Metal Ion Transporter SLC39A14-Mediated Ferroptosis and Glycosylation Modulate the Tumor Immune Microenvironment: A Pan-Cancer Multi-Omics Exploration of Therapeutic Potential

Metal Ion Transporter SLC39A14-Mediated Ferroptosis and Glycosylation Modulate the Tumor Immune Microenvironment: A Pan-Cancer Multi-Omics Exploration of Therapeutic Potential | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Metal Ion Transporter SLC39A14-Mediated Ferroptosis and Glycosylation Modulate the Tumor Immune Microenvironment: A Pan-Cancer Multi-Omics Exploration of Therapeutic Potential Yi-Chun Chiang, Chih-Yang Wang, Sachin Kumar, Chung-Bao Hsieh, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6384291/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Oct, 2025 Read the published version in Cancer Cell International → Version 1 posted 10 You are reading this latest preprint version Abstract Ferroptosis, an iron-dependent form of regulated cell death driven by lipid peroxidation, has emerged as a pivotal mechanism in cancer progression and therapeutic resistance. Concurrently, glycosylation, a key post-translational modification, plays a critical role in regulating cell signaling, immune evasion, and metastasis. Although both processes are individually implicated in tumor biology, the intersection between ferroptosis and glycosylation remains largely unexplored. We performed a comprehensive pan-cancer analysis by integrating transcriptomic, epigenomic, single-cell RNA sequencing, and pharmacogenomic datasets. Ferroptosis- and glycosylation-related genes were curated from the MSigDB, leading to the identification of metal ion transporter SLC39A14 (solute carrier family 39 member 14) as a common intersecting gene. A ferroptosis-related gene signature was constructed using LASSO Cox regression, followed by survival, immune microenvironment, and functional enrichment analyses across The Cancer Genome Atlas (TCGA) cohort. Drug sensitivity analysis and AlphaFold-based molecular docking were used to evaluate therapeutic relevance. SLC39A14 was significantly upregulated in multiple tumor types and strongly associated with poor prognosis, immune-stromal infiltration, and ferroptosis resistance. Notably, among all cancer types analyzed, glioblastoma multiforme (GBM) and kidney renal clear cell carcinoma (KIRC) exhibited the strongest prognostic associations and the most significant differential expression of SLC39A14. These two tumors also showed distinct but clinically relevant ferroptosis-immune phenotypes: GBM featured enrichment of VEGF and NRF2 oxidative stress pathways in a hypoxia-adapted, macrophage- and NK cell–infiltrated microenvironment, while KIRC was characterized by TF-induced thrombosis, DNA damage response, and immune exclusion. Single-cell transcriptomic and DNA methylation analyses further confirmed SLC39A14’s role in modulating tumor microenvironment and ferroptotic vulnerability. Functional enrichment revealed that high ferroptosis scores were enriched in angiogenesis, EMT, and cytokine signaling pathways. A nomogram integrating SLC39A14 with clinical parameters showed enhanced survival prediction. Moreover, SLC39A14 expression correlated with differential responses to ferroptosis-related drugs, suggesting translational applicability. This study highlights the dual regulatory role of SLC39A14 at the interface of ferroptosis and glycosylation, with a distinct impact on GBM and renal cancer progression. By integrating multi-omics and single-cell analyses, we reveal SLC39A14 as a promising prognostic biomarker and therapeutic target, particularly in brain and kidney cancers where ferroptosis modulation may offer novel clinical opportunities. Metal ion transporter SLC39A14 (solute carrier family 39 member 14) ferroptosis glycosylation tumor microenvironment immune infiltration bioinformatics prognostic biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1. Introduction Ferroptosis is a unique form of regulated cell death driven by iron-dependent lipid peroxidation, setting it apart from traditional forms of cell death such as apoptosis and necrosis [ 1 ]. This process is characterized by the accumulation of reactive oxygen species (ROS) and the depletion of antioxidant systems, particularly glutathione and GPX4, leading to catastrophic oxidative damage. Ferroptosis plays a dual role in cancer: it suppresses tumor growth by eliminating cancer cells in some contexts while promoting tumor progression by fostering therapy resistance and immune evasion in others. Understanding the regulation of ferroptosis is critical for developing targeted therapies, particularly as it emerges as a promising avenue for overcoming drug resistance and inducing cancer cell death. Similarly, glycosylation, a ubiquitous post-translational modification, is essential for protein folding, stability, and function. Aberrant glycosylation has been identified as a hallmark of cancer, contributing to tumor proliferation, metastasis, angiogenesis, and immune escape [ 2 , 3 ]. Glycosylation modifies key molecules involved in cell signaling, immune checkpoint regulation, and extracellular matrix remodeling, directly influencing tumor progression. Although both ferroptosis and glycosylation are individually implicated in cancer biology, their potential intersection remains underexplored. Recent evidence suggests that glycosylation may influence ferroptosis through its effects on membrane stability, iron metabolism, and immune signaling, highlighting a complex interplay that could significantly impact tumor behavior. Among regulators implicated in both pathways, SLC39A14 (ZIP14), a metal ion transporter for zinc and manganese, has emerged as a compelling candidate[ 4 ]. SLC39A14 not only modulates iron and ROS homeostasis but also influences protein glycosylation via manganese-dependent glycosyltransferases. Through pan-cancer screening across The Cancer Genome Atlas (TCGA), we identified SLC39A14 as one of the only two genes overlapping between curated ferroptosis- and glycosylation-related gene sets, suggesting its dual functionality. Several recent pan-cancer studies have adopted a strategy of broad molecular characterization followed by in-depth focus on a few cancer types, such as glioblastoma (GBM) and kidney renal clear cell carcinoma (KIRC). These cancers are highly aggressive, therapy-resistant, and immunologically distinct, providing valuable models for studying ferroptosis and immune escape mechanisms. Importantly, SLC39A14 is significantly overexpressed in both GBM and KIRC and correlates with poor prognosis, increased stromal infiltration, and epigenetic deregulation. In this study, we aimed to elucidate the biological role and clinical relevance of SLC39A14 by constructing a ferroptosis-related gene signature and validating its prognostic value across pan-cancer datasets. We integrated bulk RNA-seq, single-cell RNA-seq, methylation, and drug sensitivity datasets to systematically evaluate the ferroptosis-glycosylation interplay mediated by SLC39A14. Further, we focused our mechanistic exploration on GBM and KIRC to uncover tissue-specific implications in ferroptosis resistance, immune modulation, and potential therapeutic targeting. This approach aligns with current trends in precision oncology and integrative omics modeling. 2. Materials and Methods 2.1 Data Collection and Preprocessing This study integrated multiple publicly available datasets to investigate the interplay between glycosylation and ferroptosis pathways in cancer. Glycosylation-related genes were identified by querying the Molecular Signatures Database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb/human/ ) using the keyword "Glycosylation," yielding 663 genes for analysis. Similarly, ferroptosis-related genes were retrieved from MSigDB using the keyword "Ferroptosis," resulting in a total of 64 genes. These gene sets were curated to ensure relevance and accuracy, removing duplicates and redundant entries. Both gene sets were subsequently utilized for cross-referencing to identify overlapping genes (Fig. 1 ). The retrieved data underwent extensive preprocessing, including normalization and transformation, to eliminate inconsistencies and enhance comparability across different cancer datasets. 2.2 Identification of Common Genes To pinpoint genes that simultaneously regulate glycosylation and ferroptosis, the two gene sets were intersected using computational analysis in R (v4.1.2). This intersection revealed two overlapping genes, SLC39A8 and SLC39A14, which were subjected to further analysis. These genes were hypothesized to serve as critical regulators linking ferroptosis and glycosylation pathways, influencing cancer progression and the tumor microenvironment. Further validation through literature review and database cross-checking was performed to confirm the biological relevance of these candidate genes in cancer research contexts. 2.3 Gene Expression Data and Survival Analysis Normalized RNA sequencing data and associated clinical annotations were obtained from The Cancer Genome Atlas (TCGA) database via UCSC Xena ( http://xena.ucsc.edu ). The dataset included transcriptomic profiles for 33 tumor types, covering over 10,000 patient samples. Expression data were log2-transformed as log2(TPM + 1) to normalize distribution and mitigate batch effects[ 5 ]. Clinical variables, including overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI), were curated for survival analyses. Tumor and normal tissue samples were analyzed to evaluate differential expression patterns of SLC39A14. Additional exploratory analyses were performed to assess potential co-expression networks and correlations among ferroptosis-related genes. Survival analyses were conducted using Kaplan-Meier plots and univariate Cox proportional hazards regression models to evaluate the prognostic significance of SLC39A14 across multiple cancers. Hazard ratios (HR) with 95% confidence intervals (CI) and log-rank tests were employed to assess statistical significance. Patients were stratified into high- and low-expression groups based on median expression values, and subgroup analyses were conducted to explore associations across various demographic and clinical strata. Meanwhile, a prognostic nomogram was constructed integrating SLC39A14 expression, ferroptosis scores, and clinical parameters using multivariate Cox regression models. The predictive accuracy of the nomogram was validated by calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA), highlighting its clinical utility in personalized patient management. Cross-validation techniques were employed to enhance the reliability and robustness of the nomogram's predictions, supporting its applicability for clinical decision-making in cancer prognosis. 2.4 DNA Methylation Analysis and Clinical Samples DNA methylation data for SLC39A14 CpG sites were downloaded from TCGA. Methylation levels were analyzed via MethSurv database ( https://biit.cs.ut.ee/methsurv/ )[ 6 ]. To assess correlations with gene expression and clinical outcomes. Differential methylation analysis was conducted to identify epigenetic regulation of SLC39A14, providing insights into the underlying mechanisms influencing its differential expression in tumors versus normal tissues. We utilized the Human Protein Atlas (HPA) for immunohistochemistry (IHC) and immunofluorescence (IFC) to analyze the expression profiles of SLC39A14 in various cancer. The HPA provides comprehensive data on the localization and expression of proteins in human tissues and cells, using techniques such as antibody-based imaging, transcriptomics, and proteomic [ 7 ]. 2.5 Immune and Stromal Infiltration and Functional Enrichment Analyses: The Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm was used to quantify immune and stromal cell infiltration within tumor microenvironments across pan-cancer datasets. Pearson correlation analyses were performed to evaluate associations between SLC39A14 expression and immune/stromal scores via TIMER ( https://cistrome.shinyapps.io/timer/ ) [ 8 ].Visualization included scatter plots with regression lines to illustrate the strength and significance of these associations, with specific attention to cancers such as glioblastoma multiforme (GBM), kidney renal clear cell carcinoma (KIRC), and liver hepatocellular carcinoma (LIHC). Differentially expressed genes (DEGs) associated with SLC39A14 expression levels were identified using the R package "limma." DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the "clusterProfiler" package to elucidate underlying biological mechanisms. GO analyses covered biological processes (BP), molecular functions (MF), and cellular components (CC), while KEGG analyses identified significantly enriched signaling pathways. Enrichment terms were considered statistically significant at an adjusted false discovery rate (FDR) < 0.05. Afterward, MetaCore ( https://portal.genego.com ) was used to explore biomarker networks and cell signaling pathways, as we previous described [ 9 ]. 2.6 Single-Cell RNA Sequencing Analysis To gain deeper insights into the tumor microenvironment (TME), a single-cell (sc)RNA-Seq analysis was conducted using a processed version of the data, which included only lung tissue samples. The original dataset, available in Hierarchical Data Format version 5 (h5ad), was converted to R Data Serialization (rds) format using the "convert Format" method from the sceasy package (vers. 0.0.7), as the analysis was performed in R Studio[ 10 ]. Single-cell RNA sequencing data from publicly available cancer datasets were obtained and analyzed using the "Seurat" R package[ 11 ]. The Single-Cell Sequencing Pipeline (SCP) R package (version 0.5.6) was employed to perform the scRNA-Seq analysis, enabling systematic dimensionality reduction, clustering, and data pre-processing[ 12 – 14 ]. Cells were clustered based on expression profiles, and SLC39A14 expression was examined across distinct cell populations to identify cell-specific roles, particularly in immune cells such as NK cells and macrophages. Further dimensionality reduction techniques, including t-distributed stochastic neighbor embedding (t-SNE), were utilized for visualization and clustering refinement. 2.7 Drug Sensitivity and Molecular Docking Validation: The GSCA and the Cancer Therapeutics Response Portal (CTRP) database were utilized to collect information to determine the drug sensitivity results of gene( http://bioinfo.life.hust.edu.cn/GSCA/#/drug ) [ 15 ]. Significant correlations between drug sensitivity and SLC39A14 expression were identified to suggest potential therapeutic interventions. Additional validation of drug sensitivity results was performed using literature and external pharmacogenomic datasets. Identified drugs based on GSCA and CTRP shown to have high sensitivity was validated using molecular docking methods. The SDF structure file of the drugs was retrieved from PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ) followed by preprocessing using PyMol and AutoDockTools before docking. The protein structure of SLC39A14 was taken from the AlphaFold ( https://alphafold.ebi.ac.uk ) with the structure ID Q15043. The protein was done preprocessed according to best practices with PyMol and AutoDockTools. CastPFold was then utilized to determine potential binding site before being mapped. Docking was then performed using Vina with energy range set to 4 and exhaustiveness set to 8. 3D and 2D visualization of the docked structure was then done using PyMol and LigPlot + package. 2.8 Statistical Analysis The bioinformatics analyses in this study were conducted using online databases. Data visualization and statistical analyses were performed with ggplot2, SPSS (IBM, Armonk, NY, USA), and ImageJ (National Institutes of Health, Bethesda, MD, USA). Omics Playground vers. 3.4.1[ 16 ] and SRPlot[ 17 ] are online statistical analytical tools that store a collection of data from the public database. Results are presented as the mean ± SD, derived from a minimum of three independent experiments. Statistical analyses were carried out using one- and two-way analysis of variance (ANOVA). Survival analyses were conducted using the Kaplan-Meier method, with differences assessed by the log-rank test. The Bonferroni method was employed to determine the significance value and p-value. Differences between groups were considered significant at p < 0.05, as previously described [ 18 – 20 ]. 3. Results 3.1 Construction and Validation of a Prognostic Ferroptosis-Related Gene Signature: To systematically assess the clinical relevance of ferroptosis in cancer, we first identified 50 genes previously reported to participate in ferroptosis regulation. Using LASSO Cox regression analysis on TCGA pan-cancer data, we reduced this list to a robust 15-gene signature optimized for survival prediction. This process involved penalized regression modeling to avoid overfitting while retaining maximum prognostic power. The 15-gene panel included both ferroptosis suppressors (e.g., GPX4, SLC7A11) and pro-ferroptotic drivers (e.g., ACSL4, ALOX15), suggesting the model captured a balance of ferroptotic control mechanisms (Fig. 2 A). A ferroptosis score was calculated per patient as a weighted sum of gene expression values, where the weights were derived from the model coefficients (Fig. 2 B). Patients were then stratified into high- and low-score groups based on the median value. Survival analysis revealed that high ferroptosis scores correlated significantly with worse OS and DSS across several cancer types. Heatmaps and forest plots illustrated inter-gene correlations and individual gene contributions to patient risk, respectively (Fig. 2 C). Genes such as SLC7A11 and GPX4 demonstrated hazard ratios (HRs) > 1.5, emphasizing their tumor-promoting, anti-ferroptotic roles. The model’s robustness was further validated in two external cohorts: the Chinese Glioma Genome Atlas (CGGA) and METABRIC (breast cancer). In both, the high-score group showed significantly worse clinical outcomes, confirming the model's reproducibility and translational potential. In addition, functional annotation of the gene set linked high ferroptosis scores to aggressive phenotypes including resistance to oxidative stress, upregulated cell division, and immune escape, consistent with ferroptosis suppression promoting tumor cell survival and therapy evasion. 3.2 Prognostic Evaluation Across Cancer Types and Cohorts: To explore ferroptosis score heterogeneity, we evaluated its distribution across 33 TCGA cancer types. Violin plots revealed wide variation: cancers such as GBM, LGG, COAD, and LIHC exhibited notably high ferroptosis scores, whereas endocrine-related cancers (e.g., THCA, PRAD) had lower scores (Fig. 3 A). Univariate Cox regression across cancer types showed that high ferroptosis scores were significantly associated with increased mortality risk in at least 12 tumor types (Fig. 3 B and C). These included gliomas, renal cell carcinomas, lung adenocarcinoma, and colorectal adenocarcinoma. In contrast, the prognostic significance was weaker in tumors with more stable epigenetic or metabolic profiles. Survival analysis using Kaplan-Meier plots in both the TCGA training and test cohorts confirmed these trends. Importantly, the ferroptosis score remained an independent predictor of survival even after adjusting for tumor stage and age in multivariate Cox models, further supporting its robustness as a pan-cancer biomarker (Fig. 3 D and E). 3.3 Kaplan-Meier Survival Analysis for Specific Cancer Types: We next examined cancer-type-specific prognostic implications of the ferroptosis score in more detail. In LGG, high ferroptosis scores were strongly linked to poor survival outcomes across all endpoints (OS, DSS, PFI), with p-values < 0.001 (Fig. 4 A). These results are particularly important given the known metabolic plasticity and redox sensitivity of glioma cells. In GBM, patients with high scores had shorter survival intervals (OS: p = 0.038), suggesting that ferroptosis inhibition contributes to the aggressive phenotype of these tumors (Fig. 4 B). In KIRC and KIRP, the ferroptosis score effectively stratified patients into high- and low-risk groups. In KIRC, all survival endpoints (OS, DSS, PFI) showed p-values < 0.001, underlining the impact of iron metabolism and lipid peroxidation suppression in renal tumor biology (Fig. 4 C and D). These consistent patterns across multiple cancer types suggest that ferroptosis regulation may represent a common driver of tumor aggressiveness and serve as a useful therapeutic vulnerability. 3.4 Development and Validation of a Prognostic Nomogram: To enhance clinical applicability, we developed a nomogram that integrates ferroptosis scores with key clinical parameters including patient age, tumor stage, and cancer type. The nomogram assigns weighted points to each variable and calculates a total risk score, which can then be used to estimate 1-, 3-, 5-, and 10-year survival probabilities. The nomogram structure, showing the cumulative contribution of each factor (Fig. 5 A). The ferroptosis score emerged as the most influential variable, reaffirming its biological and prognostic significance. Calibration curves (Fig. 5 B) showed excellent agreement between predicted and observed survival rates across multiple time points. ROC curve analysis (Fig. 5 C) revealed high predictive accuracy, with AUCs of 0.78 in the training cohort and 0.81 in the test cohort at the 5-year mark. Time-dependent AUC analyses (Fig. 5 D) further demonstrated that the nomogram consistently outperformed the ferroptosis score alone across all time intervals. Decision curve analysis (DCA) (Figs. 5 E and F) confirmed the model's superior net clinical benefit, especially in intermediate-to-high risk ranges, where clinical decision-making is often uncertain. These results suggest that integrating molecular signatures like ferroptosis scores with standard clinical features can improve survival prediction and inform individualized patient management. 3.5 Correlation Between Ferroptosis Scores and Malignant Features: To elucidate the biological implications of ferroptosis scores, we assessed their correlation with hallmark cancer traits across pan-cancer data. Significant positive correlations were found between ferroptosis scores and pathways related to epithelial-mesenchymal transition (EMT), angiogenesis, and cell cycle progression. As shown in Figs. 6 A–C, ferroptosis scores were moderately to strongly associated with these processes across various tumor types (e.g., EMT: R = 0.31; angiogenesis: R = 0.33; cell cycle: R = 0.44). These processes are known to promote tumor invasiveness, immune escape, and resistance to treatment. Cancer-specific analysis (Figs. 6 D–F) demonstrated strong EMT and angiogenesis correlations in KIRC, COAD, and GBM, while HCC and ovarian cancers showed stronger links to cell cycle dysregulation. These observations support the hypothesis that ferroptosis suppression enhances malignant phenotypes, possibly by stabilizing tumor cell membranes, modulating redox-sensitive transcription factors (e.g., NRF2), or promoting immunosuppressive microenvironments. 3.6 Functional Enrichment of Differentially Expressed Genes: We conducted functional enrichment analysis on DEGs between high- and low-ferroptosis score groups. The high-score group was significantly enriched in GO biological processes such as nuclear division, chromosome segregation, and cytokine–cytokine receptor interaction. KEGG pathway analysis highlighted the IL-17 signaling pathway, Th1/Th2 differentiation, and p53 signaling, suggesting that ferroptosis-suppressed tumors may upregulate inflammatory and proliferative pathways to drive tumor growth and immune modulation (Fig. 7 A). In contrast, the low-score group exhibited enrichment in hormonal signaling, dopaminergic synapse, and insulin secretion (Fig. 7 B). GO terms such as male sex differentiation and genitalia development suggest that these tumors retain more differentiated and less proliferative characteristics. These results highlight that tumors with high ferroptosis scores are more proliferative, immune-reactive, and potentially aggressive, whereas low-score tumors may maintain homeostatic or differentiated functions. 3.7 Identification of SLC39A14 as a Key Gene in Ferroptosis and Glycosylation By intersecting glycosylation- and ferroptosis-related gene sets from MSigDB, we identified two overlapping genes: SLC39A8 and SLC39A14. SLC39A14 was prioritized due to its stronger expression across multiple tumor types and established role in metal ion transport, particularly zinc and manganese, which are critical in redox regulation. As shown in Fig. 8 A–B, SLC39A14 was overexpressed in tumors such as GBM, KIRC, and LIHC relative to adjacent normal tissue. Univariate Cox analysis (Fig. 8 C–D) revealed that elevated SLC39A14 expression was significantly associated with poor OS and DSS, particularly in KIRP (HR = 2.39, p < 0.001) and LIHC (HR = 1.92, p = 0.001). These findings suggest that SLC39A14 may suppress ferroptosis through mechanisms related to intracellular metal ion homeostasis and membrane stability, thereby promoting tumor progression and poor clinical outcomes. 3.8 Correlation of SLC39A14 Expression with Tumor Immune and Stromal Components To assess how SLC39A14 impacts the tumor microenvironment, we analyzed its correlation with ESTIMATE-derived immune and stromal scores. In KIRC, SLC39A14 expression strongly correlated with both immune (R = 0.34, p < 2.2e-16) and stromal scores (R = 0.50, p < 2.2e-16). Similar trends were observed in GBM, LIHC, and LGG (Fig. 9 A-D). These findings imply that SLC39A14 may shape both immune and extracellular matrix dynamics, possibly promoting a fibrotic or immunosuppressive microenvironment that enhances tumor resilience. 3.9 Single-cell RNA-seq Analysis of SLC39A14 in KIRC We analyzed SLC39A14 expression at single-cell resolution using scRNA-seq datasets from kidney renal clear cell carcinoma (KIRC) and glioma samples. Heatmap analysis (Figs. 10 A & 11 A) demonstrated that SLC39A14 was highly expressed in immune cells, particularly natural killer (NK) cells and macrophages, as well as epithelial tumor cells. The t-distributed stochastic neighbor embedding (t-SNE) plots (Figs. 10 B & 11 B) further illustrated that SLC39A14-expressing cells were enriched in immune-associated clusters. Co-expression analysis (Fig. 10 C) revealed strong correlations between SLC39A14 and immune cell marker genes, particularly those associated with NK cells and macrophages. These findings suggest a functional role of SLC39A14 in immune regulation within the tumor microenvironment (TME), potentially contributing to immune cell recruitment and activity. The expression pattern observed in gliomas (Fig. 11 ) was consistent with findings from KIRC, reinforcing SLC39A14’s involvement in modulating anti-tumor immune responses. Our results align with prior studies showing that zinc transporters regulate immune function and tumor progression [ 21 ]. Given its immune-related expression, SLC39A14 may influence tumor-associated immunity by affecting macrophage polarization and NK cell cytotoxicity [ 22 ]. These data provide direct cellular evidence supporting SLC39A14’s role in shaping the immune landscape of both renal and brain tumors, potentially serving as a biomarker or therapeutic target [ 23 , 24 ]. 3.10 DNA Methylation and Protein-Level Validation of SLC39A14 Expression: To elucidate the regulatory mechanisms of SLC39A14 expression, we analyzed DNA methylation data from The Cancer Genome Atlas (TCGA). Heatmap visualization (Fig. 12 A, B) revealed significant hypomethylation of multiple CpG sites within the promoter region of SLC39A14 in tumor tissues compared to adjacent normal controls. This hypomethylation pattern exhibited a strong inverse correlation with gene expression levels, suggesting that epigenetic silencing is alleviated in renal tumors, thereby leading to SLC39A14 upregulation. Stratified analyses based on clinical parameters, including age, sex, and ethnicity, confirmed that promoter hypomethylation remained a robust predictor of gene expression independent of demographic factors. These findings indicate that SLC39A14 overexpression in renal tumors is primarily driven by epigenetic reprogramming rather than patient-specific characteristics. To validate the translational relevance of these findings, we examined immunohistochemical (IHC) staining images obtained from the Immunohistochemistry staining (Figs. 12 C & 12 D) from the Human Protein Atlas (HPA) demonstrated SLC39A14 protein expression in clinical tissue samples. Compared to normal kidney tissues, kidney cancer samples exhibited stronger SLC39A14 staining and increased positivity, indicating elevated SLC39A14 expression in cancerous tissues. Conversely, glioma samples showed minimal staining, suggesting a potential tumor-type-specific role of SLC39A14 in oncogenesis. To further investigate the subcellular localization of SLC39A14, immunofluorescence staining was performed in multiple cancer cell lines using HPA, including A-431 (human epidermoid carcinoma), U-251 MG (human glioblastoma), and U-2 OS (human osteosarcoma) (Fig. 12 E). Confocal microscopy analysis demonstrated that SLC39A14 predominantly localized to the cytoplasm, with co-localization observed in microtubules and the endoplasmic reticulum (ER). This subcellular distribution aligns with the established role of SLC39A14 as a metal ion transporter implicated in maintaining redox homeostasis and regulating ferroptosis. The cytoplasmic localization further suggests an active role in intracellular metal ion trafficking, which is critical for cellular metabolism and stress response mechanisms. Collectively, these findings suggest that promoter hypomethylation of SLC39A14 contributes to its upregulation in renal tumors, highlighting the role of epigenetic reprogramming in its dysregulation. The increased protein expression in renal tumor tissues, along with its cytoplasmic localization, underscores its potential involvement in metal ion transport and redox balance processes that are fundamental for ferroptosis regulation and cancer cell survival. These observations establish a strong mechanistic link between epigenetic modifications, SLC39A14 expression, and its functional significance in tumor pathophysiology. 3.11 MetaCore Pathway Enrichment Analysis of SLC39A14 To elucidate the downstream molecular mechanisms driven by SLC39A14, we performed pathway enrichment analyses using the MetaCore platform, focusing on differentially expressed genes (DEGs) between high and low SLC39A14 expression groups. Results were analyzed separately for kidney renal clear cell carcinoma (KIRC) and glioblastoma multiforme (GBM), highlighting both shared and context-specific signaling pathways.In KIRC, MetaCore enrichment revealed significant activation of multiple tumor-promoting pathways, visualized in Fig. 13 . The tissue factor (TF)-induced thrombin signaling pathway ranked as the most significant. This cascade is tightly linked to tumor-associated coagulation, endothelial activation, and metastatic dissemination. Notably, SLC39A14 expression was correlated with elevated levels of F3 (TF), F2R, and THBD, as illustrated in Supplementary Fig. 1 and detailed in Supplementary Table 1, sheet “KIRC_DEG”. These genes may promote vascular remodeling, a known hallmark of aggressive renal carcinomas. The DNA damage response (DDR) pathway (ranked 2nd) was enriched with genes such as CHEK1, RAD51, and ATR, indicating a potential association between SLC39A14 and replication stress or genomic instability (Supplementary Fig. 2). Actin cytoskeleton remodeling and Rho GTPase signaling (ranked 3rd and 4th) were also prominent, involving genes like RAC1, CDC42, and ACTB that modulate epithelial–mesenchymal transition (EMT) and cell motility (Supplementary Fig. 3). Interestingly, WNT/β-catenin signaling showed inhibition, with reduced expression of LEF1 and MYC, suggesting context-specific effects on stemness and immune escape mechanisms [ 25 ]. (Supplementary Fig. 4). In GBM, enrichment results were visualized in Fig. 14 , with largely overlapping but distinct pathway profiles. SLC39A14-associated DEGs strongly enriched the VEGF-driven angiogenesis and ER stress response pathways[ 26 ]. Genes such as VEGFA, FLT1, HIF1A, and ANGPT2 were highly expressed in the SLC39A14-high group and may contribute to the formation of hypoxic, vascular-rich niches (Supplementary Fig. 5). These gene lists are detailed in Supplementary Table 1, sheet “GBM_DEG”.Furthermore, the Hippo-YAP pathway (Supplementary Fig. 6) showed strong activation, with upregulation of YAP1, TEAD4, and CTGF. These genes are associated with mesenchymal GBM subtypes and therapy resistance. To validate this, we cross-referenced SLC39A14-upregulated DEGs with known GBM subtype markers. The overlap (e.g., YAP1, EGFR, SOX9) is listed in Supplementary Table 2, supporting a subtype-specific regulatory role of SLC39A14.The NRF2-mediated oxidative stress response was also significantly enriched (Supplementary Fig. 7), implicating SLC39A14 in ferroptosis suppression. Key antioxidant genes like GCLC, NQO1, and HMOX1 were upregulated, suggesting a protective adaptation against ROS-induced cell death.Lastly, MAPK-ERK and TGF-β signaling (ranked 8th and 9th) were enriched with genes such as TGFB1, MAPK1, and SMAD3 (Supplementary Fig. 8), which regulate immune modulation, microglial polarization, and glioma invasion.In summary, Fig. 13 and Supplementary Figs. 1–4 demonstrate the KIRC-specific enrichment of thrombin signaling, DDR, and cytoskeletal remodeling, while Fig. 14 and Supplementary Figs. 5–8 highlight GBM-specific pathways related to angiogenesis, oxidative stress resistance, and stemness. The corresponding DEG profiles are provided in Supplementary Table 1, and GBM subtype overlaps are listed in Supplementary Table 2. These data suggest that SLC39A14 functions as a converging regulator of oncogenic signaling across diverse cancers, but exerts distinct molecular effects depending on tumor type. In KIRC, its association with thrombosis and migration aligns with vascular invasion phenotypes, whereas in GBM, its links to redox balance, angiogenesis, and mesenchymal transformation point to immune evasion and therapy resistance. Together, these insights offer a strong rationale for targeting SLC39A14-driven signaling programs in a cancer-type-specific manner. 3.12 Drug Sensitivity Profiling Based on SLC39A14 Expression To explore the therapeutic implications of SLC39A14, we analyzed drug response data from GDSC and CTRP using expression-based correlation models. Bubble plots (Fig. 15A and 15B) demonstrate that high SLC39A14 expression was positively correlated with increased sensitivity to several FDA-approved and investigational agents. In particular, high expression levels were associated with enhanced sensitivity to: Erastin, a canonical ferroptosis inducer; ML210 and RSL3, GPX4 inhibitors; Sorafenib, a multi-kinase inhibitor known to induce ferroptosis; And certain oxidative stress modulators and proteasome inhibitors. These results suggest that tumors with elevated SLC39A14 expression may be more susceptible to ferroptosis-based therapies. Conversely, for some agents (e.g., DNA-damaging chemotherapies), high SLC39A14 was associated with resistance, potentially due to its anti-ferroptotic and stress-buffering functions. These findings support the rationale for incorporating SLC39A14 expression as a companion biomarker for drug selection and treatment stratification in ferroptosis- or redox-targeted therapy regimens. 3.12 Protein-Ligand Docking of SLC39A14 To further understand the molecular interactions of SLC39A14, molecular docking studies were performed to assess the binding affinity and interaction patterns of selected ligands with the protein. The structural visualization of the docking complexes (Fig. 15C) highlights the binding poses of various ligands within the active site of SLC39A14. Each docking simulation revealed distinct interactions involving key residues within the binding pocket. The 2D interaction diagrams illustrate hydrogen bonding, π-π stacking, and hydrophobic interactions that contribute to ligand stability within the binding site. Specifically, residues such as Asp384, His380, Met446, and Ser344 were frequently involved in ligand stabilization, suggesting their critical role in binding affinity. Notably, the top-scoring ligand exhibited a strong interaction network, forming multiple hydrogen bonds with Asp384 and His380, which are predicted to be essential for ligand recognition and stability. Additionally, hydrophobic interactions with residues such as Ile387 and Glu447 further strengthened ligand binding. These findings provide structural insights into the potential binding mechanism of SLC39A14 with various ligands. The observed interactions highlight critical residues that may play a role in modulating protein function and ligand specificity, offering valuable information for future drug design and therapeutic targeting strategies. 4. Discussion This study provides comprehensive insights into the dual role of SLC39A14 in ferroptosis and glycosylation, emphasizing its critical contributions to tumor biology and its potential as both a prognostic biomarker and therapeutic target. By integrating multi-omics bioinformatics analysis across pan-cancer datasets, we highlighted how SLC39A14 links two fundamental pathways ferroptosis, a form of iron-dependent cell death, and glycosylation, a pivotal post-translational modification thus underscoring its multifaceted influence on tumor progression and the tumor microenvironment (TME). The ferroptosis-related gene signature, developed using LASSO Cox regression, demonstrated substantial prognostic power by stratifying patients into high- and low-risk groups. Figure 2 illustrates the identification and validation of this prognostic signature, showing the LASSO coefficient profile, heatmap of gene correlations, and forest plot of hazard ratios. The inclusion of genes such as SLC39A14 in the final model underscores its central role in ferroptosis regulation. The multivariate Cox analysis revealed that SLC39A14 contributes significantly to overall survival (OS) across diverse cancer types. These findings suggest that ferroptosis-related genes, particularly SLC39A14, modulate oxidative stress, lipid peroxidation, and iron homeostasis, thereby influencing tumor aggressiveness. Future mechanistic studies, including in vitro and in vivo validation, are warranted to explore its precise function in regulating ferroptotic vulnerability in cancer cells [ 20 ]. The stratification of ferroptosis scores across tumor types, as shown in Fig. 3 , revealed marked inter-cancer variability in both expression and clinical relevance. High ferroptosis scores—largely driven by expression of key regulators such as SLC39A14—were significantly associated with poor outcomes in tumors like gliomas, renal carcinomas, and hepatocellular carcinoma. Kaplan-Meier survival analyses confirmed that these scores serve as reliable predictors of clinical prognosis. Interestingly, the impact of SLC39A14 varied across tumor types, reflecting the context-specific interactions between ferroptosis and the TME, including immune, stromal, and metabolic components. These insights emphasize the necessity of developing tumor-specific therapeutic strategies that target SLC39A14 or ferroptosis-related vulnerabilities in a personalized manner[ 27 ]. The cancer-type-specific survival analyses in Fig. 4 further validated the prognostic utility of SLC39A14. In KIRC, GBM, and KIRP, high expression of SLC39A14 was significantly associated with reduced OS, DSS, and PFI. These correlations suggest that elevated SLC39A14 expression may facilitate ferroptosis resistance, enabling tumor cells to withstand oxidative damage and adopt invasive, therapy-resistant phenotypes. Additionally, its overexpression in these cancer types might be linked to enhanced metabolic flexibility and immune evasion, both key traits of tumor aggressiveness [ 28 ]. Our nomogram model, depicted in Fig. 5 , which integrated ferroptosis scores, clinical staging, and patient age, outperformed ferroptosis score alone in survival prediction. The decision curve analysis (DCA) and calibration plots underscored the clinical value of combining molecular and clinical parameters for individualized risk prediction. These findings support SLC39A14’s integration into precision oncology pipelines, particularly in the formulation of patient-specific surveillance and treatment regimens. As illustrated in Fig. 6 , SLC39A14 expression was positively associated with angiogenesis, epithelial-mesenchymal transition (EMT), and cell cycle activity—hallmark features of aggressive cancers. This indicates that SLC39A14 may contribute to tumor growth and dissemination by promoting vascular remodeling, ECM degradation, and cell motility. These associations were particularly strong in renal and hepatic tumors, supporting its functional involvement in pro-oncogenic reprogramming of the TME. Given its correlation with multiple malignant traits, SLC39A14 could serve as a valuable target in therapeutic strategies aimed at curbing metastasis and invasion [ 29 , 30 ]. The functional enrichment analysis in Fig. 7 provided mechanistic clues, showing that high-risk tumors (with elevated SLC39A14) were enriched in pathways associated with cytokine signaling, nuclear division, and extracellular matrix remodeling. These pathways collectively promote tumor proliferation, immune suppression, and tissue invasion. Conversely, low-risk tumors showed enrichment in metabolic and hormonal processes, suggesting that low SLC39A14 activity may preserve more regulated, differentiated cellular states. The identification of SLC39A14 from the overlap between glycosylation- and ferroptosis-related gene sets, as shown in Fig. 8 , positions it uniquely as a dual-functional regulator. Its strong expression in multiple tumor types and consistent correlation with poor survival emphasize its clinical importance. Functionally, SLC39A14 may modulate iron influx, ROS buffering, and protein glycosylation patterns, all of which could synergistically promote ferroptosis resistance and oncogenesis. These findings are consistent with prior reports linking SLC39A14 to manganese/zinc transport, NRF2 activation, and inflammatory signaling cascades[ 31 ]. In Fig. 9 , the significant correlation between SLC39A14 expression and immune/stromal scores suggests a role in shaping the immune landscape and fibrotic remodeling of tumors. Elevated expression of SLC39A14 was positively associated with immune-excluded or immune-suppressed phenotypes, especially in KIRC and GBM. These findings raise the possibility that SLC39A14 may influence immune evasion via altered cytokine glycosylation, antigen presentation, or immune modulation [ 26 , 32 ]. Our single-cell RNA sequencing analysis provided detailed insights into the tumor microenvironment, particularly highlighting the expression patterns of SLC39A14 across different cell populations within tumors. Figure 10 shows significant enrichment of SLC39A14 in immune cells, notably natural killer (NK) cells and macrophages, suggesting its potential role in modulating immune responses within the tumor microenvironment. The t-distributed stochastic neighbor embedding (t-SNE) plots and heatmaps demonstrated that SLC39A14 + cells were predominantly found in immune-enriched clusters, indicating a strong association with immune cell infiltration. This cellular resolution underscores the importance of SLC39A14 in shaping the immunological landscape of tumors, potentially influencing tumor progression and therapeutic resistance. Our study provides comprehensive insights into the epigenetic regulation and expression patterns of SLC39A14 across multiple cancer types. Figure 11 illustrates the DNA methylation, immunohistochemical, and immunofluorescence validation of SLC39A14 expression. The heatmap shows significant hypomethylation of multiple CpG sites within the promoter region of SLC39A14 in tumor tissues compared to adjacent normal controls. This hypomethylation pattern suggests that epigenetic reprogramming is a key driver of SLC39A14 overexpression in cancer cells. Stratified analyses based on clinical parameters, including age, sex, and ethnicity, confirmed that promoter hypomethylation remained a robust predictor of gene expression independent of demographic factors. Immunohistochemical staining reveals markedly higher SLC39A14 protein expression in renal tumor samples compared to normal kidney tissues, consistent with transcriptomic data. This elevated protein expression further supports the role of epigenetic deregulation in driving SLC39A14 overexpression in tumor cells. Immunofluorescence microscopy demonstrates that SLC39A14 predominantly localizes to the cytoplasm, with co-localization observed in microtubules and the endoplasmic reticulum (ER). This subcellular distribution aligns with the established role of SLC39A14 as a metal ion transporter implicated in maintaining redox homeostasis and regulating ferroptosis. The cytoplasmic localization suggests an active role in intracellular metal ion trafficking, which is critical for cellular metabolism and stress response mechanisms [ 33 ]. The pathway enrichment analysis in Fig. 12 revealed significant involvement of tissue factor-induced thrombin signaling, DNA damage response, cytoskeleton remodeling, and WNT/β-catenin signaling. Each of these pathways contributes to cancer pathophysiology, supporting their relevance as potential therapeutic targets. Tissue factor-induced thrombin signaling, the most significantly enriched pathway, plays a crucial role in tumor-associated thrombosis, angiogenesis, and metastasis. Previous studies have demonstrated that tissue factor (TF) activation leads to protease-activated receptor (PAR)-mediated signaling, promoting cancer cell proliferation and invasion. Our findings further indicate that TF signaling interacts with VEGF pathways, facilitating endothelial cell migration and vascular remodeling, underscoring the potential of targeting TF signaling to inhibit tumor growth and metastasis [ 34 , 35 ]. The second most enriched pathway involved DNA damage response and intra S-phase checkpoint regulation. Genomic instability is a hallmark of cancer, and the activation of DNA damage repair mechanisms is essential for tumor survival, especially under chemotherapy-induced stress. Our study highlights upregulation of checkpoint kinases (CHK1/CHK2) and ATR-mediated responses, which are known to be key regulators of cell cycle progression in response to DNA damage. Targeting these checkpoints could enhance the efficacy of DNA-damaging agents and improve therapeutic outcomes. Cytoskeletal dynamics are fundamental in cancer cell motility and metastasis These pathways contribute to vascular invasion, replication stress, and EMT—hallmarks of poor-prognosis renal carcinomas. Conversely, in GBM, enrichment of VEGF signaling, Hippo-YAP, and NRF2 oxidative stress defense (Supplementary Figs. 5–8) highlighted its role in maintaining hypoxia-adapted, immune-resistant niches and sustaining glioma stemness [ 36 ]. Notably, NRF2 target genes such as HMOX1, NQO1, and GCLC were upregulated in SLC39A14-high GBM, suggesting ferroptosis evasion as a key mechanism of survival under oxidative stress[ 37 ]. Drug sensitivity profiling using GDSC and CTRP datasets showed that SLC39A14-high tumors were more responsive to ferroptosis inducers such as RSL3, ML210, and erastin, as well as multi-target kinase inhibitors like sorafenib. This indicates that SLC39A14 may serve as a predictive biomarker for ferroptosis-based therapies. Molecular docking simulations revealed high-affinity ligand binding at active site residues including His380, Met446, and Ser344, further supporting its druggability (Fig. 13 ). Moreover, SLC39A14 expression correlated strongly with tumor features such as angiogenesis, EMT, and cell cycle progression [ 38 ]. 5. Conclusions Collectively, this study identifies SLC39A14 as a novel molecular hub that orchestrates critical oncogenic programs by integrating ferroptosis suppression and glycosylation remodeling. Through a pan-cancer, multi-omics approach, we demonstrated that SLC39A14 is overexpressed in diverse malignancies, notably glioblastoma multiforme (GBM) and kidney renal clear cell carcinoma (KIRC), and correlates with adverse clinical outcomes including OS, DSS, and PFI. Mechanistically, SLC39A14 is implicated in modulating oxidative stress, immune infiltration, endothelial remodeling, and cell cycle regulation. Its association with tissue factor-induced thrombin signaling, VEGF angiogenesis, Hippo-YAP signaling, and NRF2-mediated oxidative stress pathways further underscores its central role in tumor progression. Moreover, our pharmacogenomic analysis and molecular docking simulations highlight SLC39A14 as a tractable therapeutic target, potentially sensitizing tumors to ferroptosis inducers and kinase inhibitors. Single-cell and epigenetic analyses also reveal its dynamic regulation and influence on the tumor microenvironment. Taken together, these findings provide a strong rationale for further exploration of SLC39A14 as both a biomarker and drug target, especially in therapy-resistant and immunosuppressive tumors. Future in vivo validation and clinical trials are warranted to harness its potential in precision oncology and develop targeted interventions for SLC39A14-driven cancers. Abbreviations TF Tissue Factor PAR Protease-Activated Receptor VEGF Vascular Endothelial Growth Factor ER Endoplasmic Reticulum CpG Cytosine-phosphate-Guanine NK Natural Killer (cells) EMT Epithelial-to-Mesenchymal Transition GDSC Genomics of Drug Sensitivity in Cancer CTRP Cancer Therapeutics Response Portal LASSO Least Absolute Shrinkage and Selection Operator TCGA The Cancer Genome Atlas OS Overall Survival DSS Disease-Specific Survival PFI Progression-Free Interval DEGs Differentially Expressed Genes GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes HPA Human Protein Atlas IHC Immunohistochemistry IFC Immunofluorescence MSigDB Molecular Signatures Database ESTIMATE Estimation of STromal and Immune cells in MAlignant Tumors using Expression data scRNA seq-Single-Cell RNA Sequencing t SNE-t-distributed Stochastic Neighbor Embedding HR Hazard Ratio CI Confidence Interval FDR False Discovery Rate ROC Receiver Operating Characteristic DCA Decision Curve Analysis NRF2 Nuclear Factor Erythroid 2-Related Factor 2 Declarations Acknowledgments: The authors appreciate the professional English editing by Daniel P. Chamberlin from the Office of Research and Development at Taipei Medical University. The authors acknowledge the online platform for data analysis and visualization (http://www.bioinformatics.com.cn/). We thank the staff of the Office of Data Science, Taipei Medical University, Taiwan, for their technical support. We would like to acknowledge Yi-Ting Wu, Chien-Cheng Chao, Yun- Yu Lin, and Yueh-Yuan Shieh for their excellent technical support at Laboratory of Research and Medical Education and Research Center, Kaohsiung Armed Forces General Hospital. Author contributions: Developed the concept and designed the study: Yi-Chun Chiang, Chih-Yang Wang, Sachin Kumar, Shun-Fa Yang, Yung-Kuo Lee. Performed data analysis and interpretation: Chung-Bao Hsieh, Kai-Fu Chang, Ching-Chung Ko, Chih-Hsuan Chang, Hui-Ru Lin, Chi-Jen Wu, Chien-Han Yuan, Do Thi Minh Xuan, Juan Lorell Ngadio, Dahlak Daniel Solomon, Fitria Sari Wulandari, Hung-Yun Lin. All authors have read and approved the final version of this manuscript. Availability of data and materials: All datasets and materials generated in this study can be provided by the corresponding author upon reasonable request. Financial support and sponsorship Funding: This research was funded by National Science and Technology Council (NSTC) of Taiwan, grant number 113-2320-B-393-001 and by Kaohsiung Armed Forces General Hospital grant number, KAFGH_D_114024, and KAFGH_D_114053. The APC was funded by Kaohsiung Armed Forces General Hospital. This work was financially supported by the Higher Education Sprout Project of the Ministry of Education (MOE) in Taiwan. Ethics declarations Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. References Dixon SJ, Lemberg KM, Lamprecht MR. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell. 2012;149(5):1060–72. !!!. INVALID CITATION !!! [2, 3]. Li Y, Cao X, Li L. The metal ion transporter SLC39A14 in cancer: expression, function and therapeutic potential. Front Oncol. 2021;11:644749. Goldman MJ, Craft B, Hastie M, Repečka K, McDade F, Kamath A, Banerjee A, Luo Y, Rogers D, Brooks AN, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38(6):675–8. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4. Pontén F, Jirström K, Uhlen M. The Human Protein Atlas—a tool for pathology. J Pathology: J Pathological Soc Great Br Irel. 2008;216(4):387–93. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108–10. Anuraga G, Lang J, Xuan DTM, Ta HDK, Jiang JZ, Sun Z, Dey S, Kumar S, Singh A, Kajla G, et al. Integrated bioinformatics approaches to investigate alterations in transcriptomic profiles of monkeypox infected human cell line model. J Infect Public Health. 2024;17(1):60–9. Xuan DTM, Wu CC, Wang WJ, Hsu HP, Ta HDK, Anuraga G, Chiao CC, Wang CY. Glutamine synthetase regulates the immune microenvironment and cancer development through the inflammatory pathway. Int J Med Sci. 2023;20(1):35–49. Hao Y, Stuart T, Kowalski MH, Choudhary S, Hoffman P, Hartman A, Srivastava A, Molla G, Madad S, Fernandez-Granda C. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol. 2024;42(2):293–304. Cassiano GC, Martinelli A, Mottin M, Neves BJ, Andrade CH, Ferreira PE, Cravo P. Whole genome sequencing identifies novel mutations in malaria parasites resistant to artesunate (ATN) and to ATN + mefloquine combination. Front Cell Infect Microbiol. 2024;14:1353057. Kwee I, Martinelli A, Khayal LA, Akhmedov M. metaLINCS: an R package for meta-level analysis of LINCS L1000 drug signatures using stratified connectivity mapping. Bioinform Adv. 2022;2(1):vbac064. Maseng MJ, Tawe L, Thami PK, Seatla KK, Moyo S, Martinelli A, Kasvosve I, Novitsky V, Essex M, Russo G, et al. Association of CYP2B6 Genetic Variation with Efavirenz and Nevirapine Drug Resistance in HIV-1 Patients from Botswana. Pharmgenomics Pers Med. 2021;14:335–47. Liu C-J, Hu F-F, Xie G-Y, Miao Y-R, Li X-W, Zeng Y, Guo A-Y. GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels. Brief Bioinform 2022, 24(1). Akhmedov M, Martinelli A, Geiger R, Kwee I. Omics Playground: a comprehensive self-service platform for visualization, analytics and exploration of Big Omics Data. NAR Genom Bioinform. 2020;2(1):lqz019. Tang D, Chen M, Huang X, Zhang G, Zeng L, Zhang G, Wu S, Wang Y. SRplot: A free online platform for data visualization and graphing. PLoS ONE. 2023;18(11):e0294236. Wickham H, Sievert C. ggplot2: elegant graphics for data analysis. Volume 10. springer New York; 2009. Xuan DTM, Yeh IJ, Su CY, Liu HL, Ta HDK, Anuraga G, Chiao CC, Wang CY, Yen MC. Prognostic and Immune Infiltration Value of Proteasome Assembly Chaperone (PSMG) Family Genes in Lung Adenocarcinoma. Int J Med Sci. 2023;20(1):87–101. Wang C-Y, Chiao C-C, Phan NN, Li C-Y, Sun Z-D, Jiang J-Z, Hung J-H, Chen Y-L, Yen M-C, Weng T-Y. Gene signatures and potential therapeutic targets of amino acid metabolism in estrogen receptor-positive breast cancer. Am J cancer Res. 2020;10(1):95. Zhang Y, Wu X, Zhu J, Lu R, Ouyang Y. Knockdown of SLC39A14 inhibits glioma progression by promoting erastin-induced ferroptosis SLC39A14 knockdown inhibits glioma progression. BMC Cancer. 2023;23(1):1120. Troche C, Beker Aydemir T, Cousins RJ. Zinc transporter Slc39a14 regulates inflammatory signaling associated with hypertrophic adiposity. Am J Physiology-Endocrinology Metabolism. 2016;310(4):E258–68. Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, Coussens LM, Gabrilovich DI, Ostrand-Rosenberg S, Hedrick CC. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 2018;24(5):541–50. Xiao P, Li C, Liu Y, Gao Y, Liang X, Liu C, Yang W. The role of metal ions in the occurrence, progression, drug resistance, and biological characteristics of gastric cancer. Front Pharmacol. 2024;15:1333543. Li J-w, Mao Y-m, Chen S-l, Ye R, Fei Y-r, Li Y, Tong S-y. Yang H-w, He Y-b: The interplay between metal ions and immune cells in glioma: pathways to immune escape. Discover Oncol. 2024;15(1):348. Iyer D, Rhind N. The intra-S checkpoint responses to DNA damage. Genes (Basel). 2017;8:74. Kamynina M, Rozenberg JM, Kushchenko AS, Dmitriev SE, Modestov A, Kamashev D, Gaifullin N, Shaban N, Suntsova M, Emelianova A. Forced Overexpression and Knockout Analysis of SLC30A and SLC39A Family Genes Suggests Their Involvement in Establishing Resistance to Cisplatin in Human Cancer Cells. Int J Mol Sci. 2024;25(22):12049. Qu Y-Y, Guo R-Y, Luo M-L, Zhou Q. Pan-cancer analysis of the solute carrier family 39 genes in relation to oncogenic, immune infiltrating, and therapeutic targets. Front Genet. 2021;12:757582. Jiang X, Stockwell BR, Conrad M. Ferroptosis: mechanisms, biology and role in disease. Nat Rev Mol Cell Biol. 2021;22(4):266–82. Liang L, Lin R, Xie Y, Lin H, Shao F, Rui W, Chen H. The role of cyclophilins in inflammatory bowel disease and colorectal cancer. Int J Biol Sci. 2021;17(10):2548. Qu H, Yu Q, Ye L, Zheng J. SLC39A14 promotes the development of esophageal squamous cell carcinoma through PI3K/Akt/mTOR signaling pathway. Int Immunopharmacol. 2025;146:113831. Su J, Zhong G, Qin W, Zhou L, Ye J, Ye Y, Chen C, Liang P, Zhao W, Xiao X. Integrating iron metabolism-related gene signature to evaluate prognosis and immune infiltration in nasopharyngeal carcinoma. Discover Oncol. 2024;15(1):112. Simonds EF, Lu ED, Liu EV, Tamaki W, Rancan C, Stultz J, Sinha M, McHenry LK, Nasholm NM, Chuntova P. Deep immune profiling reveals targetable mechanisms of immune evasion in checkpoint blockade-refractory glioblastoma. bioRxiv 2020:2020.2012. 2001.404939. Aydemir TB, Cousins RJ. The multiple faces of the metal transporter ZIP14 (SLC39A14). J Nutr. 2018;148(2):174–84. van den Berg YW, Osanto S, Reitsma PH, Versteeg HH. The relationship between tissue factor and cancer progression: insights from bench and bedside. Blood J Am Soc Hematol. 2012;119(4):924–32. Dai Y, Grant S. New insights into checkpoint kinase 1 in the DNA damage response signaling network. Clin Cancer Res. 2010;16(2):376–83. Yang M, Li S-N, Anjum KM, Gui L-X, Zhu S-S, Liu J, Chen J-K, Liu Q-F, Ye G-D, Wang W-J. A double-negative feedback loop between Wnt–β-catenin signaling and HNF4α regulates epithelial–mesenchymal transition in hepatocellular carcinoma. J Cell Sci. 2013;126(24):5692–703. Ockfen E, Filali L, Pereira Fernandes D, Hoffmann C, Thomas C. Actin cytoskeleton remodeling at the cancer cell side of the immunological synapse: good, bad, or both? Front Immunol. 2023;14:1276602. Ferrara N. The role of the VEGF signaling pathway in tumor angiogenesis. Tumor Angiogenesis: Key Target Cancer Therapy 2019:211–26. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Oct, 2025 Read the published version in Cancer Cell International → Version 1 posted Editorial decision: Revision requested 29 Jul, 2025 Reviews received at journal 22 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 08 Jul, 2025 Reviews received at journal 08 Jul, 2025 Reviewers agreed at journal 08 Jul, 2025 Reviewers invited by journal 08 Jul, 2025 Editor assigned by journal 08 Apr, 2025 Submission checks completed at journal 08 Apr, 2025 First submitted to journal 05 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6384291","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482162179,"identity":"d6436e88-0613-400f-86ea-982869aad618","order_by":0,"name":"Yi-Chun Chiang","email":"","orcid":"","institution":"Chung Shan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi-Chun","middleName":"","lastName":"Chiang","suffix":""},{"id":482162181,"identity":"07947ce6-5e8e-4a06-b48f-11353a4e3561","order_by":1,"name":"Chih-Yang Wang","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chih-Yang","middleName":"","lastName":"Wang","suffix":""},{"id":482162182,"identity":"c8872290-386c-42da-8255-b2b4146353c1","order_by":2,"name":"Sachin Kumar","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sachin","middleName":"","lastName":"Kumar","suffix":""},{"id":482162183,"identity":"aafa2f9c-8da1-4b99-a3b9-e882599fa9a9","order_by":3,"name":"Chung-Bao Hsieh","email":"","orcid":"","institution":"Tri-Service General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chung-Bao","middleName":"","lastName":"Hsieh","suffix":""},{"id":482162184,"identity":"3fb99423-0a20-4c9f-ba0f-3791a536abaa","order_by":4,"name":"Kai-Fu Chang","email":"","orcid":"","institution":"Kaohsiung Armed Forces General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kai-Fu","middleName":"","lastName":"Chang","suffix":""},{"id":482162185,"identity":"41042311-cd4f-4d68-a9da-96b2a381c500","order_by":5,"name":"Ching-Chung Ko","email":"","orcid":"","institution":"Chi-Mei Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Ching-Chung","middleName":"","lastName":"Ko","suffix":""},{"id":482162186,"identity":"5c4696a5-4c2b-43b7-b88f-23fa60295ced","order_by":6,"name":"Chih-Hsuan Chang","email":"","orcid":"","institution":"Kaohsiung Armed Forces General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chih-Hsuan","middleName":"","lastName":"Chang","suffix":""},{"id":482162187,"identity":"0b5de267-db4c-44ae-928b-7661f458f420","order_by":7,"name":"Hui-Ru Lin","email":"","orcid":"","institution":"National Sun Yat-Sen University","correspondingAuthor":false,"prefix":"","firstName":"Hui-Ru","middleName":"","lastName":"Lin","suffix":""},{"id":482162188,"identity":"7a7c9c34-b702-4dce-9809-871e3e2d278b","order_by":8,"name":"Chi-Jen Wu","email":"","orcid":"","institution":"Kaohsiung Armed Forces General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chi-Jen","middleName":"","lastName":"Wu","suffix":""},{"id":482162189,"identity":"06a8a873-7267-41f3-a69f-9e064df49487","order_by":9,"name":"Chien-Han Yuan","email":"","orcid":"","institution":"Kaohsiung Armed Forces General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chien-Han","middleName":"","lastName":"Yuan","suffix":""},{"id":482162190,"identity":"7b942517-93bf-4db7-a948-761508763c4c","order_by":10,"name":"Do Thi Minh Xuan","email":"","orcid":"","institution":"Van Lang University","correspondingAuthor":false,"prefix":"","firstName":"Do","middleName":"Thi Minh","lastName":"Xuan","suffix":""},{"id":482162191,"identity":"42276c8e-dbfb-4ac7-a195-9d034b7cab07","order_by":11,"name":"Juan Lorell Ngadio","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Lorell","lastName":"Ngadio","suffix":""},{"id":482162192,"identity":"8b6fca1d-4499-472f-9f2b-2090e1cfbf33","order_by":12,"name":"Dahlak Daniel Solomon","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dahlak","middleName":"Daniel","lastName":"Solomon","suffix":""},{"id":482162193,"identity":"dcda5d38-0249-4b0e-8a6b-02dc82529d94","order_by":13,"name":"Fitria Sari Wulandari","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fitria","middleName":"Sari","lastName":"Wulandari","suffix":""},{"id":482162194,"identity":"e58ed19d-97e2-47e0-9a3e-d6f5177f3c9e","order_by":14,"name":"Hung-Yun Lin","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hung-Yun","middleName":"","lastName":"Lin","suffix":""},{"id":482162195,"identity":"e33cb8a9-1a4b-4315-97a2-117a6b2492d1","order_by":15,"name":"Shun-Fa Yang","email":"","orcid":"","institution":"Chung Shan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shun-Fa","middleName":"","lastName":"Yang","suffix":""},{"id":482162196,"identity":"29276d26-418b-4915-b23e-a7fc5d791d44","order_by":16,"name":"Yung-Kuo Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYJACxgYGBhkGBuYDQLaEDNFaeBgY2BJAWnhI0cJjAOIQ1mJwI/fwy5ltNjz87D2fX92oseBhYD98dAN+LXlplhvb0ngke85us845BnQYT1raDfxacswMH7Yd5gFat804hw2oRYLHjDgt9vffPDPO+UecFuOHG0G2SPAwP85tI0KL5Jk3ZowzzqXxSJxJM2PO7ZPgYSPkF77jOcYfe8ps5PjbDz/+nPOtTo6f/fAxvFoUDjCwSUDZEAYbPuUgIN/AwPwByoYzRsEoGAWjYBSgAAD6N0kGflyN7QAAAABJRU5ErkJggg==","orcid":"","institution":"Kaohsiung Armed Forces General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yung-Kuo","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2025-04-06 01:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6384291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6384291/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12935-025-04003-6","type":"published","date":"2025-10-21T16:16:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86451849,"identity":"9fd57274-928c-4e58-b89e-d672bac1b04b","added_by":"auto","created_at":"2025-07-10 19:47:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":183110,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the integrated bioinformatics workflow for identifying key genes bridging glycosylation and ferroptosis in pan-cancer analysis.\u003c/strong\u003e RNA-sequencing data from TCGA and UCSC Xena databases were analyzed to identify candidate genes from two distinct biological processes: glycosylation (663 genes) and ferroptosis (64 genes). Subsequent comprehensive analyses—including differential gene expression, survival analyses (Kaplan-Meier plots and forest plots), immune and stromal microenvironment correlation analyses, and construction of prognostic nomogram models with ROC curve validation—were systematically conducted. The workflow aims to pinpoint the prognostic and therapeutic relevance of SLC39A14 as a critical mediator linking glycosylation and ferroptosis in cancer progression.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/cd3ea951816cb0e8881c3f0b.jpg"},{"id":86452374,"identity":"645fda25-0e14-478d-958c-8c3dc109aadc","added_by":"auto","created_at":"2025-07-10 19:55:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":447897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and validation of ferroptosis-related prognostic signature through LASSO Cox regression analysis. (A)\u003c/strong\u003e LASSO coefficient profile plot (left) displaying trajectories of ferroptosis-related genes against varying lambda (λ) values, and ten-fold cross-validation plot (right) determining the optimal λ value (λ = 0.0014, dashed vertical line) for minimizing partial likelihood deviance. \u003cstrong\u003e(B)\u003c/strong\u003e Heatmap illustrating the pairwise correlations among selected ferroptosis-related genes. Red represents positive correlation, and blue indicates negative correlation. \u003cstrong\u003e(C)\u003c/strong\u003e Forest plot of hazard ratios (HRs) with corresponding 95% confidence intervals (CIs) for each ferroptosis-related gene. Red dots indicate genes associated with unfavorable prognosis (HR \u0026gt; 1), while blue dots denote genes associated with favorable prognosis (HR \u0026lt; 1). The prognostic significance of these genes underscores their potential as biomarkers in cancer prognosis.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/bfd391f15bcc5931d806a630.jpg"},{"id":86452495,"identity":"11eba085-d528-4dd0-a52e-8ce58dc93c1e","added_by":"auto","created_at":"2025-07-10 20:03:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":287022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePan-cancer evaluation of the ferroptosis-related prognostic signature (ferroptosis score) across different cancer types. (A)\u003c/strong\u003e Violin plots illustrating the distribution of ferroptosis scores across multiple TCGA cancer cohorts (upper panel). The lower panel represents bubble plots displaying correlations between ferroptosis scores and clinical prognosis, including overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). Bubble size corresponds to the significance level (–log10 p-value). \u003cstrong\u003e(B, C)\u003c/strong\u003e Forest plots summarizing univariate Cox regression analyses, showing hazard ratios (HR) and corresponding confidence intervals (CI) for ferroptosis scores across various cancer types. Red dots represent significantly increased risk (HR \u0026gt; 1), while green dots indicate significantly reduced risk (HR \u0026lt; 1). \u003cstrong\u003e(D, E)\u003c/strong\u003e Kaplan-Meier survival curves demonstrating significant differences in OS, DSS, and PFI between patients with high (red) and low (green) ferroptosis scores in representative cancers, emphasizing the prognostic relevance of the ferroptosis signature.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/d74af452c0a97603fa657b58.jpg"},{"id":86451852,"identity":"c83ee3c1-b267-40af-85dc-17fca4ec680f","added_by":"auto","created_at":"2025-07-10 19:47:28","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":251274,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival analysis of the ferroptosis-related prognostic signature (ferroptosis score) in four representative cancers. \u003c/strong\u003eSurvival curves compare high (red line) and low (green line) ferroptosis score groups across three clinical outcomes: overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). Significant prognostic distinctions were observed in: \u003cstrong\u003e(A)\u003c/strong\u003e Lower-grade glioma (LGG), \u003cstrong\u003e(B)\u003c/strong\u003e Glioblastoma multiforme (GBM), \u003cstrong\u003e(C)\u003c/strong\u003e Kidney renal clear cell carcinoma (KIRC), and \u003cstrong\u003e(D)\u003c/strong\u003e Kidney renal papillary cell carcinoma (KIRP). All analyses clearly demonstrate that patients with higher ferroptosis scores exhibit significantly poorer prognosis, underscoring the clinical value of the ferroptosis-related gene signature in these specific cancer types.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/c7a5b9032d07ae58644755e6.jpg"},{"id":86452375,"identity":"f272a486-3d37-42f1-9462-a43cb1673844","added_by":"auto","created_at":"2025-07-10 19:55:28","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":265940,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of the ferroptosis-related nomogram for survival prediction across multiple cancer types. (A)\u003c/strong\u003e Nomogram integrating ferroptosis score (NETs score), patient age, and cancer type for predicting 1-, 3-, 5-, and 10-year overall survival probabilities. \u003cstrong\u003e(B)\u003c/strong\u003eCalibration curve assessing the accuracy of nomogram-predicted survival against observed survival outcomes. The diagonal dashed line represents perfect prediction. \u003cstrong\u003e(C)\u003c/strong\u003e Receiver operating characteristic (ROC) curves demonstrating predictive accuracy at 5-year survival in training and test cohorts from the TCGA dataset. \u003cstrong\u003e(D)\u003c/strong\u003e Time-dependent ROC curves evaluating predictive performance of the nomogram and NETs score over 10 years in TCGA cohorts.\u003cbr\u003e\n\u003cstrong\u003e(E, F)\u003c/strong\u003e Decision curve analysis (DCA) illustrating the net benefit of nomogram-based clinical decisions at various risk threshold probabilities in training and test cohorts, respectively. The nomogram consistently outperformed the NETs score alone, highlighting its clinical utility for individualized prognosis prediction.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/e9a6eb6a68476c0865507807.jpg"},{"id":86452496,"identity":"7621f2e0-8bf0-4c15-a4d9-45ced0ff2c04","added_by":"auto","created_at":"2025-07-10 20:03:29","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":524673,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of ferroptosis scores (NETs score) with malignant cancer hallmarks across various cancer types. (A–C)\u003c/strong\u003ePan-cancer scatterplots illustrating the association between ferroptosis scores and hallmark biological processes: \u003cstrong\u003e(A)\u003c/strong\u003e Cell cycle \u003cstrong\u003e(B)\u003c/strong\u003eEpithelial-mesenchymal transition (EMT) \u003cstrong\u003e(C)\u003c/strong\u003e Angiogenesis Each scatterplot shows Pearson’s correlation coefficient (R) and corresponding significance (p-value), demonstrating significant positive correlations across most cancer types. \u003cstrong\u003e(D–F)\u003c/strong\u003e Comprehensive pan-cancer correlation analysis highlighting overall trends between ferroptosis scores and cell cycle (D), EMT (E), and angiogenesis (F) signatures. Strong positive associations underline ferroptosis as a potential driver of tumor malignancy and progression. 4.5\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/951015c2bc3e2db0beec5d73.jpg"},{"id":86452381,"identity":"401ecb02-3b91-4889-9cde-7d196b033edf","added_by":"auto","created_at":"2025-07-10 19:55:29","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":138172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analyses of differentially expressed genes (DEGs) between high and low ferroptosis-score groups. (A)\u003c/strong\u003e Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results for genes significantly upregulated in the high ferroptosis-score group. Enriched terms include biological processes (BP), cellular components (CC), molecular functions (MF), and KEGG pathways, highlighting critical roles in cell cycle progression, mitotic nuclear division, chromosome segregation, spindle assembly, and cytokine interactions. \u003cstrong\u003e(B)\u003c/strong\u003eGO and KEGG enrichment analysis for genes significantly upregulated in the low ferroptosis-score group, identifying pathways and biological processes involved in hormonal regulation, neural signaling pathways, metabolic processes, and developmental processes. These distinct enrichment patterns reveal molecular mechanisms underlying prognostic differences mediated by ferroptosis scores.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/b38699e9a37257d9f25b0d37.jpg"},{"id":86452384,"identity":"fe9691d1-c54c-405e-a51c-765048bd888a","added_by":"auto","created_at":"2025-07-10 19:55:29","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":312794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and pan-cancer validation of SLC39A14 as a candidate gene bridging ferroptosis and glycosylation pathways. (A)\u003c/strong\u003e Venn diagram illustrating the intersection between ferroptosis-related (blue circle, 64 genes) and glycosylation-related genes (orange circle, 663 genes), highlighting \u003cstrong\u003eSLC39A14\u003c/strong\u003e as a common candidate gene. \u003cstrong\u003e(B)\u003c/strong\u003eDifferential expression analysis of SLC39A14 across various cancer types from TCGA, comparing tumor (red boxes) and normal tissues (green boxes). Statistical significance: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ns: not significant. \u003cstrong\u003e(C, D)\u003c/strong\u003e Forest plots displaying univariate Cox regression results for SLC39A14 expression associated with overall survival (C) and disease-specific survival (D) across different cancer types. Red squares indicate hazard ratio (HR) \u0026gt;1 (poor prognosis), and blue squares indicate HR \u0026lt;1 (favorable prognosis). The horizontal bars represent 95% confidence intervals. These results emphasize the prognostic potential of SLC39A14 in diverse cancers.\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/930890638391d7fa9c517b82.jpg"},{"id":86451855,"identity":"68654ac2-9247-49fc-8b50-4d7dab7e8195","added_by":"auto","created_at":"2025-07-10 19:47:29","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":259671,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of SLC39A14 expression with immune and stromal cell infiltration in specific cancer types. \u003c/strong\u003eScatterplots illustrating Pearson correlations between SLC39A14 expression levels and immune scores (left) and stromal scores (right), reflecting the extent of immune and stromal cell infiltration within the tumor microenvironment. Four representative cancers are analyzed: \u003cstrong\u003e(A)\u003c/strong\u003e Glioblastoma multiforme and lower-grade glioma (GBMLGG), \u003cstrong\u003e(B)\u003c/strong\u003e Glioblastoma multiforme (GBM), \u003cstrong\u003e(C)\u003c/strong\u003eKidney renal papillary cell carcinoma (KIRP), and \u003cstrong\u003e(D)\u003c/strong\u003e Kidney renal clear cell carcinoma (KIRC). Each plot includes Pearson’s correlation coefficient (R) and associated p-value, highlighting significant positive associations between elevated SLC39A14 expression and increased immune-stromal infiltration, particularly prominent in GBMLGG and KIRC. These findings suggest an immunomodulatory role of SLC39A14, contributing to tumor progression and clinical outcomes\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/c5e73d254840e9e3b14e1b25.jpg"},{"id":86451863,"identity":"e379fc17-9923-41cc-ba08-63609a2aec26","added_by":"auto","created_at":"2025-07-10 19:47:29","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":113482,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell RNA sequencing analysis demonstrating SLC39A14 expression patterns and associated immune infiltration in kidney renal clear cell carcinoma. (A)\u003c/strong\u003e Heatmap of SLC39A14 expression across distinct cell populations within KIRC tumor microenvironment. Colors indicate relative expression levels, with red representing high expression and blue representing low expression. Notably, prominent SLC39A14 expression is observed in immune cells, especially NK cells. \u003cstrong\u003e(B)\u003c/strong\u003et-distributed stochastic neighbor embedding (t-SNE) plot visualizing cell-type clusters within the KIRC sample, highlighting specific populations such as kidney epithelial cells, NK cells, macrophages, endothelial cells, and T cells. The bar graph (top-right inset) quantitatively illustrates the distribution of SLC39A14 expression (nTPM) across these cell clusters, confirming enriched expression particularly in NK cells and macrophages. (C) Correlation heatmap depicting gene co-expression profiles of SLC39A14 with marker genes from different renal and immune cell subpopulations. Higher correlations (yellow to purple) indicate strong gene co-expression patterns, emphasizing significant functional relationships between SLC39A14 and immune cell markers. Collectively, these results provide evidence at single-cell resolution, indicating SLC39A14's immunological role in shaping the tumor microenvironment of KIRC.\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/7aa17e0ca22b2c66b7d7a30e.jpg"},{"id":86451865,"identity":"1ca1f0a4-7b6d-4f57-a1b6-9ef66806f3be","added_by":"auto","created_at":"2025-07-10 19:47:29","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":174539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell RNA sequencing analysis demonstrating SLC39A14 expression patterns and associated immune infiltration in brain. (A)\u003c/strong\u003eHeatmap of SLC39A14 expression across distinct cell populations within glioma tumor microenvironment. Colors indicate relative expression levels, with red representing high expression and blue representing low expression. \u003cstrong\u003e(B)\u003c/strong\u003et-distributed stochastic neighbor embedding (t-SNE) plot visualizing cell-type clusters within the glioma sample. The bar graph (top-right inset) quantitatively illustrates the distribution of SLC39A14 expression (nTPM) across these cell clusters SLC39A14's immunological role in shaping the tumor microenvironment of glioma\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/011dc2ca5af484b9b167823e.jpg"},{"id":86451874,"identity":"66f90e60-6540-4c57-b522-82c058885538","added_by":"auto","created_at":"2025-07-10 19:47:29","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":836493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDNA methylation, immunohistochemical, and immunofluorescence validation of SLC39A14 expression across multiple cancer types \u003c/strong\u003eHeatmap illustrating the DNA methylation profile of SLC39A14-associated CpG sites across \u003cstrong\u003e(A)\u003c/strong\u003e KIRC, and \u003cstrong\u003e(B)\u003c/strong\u003e brain lower grade glioma samples from TCGA. Each row represents a distinct CpG site, while each column represents an individual tumor sample. Clinical annotations, including ethnicity, age, gender, and CpG island relation, are indicated at the top and left sides of the heatmap. \u003cstrong\u003e(C)\u003c/strong\u003e Immunohistochemical (IHC) staining data obtained from the Human Protein Atlas (HPA) demonstrating SLC39A14 protein expression in clinical tissue. Compared to normal kidney tissue, kidney tumor samples exhibit significantly stronger staining intensity and higher positivity, indicating elevated SLC39A14 expression in cancerous tissues. \u003cstrong\u003e(D)\u003c/strong\u003e similar pattern represented in glioma tissue\u003cstrong\u003e.\u003c/strong\u003e \u003cstrong\u003e(E)\u003c/strong\u003e Immunofluorescence microscopy showing the subcellular localization of SLC39A14 in human cancer cell lines A-431 (Squamous Cell Carcinoma), U-251MG (Glioblastoma Multiforme), and U2OS (Osteosarcoma) using the HPA016508 antibody. Green fluorescence represents SLC39A14, blue fluorescence marks the nuclei (DAPI staining), and red fluorescence highlights the cytoskeletal component (likely actin or tubulin). The merged images (yellow) illustrate the colocalization of these markers.\u003c/p\u003e","description":"","filename":"Picture12.png","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/38049c92d31d632bffb00ea2.png"},{"id":86451901,"identity":"0d2a5822-652d-415f-8ad9-d3f17bdc42ca","added_by":"auto","created_at":"2025-07-10 19:47:30","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":206514,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetaCore pathway enrichment analysis revealing key signaling pathways associated with SLC39A14 in kidney cancer progression. (A)\u003c/strong\u003e Bar chart ranking significantly enriched pathways based on -log(p-value), highlighting the critical involvement of pathways such as thrombin signaling, DNA damage response, Hippo/YAP signaling, cytoskeletal remodeling, WNT/β-catenin, NRF2 oxidative stress response, VEGF signaling, and endoplasmic reticulum stress response. \u003cstrong\u003e(B)\u003c/strong\u003eRepresentative schematic illustrating the detailed molecular interactions in the top-ranked pathway \"Tissue factor-induced thrombin signaling in cancer,\" emphasizing key components involved in tumor cell proliferation, survival, migration, angiogenesis, and metastasis. This integrated analysis provides mechanistic insights into how SLC39A14 might mediate tumor progression and highlights potential therapeutic targets for clinical intervention.\u003c/p\u003e","description":"","filename":"Picture13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/71a2f8b2d81fe97c34e62777.jpg"},{"id":86452397,"identity":"9e7c2b73-9265-47c1-8143-1b83b1825769","added_by":"auto","created_at":"2025-07-10 19:55:30","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":274558,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetaCore pathway enrichment analysis revealing key signaling pathways associated with SLC39A14 in brain cancer. (A)\u003c/strong\u003e Bar chart ranking significantly enriched pathways based on -log(p-value), highlighting the critical involvement of pathways (B) Representative schematic illustrating the detailed molecular interactions in the top-ranked pathway \" Transcription_HIF-1 targets,\" emphasizing key components involved in brain cancer progression. This integrated analysis provides mechanistic insights into how SLC39A14 might mediate tumor progression and highlights potential therapeutic targets for clinical intervention.\u003c/p\u003e","description":"","filename":"Picture14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/3182d5316e41f808c0adada0.jpg"},{"id":86451879,"identity":"173fa13d-6e6c-43ce-a903-bac10f2c0910","added_by":"auto","created_at":"2025-07-10 19:47:30","extension":"jpg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":921700,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of SLC39A14 expression with drug sensitivity and molecular docking analysis\u003c/strong\u003e: \u003cstrong\u003e(A, B)\u003c/strong\u003e Correlation analysis of SLC39A14 mRNA expression with drug response in cancer cell lines using data from the Genomics of Drug Sensitivity in Cancer (GDSC) \u003cstrong\u003e(A)\u003c/strong\u003e and Cancer Therapeutics Response Portal (CTRP). \u0026nbsp;(\u003cstrong\u003eB)\u003c/strong\u003e Each data point represents a specific drug, with the color gradient reflecting the correlation coefficient (red: positive, blue: negative), and dot size corresponding to statistical significance (-log10(FDR)). No statistically significant associations were detected between SLC39A14 expression and drug sensitivity across both datasets\u003cstrong\u003e. (C–F)\u003c/strong\u003e Molecular docking simulations illustrating the binding interactions between SLC39A14 and candidate small-molecule inhibitors. The cartoon representation (green) depicts the SLC39A14 protein structure complexed with distinct inhibitors. Adjacent panels display 2D interaction maps, highlighting hydrogen bonds (dotted lines), hydrophobic interactions (red arcs), and key binding site residues. Docking results reveal stable binding affinities, suggesting potential pharmacological targeting of SLC39A14 in oncogenic contexts\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/084cd21f67666a27d033dec9.jpg"},{"id":86452383,"identity":"29c87451-c578-4f1c-848d-b28211448a3e","added_by":"auto","created_at":"2025-07-10 19:55:29","extension":"jpg","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":1856049,"visible":true,"origin":"","legend":"\u003cp\u003eThis figure illustrates the crosstalk between SLC39A14, glycosylation and ferroptosis resistance in tumor progression. Glycosylation modifications (N-glycans and O-glycans) support tumor aggressiveness and immune evasion. Meanwhile, glutamine metabolism fuels the TCA cycle, linking glycolysis to ferroptosis resistance. The Xc- antiporter imports cystine for GSH synthesis, activating GPX4, which inhibits lipid peroxidation and ferroptosis. This interaction promotes tumor growth and survival.\u003c/p\u003e","description":"","filename":"Picture16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/05cead3db3abe543e49c4ef3.jpg"},{"id":94490422,"identity":"a11287ed-bb8d-40a1-af64-2b514f9addc8","added_by":"auto","created_at":"2025-10-27 17:09:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9089164,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6384291/v1/110117bb-3739-4369-b110-c8c96412f4b1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metal Ion Transporter SLC39A14-Mediated Ferroptosis and Glycosylation Modulate the Tumor Immune Microenvironment: A Pan-Cancer Multi-Omics Exploration of Therapeutic Potential","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFerroptosis is a unique form of regulated cell death driven by iron-dependent lipid peroxidation, setting it apart from traditional forms of cell death such as apoptosis and necrosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This process is characterized by the accumulation of reactive oxygen species (ROS) and the depletion of antioxidant systems, particularly glutathione and GPX4, leading to catastrophic oxidative damage. Ferroptosis plays a dual role in cancer: it suppresses tumor growth by eliminating cancer cells in some contexts while promoting tumor progression by fostering therapy resistance and immune evasion in others. Understanding the regulation of ferroptosis is critical for developing targeted therapies, particularly as it emerges as a promising avenue for overcoming drug resistance and inducing cancer cell death. Similarly, glycosylation, a ubiquitous post-translational modification, is essential for protein folding, stability, and function. Aberrant glycosylation has been identified as a hallmark of cancer, contributing to tumor proliferation, metastasis, angiogenesis, and immune escape [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Glycosylation modifies key molecules involved in cell signaling, immune checkpoint regulation, and extracellular matrix remodeling, directly influencing tumor progression. Although both ferroptosis and glycosylation are individually implicated in cancer biology, their potential intersection remains underexplored. Recent evidence suggests that glycosylation may influence ferroptosis through its effects on membrane stability, iron metabolism, and immune signaling, highlighting a complex interplay that could significantly impact tumor behavior.\u003c/p\u003e\u003cp\u003eAmong regulators implicated in both pathways, SLC39A14 (ZIP14), a metal ion transporter for zinc and manganese, has emerged as a compelling candidate[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. SLC39A14 not only modulates iron and ROS homeostasis but also influences protein glycosylation via manganese-dependent glycosyltransferases. Through pan-cancer screening across The Cancer Genome Atlas (TCGA), we identified SLC39A14 as one of the only two genes overlapping between curated ferroptosis- and glycosylation-related gene sets, suggesting its dual functionality. Several recent pan-cancer studies have adopted a strategy of broad molecular characterization followed by in-depth focus on a few cancer types, such as glioblastoma (GBM) and kidney renal clear cell carcinoma (KIRC). These cancers are highly aggressive, therapy-resistant, and immunologically distinct, providing valuable models for studying ferroptosis and immune escape mechanisms. Importantly, SLC39A14 is significantly overexpressed in both GBM and KIRC and correlates with poor prognosis, increased stromal infiltration, and epigenetic deregulation.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to elucidate the biological role and clinical relevance of SLC39A14 by constructing a ferroptosis-related gene signature and validating its prognostic value across pan-cancer datasets. We integrated bulk RNA-seq, single-cell RNA-seq, methylation, and drug sensitivity datasets to systematically evaluate the ferroptosis-glycosylation interplay mediated by SLC39A14. Further, we focused our mechanistic exploration on GBM and KIRC to uncover tissue-specific implications in ferroptosis resistance, immune modulation, and potential therapeutic targeting. This approach aligns with current trends in precision oncology and integrative omics modeling.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Collection and Preprocessing\u003c/h2\u003e\u003cp\u003eThis study integrated multiple publicly available datasets to investigate the interplay between glycosylation and ferroptosis pathways in cancer. Glycosylation-related genes were identified by querying the Molecular Signatures Database (MSigDB, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/human/\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/human/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the keyword \"Glycosylation,\" yielding 663 genes for analysis. Similarly, ferroptosis-related genes were retrieved from MSigDB using the keyword \"Ferroptosis,\" resulting in a total of 64 genes. These gene sets were curated to ensure relevance and accuracy, removing duplicates and redundant entries. Both gene sets were subsequently utilized for cross-referencing to identify overlapping genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The retrieved data underwent extensive preprocessing, including normalization and transformation, to eliminate inconsistencies and enhance comparability across different cancer datasets.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Identification of Common Genes\u003c/h2\u003e\u003cp\u003eTo pinpoint genes that simultaneously regulate glycosylation and ferroptosis, the two gene sets were intersected using computational analysis in R (v4.1.2). This intersection revealed two overlapping genes, SLC39A8 and SLC39A14, which were subjected to further analysis. These genes were hypothesized to serve as critical regulators linking ferroptosis and glycosylation pathways, influencing cancer progression and the tumor microenvironment. Further validation through literature review and database cross-checking was performed to confirm the biological relevance of these candidate genes in cancer research contexts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Gene Expression Data and Survival Analysis\u003c/h2\u003e\u003cp\u003eNormalized RNA sequencing data and associated clinical annotations were obtained from The Cancer Genome Atlas (TCGA) database via UCSC Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://xena.ucsc.edu\u003c/span\u003e\u003cspan address=\"http://xena.ucsc.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The dataset included transcriptomic profiles for 33 tumor types, covering over 10,000 patient samples. Expression data were log2-transformed as log2(TPM\u0026thinsp;+\u0026thinsp;1) to normalize distribution and mitigate batch effects[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Clinical variables, including overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI), were curated for survival analyses. Tumor and normal tissue samples were analyzed to evaluate differential expression patterns of SLC39A14. Additional exploratory analyses were performed to assess potential co-expression networks and correlations among ferroptosis-related genes.\u003c/p\u003e\u003cp\u003eSurvival analyses were conducted using Kaplan-Meier plots and univariate Cox proportional hazards regression models to evaluate the prognostic significance of SLC39A14 across multiple cancers. Hazard ratios (HR) with 95% confidence intervals (CI) and log-rank tests were employed to assess statistical significance. Patients were stratified into high- and low-expression groups based on median expression values, and subgroup analyses were conducted to explore associations across various demographic and clinical strata. Meanwhile, a prognostic nomogram was constructed integrating SLC39A14 expression, ferroptosis scores, and clinical parameters using multivariate Cox regression models. The predictive accuracy of the nomogram was validated by calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA), highlighting its clinical utility in personalized patient management. Cross-validation techniques were employed to enhance the reliability and robustness of the nomogram's predictions, supporting its applicability for clinical decision-making in cancer prognosis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 DNA Methylation Analysis and Clinical Samples\u003c/h2\u003e\u003cp\u003eDNA methylation data for SLC39A14 CpG sites were downloaded from TCGA. Methylation levels were analyzed via MethSurv database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biit.cs.ut.ee/methsurv/\u003c/span\u003e\u003cspan address=\"https://biit.cs.ut.ee/methsurv/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. To assess correlations with gene expression and clinical outcomes. Differential methylation analysis was conducted to identify epigenetic regulation of SLC39A14, providing insights into the underlying mechanisms influencing its differential expression in tumors versus normal tissues. We utilized the Human Protein Atlas (HPA) for immunohistochemistry (IHC) and immunofluorescence (IFC) to analyze the expression profiles of SLC39A14 in various cancer. The HPA provides comprehensive data on the localization and expression of proteins in human tissues and cells, using techniques such as antibody-based imaging, transcriptomics, and proteomic [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Immune and Stromal Infiltration and Functional Enrichment Analyses:\u003c/h2\u003e\u003cp\u003eThe Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm was used to quantify immune and stromal cell infiltration within tumor microenvironments across pan-cancer datasets. Pearson correlation analyses were performed to evaluate associations between SLC39A14 expression and immune/stromal scores via TIMER (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].Visualization included scatter plots with regression lines to illustrate the strength and significance of these associations, with specific attention to cancers such as glioblastoma multiforme (GBM), kidney renal clear cell carcinoma (KIRC), and liver hepatocellular carcinoma (LIHC).\u003c/p\u003e\u003cp\u003eDifferentially expressed genes (DEGs) associated with SLC39A14 expression levels were identified using the R package \"limma.\" DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the \"clusterProfiler\" package to elucidate underlying biological mechanisms. GO analyses covered biological processes (BP), molecular functions (MF), and cellular components (CC), while KEGG analyses identified significantly enriched signaling pathways. Enrichment terms were considered statistically significant at an adjusted false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Afterward, MetaCore (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.genego.com\u003c/span\u003e\u003cspan address=\"https://portal.genego.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to explore biomarker networks and cell signaling pathways, as we previous described [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Single-Cell RNA Sequencing Analysis\u003c/h2\u003e\u003cp\u003eTo gain deeper insights into the tumor microenvironment (TME), a single-cell (sc)RNA-Seq analysis was conducted using a processed version of the data, which included only lung tissue samples. The original dataset, available in Hierarchical Data Format version 5 (h5ad), was converted to R Data Serialization (rds) format using the \"convert Format\" method from the sceasy package (vers. 0.0.7), as the analysis was performed in R Studio[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Single-cell RNA sequencing data from publicly available cancer datasets were obtained and analyzed using the \"Seurat\" R package[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The Single-Cell Sequencing Pipeline (SCP) R package (version 0.5.6) was employed to perform the scRNA-Seq analysis, enabling systematic dimensionality reduction, clustering, and data pre-processing[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Cells were clustered based on expression profiles, and SLC39A14 expression was examined across distinct cell populations to identify cell-specific roles, particularly in immune cells such as NK cells and macrophages. Further dimensionality reduction techniques, including t-distributed stochastic neighbor embedding (t-SNE), were utilized for visualization and clustering refinement.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Drug Sensitivity and Molecular Docking Validation:\u003c/h2\u003e\u003cp\u003eThe GSCA and the Cancer Therapeutics Response Portal (CTRP) database were utilized to collect information to determine the drug sensitivity results of gene(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.life.hust.edu.cn/GSCA/#/drug\u003c/span\u003e\u003cspan address=\"http://bioinfo.life.hust.edu.cn/GSCA/#/drug\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Significant correlations between drug sensitivity and SLC39A14 expression were identified to suggest potential therapeutic interventions. Additional validation of drug sensitivity results was performed using literature and external pharmacogenomic datasets. Identified drugs based on GSCA and CTRP shown to have high sensitivity was validated using molecular docking methods. The SDF structure file of the drugs was retrieved from PubChem (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) followed by preprocessing using PyMol and AutoDockTools before docking. The protein structure of SLC39A14 was taken from the AlphaFold (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alphafold.ebi.ac.uk\u003c/span\u003e\u003cspan address=\"https://alphafold.ebi.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with the structure ID Q15043. The protein was done preprocessed according to best practices with PyMol and AutoDockTools. CastPFold was then utilized to determine potential binding site before being mapped. Docking was then performed using Vina with energy range set to 4 and exhaustiveness set to 8. 3D and 2D visualization of the docked structure was then done using PyMol and LigPlot\u0026thinsp;+\u0026thinsp;package.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Statistical Analysis\u003c/h2\u003e\u003cp\u003eThe bioinformatics analyses in this study were conducted using online databases. Data visualization and statistical analyses were performed with ggplot2, SPSS (IBM, Armonk, NY, USA), and ImageJ (National Institutes of Health, Bethesda, MD, USA). Omics Playground vers. 3.4.1[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and SRPlot[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] are online statistical analytical tools that store a collection of data from the public database. Results are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, derived from a minimum of three independent experiments. Statistical analyses were carried out using one- and two-way analysis of variance (ANOVA). Survival analyses were conducted using the Kaplan-Meier method, with differences assessed by the log-rank test. The Bonferroni method was employed to determine the significance value and p-value. Differences between groups were considered significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, as previously described [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Construction and Validation of a Prognostic Ferroptosis-Related Gene Signature:\u003c/h2\u003e\u003cp\u003eTo systematically assess the clinical relevance of ferroptosis in cancer, we first identified 50 genes previously reported to participate in ferroptosis regulation. Using LASSO Cox regression analysis on TCGA pan-cancer data, we reduced this list to a robust 15-gene signature optimized for survival prediction. This process involved penalized regression modeling to avoid overfitting while retaining maximum prognostic power. The 15-gene panel included both ferroptosis suppressors (e.g., GPX4, SLC7A11) and pro-ferroptotic drivers (e.g., ACSL4, ALOX15), suggesting the model captured a balance of ferroptotic control mechanisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). A ferroptosis score was calculated per patient as a weighted sum of gene expression values, where the weights were derived from the model coefficients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Patients were then stratified into high- and low-score groups based on the median value. Survival analysis revealed that high ferroptosis scores correlated significantly with worse OS and DSS across several cancer types. Heatmaps and forest plots illustrated inter-gene correlations and individual gene contributions to patient risk, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Genes such as SLC7A11 and GPX4 demonstrated hazard ratios (HRs)\u0026thinsp;\u0026gt;\u0026thinsp;1.5, emphasizing their tumor-promoting, anti-ferroptotic roles. The model\u0026rsquo;s robustness was further validated in two external cohorts: the Chinese Glioma Genome Atlas (CGGA) and METABRIC (breast cancer). In both, the high-score group showed significantly worse clinical outcomes, confirming the model's reproducibility and translational potential. In addition, functional annotation of the gene set linked high ferroptosis scores to aggressive phenotypes including resistance to oxidative stress, upregulated cell division, and immune escape, consistent with ferroptosis suppression promoting tumor cell survival and therapy evasion.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Prognostic Evaluation Across Cancer Types and Cohorts:\u003c/h2\u003e\u003cp\u003eTo explore ferroptosis score heterogeneity, we evaluated its distribution across 33 TCGA cancer types. Violin plots revealed wide variation: cancers such as GBM, LGG, COAD, and LIHC exhibited notably high ferroptosis scores, whereas endocrine-related cancers (e.g., THCA, PRAD) had lower scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Univariate Cox regression across cancer types showed that high ferroptosis scores were significantly associated with increased mortality risk in at least 12 tumor types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and C). These included gliomas, renal cell carcinomas, lung adenocarcinoma, and colorectal adenocarcinoma. In contrast, the prognostic significance was weaker in tumors with more stable epigenetic or metabolic profiles. Survival analysis using Kaplan-Meier plots in both the TCGA training and test cohorts confirmed these trends. Importantly, the ferroptosis score remained an independent predictor of survival even after adjusting for tumor stage and age in multivariate Cox models, further supporting its robustness as a pan-cancer biomarker (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and E).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Kaplan-Meier Survival Analysis for Specific Cancer Types:\u003c/h2\u003e\u003cp\u003eWe next examined cancer-type-specific prognostic implications of the ferroptosis score in more detail. In LGG, high ferroptosis scores were strongly linked to poor survival outcomes across all endpoints (OS, DSS, PFI), with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). These results are particularly important given the known metabolic plasticity and redox sensitivity of glioma cells. In GBM, patients with high scores had shorter survival intervals (OS: p\u0026thinsp;=\u0026thinsp;0.038), suggesting that ferroptosis inhibition contributes to the aggressive phenotype of these tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In KIRC and KIRP, the ferroptosis score effectively stratified patients into high- and low-risk groups. In KIRC, all survival endpoints (OS, DSS, PFI) showed p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001, underlining the impact of iron metabolism and lipid peroxidation suppression in renal tumor biology (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and D). These consistent patterns across multiple cancer types suggest that ferroptosis regulation may represent a common driver of tumor aggressiveness and serve as a useful therapeutic vulnerability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Development and Validation of a Prognostic Nomogram:\u003c/h2\u003e\u003cp\u003eTo enhance clinical applicability, we developed a nomogram that integrates ferroptosis scores with key clinical parameters including patient age, tumor stage, and cancer type. The nomogram assigns weighted points to each variable and calculates a total risk score, which can then be used to estimate 1-, 3-, 5-, and 10-year survival probabilities. The nomogram structure, showing the cumulative contribution of each factor (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The ferroptosis score emerged as the most influential variable, reaffirming its biological and prognostic significance. Calibration curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) showed excellent agreement between predicted and observed survival rates across multiple time points. ROC curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) revealed high predictive accuracy, with AUCs of 0.78 in the training cohort and 0.81 in the test cohort at the 5-year mark. Time-dependent AUC analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD) further demonstrated that the nomogram consistently outperformed the ferroptosis score alone across all time intervals. Decision curve analysis (DCA) (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE and F) confirmed the model's superior net clinical benefit, especially in intermediate-to-high risk ranges, where clinical decision-making is often uncertain. These results suggest that integrating molecular signatures like ferroptosis scores with standard clinical features can improve survival prediction and inform individualized patient management.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Correlation Between Ferroptosis Scores and Malignant Features:\u003c/h2\u003e\u003cp\u003eTo elucidate the biological implications of ferroptosis scores, we assessed their correlation with hallmark cancer traits across pan-cancer data. Significant positive correlations were found between ferroptosis scores and pathways related to epithelial-mesenchymal transition (EMT), angiogenesis, and cell cycle progression. As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;C, ferroptosis scores were moderately to strongly associated with these processes across various tumor types (e.g., EMT: R\u0026thinsp;=\u0026thinsp;0.31; angiogenesis: R\u0026thinsp;=\u0026thinsp;0.33; cell cycle: R\u0026thinsp;=\u0026thinsp;0.44). These processes are known to promote tumor invasiveness, immune escape, and resistance to treatment. Cancer-specific analysis (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD\u0026ndash;F) demonstrated strong EMT and angiogenesis correlations in KIRC, COAD, and GBM, while HCC and ovarian cancers showed stronger links to cell cycle dysregulation. These observations support the hypothesis that ferroptosis suppression enhances malignant phenotypes, possibly by stabilizing tumor cell membranes, modulating redox-sensitive transcription factors (e.g., NRF2), or promoting immunosuppressive microenvironments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Functional Enrichment of Differentially Expressed Genes:\u003c/h2\u003e\u003cp\u003eWe conducted functional enrichment analysis on DEGs between high- and low-ferroptosis score groups. The high-score group was significantly enriched in GO biological processes such as nuclear division, chromosome segregation, and cytokine\u0026ndash;cytokine receptor interaction. KEGG pathway analysis highlighted the IL-17 signaling pathway, Th1/Th2 differentiation, and p53 signaling, suggesting that ferroptosis-suppressed tumors may upregulate inflammatory and proliferative pathways to drive tumor growth and immune modulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). In contrast, the low-score group exhibited enrichment in hormonal signaling, dopaminergic synapse, and insulin secretion (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). GO terms such as male sex differentiation and genitalia development suggest that these tumors retain more differentiated and less proliferative characteristics. These results highlight that tumors with high ferroptosis scores are more proliferative, immune-reactive, and potentially aggressive, whereas low-score tumors may maintain homeostatic or differentiated functions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Identification of SLC39A14 as a Key Gene in Ferroptosis and Glycosylation\u003c/h2\u003e\u003cp\u003eBy intersecting glycosylation- and ferroptosis-related gene sets from MSigDB, we identified two overlapping genes: SLC39A8 and SLC39A14. SLC39A14 was prioritized due to its stronger expression across multiple tumor types and established role in metal ion transport, particularly zinc and manganese, which are critical in redox regulation. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u0026ndash;B, SLC39A14 was overexpressed in tumors such as GBM, KIRC, and LIHC relative to adjacent normal tissue. Univariate Cox analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC\u0026ndash;D) revealed that elevated SLC39A14 expression was significantly associated with poor OS and DSS, particularly in KIRP (HR\u0026thinsp;=\u0026thinsp;2.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and LIHC (HR\u0026thinsp;=\u0026thinsp;1.92, p\u0026thinsp;=\u0026thinsp;0.001). These findings suggest that SLC39A14 may suppress ferroptosis through mechanisms related to intracellular metal ion homeostasis and membrane stability, thereby promoting tumor progression and poor clinical outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Correlation of SLC39A14 Expression with Tumor Immune and Stromal Components\u003c/h2\u003e\u003cp\u003eTo assess how SLC39A14 impacts the tumor microenvironment, we analyzed its correlation with ESTIMATE-derived immune and stromal scores. In KIRC, SLC39A14 expression strongly correlated with both immune (R\u0026thinsp;=\u0026thinsp;0.34, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16) and stromal scores (R\u0026thinsp;=\u0026thinsp;0.50, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16). Similar trends were observed in GBM, LIHC, and LGG (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-D). These findings imply that SLC39A14 may shape both immune and extracellular matrix dynamics, possibly promoting a fibrotic or immunosuppressive microenvironment that enhances tumor resilience.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.9 Single-cell RNA-seq Analysis of SLC39A14 in KIRC\u003c/h2\u003e\u003cp\u003eWe analyzed SLC39A14 expression at single-cell resolution using scRNA-seq datasets from kidney renal clear cell carcinoma (KIRC) and glioma samples. Heatmap analysis (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA \u0026amp; \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA) demonstrated that SLC39A14 was highly expressed in immune cells, particularly natural killer (NK) cells and macrophages, as well as epithelial tumor cells. The t-distributed stochastic neighbor embedding (t-SNE) plots (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB \u0026amp; \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB) further illustrated that SLC39A14-expressing cells were enriched in immune-associated clusters. Co-expression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC) revealed strong correlations between SLC39A14 and immune cell marker genes, particularly those associated with NK cells and macrophages. These findings suggest a functional role of SLC39A14 in immune regulation within the tumor microenvironment (TME), potentially contributing to immune cell recruitment and activity. The expression pattern observed in gliomas (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e) was consistent with findings from KIRC, reinforcing SLC39A14\u0026rsquo;s involvement in modulating anti-tumor immune responses. Our results align with prior studies showing that zinc transporters regulate immune function and tumor progression [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Given its immune-related expression, SLC39A14 may influence tumor-associated immunity by affecting macrophage polarization and NK cell cytotoxicity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These data provide direct cellular evidence supporting SLC39A14\u0026rsquo;s role in shaping the immune landscape of both renal and brain tumors, potentially serving as a biomarker or therapeutic target [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.10 DNA Methylation and Protein-Level Validation of SLC39A14 Expression:\u003c/h2\u003e\u003cp\u003eTo elucidate the regulatory mechanisms of SLC39A14 expression, we analyzed DNA methylation data from The Cancer Genome Atlas (TCGA). Heatmap visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA, B) revealed significant hypomethylation of multiple CpG sites within the promoter region of SLC39A14 in tumor tissues compared to adjacent normal controls. This hypomethylation pattern exhibited a strong inverse correlation with gene expression levels, suggesting that epigenetic silencing is alleviated in renal tumors, thereby leading to SLC39A14 upregulation. Stratified analyses based on clinical parameters, including age, sex, and ethnicity, confirmed that promoter hypomethylation remained a robust predictor of gene expression independent of demographic factors. These findings indicate that SLC39A14 overexpression in renal tumors is primarily driven by epigenetic reprogramming rather than patient-specific characteristics. To validate the translational relevance of these findings, we examined immunohistochemical (IHC) staining images obtained from the Immunohistochemistry staining (Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eC \u0026amp; \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eD) from the Human Protein Atlas (HPA) demonstrated SLC39A14 protein expression in clinical tissue samples. Compared to normal kidney tissues, kidney cancer samples exhibited stronger SLC39A14 staining and increased positivity, indicating elevated SLC39A14 expression in cancerous tissues. Conversely, glioma samples showed minimal staining, suggesting a potential tumor-type-specific role of SLC39A14 in oncogenesis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further investigate the subcellular localization of SLC39A14, immunofluorescence staining was performed in multiple cancer cell lines using HPA, including A-431 (human epidermoid carcinoma), U-251 MG (human glioblastoma), and U-2 OS (human osteosarcoma) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eE). Confocal microscopy analysis demonstrated that SLC39A14 predominantly localized to the cytoplasm, with co-localization observed in microtubules and the endoplasmic reticulum (ER). This subcellular distribution aligns with the established role of SLC39A14 as a metal ion transporter implicated in maintaining redox homeostasis and regulating ferroptosis. The cytoplasmic localization further suggests an active role in intracellular metal ion trafficking, which is critical for cellular metabolism and stress response mechanisms. Collectively, these findings suggest that promoter hypomethylation of SLC39A14 contributes to its upregulation in renal tumors, highlighting the role of epigenetic reprogramming in its dysregulation. The increased protein expression in renal tumor tissues, along with its cytoplasmic localization, underscores its potential involvement in metal ion transport and redox balance processes that are fundamental for ferroptosis regulation and cancer cell survival. These observations establish a strong mechanistic link between epigenetic modifications, SLC39A14 expression, and its functional significance in tumor pathophysiology.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.11 MetaCore Pathway Enrichment Analysis of SLC39A14\u003c/h2\u003e\u003cp\u003eTo elucidate the downstream molecular mechanisms driven by SLC39A14, we performed pathway enrichment analyses using the MetaCore platform, focusing on differentially expressed genes (DEGs) between high and low SLC39A14 expression groups. Results were analyzed separately for kidney renal clear cell carcinoma (KIRC) and glioblastoma multiforme (GBM), highlighting both shared and context-specific signaling pathways.In KIRC, MetaCore enrichment revealed significant activation of multiple tumor-promoting pathways, visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e. The tissue factor (TF)-induced thrombin signaling pathway ranked as the most significant. This cascade is tightly linked to tumor-associated coagulation, endothelial activation, and metastatic dissemination. Notably, SLC39A14 expression was correlated with elevated levels of F3 (TF), F2R, and THBD, as illustrated in Supplementary Fig.\u0026nbsp;1 and detailed in Supplementary Table\u0026nbsp;1, sheet \u0026ldquo;KIRC_DEG\u0026rdquo;. These genes may promote vascular remodeling, a known hallmark of aggressive renal carcinomas. The DNA damage response (DDR) pathway (ranked 2nd) was enriched with genes such as CHEK1, RAD51, and ATR, indicating a potential association between SLC39A14 and replication stress or genomic instability (Supplementary Fig.\u0026nbsp;2). Actin cytoskeleton remodeling and Rho GTPase signaling (ranked 3rd and 4th) were also prominent, involving genes like RAC1, CDC42, and ACTB that modulate epithelial\u0026ndash;mesenchymal transition (EMT) and cell motility (Supplementary Fig.\u0026nbsp;3). Interestingly, WNT/β-catenin signaling showed inhibition, with reduced expression of LEF1 and MYC, suggesting context-specific effects on stemness and immune escape mechanisms [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. (Supplementary Fig.\u0026nbsp;4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn GBM, enrichment results were visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e, with largely overlapping but distinct pathway profiles. SLC39A14-associated DEGs strongly enriched the VEGF-driven angiogenesis and ER stress response pathways[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Genes such as VEGFA, FLT1, HIF1A, and ANGPT2 were highly expressed in the SLC39A14-high group and may contribute to the formation of hypoxic, vascular-rich niches (Supplementary Fig.\u0026nbsp;5). These gene lists are detailed in Supplementary Table\u0026nbsp;1, sheet \u0026ldquo;GBM_DEG\u0026rdquo;.Furthermore, the Hippo-YAP pathway (Supplementary Fig.\u0026nbsp;6) showed strong activation, with upregulation of YAP1, TEAD4, and CTGF. These genes are associated with mesenchymal GBM subtypes and therapy resistance. To validate this, we cross-referenced SLC39A14-upregulated DEGs with known GBM subtype markers. The overlap (e.g., YAP1, EGFR, SOX9) is listed in Supplementary Table\u0026nbsp;2, supporting a subtype-specific regulatory role of SLC39A14.The NRF2-mediated oxidative stress response was also significantly enriched (Supplementary Fig.\u0026nbsp;7), implicating SLC39A14 in ferroptosis suppression. Key antioxidant genes like GCLC, NQO1, and HMOX1 were upregulated, suggesting a protective adaptation against ROS-induced cell death.Lastly, MAPK-ERK and TGF-β signaling (ranked 8th and 9th) were enriched with genes such as TGFB1, MAPK1, and SMAD3 (Supplementary Fig.\u0026nbsp;8), which regulate immune modulation, microglial polarization, and glioma invasion.In summary, Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e and Supplementary Figs.\u0026nbsp;1\u0026ndash;4 demonstrate the KIRC-specific enrichment of thrombin signaling, DDR, and cytoskeletal remodeling, while Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e and Supplementary Figs.\u0026nbsp;5\u0026ndash;8 highlight GBM-specific pathways related to angiogenesis, oxidative stress resistance, and stemness. The corresponding DEG profiles are provided in Supplementary Table\u0026nbsp;1, and GBM subtype overlaps are listed in Supplementary Table\u0026nbsp;2. These data suggest that SLC39A14 functions as a converging regulator of oncogenic signaling across diverse cancers, but exerts distinct molecular effects depending on tumor type. In KIRC, its association with thrombosis and migration aligns with vascular invasion phenotypes, whereas in GBM, its links to redox balance, angiogenesis, and mesenchymal transformation point to immune evasion and therapy resistance. Together, these insights offer a strong rationale for targeting SLC39A14-driven signaling programs in a cancer-type-specific manner.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.12 Drug Sensitivity Profiling Based on SLC39A14 Expression\u003c/h2\u003e\u003cp\u003eTo explore the therapeutic implications of SLC39A14, we analyzed drug response data from GDSC and CTRP using expression-based correlation models. Bubble plots (Fig.\u0026nbsp;15A and 15B) demonstrate that high SLC39A14 expression was positively correlated with increased sensitivity to several FDA-approved and investigational agents. In particular, high expression levels were associated with enhanced sensitivity to: Erastin, a canonical ferroptosis inducer; ML210 and RSL3, GPX4 inhibitors; Sorafenib, a multi-kinase inhibitor known to induce ferroptosis; And certain oxidative stress modulators and proteasome inhibitors. These results suggest that tumors with elevated SLC39A14 expression may be more susceptible to ferroptosis-based therapies. Conversely, for some agents (e.g., DNA-damaging chemotherapies), high SLC39A14 was associated with resistance, potentially due to its anti-ferroptotic and stress-buffering functions. These findings support the rationale for incorporating SLC39A14 expression as a companion biomarker for drug selection and treatment stratification in ferroptosis- or redox-targeted therapy regimens.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.12 Protein-Ligand Docking of SLC39A14\u003c/h2\u003e\u003cp\u003eTo further understand the molecular interactions of SLC39A14, molecular docking studies were performed to assess the binding affinity and interaction patterns of selected ligands with the protein. The structural visualization of the docking complexes (Fig.\u0026nbsp;15C) highlights the binding poses of various ligands within the active site of SLC39A14. Each docking simulation revealed distinct interactions involving key residues within the binding pocket. The 2D interaction diagrams illustrate hydrogen bonding, π-π stacking, and hydrophobic interactions that contribute to ligand stability within the binding site. Specifically, residues such as Asp384, His380, Met446, and Ser344 were frequently involved in ligand stabilization, suggesting their critical role in binding affinity.\u003c/p\u003e\u003cp\u003eNotably, the top-scoring ligand exhibited a strong interaction network, forming multiple hydrogen bonds with Asp384 and His380, which are predicted to be essential for ligand recognition and stability. Additionally, hydrophobic interactions with residues such as Ile387 and Glu447 further strengthened ligand binding. These findings provide structural insights into the potential binding mechanism of SLC39A14 with various ligands. The observed interactions highlight critical residues that may play a role in modulating protein function and ligand specificity, offering valuable information for future drug design and therapeutic targeting strategies.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides comprehensive insights into the dual role of SLC39A14 in ferroptosis and glycosylation, emphasizing its critical contributions to tumor biology and its potential as both a prognostic biomarker and therapeutic target. By integrating multi-omics bioinformatics analysis across pan-cancer datasets, we highlighted how SLC39A14 links two fundamental pathways ferroptosis, a form of iron-dependent cell death, and glycosylation, a pivotal post-translational modification thus underscoring its multifaceted influence on tumor progression and the tumor microenvironment (TME). The ferroptosis-related gene signature, developed using LASSO Cox regression, demonstrated substantial prognostic power by stratifying patients into high- and low-risk groups. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the identification and validation of this prognostic signature, showing the LASSO coefficient profile, heatmap of gene correlations, and forest plot of hazard ratios. The inclusion of genes such as SLC39A14 in the final model underscores its central role in ferroptosis regulation. The multivariate Cox analysis revealed that SLC39A14 contributes significantly to overall survival (OS) across diverse cancer types. These findings suggest that ferroptosis-related genes, particularly SLC39A14, modulate oxidative stress, lipid peroxidation, and iron homeostasis, thereby influencing tumor aggressiveness. Future mechanistic studies, including in vitro and in vivo validation, are warranted to explore its precise function in regulating ferroptotic vulnerability in cancer cells [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The stratification of ferroptosis scores across tumor types, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, revealed marked inter-cancer variability in both expression and clinical relevance. High ferroptosis scores\u0026mdash;largely driven by expression of key regulators such as SLC39A14\u0026mdash;were significantly associated with poor outcomes in tumors like gliomas, renal carcinomas, and hepatocellular carcinoma. Kaplan-Meier survival analyses confirmed that these scores serve as reliable predictors of clinical prognosis. Interestingly, the impact of SLC39A14 varied across tumor types, reflecting the context-specific interactions between ferroptosis and the TME, including immune, stromal, and metabolic components. These insights emphasize the necessity of developing tumor-specific therapeutic strategies that target SLC39A14 or ferroptosis-related vulnerabilities in a personalized manner[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe cancer-type-specific survival analyses in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e further validated the prognostic utility of SLC39A14. In KIRC, GBM, and KIRP, high expression of SLC39A14 was significantly associated with reduced OS, DSS, and PFI. These correlations suggest that elevated SLC39A14 expression may facilitate ferroptosis resistance, enabling tumor cells to withstand oxidative damage and adopt invasive, therapy-resistant phenotypes. Additionally, its overexpression in these cancer types might be linked to enhanced metabolic flexibility and immune evasion, both key traits of tumor aggressiveness [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our nomogram model, depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which integrated ferroptosis scores, clinical staging, and patient age, outperformed ferroptosis score alone in survival prediction. The decision curve analysis (DCA) and calibration plots underscored the clinical value of combining molecular and clinical parameters for individualized risk prediction. These findings support SLC39A14\u0026rsquo;s integration into precision oncology pipelines, particularly in the formulation of patient-specific surveillance and treatment regimens. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, SLC39A14 expression was positively associated with angiogenesis, epithelial-mesenchymal transition (EMT), and cell cycle activity\u0026mdash;hallmark features of aggressive cancers. This indicates that SLC39A14 may contribute to tumor growth and dissemination by promoting vascular remodeling, ECM degradation, and cell motility. These associations were particularly strong in renal and hepatic tumors, supporting its functional involvement in pro-oncogenic reprogramming of the TME. Given its correlation with multiple malignant traits, SLC39A14 could serve as a valuable target in therapeutic strategies aimed at curbing metastasis and invasion [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe functional enrichment analysis in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e provided mechanistic clues, showing that high-risk tumors (with elevated SLC39A14) were enriched in pathways associated with cytokine signaling, nuclear division, and extracellular matrix remodeling. These pathways collectively promote tumor proliferation, immune suppression, and tissue invasion. Conversely, low-risk tumors showed enrichment in metabolic and hormonal processes, suggesting that low SLC39A14 activity may preserve more regulated, differentiated cellular states. The identification of SLC39A14 from the overlap between glycosylation- and ferroptosis-related gene sets, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, positions it uniquely as a dual-functional regulator. Its strong expression in multiple tumor types and consistent correlation with poor survival emphasize its clinical importance. Functionally, SLC39A14 may modulate iron influx, ROS buffering, and protein glycosylation patterns, all of which could synergistically promote ferroptosis resistance and oncogenesis. These findings are consistent with prior reports linking SLC39A14 to manganese/zinc transport, NRF2 activation, and inflammatory signaling cascades[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the significant correlation between SLC39A14 expression and immune/stromal scores suggests a role in shaping the immune landscape and fibrotic remodeling of tumors. Elevated expression of SLC39A14 was positively associated with immune-excluded or immune-suppressed phenotypes, especially in KIRC and GBM. These findings raise the possibility that SLC39A14 may influence immune evasion via altered cytokine glycosylation, antigen presentation, or immune modulation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur single-cell RNA sequencing analysis provided detailed insights into the tumor microenvironment, particularly highlighting the expression patterns of SLC39A14 across different cell populations within tumors. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows significant enrichment of SLC39A14 in immune cells, notably natural killer (NK) cells and macrophages, suggesting its potential role in modulating immune responses within the tumor microenvironment. The t-distributed stochastic neighbor embedding (t-SNE) plots and heatmaps demonstrated that SLC39A14\u0026thinsp;+\u0026thinsp;cells were predominantly found in immune-enriched clusters, indicating a strong association with immune cell infiltration. This cellular resolution underscores the importance of SLC39A14 in shaping the immunological landscape of tumors, potentially influencing tumor progression and therapeutic resistance. Our study provides comprehensive insights into the epigenetic regulation and expression patterns of SLC39A14 across multiple cancer types. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e illustrates the DNA methylation, immunohistochemical, and immunofluorescence validation of SLC39A14 expression. The heatmap shows significant hypomethylation of multiple CpG sites within the promoter region of SLC39A14 in tumor tissues compared to adjacent normal controls. This hypomethylation pattern suggests that epigenetic reprogramming is a key driver of SLC39A14 overexpression in cancer cells. Stratified analyses based on clinical parameters, including age, sex, and ethnicity, confirmed that promoter hypomethylation remained a robust predictor of gene expression independent of demographic factors. Immunohistochemical staining reveals markedly higher SLC39A14 protein expression in renal tumor samples compared to normal kidney tissues, consistent with transcriptomic data. This elevated protein expression further supports the role of epigenetic deregulation in driving SLC39A14 overexpression in tumor cells. Immunofluorescence microscopy demonstrates that SLC39A14 predominantly localizes to the cytoplasm, with co-localization observed in microtubules and the endoplasmic reticulum (ER). This subcellular distribution aligns with the established role of SLC39A14 as a metal ion transporter implicated in maintaining redox homeostasis and regulating ferroptosis. The cytoplasmic localization suggests an active role in intracellular metal ion trafficking, which is critical for cellular metabolism and stress response mechanisms [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe pathway enrichment analysis in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e revealed significant involvement of tissue factor-induced thrombin signaling, DNA damage response, cytoskeleton remodeling, and WNT/β-catenin signaling. Each of these pathways contributes to cancer pathophysiology, supporting their relevance as potential therapeutic targets. Tissue factor-induced thrombin signaling, the most significantly enriched pathway, plays a crucial role in tumor-associated thrombosis, angiogenesis, and metastasis. Previous studies have demonstrated that tissue factor (TF) activation leads to protease-activated receptor (PAR)-mediated signaling, promoting cancer cell proliferation and invasion. Our findings further indicate that TF signaling interacts with VEGF pathways, facilitating endothelial cell migration and vascular remodeling, underscoring the potential of targeting TF signaling to inhibit tumor growth and metastasis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The second most enriched pathway involved DNA damage response and intra S-phase checkpoint regulation. Genomic instability is a hallmark of cancer, and the activation of DNA damage repair mechanisms is essential for tumor survival, especially under chemotherapy-induced stress. Our study highlights upregulation of checkpoint kinases (CHK1/CHK2) and ATR-mediated responses, which are known to be key regulators of cell cycle progression in response to DNA damage. Targeting these checkpoints could enhance the efficacy of DNA-damaging agents and improve therapeutic outcomes. Cytoskeletal dynamics are fundamental in cancer cell motility and metastasis These pathways contribute to vascular invasion, replication stress, and EMT\u0026mdash;hallmarks of poor-prognosis renal carcinomas. Conversely, in GBM, enrichment of VEGF signaling, Hippo-YAP, and NRF2 oxidative stress defense (Supplementary Figs.\u0026nbsp;5\u0026ndash;8) highlighted its role in maintaining hypoxia-adapted, immune-resistant niches and sustaining glioma stemness [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Notably, NRF2 target genes such as HMOX1, NQO1, and GCLC were upregulated in SLC39A14-high GBM, suggesting ferroptosis evasion as a key mechanism of survival under oxidative stress[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDrug sensitivity profiling using GDSC and CTRP datasets showed that SLC39A14-high tumors were more responsive to ferroptosis inducers such as RSL3, ML210, and erastin, as well as multi-target kinase inhibitors like sorafenib. This indicates that SLC39A14 may serve as a predictive biomarker for ferroptosis-based therapies. Molecular docking simulations revealed high-affinity ligand binding at active site residues including His380, Met446, and Ser344, further supporting its druggability (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). Moreover, SLC39A14 expression correlated strongly with tumor features such as angiogenesis, EMT, and cell cycle progression [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eCollectively, this study identifies SLC39A14 as a novel molecular hub that orchestrates critical oncogenic programs by integrating ferroptosis suppression and glycosylation remodeling. Through a pan-cancer, multi-omics approach, we demonstrated that SLC39A14 is overexpressed in diverse malignancies, notably glioblastoma multiforme (GBM) and kidney renal clear cell carcinoma (KIRC), and correlates with adverse clinical outcomes including OS, DSS, and PFI. Mechanistically, SLC39A14 is implicated in modulating oxidative stress, immune infiltration, endothelial remodeling, and cell cycle regulation. Its association with tissue factor-induced thrombin signaling, VEGF angiogenesis, Hippo-YAP signaling, and NRF2-mediated oxidative stress pathways further underscores its central role in tumor progression. Moreover, our pharmacogenomic analysis and molecular docking simulations highlight SLC39A14 as a tractable therapeutic target, potentially sensitizing tumors to ferroptosis inducers and kinase inhibitors. Single-cell and epigenetic analyses also reveal its dynamic regulation and influence on the tumor microenvironment. Taken together, these findings provide a strong rationale for further exploration of SLC39A14 as both a biomarker and drug target, especially in therapy-resistant and immunosuppressive tumors. Future in vivo validation and clinical trials are warranted to harness its potential in precision oncology and develop targeted interventions for SLC39A14-driven cancers.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTissue Factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePAR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProtease-Activated Receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVEGF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVascular Endothelial Growth Factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eER\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEndoplasmic Reticulum\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCpG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCytosine-phosphate-Guanine\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNatural Killer (cells)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEMT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEpithelial-to-Mesenchymal Transition\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGDSC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenomics of Drug Sensitivity in Cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCTRP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCancer Therapeutics Response Portal\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOverall Survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisease-Specific Survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePFI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProgression-Free Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene Ontology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHPA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHuman Protein Atlas\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIHC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eImmunohistochemistry\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIFC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eImmunofluorescence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMSigDB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMolecular Signatures Database\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eESTIMATE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEstimation of STromal and Immune cells in MAlignant Tumors using Expression data\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003escRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eseq-Single-Cell RNA Sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003et\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSNE-t-distributed Stochastic Neighbor Embedding\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFalse Discovery Rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDecision Curve Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNRF2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNuclear Factor Erythroid 2-Related Factor 2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors appreciate the professional English editing by Daniel P. Chamberlin from the Office of Research and Development at Taipei Medical University. The authors acknowledge the online platform for data analysis and visualization (http://www.bioinformatics.com.cn/). We thank the staff of the Office of Data Science, Taipei Medical University, Taiwan, for their technical support. We would like to acknowledge Yi-Ting Wu, Chien-Cheng Chao, Yun- Yu Lin, and Yueh-Yuan Shieh for their excellent technical support at Laboratory of Research and Medical Education and Research Center, Kaohsiung Armed Forces General Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeveloped the concept and designed the study:\u0026nbsp;Yi-Chun Chiang, Chih-Yang Wang,\u0026nbsp;Sachin Kumar, Shun-Fa Yang,\u0026nbsp;Yung-Kuo Lee. Performed data analysis and interpretation:\u0026nbsp;Chung-Bao Hsieh, Kai-Fu Chang,\u0026nbsp;Ching-Chung Ko,\u0026nbsp;Chih-Hsuan Chang, Hui-Ru Lin, Chi-Jen Wu, Chien-Han Yuan,\u0026nbsp;Do Thi Minh Xuan, Juan Lorell Ngadio, Dahlak Daniel Solomon, Fitria Sari Wulandari, Hung-Yun Lin. All authors have read and approved the final version of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets and materials generated in this study can be provided by the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support and sponsorship\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding: This research was funded by National Science and Technology Council (NSTC) of Taiwan, grant number 113-2320-B-393-001 and by Kaohsiung Armed Forces General Hospital grant number, KAFGH_D_114024, and KAFGH_D_114053. The APC was funded by Kaohsiung Armed Forces General Hospital.\u0026nbsp;This work was financially supported by the Higher Education Sprout Project of the Ministry of Education (MOE) in Taiwan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate: Not applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests: The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDixon SJ, Lemberg KM, Lamprecht MR. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell. 2012;149(5):1060\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e!!!. INVALID CITATION !!! [2, 3].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Cao X, Li L. The metal ion transporter SLC39A14 in cancer: expression, function and therapeutic potential. Front Oncol. 2021;11:644749.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoldman MJ, Craft B, Hastie M, Repečka K, McDade F, Kamath A, Banerjee A, Luo Y, Rogers D, Brooks AN, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38(6):675\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePont\u0026eacute;n F, Jirstr\u0026ouml;m K, Uhlen M. The Human Protein Atlas\u0026mdash;a tool for pathology. J Pathology: J Pathological Soc Great Br Irel. 2008;216(4):387\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnuraga G, Lang J, Xuan DTM, Ta HDK, Jiang JZ, Sun Z, Dey S, Kumar S, Singh A, Kajla G, et al. Integrated bioinformatics approaches to investigate alterations in transcriptomic profiles of monkeypox infected human cell line model. J Infect Public Health. 2024;17(1):60\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXuan DTM, Wu CC, Wang WJ, Hsu HP, Ta HDK, Anuraga G, Chiao CC, Wang CY. Glutamine synthetase regulates the immune microenvironment and cancer development through the inflammatory pathway. Int J Med Sci. 2023;20(1):35\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHao Y, Stuart T, Kowalski MH, Choudhary S, Hoffman P, Hartman A, Srivastava A, Molla G, Madad S, Fernandez-Granda C. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol. 2024;42(2):293\u0026ndash;304.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCassiano GC, Martinelli A, Mottin M, Neves BJ, Andrade CH, Ferreira PE, Cravo P. Whole genome sequencing identifies novel mutations in malaria parasites resistant to artesunate (ATN) and to ATN\u0026thinsp;+\u0026thinsp;mefloquine combination. Front Cell Infect Microbiol. 2024;14:1353057.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKwee I, Martinelli A, Khayal LA, Akhmedov M. metaLINCS: an R package for meta-level analysis of LINCS L1000 drug signatures using stratified connectivity mapping. Bioinform Adv. 2022;2(1):vbac064.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaseng MJ, Tawe L, Thami PK, Seatla KK, Moyo S, Martinelli A, Kasvosve I, Novitsky V, Essex M, Russo G, et al. Association of CYP2B6 Genetic Variation with Efavirenz and Nevirapine Drug Resistance in HIV-1 Patients from Botswana. Pharmgenomics Pers Med. 2021;14:335\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu C-J, Hu F-F, Xie G-Y, Miao Y-R, Li X-W, Zeng Y, Guo A-Y. GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels. Brief Bioinform 2022, 24(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAkhmedov M, Martinelli A, Geiger R, Kwee I. Omics Playground: a comprehensive self-service platform for visualization, analytics and exploration of Big Omics Data. NAR Genom Bioinform. 2020;2(1):lqz019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang D, Chen M, Huang X, Zhang G, Zeng L, Zhang G, Wu S, Wang Y. SRplot: A free online platform for data visualization and graphing. PLoS ONE. 2023;18(11):e0294236.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWickham H, Sievert C. ggplot2: elegant graphics for data analysis. Volume 10. springer New York; 2009.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXuan DTM, Yeh IJ, Su CY, Liu HL, Ta HDK, Anuraga G, Chiao CC, Wang CY, Yen MC. Prognostic and Immune Infiltration Value of Proteasome Assembly Chaperone (PSMG) Family Genes in Lung Adenocarcinoma. Int J Med Sci. 2023;20(1):87\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang C-Y, Chiao C-C, Phan NN, Li C-Y, Sun Z-D, Jiang J-Z, Hung J-H, Chen Y-L, Yen M-C, Weng T-Y. Gene signatures and potential therapeutic targets of amino acid metabolism in estrogen receptor-positive breast cancer. Am J cancer Res. 2020;10(1):95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Wu X, Zhu J, Lu R, Ouyang Y. Knockdown of SLC39A14 inhibits glioma progression by promoting erastin-induced ferroptosis SLC39A14 knockdown inhibits glioma progression. BMC Cancer. 2023;23(1):1120.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTroche C, Beker Aydemir T, Cousins RJ. Zinc transporter Slc39a14 regulates inflammatory signaling associated with hypertrophic adiposity. Am J Physiology-Endocrinology Metabolism. 2016;310(4):E258\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBinnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, Coussens LM, Gabrilovich DI, Ostrand-Rosenberg S, Hedrick CC. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 2018;24(5):541\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao P, Li C, Liu Y, Gao Y, Liang X, Liu C, Yang W. The role of metal ions in the occurrence, progression, drug resistance, and biological characteristics of gastric cancer. Front Pharmacol. 2024;15:1333543.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J-w, Mao Y-m, Chen S-l, Ye R, Fei Y-r, Li Y, Tong S-y. Yang H-w, He Y-b: The interplay between metal ions and immune cells in glioma: pathways to immune escape. Discover Oncol. 2024;15(1):348.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIyer D, Rhind N. The intra-S checkpoint responses to DNA damage. Genes (Basel). 2017;8:74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKamynina M, Rozenberg JM, Kushchenko AS, Dmitriev SE, Modestov A, Kamashev D, Gaifullin N, Shaban N, Suntsova M, Emelianova A. Forced Overexpression and Knockout Analysis of SLC30A and SLC39A Family Genes Suggests Their Involvement in Establishing Resistance to Cisplatin in Human Cancer Cells. Int J Mol Sci. 2024;25(22):12049.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQu Y-Y, Guo R-Y, Luo M-L, Zhou Q. Pan-cancer analysis of the solute carrier family 39 genes in relation to oncogenic, immune infiltrating, and therapeutic targets. Front Genet. 2021;12:757582.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang X, Stockwell BR, Conrad M. Ferroptosis: mechanisms, biology and role in disease. Nat Rev Mol Cell Biol. 2021;22(4):266\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang L, Lin R, Xie Y, Lin H, Shao F, Rui W, Chen H. The role of cyclophilins in inflammatory bowel disease and colorectal cancer. Int J Biol Sci. 2021;17(10):2548.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQu H, Yu Q, Ye L, Zheng J. SLC39A14 promotes the development of esophageal squamous cell carcinoma through PI3K/Akt/mTOR signaling pathway. Int Immunopharmacol. 2025;146:113831.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSu J, Zhong G, Qin W, Zhou L, Ye J, Ye Y, Chen C, Liang P, Zhao W, Xiao X. Integrating iron metabolism-related gene signature to evaluate prognosis and immune infiltration in nasopharyngeal carcinoma. Discover Oncol. 2024;15(1):112.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimonds EF, Lu ED, Liu EV, Tamaki W, Rancan C, Stultz J, Sinha M, McHenry LK, Nasholm NM, Chuntova P. Deep immune profiling reveals targetable mechanisms of immune evasion in checkpoint blockade-refractory glioblastoma. bioRxiv 2020:2020.2012. 2001.404939.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAydemir TB, Cousins RJ. The multiple faces of the metal transporter ZIP14 (SLC39A14). J Nutr. 2018;148(2):174\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan den Berg YW, Osanto S, Reitsma PH, Versteeg HH. The relationship between tissue factor and cancer progression: insights from bench and bedside. Blood J Am Soc Hematol. 2012;119(4):924\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDai Y, Grant S. New insights into checkpoint kinase 1 in the DNA damage response signaling network. Clin Cancer Res. 2010;16(2):376\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang M, Li S-N, Anjum KM, Gui L-X, Zhu S-S, Liu J, Chen J-K, Liu Q-F, Ye G-D, Wang W-J. A double-negative feedback loop between Wnt\u0026ndash;β-catenin signaling and HNF4α regulates epithelial\u0026ndash;mesenchymal transition in hepatocellular carcinoma. J Cell Sci. 2013;126(24):5692\u0026ndash;703.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOckfen E, Filali L, Pereira Fernandes D, Hoffmann C, Thomas C. Actin cytoskeleton remodeling at the cancer cell side of the immunological synapse: good, bad, or both? Front Immunol. 2023;14:1276602.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerrara N. The role of the VEGF signaling pathway in tumor angiogenesis. Tumor Angiogenesis: Key Target Cancer Therapy 2019:211\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metal ion transporter SLC39A14 (solute carrier family 39 member 14), ferroptosis, glycosylation, tumor microenvironment, immune infiltration, bioinformatics, prognostic biomarker","lastPublishedDoi":"10.21203/rs.3.rs-6384291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6384291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFerroptosis, an iron-dependent form of regulated cell death driven by lipid peroxidation, has emerged as a pivotal mechanism in cancer progression and therapeutic resistance. Concurrently, glycosylation, a key post-translational modification, plays a critical role in regulating cell signaling, immune evasion, and metastasis. Although both processes are individually implicated in tumor biology, the intersection between ferroptosis and glycosylation remains largely unexplored. We performed a comprehensive pan-cancer analysis by integrating transcriptomic, epigenomic, single-cell RNA sequencing, and pharmacogenomic datasets. Ferroptosis- and glycosylation-related genes were curated from the MSigDB, leading to the identification of metal ion transporter SLC39A14 (solute carrier family 39 member 14) as a common intersecting gene. A ferroptosis-related gene signature was constructed using LASSO Cox regression, followed by survival, immune microenvironment, and functional enrichment analyses across The Cancer Genome Atlas (TCGA) cohort. Drug sensitivity analysis and AlphaFold-based molecular docking were used to evaluate therapeutic relevance. SLC39A14 was significantly upregulated in multiple tumor types and strongly associated with poor prognosis, immune-stromal infiltration, and ferroptosis resistance. Notably, among all cancer types analyzed, glioblastoma multiforme (GBM) and kidney renal clear cell carcinoma (KIRC) exhibited the strongest prognostic associations and the most significant differential expression of SLC39A14. These two tumors also showed distinct but clinically relevant ferroptosis-immune phenotypes: GBM featured enrichment of VEGF and NRF2 oxidative stress pathways in a hypoxia-adapted, macrophage- and NK cell\u0026ndash;infiltrated microenvironment, while KIRC was characterized by TF-induced thrombosis, DNA damage response, and immune exclusion. Single-cell transcriptomic and DNA methylation analyses further confirmed SLC39A14\u0026rsquo;s role in modulating tumor microenvironment and ferroptotic vulnerability. Functional enrichment revealed that high ferroptosis scores were enriched in angiogenesis, EMT, and cytokine signaling pathways. A nomogram integrating SLC39A14 with clinical parameters showed enhanced survival prediction. Moreover, SLC39A14 expression correlated with differential responses to ferroptosis-related drugs, suggesting translational applicability. This study highlights the dual regulatory role of SLC39A14 at the interface of ferroptosis and glycosylation, with a distinct impact on GBM and renal cancer progression. By integrating multi-omics and single-cell analyses, we reveal SLC39A14 as a promising prognostic biomarker and therapeutic target, particularly in brain and kidney cancers where ferroptosis modulation may offer novel clinical opportunities.\u003c/p\u003e","manuscriptTitle":"Metal Ion Transporter SLC39A14-Mediated Ferroptosis and Glycosylation Modulate the Tumor Immune Microenvironment: A Pan-Cancer Multi-Omics Exploration of Therapeutic Potential","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 19:47:24","doi":"10.21203/rs.3.rs-6384291/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-29T09:02:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T09:20:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39274025511557712458663069113937078982","date":"2025-07-10T06:08:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5108677972088297838646983691548615951","date":"2025-07-08T06:08:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-08T06:07:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114573819342075704447122185296146243660","date":"2025-07-08T05:50:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-08T04:29:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-08T14:29:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-08T14:23:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Cell International","date":"2025-04-06T01:42:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f1511285-18fe-4766-98ac-39d2b283a1c4","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:26:38+00:00","versionOfRecord":{"articleIdentity":"rs-6384291","link":"https://doi.org/10.1186/s12935-025-04003-6","journal":{"identity":"cancer-cell-international","isVorOnly":false,"title":"Cancer Cell International"},"publishedOn":"2025-10-21 16:16:48","publishedOnDateReadable":"October 21st, 2025"},"versionCreatedAt":"2025-07-10 19:47:24","video":"","vorDoi":"10.1186/s12935-025-04003-6","vorDoiUrl":"https://doi.org/10.1186/s12935-025-04003-6","workflowStages":[]},"version":"v1","identity":"rs-6384291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6384291","identity":"rs-6384291","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}