Skip to main content
NCBI home page
Discover Oncology logoLink to Discover Oncology
. 2025 Jun 16;16:1119. doi: 10.1007/s12672-025-02974-6

Identification of DCAF5 as a novel cancer prognostic and immunotherapy biomarker through pan-cancer analysis and renal clear cell carcinoma clinical data validation

Huaqing Yan 1,✉, Liqi Zhang 2, Fan Xu 1, Tieding Chen 1, Rubing Li 1,✉
PMCID: PMC12170971  PMID: 40522583

Abstract

Background

DCAF5 encodes a component of ubiquitin ligase complex, thereby influencing protein ubiquitination. Recent research underscored its potential to revert the malignant phenotype, as evidenced in vivo studies, while the scarcity of research about DCAF5 challenges for its development as a novel target for cancer treatment.

Methods

We conducted an immunohistochemical analysis to compare the expression levels of DCAF5 in renal clear cell carcinoma tissues with those in adjacent non-neoplastic tissues. Subsequently, we performed survival analyses to explore the correlation between DCAF5 expression and patient outcomes. Multiple public databases were used such as the UCSC Xena platform, The Cancer Genome Atlas, and the Genotype-Tissue Expression project. The raw data extracted from the UCSC Xena platform was subsequently processed utilizing online analytical tools to facilitate further in-depth examination.

Results

Our investigation has discovered a significant reduction in DCAF5 expression within renal clear cell carcinoma tissues when juxtaposed with adjacent non-cancerous tissues. Further experimental validation demonstrated that patients with higher pathology grades exhibited lower DCAF5 expression. In a broader oncological context, our comprehensive analysis across various cancer types revealed a heterogeneous expression pattern of DCAF5. Concurrently, an examination of publicly accessible databases has highlighted a notable correlation between DCAF5 expression and both immune scores and immune cell infiltration in a range of cancer types. It is noteworthy that DCAF5 ‘s potential as an immunotherapy biomarker was recognized, distinguishing it from several established markers in this domain. Finally, our analysis of genetic alterations identified gene amplification as the predominant form of aberration affecting the DCAF5 gene.

Conclusion

Our findings reveal that DCAF5 exhibits context-dependent dual roles across cancers and demonstrates superior predictive value over traditional biomarkers like TMB in immunotherapy. This highlights its potential as a dual-functional biomarker for prognosis and immunotherapy personalization, particularly in SMARCB1-deficient malignancies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-02974-6.

Keywords: Pan-cancer, DCAF5, Immunization, Prognosis, Bioinformatic analysis

Introduction

Worldwide, cancer remains the predominant contributor to morbidity and mortality, imposing a substantial impact on public health and economic systems. For the year 2024 in the United States, it is estimated that cancer will claim the lives of approximately 611,720 individuals, with an expected total of 2,001,140 new cancer diagnoses [1]. Renal cancer is the fifth most commonly diagnosed cancer among males and the ninth among females. Although the majority of renal tumors are localized, approximately 1 out of 5 patients present with metastatic disease at diagnosis [2]. Furthermore, patients with localized renal cancer may experience metastasis or recurrence following surgical resection [3]. Traditional treatments, including chemotherapy, radiotherapy and immunotherapy with high-dose interleukin 2, have shown undesirable clinical benefits and are associated with significant side effects, thus precluding their use as first-line treatment options [3]. Over the past decade, there has been a significant evolution in the therapeutic landscape for renal cancer. The advent of targeted therapies and immune checkpoint inhibitors(e.g., nivolumab, pembrolizumab) has offered new hope for patients with advanced renal cancer; however, some patients exhibit initial resistance to these agents, while the majority of initially responsive patients eventually develop acquired resistance during the course of therapy [4, 5]. The emergence of resistance in renal cancer patients diminishes the clinical benefits of targeted therapies. In a recent large-scale real-world cohort study comprising 230 patients with advanced clear cell renal cell carcinoma who underwent immune checkpoint blockade therapy, genomic sequencing and subsequent analysis identified APOE-positive macrophages as pivotal mediators of immunosuppression and therapeutic resistance, which underscore a novel mechanistic pathway through which tumor-associated macrophages may drive immune evasion in renal cell carcinoma [6]. Meanwhile, researchers have examined the integration of PD-1/PD-L1 inhibitors with various therapeutic modalities, including tyrosine kinase inhibitors, CTLA-4 inhibitors, and emerging CXCR4 antagonists [7–10]. Consequently, elucidating the molecular mechanisms underlying renal cancer invasion and metastasis from novel perspectives and identifying new intervention strategies is of particular importance and urgency.

DDB1 and CUL4 associated factor 5 (DCAF5 ) is situated on the chromosome 14, at the q24.1 band with 13 exons. The gene DCAF5 encodes a protein that is a component of the Cul4-RING E3 ubiquitin ligase complex, which is anticipated to play a role in the process of protein ubiquitination. This complex is crucial for various cellular functions, including the regulation of protein stability and signaling pathways through the attachment of ubiquitin molecules to target proteins [11, 12]. DCAF5 has received little attention until in 2024, with advancements in cancer research have highlighting its coherence and relevance in the viability of cancers harboring SMARCB1 gene mutations. Specifically, the absence of DCAF5 leads to the reformation of SMARCB1-deficient SWI/SNF complexes, which upon binding to their genomic targets, can reinstate gene expression patterns mediated by SWI/SNF. This restoration is of such magnitude that it has the potential to revert the malignant phenotype, as evidenced by in vivo studies, thus offering new therapeutic insights into this subset of cancers [13]. The present investigation on DCAF5 prompts a contemplation of the potential for DCAF5 to serve as a therapeutic target, presenting promising prospects for genetic interventions. However, the scarcity of research pertaining to DCAF5 expression, prognostic value, and its association with immunotherapy within oncology poses significant challenges for its development as a novel target for cancer treatment.

Consequently, there is an imperative need for a comprehensive, pan-cancer examination of DCAF5 to ascertain its viability as a biomarker in cancer detection, prognostic assessment, and therapeutic strategy formulation. Our research group is dedicated to exploring the correlation between DCAF5 expression and the progression of tumors as well as its interaction with immune microenvironments, with the goal of establishing its significance as a biomarker for various cancers [14–27].

Materials and methods

Clinical sample acquisition and immunohistochemical staining

Renal clear cell carcinoma is the most common type of renal cancer. We purchased renal clear cell carcinoma tissues and adjacent para-cancer tissues with clinical data from Xinchao Biotech, Shanghai, China (Catalog Number HKidE160CS04). Rabbit polyclonal DCAF5 antibody was bought from Biorbyt Ltd., United Kingdom (Catalog Number orb419696). The immunohistochemical staining (IHC) process and method for IHC score calculation was described in our previous study [25].

Statistical analysis

Prism 7 (GraphPad Software, Inc., La Jolla, CA) was utilized to perform the statistical analyses. The determination of statistical significance was achieved through the application of a two-tailed unpaired Student’s t-test, with a stringent threshold set at p < 0.05. To delve into the multivariate analysis concerning the overall survival in renal cancer, an initial step of univariate analysis was undertaken. Subsequently, the variables that emerged as significant in this preliminary analysis were incorporated into the subsequent multivariate examination.

Data source

To conduct a comprehensive analysis of the DCAF5 gene across human cancers, we procured an array of data encompassing gene expression profiles, clinical characteristics, and mutational spectra from the UCSC Xena database (https://xena.ucsc.edu/) and the TCGA/GTEx databases. The transcriptomic datasets, which included both neoplastic and normal tissue samples, underwent rigorous processing via the SangerBox platform (http://vip.sangerbox.com/tool.html) [28]. The SangerBox platform is highly reputable and widely utilized across a diverse array of biological and medical research projects [14, 25, 29–31]. Subsequently, these datasets were meticulously transformed from the FPKM (Fragments Per Kilobase of transcript per Million mapped reads) scale to the TPM (Transcripts Per Million) scale, facilitated by a log2 conversion to ensure standardized and comparable data analysis.

Localization, topology and protein interaction network of DCAF5 proteins

The Human Protein Atlas (http://www.proteinatlas.org), an open-access resource, offers a comprehensive analysis of protein expression across various subcellular compartments, encompassing a wide range of tissues, individual cells, immune cells, and cell lines. In our study, we focused on the subcellular localization of the DCAF5 protein within the A-431, U-251MG, and U-2 OS cell lines, examining its interactions with key cellular structures such as the endoplasmic reticulum, microtubules, and nucleus. Furthermore, we utilized the PROTTER tool (http://wlab.ethz.ch/protter/start/), which is designed to predict sequence characteristics and visualize the protein structure based on empirical proteomics data [32]. Additionally, we employed the STRING database (cn.string-db.org/), an open-source repository, to predict the networks of protein-protein interactions (PPIs). By applying a stringent confidence score threshold of 0.7, we were able to identify robust PPIs that contribute to our understanding of the complex interplay between proteins within biological systems.

Expression level and prognostic value exploration of DCAF5 in pan-cancer

To elucidate the expression patterns of DCAF5 in the context of human cancers and their adjacent normal tissues, we employed the TIMER platform (https://cistrome.shinyapps.io/timer/) and the UCSC Xena databases [33]. Expanding our investigation to renal cancer specifically, we utilized the UALCAN database (https://ualcan.path.uab.edu/) to examine DCAF5 expression profiles stratified by tumor grade and nodal metastasis status [34]. To evaluate the clinical significance of DCAF5 expression, we applied the Kaplan-Meier method and univariate Cox regression analysis with the aid of the UCSC Xena database and SangerBox tools.

Genes related to RNA modification and its correlation to DCAF5 expression

To investigate the association of DCAF5 with RNA modification genes across various human cancers, we sourced gene expression data from the UCSC Xena database including 10 m1A, 13 m5C, and 21 m6A genes. We employed Pearson correlation to assess the relationship between DCAF5 expression and these RNA modification genes, with statistical significance defined as p < 0.05.

Determining the correlation between DCAF5 and the immune and molecular subtypes

The Tumor Immune System Interaction Database (TISIDB, http://cis.hku.hk/TISIDB/index.php) is a comprehensive resource that amalgamates diverse datasets to explore the dynamic interplay between neoplastic growth and immune responses [35]. We harnessed this platform to delineate the association between DCAF5 gene expression and the immunological and molecular stratifications observed in a spectrum of cancers. The analysis was conducted with a threshold for statistical significance set at p < 0.05.

Verifying the relationship of DCAF5 to immune checkpoint genes

Employing the UCSC Xena database, we conducted a study to evaluate the relationship between the expression levels of the DCAF5 gene and those of immune checkpoint genes. In this context, immune checkpoint genes have been categorized into two distinct groups—inhibitory and stimulatory—as per the findings of a prior scholarly investigation [36].

The relationship between DCAF5 expression and the tumor immune microenvironment in pan-cancer

In our investigation of the tumor microenvironment in human cancers, we employed the SangerBox tool to compute stromal, immune, and ESTIMATE scores. We focused our analysis solely on results that achieved a statistical significance threshold of p < 0.05. Furthermore, leveraging the Timer method from the IOBR package, we reassessed the infiltration scores of various immune cell types, including B cells, T cells (both CD4 and CD8 subtypes), neutrophils, macrophages, and dendritic cells (DCs), for each patient’s tumor. This re-evaluation was conducted in the context of DCAF5 gene expression [37, 38].

Identifying the correlation between DCAF5 gene alteration and tumor immunity

We leveraged the Tumor Immune Dysfunction and Exclusion (TIDE) Database to prognosticate the potential efficacy of immune checkpoint blockade therapies (http://tide.dfci.harvard.edu/) [39]. The predictive capacity of DCAF5 was scrutinized against a panel of well-established markers within TIDE’s immune checkpoint blockade cohorts. To complement our analysis, the cBioportal database (http://www.cbioportal.org/) was engaged to dissect DCAF5’s copy number variations and genetic mutations across various cancer types [40].

Results

DCAF5 is downregulated in renal clear cell carcinoma and associated with tumor grade

To verify the expression level of DCAF5 in renal clear cell carcinoma, we collected renal clear cell carcinoma tissue samples and paired adjacent para-cancer tissues along with clinical data. An immunohistochemical staining assay was conducted (Fig. 1A–C). Overall, the expression level of cytoplasm DCAF5 was significantly lower in renal clear cell carcinoma tissues compared to adjacent para-tumor tissues (Fig. 1D). Further analysis revealed an inverse trend between the renal clear cell carcinoma pathological grade and DCAF5 expression levels, suggesting that DCAF5 may function as a tumor suppressor in renal clear cell carcinoma (Fig. 1E).

Fig. 1.

Fig. 1

Open in a new tab

DCAF5 is downregulated in renal clear cell carcinoma and associated with pathological grade. A–C Representative images of immunohistochemical staining showed the expression level of DCAF5 in renal clear cell carcinoma tissues and adjacent para-cancer tissues. D The expression level of cytoplasmic DCAF5 is downregulated in renal clear cell carcinoma compared with para-cancer tissues. E DCAF5 is inversely associated with renal clear cell carcinoma pathological grade

Localization, topology and protein interaction network of DCAF5 proteins

We conducted a comprehensive analysis of DCAF5 scrutinizing its subcellular localization and interaction with other proteins. Physiological examination revealed the intracellular membrane presence of DCAF5 (Fig. 2A). Utilizing data from The Human Protein Atlas, we discerned that DCAF5 exhibited a non-uniform distribution across the nucleus, microtubules, and endoplasmic reticulum in A-431, U-251MG, and U-2 OS cell lines (Fig. 2B). The DCAF5 protein was predominantly localized to the mitochondria, as illustrated in the cell schematic (Fig. 2C). Protein-protein interaction studies indicated that DCAF5 is associated with members of the DCAF family, as well as with DDB1, DDB2, CUL4A, CUL4B, and TRPC4AP (Fig. 2D).

Fig. 2.

Fig. 2

Open in a new tab

Topology, localization and protein interaction network of DCAF5 proteins. A The topology of DCAF5 proteins is depicted, which indicates that this protein predominantly localizes to intracellular membranes. B The intracellular localization pattern of DCAF5 protein was determined using immunofluorescence staining, revealing nuclear, microtubular, and endoplasmic reticulum staining in HEL, REH, and U-2 OS cell lines, as extracted from the Human Protein Atlas database. C The distribution of DCAF5 expression, which was mainly detected in the cytoplasm, especially in the Mitochondria. D The protein interaction network of DCAF5 proteins, highlighting the various protein-protein interactions and associations from the String database

Expression level of DCAF5 in pan-cancer

We explored the association between DCAF5 and various malignancies by examining its mRNA expression across diverse tissues, leveraging multiple genomic databases. TCGA dataset was employed to delineate the expression profiles of DCAF5 in cancerous and adjacent non-cancerous tissues (Fig. 3A). Our analysis revealed that DCAF5 mRNA levels were markedly elevated in CHOL, HNSC and LIHC, with statistical significance (p < 0.05, cancer type abbreviations are shown in supplementary Table 1). Conversely, suppressed DCAF5 expression was observed in 11 distinct cancer types, encompassing LUAD, COAD, COADREAD, BRCA, KIRP, PRAD, UCEC, LUSC, THCA, READ, and BLCA.

Fig. 3.

Fig. 3

Open in a new tab

The expression profile of DCAF5 in pan-cancer. A The expression levels of DCAF5 in human cancers, as extracted from The Cancer Genome Atlas (TCGA) database. B The expression levels of DCAF5 in human cancers, as determined by the TIMER 2.0 database, which is also based on the TCGA database. C The expression levels of DCAF5 in human cancers as well as normal tissues, based on data from both the TCGA and Genotype-Tissue Expression (GTEx) databases. D The expression levels of DCAF5 in renal clear cell carcinoma based on tumor grade. E The expression levels of DCAF5 in renal clear cell carcinoma based on nodal metastasis status. *p < 0.05; **p < 0.01; ***p < 0.001

Utilizing the TIMER 2.0 database, which integrates TCGA data, we further characterized DCAF5 expression patterns in cancers (Fig. 3B). Integrating data from both TCGA and the GTEx project (Fig. 3C), we discerned an upregulation of DCAF5 in 11 cancer types, including GBM, GBMLGG, LGG, ESCA, HNSC, KIRC, WT, PAAD, ALL, LAML and CHOL. In stark contrast, DCAF5 mRNA was significantly downregulated in 19 cancers, including UCEC, BRCA, CESC, LUAD, STES, KIRP, COAD, COADREAD, PRAD, LUSC, LIHC, SKCM, BLCA, THCA, READ, OV, TGCT, UCS and ACC. Together, our analysis of TCGA and GTEx data reveals a bidirectional trend in DCAF5 expression across the majority of examined cancer types.

Although we identified a significant upregulation of DCAF5 in kidney renal clear cell carcinoma, we proceeded to investigate its expression levels across varying tumor grades and nodal metastasis statuses within KIRC using the UALCAN database. Figure 3D illustrates a significant downregulation of DCAF5 expression as tumor grade increases in KIRC, whereas Fig. 3E reveals a significant downregulation of DCAF5 expression in KIRC cases with lymph node metastasis. Our findings indicate that, despite an overall upregulation of DCAF5 in KIRC, its expression significantly decreases with increasing tumor grade and the presence of nodal metastasis.

Prognostic value exploration of DCAF5 in pan-cancer

We evaluated the impact of DCAF5 expression on the clinical outcomes of patients across a spectrum of cancers using data from the UCSC Xena database. A Cox proportional hazards regression model was employed to analyze the overall survival across various cancers (Fig. 4A). Elevated DCAF5 expression was significantly correlated with poor outcomes in three specific cancers: TARGET-LAML, TCGA-LAML, and TCGA-PCPG. In contrast, downregulated DCAF5 levels were significantly associated with poor outcomes in four cancers, namely TCGA-GBMLGG, TCGA-KIRC, TARGET-NB, and TARGET-ALL. Furthermore, Kaplan-Meier survival analysis was conducted to substantiate the influence of DCAF5 expression on patient prognosis in a selection of cancers (Fig. 4B–G). Collectively, these findings suggest that DCAF5 may exert heterogeneous effects on the prognosis of different cancer types.

Fig. 4.

Fig. 4

Open in a new tab

The prognostic value of DCAF5 in pan-cancer. A The relationship between DCAF5 expression levels and tumor overall survival across multiple cancer types. B–G Kaplan-Meier survival curves for various cancer types, showing the association between DCAF5 expression levels and OS

RNA modification related genes and its relationship to DCAF5 expression

We investigated the association between DCAF5 expression and the expression of genes involved in RNA modification across a range of cancers using the UCSC Xena database. Expression data for three categories of RNA modification genes—comprising 10 genes associated with m1A modification, 13 with m5C modification, and 21 with m6A modification—were retrieved and subjected to Pearson correlation analysis. The results revealed a significant correlation between DCAF5 and the majority of RNA modification genes in multiple cancer types (Fig. 5). Notably, in KIRC, DCAF5 demonstrated a correlation with nearly all genes related to RNA modification, with the exception of NSUN5, suggesting a potentially extensive role of DCAF5 in RNA modification processes.

Fig. 5.

Fig. 5

Open in a new tab

DCAF5 demonstrated a significant correlation with the majority of RNA modification genes across several types of cancer

Determining the correlation between DCAF5 and the immune and molecular subtypes

We utilized the TISIDB database to explore the correlation between DCAF5 expression and both immune and molecular subtypes across a spectrum of cancers [41]. Our analysis identified a significant association between DCAF5 expression and molecular subtypes in various cancer types, including BRCA, ESCA, GBM, HNSC, KIRP, LGG, LUSC, OV, PCPG, PRAD, STAD and UCEC (Fig. 6). Furthermore, we detected a significant relationship between DCAF5 expression and the six defined immune subtypes—C1 (wound healing), C2 (IFN-gamma dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet), and C6 (TGF-b dominant)—in 12 cancer types, namely BLCA, BRCA, COAD, HNSC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, STAD and UCEC (Fig. 7). Of particular interest, DCAF5 expression showed a significant correlation with both immune and molecular subtypes in several cancers, such as BRCA, HNSC, KIRP, LGG, LIHC, STAD and UCEC. These findings suggest that DCAF5 may play a pivotal role in these malignancies, warranting further investigation into its potential as a targeted therapeutic agent.

Fig. 6.

Fig. 6

Open in a new tab

The correlation between DCAF5 and molecular subtypes across various human cancers. A Analysis of its association in BRCA; B analysis of its association in ESCA; C analysis of its association in GBM; D Analysis of its association in HNSC; E analysis of its association in KIRP; F analysis of its association in LGG; G analysis of its association in LUSC; H analysis of its association in OV; I analysis of its association in PCPG; J analysis of its association in PRAD; K analysis of its association in STAD; L analysis of its association in UCEC

Fig. 7.

Fig. 7

Open in a new tab

The correlation between DCAF5 and and immune subtypes across various human cancers. A Analysis of its association in BLCA; B analysis of its association in BRCA; C analysis of its association in COAD; D analysis of its association in HNSC; E analysis of its association in KIRC; F analysis of its association in KIRP; G analysis of its association in LGG; H analysis of its association in LIHC; I analysis of its association in LUAD; J analysis of its association in MESO; K analysis of its association in STAD; L analysis of its association in UCEC

Verifying the relationship of DCAF5 to immune checkpoint genes

We explored the potential role of DCAF5 in immunotherapy by examining its association with immune checkpoint genes in human cancers. Utilizing data from the UCSC Xena database, we assessed the expression profiles of DCAF5 alongside 24 inhibitory and 36 stimulatory immune checkpoint genes across 40 human cancer types. The calculated correlations between DCAF5 and these immune checkpoint genes are presented in Fig. 8. Notably, a substantial correlation was observed between DCAF5 and the majority of immune checkpoint genes in several cancers, including OV, PAAD, COAD, KIRC, PRAD, etc. Specifically, in KIRC, 45 out of 60 immune checkpoint genes demonstrated a significant correlation with DCAF5 expression. These findings suggest that DCAF5 may modulate the expression of immune checkpoint genes across various biological pathways, potentially influencing the efficacy of immunotherapy. Our analysis implies that DCAF5 may serve as a prognostic biomarker for immunotherapy outcomes or as a novel therapeutic target to enhance the effectiveness of immunotherapeutic interventions.

Fig. 8.

Fig. 8

Open in a new tab

The potential correlation between DCAF5 and immune checkpoint genes, comprising of 24 inhibitory genes and 36 stimulatory genes, across forty human cancers *p < 0.05; **p < 0.01; ***p < 0.001

DCAF5 expression and the tumor immune microenvironment in pan-cancer

Emerging evidence underscores the pivotal role of the tumor immune microenvironment in the etiology and progression of human cancers. In pursuit of elucidating the interplay between DCAF5 expression and immune signatures, we quantified the stromal score, immune score, and ESTIMATE score. Our comprehensive analysis encompassing 44 cancer types disclosed that COADREAD, COAD and BRCA exhibited the most robust correlation between DCAF5 expression and immune score (Fig. 9A). Paralleling this, THCA, COADREAD and COAD demonstrated the most pronounced association between DCAF5 expression and stromal score (Fig. 9B). Interestingly, the top three malignancies with the most significant link between DCAF5 expression and the ESTIMATE score mirrored those observed for the stromal score, namely THCA, COADREAD, and COAD (Fig. 9C). These correlations imply that elevated DCAF5 expression in BRCA, THCA, COADREAD, and COAD is positively associated with the presence of immune and stromal cell infiltrates.

Fig. 9.

Fig. 9

Open in a new tab

The potential associations between DCAF5 expression and immune infiltration scores across numerous human cancers, A the top 3 cancer types with the most significant relationship between DCAF5 expression and immune score was presented; B the top 3 cancer types with the most significant relationship between DCAF5 expression and stromal score was presented; C the top 3 cancer types with the most significant relationship between DCAF5 expression and ESTIMATE score was presented

Furthermore, we probed the potential nexus between DCAF5 expression and the infiltration of key immune cell types (Fig. 10A). Examination across 38 human cancer types unveiled a significant relationship between DCAF5 expression and immune cell infiltration in 37 cancer types. Notably, a robust correlation was identified with B cells in 21 cancer types, CD4 + T cells in 24 cancer types, CD8 + T cells in 22 cancer types, neutrophils in 29 cancer types, macrophages in 24 cancer types, and dendritic cells (DC) in 25 cancer types.

Fig. 10.

Fig. 10

Open in a new tab

The potential correlation between DCAF5 gene alteration and tumor immunity. A The potential association between infiltration of immune cells and DCAF5 expression. B The effectiveness of DCAF5 as a biomarker compared to traditional immunotherapy biomarkers. C The copy-number alterations of DCAF5, where amplification was the most common type of copy-number alteration in DCAF5. D The genomic alteration of DCAF5 in various cancers, where mutation was the most frequent alteration type

Identifying the correlation between DCAF5 gene alteration and tumor immunity

We conducted an analysis to determine the suitability of DCAF5 as a biomarker for immunotherapy, comparing its performance with that of established biomarkers within the TIDE dataset. Our results indicated that DCAF5 exhibited an area under the curve (AUC) value exceeding 0.5 in more than half of the immunotherapy cohorts examined (Fig. 10B), suggesting its potential utility in this context. Although DCAF5 showed a lower predictive capacity compared with MSI.Score and CD274, it may surpass the predictive value of TMB, T. Clonality, and B. Clonality.

Subsequently, we utilized the cBioPortal database to investigate genomic alterations associated with DCAF5. Our analysis revealed that gene amplification was the predominant form of genetic alteration, with gene gain and diploid states occurring less frequently (Fig. 10C). Furthermore, we examined the distribution of copy-number alterations of DCAF5 across various cancer types (Fig. 10D), identifying mutation as the most common event, followed by amplification.

Discussion

The DCAF5 gene, which has received limited research attention, has been implicated in the processes of tumorigenesis and hematopoietic development in recent years [42, 43]. In 2018, Zhang et al. first reported that DCAF5 is a pivotal component of the CRL4 ubiquitin ligase complex, which mediates the degradation of DNMT1, a predominant DNA methyltransferase enzyme. This enzyme is crucial for the epigenetic maintenance of DNA methylation signatures throughout the DNA replication process. Consequently, the study suggests that DCAF5 influences the stability of numerous methylated non-histone proteins, thereby implicating its role in epigenetic regulation [11]. Recently a significant achievement has been made in the study of DCAF5 revealing that targeting DCAF5 can suppress SMARCB1-mutant cancers by restoring the function of the SWI/SNF chromatin-remodeling complex, offering a potential therapeutic strategy for these aggressive malignancies [13]. Moreover, DCAF5 null mutations lead to the accumulation of PRC2 and elevated levels of H3K27 trimethylation, thereby modulating developmental processes and cellular outcomes through the transcriptional silencing mechanism, which is implicated in the pathogenesis of diverse cancers [43]. These discovery reveals the important role of DCAF5 as post-translational regulator capable of influencing gene expression patterns, offering promising avenues for genetic therapeutic interventions. Given the urgency to elucidate the prognostic implications and immune-related functions of DCAF5 across the spectrum of human cancers, a comprehensive pan-cancer analysis was conducted, with subsequent confirmation of our results within renal clear cell carcinoma specimens.

We initially investigated the expression of DCAF5 in renal clear cell carcinoma tissues. Downregulated DCAF5 expression is associated with renal clear cell carcinoma progression and consequently, DCAF5 may serve as a tumor suppressor in renal clear cell carcinoma, a role that contrasts with its function in SMARCB1-mutant cancers. This intriguing phenomenon spurred us to investigate the role of DCAF5 in the context of human cancers, leading us to undertake a pan-cancer analysis. Of the 34 tumors we examined in GTEx and TCGA databases, DCAF5 gene expression level was significantly higher than normal tissues in 11 kinds of tumors and lower in 19 kinds of tumors, revealing a bidirectional trend in DCAF5 expression across the majority of examined cancer types. This result suggests a context-dependent dual role and complex molecular regulatory mechanism of DCAF5 across different tumor types. In SMARCB1-deficient tumors, DCAF5 has been established as a quality control factor that degrades incomplete SWI/SNF complexes through the CRL4-DCAF5 ubiquitin ligase pathway, thereby silencing SWI/SNF-mediated tumor-suppressive programs [13]. Genetic or pharmacologic inhibition of DCAF5 restores SWI/SNF chromatin remodeling activity and reverses malignant phenotypes in vivo. Conversely, renal clear cell carcinoma is characterized by DCAF5 downregulation that correlates with tumor grade (Fig. 1D and E). We hypothesize that DCAF5 may stabilize non-SWI/SNF tumor suppressors (e.g., the VHL/p53 axis) or modulate immune checkpoints (Fig. 8), findings supported by its robust association with immune infiltration scores and RNA modification genes in renal clear cell carcinoma (Figs. 5 and 9). Future experimental endeavors may focus on elucidating SWI/SNF-independent mechanisms of DCAF5, such as analyzing RNA modification dynamics (e.g., m6A and m5C) in DCAF5-deficient renal clear cell carcinoma cells using MeRIP-seq or RNA bisulfite sequencing. Additionally, validating the co-regulation of immune checkpoints through siRNA-mediated knockdown of DCAF5, followed by PD-L1 and CTLA-4 flow cytometry, may provide further insights into its multifaceted role.

To elucidate the potential influence of DCAF5 on tumor progression and the tumor immune context, we conducted an assessment of DCAF5 expression across a spectrum of molecular and immunological cancer subtypes. Our research has uncovered pronounced variations in DCAF5 expression among diverse molecular and immunological subtypes within the majority of cancer types studied. These observations propose that DCAF5 could emerge as a robust biomarker for pan-cancer diagnostics, concurrently exerting a significant influence on the modulation of the tumor’s immune microenvironment. To deepen our understanding of the interplay between DCAF5 and immune components within the tumor microenvironment, we determined the levels of immune cell infiltration and computed corresponding immune scores. Our comprehensive analysis has demonstrated a substantial association between DCAF5 expression and immune scores in 20 of the 44 cancer types evaluated, underscoring the intricate relationship between DCAF5 and the immune landscape of human tumors. Subsequent in-depth scrutiny of immune cell infiltration within the neoplastic milieu disclosed a marked correlation between DCAF5 levels and the presence of immune cells in the majority of cancer types examined. The heterogeneity observed in the interaction between immune cells and DCAF5 expression patterns across human cancers prompts us to theorize the existence of a sophisticated interplay between the immune response and DCAF5 expression. Furthermore, our examination of the nexus between DCAF5 and immune checkpoint genes suggests a significant role for DCAF5 in the intricate network of molecular pathways that govern tumor immune responses. Future investigations should prioritize molecular subtype-stratified analyses (e.g., TCGA-defined molecular subtypes in renal clear cell carcinoma) to dissect context-specific immune interactions of DCAF5. Such focused studies will clarify whether its immunomodulatory effects are conserved across molecularly distinct subgroups or restricted to specific genomic contexts.

Finally, our findings propose that DCAF5 may surpass traditional biomarkers, such as TMB, T cell clonality, and B cell clonality, in its potential as an immunotherapy predictive marker. In summation, our research underscores the robust correlation between DCAF5 and the immune response in human cancers, positioning it as a promising candidate for predicting responses to immunotherapeutic interventions. Additional studies are warranted to fully dissect the molecular underpinnings of DCAF5’s role in cancer progression and immune response dynamics.

While our extensive examination of DCAF5’s prognostic significance and immunological implications across a broad spectrum of cancers has been thorough, we recognize several inherent limitations. Initially, our analysis was confined to publicly accessible data sources, which may have introduced potential biases, such as publication and selection biases, given the exclusion of unpublished data. Additionally, discrepancies across various databases could imply a level of heterogeneity that necessitates further validation. We were also unable to perform subgroup analyses (e.g., DNA methylation status, metabolic RNA expression, and immune RNA expression) for each cancer type in the survival analysis. Lastly, we acknowledge that the experimental validation of DCAF5’s role and the mechanisms underlying its action in renal clear cell carcinoma were beyond the scope of this study, indicating a need for future research.

Conclusion

Our study has established that DCAF5 is downregulated in renal clear cell carcinoma compared to adjacent para-cancer tissue. Further investigation has disclosed an inverse correlation between the pathological grading of renal clear cell carcinoma and the expression levels of DCAF5. Expanding our scope through a pan-cancer analysis of DCAF5, we observed variations in its expression patterns and prognostic significance across different tumor types. Additionally, our findings indicate a significant link between DCAF5 expression and the composition of the tumor microenvironment, including immune cell infiltration, which differs among cancer subtypes. These insights into the intricate role of DCAF5 in oncogenesis could pave the way for the development of more tailored and precise immunotherapeutic strategies in future clinical applications.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (10.8KB, xlsx)

Author contributions

Yan Huaqing and Zhang Liqi designed this study. Yan Huaqing drafted the manuscript. Tieding Chen and Fan Xu conducted statistical analysis. Rubing Li provided critical comments, suggestions, and revised the manuscript.

Funding

This work was supported by The Natural Science Foundation of Ningbo (202003N4258, 2022J259, 2023J145) and Project of Zhejiang Medical and Health Science and technology plan (2024KY300, 2025KY1285).

Data availability

Data availability: Some database that support the findings of this study are openly available described in ‘Materials and methods’, including UCSC Xena database [44], UALCAN [34], TCGA [45], GTEx [46], human protein atlas (www.proteinatlas.org), PROTTER [47], String [48), TIMER (33), BioGPS (49), TISIDB (41), TIDE (39) and cBioportal database [50, 51]. Other data are available from corresponding author.

Declarations

Ethics approval and consent to participate

The renal clear cell carcinoma and adjacent tissue samples was bought from Xinchao Biotech, Shanghai, China (Catalog Number HKidE160CS04). Other data and analyses were based on previous published studies; thus, no ethical approval and patient consent are required.

Consent for publication

All authors gave their consent for publication.

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

Huaqing Yan, Email: yanhuaqing@zju.edu.cn.

Rubing Li, Email: lhluro@163.com.

References

  • 1.Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. Cancer J Clin. 2024;74(1):12–49. 10.3322/caac.21820. [DOI] [PubMed] [Google Scholar]
  • 2.Padala SA, Barsouk A, Thandra KC, Saginala K, Mohammed A, Vakiti A, et al. Epidemiology of renal cell carcinoma. World J Oncol. 2020;11(3):79–87. 10.14740/wjon1279. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Capitanio U, Montorsi F. Renal cancer. Lancet (London England). 2016;387(10021):894–906. 10.1016/s0140-6736(15)00046-x. [DOI] [PubMed] [Google Scholar]
  • 4.Nishida J, Momoi Y, Miyakuni K, Tamura Y, Takahashi K, Koinuma D, et al. Epigenetic remodelling shapes inflammatory renal cancer and neutrophil-dependent metastasis. Nat Cell Biol. 2020;22(4):465–75. 10.1038/s41556-020-0491-2. [DOI] [PubMed] [Google Scholar]
  • 5.Díaz-Montero CM, Rini BI, Finke JH. The immunology of renal cell carcinoma. Nat Rev Nephrol. 2020;16(12):721–35. 10.1038/s41581-020-0316-3. [DOI] [PubMed] [Google Scholar]
  • 6.Ge Q, Meng J, Wang Z, Anwaier A, Lu J, Tian X, et al. Spatially segregated APOE(+) macrophages restrict immunotherapy efficacy in clear cell renal cell carcinoma. Theranostics. 2025;15(11):5312–36. 10.7150/thno.109097. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Zhu Z, Jin Y, Zhou J, Chen F, Chen M, Gao Z, et al. PD1/PD-L1 Blockade in clear cell renal cell carcinoma: mechanistic insights, clinical efficacy, and future perspectives. Mol Cancer. 2024;23(1):146. 10.1186/s12943-024-02059-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Liao C, Hu L, Zhang Q. Von Hippel-Lindau protein signalling in clear cell renal cell carcinoma. Nat Rev Urol. 2024;21(11):662–75. 10.1038/s41585-024-00876-w. [DOI] [PubMed] [Google Scholar]
  • 9.Soler MF, Abaurrea A, Azcoaga P, Araujo AM, Caffarel MM. New perspectives in cancer immunotherapy: targeting IL-6 cytokine family. J Immunother Cancer. 2023;11(11). 10.1136/jitc-2023-007530. [DOI] [PMC free article] [PubMed]
  • 10.Nair SS, Chakravarty D, Patel V, Bhardwaj N, Tewari AK. Genitourinary cancer neoadjuvant therapies: current and future approaches. Trends cancer. 2023;9(12):1041–57. 10.1016/j.trecan.2023.07.011. [DOI] [PubMed] [Google Scholar]
  • 11.Leng F, Yu J, Zhang C, Alejo S, Hoang N, Sun H, et al. Methylated DNMT1 and E2F1 are targeted for proteolysis by L3MBTL3 and CRL4(DCAF5) ubiquitin ligase. Nat Commun. 2018;9(1):1641. 10.1038/s41467-018-04019-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Zhang C, Leng F, Saxena L, Hoang N, Yu J, Alejo S, et al. Proteolysis of methylated SOX2 protein is regulated by L3MBTL3 and CRL4(DCAF5) ubiquitin ligase. J Biol Chem. 2019;294(2):476–89. 10.1074/jbc.RA118.005336. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Radko-Juettner S, Yue H, Myers JA, Carter RD, Robertson AN, Mittal P, et al. Targeting DCAF5 suppresses SMARCB1-mutant cancer by stabilizing SWI/SNF. Nature. 2024;628(8007):442–9. 10.1038/s41586-024-07250-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Cui X, Zhang C, Zhang L, Yan H. The prognostic and immunological role of MYB: from bladder cancer validation to pan-cancer analysis. Biosci Rep. 2023;43(4). 10.1042/bsr20222627. [DOI] [PMC free article] [PubMed]
  • 15.Li J, Xie H, Ying Y, Chen H, Yan H, He L, et al. YTHDF2 mediates the mRNA degradation of the tumor suppressors to induce AKT phosphorylation in N6-methyladenosine-dependent way in prostate cancer. Mol Cancer. 2020;19(1):152. 10.1186/s12943-020-01267-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Li J, Ying Y, Xie H, Jin K, Yan H, Wang S, et al. Dual regulatory role of CCNA2 in modulating CDK6 and MET-mediated cell-cycle pathway and EMT progression is blocked by miR-381-3p in bladder cancer. FASEB J: Off Publ Federation Am Soc Exp Biol. 2019;33(1):1374–88. 10.1096/fj.201800667R. [DOI] [PubMed] [Google Scholar]
  • 17.Xiang J, Yan H, Li J, Wang X, Chen H, Zheng X. Transperineal versus transrectal prostate biopsy in the diagnosis of prostate cancer: a systematic review and meta-analysis. World J Surg Oncol. 2019;17(1):31. 10.1186/s12957-019-1573-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Xie H, Li J, Ying Y, Yan H, Jin K, Ma X, et al. METTL3/YTHDF2 m(6) A axis promotes tumorigenesis by degrading SETD7 and KLF4 mRNAs in bladder cancer. J Cell Mol Med. 2020;24(7):4092–104. 10.1111/jcmm.15063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Yan H, Cui X, Zhang P, Li R. Fruit and vegetable consumption and the risk of prostate cancer: a systematic review and meta-analysis. Nutr Cancer. 2022;74(4):1235–42. 10.1080/01635581.2021.1952445. [DOI] [PubMed] [Google Scholar]
  • 20.Yan H, Li J, Ying Y, Xie H, Chen H, Xu X, et al. MIR-300 in the imprinted DLK1-DIO3 domain suppresses the migration of bladder cancer by regulating the SP1/MMP9 pathway. Cell Cycle (Georgetown Tex). 2018;17(24):2790–801. 10.1080/15384101.2018.1557490. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Yan H, Wu Y, Cui X, Zheng S, Zhang P, Li R. From cognitive MR-targeted fusion prostate biopsy to radical prostatectomy: incidence and predictors of Gleason grade group upgrading in a Chinese cohort. Biomed Res Int. 2022;2022:7944342. 10.1155/2022/7944342. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Yan H, Xie H, Ying Y, Li J, Wang X, Xu X, et al. Pioglitazone use in patients with diabetes and risk of bladder cancer: a systematic review and meta-analysis. Cancer Manage Res. 2018;10:1627–38. 10.2147/cmar.S164840. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Yan H, Ying Y, Xie H, Li J, Wang X, He L, et al. Secondhand smoking increases bladder cancer risk in nonsmoking population: a meta-analysis. Cancer Manage Res. 2018;10:3781–91. 10.2147/cmar.S175062. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Yan H, Zhang L, Cui X, Zheng S, Li R. Roles and mechanisms of the m(6)A reader YTHDC1 in biological processes and diseases. Cell Death Discov. 2022;8(1):237. 10.1038/s41420-022-01040-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Yan H, Zhang L, Li R. Identification of m6A suppressor EIF4A3 as a novel cancer prognostic and immunotherapy biomarker through bladder cancer clinical data validation and pan-cancer analysis. Sci Rep. 2023;13(1):16457. 10.1038/s41598-023-43500-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Ying Y, Li J, Xie H, Yan H, Jin K, He L, et al. CCND1, NOP14 and DNMT3B are involved in miR-502-5p-mediated Inhibition of cell migration and proliferation in bladder cancer. Cell Prolif. 2020;53(2):e12751. 10.1111/cpr.12751. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Zhang L, Yan H. Genetically identification of endometriosis and cancers risk in women through a two-sample Mendelian randomization study. Sci Rep. 2024;14(1):8382. 10.1038/s41598-024-58950-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Chen D, Xu L, Xing H, Shen W, Song Z, Li H, et al. Sangerbox 2: enhanced functionalities and update for a comprehensive clinical bioinformatics data analysis platform. iMeta. 2024;3(5):e238. 10.1002/imt2.238. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Wang W, Gao Y, Liu Y, Xia S, Xu J, Qin L, et al. Pan-cancer analysis reveals MTTP as a prognostic and immunotherapeutic biomarker in human tumors. Front Immunol. 2025;16:1549965. 10.3389/fimmu.2025.1549965. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Chen L, He Y, Duan M, Yang T, Chen Y, Wang B, et al. Exploring NUP62’s role in cancer progression, tumor immunity, and treatment response: insights from multi-omics analysis. Front Immunol. 2025;16:1559396. 10.3389/fimmu.2025.1559396. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Zheng Y, Zhao J, Liu M, Liu Y, Ding Y, Xie T. Investigating the role of eight SNPs in CHRNA3 for COPD susceptibility in the Chinese elderly population. Ann Med. 2025;57(1):2474726. 10.1080/07853890.2025.2474726. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Omasits U, Ahrens CH, Müller S, Wollscheid B. Protter: interactive protein feature visualization and integration with experimental proteomic data. Bioinformatics. 2014;30(6):884–6. 10.1093/bioinformatics/btt607. [DOI] [PubMed] [Google Scholar]
  • 33.Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509–14. 10.1093/nar/gkaa407. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Chandrashekar DS, Karthikeyan SK, Korla PK, Patel H, Shovon AR, Athar M, et al. UALCAN: an update to the integrated cancer data analysis platform. Neoplasia (New York NY). 2022;25:18–27. 10.1016/j.neo.2022.01.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Ru B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, et al. TISIDB: an integrated repository portal for tumor–immune system interactions. Bioinformatics. 2019;35(20):4200–2. 10.1093/bioinformatics/btz210. [DOI] [PubMed] [Google Scholar]
  • 36.Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, et al. The immune landscape of cancer. Immunity. 2018;48(4):812–30. 10.1016/j.immuni.2018.03.023. e14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Zeng D, Ye Z, Shen R, Yu G, Wu J, Xiong Y, et al. IOBR: multi-omics immuno-oncology biological research to Decode tumor microenvironment and signatures. Front Immunol. 2021;12:687975. 10.3389/fimmu.2021.687975. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612. 10.1038/ncomms3612. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Fu J, Li K, Zhang W, Wan C, Zhang J, Jiang P, et al. Large-scale public data reuse to model immunotherapy response and resistance. Genome Med. 2020;12(1):21. 10.1186/s13073-020-0721-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.de Bruijn I, Kundra R, Mastrogiacomo B, Tran TN, Sikina L, Mazor T, et al. Analysis and visualization of longitudinal genomic and clinical data from the AACR project GENIE biopharma collaborative in cBioPortal. Cancer Res. 2023;83(23):3861–7. 10.1158/0008-5472.Can-23-0816. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Ru B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinf (Oxford England). 2019;35(20):4200–2. 10.1093/bioinformatics/btz210. [DOI] [PubMed] [Google Scholar]
  • 42.Sun H, Zhang H. Lysine methylation-dependent proteolysis by the malignant brain tumor (MBT) domain proteins. Int J Mol Sci. 2024;25(4). 10.3390/ijms25042248. [DOI] [PMC free article] [PubMed]
  • 43.Guo P, Lim RC, Rajawasam K, Trinh T, Sun H, Zhang H. A methylation-phosphorylation switch controls EZH2 stability and hematopoiesis. eLife. 2024;13. 10.7554/eLife.86168. [DOI] [PMC free article] [PubMed]
  • 44.Goldman MJ, Craft B, Hastie M, Repečka K, McDade F, Kamath A, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38(6):675–8. 10.1038/s41587-020-0546-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Blum A, Wang P, Zenklusen JC, SnapShot. TCGA-Analyzed tumors. Cell. 2018;173(2):530. 10.1016/j.cell.2018.03.059. [DOI] [PubMed] [Google Scholar]
  • 46.Carithers LJ, Moore HM. The genotype-tissue expression (GTEx) project. Biopreserv Biobank. 2015;13(5):307–8. 10.1089/bio.2015.29031.hmm. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Omasits U, Ahrens CH, Müller S, Wollscheid B. Protter: interactive protein feature visualization and integration with experimental proteomic data. Bioinf (Oxford England). 2014;30(6):884–6. 10.1093/bioinformatics/btt607. [DOI] [PubMed] [Google Scholar]
  • 48.Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605–12. 10.1093/nar/gkaa1074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Wu C, Jin X, Tsueng G, Afrasiabi C, Su AI. BioGPS: Building your own mash-up of gene annotations and expression profiles. Nucleic Acids Res. 2016;44(D1):D313–6. 10.1093/nar/gkv1104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4. 10.1158/2159-8290.Cd-12-0095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1. 10.1126/scisignal.2004088. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (10.8KB, xlsx)

Data Availability Statement

Data availability: Some database that support the findings of this study are openly available described in ‘Materials and methods’, including UCSC Xena database [44], UALCAN [34], TCGA [45], GTEx [46], human protein atlas (www.proteinatlas.org), PROTTER [47], String [48), TIMER (33), BioGPS (49), TISIDB (41), TIDE (39) and cBioportal database [50, 51]. Other data are available from corresponding author.

Articles from Discover Oncology are provided here courtesy of Springer

RESOURCES