A prognostic predicting model integrating preoperative 18F-FDG PET/CT metabolic parameter and clinicopathological biomarkers for patients with ccRCC

A prognostic predicting model integrating preoperative 18F-FDG PET/CT metabolic parameter and clinicopathological biomarkers for patients with ccRCC | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A prognostic predicting model integrating preoperative 18 F-FDG PET/CT metabolic parameter and clinicopathological biomarkers for patients with ccRCC Yuhui Cao, Caixia Wu, Keting Tong, Yulong Chen, Jinzhi Chen, Yuan Gao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8559986/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Accurate risk stratification is critical for the management of patients with clear cell renal cell carcinoma (ccRCC). This study aims to explore the predictive value of preoperative 18 F-FDG PET/CT metabolic parameter combined with clinicopathological features for postoperative disease-free survival (DFS) in ccRCC patients. Methods: Newly diagnosed ccRCC patients who underwent 18 F-FDG PET/CT prior to surgery were retrospectively reviewed. Maximum standardized uptake value (SUVmax) was acquired from the preoperative 18 F-FDG PET/CT. Clinicopathological features, including the tumor node metastasis (TNM) stage, body mass index (BMI), hemoglobin (Hb), World Health Organization (WHO)/the International Society of Urological Pathology (ISUP) grade, primary tumor size, carbonic anhydrase IX (CAIX), tumor-infiltrating lymphocytes (TILs), etc., were also obtained. Cox proportional hazards analyses were executed to identify prognostic factors for DFS. The predictive efficacy of the model was assessed by the area under the curve (AUC). Glycolysis genes in ccRCC were analyzed using the TCGA database. Results : 59 ccRCC patients were included and 26 (44.1%) cases developed disease progression. On univariate analysis, BMI (≤ 25.45 kg/m2), Hb (≤ 130 g/L), clinical symptoms, TNM stage (III/ IV), SUVmax (> 4.20), primary tumor size (> 5.75 cm), WHO/ISUP grade (G3/4), CAIX expression (1+), and high TILs were significant prognostic factors of inferior DFS ( P < 0.05). On multivariate analysis, SUVmax ( P =0.026, HR=4.248; 95%CI: 1.184-15.239), BMI ( P =0.002; HR=0.233; 95%CI: 0.094-0.580), and WHO/ISUP grade ( P =0.005, HR=5.888; 95%CI: 1.689-20.524) still maintained independency in prognosis prediction. The prognostic model composed of the above three independent predictors achieved excellent predictive efficacy by virtue of a C-index of 0.89, with AUC values of 0.922, 0.919, and 0.899 at 1-, 3-, and 5-year, respectively. Moreover, the glycolysis-related genes of GCKR and GCK were obviously upregulated in the disease-progression group. Conclusion: ccRCC patients with low preoperative BMI, elevated SUVmax, and high WHO/ISUP grade are more likely to develop disease progression after operation. Implementing closer surveillance or aggressive early intervention for these patients is recommended to optimize prognosis. clear cell renal cell carcinoma (ccRCC) 18F-FDG PET/CT maximum standardized uptake value (SUVmax) prognosis body mass index (BMI) World Health Organization (WHO)/the International Society of Urological Pathology (ISUP) grade Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background Clear cell renal cell carcinoma (ccRCC) is the predominant histological subtype of renal cell carcinoma (RCC) ( 1 ). While many patients are diagnosed with localized ccRCC amenable to curative treatment, approximately 30% patients present with locally advanced or metastatic disease ( 2 ). Moreover, approximately 20%-30% ccRCC patients with aggressive tumor biology may develop disease progression after surgery. Considering the differences in ccRCC patients’ outcomes, accurate risk stratification is important to identify those who may benefit from more intensive initial treatment, more active surveillance, or adjuvant therapies. In this circumstance, it is critical to define prognostic biomarkers associated with clinical outcome to stratify patients. Currently, the primary prognostic biomarkers for ccRCC are anatomical (such as tumor node metastasis (TNM) stage), histological (such as pathological grade), clinical (such as some laboratory indicators), and molecular factors (such as genetic signatures) ( 3 ). But no single biomarker is sufficient to predict patient outcomes and the molecular factors are not routinely used in clinical practice. Some prognostic models were developed based on clinicopathological factors, such as UISS, SSIGN, MSKCC, and IMDC model, which are sub-optimal in accurately identifying high-risk patients ( 4 ). When molecular targeted therapy and immunotherapy are rising, a non-invasive approach for reflecting tumor biological activity and predicting the prognosis of patients with ccRCC is urgently needed. 18 F-fluorodexoxyglucose ( 18 F-FDG) positron emission tomography/computed tomography (PET/CT) as a molecular imaging technology, has been widely used in the diagnosis and management of malignancies. But 18 F-FDG PET/CT features mainly reveal the tumor glucose metabolism, representing a partial view of the tumor. As ccRCC is characterized as both an immunogenic tumor and a metabolic disorder ( 5 ), the related features such as carbonic anhydrase IX (CAIX), glucose transporter protein 1 (GLUT-1), programmed death-ligand 1 (PD-L1), and tumor-infiltrating lymphocytes (TILs) are also essential for prognosis. The incorporation of 18 F-FDG PET/CT characteristics and clinicopathological biomarkers into the prognostic model of ccRCC has not been implemented yet. This study aims to explore the predictive value of preoperative 18 F-FDG PET/CT metabolic parameter combined with clinical and pathological features for postoperative disease-free survival (DFS) in patients with ccRCC. Moreover, we will exploit the underlying molecular mechanisms for the prognostic role of 18 F-FDG PET/CT for ccRCC. 2. Methods 2.1 Patients Data of consecutive ccRCC patients who underwent preoperative 18 F-FDG PET/CT from March 2014 to July 2020 at Peking University First Hospital were retrospectively analyzed. Inclusion criteria: ( 1 ) diagnosed as ccRCC by histological examination; ( 2 ) underwent radical or partial nephrectomy at our institution; ( 3 ) underwent 18 F-FDG PET/CT prior to surgery and systemic therapy; ( 4 ) available postoperative follow-up information ≥ 6 months for the progression-free patients. Exclusion criteria: ( 1 ) with a history of malignant tumor, including ccRCC or other malignancy; ( 2 ) with bilateral synchronous ccRCC. 2.2 Clinical and pathological features Clinical data comprised of age, gender, weight, height, body mass index (BMI), clinical symptoms, hemoglobin (Hb), primary tumor size, venous tumor thrombus, World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade ( 6 ), and TNM stage ( 7 ). Pathological features of CAIX, GLUT-1, PD-L1, and TILs were also included. BMI = weight (kg)/ height (m) 2 . Clinical symptoms were defined as hematuria, lumbar/abdominal/back pain, abdominal mass or discomfort, nausea, fatigue, fever, wasting, lower limb edema, and metastatic manifestations (e.g., cough and bone pain). The primary tumor size was the largest diameter in the surgical gross specimen. An experienced urologic pathologist evaluated the ccRCC specimen for renal vein or inferior vena cava thrombus. PD-L1 expression in the tumor nest was analyzed as our previous study ( 8 ), which involved both the tumor cells and tumor-infiltrating immune cells. TILs were also assessed as our previous study ( 8 ). The score of GLUT-1 expression was defined according to the intensity of the tumor cell staining using immunohistochemistry (IHC) as score 0, 1, 2, and 3 ( 9 ). The immunoreactivity of CAIX expression was evaluated by multiplying the staining intensity by the percentage of tumor cells, and defined as negative, 1+, 2+, or 3+ ( 10 ). All patient information was anonymized prior to data analysis. This study was approved by the Institutional Review Board of Peking University First Hospital, waiving the need for written informed consent. 2.3 18 F-FDG PET/CT imaging The preoperative 18 F-FDG PET/CT images were obtained as previously documented ( 8 ) and independently interpreted by two experienced senior nuclear medicine specialists blinded to the patients’ information. If the results were not completely aligned, they discussed and then reached a consensus. We delineated a volume of interest (VOI) on the primary lesion to covere the entire tumor as much as possible while avoiding the calycles. Then maximum standardized uptake value (SUVmax) of the primary tumor was measured. 2.4 Follow-up information Postoperative follow-up data included abdominal ultrasonography, abdominal CT scan, chest X-ray, and laboratory examination. Follow-up was conducted every 3 months for the first 2 years, semiannually for the 3rd to 5th year, and annually after 5 years ( 11 ). Disease free survival (DFS) was calculated as the interval between the date of surgery and the time of disease-progression or the last follow-up for the censored patients. Disease-progression was defined as local recurrence, distant metastasis, progression of the pre-existing metastases confirmed by Response Evaluation Criteria in Solid Tumor (RECIST, Version 1.1), or death of any reason, whichever came first. 2.5 The Cancer Gene Atlas (TCGA) dataset anylasis RNA-seq data of 533 ccRCC tumor tissues from the TCGA-KIRC (kidney renal clear cell carcinoma) project were downloaded from TCGA database ( https://portal.gdc.cancer.gov/ ) and compiled. Clinical outcomes were clearly documented in 328 of these cases. Differential expressed genes (DEGs) between disease-progression group and disease-free group were screened using fold change (FC) > 1.5 and P < 0.05 as the criteria. The gene sets related to the glycolysis pathway (HALLMARK_GLYCOLYSIS, REACTOME_GLYCOLYSIS, and KEGG_GLYCOLYSIS_GLUCONEOGENESIS) downloaded from the Molecular Signatures Database (MSigDB) ( https://www.gsea-msigdb.org/gsea/msigdb ) were intersected with the up-regulated DEGs in the disease-progression group, and the overlapping genes were defined as glycolysis-related genes. 2.6 Statistical analysis Normally distributed data were expressed as \(\:\stackrel{-}{x}\) ± s and the differences between groups were compared by Student t-test, whereas non-normally distributed variables were described as median (range) or number (percentage), and Mann-Whitney U test or Chi-square test was applied to calculate the differences between groups. Continuous variables were transformed into binary variables according to the optimal cut-off values analyzed by the receiver operating characteristic (ROC) curves. The univariate and multivariate Cox proportional hazards analyses were executed to identify the prognostic factors for DFS, and the hazard ratios (HR) and 95% confidence intervals (CI) of the predictors were calculated. The area under the curve (AUC) values of 1-, 3- and 5-year ROC curves were used to assess the predictive efficacy. Survival analysis was evaluated by the Kaplan-Meier curves, and the Log-rank test was used to compare the survival rates. All statistical analyses were conducted by SPSS 26.0 software (SPSS Software Inc., Chicago, IL, USA) and GraphPad Prism 8.0 software (GraphPad Software Inc., La Jolla, CA, USA). P < 0.05 was considered statistically significant. 3. Results 3.1 General characteristics 59 patients with histopathologically confirmed ccRCC were included, including 40 males (67.8%) and 19 females (32.2%), with a median age of 58 (33 ~ 78 years). The median survival time was 46 months (1 ~ 105 months). 26 (44.1%) cases developed disease progression, among which 9 patients died, and 17 patients had tumor recurrence, metastasis, or progression of the pre-existing metastases. BMI, Hb, clinical symptoms, TNM stage, SUVmax, primary tumor size, WHO/ISUP grade, CAIX expression, and TILs showed significant differences between the disease-progression and disease-free group (Table 1 ). Table 1 General characteristics of ccRCC patients Characteristic Total Disease-free group Disease-progression group P- value Total, N (%) 59 (100.0) 33 (55.9) 26 (44.1) Clinical features Age, years 0.753 \(\:\stackrel{-}{x}\) ±s 57.3 ± 10.2 56.9 ± 10.7 57.7 ± 9.8 Gender 0.725 Male 40 (67.8) 23 (69.7) 17 (65.4) Female 19 (32.2) 10 (30.3) 9 (34.6) Weight, kg 0.062 \(\:\stackrel{-}{x}\) ±s 72.3 ± 10.7 74.6 ± 10.4 69.3 ± 10.6 Height, m 0.691 \(\:\stackrel{-}{x}\) ±s 1.7 ± 0.1 1.7 ± 0.1 1.7 ± 0.1 BMI, kg/m 2 0.045 \(\:\stackrel{-}{x}\) ±s 25.6 ± 3.0 26.3 ± 2.8 24.7 ± 3.2 Hb, g/L 0.005 \(\:\stackrel{-}{x}\) ±s 133.7 ± 22.3 140.8 ± 18.5 124.7 ± 23.8 Clinical symptoms 0.025 Absence 18 (30.5) 14 (42.4) 4 (15.4) Presence 41 (69.5) 19 (57.6) 22 (84.6) TNM stage < 0.001 I/ II 23 (39.0) 20 (60.6) 3 (11.5) III/ IV 36 (61.0) 13 (39.4) 23 (88.5) Metabolic parameter SUVmax < 0.001 Median (Q1, Q3) 4.3 (2.9, 8.1) 3.2 (2.7, 4.1) 7.3 (5.1, 11.2) Pathological indicators Primary tumor size, cm 0.014 Median (Q1, Q3) 6.5 (4.5, 9.5) 5.0 (3.5, 8.5) 7.4 (6.2, 9.5) WHO/ISUP grade < 0.001 G1/G2 33 (55.9) 29 (87.9) 4 (15.4) G3/G4 26 (44.1) 4 (12.1) 22 (84.6) Venous tumor thrombus 0.239 Absence 41 (69.5) 25 (75.8) 16 (61.5) Presence 18 (30.5) 8 (24.2) 10 (38.5) CAIX 0.035 1 19 (32.2) 6 (18.2) 13 (50.0) 2 3 (5.1) 3 (9.1) 0 (0.0) 3 37 (62.7) 24 (72.7) 13 (50.0) GLUT-1 0.351 0 6 (10.2) 1 (3.0) 5 (19.2) 1 11 (18.6) 7 (21.2) 4 (15.4) 2 20 (33.9) 12 (36.4) 8 (30.8) 3 22 (37.3) 13 (39.4) 9 (34.6) PD-L1 1.000 Yes 11 (18.3%) 6 (18.2%) 5 (18.5%) No 49 (81.7%) 27 (81.8%) 22 (81.5%) TIL 0.041 1 26 (44.1) 18 (54.5) 8 (30.8) 2 26 (44.1) 13 (39.4) 13 (50.0) 3 7 (11.9) 2 (6.1) 5 (19.2) ccRCC, clear cell renal cell carcinoma; BMI, body mass index; Hb, hemoglobin; SUVmax, maximum standardized uptake value; WHO/ISUP grade, World Health Organisation/International Society of Urological Pathology grade; TNM, tumor node metastasis; CAIX, carbonic anhydrase IX; GLUT-1, glucose transporter protein 1; PD-L1, programmed death- ligand 1; TIL, tumor-infiltrating lymphocyte. 3.2 Prognostic factor analysis The cut-off values of the continuous variables (age, weight, height, BMI, Hb, primary tumor size, SUVmax) were obtained according to the ROC curves. The cut-off values, AUC, P -values, sensitivity and specificity of each index were shown in Table 2 . Table 2 Results for ROC curve analyses Variable cutoff AUC P- value Sensitivity Specificity Age, years 44.5 0.505 0.951 0.923 0.182 Weight, kg 77.5 0.640 0.067 0.515 0.808 Height, m 1.765 0.510 0.891 0.152 0.962 BMI, kg/m 2 25.45 0.672 0.024 0.697 0.692 Hb, g/L 130 0.695 0.011 0.788 0.615 Primary tumor size, cm 5.75 0.688 0.014 0.885 0.576 SUVmax 4.20 0.793 < 0.001 0.846 0.758 ROC, the receiver operating characteristic; AUC, area under the curve; BMI, body mass index; Hb, hemoglobin; SUVmax, maximum standardized uptake value. Univariate Cox proportional hazards analysis was performed for the above indicators that were significantly different in the disease-progression and disease-free group. The results suggested that BMI (≤ 25.45 kg/m 2 ), Hb (≤ 130 g/L), clinical symptoms, TNM stage (III/ IV), SUVmax (> 4.20), primary tumor size (> 5.75 cm), WHO/ISUP grade (G3/4), CAIX expression (1+), and high TILs (50%-90%) were significant prognostic factors of inferior DFS (Table 3 ). Patient with 1 + expression of CAIX demonstrated worse DFS than those with 3 + and patient with strong TILs staining harbored a higher risk of progression than those with low staining. Table 3 Univariate analysis of prognostic factors for DFS Variable N (%) HR (95%CI) P- value Clinical features BMI, kg/m 2 0.005 ≤ 25.45 28 (47.5) 1.000 (Reference) > 25.45 31 (52.5) 0.305 (0.132 ~ 0.704) Hb, g/L 0.001 ≤ 130 23 (39.0) 1.000 (Reference) > 130 36 (61.0) 0.266 (0.120 ~ 0.590) Clinical symptoms 0.041 Absence 18 (30.5) 1.000 (Reference) Presence 41 (69.5) 3.049 (1.049 ~ 8.864) TNM stage < 0.001 I/ II 23 (39.0) 1.000 (Reference) III/ IV 36 (61.0) 7.909 (2.351 ~ 26.611) Metabolic parameters SUVmax 4.20 30 (50.8) 8.824 (3.017 ~ 25.808) Pathological indicators Primary tumor size, cm 0.003 ≤ 5.75 22 (37.3) 1.000 (Reference) > 5.75 37 (62.7) 6.429 (1.922 ~ 21.507) WHO/ISUP grade < 0.001 G1/G2 33 (55.9) 1.000 (Reference) G3/G4 26 (44.1) 13.268 (4.514 ~ 39.000) CAIX 1 19 (32.2) 1.000 (Reference) 2 3 (5.1) 0.000 (0.000 ~ Inf) 0.998 3 37 (62.7) 0.423 (0.196 ~ 0.915) 0.029 TIL 0–10% 26 (44.1) 1.000 (Reference) 20–40% 26 (44.1) 1.674 (0.693 ~ 4.044) 0.252 50–90% 7 (11.9) 4.285 (1.376 ~ 13.341) 0.012 DFS, disease-free survival; HR, hazard ratio; CI, confidence interval; BMI, body mass index; Hb, hemoglobin; TNM, tumor node metastasis; SUVmax, maximum standardized uptake value; WHO/ISUP, World Health Organization/International Society of Urological Pathology; CAIX, carbonic anhydrase IX; TIL, tumor-infiltrating lymphocyte. Multivariate Cox proportional hazards analysis was further conducted. As a result, only BMI ( P = 0.002; HR = 0.233; 95%CI: 0.094–0.580), SUVmax ( P = 0.026, HR = 4.248; 95%CI: 1.184–15.239), and WHO/ISUP grade ( P = 0.005, HR = 5.888; 95%CI: 1.689–20.524) still maintained their independency in prognosis prediction (Fig. 1 ). 3.3 Performance of the predictive model The prognostic model composed of the above three independent predictors (BMI, SUVmax, and WHO/ISUP grade) as variables achieved excellent predictive efficacy by virtue of a C-index of 0.89, with AUC values of 0.922, 0.919, and 0.899 at 1-, 3-, and 5-year, respectively (Fig. 2 a). Kaplan-Meier survival curves showed that DFS was significantly decreased in patients with low BMI (≤ 25.45 kg/m 2 ), elevated SUVmax (> 4.20), and high WHO/ISUP grade (G3/4) (Fig. 2 b). Typical cases were illustrated in Fig. 3 . 3.4 Upregulation of glycolysis-related genes in patients of the disease-progression group Of the 328 patients with explicitly documented clinical outcomes from the TCGA-KIRC project, 227 were categorized into the disease-progression group and 101 into the disease-free group. Comparing the transcriptome profiling, we screened 3947 up-regulated genes and 1605 down-regulated genes in disease-progression group (Fig. 4 a), and compared the up-regulated genes with four gene sets related to the glycolysis pathway in the MSigDB database. Among them, 11 genes, including GCK , GCKR , KIF20A , DCN , COL5A1 , ARTN , CENPA , PLOD2 , TGFBI , DEPDC1 , and KDELR3 , were defined as glycolysis-related genes (Fig. 4 b). Notably, GCKR and GCK were most obviously upregulated in the disease-progression group with FC values of 2.88 and 2.00, respectively, which may predict the negative impact of glycolysis metabolic reprogramming in ccRCC tumor cells on patients’ prognosis. 4. Discussion Tumors with high malignancy undergo robust glucose metabolism according to the Warburg effect, leading to elevated 18 F-FDG uptake in PET/CT imaging. The feasibility of 18 F-FDG PET/CT imaging in prognostic prediction of patients with various malignant tumors has been strongly recognized. Our study also observed that the postoperative 18 F-FDG PET/CT semi-quantitative parameter, SUVmax, showed predictive value in prognostic assessment of ccRCC patients. Moreover, the prognostic model integrating of pretreatment PET quantitative imaging feature to conventional clinicopathological factors enabled the identification of ccRCC patients with a high risk of disease progression. Importantly, the three predictors in our model are relatively easy to obtain in clinical practice and simple to use for clinicians. SUVmax represents the most extensively utilized semi-quantitative metabolic parameter in 18 F-FDG PET/CT imaging. As to RCC patients, most studies confirm that patients with relatively high SUVmax of the primary tumor or high SUVmax of the whole body tumor-related lesions, tend to have inferior outcomes, although the cutoff values are not consistent across studies ( 12 – 14 ). But the results are controversial in terms of its independent prognostic significance. Fifty-nine patients diagnosed with stage I ~ IV ccRCC were included in our study, and the findings implicated that preoperative SUVmax was effective in predicting postoperative disease progression, confirmed by both univariate and multivariate analyses. Patients with elevated preoperative SUVmax (> 4.20) were more likely to develop disease progression. The potential mechanism underlying the poor prognosis associated with increased preoperative tumor glucose metabolism in ccRCC patients was further explored in our study. Comprehensive analyses of transcriptomics, metabolomics, and lipidomics have demonstrated intricate metabolic reprogramming in ccRCC, with aberrant glycolysis emerging as a pivotal metabolic process influencing ccRCC progression ( 15 ). The characteristic clear cytoplasm of ccRCC is a pathological manifestation resulting from the accumulation of glycogen and lipids, linked to a hypoxic tumor microenvironment induced by hypoxia-inducible factor (HIF) accumulation secondary to von Hippel-Lindau (VHL) gene mutations ( 16 ). Analysis of DEGs between the disease-progression and disease-free group from the TCGA database further reveals that the upregulation of glycolysis-related genes is a crucial molecular event driving malignant prognosis in ccRCC patients. GCK encodes a glucokinase, a key enzyme directly catalyzing glucose phosphorylation, enabling tumor cells to obtain sufficient energy through the Warburg effect. GCKR encodes a glucose kinase regulatory protein and also influences the hepatic cytosolic NADH/NAD + ratio, thereby modulating metabolic traits such as oxidative stress ( 17 ). The abnormal upregulation of the GCK - GCKR axis in the disease-progression group is likely a significant molecular mechanism underlying metabolic reprogramming in tumors. Recently, Di et al. demonstrated that DEPDC1 may promote glycolysis in RCC cells via the AKT/mTOR/HIF-1α pathway, and RCC cells overexpressing DEPDC1 exhibit enhanced proliferation, migration, and invasion capabilities ( 18 ). Additionally, previous studies have discovered that glycolysis-related genes such as KIF20A , DCN , and COL5A1 are significantly associated with poor prognosis including immunotherapy resistance, progression, and metastasis in ccRCC ( 19 , 20 ). Therefore, early detection of abnormal upregulation of tumor glycolysis is crucial for improving the survival outcomes of ccRCC patients. But we noticed that the protein of GLUT-1, as one of the major glucose transporters, did not possess any prognostic significance. Hironori B et al. also depicted that GLUT-1 mRNA expression showed no association with OS and recurrence-free survival in ccRCC patients ( 21 ). They postulated that some other substances related to glucose uptake besides GLUT-1 may be involved in the glycolysis in ccRCC, such as the sodium glucose transporter, glucose 6-phosphatase, and hexokinase 2. Their speculation may also explain our research findings. BMI usually serves as an important index for assessing obesity degree. It is widely acknowledged as a risk factor for the development of RCC, but our study also observed that individuals with higher BMIs exhibited more favorable prognosis, aligning with the notion of the "obesity paradox" ( 22 ). Multiple studies focused on both metastatic and non-metastatic RCC have consistently demonstrated a significant association between higher BMI and better prognosis ( 23 ). Turco et al. summarized this "obesity paradox" in RCC patients as stemming from superior nutritional status, more indolent tumors, distinct gene expression, and molecular profiles in obese individuals ( 23 ). The VHL/HIF pathway upregulates lipid metabolism-related genes, including fatty acid synthase ( FASN ), which is implicated as a key driver of the "pseudo-hypoxic" tumor microenvironment and adversely impacts the prognosis of ccRCC ( 24 ). Intriguingly, FASN levels were found to be significantly lower in the obese population compared to the normal-weight population, potentially elucidating why higher BMI predicts better prognosis in ccRCC patients. The WHO/ISUP grade is recognized as a significant prognosis factor of ccRCC patients according to the guidelines of the European Association of Urology (EAU) ( 25 ). In this study, high WHO/ISUP grade (G3/4) significantly predicted inferior outcome in ccRCC patients, consistent with the guidelines. The pathological features of CAIX expression, PD-L1 expression, and TILs, were also analyzed in this study. In the vast majority of ccRCC cases, the VHL gene is mutated or silenced, leading to the constitutive accumulation of HIF-1α, which activates a battery of "hypoxic response" genes, and the CAIX gene is one of its most classic and sensitive downstream targets ( 26 ). It is reported that more than 95% ccRCC is CAIX positive, which can serve as a biomarker for diagnosing ccRCC ( 27 ). Notably, the prognostic value of CAIX expression in ccRCC patients is also unequivocal ( 28 ). Multiple retrospective studies show that patients with high CAIX expression have significantly longer survival compared to those with low CAIX expression in ccRCC patients ( 26 , 29 , 30 ). Courcier J et al. speculated that high CAIX expression may infer the tumor still heavily dependent on the canonical VHL-HIF pathway, while low CAIX expression may reflect the tumor de-differentiation and aggressiveness, leading to more aggressive behaviors and worse prognosis ( 26 ). Zhang BY et al. concluded from 730 ccRCC patients that low CAIX expression was related to an increased risk of RCC death in the univariate analysis, but it was not an independent prognostic factor after adjusting for tumor grade and coagulative tumor necrosis ( 29 ). Patients in our study were all CAIX positive in IHC. We clarified that patient with 1 + expression of CAIX harbored worse DFS than those with 3 + expression, but it was not an independent predictor in the multivariate analysis, in keeping with the previous studies. PD-L1 is one of the most extensively studied immune checkpoint molecules, and also a promising therapeutic target in RCC. But its prognostic value in RCC patients is discrepant across studies. In the KEYNOTE 426 ( 31 ) and the Checkmate 214 ( 32 ) studies, patients treated with ICIs possessed better survival compared to those treated with sunitinib, regardless of the PD-L1 status. In our study, PD-L1 expression did not emerge as a significant predictor of prognosis. However, Tamada S et al. depicted that PD-L1 expression may predict the OS and tumor recurrence in high-risk patients with localized RCC ( 33 ). The prognostic discordance may be partly explained by the inherent limitations of IHC assay for detecting PD-L1 expression, variations in the sample size, difference in the endpoints, and the heterogeneity in treatment regimens. RCC is an immunogenic tumor characterized by extensive T-cell infiltration. However, most of these TILs remain unactivated or functionally impaired, thereby fostering an immunosuppressive tumor microenvironment ( 5 ). It is reported that elevated CD8 + T-cell infiltration is related to unfavorable prognosis in RCC ( 34 ), which is opposite with most other cancers. This may be explained in part by the high expressions of CTLA and PD-1 on T cells. We also found that patient with high TILs harbored a higher risk of progression than those with low TILs. But it was not an independent prognostic factor in the multivariate analysis neither. This may be due to our failure to account for the specific subtypes of TILs and their spatial distribution within the tumor tissue. No single biomarker is sufficient to predict patient outcome, so we incorporated the metabolic factor of SUVmax and the clinicopathological features of BMI and WHO/ISUP grade, and generated a prognostic model to predict the prognosis of ccRCC patients. It yielded excellent efficacy in predicting patients' 1-year, 3-year, and 5-year DFS. This model may enhance the predictive validity for patient prognosis, so as to aid clinicians in personalized clinical decisions rather than population-level treatment strategies. Although this study is the first to construct a clinically applicable prognostic model by combining metabolic imaging with clinical features, there are still some limitations in this study. Firstly, it is a retrospective, small sized, and single-center study, which inevitably leads to selective bias. Secondly, this study did not include metrics that reflect the overall tumor burden, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Thirdly, while this study identified upregulation of glycolysis-related DEGs in tumor tissues from ccRCC patients with poor prognosis based on TCGA data, this finding remains to be directly validated in our patient cohort by correlating the semi-quantitative parameter of 18 F-FDG PET/CT with the glycolysis-related DEGs, warranting further investigation in future. In conclusion, ccRCC patients with low preoperative BMI (≤ 25.45), elevated SUVmax (> 4.20), and high WHO/ISUP grade (G3/4) are more likely to develop disease progression after operation. Implementing closer surveillance or aggressive early intervention in routine practice for these patients is recommended to optimize prognosis. Declarations Ethics approval and consent to participate This retrospective study was approved by Ethics Committee of Peking University First Hospital, waiving the need for written informed consent. Consent for publication Not applicable. Competing interests The authors declare that they have no conflict of interest. Availability of data and material The data used in the current study is available from the corresponding authors on reasonable request. Funding This study was supported by grants from the National Natu­ral Science Foundation of China (82172052), Beijing Natural Science Foundation (Z210007). Authors’ contributions Conceptualization: [Meng Liu]; Methodology: [Meng Liu], [Yuhui Cao], [Caixia Wu]; Formal analysis and investigation: [Yuhui Cao], [Caixia Wu], [Keting Tong], [Yulong Chen], [Jinzhi Chen], [Yuan Gao], [Zijian Fu], [Xin Wang]; Writing-original draft preparation: [Yuhui Cao], [Caixia Wu]; Writing-review and editing: [Meng Liu]; Funding acquisition: [Meng Liu]; Supervision: [Meng Liu]. All authors have read and approved the final manuscript. Acknowledgements Not applicable. References Schiavoni V, Campagna R, Pozzi V, Cecati M, Milanese G, Sartini D, et al. 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Prognostic Value of Metabolic Tumor Volume and Total Lesion Glycolysis on Preoperative 18 F-FDG PET/CT in Patients With Renal Cell Carcinoma. Clin Nucl Med. 2017;42(4):e177-e82. di Meo NA, Lasorsa F, Rutigliano M, Loizzo D, Ferro M, Stella A, et al. Renal Cell Carcinoma as a Metabolic Disease: An Update on Main Pathways, Potential Biomarkers, and Therapeutic Targets. Int J Mol Sci. 2022;23(22). Li B, Qiu B, Lee DS, Walton ZE, Ochocki JD, Mathew LK, et al. Fructose-1,6-bisphosphatase opposes renal carcinoma progression. Nature. 2014;513(7517):251-255. Singh C, Jin B, Shrestha N, Markhard AL, Panda A, Calvo SE, et al. ChREBP is activated by reductive stress and mediates GCKR-associated metabolic traits. Cell Metab. 2024;36(1):144-58.e7. Di SC, Chen WJ, Yang W, Zhang XM, Dong KQ, Tian YJ, et al. DEPDC1 as a metabolic target regulates glycolysis in renal cell carcinoma through AKT/mTOR/HIF1α pathway. Cell Death Dis. 2024;15(7):533. Gao S, Yan L, Zhang H, Fan X, Jiao X, Shao F. 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Obesity and renal cell carcinoma: Biological mechanisms and perspectives. Semin Cancer Biol. 2023;94:21-33. Ljungberg B, Albiges L, Abu-Ghanem Y, Bedke J, Capitanio U, Dabestani S, et al. European Association of Urology Guidelines on Renal Cell Carcinoma: The 2022 Update. Eur Urol. 2022;82(4):399-410. Courcier J, de la Taille A, Nourieh M, Leguerney I, Lassau N, Ingels A. Carbonic Anhydrase IX in Renal Cell Carcinoma, Implications for Disease Management. Int J Mol Sci. 2020;21(19). Verhoeff SR, van Es SC, Boon E, van Helden E, Angus L, Elias SG, et al. Lesion detection by [(89)Zr]Zr-DFO-girentuximab and [(18)F]FDG-PET/CT in patients with newly diagnosed metastatic renal cell carcinoma. Eur J Nucl Med Mol Imaging. 2019;46(9):1931-1939. Chamie K, Donin NM, Klöpfer P, Bevan P, Fall B, Wilhelm O, et al. Adjuvant Weekly Girentuximab Following Nephrectomy for High-Risk Renal Cell Carcinoma: The ARISER Randomized Clinical Trial. JAMA Oncol. 2017;3(7):913-920. Zhang BY, Thompson RH, Lohse CM, Dronca RS, Cheville JC, Kwon ED, et al. Carbonic anhydrase IX (CAIX) is not an independent predictor of outcome in patients with clear cell renal cell carcinoma (ccRCC) after long-term follow-up. BJU Int. 2013;111(7):1046-1053. Ingels A, Hew M, Algaba F, de Boer OJ, van Moorselaar RJ, Horenblas S, et al. Vimentin over-expression and carbonic anhydrase IX under-expression are independent predictors of recurrence, specific and overall survival in non-metastatic clear-cell renal carcinoma: a validation study. World J Urol. 2017;35(1):81-87. Rini BI, Plimack ER, Stus V, Gafanov R, Hawkins R, Nosov D, et al. Pembrolizumab plus Axitinib versus Sunitinib for Advanced Renal-Cell Carcinoma. N Engl J Med. 2019;380(12):1116-27. Motzer RJ, Rini BI, McDermott DF, Arén Frontera O, Hammers HJ, Carducci MA, et al. Nivolumab plus ipilimumab versus sunitinib in first-line treatment for advanced renal cell carcinoma: extended follow-up of efficacy and safety results from a randomised, controlled, phase 3 trial. Lancet Oncol. 2019;20(10):1370-1385. Tamada S, Nozawa M, Ohba K, Mizuno R, Takamoto A, Ohe C, et al. Prognostic value of PD-L1 expression in recurrent renal cell carcinoma after nephrectomy: a secondary analysis of the ARCHERY study. Int J Clin Oncol. 2023;28(2):289-298. Qi Y, Xia Y, Lin Z, Qu Y, Qi Y, Chen Y, et al. Tumor-infiltrating CD39(+)CD8(+) T cells determine poor prognosis and immune evasion in clear cell renal cell carcinoma patients. Cancer Immunol Immunother. 2020;69(8):1565-1576. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":380113,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the prognostic factors for ccRCC patients in the multivariate Cox hazards analysis.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8559986/v1/55b45ec97120485ff4a94ef7.png"},{"id":100400990,"identity":"95ecffaa-728a-4773-bcf4-be7ad9826c2f","added_by":"auto","created_at":"2026-01-16 11:58:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":710994,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive performance of the prognostic model for clear cell renal cell carcinoma (ccRCC) patients. The model consisted body mass index (BMI), maximum standardized uptake value (SUVmax), and WHO/ISUP grade. (a) The area under the curve (AUC) values of receiver operating characteristic (ROC) curves predicted 1‐, 3‐, and 5‐year disease-free survival (DFS) in ccRCC patients. (b) Kaplan-Meier curves showed significant differences in DFS between patients categorized by the above three factors.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8559986/v1/0c63739c9b123f5bd5b9f6ca.png"},{"id":100401915,"identity":"abbb68ae-d065-466c-953a-390803319820","added_by":"auto","created_at":"2026-01-16 11:59:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3334064,"visible":true,"origin":"","legend":"\u003cp\u003eThe typically clinical cases\u003cstrong\u003e \u003c/strong\u003efor the model.\u003cstrong\u003e a-c\u003c/strong\u003e A 52-year-old male patient with a body mass index (BMI) of 23.1 kg/m2 presented with clear cell renal cell carcinoma (ccRCC) of the left kidney, classified as WHO/ISUP grade 3. Axial view of non-contrast CT scan revealed a mass in the mid-to-lower pole of the left kidney (b, white arrow). Maximum intensity projection (MIP) image (a, red arrow) and the axial fused image (c, white arrow) demonstrated significantly intense \u003csup\u003e18\u003c/sup\u003eF-FDG uptake within the mass, with a maximum standardized uptake value (SUVmax) of 15.1. The tumor thrombus in the left renal vein showed elevated avidity of \u003csup\u003e18\u003c/sup\u003eF-FDG (a, black arrow). The patient underwent radical nephrectomy of the left kidney combined with the removal of the venous tumor thrombus. However, 4 months later, pulmonary and skeletal metastases were observed. \u003cstrong\u003ed-f\u003c/strong\u003e A 40-year-old male patient with a BMI of 29.8 kg/m2 diagnosed with ccRCC of the right kidney, classified as WHO/ISUP grade 2. Axial non-contrast CT scan (e, white arrow) exhibited a cystic-solid mass in the lower pole of the right kidney, protruding beyond the renal contour. The MIP image (d, red arrow) and axial fused image (f, white arrow) showed mild \u003csup\u003e18\u003c/sup\u003eF-FDG uptake within the mass, with a SUVmax of 2.90. For a period of 101 months following the radical nephrectomy of the right kidney, no definitive signs of disease progression were observed.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8559986/v1/475616cdb6df044a8a9cf898.png"},{"id":100402153,"identity":"79c8489b-57f7-4a16-811c-221bcc14fc3d","added_by":"auto","created_at":"2026-01-16 11:59:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1897460,"visible":true,"origin":"","legend":"\u003cp\u003eThe panel of glycolysis-related genes. Volcano plot (a) and boxplot (b) of the upregulated differentially expressed genes (DEGs) involving glycolysis pathway in the disease-progression group compared to the disease-free group.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8559986/v1/3c12317c82d8d7ce85b7f2de.png"},{"id":105576457,"identity":"c7d4e61a-df6e-4b38-bc79-d08b377c2e39","added_by":"auto","created_at":"2026-03-27 13:44:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7282087,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8559986/v1/cfae8e16-e0a2-4e6b-9df8-dcc5d3b81536.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eA prognostic predicting model integrating preoperative \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT metabolic parameter\u003cstrong\u003e \u003c/strong\u003eand clinicopathological biomarkers for patients with ccRCC\u003c/p\u003e","fulltext":[{"header":"1. Background","content":"\u003cp\u003eClear cell renal cell carcinoma (ccRCC) is the predominant histological subtype of renal cell carcinoma (RCC) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). While many patients are diagnosed with localized ccRCC amenable to curative treatment, approximately 30% patients present with locally advanced or metastatic disease (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Moreover, approximately 20%-30% ccRCC patients with aggressive tumor biology may develop disease progression after surgery. Considering the differences in ccRCC patients\u0026rsquo; outcomes, accurate risk stratification is important to identify those who may benefit from more intensive initial treatment, more active surveillance, or adjuvant therapies. In this circumstance, it is critical to define prognostic biomarkers associated with clinical outcome to stratify patients.\u003c/p\u003e \u003cp\u003eCurrently, the primary prognostic biomarkers for ccRCC are anatomical (such as tumor node metastasis (TNM) stage), histological (such as pathological grade), clinical (such as some laboratory indicators), and molecular factors (such as genetic signatures) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). But no single biomarker is sufficient to predict patient outcomes and the molecular factors are not routinely used in clinical practice. Some prognostic models were developed based on clinicopathological factors, such as UISS, SSIGN, MSKCC, and IMDC model, which are sub-optimal in accurately identifying high-risk patients (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). When molecular targeted therapy and immunotherapy are rising, a non-invasive approach for reflecting tumor biological activity and predicting the prognosis of patients with ccRCC is urgently needed.\u003c/p\u003e \u003cp\u003e \u003csup\u003e18\u003c/sup\u003eF-fluorodexoxyglucose (\u003csup\u003e18\u003c/sup\u003eF-FDG) positron emission tomography/computed tomography (PET/CT) as a molecular imaging technology, has been widely used in the diagnosis and management of malignancies. But \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT features mainly reveal the tumor glucose metabolism, representing a partial view of the tumor. As ccRCC is characterized as both an immunogenic tumor and a metabolic disorder (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), the related features such as carbonic anhydrase IX (CAIX), glucose transporter protein 1 (GLUT-1), programmed death-ligand 1 (PD-L1), and tumor-infiltrating lymphocytes (TILs) are also essential for prognosis. The incorporation of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT characteristics and clinicopathological biomarkers into the prognostic model of ccRCC has not been implemented yet.\u003c/p\u003e \u003cp\u003eThis study aims to explore the predictive value of preoperative \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT metabolic parameter combined with clinical and pathological features for postoperative disease-free survival (DFS) in patients with ccRCC. Moreover, we will exploit the underlying molecular mechanisms for the prognostic role of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT for ccRCC.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003eData of consecutive ccRCC patients who underwent preoperative \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT from March 2014 to July 2020 at Peking University First Hospital were retrospectively analyzed.\u003c/p\u003e \u003cp\u003eInclusion criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) diagnosed as ccRCC by histological examination; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) underwent radical or partial nephrectomy at our institution; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) underwent \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT prior to surgery and systemic therapy; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) available postoperative follow-up information\u0026thinsp;\u0026ge;\u0026thinsp;6 months for the progression-free patients.\u003c/p\u003e \u003cp\u003eExclusion criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) with a history of malignant tumor, including ccRCC or other malignancy; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) with bilateral synchronous ccRCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical and pathological features\u003c/h2\u003e \u003cp\u003eClinical data comprised of age, gender, weight, height, body mass index (BMI), clinical symptoms, hemoglobin (Hb), primary tumor size, venous tumor thrombus, World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), and TNM stage (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Pathological features of CAIX, GLUT-1, PD-L1, and TILs were also included. BMI\u0026thinsp;=\u0026thinsp;weight (kg)/ height (m)\u003csup\u003e2\u003c/sup\u003e. Clinical symptoms were defined as hematuria, lumbar/abdominal/back pain, abdominal mass or discomfort, nausea, fatigue, fever, wasting, lower limb edema, and metastatic manifestations (e.g., cough and bone pain). The primary tumor size was the largest diameter in the surgical gross specimen. An experienced urologic pathologist evaluated the ccRCC specimen for renal vein or inferior vena cava thrombus.\u003c/p\u003e \u003cp\u003ePD-L1 expression in the tumor nest was analyzed as our previous study (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), which involved both the tumor cells and tumor-infiltrating immune cells. TILs were also assessed as our previous study (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The score of GLUT-1 expression was defined according to the intensity of the tumor cell staining using immunohistochemistry (IHC) as score 0, 1, 2, and 3 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The immunoreactivity of CAIX expression was evaluated by multiplying the staining intensity by the percentage of tumor cells, and defined as negative, 1+, 2+, or 3+ (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll patient information was anonymized prior to data analysis. This study was approved by the Institutional Review Board of Peking University First Hospital, waiving the need for written informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT imaging\u003c/h2\u003e \u003cp\u003eThe preoperative \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT images were obtained as previously documented (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and independently interpreted by two experienced senior nuclear medicine specialists blinded to the patients\u0026rsquo; information. If the results were not completely aligned, they discussed and then reached a consensus. We delineated a volume of interest (VOI) on the primary lesion to covere the entire tumor as much as possible while avoiding the calycles. Then maximum standardized uptake value (SUVmax) of the primary tumor was measured.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Follow-up information\u003c/h2\u003e \u003cp\u003ePostoperative follow-up data included abdominal ultrasonography, abdominal CT scan, chest X-ray, and laboratory examination. Follow-up was conducted every 3 months for the first 2 years, semiannually for the 3rd to 5th year, and annually after 5 years (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Disease free survival (DFS) was calculated as the interval between the date of surgery and the time of disease-progression or the last follow-up for the censored patients. Disease-progression was defined as local recurrence, distant metastasis, progression of the pre-existing metastases confirmed by Response Evaluation Criteria in Solid Tumor (RECIST, Version 1.1), or death of any reason, whichever came first.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 The Cancer Gene Atlas (TCGA) dataset anylasis\u003c/h2\u003e \u003cp\u003eRNA-seq data of 533 ccRCC tumor tissues from the TCGA-KIRC (kidney renal clear cell carcinoma) project were downloaded from TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and compiled. Clinical outcomes were clearly documented in 328 of these cases. Differential expressed genes (DEGs) between disease-progression group and disease-free group were screened using fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;1.5 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the criteria. The gene sets related to the glycolysis pathway (HALLMARK_GLYCOLYSIS, REACTOME_GLYCOLYSIS, and KEGG_GLYCOLYSIS_GLUCONEOGENESIS) downloaded from the Molecular Signatures Database (MSigDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were intersected with the up-regulated DEGs in the disease-progression group, and the overlapping genes were defined as glycolysis-related genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eNormally distributed data were expressed as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e \u0026plusmn; s and the differences between groups were compared by Student t-test, whereas non-normally distributed variables were described as median (range) or number (percentage), and Mann-Whitney U test or Chi-square test was applied to calculate the differences between groups. Continuous variables were transformed into binary variables according to the optimal cut-off values analyzed by the receiver operating characteristic (ROC) curves. The univariate and multivariate Cox proportional hazards analyses were executed to identify the prognostic factors for DFS, and the hazard ratios (HR) and 95% confidence intervals (CI) of the predictors were calculated. The area under the curve (AUC) values of 1-, 3- and 5-year ROC curves were used to assess the predictive efficacy. Survival analysis was evaluated by the Kaplan-Meier curves, and the Log-rank test was used to compare the survival rates.\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted by SPSS 26.0 software (SPSS Software Inc., Chicago, IL, USA) and GraphPad Prism 8.0 software (GraphPad Software Inc., La Jolla, CA, USA). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 General characteristics\u003c/h2\u003e \u003cp\u003e59 patients with histopathologically confirmed ccRCC were included, including 40 males (67.8%) and 19 females (32.2%), with a median age of 58 (33\u0026thinsp;~\u0026thinsp;78 years). The median survival time was 46 months (1\u0026thinsp;~\u0026thinsp;105 months). 26 (44.1%) cases developed disease progression, among which 9 patients died, and 17 patients had tumor recurrence, metastasis, or progression of the pre-existing metastases. BMI, Hb, clinical symptoms, TNM stage, SUVmax, primary tumor size, WHO/ISUP grade, CAIX expression, and TILs showed significant differences between the disease-progression and disease-free group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral characteristics of ccRCC patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDisease-free group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisease-progression group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal, \u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical features\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (69.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight, m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133.7\u0026thinsp;\u0026plusmn;\u0026thinsp;22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140.8\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124.7\u0026thinsp;\u0026plusmn;\u0026thinsp;23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI/ II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII/ IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (88.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetabolic parameter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUVmax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.3 (2.9, 8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2 (2.7, 4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3 (5.1, 11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathological indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor size, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.5 (4.5, 9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.5, 8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.4 (6.2, 9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO/ISUP grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1/G2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29 (87.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3/G4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenous tumor thrombus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAIX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37 (62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLUT-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (81.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (81.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (81.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eccRCC, clear cell renal cell carcinoma; BMI, body mass index; Hb, hemoglobin; SUVmax, maximum standardized uptake value; WHO/ISUP grade, World Health Organisation/International Society of Urological Pathology grade; TNM, tumor node metastasis; CAIX, carbonic anhydrase IX; GLUT-1, glucose transporter protein 1; PD-L1, programmed death- ligand 1; TIL, tumor-infiltrating lymphocyte.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Prognostic factor analysis\u003c/h2\u003e \u003cp\u003eThe cut-off values of the continuous variables (age, weight, height, BMI, Hb, primary tumor size, SUVmax) were obtained according to the ROC curves. The cut-off values, AUC, \u003cem\u003eP\u003c/em\u003e-values, sensitivity and specificity of each index were shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults for ROC curve analyses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight, m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor size, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUVmax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eROC, the receiver operating characteristic; AUC, area under the curve; BMI, body mass index; Hb, hemoglobin; SUVmax, maximum standardized uptake value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnivariate Cox proportional hazards analysis was performed for the above indicators that were significantly different in the disease-progression and disease-free group. The results suggested that BMI (\u0026le;\u0026thinsp;25.45 kg/m\u003csup\u003e2\u003c/sup\u003e), Hb (\u0026le;\u0026thinsp;130 g/L), clinical symptoms, TNM stage (III/ IV), SUVmax (\u0026gt;\u0026thinsp;4.20), primary tumor size (\u0026gt;\u0026thinsp;5.75 cm), WHO/ISUP grade (G3/4), CAIX expression (1+), and high TILs (50%-90%) were significant prognostic factors of inferior DFS (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Patient with 1\u0026thinsp;+\u0026thinsp;expression of CAIX demonstrated worse DFS than those with 3\u0026thinsp;+\u0026thinsp;and patient with strong TILs staining harbored a higher risk of progression than those with low staining.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of prognostic factors for DFS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical features\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;25.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.305 (0.132\u0026thinsp;~\u0026thinsp;0.704)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHb, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.266 (0.120\u0026thinsp;~\u0026thinsp;0.590)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eClinical symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.049 (1.049\u0026thinsp;~\u0026thinsp;8.864)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eI/ II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIII/ IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.909 (2.351\u0026thinsp;~\u0026thinsp;26.611)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetabolic parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSUVmax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.824 (3.017\u0026thinsp;~\u0026thinsp;25.808)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathological indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePrimary tumor size, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.429 (1.922\u0026thinsp;~\u0026thinsp;21.507)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWHO/ISUP grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eG1/G2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eG3/G4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.268 (4.514\u0026thinsp;~\u0026thinsp;39.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCAIX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000 (0.000\u0026thinsp;~\u0026thinsp;Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.423 (0.196\u0026thinsp;~\u0026thinsp;0.915)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e0\u0026ndash;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e20\u0026ndash;40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.674 (0.693\u0026thinsp;~\u0026thinsp;4.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e50\u0026ndash;90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.285 (1.376\u0026thinsp;~\u0026thinsp;13.341)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eDFS, disease-free survival; HR, hazard ratio; CI, confidence interval; BMI, body mass index; Hb, hemoglobin; TNM, tumor node metastasis; SUVmax, maximum standardized uptake value; WHO/ISUP, World Health Organization/International Society of Urological Pathology; CAIX, carbonic anhydrase IX; TIL, tumor-infiltrating lymphocyte.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariate Cox proportional hazards analysis was further conducted. As a result, only BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; HR\u0026thinsp;=\u0026thinsp;0.233; 95%CI: 0.094\u0026ndash;0.580), SUVmax (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, HR\u0026thinsp;=\u0026thinsp;4.248; 95%CI: 1.184\u0026ndash;15.239), and WHO/ISUP grade (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, HR\u0026thinsp;=\u0026thinsp;5.888; 95%CI: 1.689\u0026ndash;20.524) still maintained their independency in prognosis prediction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Performance of the predictive model\u003c/h2\u003e \u003cp\u003eThe prognostic model composed of the above three independent predictors (BMI, SUVmax, and WHO/ISUP grade) as variables achieved excellent predictive efficacy by virtue of a C-index of 0.89, with AUC values of 0.922, 0.919, and 0.899 at 1-, 3-, and 5-year, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Kaplan-Meier survival curves showed that DFS was significantly decreased in patients with low BMI (\u0026le;\u0026thinsp;25.45 kg/m\u003csup\u003e2\u003c/sup\u003e), elevated SUVmax (\u0026gt;\u0026thinsp;4.20), and high WHO/ISUP grade (G3/4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Typical cases were illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Upregulation of glycolysis-related genes in patients of the disease-progression group\u003c/h2\u003e \u003cp\u003eOf the 328 patients with explicitly documented clinical outcomes from the TCGA-KIRC project, 227 were categorized into the disease-progression group and 101 into the disease-free group. Comparing the transcriptome profiling, we screened 3947 up-regulated genes and 1605 down-regulated genes in disease-progression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), and compared the up-regulated genes with four gene sets related to the glycolysis pathway in the MSigDB database. Among them, 11 genes, including \u003cem\u003eGCK\u003c/em\u003e, \u003cem\u003eGCKR\u003c/em\u003e, \u003cem\u003eKIF20A\u003c/em\u003e, \u003cem\u003eDCN\u003c/em\u003e, \u003cem\u003eCOL5A1\u003c/em\u003e, \u003cem\u003eARTN\u003c/em\u003e, \u003cem\u003eCENPA\u003c/em\u003e, \u003cem\u003ePLOD2\u003c/em\u003e, \u003cem\u003eTGFBI\u003c/em\u003e, \u003cem\u003eDEPDC1\u003c/em\u003e, and \u003cem\u003eKDELR3\u003c/em\u003e, were defined as glycolysis-related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Notably, \u003cem\u003eGCKR\u003c/em\u003e and \u003cem\u003eGCK\u003c/em\u003e were most obviously upregulated in the disease-progression group with FC values of 2.88 and 2.00, respectively, which may predict the negative impact of glycolysis metabolic reprogramming in ccRCC tumor cells on patients\u0026rsquo; prognosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTumors with high malignancy undergo robust glucose metabolism according to the Warburg effect, leading to elevated \u003csup\u003e18\u003c/sup\u003eF-FDG uptake in PET/CT imaging. The feasibility of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT imaging in prognostic prediction of patients with various malignant tumors has been strongly recognized. Our study also observed that the postoperative \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT semi-quantitative parameter, SUVmax, showed predictive value in prognostic assessment of ccRCC patients. Moreover, the prognostic model integrating of pretreatment PET quantitative imaging feature to conventional clinicopathological factors enabled the identification of ccRCC patients with a high risk of disease progression. Importantly, the three predictors in our model are relatively easy to obtain in clinical practice and simple to use for clinicians.\u003c/p\u003e \u003cp\u003eSUVmax represents the most extensively utilized semi-quantitative metabolic parameter in \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT imaging. As to RCC patients, most studies confirm that patients with relatively high SUVmax of the primary tumor or high SUVmax of the whole body tumor-related lesions, tend to have inferior outcomes, although the cutoff values are not consistent across studies (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). But the results are controversial in terms of its independent prognostic significance. Fifty-nine patients diagnosed with stage I\u0026thinsp;~\u0026thinsp;IV ccRCC were included in our study, and the findings implicated that preoperative SUVmax was effective in predicting postoperative disease progression, confirmed by both univariate and multivariate analyses. Patients with elevated preoperative SUVmax (\u0026gt;\u0026thinsp;4.20) were more likely to develop disease progression.\u003c/p\u003e \u003cp\u003eThe potential mechanism underlying the poor prognosis associated with increased preoperative tumor glucose metabolism in ccRCC patients was further explored in our study. Comprehensive analyses of transcriptomics, metabolomics, and lipidomics have demonstrated intricate metabolic reprogramming in ccRCC, with aberrant glycolysis emerging as a pivotal metabolic process influencing ccRCC progression (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The characteristic clear cytoplasm of ccRCC is a pathological manifestation resulting from the accumulation of glycogen and lipids, linked to a hypoxic tumor microenvironment induced by hypoxia-inducible factor (HIF) accumulation secondary to von Hippel-Lindau (VHL) gene mutations (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Analysis of DEGs between the disease-progression and disease-free group from the TCGA database further reveals that the upregulation of glycolysis-related genes is a crucial molecular event driving malignant prognosis in ccRCC patients. \u003cem\u003eGCK\u003c/em\u003e encodes a glucokinase, a key enzyme directly catalyzing glucose phosphorylation, enabling tumor cells to obtain sufficient energy through the Warburg effect. \u003cem\u003eGCKR\u003c/em\u003e encodes a glucose kinase regulatory protein and also influences the hepatic cytosolic NADH/NAD\u0026thinsp;+\u0026thinsp;ratio, thereby modulating metabolic traits such as oxidative stress (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The abnormal upregulation of the \u003cem\u003eGCK\u003c/em\u003e-\u003cem\u003eGCKR\u003c/em\u003e axis in the disease-progression group is likely a significant molecular mechanism underlying metabolic reprogramming in tumors. Recently, Di \u003cem\u003eet al.\u003c/em\u003e demonstrated that \u003cem\u003eDEPDC1\u003c/em\u003e may promote glycolysis in RCC cells via the AKT/mTOR/HIF-1α pathway, and RCC cells overexpressing \u003cem\u003eDEPDC1\u003c/em\u003e exhibit enhanced proliferation, migration, and invasion capabilities (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Additionally, previous studies have discovered that glycolysis-related genes such as \u003cem\u003eKIF20A\u003c/em\u003e, \u003cem\u003eDCN\u003c/em\u003e, and \u003cem\u003eCOL5A1\u003c/em\u003e are significantly associated with poor prognosis including immunotherapy resistance, progression, and metastasis in ccRCC (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Therefore, early detection of abnormal upregulation of tumor glycolysis is crucial for improving the survival outcomes of ccRCC patients. But we noticed that the protein of GLUT-1, as one of the major glucose transporters, did not possess any prognostic significance. Hironori B et al. also depicted that GLUT-1 mRNA expression showed no association with OS and recurrence-free survival in ccRCC patients (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). They postulated that some other substances related to glucose uptake besides GLUT-1 may be involved in the glycolysis in ccRCC, such as the sodium glucose transporter, glucose 6-phosphatase, and hexokinase 2. Their speculation may also explain our research findings.\u003c/p\u003e \u003cp\u003eBMI usually serves as an important index for assessing obesity degree. It is widely acknowledged as a risk factor for the development of RCC, but our study also observed that individuals with higher BMIs exhibited more favorable prognosis, aligning with the notion of the \"obesity paradox\" (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Multiple studies focused on both metastatic and non-metastatic RCC have consistently demonstrated a significant association between higher BMI and better prognosis (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Turco \u003cem\u003eet al.\u003c/em\u003e summarized this \"obesity paradox\" in RCC patients as stemming from superior nutritional status, more indolent tumors, distinct gene expression, and molecular profiles in obese individuals (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The VHL/HIF pathway upregulates lipid metabolism-related genes, including fatty acid synthase (\u003cem\u003eFASN\u003c/em\u003e), which is implicated as a key driver of the \"pseudo-hypoxic\" tumor microenvironment and adversely impacts the prognosis of ccRCC (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Intriguingly, FASN levels were found to be significantly lower in the obese population compared to the normal-weight population, potentially elucidating why higher BMI predicts better prognosis in ccRCC patients.\u003c/p\u003e \u003cp\u003eThe WHO/ISUP grade is recognized as a significant prognosis factor of ccRCC patients according to the guidelines of the European Association of Urology (EAU) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In this study, high WHO/ISUP grade (G3/4) significantly predicted inferior outcome in ccRCC patients, consistent with the guidelines.\u003c/p\u003e \u003cp\u003eThe pathological features of CAIX expression, PD-L1 expression, and TILs, were also analyzed in this study. In the vast majority of ccRCC cases, the VHL gene is mutated or silenced, leading to the constitutive accumulation of HIF-1α, which activates a battery of \"hypoxic response\" genes, and the CAIX gene is one of its most classic and sensitive downstream targets (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). It is reported that more than 95% ccRCC is CAIX positive, which can serve as a biomarker for diagnosing ccRCC (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Notably, the prognostic value of CAIX expression in ccRCC patients is also unequivocal (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Multiple retrospective studies show that patients with high CAIX expression have significantly longer survival compared to those with low CAIX expression in ccRCC patients (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Courcier J et al. speculated that high CAIX expression may infer the tumor still heavily dependent on the canonical VHL-HIF pathway, while low CAIX expression may reflect the tumor de-differentiation and aggressiveness, leading to more aggressive behaviors and worse prognosis (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Zhang BY et al. concluded from 730 ccRCC patients that low CAIX expression was related to an increased risk of RCC death in the univariate analysis, but it was not an independent prognostic factor after adjusting for tumor grade and coagulative tumor necrosis (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Patients in our study were all CAIX positive in IHC. We clarified that patient with 1\u0026thinsp;+\u0026thinsp;expression of CAIX harbored worse DFS than those with 3\u0026thinsp;+\u0026thinsp;expression, but it was not an independent predictor in the multivariate analysis, in keeping with the previous studies.\u003c/p\u003e \u003cp\u003ePD-L1 is one of the most extensively studied immune checkpoint molecules, and also a promising therapeutic target in RCC. But its prognostic value in RCC patients is discrepant across studies. In the KEYNOTE 426 (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) and the Checkmate 214 (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) studies, patients treated with ICIs possessed better survival compared to those treated with sunitinib, regardless of the PD-L1 status. In our study, PD-L1 expression did not emerge as a significant predictor of prognosis. However, Tamada S et al. depicted that PD-L1 expression may predict the OS and tumor recurrence in high-risk patients with localized RCC (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The prognostic discordance may be partly explained by the inherent limitations of IHC assay for detecting PD-L1 expression, variations in the sample size, difference in the endpoints, and the heterogeneity in treatment regimens. RCC is an immunogenic tumor characterized by extensive T-cell infiltration. However, most of these TILs remain unactivated or functionally impaired, thereby fostering an immunosuppressive tumor microenvironment (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). It is reported that elevated CD8\u003csup\u003e+\u003c/sup\u003e T-cell infiltration is related to unfavorable prognosis in RCC (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), which is opposite with most other cancers. This may be explained in part by the high expressions of CTLA and PD-1 on T cells. We also found that patient with high TILs harbored a higher risk of progression than those with low TILs. But it was not an independent prognostic factor in the multivariate analysis neither. This may be due to our failure to account for the specific subtypes of TILs and their spatial distribution within the tumor tissue.\u003c/p\u003e \u003cp\u003eNo single biomarker is sufficient to predict patient outcome, so we incorporated the metabolic factor of SUVmax and the clinicopathological features of BMI and WHO/ISUP grade, and generated a prognostic model to predict the prognosis of ccRCC patients. It yielded excellent efficacy in predicting patients' 1-year, 3-year, and 5-year DFS. This model may enhance the predictive validity for patient prognosis, so as to aid clinicians in personalized clinical decisions rather than population-level treatment strategies.\u003c/p\u003e \u003cp\u003eAlthough this study is the first to construct a clinically applicable prognostic model by combining metabolic imaging with clinical features, there are still some limitations in this study. Firstly, it is a retrospective, small sized, and single-center study, which inevitably leads to selective bias. Secondly, this study did not include metrics that reflect the overall tumor burden, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Thirdly, while this study identified upregulation of glycolysis-related DEGs in tumor tissues from ccRCC patients with poor prognosis based on TCGA data, this finding remains to be directly validated in our patient cohort by correlating the semi-quantitative parameter of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT with the glycolysis-related DEGs, warranting further investigation in future.\u003c/p\u003e \u003cp\u003eIn conclusion, ccRCC patients with low preoperative BMI (\u0026le;\u0026thinsp;25.45), elevated SUVmax (\u0026gt;\u0026thinsp;4.20), and high WHO/ISUP grade (G3/4) are more likely to develop disease progression after operation. Implementing closer surveillance or aggressive early intervention in routine practice for these patients is recommended to optimize prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved by Ethics Committee of Peking University First Hospital, waiving the need for written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in the current study is available from the corresponding authors on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the National Natu\u0026shy;ral Science Foundation of China (82172052), Beijing Natural Science Foundation (Z210007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: [Meng Liu];\u003c/p\u003e\n\u003cp\u003eMethodology: [Meng Liu], [Yuhui Cao], [Caixia Wu];\u003c/p\u003e\n\u003cp\u003eFormal analysis and investigation:\u0026nbsp;[Yuhui Cao], [Caixia Wu], [Keting Tong], [Yulong Chen], [Jinzhi Chen], [Yuan Gao], [Zijian Fu], [Xin Wang];\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWriting-original draft preparation: [Yuhui Cao], [Caixia Wu];\u003c/p\u003e\n\u003cp\u003eWriting-review and editing: [Meng Liu];\u003c/p\u003e\n\u003cp\u003eFunding acquisition: [Meng Liu];\u003c/p\u003e\n\u003cp\u003eSupervision: [Meng Liu].\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSchiavoni V, Campagna R, Pozzi V, Cecati M, Milanese G, Sartini D, et al. 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Hypoxia Marker GLUT-1 (Glucose Transporter 1) is an Independent Prognostic Factor for Survival in Bladder Cancer Patients Treated with Radical Cystectomy. Bladder Cancer. 2016;2(1):101-109.\u003c/li\u003e\n\u003cli\u003eWang AX, Tian T, Liu LB, Yang F, He HY, Zhou LQ. TFEB Rearranged Renal Cell Carcinoma: Pathological and Molecular Characterization of 10 Cases, with Novel Clinical Implications: A Single Center 10-Year Experience. Biomedicines. 2023;11(2).\u003c/li\u003e\n\u003cli\u003ePeng D, He ZS, Li XS, Tang Q, Zhang L, Yang KW, et al. Prognostic Value of Inflammatory and Nutritional Scores in Renal Cell Carcinoma After Nephrectomy. Clinical genitourinary cancer. 2017;15(5):582-590.\u003c/li\u003e\n\u003cli\u003eNakaigawa N, Kondo K, Tateishi U, Minamimoto R, Kaneta T, Namura K, et al. FDG PET/CT as a prognostic biomarker in the era of molecular-targeting therapies: max SUVmax predicts survival of patients with advanced renal cell carcinoma. BMC Cancer. 2016;16:67.\u003c/li\u003e\n\u003cli\u003eWang X, Li R, Chen R, Huang G, Zhou X, Liu J. Prognostic Values of TIGAR Expression and (18)F-FDG PET/CT in Clear Cell Renal Cell Carcinoma. J Cancer. 2020;11(1):1-8.\u003c/li\u003e\n\u003cli\u003eNakajima R, Matsuo Y, Kondo T, Abe K, Sakai S. Prognostic Value of Metabolic Tumor Volume and Total Lesion Glycolysis on Preoperative \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT in Patients With Renal Cell Carcinoma. Clin Nucl Med. 2017;42(4):e177-e82.\u003c/li\u003e\n\u003cli\u003edi Meo NA, Lasorsa F, Rutigliano M, Loizzo D, Ferro M, Stella A, et al. Renal Cell Carcinoma as a Metabolic Disease: An Update on Main Pathways, Potential Biomarkers, and Therapeutic Targets. Int J Mol Sci. 2022;23(22).\u003c/li\u003e\n\u003cli\u003eLi B, Qiu B, Lee DS, Walton ZE, Ochocki JD, Mathew LK, et al. Fructose-1,6-bisphosphatase opposes renal carcinoma progression. Nature. 2014;513(7517):251-255.\u003c/li\u003e\n\u003cli\u003eSingh C, Jin B, Shrestha N, Markhard AL, Panda A, Calvo SE, et al. ChREBP is activated by reductive stress and mediates GCKR-associated metabolic traits. Cell Metab. 2024;36(1):144-58.e7.\u003c/li\u003e\n\u003cli\u003eDi SC, Chen WJ, Yang W, Zhang XM, Dong KQ, Tian YJ, et al. DEPDC1 as a metabolic target regulates glycolysis in renal cell carcinoma through AKT/mTOR/HIF1\u0026alpha; pathway. Cell Death Dis. 2024;15(7):533.\u003c/li\u003e\n\u003cli\u003eGao S, Yan L, Zhang H, Fan X, Jiao X, Shao F. Identification of a Metastasis-Associated Gene Signature of Clear Cell Renal Cell Carcinoma. Front Genet. 2020;11:603455.\u003c/li\u003e\n\u003cli\u003eFang K, Gong M, Liu D, Liang S, Li Y, Sang W, et al. FOXM1/KIF20A axis promotes clear cell renal cell carcinoma progression via regulating EMT signaling and affects immunotherapy response. Heliyon. 2023;9(12):e22734.\u003c/li\u003e\n\u003cli\u003eBetsunoh H, Sakamoto S, Kaji Y, Nukui A, Kobayashi M, Yashi M, et al. Clinical Significance of (18)F-fluorodeoxyglucose and Glucose Transporter 1 mRNA in Clear Cell Renal Cell Carcinoma. Anticancer Res. 2021;41(10):5179-5188.\u003c/li\u003e\n\u003cli\u003eSimati S, Kokkinos A, Dalamaga M, Argyrakopoulou G. Obesity Paradox: Fact or Fiction? Curr Obes Rep. 2023;12(2):75-85.\u003c/li\u003e\n\u003cli\u003eTurco F, Tucci M, Di Stefano RF, Samuelly A, Bungaro M, Audisio M, et al. Renal cell carcinoma (RCC): fatter is better? A review on the role of obesity in RCC. Endocr Relat Cancer. 2021;28(7):R207-r16.\u003c/li\u003e\n\u003cli\u003eVenkatesh N, Martini A, McQuade JL, Msaouel P, Hahn AW. Obesity and renal cell carcinoma: Biological mechanisms and perspectives. Semin Cancer Biol. 2023;94:21-33.\u003c/li\u003e\n\u003cli\u003eLjungberg B, Albiges L, Abu-Ghanem Y, Bedke J, Capitanio U, Dabestani S, et al. European Association of Urology Guidelines on Renal Cell Carcinoma: The 2022 Update. Eur Urol. 2022;82(4):399-410.\u003c/li\u003e\n\u003cli\u003eCourcier J, de la Taille A, Nourieh M, Leguerney I, Lassau N, Ingels A. Carbonic Anhydrase IX in Renal Cell Carcinoma, Implications for Disease Management. Int J Mol Sci. 2020;21(19).\u003c/li\u003e\n\u003cli\u003eVerhoeff SR, van Es SC, Boon E, van Helden E, Angus L, Elias SG, et al. Lesion detection by [(89)Zr]Zr-DFO-girentuximab and [(18)F]FDG-PET/CT in patients with newly diagnosed metastatic renal cell carcinoma. Eur J Nucl Med Mol Imaging. 2019;46(9):1931-1939.\u003c/li\u003e\n\u003cli\u003eChamie K, Donin NM, Kl\u0026ouml;pfer P, Bevan P, Fall B, Wilhelm O, et al. Adjuvant Weekly Girentuximab Following Nephrectomy for High-Risk Renal Cell Carcinoma: The ARISER Randomized Clinical Trial. JAMA Oncol. 2017;3(7):913-920.\u003c/li\u003e\n\u003cli\u003eZhang BY, Thompson RH, Lohse CM, Dronca RS, Cheville JC, Kwon ED, et al. Carbonic anhydrase IX (CAIX) is not an independent predictor of outcome in patients with clear cell renal cell carcinoma (ccRCC) after long-term follow-up. BJU Int. 2013;111(7):1046-1053.\u003c/li\u003e\n\u003cli\u003eIngels A, Hew M, Algaba F, de Boer OJ, van Moorselaar RJ, Horenblas S, et al. Vimentin over-expression and carbonic anhydrase IX under-expression are independent predictors of recurrence, specific and overall survival in non-metastatic clear-cell renal carcinoma: a validation study. World J Urol. 2017;35(1):81-87.\u003c/li\u003e\n\u003cli\u003eRini BI, Plimack ER, Stus V, Gafanov R, Hawkins R, Nosov D, et al. Pembrolizumab plus Axitinib versus Sunitinib for Advanced Renal-Cell Carcinoma. N Engl J Med. 2019;380(12):1116-27.\u003c/li\u003e\n\u003cli\u003eMotzer RJ, Rini BI, McDermott DF, Ar\u0026eacute;n Frontera O, Hammers HJ, Carducci MA, et al. Nivolumab plus ipilimumab versus sunitinib in first-line treatment for advanced renal cell carcinoma: extended follow-up of efficacy and safety results from a randomised, controlled, phase 3 trial. Lancet Oncol. 2019;20(10):1370-1385.\u003c/li\u003e\n\u003cli\u003eTamada S, Nozawa M, Ohba K, Mizuno R, Takamoto A, Ohe C, et al. Prognostic value of PD-L1 expression in recurrent renal cell carcinoma after nephrectomy: a secondary analysis of the ARCHERY study. Int J Clin Oncol. 2023;28(2):289-298.\u003c/li\u003e\n\u003cli\u003eQi Y, Xia Y, Lin Z, Qu Y, Qi Y, Chen Y, et al. Tumor-infiltrating CD39(+)CD8(+) T cells determine poor prognosis and immune evasion in clear cell renal cell carcinoma patients. Cancer Immunol Immunother. 2020;69(8):1565-1576.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"clear cell renal cell carcinoma (ccRCC), 18F-FDG PET/CT, maximum standardized uptake value (SUVmax), prognosis, body mass index (BMI), World Health Organization (WHO)/the International Society of Urological Pathology (ISUP) grade","lastPublishedDoi":"10.21203/rs.3.rs-8559986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8559986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Accurate risk stratification is critical for the management of patients with clear cell renal cell carcinoma (ccRCC). This study aims to explore the predictive value of preoperative \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT metabolic parameter combined with clinicopathological features for postoperative disease-free survival (DFS) in ccRCC patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Newly diagnosed ccRCC patients who underwent \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT prior to surgery were retrospectively reviewed. Maximum standardized uptake value (SUVmax) was acquired from the preoperative \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT. Clinicopathological features, including the tumor node metastasis (TNM) stage, body mass index (BMI), hemoglobin (Hb), World Health Organization (WHO)/the International Society of Urological Pathology (ISUP) grade, primary tumor size, carbonic anhydrase IX (CAIX), tumor-infiltrating lymphocytes (TILs), etc., were also obtained. Cox proportional hazards analyses were executed to identify prognostic factors for DFS. The predictive efficacy of the model was assessed by the area under the curve (AUC). Glycolysis genes in ccRCC were analyzed using the TCGA database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: 59 ccRCC patients were included and 26 (44.1%) cases developed disease progression. On univariate analysis, BMI (≤ 25.45 kg/m2), Hb (≤ 130 g/L), clinical symptoms, TNM stage (III/ IV), SUVmax (\u0026gt; 4.20), primary tumor size (\u0026gt; 5.75 cm), WHO/ISUP grade (G3/4), CAIX expression (1+), and high TILs were significant prognostic factors of inferior DFS (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). On multivariate analysis, SUVmax (\u003cem\u003eP\u003c/em\u003e=0.026, HR=4.248; 95%CI: 1.184-15.239), BMI (\u003cem\u003eP\u003c/em\u003e=0.002; HR=0.233; 95%CI: 0.094-0.580), and WHO/ISUP grade (\u003cem\u003eP\u003c/em\u003e=0.005, HR=5.888; 95%CI: 1.689-20.524) still maintained independency in prognosis prediction. The prognostic model composed of the above three independent predictors achieved excellent predictive efficacy by virtue of a C-index of 0.89, with AUC values of 0.922, 0.919, and 0.899 at 1-, 3-, and 5-year, respectively. Moreover, the glycolysis-related genes\u003cem\u003e \u003c/em\u003eof\u003cem\u003e GCKR\u003c/em\u003e and \u003cem\u003eGCK\u003c/em\u003e were obviously upregulated in the disease-progression group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e ccRCC patients with low preoperative BMI, elevated SUVmax, and high WHO/ISUP grade are more likely to develop disease progression after operation. Implementing closer surveillance or aggressive early intervention for these patients is recommended to optimize prognosis.\u003c/p\u003e","manuscriptTitle":"A prognostic predicting model integrating preoperative 18F-FDG PET/CT metabolic parameter and clinicopathological biomarkers for patients with ccRCC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 09:06:53","doi":"10.21203/rs.3.rs-8559986/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"553659f8-f7dd-4efb-8709-0cfd017dc77e","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T13:38:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 09:06:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8559986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8559986","identity":"rs-8559986","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}