We downloaded the GSE11151 (22), GSE6344 (23) and GSE781 (24) datasets from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), which is a public and shared cancer database. We took the top ten up-regulated genes in these three datasets to analyze the intersection genes.

The differentially expressed genes (DEGs) of GSE11151, GSE6344 and GSE781 datasets were identified using GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/), an available online analysis software for the GEO database, which was dependent on R language programming. According to the criteria of logFC ≥2 or logFC ≤-2 and adjusted p value < 0.05, DEGs from the three datasets were identified. We used the Wayne diagram to screen the intersection of three datasets with the top 10 up-regulated DEGs.

The mRNA expression data of SAA1 in ccRCC tissues and para-cancer tissues and clinicopathological features including gender, age, T stage, tumor grade, M stage, N stage, histopathological stage, overall survival (OS) were downloaded from TCGA-KIRC datasets (https://xenabrowser.net/heatmap/). Kaplan-Meier curves and ROC curves were analyzed using mRNA levels from TCGA-KIRC dataset.

SAA1 mRNA, protein expression and promoter methylation levels were evaluated using the UALCAN online analysis software (http://ualcan.path.uab.edu/index.html) (25).

SAA1 mRNA, overall survival and disease-free survival were also evaluated using GEPIA online analysis software (http://gepia.cancer-pku.cn/) (26).

The potential diagnostic value of SAA1 was evaluated using the receiver operating characteristic (ROC) curves by Graphpad Prism software.

Human RCC cell lines 786-O, ACHN, A-498, Caki-1 and normal renal tubular epithelial cells HK-2 were obtained from ATCC. OS-RC-2 cell line was a gift from the Department of Urology of Wuhan Tongji Hospital. All cell lines were cultured in DMEM medium containing 1% penicillin-streptomycin and 10% FBS. Small interfering RNA (si-RNA) against SAA1 and corresponding negative control (si-NC) were purchased from Ribobio Biological Co., Ltd. (Guangzhou, China). si-SAA1 sequences and si-NC sequences were transfected into cells using Lipofectamine 2000 reagent.

ccRCC tissues and adjacent normal tissues were fixed in 10% formalin, dehydrated, and embedded in paraffin sequentially. The paraffin sections were incubated with anti-SAA1 antibody overnight at 4°C. Transwell migration and invasion were performed using 24-well transwell chambers. The specific details of these experiments were previously described (27).

We collected ccRCC tissues and adjacent normal tissues of 30 case patients who were subjected to partial nephrectomies or nephrectomies at Wuhan Union Hospital between 2018 and 2019. All patients had signed an informed consent form. This study was approved by the Ethics Committee of Huazhong University of Science and Technology.

SPSS statistical software and Graphpad Prism 7.0 were used for statistical analysis. The SAA1 mRNA levels were analyzed among different clinicopathological features of ccRCC using the Mann-Whitney test. Pearson’s chi-square test was used to analyze the correlation between SAA1 expression levels and clinicopathological features of ccRCC. The ROC curve was used to distinguish ccRCC patients and obtain the area under the curve (AUC). The Kaplan-Meier curve was used to analyze the relationship between the expression level of SAA1 and the overall survival and progression-free survival of ccRCC patients. Each group of data is presented as mean ± SD. The p value<0.05 was considered statistically significant.

By analyzing three public cancer datasets, we found two intersection genes, SAA1 and CCL20, in these three datasets ( Figure 1A ). Next, we analyzed the correlation between the expression levels of these two genes and the overall and disease-free survival of patients with ccRCC. We found that only SAA1 expression was associated with prognosis in patients with ccRCC and high SAA1 expression indicated a worse prognosis ( Figures 1B, C ).

To verify the reliability of the above three studies, we analyzed the expression of SAA1 and its association with clinical pathological parameters in the TCGA database. As shown in Figure 2A , SAA1 expression in tumor tissues is upregulated at mRNA levels. Next, we analyzed the mRNA expression levels of SAA1 against T stage, N stage, M stage, Grade classification, histopathological stage and its correlation with these clinicopathological parameters in patients with ccRCC ( Figures 2B–F and Table 1 ). Our analysis found that the mRNA expression of SAA1 was positively correlated with these clinicopathological parameters and that the mRNA expression of SAA1 increased significantly in higher tumor stages but did not increase or show a downward trend in early tumor stages ( Figures 2B–F ). These results suggested that elevated mRNA expression of SAA1 was predictive of advanced tumor stages. To make our study more precise, we also analyzed the expression of SAA1 in the UALCAN and GEPIA online tumor database websites. As shown in Figures 3A–F , high SAA1 expression mainly occurred in patients with advanced and metastatic ccRCC, which were consistent with the analysis results of the TCGA database. Similarly, we also analyzed the expression of SAA1 at the protein level, and the results showed that SAA1 protein expression increased significantly in patients with advanced ccRCC ( Figures 4A–C ). The above results indicate that SAA1 is highly expressed at mRNA and protein levels and predicts high stage risk in patients with advanced and metastatic ccRCC.

DNA methylation modification is an important component of epigenetics, which can silence the expression of methylated genes. To understand the cause of SAA1 overexpression in advanced and metastatic ccRCC, we analyzed the methylation status of SAA1 gene through the UALCAN online database. As shown in Figure 5A , the SAA1 gene was hypomethylated in ccRCC tissues, while it was hypermethylated in normal kidney tissues. Moreover, with the increase of tumor stage and grade, the degree of SAA1 gene methylation decreased accordingly, which means that the degree of SAA1 gene methylation was inversely related to the tumor’s stage ( Figures 5B–D ). These results indicate that the upregulation of SAA1 expression is due to the low methylation levels of the SAA1 promoter region in ccRCC and its methylation levels are inversely correlated with tumor stage and grade.

Biomarkers for tumor progression are still lacking in ccRCC patients. We found that SAA1 expression was significantly up-regulated at the mRNA and protein levels in patients with advanced and metastatic ccRCC. We wondered whether SAA1 could accurately diagnose patients with advanced and metastatic ccRCC. To determine the diagnostic value of SAA1, we performed ROC curve analysis between tumor tissues with different stage or grade and normal tissues. As shown in Figures 6A–D , high SAA1 levels could effectively distinguish advanced and metastatic ccRCC tissues from normal tissues (Normal/T4 ( Figure 6A , AUC = 0.8157, p = 0.0008); Normal/N1 ( Figure 6B , AUC = 0.8481, p < 0.0001); Normal/M1 ( Figure 6B , AUC = 0.7917, p < 0.0001); Normal/G4 ( Figure 6C , AUC = 0.8862, p < 0.0001); Normal/Stage IV ( Figure 6D , AUC = 0.7737, p < 0.0001), but could not distinguish early ccRCC tissues from normal tissues. These results suggested that SAA1 could serve as a new diagnostic marker for patients with advanced and metastatic ccRCC. We wondered whether SAA1 predicts a similar prognosis in ccRCC patients with different stages and clinical parameters.

Our previous results have confirmed that high expression of SAA1 predicts poor overall and disease-free survival in patients with ccRCC ( Figure 1B ). Moreover, our COX regression analysis found that SAA1 could be used as an independent prognostic factor for ccRCC ( Table 2 ). We wondered whether SAA1 predicted a similar prognosis in ccRCC patients with different stages and clinical parameters. Therefore, we performed Kaplan Meier curve analysis towards the expression of SAA1 in ccRCC patients with different stages and clinical parameters ( Figures 7A–L ). Our results indicated that high SAA1 expression could serve as a potential prognostic factor for ccRCC patients with T1 + T2 stage ( Figure 7A , p = 0.0002), T3 + T4 stage ( Figure 7B , p = 0.0030), N0 stage ( Figure 7C , p < 0.0001), M0 stage ( Figure 7D , p < 0.0001), G1 + G2 stage ( Figure 7E , p = 0.0088), G3 + G4 stage ( Figure 7F , p = 0.0014), Stage I+II ( Figure 7G , p = 0.0004), Stage III+IV ( Figure 7H , p = 0.0081), Female ( Figure 7I , p < 0.0001), Male ( Figure 7J , p = 0.0009), Age ≥ 60 years ( Figure 7K , p < 0.0001), Age < 60 years ( Figure 7L , p = 0.0044).

To further confirm the results of the UALCAN, GEPIA and TCGA databases, SAA1 was subjected to western blotting in RCC cell lines and tissues. As shown in Figures 8A, B , the protein levels of SAA1 in RCC cell lines were significantly up-regulated compared with normal renal epithelial cell HK-2, and the protein levels of SAA1 in ccRCC tissues was also obviously overexpressed compared with adjacent normal tissues. The protein levels of SAA1 were also examined by immunohistochemistry (IHC) in paired ccRCC tissues, and the IHC results were consistent with the results of western blotting ( Figure 8C ). These results indicate that SAA1 is up-regulated in ccRCC cell lines and tissues, consistent with the results of UALCAN, GEPIA and TCGA database.

To investigate whether SAA1 affects the migration and invasion of RCC cells, we performed the transwell assays. As shown in Figures 9A, B , knockdown of SAA1 significantly impaired the migration and invasion capability of 786-O and ACHN cells. These results reversely suggest that SAA1 promotes migration and invasion of RCC cells.