Prediction of Ki-67 expression in bladder cancer based on CT radiomics nomogram.

Objectives: This study aimed to create and validate a radiomics nomogram for non-invasive preoperative Ki-67 expression level prediction in patients with bladder cancer (BCa) using contrast-enhanced CT radiomics features. Methods: A retrospective analysis of 135 patients was conducted, 79 of whom had high levels of Ki-67 expression and 56 of whom had low levels. For the dimensionality reduction analysis, the best features were chosen using the least absolute shrinkage selection operator and one-way analysis of variance. Then, a radiomics nomogram was created using multiple logistic regression analysis based on radiomics features and clinical independent risk factors. The performance of the model was assessed using the Akaike information criterion (AIC) value, the area under the curve (AUC) value, accuracy, sensitivity, and specificity. The clinical usefulness of the model was assessed using decision curve analysis (DCA). Results: Finally, to establish a radiomics nomogram, the best 5 features were chosen and integrated with the independent clinical risk factors (T stage) and Rad-score. This radiomics nomogram demonstrated significant correction and discriminating performance in both the training and validation sets, with an AUC of 0.836 and 0.887, respectively. This radiomics nomogram had the lowest AIC value (AIC = 103.16), which was considered to be the best model. When compared to clinical factor model and radiomics signature, DCA demonstrated the more value of the radiomics nomogram. Conclusion: Enhanced CT-based radiomics nomogram can better predict Ki-67 expression in BCa patients and can be used for prognosis assessment and clinical decision making.

Analysis of the origin and invasion of prostate cancer from autopsy and radical prostatectomy specimens.

Background: Without a pseudocapsule, prostate cancer is invasive in volume growth and has some regularity in spatial distribution. Our study aims to explore the specific origin location, invasive characteristics, and morphology of prostate cancer. Methods: Ninety-eight clinical specimens with tumor volume equal to or less than one-third of the organ volume and 111 autopsy specimens were retrospectively analyzed. The origin location and invasion of prostate cancer in four horizontal quadrants and 11 vertical slides were demonstrated. In addition, the median maximum anteroposterior, left-right, horizontal, and vertical diameters of lesions were compared, and the spatial morphology of lesions was described. Results: There were 335 lesions in the autopsy and clinical specimens. There was no significant difference in the distribution of lesions confined to the horizontal quarter quadrant (P=0.064). The number of lesions with a single positive slide above the apex 0.5-1.4 cm was 75 (49.7%). No significant difference was found when compared with the maximum vertical and horizontal diameters (P=0.421). However, the maximum left-right and horizontal diameters were longer than the maximum anteroposterior diameter (P=0.046 and P<0.001). The number of lesions with a tumor area that decreased from the center to both sides was 85 (46.2%) and decreased from the center to one side was 81 (44.0%). Conclusions: Approximately 50% of the lesions originated from the apex above 0.5-1.4 cm. The invasive tendency of prostate cancer was consistent in the horizontal and vertical dimensions but less so in the anteroposterior direction. About ninety percent of lesions with tumor area decreased from the center to both sides or one side.

Comparisons of mpMRI, 68Ga-PSMA PET/CT and mpMRI combined with 68Ga-PSMA PET/CT in diagnosing prostate cancer based on tumor detection, localization and staging.

PURPOSE: To evaluate the diagnostic ability of mpMRI, 68Ga-PSMA PET/CT and mpMRI combined with 68Ga-PSMA PET/CT in detecting and localizing lesions, and further clarify the accuracy of these examinations in tumor staging. METHODS: Seventy patients who underwent mpMRI, 68Ga-PSMA PET/CT and radical prostatectomy were enrolled. The abilities to detect index and clinically significant lesions by three examinations were compared. We further evaluated the ability of these examinations to localize lesions to the superior, inferior, anterior, posterior, left and right halves of the prostate and analyzed their accuracy in local and lymph node staging. RESULTS: There were no significant differences among mpMRI, 68Ga-PSMA PET/CT and mpMRI combined with 68Ga-PSMA PET/CT in their ability to detect index (p = 0.48, p = 0.23 and p = 0.07) and clinically significant lesions (p = 0.30, p = 0.29 and p = 0.06) or to localize lesions in six half divisions of the prostate. With postoperative pathology as reference, both mpMRI (p = 0.10) and mpMRI combined with 68Ga-PSMA PET/CT (p = 0.10) can accurately assess the local staging of prostate cancer. However, 68Ga-PSMA PET/CT underestimates the local staging of prostate cancer (p < 0.01). Regarding lymph node staging, the three types of examination showed no significant differences compared to postoperative pathology (p = 0.63, p = 0.51 and p = 0.14). CONCLUSIONS: With postoperative pathology as reference, 68Ga-PSMA PET/CT underestimates the local tumor staging. MpMRI combined with 68Ga-PSMA PET/CT has no obvious advantages in detecting, localizing or staging prostate cancer compared with mpMRI.

Upregulation of BMP1 through ncRNAs correlates with adverse outcomes and immune infiltration in clear cell renal cell carcinoma.

BACKGROUND: Renal cell carcinoma (RCC) accounts for approximately 2-3% of all adult malignancies. Clear cell renal cell carcinoma (ccRCC), which comprises 70-80% of all RCC cases, is the most common histological subtype. METHODS: ccRCC transcriptome data and clinical information were downloaded from the TCGA database. We used the TCGA and GEPIA databases to analyze relative expression of BMP1 in various types of human cancer. GEPIA was used to perform survival analysis for BMP1 in various cancer types. Upstream binding miRNAs of BMP1 were obtained through several important target gene prediction tools. StarBase was used to predict candidate miRNAs that may bind to BMP1 and candidate lncRNAs that may bind to hsa-miR-532-3p. We analyzed the association between expression of BMP1 and immune cell infiltration levels in ccRCC using the TIMER website. The relationship between BMP1 expression levels and immune checkpoint expression levels was also investigated. RESULTS: BMP1 was upregulated in GBM, HNSC, KIRC, KIRP and STAD and downregulated in KICH and PRAD. Combined with OS and DFS, BMP1 can be used as a biomarker for poor prognosis among patients with KIRC. Through expression analysis, survival analysis and correlation analysis, LINC00685, SLC16A1-AS1, PVT1, VPS9D1-AS1, SNHG15 and the CCDC18-AS1/hsa-miR-532-3p/BMP1 axis were established as the most potential upstream ncRNA-related pathways of BMP1 in ccRCC. Furthermore, we found that BMP1 levels correlated significantly positively with tumor immune cell infiltration, biomarkers of immune cells, and immune checkpoint expression. CONCLUSION: Our results demonstrate that ncRNA-mediated high expression of BMP1 is associated with poor prognosis and tumor immune infiltration in ccRCC.

A CT-based radiomics nomogram for differentiation of benign and malignant small renal masses (≤4 cm).

RATIONALE AND OBJECTIVES: Based on radiomics signature and clinical data, to develop and verify a radiomics nomogram for preoperative distinguish between benign and malignant of small renal masses (SRM). MATERIALS AND METHODS: One hundred and fifty-six patients with malignant (n = 92) and benign (n = 64) SRM were divided into the following three categories: category A, typical angiomyolipoma (AML) with visible fat; category B, benign SRM without visible fat, including fat-poor angiomyolipoma (fp-AML), and other rare benign renal tumors; category C, malignant renal tumors. At the same time, one hundred and fifty-six patients included in the study were divided into the training set (n = 108) and test set (n = 48). Respectively from corticomedullary phase (CP), nephrogram phase (NP) and excretory phase (EP) CT images to extract the radiomics features, and the optimal features were screened to establish the logistic regression model and decision tree model, and computed the radiomics score (Rad-score). Demographics and CT findings were evaluated and statistically significant factors were selected to construct a clinical factors model. The radiomics nomogram was established by merging Rad-score and selected clinical factors. The Akaike information criterion (AIC) values and the area under the curve (AUC) were used to compare model discriminant performance, and decision curve analysis (DCA) was used to assess clinical usefulness. RESULTS: Seven, fifteen, nineteen, and seventeen distinguishing features were obtained in the CP, NP, EP, and three-phase joint, respectively, and the logistic regression and decision tree models were built based on this features. In the training set, the logistic regression model works better than the decision tree model for distinguishing categories A and B from category C, with the AUC of CP, NP, EP and three-phase joint were 0.868, 0.906, 0.937 and 0.975, respectively. The radiomics nomogram constructed based on the three-phase joint Rad-score and selected clinical factor performed well on the training set (AUC, 0.988; 95% CI, 0.974-1.000) for differentiation of categories A and B from category C. In the test set, the AUC of clinical factors model, radiomics signature and radiomics nomogram for discriminating categories A and B from category C were 0.814, 0.954 and 0.968, respectively; for the identification of category A from category C, the AUC of the three models were 0.789, 0.979, 0.985, respectively; for discriminating category B from category C, the AUC of the three models were 0.853, 0.915, 0.946, respectively. The radiomics nomogram had better discriminative than the clinical factors model in both training and test sets (P < 0.05). The radiomics nomogram (AIC = 40.222) with the lowest AIC value was considered the best model compared with that of the clinical factors model (AIC = 106.814) and the radiomics signature (AIC = 44.224). The DCA showed that the radiomics nomogram have better clinical utility than the clinical factors model and radiomics signature. CONCLUSIONS: The logistic regression model has better discriminative performance than the decision tree model, and the radiomics nomogram based on Rad-score of three-phase joint and clinical factors has a good predictive effect in differentiating benign from malignant of SRM, which may help clinicians develop accurate and individualized treatment strategies.

CT-based deep learning radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer.

Objectives: Although the preoperative assessment of whether a bladder cancer (BCa) indicates muscular invasion is crucial for adequate treatment, there currently exist some challenges involved in preoperative diagnosis of BCa with muscular invasion. The aim of this study was to construct deep learning radiomic signature (DLRS) for preoperative predicting the muscle invasion status of BCa. Methods: A retrospective review covering 173 patients revealed 43 with pathologically proven muscle-invasive bladder cancer (MIBC) and 130 with non-muscle-invasive bladder cancer (non- MIBC). A total of 129 patients were randomly assigned to the training cohort and 44 to the test cohort. The Pearson correlation coefficient combined with the least absolute shrinkage and selection operator (LASSO) was utilized to reduce radiomic redundancy. To decrease the dimension of deep learning features, Principal Component Analysis (PCA) was adopted. Six machine learning classifiers were finally constructed based on deep learning radiomics features, which were adopted to predict the muscle invasion status of bladder cancer. The area under the curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of the model. Results: According to the comparison, DLRS-based models performed the best in predicting muscle violation status, with MLP (Train AUC: 0.973260 (95% CI 0.9488-0.9978) and Test AUC: 0.884298 (95% CI 0.7831-0.9855)) outperforming the other models. In the test cohort, the sensitivity, specificity and accuracy of the MLP model were 0.91 (95% CI 0.551-0.873), 0.78 (95% CI 0.594-0.863) and 0.58 (95% CI 0.729-0.827), respectively. DCA indicated that the MLP model showed better clinical utility than Radiomics-only model, which was demonstrated by the decision curve analysis. Conclusions: A deep radiomics model constructed with CT images can accurately predict the muscle invasion status of bladder cancer.

Biparametric MRI-based radiomics classifiers for the detection of prostate cancer in patients with PSA serum levels of 4∼10 ng/mL.

Purpose: To investigate the predictive performance of the combined model by integrating clinical variables and radiomic features for the accurate detection of prostate cancer (PCa) in patients with prostate-specific antigen (PSA) serum levels of 4-10 ng/mL. Methods: A retrospective study of 136 males (mean age, 67.3 ± 8.4 years) with Prostate Imaging-Reporting and Data System (PI-RADS) v2.1 category ≤3 lesions and PSA serum levels of 4-10 ng/mL were performed. All patients underwent multiparametric MRI at 3.0T and transrectal ultrasound-guided systematic prostate biopsy in their clinical workup. Radiomic features were extracted from axial T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) maps of each patient using PyRadiomics. Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were implemented to identify the most significant radiomic features. Independent clinic-radiological factors were identified via univariate and multivariate regression analyses. Seven machine-learning algorithms were compared to construct a single-layered radiomic score (ie, radscore) and multivariate regression analysis was applied to construct the fusion radscore. Finally, the radiomic nomogram was further developed by integrating useful clinic-radiological factors and fusion radscore using multivariate regression analysis. The discriminative power of the nomogram was evaluated by area under the curve (AUC), DeLong test, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results: The transitional zone-specific antigen density was identified as the only independent clinic-radiological factor, which yielded an AUC of 0.592 (95% confidence interval [CI]: 0.527-0.657). The ADC radscore based on six features and Naive Bayes achieved an AUC of 0.779 (95%CI: 0.730-0.828); the T2WI radscore based on 13 features and Support Vector Machine yielded an AUC of 0.808 (95%CI: 0.761-0.855). The fusion radscore obtained an improved AUC of 0.844 (95%CI: 0.801-0.887), which was higher than the single-layered radscores (both P<0.05). The radiomic nomogram achieved the highest value among all models (all P<0.05), with an AUC of 0.872 (95%CI: 0.835-0.909). Calibration curve showed good agreement and DCA together with CIC confirmed the clinical benefits of the radiomic nomogram. Conclusion: The radiomic nomogram holds the potential for accurate and noninvasive identification of PCa in patients with PI-RADS ≤3 lesions and PSA of 4-10 ng/mL, which could reduce unnecessary biopsy.

OIP5 Is a Novel Prognostic Biomarker in Clear Cell Renal Cell Cancer Correlating With Immune Infiltrates.

Opa interacting protein 5 (OIP5), overexpressed in some types of human cancers, has been reported to be associated with the carcinogenesis of human cancer. However, its contribution to cancer immunity remains unknown. Furthermore, the relationship between OIP5 and cancer immunity remains uncertain. In our research, we explored the different expression of OIP5 between 539 ccRCC and 72 normal renal tissues base on TCGA data set. We analyzed the associations between OIP5 expression with ccRCC progression and survival. Next, we compared immune cell profiles in cancer tissues and normal tissues in the Cancer Genome Atlas (TCGA) ccRCC cohort. We found that the level of immune cell infiltration was correlated with the copy number of OIP5 gene in ccRCC. The effect of OIP5 on immune activity was verified by Gene Set Enrichment Analysis of RNA-seq data from 32 ccRCC cell lines in the public database. Moreover, a pathway enrichment analysis of 49 OIP5-associated immunomodulators demonstrated the involvement of the T cell receptor signaling pathway, the JAK-STAT signaling pathway, the NF-kappa B signaling pathway and the primary immunodeficiency pathway. In addition, using OIP5-associated immunomodulators, we constructed multiple-gene risk prediction signatures using the Cox regression model. Our results provided insights into the role of OIP5 in tumor immunity and revealed that OIP5 may be a potential immunotherapeutic target for ccRCC. Designated immune signature is a promising prognostic biomarker in ccRCC.

Enhanced Expression of CNTD2/CCNP Predicts Poor Prognosis in Bladder Cancer Based on the GSE13507.

Bladder cancer is one of the most common urogenital malignancies in the world, and there are no adequate prognostic indicators. CNTD2 is one of the atypical cyclins, which may be related to the cell cycle and even the development of cancers. Early studies have shown that CNTD2 is closely related to the occurrence and development of many malignant tumors. However, the mechanism of CNTD2 in bladder cancer has not been reported. In our research, we explored the different expressions of CNTD2 between 411 bladder cancers and 19 normal bladder tissues based on the TCGA dataset. CNTD2-related signaling pathways were identified through the GSEA. We analyzed the associations of CNTD2 expression and bladder cancer progression and survival using GSE13507. Compared with 19 cases of normal bladder tissue, CNTD2 gene expression was increased in 411 cases of bladder cancer. The high expression of CNTD2 strongly correlated with grade (P < 0.0001), T classification (P = 0.0001), N classification (P = 0.00011), M classification (P = 0.044), age (P = 0.027), and gender (P = 0.0012). Bladder cancer patients with high CNTD2 expression had shorter overall survival (P < 0.001). In the meantime, univariate and multivariate analyses showed that the increased expression of CNTD2 was an independent factor for poor prognosis in bladder cancer patients (P < 0.001 and P < 0.001, respectively). CNTD2 expression is closely related to bladder cancer progression, and the high expression of CNTD2 may be an adverse biomarker in bladder cancer patients.

The characteristics and spatial distributions of prostate cancer in autopsy specimens.

BACKGROUND: The characteristics of prostate cancer on autopsy and early-stage prostate cancer are identical. Using autopsy specimens, we analysed prostate cancer characteristics and clarified the spatial distributions of lesions. METHOD: We obtained prostate specimens from Chinese donors without a prostate cancer diagnosis and analyzed prostate cancer pathological characteristics on autopsy by whole-mount sampling. We determined the distributions of lesions in horizontal and vertical dimensions. The horizontal dimension included four horizontal quadrants (left-anterior, left-posterior, right-anterior, and right-posterior quadrants), the peripheral zone, and the transition zone. RESULT: The overall positive rate of prostate cancer among 113 specimens was 35.4%. There were 73 lesions in 40 prostates with prostate cancer. The positive rates of lesions in the left-anterior, left-posterior, right-anterior, and right-posterior quadrants were 24.7% (18/73), 27.4% (20/73), 26.0% (19/73), and 21.9% (16/73), respectively. The positive rate of prostate cancer was 74% in the areas between the apex above 0.5-0.8 cm and the middle slice. There were 22 (30.1%) and 51 (69.9%) lesions in the superior and inferior half of the prostate. There were no significant differences in the median volume and Gleason grade group between the superior and inferior half (p = .876 and p = .228). CONCLUSION: In the horizontal dimension, the positive rate of prostate cancer was consistent in the four quadrants. Prostate cancer mainly originated from the areas between the apex above 0.5-0.8 cm and the middle slice. Compared with the superior half, the inferior half of the prostate had a higher positive rate but the same lesion characteristics.

Ginsenoside Rh2 Inhibits Angiogenesis in Prostate Cancer by Targeting CNNM1.

To explore the molecular mechanism by which ginsenoside Rh2 (G-Rh2) inhibits prostate cancer by regulating vascular growth. Different concentrations of G-Rh2 with three prostate cancer cell lines (LNCaP, PC3 and DU145) were transplanted in nude mice, and tumor mass volume was measured over time. LNCaP, PC3 and DU145 were co-cultured with vascular endothelial cells to determine the optimal concentration of G-Rh2 by MTT assay. LNCaP, PC3 and DU145 were cultured under the selected concentration (0, 0.01, 0.05, 0.1, 0.5 and 1 mg/mL) of G-Rh2, and the expression levels of CD31, VEGF, PDGF and CNNM1 detected by qRT-PCR and western blot. The expression pattern of CD31 was detected in CNNM1 overexpressed and knockout LNCaP, PC3 and DU145 cells under G-Rh2. G-Rh2 significantly inhibited the growth of all three prostate cancer cell lines in the dorsum of nude mice (P <0.05), and the increment rate of vascular endothelial cells co-cultured with LNCaP, PC3 and DU145 (P <0.05). The expression of CD31, VEGF, PDGF and CNNM1 genes in LNCaP, PC3 and DU145 cells was inhibited by G-Rh2. Overexpression of CNNM1 reversed the inhibitory effect of G-Rh2 on the expression of CD31 in these cells (P <0.05), while the function of knockout of CNNM1 and the inhibitory effect of G-Rh2 appeared to be similar (P <0.05). In conclusion, G-Rh2 inhibited prostate cancer growth by inhibiting its angiogenesis through decreasing the expression of CNNM1 in the cancer cells.

Opposing roles of ICAT and Wnt/ß-catenin signaling in NSC67657-induced monocytic differentiation.

NSC67657 is a new steroid drug that induces monocytic differentiation of acute myeloid leukemia cells. Here, we demonstrate that NSC67657 has opposing effects on expression of downstream targets of inhibitor of ß-catenin and TCF (ICAT) and Wnt signaling in HL60 cells. ICAT binds to ß-catenin, and this interaction is further increased in NSC67657-differentiated cells. ICAT overexpression decreases expression of Wnt downstream targets and increases sensitivity of HL60 cells to NSC67657, while ICAT silencing increases Wnt signaling and delays the NSC67657-induced cell differentiation. In addition, pharmacological inhibition of Wnt/ß-catenin signaling increases the NSC67657-induced cell differentiation, while activation of Wnt/ß-catenin signaling inhibits the differentiation, indicating Wnt/ß-catenin signaling inhibits NSC67657-induced monocytic differentiation of HL60 cells. Our data demonstrate the opposing roles of ICAT and Wnt signaling in the NSC67657-induced monocytic differentiation, and suggest that ICAT and Wnt signaling may serve as therapeutic targets for leukemia chemotherapy.

miR­30c suppresses prostate cancer survival by targeting the ASF/SF2 splicing factor oncoprotein.

Our previous study revealed that microRNA (miR) ­30c represents a potential tumor suppressor gene, the expression of which is associated with decreased oncogenic potential in prostate cancer (PCa) cell lines. However, the functional role and underlying mechanisms of miR­30c in PCa remain to be fully elucidated. Reverse transcription­quantitative polymerase chain reaction and immunohistochemical analysis were used to detect the expression levels of alternative splicing factor/splicing factor 2 (ASF/SF2) in PCa tissues. A luciferase reporter assay was used to investigate whether ASF/SF2 may be a direct target gene of miR­30c. In addition, the effects of miR­30c on the proliferation and apoptosis of PCa cell lines were examined, following transfection with miR­30c mimics. Furthermore, correlation analysis was performed to investigate the relationship between the expression of miR­30c and ASF/SF2 and various clinicopathological parameters of patients with PCa. The present results demonstrated that PCa tissues exhibited higher levels of alternative splicing factor/splicing factor 2 (ASF/SF2), compared with normal tissues. In addition, miR­30c was revealed to targete the 3'­untranslated region of the ASF/SF2 gene, causing a decrease in the mRNA and protein levels of ASF/SF2. Furthermore, miR­30c was reported to decrease cell proliferation, increase the percentage of cells in the G1 cell cycle phase, and promote apoptosis through the inhibition of ASF/SF2. Following correlation analysis using patient samples, the expression of ASF/SF2 was revealed to be tightly correlated with the pathological stage of PCa and biochemical recurrence (BCR). In addition, patients with PCa exhibiting low expression levels of miR­30c and high expression of ASF/SF2 had significantly lower rates of BCR­free survival. In conclusion, the present study suggested that the tumor suppressor miR­30c may be involved in PCa tumorigenesis, possibly via targeting ASF/SF2. The combined analysis of the expression of ASF/SF2 and miR­30c may be a valuable tool for early prediction of BCR in patients with PCa following radical prostatectomy.

Associations of Prostate-Specific Antigen, Prostate Carcinoma Tissue Gleason Score, and Androgen Receptor Expression with Bone Metastasis in Patients with Prostate Carcinoma.

BACKGROUND Prostate carcinoma (PCa) is often not diagnosed until advanced disease with bone metastasis. Predictive factors for bone metastasis are required to improve patient outcomes. The study aimed to analyze the factors associated with bone metastases in newly diagnosed patients with PCa. MATERIAL AND METHODS This was a retrospective study of 80 patients newly diagnosed with PCa by pathological examination between January 2012 and December 2014. Bone metastases were diagnosed by positron emission computed tomography. Clinical data, serological laboratory results, and pathological examination results were collected. RESULTS Among the 80 patients, 45 (56%) had bone metastases. Age, serum alkaline phosphatase, prostate-specific antigen (PSA), erythrocyte sedimentation rate, PCa tissue Gleason score, androgen receptor (AR) expression, and Ki-67 expression were higher in patients with bone metastasis compared with those without (all P<0.05). Multivariate logistic regression showed that PSA (OR: 1.005; 95%CI: 1.001-1.010; P=0.016), Gleason score (OR: 4.095; 95%CI: 1.592-10.529; P=0.003), and AR expression (OR: 14.023; 95%CI: 3.531-55.6981; P=0.005) were independently associated with bone metastases. Cut-off values for PSA, Gleason score, and AR expression were 67.1 ng/ml (sensitivity: 55.6%; specificity: 97.1%), 7.5 (sensitivity: 75.6%; specificity: 82.9%), and 2.5 (sensitivity: 84.0%; specificity: 91.4%), respectively. CONCLUSIONS PSA, Gleason score, and AR expression in PCa tissues were independently associated with PCa bone metastases. These results could help identifying patients with PCa at high risk of bone metastases.

Vascular endothelial growth factor gene polymorphisms and the risk of renal cell carcinoma: Evidence from eight case-control studies.

BACKGROUND: Vascular endothelial growth factor (VEGF) protein plays important role in renal cell carcinoma (RCC) development and progression. VEGF gene polymorphisms can alter the protein concentrations and might be associated with renal cell carcinoma risk. However, the results of studies investigating the association between VEGF polymorphisms and renal cell carcinoma risk are inconsistent. Thus, a meta-analysis was performed. METHODS: We selected eligible studies via electronic searches. Only high-quality studies were included based on specific inclusion criteria and the Newcastle-Ottawa Scale (NOS). RESULTS: Eight studies primarily focusing on seven polymorphisms were included in our meta-analysis. Our results showed dramatically high risks for renal cell carcinoma were found regarding most genetic models and alleles of the +936C/T polymorphism (except CT vs. CC). In addition, significant increased renal cell carcinoma risks were found regarding all genetic models and alleles of the -2578C/A polymorphism. However, no significant associations were found between renal cell carcinoma risk and the +1612G/A, -460T/C, -634G/C, -405G/C or -1154G/A polymorphisms. CONCLUSIONS: Our meta-analysis indicates that the +936C/T and -2578C/A polymorphisms of VEGF are associated with an increased risk for renal cell carcinoma. Additional rigorous analytical studies are needed to confirm our results.