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Comprehensive pan-cancer analysis of ZNF337 as a potential diagnostic, immunological, and prognostic biomarker

BMC Cancer volume 24, Article number: 987 (2024) Cite this article

Abstract

Background

Zinc Finger Protein 337 (ZNF337) is a novel Zinc Finger (ZNF) protein family member. However, the roles of ZNF337 in human cancers have not yet been investigated.

Methods

In this study, with the aid of TCGA databases, GTEx databases, and online websites, we determined the expression levels of ZNF337 in pan-cancer and its potential value as a diagnostic and prognostic marker for pan-cancer and analyzed the relationship between ZNF337 expression and immune cell infiltration and immune checkpoint genes. We then focused our research on the potential of ZNF337 as a biomarker for diagnostic and prognostic in KIRC (kidney renal clear cell carcinoma) and validated in the E-MTAB-1980 database. Moreover, the expression of ZNF337 was detected through qRT-PCR and Western blotting (WB). CCK-8 experiment, colony formation experiment, and EDU experiment were performed to evaluate cell proliferation ability. Wound healing assay and transwell assay were used to analyze its migration ability. The qRT-PCR and WB were used to detect the expression of ZNF337 in tumor tissues and paracancerous tissues of KIRC patients.

Results

The pan-cancer analysis revealed that abnormal ZNF337 expression was found in multiple human cancer types. ZNF337 had a high diagnostic value in pan-cancer and a significant association with the prognosis of certain cancers, indicating that ZNF337 may be a valuable prognostic biomarker for multiple cancers. Further analysis demonstrated that the expression level of ZNF337 displayed significant correlations with cancer-associated fibroblasts, immune cell infiltration, and immune checkpoint genes in many tumors. Additionally, ZNF337 was observed to have a high expression in KIRC. Its expression was significantly associated with poor prognosis [overall survival (OS), disease-specific survival (DSS)], age, TNM stage, histologic grade, and pathologic stage. The high ZNF337 expression was associated with poor prognosis in the E-MTAB-1980 validation cohort. The in vitro experiments suggested that the expression of ZNF337 in KIRC tumor tissues was higher than in adjacent tissues, and ZNF337 knockdown inhibited the proliferation and migration of KIRC cells, whereas overexpression of ZNF337 had the opposite effects.

Conclusions

ZNF337 might be an important prognostic and immunotherapeutic biomarker for pan-cancer, especially in KIRC.

Peer Review reports

Introduction

Current cancer treatment strategies primarily include surgery, chemotherapy, radiotherapy, molecular-targeted therapy, and immunotherapy. Despite the advances in cancer management, prognosis remains below standard [1]. In recent years, immunotherapy has shown tremendous promise in cancer treatment, particularly immune checkpoint inhibitors [2,3,4]. Furthermore, developing sequencing technologies, such as TCGA (The Cancer Genome Atlas), have generated many bioinformatics databases. Various bioinformatics approaches can obtain many tumor-related functional genomic datasets, and the correlation of individual genes with cancer prognosis and immune infiltration can be mined [5,6,7]. Therefore, it is essential to identify an accurate and efficient prognosis biomarker and immunotherapies target for cancer.

Zinc finger (ZNF) proteins represent one of the largest families of transcription factors in the human genome [8], and they possess different zinc finger structural domains that selectively bind to specific DNA or RNA [9]. The ZNF proteins are involved in multiple biological processes, including DNA recognition, RNA packaging, protein folding and assembly, transcriptional activation, and regulation of apoptosis [10, 11]. In addition, members of the ZNF proteins family were found to be involved in regulating the development of various tumors [12, 13]. ZNF750 was associated with the prognosis of esophageal squamous cell carcinoma [14]. ZNF545 inhibited breast tumor cell proliferation through the induction of apoptosis in breast cancer [15]. ZNF337 is a member of the ZNF proteins family, a protein-coding gene. Previous reports reported potentially deleterious somatic variants in ZNF337 were associated with focal cortical dysplasia type II [16]. However, little is known about the role of ZNF337 in human cancer.

Thus, we performed a systematic pan-cancer analysis to investigate the potential value of ZNF337 as a diagnostic and prognostic marker for human cancers for the first time. We also explored ZNF337 expression for its association with immune cell infiltration and immune checkpoint genes in pan-cancer. Next, we focused on KIRC and identified ZNF337 as an independent prognostic factor for KIRC. We also analyzed the biological function of ZNF337 in KIRC cell lines by in vitro functional assays. There is a discovery that ZNF337 has high potential as a diagnostic, prognostic, and immunotherapeutic biomarker for pan-cancer, particularly for KIRC.

Materials and methods

ZNF337 expression analysis

Using data from Human Protein Atlas (HPA) (http://www.proteinatlas.org/) [17, 18], mRNA levels of ZNF337 were detected in various human cell lines. To evaluate the differences in the protein level of ZNF337, we downloaded immunohistochemistry (IHC) images of ZNF337 protein expression in normal and tumor tissues of the kidney, prostate, endometrium, and stomach from HPA. The ZNF337 gene expression profile between tumor types and adjacent normal tissues was analyzed using the TIMER 2.0 database (http://timer.cistrome.org/) [19, 20]. Public RNA-seq data (including 18102 samples across 33 tumor types and normal tissues) was accessed from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. The RNA-seq data was processed using the transcripts per million reads (TPM) method and transformed into log2(TPM + 1) to normalize their distribution. Statistical analysis was performed using R language (version 3.6.3) [21]. The Wilcoxon rank sum test assessed the differential expression between the two groups. Differences were considered statistically significant when P < 0.05 (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001). The false discovery rate test (FDR) correction was used for multiple test corrections.

ZNF337-related gene enrichment analysis

We predicted the proteins binding to ZNF337 by the STRING website (https://string-db.org/) and GEPIA (http://gepia.cancerpku.cn/index.html). The obtained related genes were subjected to Protein-Protein Interaction (PPI) network analysis. The parameter settings were as follows: minimum required interaction score [“low confidence (0.150)”], meaning of network edges (“evidence”), max number of interactors to show (“no more than 50 interactors” in 1st shell) and active interaction sources (“Experiments, Text mining, Databases”). The Cytoscape (v3.9.1) was employed to visualize the PPI network and subsequent analysis. The ZNF337-related genes were conducted Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and the results were visualized by the R package “ggplot2” and “clusterProfiler” [22]. In addition, the top 100 genes associated with the ZNF337 gene were analyzed with the GEPIA database. Then, we performed a gene-gene Pearson correlation analysis of the selected genes with the ZNF337 gene.

Diagnostic value analysis

The receiver operating characteristic (ROC) curve analysis was conducted to measure the area under the ROC curve (AUC) to determine the diagnostic value of ZNF337 for pan-cancer. The ROC curves were performed by “timeROC”, “survminer”, and “survival” packages [23]. The closer the AUC was to 1, the better the overall diagnostic performance was. We also obtained RNA expression from KIRC patients (101 tumor samples) and clinical data from E-MTAB-1980 (101 tumor samples) from ArrayExpress (https://www.ebi.ac.uk/arrayexpress/) as a validation cohort.

Survival prognosis analysis

According to the median level of ZNF337 expression, the patients in the cohort were divided into two groups (high expression and low expression). The overall survival (OS) and disease-specific survival (DSS) association with ZNF337 expression was analyzed in different cancer types by Log-rank test in a TCGA cohort, and forest plots were generated using the R package “forestplot” to visualize the analysis results. Kaplan-Meier (K-M) analysis through the R packages “survminer” and “survival” was applied to evaluate the prognostic significance of ZNF337 expression on OS and DSS in pan-cancer. In this study, we further examined the relationship between the expression level of ZNF337 and the prognosis of different clinical subgroups of KIRC. Visualization was done using the “survminer” package, and statistical analysis was done using the “survival” package. P < 0.05 was considered statistically significant.

Prognostic model generation and prediction in KIRC

Univariate and multivariate Cox regression models were used to identify the prognostic significance of ZNF337 and clinical variables for OS and DSS in KIRC. Univariate and multivariate Cox regression analysis was performed using the R packages survival and survminer. The ggforest method in the “survminer” package was used to construct the forest plot. A P-value < 0.05 was regarded as statistically significant. To personalize the predicted OS probability, we constructed a nomogram using the “rms” package and developed a calibration plot for the nomogram to evaluate its prediction accuracy.

Immune infiltration analysis

The present study utilized the “GENE” module of TIMER 2.0 to examine the relevance of ZNF337 to cancer-associated fibroblasts (CAFs) in pan-cancer. The “ggplot2” package visualized the results. Moreover, correlation analysis was conducted between ZNF337 expression and immune cells using the TIMER database.

Immune checkpoint genes correlation analysis

A uniformly normalized pan-cancer dataset combining the TCGA and GTEX databases was obtained from UCSC (https://xenabrowser.net/). Analysis in the present study was conducted via the “SangerBox” online tool (http://sangerbox.com/Tool) to identify the association between the expression of common immune checkpoint genes and ZNF337. The Pearson correlation test was used for the correlation analysis. P-value < 0.05 was regarded as statistically significant.

Analysis of differences between high and low ZNF337 expression groups in KIRC

KIRC patients in TCGA were classified into high and low-expression groups based on the median score of ZNF337 expression. The differentially expressed genes (DEGs) positively and negatively correlated with ZNF337 expression were further analyzed. Volcano plots and heatmap plots were done with the “ggplot2” package. The parameters were: FDR adjusted p-value < 0.05, and |log2-fold-change (FC)|>2. The correlation was assessed using the Pearson correlation coefficient. GO and KEGG enrichment analysis of differential genes grouped by high and low expression of ZNF337 in KIRC based on TCGA database using “GOplot”, “ggplot2,” and “clusterProfiler” package, and construction of PPI network to identify hub genes for visualization by Cytoscape (v3.9.1). Hub genes were selected by cytoHubba in Cytoscape.

ZNF337 co-expression gene analysis in the KIRC

This study analyzed the top 50 co-expressed genes that positively or inversely correlated with ZNF337 expression in KIRC using the “stat” package. Co-expression heatmaps were generated with the “ggplot2” package. The relationship between ZNF337 expression and the top 10 gene expression in the heatmap was evaluated with the Pearson correlation coefficient. The results were visualized using the “ggplot2” package.

Specimen collection

KIRC tissues and adjacent tissues were collected from Yantai Yuhuangding Hospital, affiliated with Qingdao University. The fresh tissue specimens were immediately frozen in liquid nitrogen after surgery and stored at -80 °C. This study was approved by the institutional ethics committee and was conducted after obtaining written informed consent from each subject.

Cell culture and transfection

Normal human renal epithelial cell lines (HK2) and KIRC cell lines (Caki-1 and 786-O) were received from the Chinese Academy of Sciences Cell Bank (Shanghai, China). Cell lines were cultured according to their instruction. All siRNAs (si-NC and si-ZNF337) were purchased from Suzhou Jima Gene Co. Ltd. (Suzhou, China), and transfected to cells by RNAiMAX (Invitrogen, USA). The siRNA sequences were as follows: negative control: 5’-UUCUCCGAACGUGUCACGUTT-3’ sense and 5’- ACGUGACACGUUCGGAGAATT-3’ antisense; si-ZNF337: 5’-GCUAUACCAGUAAGUCAUATT-3’ sense and 5’-UAUGACUUACUGGUAUAGCTT-3’ antisense. For ZNF337 overexpression (OE-ZNF337), OE-ZNF337 plasmid was constructed based on the vector pcDNA 4.0 (Invitrogen, USA), and the empty pcDNA 4.0 was used as the negative control (NC). According to the manufacturer’s protocol, overexpression plasmids were transiently transfected using jetPRIME reagents (Polyplus, France).

Quantitative reverse Transcription-PCR (qRT-PCR)

Total RNA was extracted using an RNA isolation kit (Dakewe, China). Reverse transcription was performed using the TaKaRa RT-PCR kit (TaKaRa, Japan). qRT-PCR was performed using the SYBR Green PCR kit (Vazyme, China). The qRT-PCR experiments were done in triplicate. The sequences are as follows: ZNF337: 5’-TCACCCAGAAGGAATGGAGGCT and 3’- TTGCTCTAGCCGCCTGATGAGT; β-actin: 5’- CTTCGCGGGCGACGAT and 3’- ATAGGAATCCTTCTGACCCATGC.

Western blotting (WB)

The total protein of cells was extracted with RIPA buffer containing phosphatase inhibitors and protease inhibitors. The BCA Kit determined the protein concentration (Thermo Scientific, USA). The sodium dodecyl sulfate-polyacrylamide gel electrophoresis separated protein extracts (60 µg). Then, the protein was transferred to polyvinylidene difluoride membranes and blocked with 5% skim milk. Subsequently, the membrane was incubated with specific primary antibodies (ZNF337, Thermo Scientific, USA; β-actin, Abcam, USA; GAPDH, Abcam, USA) at 4 °C overnight. After incubation with a second antibody, the membrane was reacted with a chemiluminescence detection reagent (Merck, Germany). Quantifications were performed in ImageJ.

Cell counting Kit-8 (CCK-8) experiment

Cell viability was evaluated using a CCK-8 (MedChemExpress, China) experiment. Cells were inoculated into 96-well plates at 5 × 103 cells/well density. Then, absorbance values of 450 nm were measured daily for 3 days according to the manufacturer’s protocol.

5-Ethynyl-2′-deoxyuridine (EdU) experiment

EdU experiment was conducted using an EdU kit (Biyuntian, China). The operation procedure was carried out manufacturer’s instructions. Samples were then examined by flow cytometric analysis.

Colony formation experiment

The cells were seeded in six-well plates (500 cells per well). The plates were incubated at 37 ℃, 5% CO2, and 95% humidity for 7–14 days. Colonies were counted under an inverted microscope. Clusters of 10 or more cells were counted as a colony. After the colony formed, the cells were fixed with 4% paraformaldehyde and stained with 0.05% crystal violet staining solution.

Wound healing experiment

The cells were seeded in six-well plates, grown to 80–90% confluency, and transfected with siRNA. After 48 h, a 10 µ L pipette tip was used to make a scratch, and then the serum-free medium was replaced. The distances of wound healing were observed, and images were collected at 0 h, 6 h, 12 h, and 24 h after injury.

Transwell experiment

Cells of 5 × 103 were seeded into the serum-free medium in the upper chamber, and the lower chamber of the Transwell was added with a complete medium. After 48 h, the cells were fixed with 4% paraformaldehyde and stained with 0.05% crystal violet staining solution. Cells at the bottom of the chamber were counted.

Results

ZNF337 expression in pan-cancer

The flow chart of this research study is presented in Supplementary Fig. 1. We analyzed the mRNA expression levels of ZNF337 in human cell lines with data from the HPA database. The results from the consensus dataset (FANTOM5, HPA, and GTEx) indicated that ZNF337 expression was observed in multiple cell types (Fig. 1A). The TIMEER 2.0 was used to ascertain the differential expression of ZNF337 between TCGA tumors and adjacent normal tissues. As Fig. 1B shows, the expression level of ZNF337 was highly expressed in many tumor tissues, including bladder urothelial carcinoma (BLCA), cholangiocarcinoma (CHOL), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), paraganglioma (PCPG), and stomach adenocarcinoma (STAD). In contrast, low expression of ZNF337 was examined in breast invasive carcinoma (BRCA), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC). The ZNF337 expression differences in paired tumor and adjacent normal tissues in pan-cancer data of TCGA were presented in Fig. 1C.

Fig. 1
figure 1

Expression level of ZNF337 gene in tumors and normal tissues. (A) ZNF337 expression in human cell lines; (B) ZNF337 expression in TCGA tumors and adjacent tissues; (C) ZNF337 expression in TCGA paired tumor and adjacent normal tissues in pan-cancer; (D) ZNF337 expression in TCGA unpaired tumors and normal tissues with the data of the GTEx database as controls (*p < 0.05, **p < 0.01, ***p < 0.001)

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Moreover, we validated the association between the expression differences of ZNF337 between the tumor and normal tissues by combining the GTEx dataset. The unpaired analysis revealed that ZNF337 mRNA expression was increased in KIRC, KIRP, acute myeloid leukemia (LAML), brain lower grade glioma (LGG), pancreatic adenocarcinoma (PAAD), and PCPG compared with normal tissues (Fig. 1D).

IHC results in the HPA database analyzed the ZNF337 expression differences at the protein level. The results showed that ZNF337 was highly expressed in renal cancer tissues and lowly expressed in prostate, endometrial, and stomach cancer tissues, consistent with the ZNF337 gene expression data from TCGA (Fig. 2A and D).

Fig. 2
figure 2

The expression of ZNF337 mRNA and protein in different cancers. (A) Kidney; (B) Prostate; (C) Endometrium; (D) Stomach (***p < 0.001)

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PPI network and enrichment analysis of ZNF337-related genes

The PPI network of the top 50 proteins correlated with ZNF337 was established by the STRING website and GEPIA (Supplementary Fig. 2A). Then, the top 50 targeted binding proteins were subjected to GO and KEGG enrichment analysis (Supplementary Fig. 2B). The GO analysis showed that ZNF337-binding proteins were mainly involved in RNA splicing, regulation of mRNA splicing, termination of RNA polymerase I transcription, transcription elongation from RNA polymerase I promoter, and protein serine/threonine kinase activity (Supplementary Fig. 2C). And the expression of ZNF337 positively correlated with that of NPIPA1, NEURL4, ZNF767P, EZH1, and NRF1 (Supplementary Fig. 2D).

Diagnostic value of ZNF337 in pan-cancer

We analyzed the diagnostic value of ZNF337 in pan-cancer by ROC curves, which are used to assess the diagnostic accuracy of gene signature. The results showed that ZNF337 had a certain prediction accuracy (AUC > 0.7) in pan-cancer, including KIRC (AUC = 0.868), KIRP (AUC = 0.726), PRAD (AUC = 0.823), ACC (AUC = 0.747), BRCA (AUC = 0.729), CESC (AUC = 0.901), CHOL (AUC = 0.966), COAD (AUC = 0.800), esophageal carcinoma (ESCA) (AUC = 0.865), HNSC (AUC = 0.729), LUSC (AUC = 0.806), OV (AUC = 0.996), PAAD (AUC = 0.772), rectum adenocarcinoma (READ) (AUC = 0.829), SKCM (AUC = 0.812), STAD (AUC = 0.738), THCA (AUC = 0.937), THYM (AUC = 0.844), UCEC (AUC = 0.817), and UCS (AUC = 0.980). Of these, ZNF337 showed high diagnostic accuracy (AUC > 0.9) for CESC, CHOL, OV, THCA, and UCS (Fig. 3A and T). ROC curves analysis showed that ZNF337 was a valuable diagnostic biomarker for multiple types of cancer.

Fig. 3
figure 3

The diagnostic value of ZNF337 in pan-cancer. (A–T). The ROC curves of ZNF337 in KIRC, KIRP, PRAD, ACC, BRCA, CESC, CHOL, COAD, ESCA, HNSC, LUSC, OV, PAAD, READ, SKCM, STAD, THCA, THYM, UCEC, and UCS

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Survival analysis of ZNF337 in the pan-cancer

The relationship between ZNF337 expression and survival outcome (OS and DSS) in 33 TCGA tumors was investigated by univariate survival analysis. The pooled analysis forest plot (Fig. 4A and B) found that an increased ZNF337 expression was associated with poor OS in KIRC (HR = 1.73, P < 0.01), LIHC (HR = 1.46, P = 0.03), but a better prognosis in READ (HR = 0.26, P < 0.01). The correlation analysis of DSS revealed that ZNF337 plays a risk role for KIRC (HR = 1.75, P = 0.04) and LIHC (HR = 1.80, P = 0.009), and a protective role for PAAD (HR = 0.62, P = 0.045). In the TCGA cohort, the K-M curves analysis showed that high ZNF337 expression was correlated with poor OS and DSS in KIRC and LIHC (Fig. 4C, D, E and F). This discrepancy could result from the heterogeneity among different cancer types. ZNF337 may play a different role in different kinds of cancer.

Fig. 4
figure 4

Correlations between ZNF337 expression and the prognosis (OS and DSS) of cancers. (A) Forest plot of ZNF337 OS in cancers; (B) Forest plot of ZNF337 DSS in cancers; (C) OS K-M plot of high and low ZNF337 expression in KIRC; (D) OS K-M plot of high and low ZNF337 expression in LIHC; (E) DSS K-M plot of high and low ZNF337 expression in KIRC; (F) DSS K-M plot of high and low ZNF337 expression in LIHC

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Fig. 5
figure 5

Correlation analysis between ZNF337 expression and CAFs, tumor-infiltrating immune cells. (A) Correlation analysis between ZNF337 expression and CAFs in pan-cancers; the correlation analysis between ZNF337 expression and immune infiltrating cells in (B) KIRC; (C) KIRP; (D) BLCA; (E) BRCA; (F) GBM; (G)HNSC; (H) STAD; (I) THCA

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Correlation analysis between ZNF337 and immune cell infiltration and immune checkpoint genes in pan-cancer

The TIDE, XCELL, MCPCOUNTER, and EPIC algorithms were adopted to investigate the correlation between ZNF337 expression and CAFs in pan-cancer. We found that ZNF337 expression positively correlated with the estimated infiltration value of CAFs in most tumors (Fig. 5A). Moreover, the immune cell infiltration was also analyzed using the TIMER. As presented in Fig. 6B and I, ZNF337 was correlated with tumor purity and immune cell infiltration, including B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells, in KIRC, KIRP, BLCA, BRCA, HNSC, STAD, and THCA (Fig. 5B and I). In summary, ZNF337 expression might affect immune cell infiltration.

Fig. 6
figure 6

Independent prognostic value of ZNF337 in KIRC. (A) Forest plot showing the results of univariate Cox regression analysis; (B) Forest plot showing the results of multivariate Cox regression analysis; (C) A nomogram that integrates ZNF337 and other prognostic factors in KIRC from TCGA data; (D) The calibration curve of the nomogram; (E) K-M survival curve analysis in the TCGA cohort based the nomogram; (F-H) ROC curves analysis shows 1, 3, and 5-year OS and the corresponding AUC values for KIRC patients from the TCGA cohort based the nomogram; (I) K-M survival curve analysis in the E-MTAB-1980 cohort based the nomogram; (J-L) ROC curves analysis shows 1, 3, and 5-year OS and the corresponding AUC values for KIRC patients from the E-MTAB-1980 cohort based the nomogram

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Immune checkpoints represent a set of molecules expressed on immune cells that regulate the degree of immune activation. Tumor cells achieve immune escape by activating immune checkpoints [24]. The immune checkpoints are one of the most critical targets for clinical immunotherapy strategies, and they are useful for evaluating the immune response of patients to immunotherapy. Therefore, an analysis of the correlation between the expression of common immune checkpoint genes and ZNF337 was performed using the Sangerbox tool in the present study. Our results revealed that these correlations were significant in many tumors (Supplementary Fig. 3). In KIRC, lymphoid neoplasm diffuses large B-cell lymphoma (DLBC), LIHC, OV, and UVM, and the expression of ZNF337 showed a positive correlation with the expression of most immune checkpoint genes. ZNF337 expression positively correlated with 53 out of 60 immune checkpoint genes in KIRC and 54 out of 60 immune checkpoint genes in LIHC, suggesting that ZNF337 was closely related to the sensitivity of tumor immune checkpoint inhibitors. In BLCA, glioblastoma multiforme (GBM), brain lower grade glioma (LGG), and sarcoma (SARC), ZNF337 expression was negatively correlated with immune checkpoint genes. It suggested that patients with high ZNF337 expression in these tumors may experience an inferior response to immunotherapy targeting the immune checkpoint genes.

ZNF337 is correlated with different clinical characteristics in KIRC

Based on these results, we focused on KIRC for further analysis. In addition to the TCGA-KIRC cohort, we used the E-MTAB-1980 cohort as an external cohort to further evaluate whether prognostic signature had similar predictive performance and accuracy in other cohorts of KIRC patients. The K-M curves analysis showed that high levels of ZNF337 expression in the E-MTAB-1980 cohort also suggested a poor prognosis (Supplementary Fig. 4A). The predicted AUCs of 1-, 3-, and 5 years were 0.681, 0.743, and 0.708 (Supplementary Fig. 4B-D). The results showed that the expression level of ZNF337 had a very important potential prognostic value for KIRC. We further constructed a survival analysis of subgroups to evaluate the prognostic power of ZNF337 in clinical subgroups of KIRC. The clinical characteristics of ZNF337-high and -low expressed subgroups of KIRC are shown in Table 1. With regards to OS, subgroup analysis indicated that the group of high ZNF337 expression presented a worse survival rate in a subgroup of age > 60 (Fig. 7A), a subgroup of T3 stage (Fig. 7B), a subgroup of T4 stage (Fig. 7C), a subgroup of N0 stage (Fig. 7D), a subgroup of M0 stage (Fig. 7E), a subgroup of M1 stage (Fig. 7F), a subgroup of pathologic stage II (Fig. 7G), and a subgroup of pathologic stage IV (Fig. 7H). For DSS, the group of high ZNF337 expression presented a worse survival rate in a subgroup of T3 (Fig. 7I), a subgroup of M1 (Fig. 7J), a subgroup of pathologic stage II (Fig. 7K), and histologic stage G4 (Fig. 7L).

Fig. 7
figure 7

Associations between ZNF337 expression and OS, DSS in different clinical subgroups of KIRC. (A) age > 60; (B) T3 stage; (C) T4 stage; (D) N0 stage; (E) M0 stage; (F) M1 stage; (G) pathologic stage II; (H) pathologic stage IV; (I) T3; (J) M1; (K) pathologic stage II; (L) histologic stage G4

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Table 1 Clinical characteristics of KIRC patients
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Independent prognostic value of ZNF337 in KIRC

Univariate and multivariate survival analyses were conducted using Cox regression models, to study the independent prognostic value of ZNF337 in KIRC. Pooled results were shown in the forest plots (Fig. 6A and B). The results demonstrated that there was a significant survival difference in OS between the groups of low- and high-expression levels of ZNF337 on both univariate (hazard ratio [HR], 1.298; 95% confidence interval CI [1.038–1.624]; P = 0.022) and multivariate (HR, 1.613; 95% CI [1.180–2.205]; P = 0.003) analysis. ZNF337 expression, age, M stage, and histologic grade were independent prognostic factors of KIRC.

To substantiate this conclusion, we constructed a nomogram based on independent factors of OS to explain ZNF337 and other prognostic factors, including age, M stage, and histologic grade (Fig. 6C). The points for each variable were mapped to the corresponding horizontal line, and then the total points for each patient were calculated and standardized to a distribution of 0 ~ 100. A higher total point on the nomogram predicts a worse prognosis. By drawing a vertical line between the total score axis and each prognostic axis, we assessed the 1-, 3-, and 5-year survival rates of KIRC patients, which can be used to inform clinical decisions. The calibration curves of predicted results showed good agreement with these observed results, and the nomogram’s concordance index (C-index) value was 0.718 (Fig. 6D). In addition, to further determine the reliability and robustness of the nomogram, we performed internal and external validation using TCGA and E-MTAB-1980 cohorts, respectively. K-M survival curve analysis revealed that the nomogram accurately identified KIRC patients with a lower probability of survival in the TCGA (P < 0.001) and E-MTAB-1980 cohorts (P < 0.001) (Fig. 6E and I). Based on the nomogram, the predicted AUC for 1, 3, and 5 years in the TCGA cohort were 0.811, 0.790, and 0.776 (Fig. 6F and H). In the E-MTAB-1980 dataset, the AUC for predicting 1, 3, and 5 years was 0.936, 0.863, and 0.869, respectively (Fig. 6J and L). The results indicated that the nomogram had good predictive power and accuracy.

Functional enrichment analysis of DEGs between ZNF337 high - and low-expression groups in KIRC

To explain the underlying molecular mechanisms by which ZNF337 induced tumorigenesis, we obtained DEGs high - and low-expression groups in KIRC. There were 9256 DEGs, of which 8707 DEGs were up-regulated, and 549 DEGs were down-regulated (adjusted P-value < 0.05, |log2-FC| > 1). The volcano plot and heatmap of the DEG expression are shown in Supplementary Fig. 5A and 5B. Selected DEGs were subjected to GO and KEGG enrichment analysis (Supplementary Fig. 5C). GO enrichment analysis, including biological processes (BP), cellular compositions (CC), and molecular functions (MF), showed that DEGs were mainly enriched in substrate-specific channel activity, serine-type peptidase activity, serine hydrolase activity, blood microparticle, and icosanoid metabolic process (Supplementary Fig. 5D). KEGG pathway analysis indicated that DEGs were mainly enriched in collecting duct acid secretion, neuroactive ligand-receptor interaction, ovarian steroidogenesis, linoleic acid metabolism, and alpha-linolenic acid metabolism (Supplementary Fig. 5E). In addition, we further investigated potential interactions between DEGs. The top 10 hub genes were identified, including H3F3B, HIST1H3A, HIST1H3C, HIST1H3D, HIST1H4F, HIST1H3J, HIST1H2AI, BRD2, BRD4 and HIST1H2BC (Supplementary Fig. 5F). The top five of these hub genes were H3F3B, HIST1H3A, HIST1H3C, HIST1H4F, and HIST1H2AI (Supplementary Fig. 5G), and the top three hub genes were HIST1H3A, HIST1H3C, and HIST1H4F (Supplementary Fig. 5H).

Co-expression gene analysis of ZNF337 in KIRC

We showed the top 50 genes positively and negatively correlated with ZNF337 in KIRC as a heatmap (Supplementary Fig. 6A and 7 A). A correlation analysis was performed for the top 10 genes among them. Analysis showed that a significant positive correlation was found between the expression of ZNF337 and JMJD7-PLA2G4B (Supplementary Fig. 6B), NKTR (Supplementary Fig. 6C), DMTF1 (Supplementary Fig. 6D), L3MBTL1 (Supplementary Fig. 6E), FNBP4 (Supplementary Fig. 6F), TTLL3 (Supplementary Fig. 6G), WDR27 (Supplementary Fig. 6H), DNAH1 (Supplementary Fig. 6I), DDX39B (Supplementary Fig. 6J), and LENG8 (Supplementary Fig. 6K). In comparison, a negative correlation was observed with UQCRQ (Supplementary Fig. 7B), ATP5F1E (Supplementary Fig. 7C), COX6A1 (Supplementary Fig. 7D), COX5A (Supplementary Fig. 7E), UQCR10 (Supplementary Fig. 7F), COX4I1 (Supplementary Fig. 7G), COX7B (Supplementary Fig. 9H), NDUFA4 (Supplementary Fig. 7I), PRADC1 (Supplementary Fig. 7J), and ATP6V0E1 (Supplementary Fig. 7K).

ZNF337 promoted the proliferation and migration of KIRC cells in vitro

Next, we performed the in vitro experiments to further validate the bioinformatics analysis results. QRT-PCR and WB were performed in KIRC and the corresponding normal adjacent tissues. We found that ZNF337 expression was significantly upregulated in KIRC tissues compared with paracancerous tissues (Fig. 8A and B). In addition, ZNF337 was also significantly upregulated in KIRC cell lines (Fig. 8C and D). We then used specific ZNF337-targeting siRNAs to knockdown the expression levels of ZNF337 in the Caki-1 and 786-O cells. WB analysis and qRT-PCR confirmed the efficiency of the knockdown (Fig. 8E and F). CCK8 experiments showed that cell viability in Caki-1 and 786-O cell lines was significantly decreased in the si-ZNF337 group compared to the control group during 24–72 h (Fig. 9A). The capability of cell proliferation was tested using the colony formation experiment and EdU experiment. We observed that the knockdown of ZNF337 reduced the number of colonies and proliferative capacity of Caki-1 and 786-O cell lines (Fig. 9B and C). In addition, we performed transwell and wound healing experiments to verify the effect of ZNF337 on the migration ability of KIRC cells. The results showed that ZNF337 knockdown suppressed the function of KIRC cell migration (Fig. 9D and E).

Fig. 8
figure 8

ZNF337 expression in human KIRC tissue and cell lines. (A) and (B) transcription level and protein level of ZNF337 in fresh KIRC tissues and adjacent tissues. (C) and (D) transcription level and protein level of ZNF337 in KIRC cell lines and HK2. (E) and (F) verification of knockdown efficiency of ZNF337 in Caki-1 and 786-O cell lines by qRT-PCR and WB. (G) and (H) verification of overexpression efficiency of ZNF337 in Caki-1 and 786-O cell lines by qRT-PCR and WB (*p < 0.05, **p < 0.01, ***p < 0.001)

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Fig. 9
figure 9

Knockdown of ZNF337 inhibited proliferation and migration of KIRC cells. (A-C) cell proliferation was assessed by CCK8 experiment, clone formation experiment, and EdU experiment. (D-E) cell migration was assessed by transwell experiment and wound healing experiment (*p < 0.05, **p < 0.01, ***p < 0.001)

Full size image

To further confirm the effects of ZNF337 in KIRC, the recombinant plasmid with high expression of ZNF337 was constructed and transfected into the Caki-1 and 786-O cells. The qRT-qPCR and WB results indicated that Caki-1 and 786-O cells with ZNF337 overexpression were successfully established (Fig. 8G and H). The results of the CCK8 experiments revealed that OE-ZNF337 promoted the viability of Caki-1 and 786-O cells (Fig. 10A). Besides that, the number of colonies and proliferative capacity was increased in cells overexpressing ZNF337 compared with that of the control group (Fig. 10B and C). We further investigated the effect of OE-ZNF337 on the migration ability of KIRC cells using transwell and wound healing experiments. As shown in Fig. 10D and E, the migration ability of KIRC cells overexpressing ZNF337 was significantly increased (P < 0.05). These results showed that ZNF337 promoted the proliferation and migration of KIRC cells in vitro.

Fig. 10
figure 10

Overexpression of ZNF337 promoted proliferation and migration of KIRC cells. (A-C) cell proliferation was assessed by CCK8 experiment, clone formation experiment, and EdU experiment. (D-E) cell migration was assessed by transwell experiment and wound healing experiment (*p < 0.05, **p < 0.01, ***p < 0.001)

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Discussion

The ZNF proteins were first discovered in transcription factor IIIA (TFIIIA) of Xenopus oocytes in 1983 [25], containing a stable “finger”-like structure formed by the self-folding of Zn2+ [26]. ZNF is divided into different types based on the binding of Zn2+ to varying amounts of Cys and His residues. Due to the diversity of zinc finger motifs and structural domains, ZNF can play different regulatory roles in other cellular contexts and stimuli. The effects of many members of the ZNF family have been reported in various human cancers, such as nasopharyngeal cancer, esophageal cancer, gastric cancer, colorectal cancer, and prostate cancer [27,28,29,30,31]. Currently, there are no studies about the biological significance of ZNF337 in pan-cancer.

In the present study, we conducted a pan-cancer analysis of ZNF337 for the first time and identified the expression levels of ZNF337 in various cancers. Our findings confirmed that ZNF337 was abnormally expressed in many cancer types, including KIRC, suggesting that ZNF337 held the potential to be a biomarker for multiple tumor types. As for the diagnostic and prognostic value of ZNF337 in pan-cancer, we analyzed the ROC curves, which showed that ZNF337 had some certain predicting accuracy (AUC > 0.7) for 20 cancer types, including KIRC, KIRP, PRAD, ACC, BRCA, CESC, CHOL, COAD, ESCA, HNSC, LUSC, OV, PAAD, READ, SKCM, STAD, THCA, THYM, UCEC, and UCS. Next, the potential value of ZNF337 in cancer prognosis was verified by the K-M curve. Survival analysis showed that high ZNF337 expression significantly correlated with poor prognosis for OS and DSS in KIRC and LIHC, but seemed to be protective in READ and PAAD. This difference may be due to cancer type dependence and tissue specificity of cancer-related genes during tumorigenesis [32]. Previous studies have reported that up-regulation of ZNF337 is associated with radiotherapy response and involved in multiple processes of cellular biology in READ [33]. Fan et al. showed that low expression of ZNF337 gene in prostate cancer is associated with progression-free survival [34]. We speculated that ZNF337 may play different functions in different cancers due to the complexity of molecular mechanisms.

The tumor microenvironment has recently been a focus of tumor research [35]. CAFs were the most predominant component of the tumor microenvironment, which play an essential role in tumor development. Previous studies showed that CAFs could facilitate tumor progression and immune escape by secreting growth factors, cytokines, and extracellular matrix [36, 37]. Besides, as another important component of the tumor microenvironment, immune cell infiltration in tumors has also been shown to influence the development of many tumors and the sensitivity of immunotherapy [38, 39]. The results of this study confirmed that ZNF337 expression might be associated with CAFs and immune cell infiltration in a variety of tumors, suggesting that ZNF337 may influence tumor progression and prognosis by regulating the tumor microenvironment. With the advent of immune checkpoint inhibitors, identifying valuable biomarkers has become a focus. Herein, ZNF337 expression was significantly associated with immune checkpoint genes in most cancer types. In KIRC, ZNF37 expression showed significant positive correlations with many important immune checkpoint inhibitor action targets, including CTLA-4 and PDCD1. As such, ZNF337 has strong potential as a novel drug target for immunotherapy.

In this study, we focused on the role of ZNF337 in KIRC. We further found that ZNF337 higher expression might lead to worse OS in multiple clinical subgroups of KIRC, including a subgroup of age > 60, a subgroup of T3 stage, a subgroup of T4 stage, a subgroup of N0 stage, a subgroup of M0 stage, a subgroup of M1 stage, a subgroup of pathologic stage II, and a subgroup of pathologic stage IV. Similarly, in the KIRC patient subgroup of T3, M1, pathologic stage II, and histologic stage G4, high ZNF337 expression was significantly correlated with worse DSS. Univariate and multivariate survival analysis also yielded consistent results, demonstrating that ZNF337 may be an independent prognostic factor for KIRC. We then built a nomogram including ZNF337 expression, age, M stage, histologic grade, and OS predictive risk score for KIRC patients. The TCGA and E-MTAB-1980 cohorts were used to carry out internal and external validation to ensure the robustness of the nomogram. Moreover, our vitro experiments also showed that ZNF337 was highly expressed in KIRC tissues and cell lines, and knockdown of ZNF337 may significantly inhibit the proliferation and migration ability of Caki-1 and 786-O cells. Conversely, the overexpression of ZNF337 promoted cell proliferation and migration.

In addition to this, co-expression analysis detected 9256 DEGs that were ZNF337-related genes in KIRC. By utilizing enrichment analysis on selected DEGs, we recognized that “ovarian steroidogenesis,” “linoleic acid metabolism,” and “alpha-linolenic acid metabolism” may be potential mechanisms for the involvement of ZNF337 in KIRC pathogenesis. It has previously been demonstrated that steroid metabolic processes and fatty acid metabolism are classic ferroptosis-related metabolic pathways [40, 41]. It remains to be elucidated whether ZNF337 affects KIRC through the ferroptosis pathway.

In the present study, we are the first to perform a systematic pan-cancer analysis of ZNF337 and demonstrate statistical correlations of ZNF337 expression with clinical prognosis in KIRC. However, certain limitations still exist in this study. Firstly, our study only conducted in vitro experiments and lacked in vivo animal experiments. In future studies, we will employ further experiments for validation in vivo. Secondly, due to differences between the samples collected from different databases, the diagnostic and prognostic value of ZNF337 in partial cancers needed further confirmation with a larger sample size. Finally, while the findings of the pan-cancer analysis support the association of ZNF337 expression with immune cell infiltration in various tumors, the underlying mechanism remains unclear. This is exactly where we will continue to focus our research next.

Conclusions

ZNF337 may be utilized as a biomarker for the diagnosis and prognosis in pan-cancer, especially in KIRC. In addition, we confirmed for the first time that ZNF337 is highly expressed in KIRC cells and contributes to proliferation and migration. These results provided a promising target for immunotherapy in diverse cancer types and revealed the underlying molecular mechanisms of ZNF337 in KIRC.

Data availability

Publicly available data sets were utilized in this study. The details are as follows: TIMER database (http://timer.cistrome.org/), the Human Protein Atlas (HPA) (http://www.proteinatlas.org/), The Cancer Genome Atlas (TCGA) (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga), GTEx (https://commonfund.nih.gov/GTEx), STRING website (https://string-db.org/) and GEPIA (http://gepia2.cancer-pku.cn/#index).

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Author notes

  1. Dongxu Zhang and Pu Liang contributed equally to this work as co-first authors.

Authors and Affiliations

  1. Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, NO. 8 Gongti South Road, Beijing, China

    Dongxu Zhang, Bowen Xia & Xiaopeng Hu

  2. Institute of Urology, Capital Medical University, Beijing, China

    Dongxu Zhang, Bowen Xia & Xiaopeng Hu

  3. Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China

    Pu Liang

  4. Beijing Institute of Infectious Diseases, Beijing, 100015, China

    Pu Liang

  5. National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China

    Pu Liang

  6. National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, 100015, China

    Pu Liang

  7. Department of Urology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China

    Jitao Wu

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Contributions

HXP and ZDX designed the research, interpreted the data, and revised the paper. ZDX, LP, XBW, ZX, performed the data analysis. WJT collected the tissue specimen. ZDX and LP performed validation experiments and drafted the paper. All of the authors approved the submitted and final versions.

Corresponding author

Correspondence to Xiaopeng Hu.

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This study was approved by the Ethics Committee of Yantai Yuhuangding Hospital affiliated to Qingdao University (batch number: 2023 − 246). (The name and affiliation of the ethics committee that approved this study: Ethics Committee of Yantai Yuhuangding Hospital, No. 2023 − 246).

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The study was conducted according to the guidelines laid down in the Declaration of Helsinki.

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Zhang, D., Liang, P., Xia, B. et al. Comprehensive pan-cancer analysis of ZNF337 as a potential diagnostic, immunological, and prognostic biomarker. BMC Cancer 24, 987 (2024). https://doi.org/10.1186/s12885-024-12703-x

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BMC Cancer

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