Biomarker For Renal Cell Carcinoma Research Articles

Tissue factor pathway inhibitor 2 (TFPI2) is a potential serum biomarker for clear cell renal carcinoma.

Renal and ovarian clear cell carcinoma (CCC) are both characterized by a clear cytoplasm and exhibit similar genomic alterations and clinical characteristics. We hypothesized that both CCCs may share clinical biomarker. Tissue factor pathway inhibitor 2 (TFPI2), a serine protease inhibitor, has emerged as a promising serum biomarker for ovarian CCC, and we evaluated the efficacy of TFPI2 as a biomarker for renal cell carcinoma (RCC). Serum samples were collected from patients with RCC and healthy volunteers, and TFPI2 levels were measured. Expression of TFPI2 in each cell type was evaluated using single-cell RNA sequencing. Survival analyses according to TFPI2 expression levels were performed based on publicly available databases. Serum TFPI2 was significantly elevated in patients with RCC compared to healthy volunteers, particularly those with clear cell histology. Metastatic RCC tumors exhibited higher TFPI2 than localized RCCs. Moreover, higher TFPI2 correlated with higher Fuhrman grades in clear cell RCC. Publicly available databases showed an association between TFPI2 expression and overall survival, particularly in clear cell RCC. Single-cell RNA sequencing confirmed TFPI2 expression in clear cell RCC and normal kidney tubular epithelial cells. TFPI2 has emerged as a potential serum biomarker for RCC, offering avenues for improved detection and prognostication.

Meta-analysis of the diagnostic value of exosomal microRNAs in renal cell carcinoma.

This meta-analysis aims to evaluate the potential of exosomal microRNAs(Exo-miRs) as diagnostic biomarkers for renal cell carcinoma(RCC). Clinical studies reporting the use of Exo-miRs in the diagnosis of RCC were retrieved from PubMed, Web of Science, Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI), Wanfang, VIP, and Chinese Biomedical Literature Database (SinoMed). After relevant data were screened and extracted, the quality of the included studies was assessed using the QUADAS-2 tool. The Meta-disc (version 1.4) software was used to analyze the heterogeneity of threshold/non-threshold effects in the included studies. The Stata MP (version 16.0) software was used to calculate sensitivity(Sen), specificity(Spe), positive likelihood ratio(+LR), negative likelihood ratio(-LR), area under the curve(AUC), diagnostic odds ratio(DOR), and publication bias. A total of 11 studies were included in this meta-analysis. Spearman correlation coefficient was 0.319 (P = 0.075; >0.05), indicating no threshold effects. The pooled Sen, Spe, +LR, -LR, DOR, and AUC were 0.73 (95% CI, 0.68-0.78), 0.81 (95% CI, 0.76-0.85), 3.80 (95% CI, 3.02-4.77), 0.33 (95% CI, 0.28-0.40), 11.48 (95% CI, 8.27-15.95), and 0.84 (95% CI, 0.80-0.87), respectively. No publication bias was detected among the included studies. The expression of Exo-miRs plays an important role in the diagnosis of RCC. However, owing to the limited number of included studies and heterogeneity among them, further clinical research is necessary to verify the findings of this meta-analysis. https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023445956.

Identifying CDCA3 as a pivotal biomarker for predicting outcomes and immunotherapy efficacy in pan-renal cell carcinoma.

Renal cell carcinoma (RCC) is a heterogeneous disease. Identifying effective biomarkers is crucial for improving prognostic accuracy and therapy outcomes. This study investigates cell division cycle-associated 3 (CDCA3) as a novel biomarker for prognostic assessment and immunotherapy response in RCC. This study analyzed multi-omics data from The Cancer Genome Atlas (TCGA) for kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), and kidney chromophobe (KICH) subtypes to evaluate CDCA3 expression and its clinical implications. Functional enrichment and immune infiltration analyses were performed using bioinformatics tools gene set enrichment analysis (GSEA) and xCell. The prognostic value of CDCA3 was assessed through Cox regression and Kaplan-Meier survival analysis. Immunohistochemistry (IHC) was employed to confirm CDCA3 expression at the protein level in RCC samples. Higher CDCA3 expression correlated with poor survival outcomes and reduced response to programmed cell death protein 1 (PD-1) blockade therapies. Statistical analysis indicated that CDCA3 was an independent prognostic factor for poor survival in RCC. CDCA3 was consistently overexpressed in RCC tissues compared to normal tissues and was associated with adverse clinical features, including high Th2 cell infiltration and suppression of immune pathways. CDCA3 is a promising biomarker for RCC, offering insights into the tumor's prognosis and potential response to immunotherapy. The strong association between CDCA3 expression and poor therapeutic outcomes suggests that CDCA3 could be targeted in future therapeutic strategies.

Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images

There is an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal cell carcinoma (RCC). We proposed deep learning-based artificial intelligence strategies. The study included 1752 whole slide images from multiple centres. Based on the pixel-level of RCC segmentation, the diagnosis diagnostic model achieved an area under the receiver operating characteristic curve (AUC) of 0.977 (95% CI 0.969-0.984) in the external validation cohort. In addition, our diagnostic model exhibited excellent performance in the differential diagnosis of RCC from renal oncocytoma, which achieved an AUC of 0.951 (0.922-0.972). The graderisk for the recognition of high-grade tumour achieved AUCs of 0.840 (0.805-0.871) in the Cancer Genome Atlas (TCGA) cohort, 0.857 (0.813-0.894) in the Shanghai General Hospital (General) cohort, and 0.894 (0.842-0.933) in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohort, for the recognition of high-grade tumour. The OSrisk for predicting 5-year survival status achieved an AUC of 0.784 (0.746-0.819) in the TCGA cohort, which was further verified in the independent general cohort and the CPTAC cohort, with AUCs of 0.774 (0.723-0.820) and 0.702 (0.632-0.765), respectively. Moreover, the competing-risk nomogram (CRN) showed its potential to be a prognostic indicator, with a hazard ratio (HR) of 5.664 (3.893-8.239, p<0.0001), outperforming other traditional clinical prognostic indicators. Kaplan-Meier survival analysis further illustrated that our CRN could significantly distinguish patients with high survival risk. Deep learning-based artificial intelligence could be a useful tool for clinicians to diagnose and predict the prognosis of RCC patients, thus improving the process of individualised treatment.

MiR-4429 inhibits ccRCC proliferation, migration, and invasion by directly targeting CD274

Clear cell renal cell carcinoma (ccRCC) is one of the most aggressive urological malignancies and a highly immunogenic cancer. Yet, its pathogenesis is still not fully understood. This study analyzed the role of the miR-320 family in ccRCC using bioinformatics algorithms and a series of in vitro experiments. miR-4429 was found to be significantly down-regulated in ccRCC tissues and cell lines, while overexpression of miR-4429 significantly inhibited renal cancer cell proliferation, migration, and invasion in vitro. In addition, the UALCAN database, immunohistochemistry, and protein blotting results showed that CD274 expression was up-regulated in ccRCC tissues and correlated with higher histologic grading. Dual luciferase assay indicated that CD274 was a direct target of miR-4429. Overexpression of miR-4429 in 786-O, Caki-2 cells significantly inhibited CD274 expression. KEGG results indicated that the potential target function of miR-4429 was associated with the PI3K/AKT signaling pathway, and protein blotting verified the results. In summary, this data shows that miR-4429 targets CD274 and inhibits ccRCC proliferation, migration, and invasion by regulating PI3K/AKT signaling, thus potentially providing a promising therapeutic target and prognostic biomarker for renal cell carcinoma patients.Graphical

Immunotherapy Applications for Thymine Dimers and WT1 Antigen in Renal Cancers: A Comparative Statistical Analysis.

Renal cell carcinoma (RCC) remains incurable in advanced stages. Biomarkers have proven to be quite useful in cancer therapeutics. Herein, we provide a comparative/integrative statistical analysis of seminal immunohistochemistry (IHC) findings for Wilms' Tumor 1 antigen (WT1) and thymine dimers (TDs), emerging as atypical, yet promising, potential biomarkers for RCCs. We assessed WT1/TD reactivity in adult RCC tumor cells, tumor microenvironment (TME), and tumor-adjacent healthy renal tissue (HRT). WT1 positivity was scarce and strictly nuclear in tumor cells, whereas TD-reactive tumor tissues were prevalent. We report statistically significant positive correlations between the density of reactive RCC cellularity and the intensity of nuclear staining for both biomarkers (WT1 - rho = 0.341, p-value = 0.036; TDs - rho = 0.379, p-value = 0.002). RCC stromal TME TD-positivity was much more frequent than WT1 reactivity, apparently proportional to that of the proper RCC cellularity and facilitated by extensive RCC inflammatory infiltration. TDs exhibited nuclear reactivity for most TME cell lines, while RCC TME WT1 expression was rare and inconsistent. In HRTs, TDs were entirely restricted to renal tubular cells, the likely cellular progenitor of most conventional RCC subtypes. In lieu of proper validation, these early findings have significant implications regarding the origins/biology of RCCs and may inform RCC therapeutics, both accounting for the high frequency of immunotherapy-permissive frameshift indels in RCCs, but also hinting at novel predictive clinical tools for WT1-targeted immunotherapy. Overall, the current study represents a meek yet hopefully significant step towards understanding the molecular biology and potential therapeutic targets of RCCs.

Metastatic Kidney Cancer: Does the Location of the Metastases Matter? Moving towards Personalized Therapy for Metastatic Renal Cell Carcinoma.

The management of renal cell carcinoma (RCC) has been revolutionized over the past two decades with several practice-changing treatments. Treatment for RCC often requires a multimodal approach: Local treatment, such as surgery or ablation, is typically recommended for patients with localized tumors, while stage IV cancers often require both local and systemic therapy. The treatment of advanced RCC heavily relies on immunotherapy and targeted therapy, which are highly contingent upon histological subtypes. Despite years of research on biomarkers for RCC, the standard of care is to choose systemic therapy based on the risk profile according to the International Metastatic RCC Database Consortium and Memorial Sloan Kettering Cancer Centre models. However, many questions still need to be answered. Should we consider metastatic sites when deciding on treatment options for metastatic RCC? How do we choose between dual immunotherapy and combinations of immunotherapy and tyrosine kinase inhibitors? This review article aims to answer these unresolved questions surrounding the concept of personalized medicine.

The role of natural products versus miRNA in renal cell carcinoma: implications for disease mechanisms and diagnostic markers.

Natural products are chemical compounds produced by living organisms. They are isolated and purified to determine their function and can potentially be used as therapeutic agents. The ability of some bioactive natural products to modify the course of cancer is fascinating and promising. In the past 50years, there have been advancements in cancer therapy that have increased survival rates for localized tumors. However, there has been little progress in treating advanced renal cell carcinoma (RCC), which is resistant to radiation and chemotherapy. Oncogenes and tumor suppressors are two roles played by microRNAs (miRNAs). They are involved in important pathogenetic mechanisms like hypoxia and epithelial-mesenchymal transition (EMT); they control apoptosis, cell growth, migration, invasion, angiogenesis, and proliferation through target proteins involved in various signaling pathways. Depending on their expression pattern, miRNAs may identify certain subtypes of RCC or distinguish tumor tissue from healthy renal tissue. As diagnostic biomarkers of RCC, circulating miRNAs show promise. There is a correlation between the expression patterns of several miRNAs and the prognosis and diagnosis of patients with RCC. Potentially high-risk primary tumors may be identified by comparing original tumor tissue with metastases. Variations in miRNA expression between treatment-sensitive and therapy-resistant patients' tissues and serum allow for the estimation of responsiveness to target therapy. Our knowledge of miRNAs' function in RCC etiology has a tremendous uptick. Finding and validating their gene targets could have an immediate effect on creating anticancer treatments based on miRNAs. Several miRNAs have the potential to be used as biomarkers for diagnosis and prognosis. This review provides an in-depth analysis of the current knowledge regarding natural compounds and their modes of action in combating cancer. Also, this study aims to give information about the diagnostic and prognostic value of miRNAs as cancer biomarkers and their involvement in the pathogenesis of RCC.

Molecular subtypes as potential biomarkers in renal cell carcinoma

The increasing number of approved treatment combinations for metastatic renal cell carcinoma highlights the need for actionable biomarkers to guide treatment selection. In this issue of Cancer Cell, Saliby etal. validate the distinct clinico-genomic profiles of expression-based molecular clusters identified in the IMmotion151 trial in the JAVELIN Renal 101 dataset.

A three miRNAs panel in paraffin tissue serves as tool for predicting prognosis of renal cell carcinoma.

Renal cell carcinoma (RCC) stands as the most prevalent form of urogenital cancer. However, there is currently no universally accepted method for predicting the prognosis of RCC. MiRNA holds great potential as a prognostic biomarker for RCC. A total of 100 cases with complete paraffin specimens and over 5-year follow-up data meeting the requirements were collected. Utilizing the clinical information and follow-up data of the specimens, an information model was developed. The expression levels of eight microRNAs were identified using RT-qPCR. Finally, determine and analyze the clinical application value of these microRNAs as prognostic markers for RCC. Significant differences were observed in the expression of two types of miRNAs (miR-378a-5p, miR-23a-5p) in RCC tissue, and three types of miRNAs (miR-378a-5p, miR-642a-5p, miR-23a-5p) were found to be linked to the prognosis of RCC. Establish biomarker combinations of miR-378a-5p, miR-642a-5p, and miR-23a-5p to evaluate RCC prognosis. The combination of three microRNA groups (miR-378a-5p, miR-642a-5p, and miR-23a-5p) identified in paraffin section specimens of RCC in this study holds significant potential as biomarkers for assessing RCC prognosis.

Wide-Targeted Semi-Quantitative Analysis of Acidic Glycosphingolipids in Cell Lines and Urine to Develop Potential Screening Biomarkers for Renal Cell Carcinoma.

Glycosphingolipids (GSLs), mainly located in the cell membrane, play various roles in cancer cell function. GSLs have potential as renal cell carcinoma (RCC) biomarkers; however, their analysis in body fluids is challenging because of the complexity of numerous glycans and ceramides. Therefore, we applied wide-targeted lipidomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS) with selected reaction monitoring (SRM) based on theoretical mass to perform a comprehensive measurement of GSLs and evaluate their potency as urinary biomarkers. In semi-quantitative lipidomics, 240 SRM transitions were set based on the reported/speculated structures. We verified the feasibility of measuring GSLs in cells and medium and found that disialosyl globopentaosylceramide (DSGb5 (d18:1/16:0)) increased GSL in the ACHN medium. LC-MS/MS analysis of urine samples from clear cell RCC (ccRCC) patients and healthy controls showed a significant increase in the peak intensity of urinary DSGb5 (d18:1/16:0) in the ccRCC group compared with that in the control group. Receiver operating characteristic analysis indicated that urinary DSGb5 could serve as a sensitive and specific marker for RCC screening, with an AUC of 0.89. This study demonstrated the possibility of urinary screening using DSGb5 (d18:1/16:0). In conclusion, urinary DSGb5 (d18:1/16:0) was a potential biomarker for cancer screening, which could contribute to the treatment of RCC patients.

Pan-Cancer Analysis Identifies BCLAF1 as a Potential Biomarker for Renal Cell Carcinoma.

B-cell lymphoma-2-associated transcription factor 1 (BCLAF1) is a versatile protein involved in the regulation of gene transcription and post-transcriptional processing. Although BCLAF1 exerts a broad tumor suppressor effect or tumor promoter effect in many cancer types, the specific roles concerning its expression levels, and its impact on tumorigenesis in Renal cell carcinoma (RCC) remain unclear. Here, we utilized the Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) datasets alongside R software and online tools to unravel the specific roles of BCLAF1 in 33 cancer types, including its expression levels, tumor immune and molecular subtypes, and its correlation with prognosis, diagnosis, DNA methylation, and immune microenvironment. Additionally, we carried out cell biology experiments to independently investigate the expression of BCLAF1 in RCC and its effects on tumor progression. BCLAF1 was differentially expressed in tumor tissues compared to normal tissues across various cancer types and was also differentially expressed in different immune and molecular subtypes. In RCC, patients with high BCLAF1 expression had a better prognosis and BCLAF1 was tightly correlated with the stage, gender, and histological grade of patients. Furthermore, BCLAF1 had higher DNA methylation levels and higher immune infiltration levels in tumor tissues. Additionally, cell functional experiments confirmed the low expression of BCLAF1 in RCC and that BCLAF1 significantly inhibited the proliferation, migration, and invasion, while inducing apoptosis and cell cycle arrest in RCC cells in vitro. Our study under-scored the potential of BCLAF1 as an important actor in tumorigenesis, especially concerning RCC where it may serve as an effective prognostic marker.

Abstract 1056: Development of a B-cell epitope classifier for early detection of renal cell carcinoma

Abstract A blood biomarker for early detection of renal cell carcinoma (RCC) would help decrease the burden of 15,000 deaths annually in the U.S. (1). Antibodies against tumor (neo)antigens are thought to arise early in cancer development and might serve as a more sensitive biomarker for RCC than tumor-derived biomolecules. Serum Epitope Repertoire Analysis (SERA) of human serum/plasma uses a random bacterial peptide display library and next generation sequencing to allow for unbiased highly multiplexed profiling of B cell epitopes. In combination with bioinformatic methods and machine learning, a classifier was trained to predict the presence of RCC using a cohort of 163 treatment-naïve patients (median age 62, stage: I 35%, II 5%, III 20%, IV 40%) with clear cell RCC (ccRCC) and 438 age-matched healthy controls (median age 61). Classifier performance was evaluated by sensitivity, specificity, and receiver operator curve (ROC) area under the curve (AUC) analysis in an independent validation cohort of 43 treatment-naïve patients with ccRCC (age 22-86, median 63, stage: I 33%, II 2%, III 26%, IV 39%) and 1,759 healthy controls (age 18-89, median 40). A total of 1,465 individual B cell epitope features were discovered and enriched in ccRCC vs. controls and included in a random forest model. In the validation cohort, the ROC-AUC was 0.65, and sensitivities of 9%, 16%, 30%, and 36% were achieved at specificities of 99%, 98%, 95%, and 90%, respectively. Performance was better in both early stage ccRCC and low tumor grade (grade 1 or 2). For stage I, the ROC-AUC was 0.88, and sensitivities of 7%, 14%, 36%, and 57% were achieved at specificities of 99%, 98%, 95%, and 90%, respectively. For low tumor grade, the ROC-AUC was 0.86, and sensitivities of 10%, 20%, 50%, and 60% of were achieved at specificities of 99%, 98%, 95%, and 90%, respectively. The classifier did not miscategorize benign renal lesions as ccRCC (Mann-Whitney p-value 0.52, ROC-AUC 0.54) in a separate cohort of 23 patients with benign renal tumors vs. the 1,759 healthy controls. This preliminary performance for a B cell epitope-based classifier is promising: the better performance in early stage ccRCC suggests that B cell epitope biomarkers may complement the lower sensitivity of cell-free DNA for detecting early-stage cancer (2). Future work will further validate these results in larger multi-institutional cohorts to improve classification performance and explore if B cell epitope biomarkers can be combined with existing assays to develop an early detection composite biomarker for RCC and tumor grade. (1)Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023 Jan;73(1):17-48.(2)Francini, E.; Fanelli, G.N.; Pederzoli, F.; Spisak, S.; Minonne, E.; Raffo, M.; Pakula, H.; Tisza, V.; Scatena, C.; Naccarato, A.G.; et al. Circulating Cell-Free DNA in Renal Cell Carcinoma: The New Era of Precision Medicine. Cancers 2022, 14, 4359. Citation Format: Thomas W. Campbell, Christian R. Hoerner, Elizabeth Alli, John T. Leppert, John C. Shon, Alice C. Fan. Development of a B-cell epitope classifier for early detection of renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1056.

Abstract 3689: Optimizing CTC isolation techniques for molecular characterization of circulating tumor cells in clear cell renal cell carcinoma: A comparative study of EpCAM-based and density-based methods

Abstract Introduction: Renal cell carcinoma (RCC) belongs to the most invasive cancers with the high molecular intrapatient heterogeneity. Circulating tumor cells (CTCs) are cells that detach from the tumor, reflecting the activity of the metastatic procedure. The most popular CTCs isolation methods are based on EpCAM signal detection on epithelial tumor cells. The clear cell RCC (ccRCC) originate from epithelial entity, since that is the EpCAM-based method suitable to isolate CTCs in this cancer. However, the prone of CTCs to EMT is known, which may in consequence downregulate the EpCAM expression and change the cell profile (hybrid phenotype). This is one of the points challenging the CTCs isolation by RCC disease. In our study, we compare the effectivity of antibody-based and density-based isolations technics in ccRCC patients to detect potential markers in CTCs. Purpose: Our aim was to find a precise and costs effective method to isolate and characterize CTCs from ccRCC patients. Materials and Methods: Prospective blood samples (3mL) from pulmonary and/or osseous metastatic ccRCC patients were collected during the immunotherapy and from the healthy donors. CTCs were isolated using EpCAM beads (RCC n=13; healthy n=15) or density-based (RCC n=13; healthy n=15) method. Expression of potential RCC biomarkers (mucin-1 (Muc1), androgen receptor (AR)) was identified using qPCR (Real-Time PCR) followed by dPCR (digital PCR), the results status received from both methods was evaluated. Summary: Based on our prior results, we first isolated CTCs based on EpCAM beads. Using qPCR we analyzed expression of genes, which we previously associated to the RCC (Muc1, PD-L1, AR). The results revealed differences in Muc1 (40% of tested patients positive but none of the healthy donors), when PD-L1 did not differed and AR was not detected. Applying more precise dPCR did neither show any expression. We continued our investigations with isolation of CTCs using density-based method. We confirmed more frequent expression of Muc1 by RCC Patients than by donors, as in our EpCAM cohort. PD-L1 was similar in both groups. Results of AR obtained using qPCR were confusing, showing deviations within same patients. The use of dPCR displayed significant differences in expression between RCC patients (77%; median [range] 0.28 [0.00 - 0.62] and donors (60%; 0.04 [0.00 - 0.51] confirming dPCR as optimal tool for molecular CTC characterization. Conclusions: Based on our results we demonstrate higher expression of Muc1 and AR in CTCs of metastatic ccRCC patients. The isolation technic should be adapted to the disease status and treatment choice. Further investigations confirming Muc1 and AR as ccRCC biomarkers are required. Citation Format: Joanna Bialek, Anne Muthe, Stefan Yankulov, Felix Kawan, Georgios Gakis, Gerit Theil. Optimizing CTC isolation techniques for molecular characterization of circulating tumor cells in clear cell renal cell carcinoma: A comparative study of EpCAM-based and density-based methods [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3689.

NLR Outperforms Low Hemoglobin and High Platelet Count as Predictive and Prognostic Biomarker in Metastatic Renal Cell Carcinoma Treated with Immune Checkpoint Inhibitors

BackgroundReliable biomarkers in renal cell carcinoma (RCC) remain elusive. While several markers have been shown to be associated with prognosis, and may aid in risk assessment, predictive biomarkers of response to immune checkpoint inhibitors (ICIs) have not been established. Previous studies have shown that a high pretreatment neutrophil-lymphocyte ratio (NLR) is a negative prognostic factor in RCC. However, a clinically useful cut-off for the predictive and prognostic value of NLR has not been well defined. MethodsWe conducted a retrospective analysis of 132 patients with previously untreated metastatic clear cell RCC (ccRCC) who received first line ICI-based therapy. ICI-based therapy included anti-PD-1/PD-L1 alone or in combination with anti-CTLA-4 or VEGF-TKI. Platelet, haemoglobin, neutrophil and lymphocyte counts were collected prior to treatment and at 12-weeks after treatment initiation. Radiologic response at 12-weeks and overall survival (OS) data was also collected. ResultsLow haemoglobin, high platelet count, and NLR ≥3 were statistically significant negative predictive biomarkers when assessed at 12-weeks, but not at baseline. Median OS was shorter in patients with low haemoglobin (20.3 months vs. 51.6 months, P = .009), high platelet count (14.3 months vs. 43.8 months, P = .003), and NLR ≥ 3 (17.5 months vs. 40.3 months, P < .001) at 12-weeks. In an IMDC-risk adjusted analysis, only NLR ≥3 at 12-weeks remained statistically significant (OR of 2.11, P = .003) A dynamic change towards lower absolute NLR overtime was associated with longer OS. In patients who had baseline NLR ≥ 3, those who achieved NLR < 3 at 12-weeks demonstrated significant longer median OS compared to those whose NLR remained persistently ≥ 3 (40.3 months vs. 14.7 months, P = .004). ConclusionNLR ≥3, low haemoglobin and elevated platelet count after 12-weeks of ICI-based first line therapy were negatively prognostic and predictive in patients with metastatic RCC. Normalization of NLR in patients with baseline elevation was associated with longer median OS and response to therapy. These results suggest that monitoring of routine haematologic biomarkers during therapy may provide important predictive and prognostic information, beyond what is available with baseline risk assessment scoring systems.

KIDNEY-PAGER: analysis of circulating tumor DNA as a biomarker in renal cancer - an observational trial. Study protocol.

Management of localized renal cell carcinoma (RCC) is challenged by inaccurate methods to assess the risk of recurrence and deferred detection of relapse and residual disease after radical or partial nephrectomy. Circulating tumor DNA (ctDNA) has been proposed as a potential biomarker in RCC. Conduction of an observational study to evaluate the validity of ctDNA as a biomarker of the risk of recurrence and subclinical residual disease to improve postoperative surveillance. Urine and blood will be prospectively collected before and after surgery of the primary tumor from up to 500 patients until 5 years of follow-up. ctDNA analysis will be performed using shallow whole genome sequencing and cell-free methylated DNA immunoprecipitation sequencing. ctDNA levels in plasma and urine will be correlated to oncological outcomes. Residual blood and urine as well as tissue biopsies will be biobanked for future research. Results will pave the way for future ctDNA-guided clinical trials aiming to improve RCC management.

Diagnostic, prognostic, and therapeutic potential of exosomal microRNAs in renal cancer.

Renal cell carcinoma (RCC) arises from the tubular epithelial cells of the nephron. It has the highest mortality rate among urological cancers. There are no effective therapeutic approaches and no non-invasive biomarkers for diagnosis and follow-up. Thus, suitable novel biomarkers and therapeutic targets are essential for improving RCC diagnosis/prognosis and treatment. Circulating exosomes such as exosomal microRNAs (Exo-miRs) provide non-invasive prognostic/diagnostic biomarkers and valuable therapeutic targets, as they can be easily isolated and quantified and show high sensitivity and specificity. Exosomes secreted by an RCC can exhibit alterations in the miRs' profile that may reflect the cellular origin and (patho)physiological state, as a ''signature'' or ''fingerprint'' of the donor cell. It has been shown that the transportation of renal-specific miRs in exosomes can be rapidly detected and measured, holding great potential as biomarkers in RCC. The present review highlights the studies reporting tumor microenvironment-derived Exo-miRs with therapeutic potential as well as circulating Exo-miRs as potential diagnostic/prognostic biomarkers in patients with RCC.

Use of Droplet Digital Polymerase Chain Reaction to Identify Biomarkers for Differentiation of Benign and Malignant Renal Masses.

Several microRNAs (miRNAs) have been identified as cell-free biomarkers for detecting renal cell carcinoma (RCC). Droplet digital polymerase chain reaction (ddPCR) is a unique technology for nucleic acid quantification. It has the potential for superior precision, reproducibility, and diagnostic performance in identifying circulating miRNA biomarkers compared to conventional quantitative real-time PCR (qRT-PCR). This study aims to evaluate the performance of ddPCR compared to qRT- PCR in identifying miRNA biomarkers that differentiate malignant from benign renal masses. Potential biomarkers of RCC were identified from a literature review. RNA was extracted from the plasma of 56 patients. All the samples underwent analysis via ddPCR as well as qRT-PCR, and expression levels were recorded for the following miRNAs: miR-93, -144, -210, -221, and -222. Tumors were grouped into low-grade ccRCC, high-grade ccRCC, papillary RCC, and benign masses (primarily angiomyolipoma). The miRNA miR-210 (p = 0.034) and the combination of miRs-210 and miR-222 (p = 0.003) were expressed at significantly higher rates among those with RCC than those with benign masses, as measured by ddPCR. Using the combination of miR-210 and miR-222, ddPCR identified significant differences between the subgroups: papillary RCC versus benign (p = 0.03), low-grade ccRCC versus benign (p = 0.026), and high-grade ccRCC versus benign (p = 0.002). The only significant difference between these subgroups using qRT-PCR was between high-grade ccRCC and benign (p = 0.045). All the AUCs were significant when comparing each RCC subgroup with benign for both PCR technologies. Using a combination of miR-210 and miR-222, ddPCR identified significant differences between benign and malignant renal masses that were not identified as significant by conventional qRT-PCR.

An ensemble learning-based feature selection algorithm for identification of biomarkers of renal cell carcinoma.

Feature selection plays a crucial role in classification tasks as part of the data preprocessing process. Effective feature selection can improve the robustness and interpretability of learning algorithms, and accelerate model learning. However, traditional statistical methods for feature selection are no longer practical in the context of high-dimensional data due to the computationally complex. Ensemble learning, a prominent learning method in machine learning, has demonstrated exceptional performance, particularly in classification problems. To address the issue, we propose a three-stage feature selection algorithm framework for high-dimensional data based on ensemble learning (EFS-GINI). Firstly, highly linearly correlated features are eliminated using the Spearman coefficient. Then, a feature selector based on the F-test is employed for the first stage selection. For the second stage, four feature subsets are formed using mutual information (MI), ReliefF, SURF, and SURF* filters in parallel. The third stage involves feature selection using a combinator based on GINI coefficient. Finally, a soft voting approach is proposed to employ for classification, including decision tree, naive Bayes, support vector machine (SVM), k-nearest neighbors (KNN) and random forest classifiers. To demonstrate the effectiveness and efficiency of the proposed algorithm, eight high-dimensional datasets are used and five feature selection methods are employed to compare with our proposed algorithm. Experimental results show that our method effectively enhances the accuracy and speed of feature selection. Moreover, to explore the biological significance of the proposed algorithm, we apply it on the renal cell carcinoma dataset GSE40435 from the Gene Expression Omnibus database. Two feature genes, NOP2 and NSUN5, are selected by our proposed algorithm. They are directly involved in regulating m5c RNA modification, which reveals the biological importance of EFS-GINI. Through bioinformatics analysis, we shows that m5C-related genes play an important role in the occurrence and progression of renal cell carcinoma, and are expected to become an important marker to predict the prognosis of patients.

Update on Biomarkers in Renal Cell Carcinoma.

Immune checkpoint inhibitors have significantly transformed the treatment paradigm for metastatic renal cell carcinoma (RCC), offering prolonged overall survival and achieving remarkable deep and durable responses. However, given the multiple ICI-containing, standard-of-care regimens approved for RCC, identifying biomarkers that predict therapeutic response and resistance is of critical importance. Although tumor-intrinsic features such as pathological characteristics, genomic alterations, and transcriptional signatures have been extensively investigated, they have yet to provide definitive, robust predictive biomarkers. Current research is exploring host factors through in-depth characterization of the immune system. Additionally, innovative technological approaches are being developed to overcome challenges presented by existing techniques, such as tumor heterogeneity. Promising avenues in biomarker discovery include the study of the microbiome, radiomics, and spatial transcriptomics.