Loss of GLS2 function is essential for obtaining oncogenic functions and promotes the progression of clear cell renal cell carcinoma.

The incidence of RCC has drastically increased in recent years. The large intratumor heterogeneity of RCC, especially ccRCC, usually leads to treatment failure. In addition, single biomarkers have a limited ability to predict prognosis. Therefore, we performed this study to select variables and provided a simple but efficient way to predict prognosis. Three studies from the GEO database were involved in the selection of DEGs. A total of 840 RCC patients and 524 ccRCC patients from the TCGA database were involved in the prognostic analyses. Nomograms based on the Cox regression model were used to select variables to predict the prognosis, and GSEA was used to demonstrate the potential pathways altered by gene expression. These results revealed that with could and advanced According the results, alter cell cycle by E2F


Background
Renal cell carcinoma (RCC), which accounts for more than 90% of cancers derived from the kidney [1], has a drastically increasing incidence worldwide in recent years [2], and more than 350,000 patients worldwide are diagnosed with RCC per year [3]. Clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC) are major subtypes (≥ 5%) that account for approximately 75%, 15% and 5% of RCCs, respectively [1,4]. RCC has been revealed to be a malignant disease with high heterogeneity, and molecular alterations may play important roles in obtaining oncogenic functions, including invasiveness and the ability to metastasize [5]. In addition to orthodox therapeutics for primary RCC, approved drugs targeting driver genomic alterations for advanced RCC have been widely used [6]. Axitinib [7], sorafenib [8], sunitinib [9,10], lenvatinib [11] and other approved drugs, such as bevacizumab [12] and immune checkpoint inhibitors (ICIs) [13], provide advanced RCC patients with more options. Unfortunately, the treatment response varies among advanced RCC patients because of tumor heterogeneity, different drug action mechanisms and varying cancerous biological behaviors [6,14]. With the development of genomic sequencing, great improvements have been made in precision medicine for RCC in the past decade [6,15,16,17]. Biomarkers for predicting the prognosis of RCC have been detected for clinical use, such as Von Hippel-Lindau tumor suppressor (VHL) and epidermal growth factor receptor (EGFR). However, given that RCC shows great heterogeneity in tumor biological behaviors, the differentially expressed genes (DEGs) in primary and metastatic RCC tissues identified in different studies also show great heterogeneity, and it is difficult to conclude possible biomarkers for predicting the prognosis of RCC.
The Gene Expression Omnibus (GEO) database is a commonly used database for analyses of genomic profiles based on RNA sequencing (RNA-seq) [18]. In our study, we used three previously published studies on the DEGs between primary and metastatic RCC or ccRCC tissues, the RNA-seq data of which can be acquired from the GEO database, including the GSE105261 (ccRCC), GSE47352 (ccRCC) and GSE23629 (RCC) datasets. Seven DEGs (existing in ≥ 2 studies) were detected in our study, and the area under the curve (AUC) of the ROC curves and prognostic nomograms were used for the detection of the most valuable biomarkers for RCC, especially ccRCC. Overall, our study confirmed the important role of glutaminase 2 (GLS2) functional loss in the metastasis of ccRCC and the prognostic value of GLS2 in ccRCC and RCC. In addition, we demonstrated that the E2F pathway was the most likely signaling pathway activated by GLS2 functional loss and that molecules from the E2F family were related to a poor prognosis in ccRCC and RCC.

Bioinformatic Analyses
In this study, we used GEO2R online tools to identify DEGs between primary and metastatic tissues. A P value < 0.01 and a ratio of the FPKM values between the two groups (fold change) ≥ 2 were used for the determination of DEGs. DEGs that existed in more than two studies selected from the GEO database were identified as valuable DEGs and used for the next analyses. In addition, the DEGs of each study were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses through DAVID and KOBAS 3.0 online tools to obtain a comprehensive set of functions of these altered genes. The mutual pathways between the three studies were detected by a Venn plot to identify the most likely pathways altered in metastatic tissues and provide researchers with biological information for further studies. Gene set enrichment analysis (GSEA) was used to confirm the differential signaling pathways and molecules in two sets of patients from the TCGA database grouped by a selected gene. We used OncoLnc [19] and GEPIA [20] to draw Kaplan-Meier plots, and the median expression values of selected genes were used as cutoffs for the univariate analysis. The expression data of selected genes in different samples, TNM stages, metastatic lymph nodes and tumor grades were collected from UALCAN [21].

Statistical Analyses
ROC curves of DEGs and E2F family molecules were generated in this study, and the AUC was used to identify the efficiency in predicting the prognosis of RCC or ccRCC patients.
Student's t test was used for the detection of statistical significance in the comparison of gene expression between the group of patients who were alive and the group of patient who had died. Multivariate analysis using the Cox proportional hazard model and a P < 0.05 was used to integrate variables into our prognostic nomograms. We used the "rms" package of R software version 3.1.2 (The R Foundation for Statistical Computing, Vienna, Austria) to construct nomograms. Discrimination and calibration were conducted to evaluate the internal validity of nomograms. Harrell's C-indexes ranging from 0.5 (no discrimination) to 1 (perfect discrimination) were used to verify discrimination [22]. Visual calibration plots were used to verify calibration [23]. Bootstrap analyses with 1,000 resamples were used for these analyses. We compared the AUC of each variable with Harrell's C-indexes of the nomograms to determine a better way to predict the prognosis of RCC and ccRCC patients [24]. All figures and statistical processes in our study were performed by R software version 3.1.2 (The R Foundation for Statistical Computing, Vienna, Austria), SPSS 23.0 (SPSS, Inc.) and GraphPad Prisma 8.0 software. P values were two-tailed for all tests, and a P < 0.05 was used to define statistical significance.

Identification of DEGs and enrichment analyses
Three studies including GSE105261, GSE47352 and GSE23629 from the GEO database were involved in the analyses. As the volcano plots show in Fig

Construction of a prognostic nomogram for RCC patients based on DEGs
840 RCC patients and their basic characteristics were obtained from the TCGA database.
The mean age of all RCC patients was 60.2 years, ranging from 17 to 90 years. We divided all RCC patients into two groups according to their outcomes, including a group of patients who were alive and a group of patients who died, and compared the expression status of the seven selected DEGs between the two groups, as shown in Figure D5-D8. GLS2, ADGRF1 and KATNAL2 showed a significant reduction in the group of patients who died, which indicated that the functional loss of these genes was associated with a poor prognosis in RCC.
Univariate analyses were performed to demonstrate the relationship between selected DEGs and the prognosis of RCC patients, as shown in Fig. 2A-D. The four selected DEGs were tightly associated with prognosis. High expression of GLS2, ADGRF1 and KATNAL2 was related to a good prognosis both in terms of disease-free survival (DFS) and overall survival (OS). However, high expression of OGN was related to poor DFS and OS.
Multivariate analysis was performed to integrate the variables into a nomogram. As shown in Table 1, age (P = 0.014), TNM stage (P < 0.001) and GLS2 expression (P = 0.001) were selected for the nomograms based on multivariate analysis with the Cox regression model.
The nomogram for OS of RCC patients was constructed based on the three variables above, as shown in Fig. 2E. In the nomogram, every variable produced a score, and the total score was easy to calculate. By correlating the total score with the 1-year to 5-year OS values, the probability of survival for every patient could be obtained.  E2F family and the prognosis of RCC and ccRCC patients The E2F pathway and molecules of the E2F family were found to be activated in the GLS2-L group of ccRCC patients through GSEA. Therefore, we further determined the relationship between the E2F family and the prognosis of ccRCC and RCC patients. E2F1 to E2F8, which are E2F family molecules detected by researchers so far [25], were included in the prognostic analyses in our study. ROC curves of these eight molecules were constructed in our study, and E2F1, E2F2, E2F3, E2F4, E2F5 and E2F7 showed satisfactory AUC values for predicting the prognosis of ccRCC patients, as shown in Fig. 4B1-B6.
Survival analyses for the six selected genes above were performed, and the results are displayed in Fig. 4C1 including age (P = 0.001), TNM stage (P < 0.001), E2F1 (P = 0.037), E2F4 (P = 0.012) and E2F5 (P = 0.015), were selected for the construction of a nomogram, as shown in Fig. 5A.
The details of the multivariate analysis are summarized in Table 2. Interestingly, although the univariate analysis of E2F5 showed that it had no significance in predicting the prognosis of ccRCC patients, E2F5 may influence prognosis in combination with other factors. E2F1 demonstrated a robust ability to predict the prognosis of ccRCC patients and was activated in the GLS2-L group. We further determined the expression status of E2F1 in the different groups, and the comparisons are shown in Fig. 5B-E. In contrast to that of GLS2, higher E2F1 expression was detected in tumor tissue than in normal tissue, and higher E2F1 expression was related to more metastatic lymph nodes, higher TNM stage and higher tumor grade. The ability of GLS2 and the E2F family to predict the prognosis of RCC patients was then estimated. E2F2, E2F3, E2F7 and E2F8 demonstrated satisfactory AUC values, as shown in Table 3, and were convincing biomarkers for predicting the prognosis according to the univariate analyses, the findings of which are summarized in Table 2. Age (P < 0.001), TNM stage (P < 0.001), GLS2 (P = 0.036) and E2F7 (P = 0.021) were selected for the construction of a nomogram based on the findings of the multivariate analysis, as shown in Fig. 6A. Given the heterogeneity of RCC, E2F7 is likely to be an important biomarker for prognosis. As shown in Fig. 6B-D, higher expression of E2F7 was detected in the group of RCC patients who died than in the group of RCC patients who were alive, and high expression of E2F7 was related to a poor prognosis in terms of both DFS and OS in RCC patients. Table 3 The comparison of AUC and C-index.

Validation of Nomogram Performance
Harrell's C-indexes were calculated to evaluate the discrimination ability of the nomograms and were involved in the comparison of the abilities of the nomograms and other biomarkers in predicting prognosis. The comparisons of AUC values and C-indexes are shown in Table 3. The nomograms demonstrated a more robust ability to predict prognosis than any other single variable selected in this study in RCC patients and ccRCC patients. The calibration plots are shown in Figure S1. The probabilities of our prognostic models agreed with the accuracy probabilities on acceptable scales (dashed lines in the calibration plots correspond to a 10% margin of error) except for that of the 5-year OS model.

Discussion
Glutaminase initiates glutamine catabolism, which is essential for tumorigenesis, and glutaminase is encoded by two genes in mammals, GLS1 and GLS2 [ 26]. GLS2, which is also called liver-type glutaminase, is primarily expressed in liver, pancreas and brain tissue [27]. Previous studies have proven that GLS2 serves as a tumor suppressor in liver and brain cancer [28,29]. GLS2 is a target gene of p53 [30] and is related to DNA hypermethylation. In breast cancer, GLS2 expression differs across the luminal subtypes [31], with higher expression of GLS2 in luminal A and B types than in basal-subtype breast cancer. In addition, GLS2 is a druggable metabolic node for breast cancer. In RCC, metabolic reprogramming is closely associated with disease progression and metastasis; as a result, glutaminase inhibitors are a novel strategy for RCC treatment [32]. Although GLS2 is predominantly found in liver tissue, its function as a tumor suppressor in RCC requires further discussion.
In this study, we estimated the relationship between GLS2 and the prognosis of RCC and ccRCC patients and revealed that GLS2 serves as a tumor suppressor in renal cancer. Low GLS2 expression was associated with a poor prognosis in RCC and ccRCC patients and a significant tumorigenesis tendency. We found that low GLS2 expression endowed ccRCC cells with invasiveness and the ability to metastasize because patients with lower GLS2 expression had a worse TNM stage than those with higher GLS2 expression. In addition, low GLS2 expression also increased the malignant degree of ccRCCs, which participated in its ability to predict prognosis in ccRCC. Our prognostic nomogram confirmed the tumor suppressor function of GLS2.
GSEA demonstrated that the E2F pathway was a potential signaling pathway activated by low GLS2 expression. Previous studies confirmed that the E2F pathway controlled tumor cell growth [33,34]   The data used during the current study is available from the corresponding author on reasonable request.

-Competing interests
The authors declared no conflicts on interests.      The relationship between E2F family members and RCC prognosis. A: nomograms for RCC patients based on COX regression model; B: the expression of E2F7 in alive and dead patients; C: DFS of RCC patients grouped by E2F7 expression; D: OS of RCC patients grouped by E2F7 expression.

Supplementary Files
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