Gene expression (535 tumor and 72 normal samples), DNA methylation, phenotype, and survival data were downloaded from http://xena.ucsc.edu/. |logFC| > 1.5 and p < 0.01 were defined as the criteria for differentially expressed genes (DEGs), and DEGs were identified using the limma R package.

Cox univariate and multivariate analyses were performed to determine the risk factors for ccRCC prognosis using R software.

Overall survival (OS) and progression-free survival rates of patients with ccRCC were investigated by Kaplan–Meier analysis.

TIMER2.0 (http://timer.comp-genomics.org/) was used for the comprehensive analysis of the relationship between ASNS expression and tumor-infiltrating immune cell levels, namely, CD4+ T cells, Tregs, CD8+ T cells, CTLA4, and PD-L1 (CD274) (8).

The STRING (https://string-db.org/) website analyzes protein–protein interactions using a unique set of computer prediction models (9). The expression levels of hub genes in tumor and normal tissues based on the ccRCC dataset were compared using the Wilcoxon rank-sum test (p < 0.05). OS analysis for the expression of hub genes between the high- and low-expression groups was performed, with a p-value of <0.05 indicating statistical significance.

GEPIA2 (http://gepia2.cancer-pku.cn/#index) uses standard processing procedures to analyze the data using many tumor samples and normal tissue samples (10). GEPIA2 analyzes the OS or disease-free survival of cancer patients based on gene expression. GEPIA2 uses the Log-rank test for hypothesis testing. The hazard ratio and 95% confidence interval are also included in survival plots.

According to ASNS expression level, ccRCC samples were sorted and divided into two groups of high and low expression, and the genes with differential expression were screened separately (|logFC| > 0.5, p-value < 0.05). The Clusterprofiler R package was used for GO enrichment analysis, which included molecular function, biological processes, and cellular components of DEGs, and KEGG enrichment analysis.

The molecular mechanisms involved in ccRCC with different levels of ASNS expression were examined by the GSEA approach (11). The reference gene set used for GSEA was obtained from c7.immunesigdb.v7.5.1.symbols.gmt. Gene sets with a nominal p-value less than 0.05, |Normalized Enrichment Score| greater than 1, and false discovery rate less than 0.25 in the GSEA report were considered statistically significant.

Western blotting was performed as described in our previous study (12).

The expression and distribution of ASNS protein were detected by immunohistochemistry in paraffin-embedded tissue sections of each group. After routine paraffin dewaxing to water, antigen repair was performed. Hydrogen peroxide solution (3%) was added to remove endogenous catalase. Bovine serum albumin sealing solution (1%) was added, and the slides were incubated for 15 min. After the blocking solution was added, the primary antibody against ASNS (1:100 dilution) was added, and the slides were incubated overnight at 4°C. Thereafter, they were washed thrice with phosphate-buffered saline with Tween 20. The secondary antibody (1:100 dilution) was added, and the slides were incubated for 1 h at room temperature. DAB was added for color development, after which the slides were re-dyed with hematoxylin for 30 s. After washing with running water for 1 min, the slides were treated with 0.1% sodium bicarbonate to develop blue color. Then, the slides were subjected to dehydration and xylene treatment until the sections became transparent, mounted with neutral gum, observed under a microscope, and photographed.

The immunohistochemical staining intensity of ASNS was assessed using ImageJ software with the assistance of experienced pathologists in Affiliated Zhongda Hospital of Southeast University (13, 14).

Clinical data of 81 patients were collected at the Affiliated Zhongda Hospital of Southeast University from March 2019 to May 2022. All patients were diagnosed with ccRCC (unilateral) and had no other carcinomas. All patients did not undergo any preoperative therapy for the carcinoma (chemotherapy, targeted therapy, immunotherapy, etc.). Age distribution: 19–85 years old. Surgical modalities: Partial nephrectomy or radical nephrectomy. Neoplasm histological grade criteria: Fuhrman nuclear grading system. Pathological stage criteria: American Joint Committee on Cancer (AJCC).

Analyses were performed using IBM SPSS for MAC version 26.0. The Mann–Whitney test was used to compare continuous variables between the two groups. The immunohistochemical staining intensity and clinical characteristics, such as gender, age, location, tumor size, TNM stage, AJCC stage, and grade were subjected to Spearman rank correlation analysis. p-values less than 0.05 were considered statistically significant.

The expression pattern of ASNS in multiple pan-cancers was evaluated by using data from TIMER2.0, and the prognostic values of ASNS in multiple cancers (invasive breast carcinoma, cervical squamous cell carcinoma, endocervical adenocarcinoma, cholangiocarcinoma, esophageal carcinoma, kidney renal clear cell carcinoma, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, pancreatic adenocarcinoma, and stomach adenocarcinoma) are shown in the survival map prepared using GEPIA2 ( Figures 1A, B ). The pan-cancer overexpression of ASNS was linked to poor prognosis. The mRNA data of ccRCC were downloaded from TCGA (https://xenabrowser.net/datapages/) and included 535 tumor tissues and 72 normal tissues. ASNS was highly expressed in ccRCC compared with normal renal tissues (p < 0.001; Figure 1C ) and was correlated with poor prognosis, i.e., OS (p < 0.001; Figure 1D ). Furthermore, the high protein expression level of ASNS in ccRCC was verified by data from the Human Protein Atlas (HPA) databases (https://www.proteinatlas.org/) ( Figure 1E ).

For further clinical research, the expression pattern of ASNS was studied in relation to several clinicopathological features, and the data demonstrated that ASNS expression gradually increased with tumor progression ( Figure 2 ). Therefore, ASNS could be an independent factor predicting ccRCC prognosis.

Univariate independent prognostic analysis demonstrated that neoplasm histologic grades, pathological T&M stages, tumor stages, and the expression of ASNS were significant factors that could predict ccRCC prognosis (p < 0.001; hazard ratio [HR] > 1; Figure 3A ). Meanwhile, multivariate prognostic analysis showed that ASNS could be an independent prognostic factor for ccRCC (p < 0.001; HR > 1; Figure 3B ). Moreover, the receiver operating characteristic (ROC) curve was used to analyze the accuracy of ASNS to predict the survival of ccRCC. ASNS predicted the survival period of ccRCC patients at 1, 3, and 5 years, which was significant (area under the curve [AUC] > 0.6; Figure 3C ). Subsequently, serial ROC analysis showed favorable diagnostic values for ASNS to predict various clinicopathological features ( Figures 3D–K ). Ultimately, the above results were validated using nomogram and calibration plots, which showed that ASNS had excellent potential for predicting clinicopathological features in ccRCC ( Figures 3L, M ).

To further explore the regulation of ASNS in ccRCC, we performed a series of methylation analyses. Methylation levels were detected at the promoter region sites of the ASNS gene ( Figure 4A ), and ASNS expression was inversely proportional to the methylation level ( Figures 4B, C ). We further determined the relationship between ASNS gene methylation level and various clinicopathological features such as pathologic TNM stages ( Figures 4D–F ), tumor stages ( Figure 4G ), and neoplasm histologic grades ( Figure 4H ) of ccRCC. The results demonstrated that the methylation level of ASNS gradually decreased with tumor progression. Furthermore, the lower methylation level of ASNS led to a worse prognosis, such as poorer OS rates (p < 0.001; Figures 4I–K ) and poorer progression-free survival (p < 0.001; Figure 4L ). The decrease in the methylation level of ASNS with tumor progression indicates that the methylation modification of ASNS plays a key regulatory role in the expression of ASNS.

ccRCC has a relatively high stromal score and immune score among common cancers (15). High immune scores and infiltration of Tregs are significantly associated with poor OS, high tumor stage, and more chances of metastases in ccRCC (16). To further investigate the relationship between ASNS expression and the immune microenvironment in ccRCC, we performed immune infiltration analysis using TIMER2.0. The immune infiltration level of non-regulatory CD⁴⁺ T cells was negatively correlated with ASNS expression ( Figure 5B ), whereas Tregs were positively correlated ( Figure 5C ). Subsequent analyses showed that higher CD⁴⁺ T-cell infiltration was associated with better prognosis ( Figure 5E ), and higher Tregs infiltration was associated with poorer prognosis ( Figure 5F ). The correlation between CD⁸⁺ T-cell infiltration and ASNS was also analyzed; however, the results were insignificant ( Figures 5A, D ). Finally, ASNS expression correlated positively with the expression of immune checkpoints such as CTLA4, and PD-L1 (CD274) was positive ( Figures 5G, H ).

We analyzed the positively and negatively correlated genes of ASNS in ccRCC and plotted heatmaps to show the expression patterns of the top 20 ASNS-associated genes ( Figure 6A ). To further determine the functions of ASNS in tumor progression and prognosis, GO and KEGG enrichment analyses were performed. GO enrichment analysis found that ASNS protein may be located on the cell membrane and regulate the molecular transport in cells ( q -value <0.01; Figure 6B ). KEGG enrichment analysis indicated that ASNS might participate in the regulation of insulin resistance, PPAR signaling pathway, and amino acid metabolism ( q -value <0.05; Figure 6C ). Subsequent GSEA suggested that ASNS might be involved in CD 4+ T-cell infiltration-related signaling in ccRCC ( Figures 6D, E ), which is also consistent with the previous analysis results.

We performed a PPI network analysis of ASNS using the STRING website to explore potential interactions between ASNS and other proteins. The top 10 hub genes were selected from the PPI network, and the expression patterns and prognostic values in ccRCC were studied ( Figure 7 ).

To further verify the effect of ASNS on ccRCC, we evaluated the expression of ASNS in ccRCC tissues and human ccRCC cell lines. Compared with adjacent normal renal tissues, ASNS was highly expressed in ccRCC tissues ( Figure 8A ). ImageJ software was used to quantitatively analyze the expression of ASNS ( Figures 8B–E ). Finally, we evaluated the expression of ASNS protein in 786-O cells (human ccRCC) and HK-2 cells (Human Kidney-2 cells), and the results were significant ( Figure 8F ).

In addition, the above results were further validated using the data from our cohort. It was further verified that ASNS was highly expressed in ccRCC tissues compared with adjacent tissues (p < 0.01, Table 1 ). Moreover, high ASNS expression was not correlated with age, gender, and tumor location (p > 0.05, Table 2 ), but was significantly positively associated with tumor size, T stage, the American Joint Committee on Cancer (AJCC) status, and neoplasm histological grade (p < 0.001, Table 2 ). These were also consistent with our previous results.