Abstract

Purpose

   RNF213 encodes one of the largest E3 ubiquitin ligases in the human
   proteome, playing essential roles in ubiquitination, DNA damage repair,
   and immune system regulation. Although RNF213 has been extensively
   studied in Moyamoya disease, its involvement in cancer biology remains
   insufficiently understood. This study seeks to conduct a comprehensive
   pan-cancer analysis to explore RNF213 expression, mutation
   characteristics, its correlation with clinical outcomes, and its
   potential as a prognostic biomarker and a therapeutic target,
   particularly in the context of immunotherapy.

Methods

   Leveraging data from The Cancer Genome Atlas (TCGA) and additional
   publicly available datasets, we performed a systematic analysis of
   RNF213 expression patterns, mutation frequencies, and their
   relationships with clinical outcomes. We also conducted survival
   analyses to assess the impact of RNF213 expression on the efficacy of
   immune checkpoint blockade therapies.

Results

   RNF213 demonstrated variable expression levels across different cancer
   types, with notable overexpression observed in glioblastoma and
   endometrial carcinoma, while underexpression was detected in lung
   adenocarcinoma and prostate adenocarcinoma. Elevated RNF213 expression
   was linked to unfavorable survival outcomes in acute myeloid leukemia
   and low-grade glioma, whereas it was associated with better survival in
   sarcoma and skin cutaneous melanoma. RNF213 expression showed a
   positive association with immune-related genes and immune cell
   infiltration, particularly in glioblastoma and breast cancer.
   Furthermore, patients with high RNF213 expression experienced
   significant survival advantages when receiving combined anti-PD-1 and
   anti-CTLA-4 immunotherapies.

Conclusion

   This study highlights RNF213’s dual functionality in tumor progression
   and immune modulation, underscoring its potential as both a prognostic
   biomarker and a therapeutic target. The findings suggest that RNF213
   may play a pivotal role in determining immunotherapy outcomes,
   warranting further exploration into its underlying mechanisms and
   clinical applications.

Supplementary Information

   The online version contains supplementary material available at
   10.1007/s12672-025-03237-0.

   Keywords: RNF213, Pan-cancer analysis, Tumor microenvironment,
   Immunotherapy, Immune checkpoint blockade, Prognostic biomarker

Introduction

   Cancer is recognized as the second leading cause of death worldwide,
   with its global burden projected to rise significantly in the coming
   decades. It is characterized by the uncontrolled proliferation,
   invasion, and metastasis of cells, with most cancer-related fatalities
   resulting from metastatic disease [[32]1]. Advances in large-scale
   parallel sequencing technologies have enabled the systematic
   identification of genetic alterations across the cancer genome,
   offering unprecedented insights into the molecular mechanisms
   underlying tumorigenesis [[33]2–[34]4]. Despite these advancements, the
   inherent complexity of cancer necessitates a more comprehensive
   understanding of these mechanisms, which often vary considerably across
   different cancer types. Pan-cancer analyses, which simultaneously
   investigate multiple cancer types, provide a unique opportunity to
   uncover shared pathways and genetic alterations that drive
   tumorigenesis. Such studies not only deepen our understanding of cancer
   biology but also aid in identifying novel biomarkers and therapeutic
   targets.

   Ring finger protein 213 (RNF213), encoded by a gene located on
   chromosome 17q25.3, is the largest E3 ubiquitin ligase in the human
   proteome, with a molecular weight of 591 kDa. It exhibits both
   ubiquitin ligase and ATPase activities and is involved in various
   biological processes, including ubiquitination, DNA damage repair, and
   innate immune responses [[35]5–[36]7]. RNF213 is most prominently
   associated with Moyamoya disease, a rare cerebrovascular disorder
   [[37]8, [38]9]. Notably, RNF213 functionally interacts with GUCY1A3,
   another key gene implicated not only in Moyamoya disease but also in
   coronary artery disease and pulmonary artery hypertension. Both RNF213
   and GUCY1A3 influence lipid metabolism, inflammation mediated by NF-κB,
   and vascular stability through shared signaling pathways such as
   calcineurin/NFAT and caveolin. This interplay underscores their
   critical roles in vascular homeostasis and related pathologies beyond
   Moyamoya disease, highlighting the importance of investigating their
   coordinated functions [[39]10]. However, emerging evidence indicates
   its significant involvement in cancer. For example, alterations in
   RNF213 have been implicated in the progression of several cancers, such
   as gliomas, hepatocellular carcinoma, and breast cancer
   [[40]11–[41]13]. Recurrent RNF213-SLC26A11 fusion transcripts have been
   identified in gliomas and myeloid leukemia [[42]11, [43]14]while RNF213
   mutations have been reported in oral tongue squamous cell carcinoma,
   ovarian cancer, advanced gastric cancer, and gastric cancer with liver
   metastases [[44]13, [45]15–[46]17]. Furthermore, RNF213 expression has
   been linked to immune cell infiltration and prognosis in lung cancer,
   particularly non-small cell lung cancer, highlighting its potential
   role in modulating the tumor immune microenvironment and affecting
   clinical outcomes [[47]18]. These findings suggest that RNF213 may
   function as either a tumor suppressor or an oncogene, depending on the
   specific cancer type and biological context.

   Additionally, RNF213 has been implicated in the regulation of
   endoplasmic reticulum (ER) stress responses. Recent studies
   demonstrated that suppression of RNF213 inhibits ER stress by
   upregulating SEL1L, a key component of the ER-associated degradation
   (ERAD) pathway, thereby promoting cellular homeostasis under stress
   conditions [[48]19]. This suggests a potential link between RNF213 and
   ER function that may be relevant in both vascular and cancer biology.

   Despite these discoveries, a comprehensive understanding of RNF213’s
   role across diverse cancer types remains incomplete. Notably, RNF213
   displays varying expression patterns and mutation frequencies across
   different cancers, and its dysregulation has been linked to aggressive
   tumor behaviors, including enhanced proliferation, invasion, and
   metastasis. However, the precise mechanisms through which RNF213
   contributes to tumorigenesis are not yet fully elucidated. To address
   this knowledge gap, this study aims to perform a pan-cancer analysis of
   RNF213 expression and mutation patterns using publicly available
   datasets, such as TCGA and other large-scale genomic resources. By
   systematically evaluating RNF213’s expression levels, mutation
   frequencies, and their associations with clinical outcomes across
   multiple cancer types, this work seeks to clarify its role in cancer
   progression and assess its potential as a biomarker and therapeutic
   target. This study lays the groundwork for future research to
   investigate the functional mechanisms of RNF213 and its implications
   for cancer biology and treatment strategies.

Methods and materials

RNF213 expression analysis and protein distribution

   The mRNA expression levels of RNF213 in normal human tissues, as well
   as its subcellular protein distribution, were analyzed using data from
   the Human Protein Atlas (HPA) database
   ([49]https://www.proteinatlas.org). To evaluate RNF213 expression
   across various cancer types, we utilized the TCGA TARGET GTEx dataset
   (PANCAN, N = 19131, G = 60499) available through the UCSC Xena database
   ([50]https://xenabrowser.net/). Expression data for RNF213
   (ENSG00000173821) were retrieved from samples categorized as Solid
   Tissue Normal, Primary Solid Tumor, Primary Tumor, Normal Tissue,
   Primary Blood Derived Cancer - Bone Marrow, and Primary Blood Derived
   Cancer - Peripheral Blood. To ensure data quality, samples with
   expression levels of 0 were excluded. The expression values were
   normalized using a log2(x + 1) transformation. Cancer types with fewer
   than three samples were removed, resulting in data from 34 distinct
   cancer types.

   For subcellular localization analysis, indirect immunofluorescence (IF)
   staining was performed on A-431, U2OS, and U-251MG cell lines using
   antibodies obtained from Atlas Antibodies and Sigma-Aldrich (Catalog
   numbers HPA003347 and HPA026790). Cells were fixed with
   paraformaldehyde (PFA) and stained at a dilution of 1:50. The antibody
   specificity and staining patterns were validated by the Human Protein
   Atlas using standard and enhanced validation methods, including siRNA
   knockdown, GFP-tagged cell lines, and independent antibodies. Confocal
   microscopy was used to visualize RNF213 localization, with co-staining
   for microtubules and ER markers.

Genomic alterations and expression correlation analysis

   Genomic alterations of RNF213 were examined using the cBioPortal
   platform ([51]https://cbioportal.org). RNA-seq data for tumor cell
   lines were obtained from the Cancer Cell Line Encyclopedia (CCLE)
   ([52]https://sites.broadinstitute.org/ccle/). The relationship between
   copy number variations (CNVs) and RNF213 mRNA expression was analyzed
   using the Gene Set Cancer Analysis (GSCA) platform
   ([53]https://guolab.wchscu.cn/GSCA/). RSEM-normalized mRNA expression
   data and CNV data were downloaded from the TCGA database and merged
   using TCGA barcodes. Spearman correlation analysis was conducted to
   evaluate the association between RNF213 mRNA expression and CNVs, with
   p-values adjusted using the false discovery rate (FDR) [[54]20].

RNF213 expression and prognostic significance

   The association between RNF213 expression and tumor staging was
   analyzed using the Gene Expression Profiling Interactive Analysis
   database (GEPIA2; [55]http://gepia.cancer-pku.cn/), a web server that
   enables analysis of RNA sequencing expression data from 9,736 tumors
   and 8,587 normal samples derived from TCGA and the GTEx projects,
   providing a standardized processing pipeline for high-resolution
   exploration of gene expression patterns. RNF213 expression data were
   extracted from the TCGA Pan-Cancer dataset available through the UCSC
   Xena database. Samples with expression levels of 0 and cancer types
   with fewer than three samples were excluded. Expression values were
   transformed using log2(x + 0.001). Differential expression analysis
   across clinical stages was performed using R software (version 3.6.4).
   Statistical significance was determined using the unpaired Student’s
   t-test for pairwise comparisons and analysis of variance (ANOVA) for
   multiple group comparisons.

   For survival analysis, overall survival (OS) and disease-free survival
   (DFS) data were obtained from GEPIA2. Patients were divided into high
   and low RNF213 expression groups based on the top 50% and bottom 50%
   expression thresholds. Kaplan-Meier survival curves were generated, and
   the log-rank test was used to assess statistical significance.
   Additionally, Cox proportional hazards regression models were
   constructed using the coxph function from the R package survival
   (version 3.2-7) to evaluate the relationship between RNF213 expression
   and prognosis.

Immunoregulatory gene analysis

   To explore the association between RNF213 and immune pathways, RNF213
   expression data and 150 marker genes representing five immune pathway
   categories (chemokines, receptors, MHC molecules, immunoinhibitors, and
   immunostimulators) were extracted from the TCGA Pan-Cancer dataset.
   Expression values were log2(x + 0.001) transformed, and Pearson
   correlation coefficients were calculated between RNF213 and the marker
   genes. Tumor stemness scores (RNAss), derived from mRNA features, were
   integrated with RNF213 expression data to assess the relationship
   between tumor stemness and RNF213 expression [[56]21]. Pearson
   correlation coefficients were computed for 37 cancer types.

Immune infiltration analysis

   Immune infiltration scores were calculated using the ESTIMATE algorithm
   in R. Stromal, immune, and ESTIMATE scores were computed for 9554 tumor
   samples across 39 tumor types [[57]22]. Pearson correlation
   coefficients were calculated between RNF213 expression and immune
   infiltration scores. Additionally, immune cell infiltration scores for
   B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and
   dendritic cells were reassessed using the TIMER method from the R
   package IOBR [[58]23, [59]24]. Spearman correlation coefficients were
   calculated to determine the relationship between RNF213 expression and
   immune cell infiltration across 9405 tumor samples in 36 tumor types.

RNF213-associated gene analysis

   Experimentally validated RNF213 binding proteins were retrieved from
   the STRING database ([60]https://cn.string-db.org/). The top 100 genes
   associated with RNF213 were identified using the “Similar Genes
   Detection” module in GEPIA2. Expression data for the top 10 target
   genes were obtained from TIMER2.0, a comprehensive web resource
   designed for systematic analysis of immune cell infiltrates across
   diverse cancer types. TIMER2.0 estimates immune cell abundances using
   multiple deconvolution methods and allows dynamic generation of
   high-quality figures to explore tumor immunological, clinical, and
   genomic features. Expression data were visualized as heatmaps.
   Functional enrichment analysis was performed using the R package
   clusterProfiler (version 3.14.3) with KEGG Pathway and Gene Ontology
   (GO) annotations. Gene sets with a minimum size of 5 and a maximum size
   of 5000 were included, and statistical significance was defined as
   P < 0.05 and FDR < 0.01. Visualization of enrichment results was
   conducted using the Sangerbox3.0 platform
   ([61]http://sangerbox.com/home.html).

RNF213 expression and prognosis in cancer immunotherapy

   The prognostic value of RNF213 expression in patients undergoing immune
   checkpoint blockade (ICB) therapies, including anti-PD-1, anti-PD-L1,
   and anti-CTLA-4 treatments, was evaluated using the Kaplan-Meier
   Plotter. Patients were stratified into high and low RNF213 expression
   groups, and median OS was compared using the log-rank test. RNF213
   expression and mutation patterns were further analyzed using the Tumor
   Immune Syngeneic Mouse (TISMO) database, which includes both in vivo
   and in vitro datasets. Differential expression analysis was performed
   using DESeq2, with RNF213 expression quantified as log-transformed
   transcripts per million (log(TPM)) values. Statistical significance
   thresholds were set at *FDR ≤ 0.05, **FDR ≤ 0.01, and ***FDR  ≤ 0.001.

Statistical analysis

   Statistical analyses were conducted using R software and specified
   bioinformatics tools. Differential RNF213 expression across tumor
   stages was evaluated via unpaired t-test (pairwise) or ANOVA
   (multi-group). Spearman correlation with false discovery rate (FDR)
   adjustment assessed associations between RNF213 mRNA expression and
   copy number variations. Survival outcomes (OS/DFS) were analyzed using
   Kaplan-Meier curves (log-rank test) and Cox regression.

   Correlations with immune markers, tumor stemness (RNAss), and immune
   infiltration scores (ESTIMATE/TIMER) were calculated via
   Pearson/Spearman methods. Functional enrichment of RNF213-associated
   genes was performed using clusterProfiler (KEGG/GO; P < 0.05,
   FDR < 0.01). For immunotherapy cohorts, DESeq2-based differential
   expression and survival analyses (log-rank test) were applied with
   FDR-defined significance levels (*≤0.05, **≤0.01, ***≤0.001).
   Statistical significance was set at P < 0.05 unless otherwise noted.

Results

Differential expression and genomic alterations of RNF213 in normal and tumor
tissues

   To explore the potential role of RNF213 in cancer, its mRNA expression
   levels were first analyzed across normal human tissues. The results
   demonstrated that RNF213 is broadly expressed, with particularly high
   expression levels observed in the parathyroid, bone marrow, thymus,
   spleen, appendix, lymph nodes, tonsils, lungs, retina, stomach,
   thyroid, bladder, salivary glands, small intestine, gallbladder, and
   kidneys (nTPM > 10) (Fig. [62]1A).

Fig. 1.

   [63]Fig. 1
   [64]Open in a new tab

   RNF213 mRNA expression and protein localization in normal tissues and
   cancer types. A RNF213 mRNA expression levels across normal human
   tissues, showing widespread expression with notably high levels in
   specific tissues. B Differential expression of RNF213 mRNA in tumor and
   normal tissues across various cancer types, highlighting significant
   upregulation or downregulation in specific cancers. C Subcellular
   localization of RNF213 protein in cancer cell lines. Immunofluorescence
   images show RNF213 colocalized with microtubules and endoplasmic
   reticulum (ER) in A-431, U2OS, and U-251MG cells. Separate merged
   images of RNF213 with microtubules and RNF213 with ER are provided to
   illustrate predominant localization

   Next, RNF213 mRNA expression was evaluated in various tumor types using
   data from the TCGA and GTEx databases. The analysis revealed
   significant upregulation of RNF213 mRNA in multiple cancers, including
   glioblastoma (GBM), glioma (GBMLGG), low-grade glioma (LGG),
   endometrial carcinoma (UCEC), invasive breast carcinoma (BRCA),
   cervical squamous cell carcinoma and endocervical adenocarcinoma
   (CESC), esophageal carcinoma (ESCA), stomach and esophageal carcinoma
   (STES), kidney renal papillary cell carcinoma (KIRP), kidney cancer
   (KIPAN), stomach adenocarcinoma (STAD), head and neck squamous cell
   carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC),
   hepatocellular carcinoma (LIHC), bladder urothelial carcinoma (BLCA),
   rectal adenocarcinoma (READ), pancreatic adenocarcinoma (PAAD),
   testicular germ cell tumors (TGCT), acute lymphoblastic leukemia (ALL),
   acute myeloid leukemia (LAML), and cholangiocarcinoma (CHOL).
   Conversely, RNF213 mRNA was found to be significantly downregulated in
   cancers such as lung adenocarcinoma (LUAD), prostate adenocarcinoma
   (PRAD), lung squamous cell carcinoma (LUSC), Wilms tumor (WT), thyroid
   carcinoma (THCA), uterine carcinosarcoma (UCS), adrenocortical
   carcinoma (ACC), and kidney chromophobe (KICH). For other tumor types,
   including colon adenocarcinoma (COAD), colorectal adenocarcinoma
   (COADREAD), skin cutaneous melanoma (SKCM), ovarian serous
   cystadenocarcinoma (OV), and pheochromocytoma and paraganglioma (PCPG),
   no significant differences in RNF213 expression were observed
   (Fig. [65]1B).

   In the context of pan-cancer analysis, immunofluorescence was employed
   to investigate the subcellular localization of RNF213 protein in A-413,
   U2OS, and U-251MG cell lines. Separate merged images of RNF213 with
   microtubules and RNF213 with ER were obtained, demonstrating that
   RNF213 predominantly localizes to the ER and microtubules
   (Fig. [66]1C).

Genomic alterations and correlation analysis of RNF213

   Using the cBioPortal platform, we analyzed the genomic alterations of
   RNF213 across multiple cancer types. Among the detected alterations,
   mutations were the most prevalent, followed by gene amplifications
   (Fig. [67]2A). Further investigation using the GSCA platform
   demonstrated significant positive correlations between RNF213 CNVs and
   mRNA expression levels in various cancers (FDR ≤ 0.05). Notable
   correlations were observed in ACC (Spearman’s ρ = 0.384, FDR = 0.006),
   BRCA (Spearman’s ρ = 0.367, FDR < 0.0001), KICH (Spearman’s ρ = 0.378,
   FDR = 0.034), LUAD (Spearman’s ρ = 0.304, FDR < 0.0001), OV (Spearman’s
   ρ = 0.327, FDR < 0.0001), and SKCM (Spearman’s ρ = 0.303,
   FDR < 0.0001).

Fig. 2.

   [68]Fig. 2
   [69]Open in a new tab

   Genomic alterations and CNV-mRNA expression correlation of RNF213
   across cancer types. A Summary of genomic alterations of RNF213 across
   various cancer types, showing mutations as the most frequent
   alteration, followed by amplifications. B Correlation analysis between
   RNF213 CNVs and mRNA expression in different cancer types. C Positive
   correlation between RNF213 amplification and mRNA expression levels,
   highlighting the impact of CNV on gene expression

   Additional significant correlations were identified in COAD (Spearman’s
   ρ = 0.245, FDR < 0.001), LIHC (Spearman’s ρ = 0.249, FDR < 0.0001), and
   UCEC (Spearman’s ρ = 0.286, FDR < 0.001). Conversely, no significant
   correlations were found in certain cancer types, such as diffuse large
   B-cell lymphoma (DLBC, FDR = 1) and LAML (FDR = 1). While a few cancer
   types displayed negative correlations, these were not statistically
   significant (Fig. [70]2B).

   Furthermore, analysis of RNF213 amplifications revealed a strong
   positive relationship between CNV levels and mRNA expression, further
   supporting the role of CNVs in regulating RNF213 expression
   (Fig. [71]2C).

Correlation of RNF213 expression with tumor staging and survival outcomes

   The analysis of RNF213 expression across various tumor stages revealed
   significant associations that differed depending on the cancer type. In
   PAAD, OV, and SKCM, RNF213 expression was inversely correlated with
   clinical staging, indicating that higher RNF213 expression levels might
   be linked to earlier stages of these cancers (Fig. [72]3A–C). Further
   validation using data from the UCSC database supported these
   observations and identified a positive correlation between RNF213
   expression and clinical staging in KIPAN. These findings suggest that
   RNF213 may have distinct roles in tumor biology, potentially
   functioning as a tumor suppressor in cancers such as PAAD and OV, while
   promoting tumor progression in kidney-related malignancies
   (Fig. [73]3D).

Fig. 3.

   [74]Fig. 3
   [75]Open in a new tab

   Correlation of RNF213 expression with tumor stage and survival
   outcomes. A–D Correlation between RNF213 expression and clinical
   staging across different cancer types. E–I Association of RNF213
   expression with OS in selected cancer types. J, K Association of RNF213
   expression with DFS in specific cancer types

   Survival analysis provided additional insights into the prognostic
   significance of RNF213, highlighting its complex and context-dependent
   roles. Elevated RNF213 expression was associated with poorer OS in
   LAML, LGG, and Uveal Melanoma (UVM). In contrast, higher RNF213
   expression was linked to improved OS in sarcoma (SARC) and SKCM
   (Fig. [76]3E–I). Moreover, elevated RNF213 expression was found to
   correlate with reduced DFS in LGG and UVM, underscoring its potential
   as a context-specific prognostic marker (Fig. [77]3J, K). These results
   suggest that RNF213 could serve as a valuable biomarker for predicting
   survival outcomes in certain cancer types.

   To better understand the association between RNF213 expression and
   prognosis in various cancers, we analyzed standardized pan-cancer
   datasets and corresponding follow-up data from the UCSC database. Using
   the R package “survival” and the Cox proportional hazards regression
   model (coxph function), we conducted statistical significance testing
   through the log-rank test.

   The results indicated that elevated RNF213 expression was significantly
   correlated with poor prognosis in seven cancer types, including
   TCGA-GBMLGG(N = 619, p = 2.9e-4, HR = 1.44(1.18, 1.76)),
   TCGA-LGG(N = 474, p= 5.2e-7, HR = 2.19(1.62, 2.97)),
   TARGET-LAML(N = 142, p = 1.2e-4, HR = 1.46(1.20, 1.78)),
   TCGA-KIRP(N = 276, p = 4.3e-3, HR = 1.92(1.23, 3.00)),
   TCGA-KIPAN(N = 855, p = 3.0e-4, HR = 1.32(1.14, 1.54)),
   TCGA-UVM(N = 74, p = 3.7e-3, HR = 2.38(1.31, 4.32)) and
   TCGA-LAML(N = 209, p = 6.2e-3, HR = 1.27(1.07, 1.52)). Conversely,
   reduced RNF213 expression was associated with unfavorable prognosis in
   four cancer types, namely TCGA-SARC(N = 254, p = 0.01, HR = 0.70(0.53,
   0.93)), TCGA-SKCM(N = 444, p = 3.1e-7, HR = 0.66(0.56, 0.77)),
   TCGA-SKCM-M(N = 347, p = 4.7e-6, HR = 0.66(0.56, 0.79)) and
   TARGET-ALL(N = 86, p = 8.4e-3, HR = 0.75(0.61, 0.93)) (Fig. [78]4).

Fig. 4.

   [79]Fig. 4
   [80]Open in a new tab

   Prognostic associations of RNF213 expression. Forest plot of Cox hazard
   ratios (HR) across 44 cancer datasets, highlighting 11 tumors with
   significant associations (p < 0.05)

Immunomodulatory role of RNF213 in various cancer types

   To investigate the immunomodulatory role of RNF213 in tumors, we
   analyzed its association with immune regulatory genes, immune
   checkpoints, tumor stemness scores, and immune infiltration. Using a
   pan-cancer dataset from the UCSC database, we retrieved expression data
   for the RNF213 gene and 150 marker genes related to five immune
   pathways: chemokines (41 genes), receptors (18 genes), MHC molecules
   (21 genes), immune inhibitors (24 genes), and immune stimulators (46
   genes). Pearson correlation coefficients were calculated to evaluate
   the relationship between RNF213 expression and these immune pathway
   marker genes. The analysis revealed a positive correlation between
   RNF213 expression and the majority of immune regulatory genes
   (Fig. [81]5A).

Fig. 5.

   [82]Fig. 5
   [83]Open in a new tab

   Correlation of RNF213 expression with immune regulatory pathways and
   immune checkpoints. A Correlation between RNF213 expression and marker
   genes of five immune pathways, including chemokines, receptors, MHC
   molecules, immune inhibitors, and immune stimulators. B Association of
   RNF213 expression with immune checkpoints, including inhibitory and
   stimulatory checkpoints, across cancer types. C Differential
   correlation of RNF213 expression with immune checkpoints in specific
   cancer types

   We further explored the relationship between RNF213 and specific immune
   checkpoints, including 24 inhibitory and 36 stimulatory checkpoints.
   The results demonstrated significant correlations between RNF213
   expression and most immune checkpoints (Fig. [84]5B). A detailed
   analysis revealed that RNF213 expression was significantly associated
   with immune checkpoints in 22 cancer types. Specifically, RNF213 showed
   a significant positive correlation in four cancers, including
   BRCA(N = 1080)(R = 0.069, P = 0.024), STES(N = 578)(R = 0.113,
   P = 0.007), STAD(N = 399)(R = 0.124, P = 0.013) and
   PCPG(N = 176)(R = 0.172, P = 0.022), while exhibiting a significant
   negative correlation in 18 cancers, such as GBM(N = 152)(R=-0.278,
   P = 0.001), GBMLGG(N = 659)(R=-0.361,P = 9.811), LGG(N = 507)(R=-0.476,
   P = 5.400e-30), CESC(N = 301)(R=-0.129, P = 0.026),
   COAD(N = 281)(R=-0.194, P = 0.001), COADREAD(N = 369)(R=-0.204,
   P = 0.00008), LAML(N = 167)(R=-0.230, P = 0.003),
   KIRP(N = 283)(R=-0.233, P = 0.00007), KIPAN(N = 860)(R=-0.332,
   P = 1.358e-23), HNSC(N = 512)(R=-0.088, P = 0.046),
   KIRC(N = 512)(R=-0.223, P = 3.631e-7), LUSC(N = 483)(R=-0.099,
   P = 0.030), THYM(N = 119)(R=-0.415, P = 0.000003),
   THCA(N = 499)(R=-0.314, P = 6.596e-13), READ(N = 88)(R=-0.263,
   P = 0.013), PAAD(N = 156)(R=-0.255, P = 0.001), TGCT(N = 147)(R=-0.319,
   P = 0.00008) and KICH(N = 65)(R=-0.498, P = 0.00002) (Fig. [85]5C).

Correlation between RNF213 expression and immune infiltration across cancer
types

   To investigate the relationship between RNF213 expression and immune
   infiltration, we analyzed immune infiltration scores from 9554 tumor
   samples across 39 cancer types. Pearson correlation coefficients were
   calculated to assess the association between RNF213 expression and
   immune infiltration scores for each cancer type. Notably, RNF213
   expression showed a significant positive correlation with immune
   infiltration in 30 cancer types, including TCGA-GBMLGG(N = 656,
   R = 0.27, P = 1.6e-12), TCGA-LGG(N = 504, R = 0.45, P = 5.0e-27),
   TCGA-CESC(N = 291, R = 0.33, P = 1.2e-8), TCGA-LUAD(N = 500, R = 0.16,
   P = 4.8e-4), TCGA-COAD(N = 282, R = 0.38, P = 6.4e-11),
   TCGA-COADREAD(N = 373, R = 0.38, P = 1.9e-14), TCGA-LAML(N = 149,
   R = 0.39, P = 7.8e-7), TCGA-BRCA(N = 1077, R = 0.18, P = 2.0e-9),
   TCGA-ESCA(N = 181, R = 0.21, P = 4.0e-3), TCGA-STES(N = 569, R = 0.11,
   P = 0.01), TCGA-SARC(N = 258, R = 0.34, P = 1.8e-8),
   TCGA-KIPAN(N = 878, R = 0.20, P = 3.1e-9), TCGA-STAD(N = 388, R = 0.17,
   P = 7.2e-4), TCGA-PRAD(N = 495, R = 0.22, P = 4.4e-7),
   TCGA-HNSC(N = 517, R = 0.41, P = 7.1e-22), TCGA-KIRC(N = 528, R = 0.36,
   P = 1.1e-17), TCGA-LUSC(N = 491, R = 0.20, P = 1.2e-5),
   TCGA-THCA(N = 503, R = 0.28, P = 3.3e-10), TCGA-READ(N = 91, R = 0.41,
   P = 6.1e-5), TCGA-SKCM-M(N = 351, R = 0.54, P = 1.7e-28),
   TCGA-SKCM(N = 452, R = 0.51, P = 1.1e-30), TCGA-PAAD(N = 177, R = 0.35,
   P = 2.0e-6), TCGA-OV(N = 416, R = 0.32, P = 1.3e-11),
   TCGA-TGCT(N = 132, R = 0.64, P = 1.1e-16), TCGA-SKCM-P(N = 101,
   R = 0.29, P = 2.8e-3), TCGA-UVM(N = 79, R = 0.49, P = 3.7e-6),
   TCGA-UCS(N = 56, R = 0.35, P = 8.0e-3), TCGA-BLCA(N = 405, R = 0.21,
   P = 1.7e-5), TCGA-KICH(N = 65, R = 0.53, P = 6.9e-6) and
   TCGA-DLBC(N = 46, R = 0.64, P = 1.4e-6) (Fig. [86]6A–D1).

Fig. 6.

   [87]Fig. 6
   [88]Open in a new tab

   Correlation of RNF213 expression with immune infiltration across cancer
   types. A–D1 Correlation between RNF213 expression and immune
   infiltration scores in 39 cancer types, showing significant positive
   associations in 30 cancer types. E1 Association between RNF213
   expression and immune cell infiltration, including B cells, CD4 + T
   cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells,
   across multiple cancer types

   To further clarify the association between RNF213 expression and immune
   cell infiltration, we analyzed data from 9405 tumor samples across 36
   cancer types. The results revealed significant correlations between
   RNF213 expression and immune cell infiltration scores in multiple
   cancer types. Specifically, RNF213 expression was strongly associated
   with the infiltration of various immune cell types, including B cells,
   CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and dendritic
   cells, in certain tumor types. These findings suggest that RNF213 may
   play a critical role in modulating the tumor immune microenvironment
   across diverse cancer types (Fig. 6E1).

Analysis of RNF213-associated genes

   Using the STRING online tool, we identified a total of 50 proteins that
   interact with RNF213 (Fig. [89]7A, Supplementary Table [90]1). Notably,
   among the identified RNF213-interacting proteins, PTPN1 (also known as
   PTP1B) was included, consistent with previous studies demonstrating a
   functional relationship between PTP1B and RNF213 in regulating tumor
   survival under hypoxia [[91]12]. This finding supports the reliability
   of our network analysis and suggests that the PTP1B-RNF213 axis may
   play a critical role in cancer biology. Furthermore, recent work by
   Bhardwaj et al. has elucidated a mechanism involving PTP1B and RNF213
   in hypoxia-induced inflammatory cell death, further underscoring the
   biological relevance of this interaction [[92]25]. Additionally, the
   GEPIA2 platform was utilized to determine the top 100 genes associated
   with RNF213 (Supplementary Table [93]2). Among these genes, the top 10
   exhibited a strong positive correlation with RNF213 expression across
   multiple cancer types (Fig. [94]7B). To better understand the
   functional roles of these genes, we conducted KEGG and GO enrichment
   analyses.

Fig. 7.

   [95]Fig. 7
   [96]Open in a new tab

   Protein–protein interaction network, associated genes, and functional
   enrichment analysis of RNF213. A Protein–protein interaction (PPI)
   network of RNF213. B Correlation analysis of RNF213-associated genes
   across cancer types. C KEGG pathway enrichment analysis of
   RNF213-associated genes. D GO enrichment analysis of RNF213-associated
   genes

   The KEGG pathway analysis identified several pathways significantly
   linked to RNF213, shedding light on its potential involvement in
   tumorigenesis and cancer progression. The enriched pathways included
   “NOD-like receptor signaling pathway,” “Epstein-Barr virus infection,”
   and “Necroptosis,” which are associated with key processes such as
   immune evasion, modulation of the tumor microenvironment, and
   inflammation-driven cancer development (Fig. [97]7C).

   GO enrichment analysis revealed that the top 100 RNF213-associated
   genes were significantly enriched in various biological processes (BP).
   These processes included “defense response to virus,” “innate immune
   response,” “immune response,” and “response to interferon-gamma.” The
   enrichment of these immune-related pathways highlights RNF213’s
   potential role in immunoregulation and antiviral responses, suggesting
   a possible link between its immune functions and cancer progression
   (Fig. [98]7D).

RNF213 expression and its impact on immunotherapy outcomes

   Analysis of RNF213 expression using Kaplan-Meier Plotter demonstrated
   its significant effect on OS across different immunotherapy subtypes.
   In patients who did not undergo ICB therapy, high RNF213 expression was
   associated with a reduced median OS of 12.81 months, compared to 20
   months in the low expression group (P = 0.0014, FDR = 10%)
   (Fig. [99]8A). This suggests that elevated RNF213 expression negatively
   impacts survival when immunotherapy is not used.

Fig. 8.

   [100]Fig. 8
   [101]Open in a new tab

   Impact of RNF213 expression on immunotherapy outcomes and cytokine
   responses. A–E Kaplan–Meier survival analysis of RNF213 expression in
   patients receiving different immunotherapy regimens, including no
   immunotherapy, anti-PD-1, anti-PD-L1, anti-CTLA-4, and combined
   anti-PD-1/anti-CTLA-4 therapies. RNF213 expression shows varying
   effects on overall survival (OS) depending on the treatment context. F
   Changes in RNF213 expression in response to immune checkpoint blockade
   (ICB) therapy across multiple tumor models, highlighting its
   upregulation in responders in melanoma, colorectal cancer, head and
   neck squamous cell carcinoma, and breast cancer models, with
   context-dependent effects observed in gastric adenocarcinoma. G
   Upregulation of RNF213 expression following cytokine treatments (IFNβ,
   IFNγ, and TNFα) in multiple cancer cell lines, including breast cancer,
   melanoma, and colorectal cancer, indicating its involvement in
   cytokine-mediated immune responses

   In contrast, in patients treated with anti-PD-1 therapy, high RNF213
   expression was associated with a prolonged median OS of 27.1 months,
   compared to 15.51 months in the low expression group (P = 0.0052,
   FDR = 50%) (Fig. [102]8B), indicating that increased RNF213 expression
   may enhance the efficacy of anti-PD-1 therapy. However, in patients
   receiving anti-PD-L1 therapy, high RNF213 expression was linked to a
   shorter median OS of 9.56 months, compared to 12.85 months in the low
   expression group. This difference, though indicative, was not
   statistically significant (P = 0.1139, FDR = 100%) (Fig. [103]8C),
   suggesting that the role of RNF213 in this context might be limited or
   dependent on other factors.

   Interestingly, in patients undergoing anti-CTLA-4 therapy, elevated
   RNF213 expression was associated with a significantly better median OS
   of 39.47 months, compared to 20.93 months in the low expression group
   (P = 0.0283, FDR > 50%) (Fig. [104]8D). Moreover, in patients receiving
   a combination of anti-PD-1 and anti-CTLA-4 therapies, high RNF213
   expression was correlated with a notably longer OS, yielding a highly
   significant P-value of 1.2e-5 (FDR = 1%) (Fig. [105]8E). These findings
   suggest that RNF213 could serve as a potential biomarker for predicting
   immunotherapy response, with its effects varying depending on the
   specific type of immunotherapy administered.

Differential expression of RNF213 in response to ICB and cytokine treatment

   RNF213 expression exhibited significant changes in response to ICB
   therapy across multiple tumor models. In melanoma, RNF213 expression
   was significantly elevated in responders compared to baseline in the
   B16_GSE149825_antiCTLA4&antiPD1 model (FDR ≤ 0.001), suggesting its
   role in enhancing anti-tumor immunity. Similar trends were observed in
   colorectal cancer (CT26_GSE139475_antiPD1, FDR ≤ 0.01) and head and
   neck squamous cell carcinoma (MOC22_RU31562203_antiPD1, FDR ≤ 0.01). In
   breast cancer models, RNF213 expression was significantly upregulated
   in responders in both T11_GSE124821_Apobec_day3 and day7 models
   (FDR ≤ 0.01 and FDR ≤ 0.001, respectively). Interestingly, in gastric
   adenocarcinoma (YTN16_GSE146027_day14_antiCTLA4), baseline RNF213
   expression was higher than in responders (FDR ≤ 0.001), suggesting a
   context-dependent role for RNF213 (Fig. [106]8F). Overall, RNF213
   expression was elevated in most tumor models among responders,
   indicating its potential involvement in mediating ICB treatment
   response.

   In vitro, RNF213 expression was significantly upregulated following
   cytokine treatment across multiple cancer cell lines. IFNβ treatment
   induced notable increases in RNF213 expression in breast cancer
   (4T1RTM28723893, FDR ≤ 0.001), melanoma (B16GSE110708, FDR ≤ 0.001),
   and colorectal cancer cell lines (CT26RTM28723893, FDR ≤ 0.01).
   Similarly, IFNγ treatment significantly upregulated RNF213 in breast
   cancer (4T1RTM28723893, FDR ≤ 0.01) and melanoma cell lines
   (B16GSE149824, FDR ≤ 0.01). TNFα treatment also led to significant
   RNF213 upregulation in melanoma (B16RTM28723893, FDR ≤ 0.01) and
   colorectal cancer cell lines (CT26RTM28723893, FDR ≤ 0.05)
   (Fig. [107]8G). Across all cytokine-treated samples, RNF213 expression
   consistently exceeded baseline levels, suggesting its involvement in
   cytokine-mediated immune responses.

Discussion

   This study provides a comprehensive pan-cancer analysis of RNF213,
   revealing its diverse roles in tumorigenesis, immune regulation, and
   the tumor microenvironment. Previous studies have demonstrated that
   copy number variations (CNVs) in oncogenes such as MYC and TP53 are
   critical drivers of tumor progression, highlighting the importance of
   CNVs in cancer biology [[108]26, [109]27]. In our analysis, we observed
   that CNVs, particularly amplifications, were positively correlated with
   RNF213 mRNA expression, suggesting that CNVs may serve as a regulatory
   mechanism driving its overexpression in certain cancers. However, the
   independent prognostic or predictive value of RNF213 CNV status
   compared to mRNA expression alone was not directly assessed in this
   study. Future analyses incorporating patient survival and treatment
   response data stratified by CNV status are warranted to clarify the
   relative clinical utility of RNF213 genomic alterations versus
   expression levels. RNF213 exhibits cancer-type-specific expression
   patterns, with significant upregulation in glioblastoma and endometrial
   carcinoma, and downregulation in lung adenocarcinoma and prostate
   adenocarcinoma. In some cancers, such as colorectal adenocarcinoma and
   ovarian serous cystadenocarcinoma, RNF213 expression showed no
   significant differences compared to normal tissues, suggesting its role
   may be limited or context-dependent in these tumor types. These
   findings suggest that RNF213 may contribute to tumor progression
   through genomic alterations, although the precise functional
   consequences require further investigation.

   Angiogenesis is a hallmark of cancer, and dysregulated vascular
   remodeling is a key process in tumor progression [[110]28, [111]29].
   RNF213 has been shown to regulate endothelial cell function and
   vascular remodeling in Moyamoya disease and Vasospastic angina,
   indicating its potential role in angiogenesis [[112]30, [113]31]. In
   our study, RNF213 was found to predominantly localize to the ER and
   microtubules, implicating it in ER-associated processes such as protein
   folding, stress responses, and lipid metabolism, as well as
   microtubule-related functions like intracellular transport and
   cytoskeletal organization. Although reports on RNF213 and lipid
   droplets are more numerous, there have also been previous studies
   linking RNF213 to the ER, including its role in modulating ER stress
   via SEL1L upregulation [[114]19]. Regarding cytoskeletal organization,
   RNF213’s interactions with cytoskeletal components have been
   highlighted in Moyamoya disease research [[115]32]. These findings
   align with its potential involvement in tumor angiogenesis and cellular
   stress responses, processes frequently dysregulated in cancer. RNF213’s
   role in vascular remodeling may further suggest its involvement in
   modulating tumor vascularization, which warrants further exploration.

   The dual roles of genes in cancer biology are well-documented, with
   examples such as TP53 and TGF-β, which can act as tumor suppressors or
   oncogenes depending on the cellular environment and tumor
   microenvironment [[116]33, [117]34]. Similarly, our analysis revealed
   that RNF213 exhibits context-dependent associations with tumor
   progression and patient outcomes. For instance, higher RNF213
   expression correlated with early tumor stages in pancreatic
   adenocarcinoma, ovarian cancer, and skin cutaneous melanoma, suggesting
   a potential tumor-suppressive role. Conversely, in the pan-kidney
   cohort, RNF213 expression increased with tumor progression, indicating
   a possible oncogenic role. Survival analysis further highlighted
   RNF213’s dual role in cancer prognosis. High RNF213 expression was
   associated with poor overall survival (OS) in acute myeloid leukemia,
   low-grade glioma, and uveal melanoma, while it correlated with improved
   OS in sarcoma and skin cutaneous melanoma. Additionally, high RNF213
   expression was linked to reduced disease-free survival (DFS) in
   low-grade glioma and uveal melanoma. These findings underscore the
   complexity of RNF213’s prognostic value, which varies significantly
   across tumor types and contexts.

   Immune-related genes such as PD-L1 and CTLA-4 are critical modulators
   of immune cell infiltration and immune evasion in the tumor
   microenvironment [[118]35–[119]37]. Consistent with this, our analysis
   revealed that RNF213 is closely associated with immune regulation and
   the tumor microenvironment. RNF213 expression was positively correlated
   with immunoregulatory genes across key immune pathways, including
   chemokines, receptors, MHC molecules, immunoinhibitors, and
   immunostimulators. Furthermore, RNF213 showed significant associations
   with both inhibitory and stimulatory immune checkpoints, indicating its
   involvement in immune evasion mechanisms and its potential as a target
   for immunotherapy. RNF213 expression was also significantly correlated
   with immune infiltration scores in 30 cancer types, particularly in
   glioblastoma, lung adenocarcinoma, and breast cancer, where increased
   RNF213 expression may enhance immune cell presence within tumors.
   Specific immune cell types, including B cells, CD4 + and CD8 + T cells,
   neutrophils, macrophages, and dendritic cells, were strongly associated
   with RNF213 expression, further supporting its role in shaping the
   tumor immune landscape. Given these associations, RNF213 expression may
   reflect or contribute to the immune “hot” tumor phenotype characterized
   by high immune infiltration, which is generally more responsive to
   immunotherapies. Conversely, low RNF213 expression could be indicative
   of “cold” tumors with poor immune cell infiltration and limited
   immunogenicity. Thus, RNF213 has potential utility both as a biomarker
   to predict immune responsiveness and, conceivably, as a modulator to
   convert “cold” tumors into “hot” ones, enhancing immunotherapy
   efficacy. However, the mechanistic basis of such effects remains to be
   elucidated.

   Despite the robust associations observed, the mechanisms by which
   RNF213 modulates immune responses and contributes to immune evasion or
   therapy resistance remain poorly understood. It is currently unknown
   whether RNF213 directly regulates interferon signaling pathways or
   antigen presentation processes. Given RNF213’s role as an E3 ubiquitin
   ligase, it may modulate immune signaling indirectly through
   ubiquitination of key immune regulators. We propose that future
   mechanistic studies focus on dissecting RNF213’s substrates and
   downstream pathways in immune cells and tumor microenvironment contexts
   to clarify its immunomodulatory functions.

   Immune checkpoint inhibitors have shown variable efficacy depending on
   the expression of immune-related genes and the tumor immune
   microenvironment [[120]38–[121]40]. In line with this, our findings
   suggest that RNF213 plays a significant and context-dependent role in
   modulating immune therapy outcomes, highlighting its potential as a
   predictive biomarker in cancer immunotherapy. High RNF213 expression
   was associated with poorer OS in patients not receiving immune
   checkpoint blockade (ICB) therapy, suggesting its involvement in
   promoting tumor progression or immune evasion under untreated
   conditions. However, it is important to note that the prognostic impact
   of RNF213 is tumor-type-dependent. While a negative correlation between
   high RNF213 expression and prognosis is observed in several cancers
   under untreated conditions, this is not universal across all tumor
   types targeted by immune checkpoint therapies. For instance, in
   non-small cell lung cancer (NSCLC), where immune checkpoint inhibitors
   are commonly used, high RNF213 expression has been reported to
   correlate positively with immune cell infiltration and favorable
   prognosis, indicating a role in antitumor immune activation [[122]18].
   This highlights the complexity of RNF213’s role in the tumor immune
   microenvironment and underscores the necessity for stratified analyses
   by cancer type to accurately interpret its prognostic value.
   Conversely, high RNF213 expression correlated with improved OS in
   patients treated with anti-PD-1 or anti-CTLA-4 therapies, indicating
   its potential role in enhancing immune activation and amplifying
   anti-tumor responses. Notably, the significant survival advantage
   observed in patients with high RNF213 expression receiving combined
   anti-PD-1 and anti-CTLA-4 therapy suggests a synergistic effect,
   wherein RNF213 may enhance the combined efficacy of these therapies.
   However, the lack of significant survival benefit in the anti-PD-L1
   cohort underscores the complexity of RNF213’s interactions with immune
   signaling pathways, which may vary depending on the specific checkpoint
   targeted. Compared to PD-1, PD-L1 is a tumor-intrinsic factor and may
   be more directly influenced by tumor-specific changes in RNF213. Given
   that RNF213 is also expressed in immune cells, both tumor-intrinsic and
   germline alterations could impact patient outcomes. Importantly, our
   study could not differentiate between tumor-specific and blood-derived
   (germline) alterations, which represents a limitation. Notably, Liu ZX
   et al. reported that RNF213 exhibited one of the highest incidences
   (> 10%) of private germline alterations, including insertions and
   deletions, in a hereditary diffuse gastric cancer (HDGC) cohort
   [[123]41]. This finding suggests that germline variants of RNF213 may
   contribute to cancer susceptibility and patient prognosis, underscoring
   the need for future studies to dissect the relative contributions of
   tumor-specific versus germline RNF213 alterations in cancer biology and
   therapy response.

   Furthermore, it is plausible that high RNF213 expression contributes to
   an immunoinhibitory tumor microenvironment, which may partly explain
   the observed poorer prognosis in patients not receiving immune
   checkpoint blockade. The efficacy of PD-1 blockade in improving
   outcomes in such contexts aligns with this notion, as PD-1 inhibitors
   may counteract RNF213-mediated immune suppression. Additionally, recent
   studies have demonstrated that RNF213 promotes regulatory T cell (Treg)
   differentiation by facilitating K63-linked ubiquitination and nuclear
   translocation of FOXO1, a key transcription factor in Treg biology
   [[124]42]. This mechanism suggests a potential pathway through which
   tumor-intrinsic RNF213 alterations could modulate immune responses and
   contribute to immune evasion. However, linking tumor-specific RNF213
   alterations directly to immune modulation remains an important
   challenge and warrants further mechanistic investigations.

Limitations

   While our study provides valuable insights into the expression and
   mutation patterns of RNF213 across various cancers, several limitations
   should be acknowledged. First, the study relies on publicly available
   datasets, which may introduce biases related to sample heterogeneity
   and data quality. Second, the lack of experimental validation limits
   the ability to draw definitive conclusions about RNF213’s functional
   roles in specific cancers. Third, the uniform application of a 50%
   expression threshold to divide patients into high and low RNF213
   expression groups across all cancer types may not be optimal, as it
   overlooks potential non-linear (e.g., U-shaped or J-shaped)
   associations between RNF213 expression and clinical outcomes. Due to
   data constraints, cancer type-specific threshold optimization was not
   feasible in this study. This limitation may have led to inaccurate
   prognostic evaluations in certain tumor types and should be considered
   when interpreting the results. Fourth, our analyses could not
   distinguish between tumor-intrinsic and germline or blood-derived
   RNF213 alterations, which may differentially influence tumor biology
   and immune responses. This represents an important limitation and
   highlights the need for future research integrating paired tumor-normal
   sequencing and functional assays to clarify these effects. Clinically,
   while our study establishes RNF213 as a potential biomarker, its direct
   application remains to be defined. Future studies should focus on
   experimental validation of these findings and explore the molecular
   mechanisms underlying RNF213’s diverse roles in tumorigenesis and
   immune regulation. Integrating multi-omics approaches could provide a
   more comprehensive understanding of RNF213’s role in cancer biology and
   its potential as a therapeutic target. Addressing these limitations in
   future studies will enhance our understanding of RNF213’s role in
   cancer and its potential as a biomarker and therapeutic target.

Conclusion

   In summary, this study provides a comprehensive pan-cancer analysis of
   RNF213, highlighting its diverse and context-dependent roles in
   tumorigenesis, immune regulation, and the tumor microenvironment.
   RNF213 exhibits cancer-type-specific expression and genomic
   alterations, with its dysregulation linked to both tumor-suppressive
   and oncogenic functions depending on the cancer type and clinical
   context. Furthermore, RNF213 is closely associated with immune
   pathways, immune cell infiltration, and immune checkpoint regulation,
   underscoring its potential as a key modulator of tumor immunity.
   Importantly, RNF213 shows promise as a predictive biomarker for cancer
   immunotherapy, particularly in the context of immune checkpoint
   blockade therapies. While this study provides novel insights into
   RNF213’s functional and clinical significance, further experimental
   validation and mechanistic studies are required to fully elucidate its
   roles in cancer biology and to explore its potential as a therapeutic
   target. These findings lay the groundwork for future research aimed at
   leveraging RNF213 for improved cancer diagnosis, prognosis, and
   treatment strategies.

Electronic supplementary material

   Below is the link to the electronic supplementary material.
   [125]Supplementary Material 1^ (29.2KB, xlsx)
   [126]Supplementary Material 2^ (11.4KB, xlsx)

Abbreviations

   ACC
          Adrenocortical carcinoma

   BLCA
          Bladder urothelial carcinoma

   BRCA
          Breast invasive carcinoma

   CESC
          Cervical squamous cell carcinoma and endocervical adenocarcinoma

   CHOL
          Cholangiocarcinoma

   COAD
          Colon adenocarcinoma

   DLBC
          Lymphoid neoplasm diffuse large B-cell lymphoma

   ESCA
          Esophageal carcinoma

   GBM
          Glioblastoma multiforme

   HNSC
          Head and neck squamous cell carcinoma

   KICH
          Kidney chromophobe

   KIRC
          Kidney renal clear cell carcinoma

   KIRP
          Kidney renal papillary cell carcinoma

   LAML
          Acute myeloid leukemia

   LGG
          Brain lower grade glioma

   LIHC
          Liver hepatocellular carcinoma

   LUAD
          Lung adenocarcinoma

   LUSC
          Lung squamous cell carcinoma

   MESO
          Mesothelioma

   OV
          Ovarian serous cystadenocarcinoma

   PAAD
          Pancreatic adenocarcinoma

   PCPG
          Pheochromocytoma and paraganglioma

   PRAD
          Prostate adenocarcinoma

   READ
          Rectum adenocarcinoma

   SARC
          Sarcoma

   STAD
          Stomach adenocarcinoma

   SKCM
          Skin cutaneous melanoma

   TGCT
          Testicular germ cell tumors

   THCA
          Thyroid carcinoma

   THYM
          Thymoma

   UCEC
          Uterine corpus endometrial carcinoma

   UCS
          Uterine carcinosarcoma

   UVM
          Uveal melanoma

Author contributions

   All authors contributed to the study conception and design. Material
   preparation, data collection and analysis were performed by J.P.W.,
   Z.K.L. and M.J.L. The first draft of the manuscript was written by
   J.P.W. C.T.Y.and C.M.Z. commented on previous versions of the
   manuscript. supervision was managed by C.T.Y.and C.M.Z. All authors
   read and approved the final manuscript.

Funding

   This work was supported by the Natural Science Foundation of Zhejiang
   Province (LQ22H160063); the Zhejiang Medical and Health Science and
   Technology Project (2022KY064); the Project of Administration of
   Traditional Chinese Medicine of Zhejiang Province of China (2021ZQ011);
   and the Basic Scientific Research Funds of the Department of Education
   of Zhejiang Province (KYQN202111).

Data availability

   All data supporting the findings of this study are available within the
   paper and its Supplementary Information.

Declarations

Competing interests

   The authors declare no competing interests.

Footnotes

   Publisher’s note

   Springer Nature remains neutral with regard to jurisdictional claims in
   published maps and institutional affiliations.

Contributor Information

   Chuanming Zheng, Email: mingdoc@163.com.

   Changtian Yin, Email: yin454593539@163.com.

References