Exosomes in Renal Cell Cancer: Diagnostic and Therapeutic Nanovehicles.

Early diagnosis is crucial for enhancing the survival rate of renal cell cancer patients, and exosomes present potential advantages in this area. Their small size, high mobility, and lipid bilayer structure enable exosomes to cross biological membranes easily, protecting the bioactive cargo within from degradation. Exosomes significantly influence the invasion and metastasis of RCC, and they also contribute to tumor drug resistance and immune evasion.

Renal and Urinary Conditions: Kidney Cysts and Tumors.

Kidney cysts and tumors often are identified during imaging for unrelated issues. Kidney cysts can be attributable to heritable polycystic kidney diseases. These cysts are rare in children. In adults, they affect approximately 50% of individuals older than 50 years. Kidney cysts are categorized on imaging using the Bosniak Classification of Cystic Renal Masses, which determines the likelihood that cysts are malignant or benign. Asymptomatic Bosniak class I and II cysts require no further evaluation or follow-up; however, symptomatic large simple cysts might require treatment. Bosniak class III and IV cysts might be malignant and require excision. Kidney tumors also occur in children and adults. In children, the most common is Wilms tumor, but after age 10 years renal cell carcinoma (RCC) is more common. In adults, kidney tumors may be malignant or benign. RCC accounts for 85% of kidney tumors in adults, often with metastatic disease. In patients with kidney tumors, biopsy typically is avoided to prevent spread of malignant cells. Tumors that appear suspicious for cancer on imaging are managed directly, which can include total or partial nephrectomy, ablation therapy, and adjuvant therapies, along with chemotherapy and radiotherapy depending on tumor stage. For some patients, evaluation may involve consideration of genetic testing for hereditary cancer syndromes. Patients with these syndromes should undergo periodic screening for RCC.

Comprehensive pan-cancer analysis of ZNF337 as a potential diagnostic, immunological, and prognostic biomarker.

BACKGROUND: Zinc Finger Protein 337 (ZNF337) is a novel Zinc Finger (ZNF) protein family member. However, the roles of ZNF337 in human cancers have not yet been investigated. METHODS: In this study, with the aid of TCGA databases, GTEx databases, and online websites, we determined the expression levels of ZNF337 in pan-cancer and its potential value as a diagnostic and prognostic marker for pan-cancer and analyzed the relationship between ZNF337 expression and immune cell infiltration and immune checkpoint genes. We then focused our research on the potential of ZNF337 as a biomarker for diagnostic and prognostic in KIRC (kidney renal clear cell carcinoma) and validated in the E-MTAB-1980 database. Moreover, the expression of ZNF337 was detected through qRT-PCR and Western blotting (WB). CCK-8 experiment, colony formation experiment, and EDU experiment were performed to evaluate cell proliferation ability. Wound healing assay and transwell assay were used to analyze its migration ability. The qRT-PCR and WB were used to detect the expression of ZNF337 in tumor tissues and paracancerous tissues of KIRC patients. RESULTS: The pan-cancer analysis revealed that abnormal ZNF337 expression was found in multiple human cancer types. ZNF337 had a high diagnostic value in pan-cancer and a significant association with the prognosis of certain cancers, indicating that ZNF337 may be a valuable prognostic biomarker for multiple cancers. Further analysis demonstrated that the expression level of ZNF337 displayed significant correlations with cancer-associated fibroblasts, immune cell infiltration, and immune checkpoint genes in many tumors. Additionally, ZNF337 was observed to have a high expression in KIRC. Its expression was significantly associated with poor prognosis [overall survival (OS), disease-specific survival (DSS)], age, TNM stage, histologic grade, and pathologic stage. The high ZNF337 expression was associated with poor prognosis in the E-MTAB-1980 validation cohort. The in vitro experiments suggested that the expression of ZNF337 in KIRC tumor tissues was higher than in adjacent tissues, and ZNF337 knockdown inhibited the proliferation and migration of KIRC cells, whereas overexpression of ZNF337 had the opposite effects. CONCLUSIONS: ZNF337 might be an important prognostic and immunotherapeutic biomarker for pan-cancer, especially in KIRC.

A case report of renal cell carcinoma metastasis revealed through late-onset thyroid nodules.

BACKGROUND: Renal cell carcinoma (RCC) is one of the most common and prevalent cancers all around the world with a prevalence of 3%. Approximately twenty percent of patients present with metastasis at the time of diagnosis, while late metastasis in renal cell carcinoma is a quite familiar phenomenon. Head and neck and particularly thyroid metastasis from RCC are rare events. CASE: We present a case of a 75-year-old woman who developed thyroid nodules 13 years after nephrectomy for RCC. Diagnosis confirmed metastatic RCC through clinical history, histomorphology, and immunohistochemistry. Imaging studies revealed thyroid lesions without metastasis in other organs. The patient underwent total thyroidectomy and remains symptom-free after 2 years of follow-up. CONCLUSION: This case highlights the importance of considering metastatic lesions is crucial in managing thyroid nodules in patients with a history of cancer, particularly RCC.

Diagnostic, predictive and prognostic molecular biomarkers in clear cell renal cell carcinoma: A retrospective study.

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive subtype of kidney cancer. Many patients are diagnosed at advanced stages, making early detection crucial. Unfortunately, there are currently no noninvasive tests for ccRCC, emphasizing the need for new biomarkers. Additionally, ccRCC often develops resistance to treatments like radiotherapy and chemotherapy. Identifying biomarkers that predict treatment outcomes is vital for personalized care. The integration of artificial intelligence (AI), multi-omics analysis, and computational biology holds promise in bolstering detection precision and resilience, opening avenues for future investigations. The amalgamation of radiogenomics and biomaterial-basedimmunomodulation signifies a revolutionary breakthrough in diagnostic medicine. This review summarizes existing literature and highlights emerging biomarkers that enhance diagnostic, predictive, and prognostic capabilities for ccRCC, setting the stage for future clinical research.

Analysis of Metabolic Changes in Endogenous Metabolites and Diagnostic Biomarkers for Various Diseases Using Liquid Chromatography and Mass Spectrometry.

Analysis of endogenous metabolites in various diseases is useful for searching diagnostic biomarkers and elucidating the molecular mechanisms of pathophysiology. The author and collaborators have developed some LC/tandem mass spectrometry (LC/MS/MS) methods for metabolites and applied them to disease-related samples. First, we identified urinary conjugated cholesterol metabolites and serum N-palmitoyl-O-phosphocholine serine as useful biomarkers for Niemann-Pick disease type C (NPC). For the purpose of intraoperative diagnosis of glioma patients, we developed the LC/MS/MS analysis methods for 2-hydroxyglutaric acid or cystine and found that they could be good differential biomarkers. For renal cell carcinoma, we searched for various biomarkers for early diagnosis, malignancy evaluation and recurrence prediction by global metabolome analysis and targeted LC/MS/MS analysis. In pathological analysis, we developed a simultaneous LC/MS/MS analysis method for 13 steroid hormones and applied it to NPC cells, we found 6 types of reductions in NPC model cells. For non-alcoholic steatohepatitis (NASH), model mice were prepared with special diet and plasma bile acids were measured, and as a result, hydrophilic bile acids were significantly increased. In addition, we developed an LC/MS/MS method for 17 sterols and analyzed liver cholesterol metabolites and found a decrease in phytosterols and cholesterol synthetic markers and an increase in non-enzymatic oxidative sterols in the pre-onset stage of NASH. We will continue to challenge themselves to add value to clinical practice based on cutting-edge analytical chemistry methodology.

Identification of novel biomarkers to distinguish clear cell and non-clear cell renal cell carcinoma using bioinformatics and machine learning.

Renal cell carcinoma (RCC), accounting for 90% of all kidney cancer, is categorized into clear cell RCC (ccRCC) and non-clear cell RCC (non-ccRCC) for treatment based on the current NCCN Guidelines. Thus, the classification will be associated with therapeutic implications. This study aims to identify novel biomarkers to differentiate ccRCC from non-ccRCC using bioinformatics and machine learning. The gene expression profiles of ccRCC and non-ccRCC subtypes (including papillary RCC (pRCC) and chromophobe RCC (chRCC)), were obtained from TCGA. Differential expression genes (DEGs) were identified, and specific DEGs for ccRCC and non-ccRCC were explored using a Venn diagram. Gene Ontology and pathway enrichment analysis were performed using DAVID. The top ten expressed genes in ccRCC were then selected for machine learning analysis. Feature selection was operated to identify a minimum highly effective gene set for constructing a predictive model. The expression of best-performing gene set was validated on tissue samples from RCC patients using immunohistochemistry techniques. Subsequently, machine learning models for diagnosing RCC were developed using H-scores. There were 910, 415, and 835 genes significantly specific for DEGs in ccRCC, pRCC, and chRCC, respectively. Specific DEGs in ccRCC enriched in PD-1 signaling, immune system, and cytokine signaling in the immune system, whereas TCA cycle and respiratory, signaling by insulin receptor, and metabolism were enriched in chRCC. Feature selection based on Decision Tree Classifier revealed that the model with two genes, including NDUFA4L2 and DAT, had an accuracy of 98.89%. Supervised classification models based on H-score of NDUFA4L2, and DAT revealed that Decision Tree models showed the best performance with 82% accuracy and 0.9 AUC. NDUFA4L2 expression was associated with lymphovascular invasion, pathologic stage and pT stage in ccRCC. Using integrated bioinformatics and machine learning analysis, NDUFA4L2 and DAT were identified as novel biomarkers to differential diagnosis ccRCC from non-ccRCC.

Eosinophilic Solid and Cystic Renal Cell Carcinoma-A Systematic Review and Meta-Analysis.

Eosinophilic solid and cystic renal cell carcinoma (ESC-RCC) is a novel and uncommon type of renal cell carcinoma, which has been recently recognized and introduced as a distinct entity in the WHO 2022 kidney tumor classification. Previously known as "unclassified RCC", followed by "tuberous sclerosis complex (TSC)-associated RCC", ESC-RCC is now a distinct category of kidney tumor, with its own name, with specific clinical manifestations, and a unique morphological, immunohistochemical and molecular profile. Due to its recent introduction and the limited available data, the diagnosis of ESC-RCC is still a complex challenge, and it is probably frequently misdiagnosed. The secret of diagnosing this tumor lies in the pathologists' knowledge, and keeping it up to date through research, thereby limiting the use of outdated nomenclature. The aim of our case-based review is to provide a better understanding of this pathology and to enrich the literature with a new case report, which has some particularities compared to the existing cases.

Detection of two synchronous histologically different renal cell carcinoma subtypes in the same kidney: a case report and review of the literature.

INTRODUCTION: Renal cell carcinoma (RCC) is the dominant primary renal malignant neoplasm, encompassing a significant portion of renal tumors. The presence of synchronous yet histologically distinct ipsilateral RCCs, however, is an exceptionally uncommon phenomenon that is rather under-described in the literature regarding etiology, diagnosis, management, and later outcomes during follow-up. CASE PRESENTATION: We aim to present the 9th case of a combination chromophobe RCC (ChRCC) and clear cell RCC (ccRCC) in literature, according to our knowledge, for a 69-year-old North African, Caucasian female patient who, after complaining of loin pain and hematuria, was found to have two right renal masses with preoperative computed tomography (CT) and underwent right radical nephrectomy. Pathological examination later revealed the two renal masses to be of different histologic subtypes. CONCLUSION: The coexistence of dissimilar RCC subtypes can contribute to diverse prognostic implications. Further research should focus on enhancing the complex, yet highly crucial, preoperative detection and pathological examination to differentiate multiple renal lesions. Planning optimal operative techniques (radical or partial nephrectomy), selecting suitable adjuvant regimens, and reporting long-term follow-up outcomes of patients in whom synchronous yet different RCC subtypes were detected are of utmost importance.

Characterization of extrachromosomal circular DNAs in plasma of patients with clear cell renal cell carcinoma.

BACKGROUND AND PURPOSE: Extrachromosomal circular DNAs (eccDNAs) have been recognized for their significant involvement in numerous biological processes. Nonetheless, the existence and molecular characteristics of eccDNA in the peripheral blood of patients diagnosed with clear cell renal cell carcinoma (ccRCC) have not yet been reported. Our aim was to identify potentially marked plasma eccDNAs in ccRCC patients. METHODS AND MATERIALS: The detection of plasma eccDNA in ccRCC patients and healthy controls was performed using the Tn5-tagmentation and next-generation sequencing (NGS) method. Comparisons were made between ccRCC patients and healthy controls regarding the distribution of length, gene annotation, pattern of junctional nucleotide motif, and expression pattern of plasma eccDNA. RESULTS: We found 8,568 and 8,150 plasma eccDNAs in ccRCC patients and healthy controls, respectively. There were no statistical differences in the length distribution, gene annotation, and motif signature of plasma eccDNAs between the two groups. A total of 701 differentially expressed plasma eccDNAs were identified, and 25 plasma eccDNAs with potential diagnostic value for ccRCC have been successfully screened. These up-regulated plasma eccDNAs also be indicated to originate from the genomic region of the tumor-associated genes. CONCLUSION: This work demonstrates the characterization of plasma eccDNAs in ccRCC and suggests that the up-regulated plasma eccDNAs could be considered as a promising non-invasive biomarker in ccRCC.

Multiphase comparative study for WHO/ISUP nuclear grading diagnostic model based on enhanced CT images of clear cell renal cell carcinoma.

To compare and analyze the diagnostic value of different enhancement stages in distinguishing low and high nuclear grade clear cell renal cell carcinoma (ccRCC) based on enhanced computed tomography (CT) images by building machine learning classifiers. A total of 51 patients (Dateset1, including 41 low-grade and 10 high-grade) and 27 patients (Independent Dateset2, including 16 low-grade and 11 high-grade) with pathologically proven ccRCC were enrolled in this retrospective study. Radiomic features were extracted from the corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP) CT images, and selected using the recursive feature elimination cross-validation (RFECV) algorithm, the group differences were assessed using T-test and Mann-Whitney U test for continuous variables. The support vector machine (SVM), random forest (RF), XGBoost (XGB), VGG11, ResNet18, and GoogLeNet classifiers are established to distinguish low-grade and high-grade ccRCC. The classifiers based on CT images of NP (Dateset1, RF: AUC = 0.82 ± 0.05, ResNet18: AUC = 0.81 ± 0.02; Dateset2, XGB: AUC = 0.95 ± 0.02, ResNet18: AUC = 0.87 ± 0.07) obtained the best performance and robustness in distinguishing low-grade and high-grade ccRCC, while the EP-based classifier performance in poorer results. The CT images of enhanced phase NP had the best performance in diagnosing low and high nuclear grade ccRCC. Firstorder_Kurtosis and firstorder_90Percentile feature play a vital role in the classification task.

miRNA-27b-3p, let-7f-5p and miRNA-142-5p can be Used in a Novel Serum Diagnostic Panel for Clear Cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a prevalent malignant tumor affecting the urinary system. Due to its unfavorable prognosis, there is a pressing need to discover effective approaches for early diagnosis and treatment of ccRCC. Extensive research has consistently demonstrated the presence of stable microRNAs (miRNAs) in human serum. Accordingly, the objective of this study was to identify a specific panel of miRNAs in serum that can serve as a reliable and non-invasive biomarker for the early detection of ccRCC. METHODS: The study comprised of training and validation phases to identify potential biomarkers. In the training phase, a total of 10 miRNAs exhibiting the most significant differential expression among 28 ccRCC patients and 28 healthy controls (HCs) were identified using quantitative reverse transcription polymerase chain reaction (qRT-PCR). In the subsequent validation phase, these 10 miRNAs were assessed in serum samples obtained from an additional 80 ccRCC patients and 84 HCs using RT-qPCR. To construct a panel with optimal diagnostic capability, backward stepwise logistic regression analysis was conducted. Furthermore, bioinformatics analysis was performed on this selected miRNA panel. RESULTS: In ccRCC patients, the serum expression level of miRNA-142-5p was found to be significantly elevated compared to healthy controls (HCs), whereas the expression levels of let-7f-5p, miRNA-27b-3p, miRNA-212-3p, and miRNA-216-5p were significantly reduced. To assess their diagnostic potential for ccRCC, receiver operating characteristic (ROC) curve analysis was performed. The analysis revealed that miRNA-27b-3p, let-7f-5p, and miRNA-142-5p exhibited moderate diagnostic capabilities for ccRCC, with area under the curve (AUC) values of 0.826, 0.828, and 0.643, respectively. To further enhance diagnostic accuracy, a final diagnostic panel consisting of these three miRNAs was constructed, demonstrating good diagnostic value with an AUC of 0.952. CONCLUSIONS: The miRNA serum biomarker panel (miRNA-27b-3p, let-7f-5p, and miRNA-142-5p) identified in this study holds promise for early, non-invasive, and accurate diagnosis of ccRCC. This panel could potentially provide a valuable tool in clinical settings to aid in the timely detection and management of ccRCC.

A Cerebellar Tumor-to-Tumor Metastasis in a Patient With Von Hippel-Lindau Disease.

Tumor-to-tumor metastasis in the central nerve system is uncommon in our routine practice. Most reports include metastatic breast cancer into meningioma. Here we report a metastatic clear cell renal cell carcinoma (ccRCC) into a cerebellar hemangioblastoma in a patient with von Hippel-Lindau (VHL) disease. Imaging cannot distinguish metastatic ccRCC from primary cerebellar hemangioblastoma. Immuno-molecular studies are proven to be diagnostic. We also reviewed previously documented tumor-to-tumor metastasis of ccRCC to cerebellar hemangioblastoma in VHL disease. Lastly, we discussed potential mechanisms involved in the metastasis of ccRCC to hemangioblastoma in the cerebellum in patients with VHL.

Influence of Dietary Polyunsaturated Fatty Acid Intake on Potential Lipid Metabolite Diagnostic Markers in Renal Cell Carcinoma: A Case-Control Study.

Non-invasive diagnostics are crucial for the timely detection of renal cell carcinoma (RCC), significantly improving survival rates. Despite advancements, specific lipid markers for RCC remain unidentified. We aimed to discover and validate potent plasma markers and their association with dietary fats. Using lipid metabolite quantification, machine-learning algorithms, and marker validation, we identified RCC diagnostic markers in studies involving 60 RCC and 167 healthy controls (HC), as well as 27 RCC and 74 HC, by analyzing their correlation with dietary fats. RCC was associated with altered metabolism in amino acids, glycerophospholipids, and glutathione. We validated seven markers (l-tryptophan, various lysophosphatidylcholines [LysoPCs], decanoylcarnitine, and l-glutamic acid), achieving a 96.9% AUC, effectively distinguishing RCC from HC. Decreased decanoylcarnitine, due to reduced carnitine palmitoyltransferase 1 (CPT1) activity, was identified as affecting RCC risk. High intake of polyunsaturated fatty acids (PUFAs) was negatively correlated with LysoPC (18:1) and LysoPC (18:2), influencing RCC risk. We validated seven potential markers for RCC diagnosis, highlighting the influence of high PUFA intake on LysoPC levels and its impact on RCC occurrence via CPT1 downregulation. These insights support the efficient and accurate diagnosis of RCC, thereby facilitating risk mitigation and improving patient outcomes.

Automated Machine Learning and Explainable AI (AutoML-XAI) for Metabolomics: Improving Cancer Diagnostics.

Metabolomics generates complex data necessitating advanced computational methods for generating biological insight. While machine learning (ML) is promising, the challenges of selecting the best algorithms and tuning hyperparameters, particularly for nonexperts, remain. Automated machine learning (AutoML) can streamline this process; however, the issue of interpretability could persist. This research introduces a unified pipeline that combines AutoML with explainable AI (XAI) techniques to optimize metabolomics analysis. We tested our approach on two data sets: renal cell carcinoma (RCC) urine metabolomics and ovarian cancer (OC) serum metabolomics. AutoML, using Auto-sklearn, surpassed standalone ML algorithms like SVM and k-Nearest Neighbors in differentiating between RCC and healthy controls, as well as OC patients and those with other gynecological cancers. The effectiveness of Auto-sklearn is highlighted by its AUC scores of 0.97 for RCC and 0.85 for OC, obtained from the unseen test sets. Importantly, on most of the metrics considered, Auto-sklearn demonstrated a better classification performance, leveraging a mix of algorithms and ensemble techniques. Shapley Additive Explanations (SHAP) provided a global ranking of feature importance, identifying dibutylamine and ganglioside GM(d34:1) as the top discriminative metabolites for RCC and OC, respectively. Waterfall plots offered local explanations by illustrating the influence of each metabolite on individual predictions. Dependence plots spotlighted metabolite interactions, such as the connection between hippuric acid and one of its derivatives in RCC, and between GM3(d34:1) and GM3(18:1_16:0) in OC, hinting at potential mechanistic relationships. Through decision plots, a detailed error analysis was conducted, contrasting feature importance for correctly versus incorrectly classified samples. In essence, our pipeline emphasizes the importance of harmonizing AutoML and XAI, facilitating both simplified ML application and improved interpretability in metabolomics data science.

Characteristic morphology and immunohistochemical patterns of clear cell papillary renal cell tumours may be observed in renal cell carcinomas, a critical pitfall in renal biopsy cytopathology.

BACKGROUND: Clear cell papillary renal cell tumour (CCPRCT) was renamed from previous clear cell papillary renal cell carcinoma (CCPRCC) in the latest WHO Classification of Tumours. It is essential to differentiate RCC from CCPRCT in renal mass biopsies (RMB). DESIGN: RMB cases with subsequent resections were reviewed. The pathology reports and pertinent clinical information were recorded. RESULTS: Fifteen cases displaying either CCPRCT morphology (20% diffuse, 67% focal) or immunohistochemical patterns (cup-like CA9: 20% diffuse, 47% focal; CK7: 33% diffuse, 40% focal) were identified. One case was positive for TFE3. TSC mutation was identified in one case. Both cases exhibited both CCPRCT morphology and immunohistochemical patterns for CA9 and CK7, with focal high-grade nuclei. RMB diagnoses were as follows: 6 (40%) as CCRCC, 2 (13%) as CCPRCT, 2 (13%) as CCRCC versus CCPRCT, 2 (13%) as CCRCC versus PRCC, 1 (7%) as RCC with TSC mutation versus CCPRCT, 1 (7%) as TFE3-rearranged RCC versus PRCC, and 1 (7%) as cyst with low-grade atypia. 71% of patients underwent nephrectomy, 21% received systemic treatment for stage 4 RCCs, and 7% with ablation for small renal mass (1.6 cm) with low-grade CCRCC. CONCLUSIONS: Our study highlights that morphologic and immunochemical features of CCPRCT may be present in RCCs, including RCC-TFE3 expression and TSC-associated RCC, a critical pitfall to misdiagnose aggressive RCC as indolent CCPRCT and result in undertreatment. Careful examination of morphology and immunostains for CA9, CK7, and TFE3, as well as molecular tests, is crucial for distinguishing aggressive RCC from indolent CCPRCT.