Diagnostic and Prognostic Value of SCN4B in ccRCC

doi:10.21203/rs.3.rs-4839015/v1

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Diagnostic and Prognostic Value of SCN4B in ccRCC

https://doi.org/10.21203/rs.3.rs-4839015/v1

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Background

The role of the voltage-gated sodium channel β4 subunit (SCN4B) gene in clear cell renal cell carcinoma (ccRCC) remains ambiguous. Utilizing the Cancer Genome Atlas (TCGA) database, we undertook a bioinformatics analysis to delineate SCN4B's function in ccRCC.

Methods

Data from 541 ccRCC patients and 72 normal individuals were extracted from the TCGA database. The Kruskal–Wallis test and logistic regression analysis were used to assess the relationship between SCN4B expression and clinicopathological characteristics. Cox regression and Kaplan–Meier analyses were performed to evaluate the prognostic value of SCN4B. Single-sample gene set enrichment analysis (ssGSEA) was employed to explore associations between SCN4B expression and immune infiltration levels in ccRCC.

Results

SCN4B was significantly overexpressed in ccRCC (P < 0.001) and effectively distinguished tumor tissue (area under the curve = 0.835) from normal tissue. SCN4B was notably associated with pathological T stage (odds ratio (OR) = 0.429 (0.298–0.616), P < 0.001) and pathological M stage (odds ratio (OR) = 0.424 (0.256–0.704), P < 0.001). Kaplan–Meier survival analysis revealed that patients with high SCN4B expression had better overall survival (hazard ratio (HR) = 0.57, P < 0.001) and progression-free interval (hazard ratio (HR) = 0.50, P < 0.001). Cox regression analysis indicated that pathological M stage was a risk factor for progression-free interval (HR = 4.062 (2.439–6.765), P < 0.001). Using ssGSEA, SCN4B expression was positively correlated with the abundance of NK cells (R = 0.415, P < 0.001), mast cells (R = 0.405, P < 0.001), and plasmacytoid dendritic cells (pDC) (R = 0.381, P < 0.001).

Conclusion

SCN4B may serve as a crucial biomarker for predicting ccRCC prognosis and could represent a viable target for immunotherapy related to immune infiltration.

clear cell renal cell carcinoma

SCN4B

diagnostic

prognostic

ccRCC, originating from renal tubular epithelial cells, comprises approximately 70–75% of renal cell carcinomas and is noted for its propensity for metastasis and recurrence, often involving multiple organs. This subtype of renal cancer generally has a poorer prognosis compared to others and is the leading cause of death among renal carcinoma patients ^[1–3]. ccRCC exhibits limited sensitivity to radiotherapy and chemotherapy, making targeted therapy a promising avenue for treating advanced cases. Thus, identifying new therapeutic targets is crucial for improving ccRCC prognosis ^[4–7].

SCN4B, a co-subunit of the α-subunit, significantly influences cell excitation and functions as an adhesion molecule, impacting tumor cell adhesion and migration. Emerging evidence indicates that dysregulated SCN4B is linked to tumor progression, including migration and invasion, and is implicated in several cancers such as papillary, breast, and cervical cancers ^[8–10].

SCN4B also shows potential as a biomarker for diagnosis, prognosis, and disease monitoring. For instance, enhancing SCN4B expression can inhibit prostate cancer cell proliferation, migration, and invasion, and suppress the epithelial-mesenchymal transition (EMT) process in prostate cancer ^[11]. Additionally, upregulation of SCN4B in nasopharyngeal carcinoma (NPC) cells inhibits proliferation, invasion, and migration, while promoting apoptosis ^[12]. Hence, SCN4B is a promising prognostic biomarker warranting further investigation.

Utilizing the TCGA database, we explored the correlation between SCN4B and ccRCC and examined its potential as a prognostic predictor. We analyzed RNA sequencing (RNA-seq) data for kidney tumors in TCGA to assess SCN4B expression differences between ccRCC patients and normal tissues. We also investigated the relationship between SCN4B expression and prognosis, as well as its association with immune infiltration, revealing a potential mechanism for SCN4B's involvement in ccRCC progression. Our findings may provide a valuable target for ccRCC prognosis and treatment.

RNA-seq data and bioinformatics analysis

Data were downloaded from the TCGA database (https://portal.gdc.cancer.gov). RNA-seq data from the TCGA–ccRCC (KIRC) project were processed using the STAR workflow and extracted in TPM format to obtain gene expression and clinical data. Data processing and visualization were performed using Xiantao Academic (https://www.xiantao.love), wherein clinically uninformative and duplicate data were removed. This study adhered to the Declaration of Helsinki (revised in 2013) and complied with the publication guidelines provided by TCGA. It did not involve any research on humans or animals by the authors.

Receiver operating characteristic curve analysis

Data processing and visualization were also performed using Xiantao Academic.

Kaplan–Meier curve analysis

Data processing and visualization were performed using Xiantao Academic.

Immune inffltration analysis

Immune infiltration matrix data were obtained from the cloud database from Xiantao Academic. The correlation between main variables and the immune infiltration matrix data in the preprocessed data was analyzed and visualized using Xiantao Academic.

Statistical analysis

All statistical analyses were performed using Xiantao Academic. The Kruskal–Wallis test was used to compare SCN4B expression in ccRCC and normal tissues. A P-value < 0.05 was considered statistically significant, with thresholds set at *P < 0.05, **P < 0.01, and ***P < 0.001.

Differences in SCN4B expression between ccRCC and normal tissues

To confirm the association between SCN4B expression and ccRCC, we collected gene expression data for 541 ccRCC patients and 72 normal subjects from the TCGA database. The data revealed significantly higher SCN4B expression in ccRCC patients compared to normal subjects (P < 0.001, Fig. 1A). A paired comparison analysis of 72 cancer patients and 72 normal controls corroborated these findings (P < 0.001, Fig. 1B). ROC curve analysis demonstrated that SCN4B could effectively differentiate between tumor and non-tumor tissues, with an area under the curve of 0.835 (CI = 0.798–0.871) (Fig. 1C).

SCN4B expression was examined across 33 common tumor types (Fig. 1D), showing significant overexpression in seven types, including lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) (P < 0.001), ccRCC (KIRC) (P < 0.001), acute myeloid leukemia (LAML) (P < 0.001), liver hepatocellular carcinoma (LIHC) (P < 0.001), ovarian serous cystadenocarcinoma (OV) (P < 0.001), pancreatic adenocarcinoma (PAAD) (P < 0.001), and thymoma (THYM). Conversely, SCN4B was significantly underexpressed in 19 tumor types, including adrenocortical carcinoma (ACC) (P < 0.001), bladder urothelial carcinoma (BLCA) (P < 0.001), breast invasive carcinoma (BRCA) (P < 0.001), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) (P < 0.001), colon adenocarcinoma (COAD) (P < 0.001), esophageal carcinoma (ESCA) (P < 0.001), glioblastoma multiforme (GBM) (P < 0.001), head and neck squamous cell carcinoma (HNSC) (P < 0.01), kidney renal papillary cell carcinoma (KIRP) (P < 0.001), brain lower grade glioma (LGG) (P < 0.001), lung adenocarcinoma (LUAD) (P < 0.001), lung squamous cell carcinoma (LUSC) (P < 0.001), prostate adenocarcinoma (PRAD) (P < 0.001), rectum adenocarcinoma (READ) (P < 0.001), stomach adenocarcinoma (STAD) (P < 0.001), testicular germ cell tumors (TGCT) (P < 0.001), thyroid carcinoma (THCA) (P < 0.001), uterine corpus endometrial carcinoma (UCEC) (P < 0.001), and uterine carcinosarcoma (UCS) (P < 0.001).

To validate our conclusions, independent samples from two separate studies were analyzed for SCN4B expression differences (all samples were from human kidney tissue). The data from the TCGA database (Fig. 1E) included 72 normal kidney tissues and 541 ccRCC tissues, showing a log fold change of 1.955 (P < 0.001). Data from a published study by John A Copland (Fig. 1F) included 72 normal kidney tissues and 72 ccRCC tissues, showing a log fold change of 1.903 (P < 0.001).

Relationship between SCN4B expression and clinicopathologic characteristics of ccRCC

Baseline data for 541 ccRCC patients from the TCGA database were statistically analyzed (Table 1). The cohort was divided into two groups: 270 patients with relatively low SCN4B expression and 271 with relatively high SCN4B expression. Significant differences were observed in sex (P = 0.002), while no significant differences were noted in age (P = 0.576). Significant differences were found in T stage (P < 0.001) and M stage (P < 0.001), but not in N stage (P = 0.061). Differences were also significant in pathological stage (P < 0.001).

Logistic regression analysis (Table 2) revealed significant associations between SCN4B expression and clinicopathological characteristics of ccRCC. No significant differences were found in age (P = 0.576) and N stage (P = 0.072). Significant differences were observed between stages T3 and T4 compared to stages T1 and T2 (P < 0.001). Similarly, significant differences were found between stages III and IV compared to stages I and II (P < 0.001). The analysis also revealed a significant difference between M1 and M0 stages (P < 0.001).

Table 1 Correlation between SCN4B expression and clinicopathologic characteristics of ccRCC

Table 2 SCN4B expression associated with clinicopathologic characteristics(logistic

To further validate the relationship between SCN4B expression and pathological stages of ccRCC, we analyzed SCN4B expression in samples from different pathological stages. The results showed significantly higher SCN4B expression in ccRCC samples at stages T1, T2, and T3 compared to normal kidney tissues, with significant differences between stages T1 and T2, T3, and T4 (Fig. 2A). SCN4B expression was significantly higher in N0 stage compared to normal tissues, with significant differences between stages N0 and N1 (Fig. 2B). SCN4B expression was also significantly higher in M stages compared to normal tissues, with significant differences between stages M0 and M1 (Fig. 2C). Additionally, SCN4B expression was significantly higher in all four pathological stages of ccRCC compared to normal tissues, with significant differences between stages I and II, as well as stages I and III (Fig. 2D).

Role of SCN4B expression in the survival of patients with ccRCC

Kaplan–Meier survival curves were used to evaluate the prognostic value of SCN4B expression in ccRCC. The overall survival (OS) curve showed that patients with high SCN4B expression had a significantly higher survival rate compared to those with low expression (HR = 0.57, 95% CI: 0.42–0.78, P < 0.001) (Fig. 3A). A similar result was observed for the progression-free interval (PFI) (HR = 0.50, 95% CI: 0.36–0.69, P < 0.001) (Fig. 3B). To better predict recurrence-free survival after treatment, a Cox regression analysis was performed with PFI as a dependent variable (Table 3). Univariate analysis showed a significant correlation between pathological stage and PFI, including pathologic T1 and T2 vs. T3 and T4 (HR = 4.569 (3.306–6.314), P < 0.001), pathologic N0 vs. N1 (OR = 3.697 (1.899–7.198), P < 0.001), sex male vs. female (HR = 1.476 (1.043–2.090), P < 0.001), pathologic M0 vs. M1 (OR = 9.081 (6.554–12.582), P < 0.001), and pathologic stage I and II vs. stage III and IV (OR = 6.877 (4.813–9.826), P < 0.001). Multivariate analysis indicated that pathologic M0 vs. M1 (OR = 4.062 (2.439–6.765), P < 0.001) and pathologic stage I and II vs. stage III and IV (OR = 3.983 (1.676–9.463), P = 0.002) were significantly associated with PFI.

Table 3 Univariate and multivariate Cox regression analyses of prognostic factors for PFI in ccRCC

Correlation between SCN4B expression and immune inffltration

Using the ssGSEA algorithm, we assessed the correlation between SCN4B expression and 24 immune cell types related to immune infiltration. Visualization (Fig. 4A) showed the correlation between immune cell enrichment and SCN4B, with circle size representing the correlation strength and color indicating the P-value. The top three immune cells with the strongest correlation and significant P-values were selected for further analysis. SCN4B expression was positively correlated with NK cell infiltration (R = 0.415, P < 0.001), mast cell infiltration (R = 0.405, P < 0.001), and pDC infiltration (R = 0.381, P < 0.001) (Fig. 4B, C, and D). Statistical analysis revealed significantly higher infiltration levels of NK cells, mast cells, and pDC in samples with high SCN4B expression compared to those with low expression (Fig. 4E, F, and G).

Few studies have explored SCN4B's role in disease. Our findings show that SCN4B significantly influences tumorigenesis and progression. Previous research indicated that low SCN4B expression correlates with poor prognosis in non-small cell lung cancer ^[13]. Similarly, SCN4B is underexpressed in prostate cancer tissues and cell lines, with its overexpression inhibiting proliferation and invasion of the prostate cancer cell line DU145, suggesting SCN4B's association with prostate cancer progression ^[11]. However, SCN4B's relationship with ccRCC and its prognostic impact remain underexplored. Our study investigates SCN4B's effect on ccRCC prognosis and immune infiltration, proposing it as a novel diagnostic and therapeutic biomarker.

Bioinformatics analysis revealed significant SCN4B overexpression in ccRCC patients, confirmed by paired comparison analysis. ROC analysis indicated SCN4B's potential as a biomarker for distinguishing ccRCC from normal tissue. Subgroup analysis showed significant correlations between SCN4B expression and pathologic TNM stage, pathological grade, and gender. Detailed analysis revealed increased SCN4B expression in patients with N1 and M1 stages, suggesting SCN4B's efficacy as a ccRCC biomarker and initial severity assessment tool.

Kaplan–Meier survival curves indicated that high SCN4B expression correlates with higher survival rates, suggesting SCN4B as a crucial prognostic biomarker for ccRCC. The recurrence-free interval was also longer in the high SCN4B expression group, indicating its predictive value for post-treatment recurrence. Cox regression analysis identified pathological stage as a significant factor affecting recurrence, particularly the pathological M1 stage. Thus, combining SCN4B expression with pathological staging effectively predicts ccRCC prognosis.

ssGSEA and Spearman correlation analyses confirmed SCN4B's association with immune infiltration in ccRCC. NK cells, mast cells, and pDCs were positively correlated with SCN4B expression. NK cells participate in anti-tumor immune responses through various mechanisms, including ADCC effect, perforin and granzyme release, and FASL-FAS interaction ^[14]. Mast cells regulate tumor progression events, such as proliferation, growth, angiogenesis, invasion, and metastasis, with both pro-tumor and anti-tumor effects depending on tumor type and stage ^[15]. pDCs are abundant in various solid tumors and induce immune escape in the tumor microenvironment ^[16]. SCN4B may play a critical role in mediating anti-cancer immune responses, requiring further investigation.

Our study demonstrated that increased SCN4B gen e expression significantly promoted infiltration of NK cells, mast cells, and pDCs, essential for tumor killing. In conclusion, SCN4B can predict ccRCC prognosis and may serve as a new therapeutic target.

SCN4B may be an potential biomarker for ccRCC screening and prognosis prediction. SCN4B expression effectively predicts patient survival.And, combined with pathological staging, provides meaningful evidence of post-treatment recurrence. Moreover, SCN4B expression is closely associated with immune infiltration, suggesting its potential as an immunotherapeutic target for ccRCC.

Author Contribution

Hongwei Yang: wrote the main manuscript text tables and figures. Faxian Yi:reviewed the manuscript.

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No competing interests reported.

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You are reading this latest preprint version

Diagnostic and Prognostic Value of SCN4B in ccRCC

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methods

RNA-seq data and bioinformatics analysis

Receiver operating characteristic curve analysis

Kaplan–Meier curve analysis

Immune inffltration analysis

Statistical analysis

Results

Differences in SCN4B expression between ccRCC and normal tissues

Relationship between SCN4B expression and clinicopathologic characteristics of ccRCC

Role of SCN4B expression in the survival of patients with ccRCC

Correlation between SCN4B expression and immune inffltration

Discussion

Conclusion

Declarations

Author Contribution

References

Additional Declarations

Status:

Version 1