Renal Cell Carcinoma Dataset Research Articles

Evaluating trophinin associated protein as a biomarker of prognosis and therapy response in renal cell carcinoma

BackgroundTrophinin Associated Protein (TROAP) has been implicated in some tumors, yet its role in renal cell carcinoma (RCC) remains underexplored. This study aims to elucidate the prognostic and therapeutic implications of TROAP in RCC, encompassing different subtypes.MethodsFirstly, we identified the expression patterns of TROAP across various tumors within the TCGA pan-cancer cohort. Subsequently, the prognostic significance of TROAP was validated in three TCGA RCC cohorts and a local cohort. Finally, we conducted functional enrichment analysis, somatic mutations and copy number variations, assessed therapeutic response cohorts, and performed in vitro experiments to explore the biological characteristics of TROAP.ResultsTROAP serves as an unfavorable factor in both the TCGA RCC datasets and our local cohort. Functional enrichment analysis and in vitro experiments have demonstrated its oncogene effect in promoting tumor progression. Additionally, the relationship between TROAP expression and gene mutations in RCC appears to be limited. Furthermore, elevated TROAP expression is associated with reduced efficacy of RCC therapies, including nivolumab and everolimus.ConclusionsOur findings illustrate TROAP as a pivotal biomarker for prognosis and therapeutic response in RCC. Elevated TROAP expression is indicative of aggressive tumor behavior and resistance to conventional therapies, making it a valuable target for personalized treatment strategies in RCC management.

A novel convolution transformer-based network for histopathology-image classification using adaptive convolution and dynamic attention

Renal cell carcinoma (RCC), which is the primary subtype of kidney cancer, is among the leading causes of cancer. Recent breakthroughs in computer vision, particularly deep learning, have revolutionized the analysis of histopathology images, thus providing potential solutions for tasks such as the grading of renal cell carcinoma. Nevertheless, the multitude of available neural network architectures and the absence of systematic evaluations render it challenging to identify optimal models and training configurations for distinct histopathology classification tasks. Hence, we propose a novel hybrid model that effectively combines the advantages of vision transformers and convolutional neural networks. The proposed method, which is named the renal cancer grading network, comprises two essential components: an adaptive convolution (AC) block and a dynamic attention (DA) block. The AC block emphasizes efficient feature extraction and spatial representation learning via intelligently designed convolutional operations. The DA block, which is constructed on the features of the AC block, is a crucial module for histopathology-image classification. It introduces a dynamic attention mechanism and employs a transformer encoder to refine learned representations. Experiments were conducted on four publicly available histopathology datasets: RCC dataset of Kasturba medical college (KMC), colorectal cancer histology (CRCH), break cancer histology (BreakHis) and colon cancer histopathology dataset (CCH). The proposed method demonstrated an accuracy of 90.62%, precision of 91.23%, recall of 90.63%, and a weighted harmonic mean of precision and recall (F1-score) of 90.92 on the KMC dataset. Similarly, the proposed method demonstrates consistent accuracy (weighted average F1-score of 99%) on the CRCH dataset, recognition rate of 88.30% on the BreakHis dataset, and an accuracy of 99.7% on CCH dataset. These results confirm that our method outperforms the state-of-the-art methods, thus demonstrating its effectiveness and robustness across various datasets.

Construction of exosome-related genes risk model in kidney cell carcinoma predicts prognosis and immune therapy response.

Renal cell carcinoma (RCC) is one of the most prevalent types of urological cancer. Exosomes are vesicles derived from cells and have been found to promote the development of RCC, but the potential biomarker and molecular mechanism of exosomes on RCC remain ambiguous. Here, we first screened differentially expressed exosome-related genes (ERGs) by analyzing The Cancer Genome Atlas (TCGA) database and exoRBase 2.0 database. We then determined prognosis-related ERGs (PRERGs) by univariate Cox regression analysis. Gene Dependency Score (gDS), target development level, and pathway correlation analysis were utilized to examine the importance of PRERGs. Machine learning and lasso-cox regression were utilized to screen and construct a 5-gene risk model. The risk model showed high predictive accuracy for the prognosis of patients and proved to be an independent prognostic factor in three RCC datasets, including TCGA-KIRC, E-MTAB-1980, and TCGA-KIRP datasets. Patients with high-risk scores showed worse outcomes in different clinical subgroups, revealing that the risk score is robust. In addition, we found that immune-related pathways are highly enriched in the high-risk group. Activities of immune cells were distinct in high-/low-risk groups. In independent immune therapeutic cohorts, high-risk patients show worse immune therapy responses. In summary, we identified several exosome-derived genes that might play essential roles in RCC and constructed a 5-gene risk signature to predict the prognosis of RCC and immune therapy response.

TCR CDR3-CMV Antigen Chemical Complementaries Are Associated With a Worse Outcome for Renal Cell Carcinoma.

Due to still unresolved questions regarding viruses as either a primary cause or a comorbidity in cancer, we examined a potential immune response to cytomegalovirus (CMV) in the renal cell carcinoma (RCC) setting using genomics and bioinformatics approaches. Specifically, we assessed chemical complementarity scores (CSs) for solid tissue normal resident, T-cell receptor (TCR) complementarity determining region 3 (CDR3s) and CMV antigens and determined whether higher or lower CS groups were associated with a higher or lower survival probability. This was indeed the case, with all such analyses consistently indicating a lower overall and progression-free survival for the cases representing the higher TCR CDR3-CMV antigen chemical CSs. This basic result was obtained for two separate RCC datasets and multiple CMV antigens. The results raise the question, to what extent a systemic CMV infection may represent an important co-morbidity for RCC.

Effect of Annexin A2 on prognosis and sensitivity to immune checkpoint plus tyrosine kinase inhibition in metastatic renal cell carcinoma

BackgroundImmunotherapy (IO) plus tyrosine kinase inhibitor (TKI) therapy is the first-line recommendation for advanced renal cell carcinoma (RCC), but no biomarker has been approved for it. Annexin A2 (ANXA2) can induce immune escape in tumors.MethodsTwo independent cohorts of advanced RCC treated by IO + TKI were utilized for survival analysis (ZS-MRCC, n = 45; Javelin-101, n = 726). ANXA2 expression was determined by RNA-sequencing. The impact of ANXA2 on the tumor microenvironment was assessed by RNA-sequencing, flow cytometry and immunohistochemistry in two localized RCC datasets (ZS-HRRCC, n = 40; TCGA-KIRC, n = 530).ResultsANXA2 was upregulated in non-responders of IO + TKI therapy (p = 0.027). High-ANXA2 group showed poor progression-free survival (PFS) in both the ZS-MRCC cohort (HR, 2.348; 95% CI 1.084–5.085; P = 0.025) and the Javelin-101 cohort (HR, 1.472; 95% CI 1.043–2.077; P = 0.027). Multivariate Cox regression determined ANXA2 as an independent prognostic factor (HR, 2.619; 95% CI 1.194–5.746; P = 0.016). High-ANXA2 was correlated with decreased proportion of granzyme B+ CD8+ T cells (Spearman’s ρ = − 0.40, P = 0.01), and increased TIM-3+ (Spearman’s ρ = 0.43, P < 0.001) and CTLA4+ (Spearman’s ρ = 0.49, P < 0.001) tumor-infiltrating lymphocytes. A random forest (RF) score was further build by integrating ANXA2 and immune genes, which stratified patients who would benefit from IO + TKI therapy (low-RF score, IO + TKI vs TKI, HR = 0.453, 95% CI 0.328–0.626; high-RF score, IO + TKI vs TKI, HR = 0.877, 95% CI 0.661–1.165; interaction P = 0.003).ConclusionsUpregulated ANXA2 was associated with poor PFS and therapeutic resistance in RCC treated by IO + TKI therapy, and related with T cell exhaustion. The integrated RF score could stratify patients who would benefit from IO + TKI therapy.

TYROBP as a molecular target in cholangiocarcinoma, renal cancer and abdominal aortic aneurysm.

Cholangiocarcinoma occurs when there is a malignant tumor in the bile duct system. Renal cancer originates from renal tubular epithelial cells. Abdominal aortic aneurysm (AAA) is a permanently localized dilation caused by a lesion or injury to abdominal aortic wall. However, the relationship between TYROBP and cholangiocarcinoma, renal cancer and AAA remains unclear. The profiles of cholangiocarcinoma dataset GSE107943, renal cell carcinoma dataset GSE213324, and AAA dataset GSE47472 were downloaded from the GEO database using the platforms GPL18573, GPL24676, and GPL10558. DEGs were screened, WGCNA was performed as well as construction and analysis of PPI network. Functional enrichment analysis, GSEA, heat map of gene expression, survival analysis, and immune infiltration analysis were performed. The most relevant diseases to core genes were found by CTD. The GSE107943 dataset identified 3383 DEGs for cholangiocarcinoma, GSE47472 identified 95 DEGs for abdominal aortic aneurysm, and GSE213324 identified 10245 DEGs for renal cell carcinoma. For the GSE107943 cholangiocarcinoma dataset, GO analysis revealed enrichment in immune response, cell adhesion, extracellular space, and oxidoreductase activity. KEGG analysis indicated enrichment in metabolic pathways, the PI3K-Akt signaling pathway, cell apoptosis, the cell cycle, and the NF-kappa B signaling pathway. In the GSE47472 AAA dataset, GO analysis showed enrichment in neuroblast differentiation, cardiac muscle myofilament complex, and alkaline binding. KEGG analysis indicated enrichment in mRNA surveillance pathway and purine metabolism. In the GSE213324 renal cell carcinoma dataset, GO analysis indicated enrichment in immune system processes, cell adhesion, and membrane parts. KEGG analysis showed enrichment in cytokine-cytokine receptor interaction, calcium signaling pathway, and hematopoietic cell lineage. Furthermore, for cholangiocarcinoma (GSE107943), enriched terms associated with DEGs were in metabolic pathways, cell apoptosis, and the cell cycle. For AAA (GSE47472), enriched terms were in alkaline binding and cellular redox homeostasis. For renal cell carcinoma (GSE213324), enriched terms were in biological adhesion, regulation of immune system processes, and cell surface. Common core genes (ADH6, AGXT, CYP3A43, TYROBP) were identified for cholangiocarcinoma, renal cell carcinoma, and AAA. ADH6 and TYROBP were associated with cholangiocarcinoma, AAA, renal tumors, kidney diseases, atherosclerosis, and inflammation. TYROBP is abnormally expressed in cholangiocarcinoma, renal cancer and abdominal aortic aneurysm.

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.

Preoperative prognostic model for localized and locally advanced renal cell carcinoma: Michinoku Japan Urological Cancer Study Group.

The Modified International Metastatic Renal Cell Carcinoma Dataset Consortium model (mIMDC) is a preoperative prognostic model for pT3cN0M0 renal cell carcinoma (RCC). This study aimed to validate the mIMDC and to construct a new model in a localized and locally advanced RCC (LLRCC). A database was established (the Michinoku Japan Urological Cancer Study Group database) consisting of 79 patients who were clinically diagnosed with LLRCC (cT3b/c/4NanyM0) and underwent radical nephrectomy from December 2007 to May 2018. Using univariable and multivariable analyses, we retrospectively analyzed disease-free survival (DFS) and overall survival (OS) in this database, constructed a new prognostic model according to these results, and estimated the model fit using c-index on the new and mIMDC models. Independent poorer prognostic factors for both DFS and OS include the following: ≥ 1 Eastern Cooperative Oncology Group performance status, 2.0mg/dL C-reactive protein, and > upper normal limit of white blood cell count. The median DFS in the favorable (no factor), intermediate (one factor), and poor-risk group (two or three factors) was 76.1, 14.3, and 4.0 months, respectively (P < 0.001). The 3-year OS in the favorable, intermediate, and poor-risk group were 92%, 44%, and 0%, respectively (P < 0.001). The c-indices of the new and mIMDC models were 0.67 and 0.60 for DFS (P = 0.060) and 0.74 and 0.63 for OS (P = 0.012), respectively. The new preoperative prognostic model in LLRCC can be used in patient care and clinical trials.

Abstract B015: Understanding cell heterogeneity in single-cell renal cell carcinoma datasets

Abstract Studying the heterogeneity in RCC may provide insights for improved diagnosis and treatment. Cell heterogeneity may be reflected at the mRNA level, and cell deconvolution tools like CIBERSORT have been used to decipher cell-specific signals in bulk RNA interrogation. Single-cell RNA seq technologies (scRNA-seq) enable dissecting cell heterogeneity in tumor samples further. An exploratory scRNA-seq study was conducted on three subtypes of RCC: 2 samples of clear cell RCC (ccRCC1 and ccRCC2), 1 sample of type-2 papillary RCC (pRCC), and 1 sample of chromophobe RCC (chRCC) (Su et al., 2021). This study provided valuable insights, such as delineating new tumor markers and predicting drug responses. As we advance our understanding of scRNA-seq, additional tools allow annotating of more cell types and exploring ploidy levels. Here we reanalyze this dataset further to illustrate the tumor microenvironment heterogeneity in RCC samples. We downloaded the datasets (GSE152938) from Gene Expression Omnibus and reprocessed the data using Seurat. The cellular composition and ploidy levels were explored using Azimuth and CopyKAT. A total of 30261 high-quality single-cell transcriptomic information of the RCC samples were obtained for the downstream analysis. The RCC subtypes showed both intra and inter-tumoral heterogeneity. Within the clear cell carcinomas, the ccRCC2 sample showed about 63.57% immune cell infiltration compared to 45.68% in ccRCC1. Rare subpopulations of 0.86% fibroblast and 0.81% mast cells were observed only in ccRCC1. In contrast, 1.56% of B cells were observed only in ccRCC2. Using an improved cell deconvolution algorithm, we identified natural killer T cells, neutrophils, and plasma cells in the ccRCC samples that were not reported in the original publication. We also identified five subtypes of endothelial cells (instead of the two reported in the original article) which may further our understanding of angiogenesis and immune regulation in these ccRCC samples. Our analysis showed that the macrophages in the ccRCC and pRCC samples are predominantly M2 macrophages. In the chRCC sample, we also identified 0.2% B cells and 2.19% M2 macrophage subpopulations, which may further illuminate the immune landscape of the chRCC tumor microenvironment. We found the ccRCC sample to be the most immune-rich with about 56.49% immune infiltration, followed by the pRCC (46.88% infiltration) and chRCC (8.86% infiltration) samples. In the combined ccRCC samples, three of the four ccRCC cell clusters were predominantly diploids (85.6%, 97.8%, and 60%, respectively), whereas only one showed a higher proportion of aneuploidy (95.8%). Further studies with a larger sample are required to validate these observations. This reanalysis of previously published data may enrich the results of the original manuscript and provide additional information about the tumor microenvironment of these RCC subtypes. We will integrate these findings with our more extensive collection of scRNA-seq at our institution, for further interrogating and validating these results. Citation Format: Sadia Islam Kana, Lucas A. Salas. Understanding cell heterogeneity in single-cell renal cell carcinoma datasets [abstract]. In: Proceedings of the AACR Special Conference: Advances in Kidney Cancer Research; 2023 Jun 24-27; Austin, Texas. Philadelphia (PA): AACR; Cancer Res 2023;83(16 Suppl):Abstract nr B015.

Abstract PR013: An integrative genomics approach converges on a critical OXPHOS program driven by MiT/TFE fusions in translocation renal cell carcinoma

Abstract Translocation renal cell carcinoma (tRCC) is a rare and aggressive subtype of non-clear cell renal cell carcinoma (RCC) that represents 1-5% of kidney cancer in adults. Biologically, tRCC is driven by rearrangements that involve a member of the MiT/TFE transcription factor gene family, most commonly TFE3. There are currently no molecularly-targeted therapies specific to tRCC, owing in part to an incomplete mechanistic understanding of precisely how MiT/TFE gene fusions exert their oncogenic function. In prior work, we leveraged “histologic overlap” between tRCC and other RCC subtypes to identify tRCC cases from across multiple genomic, clinical trial, and retrospective RCC datasets, with the goal of defining the molecular landscape of this disease. Here, by analyzing pathways selectively activated in tRCC relative to other RCC subtypes in this aggregate dataset, we observed that tRCCs display strong enrichment for genes involved in oxidative phosphorylation (OXPHOS), reactive oxidative species (ROS) sensing, and the response to oxidative stress mediated by the transcription factor NRF2 (nuclear factor erythroid-derived2-like 2 [NFE2L2]). We performed chromatin immunoprecipitation and sequencing (ChIP-Seq) on a panel of tRCC and clear-cell RCC (ccRCC) cell lines, using antibodies against TFE3 as well as the active enhancer mark H3K27ac. We found that tRCC cell lines display a distinctive epigenomic profile characterized by activation of a host of genes involved in driving OXPHOS as well as NRF2 signaling. We further found that TFE3 fusions and NRF2 physically interact and genomically co-localize at multiple genes critical for mediating the response to antioxidant stress, suggesting that this metabolic phenotype is transcriptionally driven by TFE3 fusions. Genome-scale CRISPR screening in tRCC cell lines subsequently revealed tRCC-selective vulnerabilities in components of the OXPHOS machinery and in the NRF2 pathway, a distinction from most other kidney cancers in which aerobic glycolysis predominates. Overall, our genomic and functional studies implicate an essential transcriptional program driven by TFE3 fusions that converges on activation of the OXPHOS and NRF2 pathways. These molecular features provide mechanistic rationale for exploring the therapeutic targeting of OXPHOS and/or redox homeostasis pathways in tRCC. Citation Format: Jiao Li, Danny S. Gallant, Ananthan Sadagopan, Bingchen Li, Qingru Xu, Srinivas R. Viswanathan. An integrative genomics approach converges on a critical OXPHOS program driven by MiT/TFE fusions in translocation renal cell carcinoma [abstract]. In: Proceedings of the AACR Special Conference: Advances in Kidney Cancer Research; 2023 Jun 24-27; Austin, Texas. Philadelphia (PA): AACR; Cancer Res 2023;83(16 Suppl):Abstract nr PR013.

Normalization methods in mass spectrometry-based analytical proteomics: A case study based on renal cell carcinoma datasets

Normalization is a crucial step in proteomics data analysis as it enables data adjustment and enhances comparability between datasets by minimizing multiple sources of variability, such as sampling, sample handling, storage, treatment, and mass spectrometry measurements. In this study, we investigated different normalization methods, including Z-score normalization, median divide normalization, and quantile normalization, to evaluate their performance using a case study based on renal cell carcinoma datasets. Our results demonstrate that when comparing datasets by pairs, both the Z-score and quantile normalization methods consistently provide better results in terms of the number of proteins identified and quantified as well as in identifying statistically significant up or down-regulated proteins. However, when three or more datasets are compared at the same time the differences are found to be negligible.

New insights into fibrotic signaling in renal cell carcinoma.

Fibrotic signaling plays a pivotal role in the development and progression of solid cancers including renal cell carcinoma (RCC). Intratumoral fibrosis (ITF) and pseudo-capsule (PC) fibrosis are significantly correlated to the disease progression of renal cell carcinoma. Targeting classic fibrotic signaling processes such as TGF-β signaling and epithelial-to-mesenchymal transition (EMT) shows promising antitumor effects both preclinically and clinically. Therefore, a better understanding of the pathogenic mechanisms of fibrotic signaling in renal cell carcinoma at molecular resolution can facilitate the development of precision therapies against solid cancers. In this review, we systematically summarized the latest updates on fibrotic signaling, from clinical correlation and molecular mechanisms to its therapeutic strategies for renal cell carcinoma. Importantly, we examined the reported fibrotic signaling on the human renal cell carcinoma dataset at the transcriptome level with single-cell resolution to assess its translational potential in the clinic.

A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma

Background: Recently, researchers have been attracted in identifying the crucial genes related to cancer, which plays important role in cancer diagnosis and treatment. However, in performing the cancer molecular subtype classification task from cancer gene expression data, it is challenging to obtain those significant genes due to the high dimensionality and high noise of data. Moreover, the existing methods always suffer from some issues such as premature convergence. Methods: To address those problems, we propose a new ant colony optimization (ACO) algorithm called DACO to classify the cancer gene expression datasets, identifying the essential genes of different diseases. In DACO, first, we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed; then, a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution; finally, the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally. To demonstrate the performance of the proposed algorithm in classification, different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets. Results: The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets. It can be used to address the classification problem effectively. Moreover, a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses. Conclusion: The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.

HistoPerm: A permutation-based view generation approach for improving histopathologic feature representation learning

Deep learning has been effective for histology image analysis in digital pathology. However, many current deep learning approaches require large, strongly- or weakly labeled images and regions of interest, which can be time-consuming and resource-intensive to obtain. To address this challenge, we present HistoPerm, a view generation method for representation learning using joint embedding architectures that enhances representation learning for histology images. HistoPerm permutes augmented views of patches extracted from whole-slide histology images to improve classification performance. We evaluated the effectiveness of HistoPerm on 2 histology image datasets for Celiac disease and Renal Cell Carcinoma, using 3 widely used joint embedding architecture-based representation learning methods: BYOL, SimCLR, and VICReg. Our results show that HistoPerm consistently improves patch- and slide-level classification performance in terms of accuracy, F1-score, and AUC. Specifically, for patch-level classification accuracy on the Celiac disease dataset, HistoPerm boosts BYOL and VICReg by 8% and SimCLR by 3%. On the Renal Cell Carcinoma dataset, patch-level classification accuracy is increased by 2% for BYOL and VICReg, and by 1% for SimCLR. In addition, on the Celiac disease dataset, models with HistoPerm outperform the fully supervised baseline model by 6%, 5%, and 2% for BYOL, SimCLR, and VICReg, respectively. For the Renal Cell Carcinoma dataset, HistoPerm lowers the classification accuracy gap for the models up to 10% relative to the fully supervised baseline. These findings suggest that HistoPerm can be a valuable tool for improving representation learning of histopathology features when access to labeled data is limited and can lead to whole-slide classification results that are comparable to or superior to fully supervised methods.

Sequential reinforcement active feature learning for gene signature identification in renal cell carcinoma.

Renal cell carcinoma (RCC) is one of the deadliest cancers and mainly consists of three subtypes: kidney clear cell carcinoma (KIRC), kidney papillary cell carcinoma (KIRP), and kidney chromophobe (KICH). Gene signature identification plays an important role in the precise classification of RCC subtypes and personalized treatment. However, most of the existing gene selection methods focus on statically selecting the same informative genes for each subtype, and fail to consider the heterogeneity of patients which causes pattern differences in each subtype. In this work, to explore different informative gene subsets for each subtype, we propose a novel gene selection method, named sequential reinforcement active feature learning (SRAFL), which dynamically acquire the different genes in each sample to identify the different gene signatures for each subtype. The proposed SRAFL method combines the cancer subtype classifier with the reinforcement learning (RL) agent, which sequentially select the active genes in each sample from three mixed RCC subtypes in a cost-sensitive manner. Moreover, the module-based gene filtering is run before gene selection to filter the redundant genes. We mainly evaluate the proposed SRAFL method based on mRNA and long non-coding RNA (lncRNA) expression profiles of RCC datasets from The Cancer Genome Atlas (TCGA). The experimental results demonstrate that the proposed method can automatically identify different gene signatures for different subtypes to accurately classify RCC subtypes. More importantly, we here for the first time show the proposed SRAFL method can consider the heterogeneity of samples to select different gene signatures for different RCC subtypes, which shows more potential for the precision-based RCC care in the future.

Tumor-Educated Platelets as a Promising Biomarker for Blood-Based Detection of Renal Cell Carcinoma.

PurposeTumor-educated platelets (TEPs) are a promising liquid biopsy in many cancers. However, their role in renal cell carcinoma (RCC) is unknown. Thus, this study explored the diagnostic value of TEPs in RCC patients.MethodsPlatelets were prospectively collected from 24 RCC patients and 25 controls. RNA-seq was performed to identify the differentially expressed genes (DEGs) between RCC patients and controls. Besides, RNA-seq data of pan-cancer TEPs were downloaded and randomly divided into training and validation sets. A pan-cancer TEP model was developed in the training set using the support vector machine (SVM) and validated in the validation set and our RCC dataset. Finally, an RCC-based TEP model was developed and optimized through the SVM algorithms and recursive feature elimination (RFE) method.ResultTwo hundred three DEGs, 64 (31.5%) upregulated and 139 (68.5%) downregulated, were detected in the platelets of RCC patients compared with controls. The pan-cancer TEP model had a high accuracy in detecting cancer in the internal validation (training set, accuracy 98.8%, AUC: 0.999; validation set, accuracy 95.4%, AUC: 0.972; different tumor subtypes, accuracy 86.6%–96.1%, AUC: 0.952–1.000). However, the pan-cancer TEP model in the external validation had a scarce diagnostic value in RCC patients (accuracy 48.7%, AUC: 0.615). Therefore, to develop the RCC-based TEP model, the gene biomarkers mostly contributing to the model were selected using the RFE method. The RCC-based TEP model containing 68 gene biomarkers reached a diagnostic accuracy of 100% (AUC: 1.000) in the training set, 88.9% (AUC: 0.963) in the validation set, and 95.9% (AUC: 0.988) in the overall cohort.ConclusionTEPs could function as a minimally invasive blood biomarker in the detection of RCC.

A large-scale assay library for targeted protein quantification in renal cell carcinoma tissues.

Renal cell carcinoma (RCC) represents 2.2% of all cancer incidences; however, prognostic or predictive RCC biomarkers at protein level are largely missing. To support proteomics research of localized and metastatic RCC, we introduce a new library of targeted mass spectrometry assays for accurate protein quantification in malignant and normal kidney tissue. Aliquots of 86 initially localized RCC, 75 metastatic RCC and 17 adjacent non-cancerous fresh frozen tissue lysates were trypsin digested, pooled, and fractionated using hydrophilic chromatography. The fractions were analyzed using LC-MS/MS on QExactive HF-X mass spectrometer in data-dependent acquisition (DDA) mode. A resulting spectral library contains 77,817 peptides representing 7960 protein groups (FDR=1%). Further, we confirm applicability of this library on four RCC datasets measured in data-independent acquisition (DIA) mode, demonstrating a specific quantification of a substantially increased part of RCC proteome, depending on LC-MS/MS instrumentation. Impact of sample specificity of the library on the results of targeted DIA data extraction was demonstrated by parallel analyses of two datasets by two pan human libraries. The new RCC specific library has potential to contribute to better understanding the RCC development at molecular level, leading to new diagnostic and therapeutic targets.

Integrated analysis of immune-related gene subtype and immune index for immunotherapy in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma (RCC) with high immunogenicity. Research on immune-related gene (IRG) is of great significance in ccRCC in identifying new therapeutic targets and improving patient prognosis. In this study, the IRG patterns of ccRCC were investigated and correlated these patterns with tumor microenvironment infiltrating characteristics in immunotherapy. Moreover, an IRG score was constructed to quantify the pattern of individual tumors through the principal component analysis algorithm. Two distinct molecular subtypes (C1 and C2) were identified based on the IRGs expression profile. Subtype C1 was characterized with significantly high level of immune-checkpoint, immune score, stromal score, showed high drug sensitivity in Sorafenib, Sunitinib, Cisplatin, Vinblastine, Vinorelbine, Vorinostat, and Gemcitabine. Cytokine-cytokine receptor pathway, chemokine signaling pathway, and JAK signaling pathways were found enriched in the subtype C1 account for the poor prognosis. Subtype C2 was linked to a better survival outcome. By using the Connective Map database, subtype specific small molecular drugs identified that could facilitate the treatment of ccRCC patients. In addition, A immune index that used to evaluated the immune modification patterns and further validated in the other types RCC dataset, such as papillary renal cell carcinoma (pRCC) and chromophobe renal cell carcinoma (chRCC). Together, this study identified two distinct molecular subtypes with immune index, aid to the treatment of ccRCC and enhancing our cognition of the tumor microenvironment infiltration characterization in ccRCC immunotherapy.

Renal Cell Carcinoma-Infiltrating CD3low Vγ9Vδ1 T Cells Represent Potentially Novel Anti-Tumor Immune Players.

Due to the highly immunogenic nature of renal cell carcinoma (RCC), the tumor microenvironment (TME) is enriched with various innate and adaptive immune subsets. In particular, gamma-delta (γδ) T cells can act as potent attractive mediators of adoptive cell transfer immunotherapy because of their unique properties such as non-reliance on major histocompatibility complex expression, their ability to infiltrate human tumors and recognize tumor antigens, relative insensitivity to immune checkpoint molecules, and broad tumor cytotoxicity. Therefore, it is now critical to better characterize human γδ T-cell subsets and their mechanisms in RCCs, especially the stage of differentiation. In this study, we aimed to identify γδ T cells that might have adaptive responses against RCC progression. We characterized γδ T cells in peripheral blood and tumor-infiltrating lymphocytes (TILs) in freshly resected tumor specimens from 20 RCC patients. Furthermore, we performed a gene set enrichment analysis on RNA-sequencing data from The Cancer Genome Atlas (TCGA) derived from normal kidneys and RCC tumors to ascertain the association between γδ T-cell infiltration and anti-cancer immune activity. Notably, RCC-infiltrating CD3low Vγ9Vδ1 T cells with a terminally differentiated effector memory phenotype with up-regulated activation/exhaustion molecules were newly detected as predominant TILs, and the cytotoxic activity of these cells against RCC was confirmed in vitro. In an additional analysis of the TCGA RCC dataset, γδ T-cell enrichment scores correlated strongly with those for CTLs, Th1 cells, “exhausted” T cells, and M1 macrophages, suggesting active involvement of γδ T cells in anti-tumor rather than pro-tumor activity, and Vδ1 cells were more abundant than Vδ2 or Vδ3 cells in RCC tumor samples. Thus, we posit that Vγ9Vδ1 T cells may represent an excellent candidate for adoptive immunotherapy in RCC patients with a high risk of relapse after surgery.

Genetic variants in MAPK10 modify renal cell carcinoma susceptibility and clinical outcomes

AimsThe mitogen-activated protein kinase (MAPK) cascades integrate various upstream signals to regulate many cellular functions, including proliferation, differentiation, and survival. Dysregulation of these pathways has been implicated in the occurrence and progression of a variety of cancers. Main methodsThis study aimed to assess the association of 192 single nucleotide polymorphisms in 22 MAPK cascade genes with renal cell carcinoma (RCC) risk and survival in 312 patients and 318 controls. Key findingsAfter multiple testing correction and multivariate analysis, the minor T allele of MAPK10 rs12648265 remained associated with a lower risk of RCC (adjusted odds ratio 0.64, 95% confidence interval 0.50–0.82, P = 0.000426) and metastasis (adjusted hazard ratio 0.50, 95% confidence interval 0.30–0.82, P = 0.006). Presence of the rs12648265 T allele demonstrated a trend towards being associated with increased MAPK10 expression, and meta-analysis of four RCC datasets indicated that high MAPK10 expression is associated with a favourable prognosis. Furthermore, activation of MAPK10 by the potent agonist anisomycin inhibited RCC cell growth in vitro, suggesting an involvement of MAPK10 in RCC progression. SignificanceIn conclusion, MAPK10 may be a meaningful biomarker and a potential therapeutic target in RCC.