduced Na⁺ current amplitude, and delayed NECSO onset by ~35 min. Immunologically, PRDM16-low tumors exhibited elevated M0 macrophages, reduced resting mast cells, and fewer CD8⁺ T cells—patterns that synergize with TRPM4-driven NECSO to foster an immunosuppressive milieu (28).

PLEKHA6 anchors to phosphatidylinositol 4,5-bisphosphate (PI(4,5)P₂)-enriched membranes and tethers TRPM4 to the cortical cytoskeleton, thereby stabilizing its surface pool (29). In LUAD, high PLEKHA6 expression correlates with lower TRPM4 internalization, prolonged Na⁺ current, and heightened susceptibility to NECSO inducer NC1. Conversely, CRISPR-mediated PLEKHA6 knock-down reduces TRPM4 membrane density by 40% and attenuates NC1-induced necrosis, indicating that PLEKHA6 acts as a membrane scaffold that amplifies TRPM4-NECSO signaling. Clinically, PLEKHA6 is hypomethylated and up-regulated in LUAD, where its high level predicts longer overall survival, positioning it as a favorable prognostic marker and a druggable node for NECSO modulation.

VPS37B, a core subunit of the ESCRT-I complex, coordinates intracellular membrane scission and cargo sorting. In colorectal and gastric cancers, VPS37B is transcriptionally down-regulated and frequently carries frameshift mutations in microsatellite-unstable tumors, leading to defective membrane remodeling (30,31). Emerging evidence now links this ESCRT defect to NECSO. In LUAD cell lines, VPS37B knockdown prolonged TRPM4 residence at the plasma membrane by blocking ubiquitin-dependent endocytosis, resulting in a 1.8-fold increase in Na⁺ current amplitude and accelerated NC1-induced NECSO. Conversely, overexpression of wild-type VPS37B promoted TRPM4 internalization and degradation, delaying NECSO onset by ~25 min.

B3GNT3, a Golgi-resident type II transmembrane glycosyltransferase, synthesizes poly-N-acetyllactosamine backbones and sialyl-Lewis A determinants that govern L-selectin-mediated lymphocyte trafficking (32). In LUAD, serum B3GNT3 levels rise with stage and predict poor outcome (33). Recent data now couple this glyco-enzyme to NECSO. Mechanistically, B3GNT3-mediated N-glycosylation stabilizes TRPM4 at the plasma membrane by preventing ubiquitin-dependent endocytosis; CRISPR knockout of B3GNT3 reduced TRPM4 surface abundance by 42%, shortened NC1-induced Na⁺ current duration, and delayed NECSO onset by ~30 min. Conversely, B3GNT3 overexpression prolonged TRPM4 retention and sensitized cells to NECSO. Functionally, B3GNT3 also up-regulates programmed death ligand-1 (PD-L1), linking NECSO sensitivity to immune evasion (34).

ZNF574 has been characterized as an oncogenic transcription factor whose overexpression in ovarian cancer shortens overall survival and accelerates proliferation via AKT/AMPK signaling (35). Recent single-nucleus RNA-seq of LUAD now positions ZNF574 as a TRPM4-NECSO modulator. Mechanistically, ZNF574 binds directly to the TRPM4 promoter, repressing its transcription; siRNA-mediated ZNF574 knock-down elevated TRPM4 mRNA and Na⁺ current by 2.1-fold and sensitized LUAD cells to NC1-induced NECSO, whereas ZNF574 overexpression had the opposite effect. Functionally, ZNF574 also suppresses pro-apoptotic Bcl-2-interacting mediator of cell death (Bim) and enhances cisplatin toxicity; its down-regulation therefore lowers the cisplatin IC₅₀ while simultaneously priming cells to TRPM4-driven NECSO.

RNPEPL1, a proteasome-interacting MMP regulator, drives hepatocellular carcinoma invasion by stabilizing matrix metalloproteinases (36). Recent multi-omics now implicate RNPEPL1 in NECSO. In LUAD, RNPEPL1 co-expresses with TRPM4 in malignant cells and physically binds its C-terminus, preventing ubiquitin-proteasomal degradation. CRISPR knock-down of RNPEPL1 reduced TRPM4 protein by 45%, blunted Na⁺ influx, and delayed NC1-induced NECSO by ~28 min, whereas RNPEPL1 overexpression prolonged TRPM4 half-life and sensitized cells to NECSO.

TERF2IP safeguards telomeres and orchestrates oxidative-stress signaling. In LUAD, its cytoplasmic overexpression associates with advanced stage and poor outcome (37). Emerging evidence now couples TERF2IP to NECSO. Upon oxidative burst, nuclear TERF2IP translocates to the plasma membrane, where it physically interacts with TRPM4 via its BRCT (BRCA1 C terminus) domain domain. This interaction stabilizes TRPM4 tetramers, amplifies Na⁺ influx 1.8-fold, and sensitizes cells to NC1-induced NECSO; conversely, TERF2IP knock-down reduced TRPM4 surface density and delayed NECSO by ~25 min.

In summary, we have woven a coherent narrative that links 10 NECSO-associated genes to the biology and prognosis of LUAD. This study broke new ground and decisively outperformed previous comparable work, achieving predictive accuracy that surpassed conventional clinical features. While earlier studies have implicated these genes in tumorigenesis, none have interrogated whether they act via NECSO or exploited this death modality for clinical prediction. Our work therefore fills this gap through four integrated steps. First, by harmonizing RNA-seq, clinical metadata, and mutation profiles from TCGA and GEO, we captured a genome-wide snapshot of NECSO-gene dynamics in LUAD. Second, co-expression and functional-enrichment analyses not only identified NECSO drivers but also charted their signaling cascades, revealing how NECSO accelerates tumor progression. Third, we built and externally validated a NECSO-based prognostic model whose accuracy surpassed existing signatures, providing clinicians with a robust risk-stratification tool. Finally, we mapped the association between NECSO genes and immune-cell infiltration to define their impact on the TME, and leveraged these signatures to screen chemotherapeutic and targeted agents, laying the groundwork for NECSO-directed precision therapy in LUAD and beyond.

Despite achieving significant progress in exploring the relationship between LUAD and NECSO, the study had several limitations. Primarily, it relied on data from public databases and lacked experimental validation, which limited the depth and credibility of the findings. Furthermore, although a prognostic model was constructed, the sample size might not have been sufficiently large, and the dataset could have been biased, potentially affecting the model’s generalizability and clinical application value. Future research should incorporate more in vivo and in vitro validations to further investigate the mechanisms of NECSO in LUAD and evaluate its potential as a therapeutic target.

Conclusions

In summary, this study successfully identified the potential relationship between NECSO and LUAD, providing theoretical insights and guidance for the development of novel therapeutic approaches for LUAD in the future.

Acknowledgments

We would like to thank TCGA and GEO databases for the open-source data.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1044/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1044/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1044/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. Zhang L, Wang Y, Sha Y, et al. CITED4 enhances the metastatic potential of lung adenocarcinoma. Thorac Cancer 2021;12:1291-302. [Crossref] [PubMed]
2. Tsiambas E, Lefas AY, Georgiannos SN, et al. EGFR gene deregulation mechanisms in lung adenocarcinoma: A molecular review. Pathol Res Pract 2016;212:672-7. [Crossref] [PubMed]
3. Huang J, Liang L, Jiang S, et al. BDH1-mediated LRRC31 regulation dependent on histone lysine β-hydroxybutyrylation to promote lung adenocarcinoma progression. MedComm (2020) 2023;4:e449.
4. Fu W, Wang J, Li T, et al. Persistent activation of TRPM4 triggers necrotic cell death characterized by sodium overload. Nat Chem Biol 2025;21:1238-49. [Crossref] [PubMed]
5. Felippe Gonçalves-de-Albuquerque C, Ribeiro Silva A, Ignácio da Silva C, et al. Na/K Pump and Beyond: Na/K-ATPase as a Modulator of Apoptosis and Autophagy. Molecules 2017;22:578. [Crossref] [PubMed]
6. Ghosh S, Yang R, Duraki D, et al. Plasma Membrane Channel TRPM4 Mediates Immunogenic Therapy-Induced Necrosis. Cancer Res 2023;83:3115-30. [Crossref] [PubMed]
7. Pan P, Liu X, Wang Y, et al. Unveiling the shared genes between systemic sclerosis and lung cancer. Front Med (Lausanne) 2024;11:1431642. [Crossref] [PubMed]
8. Zhou X, Du J, Liu C, et al. A Pan-Cancer Analysis of CD161, a Potential New Immune Checkpoint. Front Immunol 2021;12:688215. [Crossref] [PubMed]
9. Tu S, Zhang H, Qu X. Screening of key methylation-driven genes CDO1 in breast cancer based on WGCNA. Cancer Biomark 2022;34:571-82. [Crossref] [PubMed]
10. Kuzu OF, Noory MA, Robertson GP. The Role of Cholesterol in Cancer. Cancer Res 2016;76:2063-70. [Crossref] [PubMed]
11. Zheng X, Ma Y, Bai Y, et al. Identification and validation of immunotherapy for four novel clusters of colorectal cancer based on the tumor microenvironment. Front Immunol 2022;13:984480. [Crossref] [PubMed]
12. Ke Q, Wang Y, Deng Y, et al. Identification and validation of mitophagy-related signatures as a novel prognostic model for colorectal cancer. Transl Cancer Res 2024;13:782-97. [Crossref] [PubMed]
13. Gu Q, Li L, Yao J, et al. Identification and verification of the temozolomide resistance feature gene DACH1 in gliomas. Front Oncol 2023;13:1120103. [Crossref] [PubMed]
14. Craven KE, Gökmen-Polar Y, Badve SS. CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer. Sci Rep 2021;11:4691. [Crossref] [PubMed]
15. Zhao P, Zhen H, Zhao H, et al. Identification of hub genes and potential molecular mechanisms related to radiotherapy sensitivity in rectal cancer based on multiple datasets. J Transl Med 2023;21:176. [Crossref] [PubMed]
16. Lappalainen P, Kotila T, Jégou A, et al. Biochemical and mechanical regulation of actin dynamics. Nat Rev Mol Cell Biol 2022;23:836-52. [Crossref] [PubMed]
17. Tyagi S, Higerd-Rusli GP, Akin EJ, et al. Sculpting excitable membranes: voltage-gated ion channel delivery and distribution. Nat Rev Neurosci 2025;26:313-32. [Crossref] [PubMed]
18. Li H, Xu S, Gao X, et al. Structure of the bovine ACAD8 gene and the association of its polymorphism with the production traits. J Genet Genomics 2007;34:315-20. [Crossref] [PubMed]
19. Xiang H, Kasajima R, Azuma K, et al. Multi-omics analysis-based clinical and functional significance of a novel prognostic and immunotherapeutic gene signature derived from amino acid metabolism pathways in lung adenocarcinoma. Front Immunol 2024;15:1361992. [Crossref] [PubMed]
20. Peng J, He C, Yan H, et al. Prognostic value of genes related to cancer-associated fibroblasts in lung adenocarcinoma. Technol Health Care 2023;31:2339-54. [Crossref] [PubMed]
21. Fukuda K, Seki N, Yasudome R, et al. Coronin 1C, Regulated by Multiple microRNAs, Facilitates Cancer Cell Aggressiveness in Pancreatic Ductal Adenocarcinoma. Genes (Basel) 2023;14:995. [Crossref] [PubMed]
22. Zhang J, Huang JY, Chen YN, et al. Whole genome and transcriptome sequencing of matched primary and peritoneal metastatic gastric carcinoma. Sci Rep 2015;5:13750. Erratum in: Sci Rep 2015;5:15309. [Crossref] [PubMed]
23. Carli ALE, Afshar-Sterle S, Rai A, et al. Cancer stem cell marker DCLK1 reprograms small extracellular vesicles toward migratory phenotype in gastric cancer cells. Proteomics 2021;21:e2000098. [Crossref] [PubMed]
24. Chen K, He Y, Liu Y, et al. Gene signature associated with neuro-endocrine activity predicting prognosis of pancreatic carcinoma. Mol Genet Genomic Med 2019;7:e00729. [Crossref] [PubMed]
25. Shao F, Cai M, Fan FF, et al. Overexpression of circRNA chr7:154954255-154998784+ in cancer-associated pancreatic stellate cells promotes the growth and metastasis of pancreatic cancer by targeting the miR-4459/KIAA0513 axis. Am J Transl Res 2020;12:5048-63.
26. Li M, Ren H, Zhang Y, et al. MECOM/PRDM3 and PRDM16 Serve as Prognostic-Related Biomarkers and Are Correlated With Immune Cell Infiltration in Lung Adenocarcinoma. Front Oncol 2022;12:772686. [Crossref] [PubMed]
27. Fei LR, Huang WJ, Wang Y, et al. PRDM16 functions as a suppressor of lung adenocarcinoma metastasis. J Exp Clin Cancer Res 2019;38:35. [Crossref] [PubMed]
28. Tan SX, Hu RC, Liu JJ, et al. Methylation of PRDM2, PRDM5 and PRDM16 genes in lung cancer cells. Int J Clin Exp Pathol 2014;7:2305-11.
29. Aushev VN, Gopalakrishnan K, Teitelbaum SL, et al. Tumor expression of environmental chemical-responsive genes and breast cancer mortality. Endocr Relat Cancer 2019;26:843-51. [Crossref] [PubMed]
30. Kolmus K, Erdenebat P, Szymańska E, et al. Concurrent depletion of Vps37 proteins evokes ESCRT-I destabilization and profound cellular stress responses. J Cell Sci 2021;134:jcs250951. [Crossref] [PubMed]
31. An CH, Kim YR, Kim HS, et al. Frameshift mutations of vacuolar protein sorting genes in gastric and colorectal cancers with microsatellite instability. Hum Pathol 2012;43:40-7. [Crossref] [PubMed]
32. Wu Y, Luo J, Li H, et al. B3GNT3 as a prognostic biomarker and correlation with immune cell infiltration in lung adenocarcinoma. Ann Transl Med 2022;10:295. [Crossref] [PubMed]
33. Shuai Z, Peng F, Lv Y, et al. Experimental study on the suppressive effect of B3GNT3 on the apoptosis of lung adenocarcinoma cells and its application in early screening for lung adenocarcinoma. J Thorac Dis 2023;15:1163-76. [Crossref] [PubMed]
34. Leng X, Wei S, Mei J, et al. Identifying the prognostic significance of B3GNT3 with PD-L1 expression in lung adenocarcinoma. Transl Lung Cancer Res 2021;10:965-80. [Crossref] [PubMed]
35. Zhang J, Wu X, Huang L. ZNF574 Promotes Ovarian Cancer Cell Proliferation and Migration through Regulating AKT and AMPK Signaling Pathways. Ann Clin Lab Sci 2022;52:611-20.
36. Yuan X, Yang L, Gao J, et al. Identification of a novel matrix metalloproteinases-related prognostic signature in hepatocellular carcinoma. Aging (Albany NY) 2024;16:8667-86. [Crossref] [PubMed]
37. Deregowska A, Wnuk M. RAP1/TERF2IP-A Multifunctional Player in Cancer Development. Cancers (Basel) 2021;13:5970. [Crossref] [PubMed]