Kidney Renal Cell Carcinoma Research Articles

Low Expression Levels of SLC22A12 Indicates a Poor Prognosis and Progresses Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) accounts for approximately 4/5 of all kidney cancers. Accumulation of minor changes in the cellular homeostasis may be one cause of ccRCC. Therefore, we downloaded the RNA sequencing and survival data of the kidney renal cell carcinoma (KIRC) cohort from the Cancer Genome Atlas (TCGA) database. After the univariate and multivariate Cox regression analyses, 19 kidney-specific differentially expressed genes (DEGs) were found. Solute Carrier Family 22 Member 12 (SLC22A12) resulted in an independent prognostic predictor for both overall survival (OS) and disease-free survival (DFS). SLC22A12 expression was lower in tumoral tissue compared to normal tissue. Moreover, patients in the SLC22A12 low expression group had a higher pathological stage and worse survival than the high expression group. Additionally, qRT-PCR assay, immunoblotting test (IBT), and immunohistochemical (IHC) analyses of cancer tissues/cells and the corresponding normal controls verified that SLC22A12 is downregulated in ccRCC. Receiver operator characteristic (ROC) curves showed that the low expression level of SLC22A12 could be a good diagnostic marker for ccRCC (AUC=0.7258; p <0.0001). Gene set enrichment analysis (GSEA) showed that SLC22A12 expression levels are related to metabolism, cell cycle, and tumor-related signaling pathways. GO and KEGG analyses revealed that SLC22A12 transports multiple organic compounds, ions, and hormones and participates in the extracellular structure organization. Furthermore, SLC22A12 over-expression in vitro inhibited the proliferation, migration, and invasion of renal cancer cells by regulating PI3K/Akt pathways. Such effects were reversed when knocking out SLC22A12. In summary, as a transporter for many vital metabolites, SLC22A12 may affect tumor cell survival through its impacts on the mentioned metabolites. In conclusion, this study uncovered that SLC22A12 is a promising prognostic and diagnostic biomarker for ccRCC.

Diagnostic and procedural intraoperative ultrasound: technique, tips and tricks for optimizing results

Intraoperative ultrasound (IOUS) is a valuable adjunctive tool that can provide real-time diagnostic information in surgery that has the potential to alter patient management and decrease complications. Lesion localization, characterization and staging can be performed, as well as surveying for additional lesions and metastatic disease. IOUS is commonly used in the liver for hepatic metastatic disease and hepatocellular carcinoma, in the pancreas for neuroendocrine tumors, and in the kidney for renal cell carcinoma. IOUS allows real-time evaluation of vascular patency and perfusion in organ transplantation and allows for early intervention for anastomotic complications. It can also be used to guide intraoperative procedures such as biopsy, fiducial placement, radiation, or ablation. A variety of adjuncts including microbubble contrast and elastography may provide additional information at IOUS. It is important for the radiologist to be familiar with the available equipment, common clinical indications, technique, relevant anatomy and intraoperative imaging appearance to optimize performance of this valuable imaging modality.

Sexual dimorphism in cancer: insights from transcriptional signatures in kidney tissue and renal cell carcinoma.

Sexual dimorphism in cancer incidence and outcome is widespread. Understanding the underlying mechanisms is fundamental to improve cancer prevention and clinical management. Sex disparities are particularly striking in kidney cancer: across diverse populations, men consistently show unexplained 2-fold increased incidence and worse prognosis.We have characterized genome-wide expression and regulatory networks of 609 renal tumors and 256 non-tumor renal tissues. Normal kidney displayed sex-specific transcriptional signatures, including higher expression of X-linked tumor suppressor genes in women. Sex-dependent genotype–phenotype associations unraveled women-specific immune regulation. Sex differences were markedly expanded in tumors, with male-biased expression of key genes implicated in metabolism, non-malignant diseases with male predominance and carcinogenesis, including markers of tumor infiltrating leukocytes. Analysis of sex-dependent RCC progression and survival uncovered prognostic markers involved in immune response and oxygen homeostasis.In summary, human kidney tissues display remarkable sexual dimorphism at the molecular level. Sex-specific transcriptional signatures further shape renal cancer, with relevance for clinical management.

Recommandations françaises du Comité de transplantation de l’association française d’urologie (CTAFU) : carcinome rénal sur reins natifs chez le patient transplanté rénal ou en attente de transplantation

To define guidelines for the management of renal cell carcinoma of the native kidney (NKRCC) in kidney transplant (KTx) recipients and renal cell carcinoma (RCC) in end-stage renal disease (ESRD) patients candidates for renal transplantation. A review of the literature following a systematic approach (Medline) was conducted by the CTAFU to report renal cell carcinoma epidemiology, screening, diagnosis and management in KTx candidates and recipients. References were assessed according to a predefined process to propose recommendations with the corresponding levels of evidence. ESRD patients are at higher risk of RCC with a standardized incidence ratio of approximately 4,5as compared with general population. NKRCC tumors occur in 1to 3% of KTx recipients with a 10to 15-fold increased risk as compared with general population, especially in patients with acquired multicystic kidney disease. Most authors suggest yearly monitoring of the native kidneys using ultrasound imaging. Radical nephrectomy (either open or laparoscopic approach) is the preferred treatment of NKRCC in KTx recipients and RCC in ESRD. Surveillance in a valid option in small or cystic renal masses. In the localized setting, change in immunosuppressive therapy is not recommended besides perioperative avoidance of mTOR inhibitor to limit morbidity. CTAFU does not recommend a mandatory waiting time after nephrectomy for RCC in ESRD patients candidates for renal tranplantation when tumor stage<T3and low ISUP grade. Follow-up modalities should follow recommendations in general population. The French recommendations should contribute to improve management of NKRCC in KTx recipients and RCC in ESRD candidates for KTx, integrating oncological objectives in the context of kidney transplantation.

Expression Profile Analysis of m6A RNA Methylation Regulators Indicates They Are Immune Signature Associated and Can Predict Survival in Kidney Renal Cell Carcinoma.

N6-Methyladenosine (m6A) refers to the methylation modification occurring at the nitrogen-6 position of adenosine. Many human physiological processes such as modulation of spermatogenesis are caused by m6A RNA modifications. However, the relationship between m6A RNA methylation regulators and kidney renal clear cell carcinoma (KIRC) remains rarely investigated. This work aimed to explore the influence of m6A RNA methylation regulators in KIRC. We examined abnormally expressed m6A RNA methylation regulators among different clinicopathological features of KIRC. We recognized three subgroups (KIRC1, KIRC2, and KIRC3) with significant differences in overall survival through consensus clustering of m6A RNA methylation regulators. Surprisingly, KIRC2 displayed elevated immune activity, but high proportions of immune-inhibitory cells (Tregs and myeloid-derived suppressor cell) based on single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT analysis. Moreover, the KIRC2 subgroup had the lowest tumor mutation burden levels and the highest expression levels of 80% (12/15) of co-inhibitory molecules. Next, correlation analysis indicated that RBM15B expression was negatively correlated with multiple immune signatures, which was verified by ssGSEA and CIBERSORT analyses. Multiple immune-related and cancer-related pathways were enriched in the group with high RBM15B expression. Furthermore, a four-m6A RNA methylation regulator-based risk signature was constructed based on an ArrayExpress (E-MTAB-3267) dataset and confirmed in the The Cancer Genome Atlas (TCGA) testing cohort. In conclusion, our study successfully classified TCGA samples into three subgroups with different immune signatures, and suggested that the worse prognosis of KIRC2 is probably mediated by immune evasion. These findings will facilitate personalized immunotherapy in patients with KIRC. In addition, the risk score system was revealed as an independent prognostic marker that can predict survival in KIRC patients.

Cancer-Specific Immune Prognostic Signature in Solid Tumors and Its Relation to Immune Checkpoint Therapies.

To elucidate the role of immune cell infiltration as a prognostic signature in solid tumors, we analyzed immune-function-related genes from four publicly available single-cell RNA-Seq data sets and twenty bulk tumor RNA-Seq data sets from The Cancer Genome Atlas (TCGA). Unsupervised clustering of pan-cancer transcriptomic signature showed two major immune function types: one related to NK-, T-, and B-cell functions and the other related to monocyte, macrophage, dendritic cell, and Toll-like receptor functions. Kaplan–Meier analysis showed differential prognosis of these two groups, dependent on the cancer type. Our analysis of TCGA solid tumors with an elastic net model identified 155 genes associated with disease-free survival in different tumor types with varied influence across different cancer types. With this gene set, we computed cancer-specific prognostic immune score models for individual cancer types that predicted disease-free and overall survival. Validation of our model on available published data of immune checkpoint blockade therapies on melanoma, kidney renal cell carcinoma, non-small cell lung cancer, gastric cancer and bladder cancer confirmed that cancer-specific higher immune scores are associated with response to immunotherapy. Our analysis provides a comprehensive map of cancer-specific immune-related prognostic gene sets that are associated with immunotherapy response.

In-silco investigation of EGFR network in kidney cancer: a drug discovery approach

Epidermal growth factor receptor (EGFR) gene plays an important role in the cell membrane including cell cycle promotion and differentiation. In many cancers studies the EGFR alteration causes the development of Oncogenesis that promote carcinogenesis and metastasis. Aim of the current study is to better understand the EGFR protein-protein interacting network in kidney cancer to identifying co-targets for drug designing. EGFR protein was as a query in STRING database and a network of 34 nodes and 123 edges was extracted. HUBBA analyzer was applied for the identification of hubs protein in the network that showed the functional association among network components. The findings of this study explored five clusters which has intra/inter-modular connections those are targeting links in therapeutic strategy. The cbioportal cancer database was used to analyze the expression levels of gene encoding hubs protein in the network and their participation in kidney cancer. In total of twenty hub proteins, seven genes, including HRAS, SHC1, PTPN1, PIK3CB, HBEGF, NRG2, AGTR1 shows overexpression and five genes like PIK3CD, ARRB1, NRG1, RAB5A, EPN1 displayed under-expression in kidney renal cell carcinoma. Outcomes of the research declared novel components mutations, mediated through EGFR signaling that are involved in ATM, MAPK, MDM2, ATK/VEGFR signaling pathways of proliferation and anti-apoptotic activities. It is evident that experimental validation of this results could be employed for the identification of new diagnostic markers for kidney cancer. Keywords: Carcinoma; Cbioportal; EGFR; Kidney cancer; String database http://dx.doi.org/10.19045/bspab.2020.90166

A comparative study of different classification algorithms on RNA-Seq cancer data

Gene mutations are the most important reason of cancer diseases, and there are different kind of causing genes across these diseases. RNA-Seq technology enables us to allow for gathering information about many genes simultaneously; hence, RNA-Seq data can be used for cancer diagnosis and classification. In this study, RNA-Seq dataset for renal cell cancer is analysed using three different developed classification methods: random forest (RF), artificial neural network (ANN) and deep learning (DL). The genes in our dataset are related to the following cancer types: kidney renal papillary cell, kidney renal clear cell and kidney chromophore carcinomas. It suggests that the DL method gives the highest accuracy rate compared to RF and ANN for 95.15%, 91.83% and 89.22%, respectively. We believe that the results acquired in this study will make a contribution to the classification of cancer types and support doctors in their processes of decision making.&#x0D; &#x0D; Keywords: Classification, gene-expression, RNA-Seq, DL.

Ciliary Genes in Renal Cystic Diseases.

Cilia are microtubule-based organelles, protruding from the apical cell surface and anchoring to the cytoskeleton. Primary (nonmotile) cilia of the kidney act as mechanosensors of nephron cells, responding to fluid movements by triggering signal transduction. The impaired functioning of primary cilia leads to formation of cysts which in turn contribute to development of diverse renal diseases, including kidney ciliopathies and renal cancer. Here, we review current knowledge on the role of ciliary genes in kidney ciliopathies and renal cell carcinoma (RCC). Special focus is given on the impact of mutations and altered expression of ciliary genes (e.g., encoding polycystins, nephrocystins, Bardet-Biedl syndrome (BBS) proteins, ALS1, Oral-facial-digital syndrome 1 (OFD1) and others) in polycystic kidney disease and nephronophthisis, as well as rare genetic disorders, including syndromes of Joubert, Meckel-Gruber, Bardet-Biedl, Senior-Loken, Alström, Orofaciodigital syndrome type I and cranioectodermal dysplasia. We also show that RCC and classic kidney ciliopathies share commonly disturbed genes affecting cilia function, including VHL (von Hippel-Lindau tumor suppressor), PKD1 (polycystin 1, transient receptor potential channel interacting) and PKD2 (polycystin 2, transient receptor potential cation channel). Finally, we discuss the significance of ciliary genes as diagnostic and prognostic markers, as well as therapeutic targets in ciliopathies and cancer.

Ferroptosis and kidney diseases.

Ferroptosis is a form of iron-dependent, non-apoptotic regulated cell death, which is characterized by the accumulation of lipid hydroperoxides to lethal levels. Ferroptosis recently has been shown to have implications in diverse kidney diseases, such as acute kidney injury, polycystic kidney disease and renal cell carcinoma. This review summarizes current research on ferroptosis, its underlying mechanisms and its role in the progression of different kidney diseases to provide more information regarding treatment and prevention of these destructive diseases.

A Gene Signature of Survival Prediction for Kidney Renal Cell Carcinoma by Multi-Omic Data Analysis.

Kidney renal cell carcinoma (KIRC), which is the most common subtype of kidney cancer, has a poor prognosis and a high mortality rate. In this study, a multi-omics analysis is performed to build a multi-gene prognosis signature for KIRC. A combination of a DNA methylation analysis and a gene expression data analysis revealed 863 methylated differentially expressed genes (MDEGs). Seven MDEGs (BID, CCNF, DLX4, FAM72D, PYCR1, RUNX1, and TRIP13) were further screened using LASSO Cox regression and integrated into a prognostic risk score model. Then, KIRC patients were divided into high- and low-risk groups. A univariate cox regression analysis revealed a significant association between the high-risk group and a poor prognosis. The time-dependent receiver operating characteristic (ROC) curve shows that the risk group performs well in predicting overall survival. Furthermore, the risk group is contained in the best multivariate model that was obtained by a multivariate stepwise analysis, which further confirms that the risk group can be used as a potential prognostic biomarker. In addition, a nomogram was established for the best multivariate model and shown to perform well in predicting the survival of KIRC patients. In summary, a seven-MDEG signature is a powerful prognosis factor for KIRC patients and may provide useful suggestions for their personalized therapy.

Integrative Analysis of Multi-Genomic Data for Kidney Renal Cell Carcinoma.

Accounting for nine out of ten kidney cancers, kidney renal cell carcinoma (KIRC) is by far the most common type of kidney cancer. In view of limited and ineffective available therapies, understanding the genetic basis of disease becomes important for better diagnosis and treatment. The present studies are based on a single type ofgenomic data. These studies do not consider interactions between genomic data types and their underlying biological relationships in the disease. However,the current availability of multiple genomic data andthe possibility of combiningit have facilitated a better understanding of the cancer's characterization. But high dimensionality andthe existence of complex interactions (within and between genomic data types) are the two main challenges of integrative methods to analyze cancer effectively. In this paper, we propose a method to build an integrative model based on Bayesian model averaging procedure for improved prediction of clinical outcome in cancer survival. The proposed method initiallyuses dimensionality reduction techniques to generate low-dimensional latent features for the predictive models and then incorporates interactions between them. It defines the latent features using principal components and their sparse version. It compares the predictive performance of models based on these two latent features on real data.These models also validate several ccRCC-specific cancer biomarkers previously reported in the literature. Applied on kidney renal cell carcinoma (KIRC) dataset of The Cancer Genome Atlas (TCGA), the method achieves better prediction with sparse principal components model by including latent feature interactions as compared to without including them.

Identification of Epigenetic Regulation on The Expression of The Aberrant Gene of Kidney Renal Clear Cell Carcinoma Patients Observed in a Specific Race

Aberrant expression of genes in cancer is mainly caused by a mutation where there is a change in DNA sequences. However, the aberrant expression was also found without a change in the DNA sequences where epigenetic modification such as DNA methylation, histone modifications and microRNA become the main regulator in another layer of the cancer mechanism. The nature of epigenetic is heritable and reversible. It is important to search for the epigenetic mechanism in disease development in order to design epigenetic therapy and drugs. Methylation inhibitors and HDAC inhibitors drug already yielded seven FDA approved epigenetic drugs for myelodysplastic syndrome, cutaneous T-cell lymphoma, Multiple myelomas, peripheral T-cell lymphoma. This study search for the epigenetic mechanism in a kidney renal cell carcinoma patients in a white race. The dataset of transcriptome profiling and epigenetic was downloaded from The Cancer Genome Atlas (TCGA) database. Some programming languages such as R, Python, Matlab and MySQL database were used to pre-processing the datasets and correlation computation part. This study found 14 aberrant genes which significantly correlated with 19 aberrant methylation probes with the correlation score less than equal to -0.7 and p-value < 0.01. Some of those down-regulated genes such as ZNF542, ZFP28, TMEM25, STK33 are correlated with hypermethylation in more than one sites. It is suggested that those methylation sites can affect the down-regulation of their expression in cancer formation. Further study is needed to validate the results through wet lab analysis.

Quantification of gold nanoparticle accumulation in tissue by two-photon luminescence microscopy.

Nanomedicine has emerged as a promising strategy to address some of the limitations of traditional biomedical sensing, imaging and therapy modalities. Its applicability and efficacy are, in part, hindered by the difficulty in both controllably delivering nanoparticles to specific regions and accurately monitoring them in tissue. Gold nanoparticles are among the most extensively used inorganic nanoparticles which benefit from high biocompatibility, flexible functionalization, strong and tunable resonant absorption, and production scalability. Moreover, their capability to enhance optical fields at their plasmon resonance enables local boosting of non-linear optical processes, which are otherwise very inefficient. In particular, two-photon induced luminescence (TPL) in gold offers high signal specificity for monitoring gold nanoparticles in a biological environment. In this article, we demonstrate that TPL microscopy provides a robust sub-micron-resolution technique able to quantify accumulated gold nanorods (GNRs) both in cells and in tissues. First, the temporal accumulation of GNRs with two different surface chemistries was measured in 786-O cells during the first 24 hours of incubation, and at different nanoparticle concentrations. Subsequently, GNR accumulation in mice, 6 h and 24 hours after tail vein injection, was quantified by TPL microscopy in biopsied tissue from kidney, spleen, liver and clear cell renal cell carcinoma (ccRCC) tumors, in good agreement with inductively coupled mass spectroscopy. Our data suggest that TPL microscopy stands as a powerful tool to understand and quantify the delivery mechanisms of gold nanoparticles, highly relevant to the development of future theranostic medicines.

Kidney stones and the risk of renal cell carcinoma and upper tract urothelial carcinoma: the Netherlands Cohort Study

BackgroundWe examined the association between kidney stones and renal cell carcinoma (RCC) and upper tract urothelial carcinoma (UTUC) risk in the Netherlands Cohort Study on diet and cancer.MethodsIn total, 120,852 participants aged 55–69 completed a self-administered questionnaire on diet, medical conditions and other risk factors for cancer at baseline (1986). After 20.3 years of cancer follow-up 4352 subcohort members, 544 RCC cases and 140 UTUC cases were eligible for case-cohort analysis. Hazard ratios (HR) and 95% confidence intervals (CIs) were estimated by multivariable-adjusted proportional hazards models.ResultsKidney stones were associated with an increased RCC risk (HR: 1.39, 95% CI 1.05–1.84), vs. no kidney stones. Kidney stones were associated with an increased risk of papillary RCC (HR: 3.08, 95% CI 1.55–6.11), but not clear-cell RCC (HR: 1.14, 95% CI 0.79–1.65). UTUC risk was increased for participants with kidney stones (HR: 1.66, 95% CI 1.03–2.68). No heterogeneity of associations was found for UTUC in the ureter and renal pelvis. An early kidney stone diagnosis (≤40 years) was associated with an increased RCC and UTUC risk, compared to later diagnosis.ConclusionKidney stones were associated with increased papillary RCC risk, but not clear-cell RCC risk. No heterogeneity was found for UTUC subtypes.

Contour-Seed Pairs Learning-Based Framework for Simultaneously Detecting and Segmenting Various Overlapping Cells/Nuclei in Microscopy Images.

In this paper, we propose a novel contour-seed pairs learning-based framework for robust and automated cell/nucleus segmentation. Automated granular object segmentation in microscopy images has significant clinical importance for pathology grading of the cell carcinoma and gene expression. The focus of the past literature is dominated by either segmenting a certain type of cells/nuclei or simply splitting the clustered objects without contours inference of them. Our method addresses these issues by formulating the detection and segmentation tasks in terms of a unified regression problem, where a cascade sparse regression chain model is trained and then applied to return object locations and entire boundaries of clustered objects. In particular, we first learn a set of online convolutional features in each layer. Then, in the proposed cascade sparse regression chain, with the input from the learned features, we iteratively update the locations and clustered object boundaries until convergence. In this way, the boundary evidences of each individual object can be easily delineated and be further fed to a complete contour inference procedure optimized by the minimum description length principle. For any probe image, our method enables to analyze free-lying and overlapping cells with complex shapes. Experimental results show that the proposed method is very generic and performs well on contour inferences of various cell/nucleus types. Compared with the current segmentation techniques, our approach achieves state-of-the-art performances on four challenging datasets, i.e., the kidney renal cell carcinoma histopathology dataset, Drosophila Kc167 cellular dataset, differential interference contrast red blood cell dataset, and cervical cytology dataset.

Loss of Fnip1 alters kidney developmental transcriptional program and synergizes with TSC1 loss to promote mTORC1 activation and renal cyst formation.

Birt-Hogg-Dube’ Syndrome (BHDS) is a rare genetic disorder in humans characterized by skin hamartomas, lung cysts, pneumothorax, and increased risk of renal tumors. BHDS is caused by mutations in the BHD gene, which encodes for Folliculin, a cytoplasmic adapter protein that binds to Folliculin interacting proteins-1 and -2 (Fnip1, Fnip2) as well as the master energy sensor AMP kinase (AMPK). Whereas kidney-specific deletion of the Bhd gene in mice is known to result in polycystic kidney disease (PKD) and renal cell carcinoma, the roles of Fnip1 in renal cell development and function are unclear. In this study, we utilized mice with constitutive deletion of the Fnip1 gene to show that the loss of Fnip1 is sufficient to result in renal cyst formation, which was characterized by decreased AMPK activation, increased mTOR activation, and metabolic hyperactivation. Using RNAseq, we found that Fnip1 disruption resulted in many cellular and molecular changes previously implicated in the development of PKD in humans, including alterations in the expression of ion and amino acid transporters, increased cell adhesion, and increased inflammation. Loss of Fnip1 synergized with Tsc1 loss to hyperactivate mTOR, increase Erk activation, and greatly accelerate the development of PKD. Our results collectively define roles for Fnip1 in regulating kidney development and function, and provide a model for how loss of Fnip1 contributes to PKD and perhaps renal cell carcinoma.

High-dimensional variable selection with the plaid mixture model for clustering

With high-dimensional data, the number of covariates is considerably larger than the sample size. We propose a sound method for analyzing these data. It performs simultaneously clustering and variable selection. The method is inspired by the plaid model. It may be seen as a multiplicative mixture model that allows for overlapping clustering. Unlike conventional clustering, within this model an observation may be explained by several clusters. This characteristic makes it specially suitable for gene expression data. Parameter estimation is performed with the Monte Carlo expectation maximization algorithm and importance sampling. Using extensive simulations and comparisons with competing methods, we show the advantages of our methodology, in terms of both variable selection and clustering. An application of our approach to the gene expression data of kidney renal cell carcinoma taken from The Cancer Genome Atlas validates some previously identified cancer biomarkers.

Metastatic renal cell carcinoma kidney in children 10 years old – possibility of targeted therapy

Renal cell carcinoma (RCC) is rare in children, its metastatic form is even rarer. At present there is no treatment protocol for children with RCC. This clinical observation presented the patient 10 year old with RCC metastases to the lungs. After radical surgery, after targeted sorafenib therapy the patient has a positive dynamics. At present his progression-free survival is 30 months.

Renal angiomyoadenomatous tumour.

Renal angiomyoadenomatous tumour is a newly described rare neoplasm. This tumour is characterised microscopically by admixture of three components- epithelial cells arranged in tubules and nests, angiomyomatous stroma and capillary sized interconnecting vascular channels in close association with the epithelial cell clusters. Microscopically it has wide range of differential diagnoses which include mixed epithelial and stromal tumour of kidney, angiomyolipoma and clear cell renal cell carcinoma with angiomyolipomatous/angiomyoadenomatous areas. Renal angiomyoadenomatous tumour should be differentiated from these tumours. Till now, only 10 cases have been reported in English medical literature. Here, we are reporting a case of renal angiomyoadenomatous tumour in a 29 year- old female patient who presented with hematuria and low backache and describing its main features so as to differentiate this entity from other renal tumours. To the best of our knowledge, this is the first case to be reported from India.