TCGA-KIRC Cohort Research Articles

Single-cell transcriptional profiling of clear cell renal cell carcinoma reveals a tumor-associated endothelial tip cell phenotype

Clear cell renal cell carcinoma (ccRCC) is the most prevalent form of renal cancer, accounting for over 75% of cases. The asymptomatic nature of the disease contributes to late-stage diagnoses and poor survival. Highly vascularized and immune infiltrated microenvironment are prominent features of ccRCC, yet the interplay between vasculature and immune cells, disease progression and response to therapy remains poorly understood. Using droplet-based single-cell RNA sequencing we profile 50,236 transcriptomes from paired tumor and healthy adjacent kidney tissues. Our analysis reveals significant heterogeneity and inter-patient variability of the tumor microenvironment. Notably, we discover a previously uncharacterized vasculature subpopulation associated with epithelial-mesenchymal transition. The cell-cell communication analysis reveals multiple modes of immunosuppressive interactions within the tumor microenvironment, including clinically relevant interactions between tumor vasculature and stromal cells with immune cells. The upregulation of the genes involved in these interactions is associated with worse survival in the TCGA KIRC cohort. Our findings demonstrate the role of tumor vasculature and stromal cell populations in shaping the ccRCC microenvironment and uncover a subpopulation of cells within the tumor vasculature that is associated with an angiogenic phenotype.

Function of NEK2 in clear cell renal cell carcinoma and its effect on the tumor microenvironment.

Previous studies have revealed the critical functions of NEK2 in controlling the cell cycle which is linked to poor prognosis in multiple tumor types, but less research has been devoted to clear cell renal cell carcinoma (ccRCC). We downloaded clinical data from the gene expression omnibus (GEO) and TCGA databases together with transcriptional and mutational datasets. Strongly coexpressed genes with NEK2 were extracted from TCGA-KIRC cohort, and were submitted to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional analyses. According to NEK2 levels, the survival status, mutational characteristics, response to immunotherapy and sensitivity to drugs of the patients were studied. The potential correlations between NEK2 levels and immune cell state as well as immune cell infiltration were examined using the GEPIA, TIMER and TISIDB databases. Double immunofluorescence (IF) was performed to identify the NEK2 overexpression and relationship with CD8 in ccRCC. The NEK2 gene was overexpressed and would enhance the nuclear division and cell cycle activities in ccRCC. ccRCC patients with high NEK2 expression had worse clinical outcomes, higher mutation burden and better therapeutic response. Moreover, NEK2 gene overexpression was positively related to various immune cell marker sets, which was also proved by validation cohort, and more infiltration of various immune cells. ccRCC patients with NEK2 high expression have a poorer prognosis than those with NEK2 low expression, resulting from its function of promoting proliferation, accompanied by increased infiltration of CD8 + T cells and Tregs and T-cell exhaustion and will respond better to proper treatments.

A three-gene expression score for predicting clinical benefit to anti-PD-1 blockade in advanced renal cell carcinoma.

In the advanced renal cell carcinoma (RCC) scenario, there are no consistent biomarkers to predict the clinical benefit patients derived from immune checkpoint blockade (ICB). Taking this into consideration, herein, we conducted a retrospective study in order to develop and validate a gene expression score for predicting clinical benefit to the anti-PD-1 antibody nivolumab in the context of patients diagnosed with advanced clear cell RCC enrolled in the CheckMate-009, CheckMate-010, and CheckMate-025 clinical trials. First, a three-gene expression score (3GES) with prognostic value for overall survival integrating HMGA1, NUP62, and ARHGAP42 transcripts was developed in a cohort of patients treated with nivolumab. Its prognostic value was then validated in the TCGA-KIRC cohort. Second, the predictive value for nivolumab was confirmed in a set of patients from the CheckMate-025 phase 3 clinical trial. Lastly, we explored the correlation of our 3GES with different clinical, molecular, and immune tumor characteristics. If the results of this study are definitively validated in other retrospective and large-scale, prospective studies, the 3GES will represent a valuable tool for guiding the design of ICB-based clinical trials in the aRCC scenario in the near future.

Prognosis and biological function of SGOL1 in clear cell renal cell carcinoma: a multiomics analysis.

Shugoshin-1 (SGOL1) is a mammalian ortholog of Shugoshin in yeast and is essential for precise chromosome segregation during mitosis and meiosis. Aberrant SGOL1 expression was reported to be closely correlated with the malignant progression of various tumors. However, the expression pattern and biological function of SGOL1 in clear cell renal cell carcinoma (ccRCC) are unclear. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provide mRNA expression data and outcome information for ccRCC patients. Immunohistochemistry (IHC) of ccRCC tissue chips verified SGOL1 protein expression in ccRCC patients. Data processing and visualization were performed with the UALCAN, TISIDB, TIMER, GSCA, LinkedOmics, and starBase databases. Gene Ontology (GO) annotation and gene set enrichment analysis (GSEA) were used to identify SGOL1-related biological functions and signaling pathways. Immune infiltration analysis was performed using the TISIDB database, ssGSEA algorithm, and TCGA-KIRC cohort. The biological role of SGOL1 in ccRCC was investigated using a series of in vitro cytological assays, including the MTT assay, EdU staining assay, flow cytometry analysis, Transwell assay, and wound healing assay. SGOL1 was highly expressed in ccRCC and linked to adverse clinicopathological parameters and unfavorable prognosis. Multivariate logistic regression and nomogram calibration suggested that SGOL1 might serve as an independent and reliable prognostic predictor of ccRCC. Functional enrichment analysis indicated that SGOL1 may be involved in the cell cycle, the p53 pathway, DNA replication, and T-cell activation. Furthermore, tumor microenvironment (TME) analysis suggested that SGOL1 was positively associated with Treg infiltration and immune checkpoint upregulation. In addition, we identified a potential SNHG17/PVT1/ZMIZ1-AS1-miR-23b-3p-SGOL1 axis correlated with ccRCC carcinogenesis and progression. Finally, we demonstrated that SGOL1 promoted ccRCC cell proliferation, migratory capacity, and invasion in vitro. SGOL1 potentially functions as an oncogene in ccRCC progression and might contribute to the immunosuppressive TME by increasing Treg infiltration and checkpoint expression, suggesting that targeting SGOL1 could be a novel therapeutic strategy for the treatment of ccRCC patients.

Development and implementation of a prognostic model for clear cell renal cell carcinoma based on heterogeneous TLR4 expression

ObjectiveClear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cell carcinomas and has the worst prognosis, originating from renal tubular epithelial cells. Toll-like receptor 4 (TLR4) plays a crucial role in ccRCC proliferation, infiltration, and metastasis. The aim of this study was to construct a prognostic scoring model for ccRCC based on TLR4 expression heterogeneity and to explore its association with immune infiltration, thereby providing insights for the treatment and prognostic evaluation of ccRCC. MethodsUsing R software, a differential analysis was conducted on normal samples and ccRCC samples, and in conjunction with the KEGG database, a correlation analysis for the clear cell renal cell carcinoma pathway (hsa05211) was carried out. We observed the expression heterogeneity of TLR4 in the TCGA-KIRC cohort and identified its related differential genes (TRGs). Based on the expression levels of TRGs, consensus clustering was employed to identify TLR4-related subtypes, and further clustering heatmaps, principal component, and single-sample gene set enrichment analyses were conducted. Overlapping differential genes (ODEGs) between subtypes were analysed, and combined with survival data, univariate Cox regression, LASSO, and multivariate Cox regression were used to establish a prognostic risk model for ccRCC. This model was subsequently evaluated through ROC analysis, risk factor correlation analysis, independent prognostic factor analysis, and intergroup differential analysis. The ssGSEA model was employed to explore immune heterogeneity in ccRCC, and the performance of the model in predicting patient prognosis was evaluated using box plots and the oncoPredict software package. ResultsIn the TCGA-KIRC cohort, TLR4 expression was notably elevated in ccRCC samples compared to normal samples, correlating with improved survival in the high-expression group. The study identified distinct TLR4-related differential genes and categorized ccRCC into three subtypes with varied survival outcomes. A risk prognosis model based on overlapping differential genes was established, showing significant associations with immune cell infiltration and key immune checkpoints (PD-1, PD-L1, CTLA4). Additionally, drug sensitivity differences were observed between risk groups. ConclusionIn the TCGA-KIRC cohort, the expression of TLR4 in ccRCC samples exhibited significant heterogeneity. Through clustering analysis, we identified that the primary immune cells across subtypes are myeloid-derived suppressor cells, central memory CD4 T cells, and regulatory T cells. Furthermore, we successfully constructed a prognostic risk model for ccRCC composed of 17 genes. This model provides valuable references for the prognosis prediction and treatment of ccRCC patients.

Identification of a novel prognostic gene signature in a clear cell renal cell carcinoma (ccRCC) population using an integrated multi-study single-cell RNAseq dataset.

465 Background: Clear cell renal cell carcinoma (ccRCC) is a highly heterogeneous disease with varying prognoses and treatment responses. Understanding the underlying molecular determinants of this diversity is key to tailoring effective treatment strategies for each patient (pt). Here, we leveraged single–cell RNA sequencing (scRNA–seq) data to assess intra–tumoral diversity and its impact on pt prognosis. Methods: scRNA–seq raw data from five published studies were processed with Seurat’s standard workflow and integrated to remove biases using Harmony. We then re–analyzed the malignant cell cluster, defined by CA9, NDUFA4L2, and IGFBP3 expression, at a higher resolution. The resulting tumor cell sub–clusters were subjected to differentially expressed gene (DEG) and gene ontology (GO) enrichment analyses. A signature based on all DEGs with a fold change > 1.25 was applied on TCGA–KIRC cohort using single–sample gene set enrichment analysis and then correlated with relapse–free survival (RFS) and overall survival (OS) by Kaplan–Meier and multivariate Cox analyses. Results: We integrated scRNA–seq data from 50 samples from 44 pt’s (40% T3–4, mean age 75, 10% females). A total of 288K cells were classified into 19 clusters. The tumor cluster was re–analyzed and three biologically distinct tumor cell sub–clusters (labeled MC1, MC2, and MC3) were identified, each with unique molecular markers. GO analysis showed enrichment in genes associated with iron sequestration, oxidative phosphorylation, and apoptotic signaling, respectively. In the KIRC cohort, a 23–DEG signature from MC2 strongly correlated with RFS (HR 0.49; 95% CI 0.35−0.67 p < 0.001; 5-RFS: 74% vs. 54%) and OS (HR 0.44; 95% CI 0.33−0.60 p < 0.001; 5-OS: 75% vs. 51%) and was independent of other clinical variables in the multivariate analysis (Table 1). Moreover, this signature identified a subset of T1–T2 tumors (47.8%) with low risk of relapse (HR 0.38; 95% CI 0.19−0.78, p = 0.008; 5-RFS: 92% vs. 80%) and longer OS (HR 0.35; 95% CI 0.20−0.62 p < 0.001; 5-RFS: 90% vs. 68%). Conclusions: We have identified three distinct tumor cell sub–populations in an integrated scRNA–seq database, each characterized by unique transcriptomic profiles. A gene expression signature based on the MC2 sub–cluster was prognostic in the TCGA dataset and may help in identifying patients with a higher risk of relapse and candidates to adjuvant therapy.[Table: see text]

Analysis of the Tumor Immune Microenvironment (TIME) in Clear Cell Renal Cell Carcinoma (ccRCC) Reveals an M0 Macrophage-Enriched Subtype: An Exploration of Prognostic and Biological Characteristics of This Immune Phenotype

There is a need to optimize the treatment of clear cell renal cell carcinoma (ccRCC) patients at high recurrence risk after nephrectomy. We sought to elucidate the tumor immune microenvironment (TIME) of localized ccRCC and understand the prognostic and predictive characteristics of certain features. The discovery cohort was clinically localized patients in the TCGA-Kidney Renal Clear Cell Carcinoma (KIRC) project (n = 382). We identified an M0 macrophage-enriched cluster (n = 25) in the TCGA-KIRC cohort. This cluster’s median progression-free survival (PFS) and overall survival (OS) were 40.4 and 45.3 months, respectively, but this was not reached in the others (p = 0.0003 and <0.0001, respectively). Gene set enrichment (GSEA) analysis revealed an enrichment of epithelial to mesenchymal transition and cell cycle progression genes within this cluster, and these patients also had a lower predicted response to immune checkpoint blockade (ICB) (4% vs. 20–34%). An M0-enriched cluster (n = 9) with shorter PFS (p = 0.0006) was also identified in the Clinical Proteomics Tumor Analysis Consortium (CPTAC) cohort (n = 94). Through this characterization of the TIME in ccRCC, a cluster of patients defined by enrichment in M0 macrophages was identified that demonstrated poor prognosis and lower predicted ICB response. Pending further validation, this signature can identify localized ccRCC patients at high risk of recurrence after nephrectomy and who may require therapeutic approaches beyond ICB monotherapy.

Prognostic impact of lactylation-associated gene modifications in clear cell renal cell carcinoma: Insights into molecular landscape and therapeutic opportunities.

Clear cell renal cell carcinoma (ccRCC) stands as a challenging subtype of kidney cancer, frequently complicating patient prognosis due to factors like postsurgical recurrences or late-stage diagnoses. In this study, we employed bioinformatics to investigate lactylation modifications in ccRCC, focusing on the TCGA-KIRC cohort. Out of 328 lactylation-associated genes, 31 emerged as differentially expressed, with 16 showing a marked correlation with overall survival. These genes exhibited strong protein-protein interactions and significant expression correlations. Intriguingly, a notable loss of gene copy numbers suggests potential implications in tumor progression. Utilizing unsupervised clustering, KIRC samples were grouped into two distinct subcategories, each showcasing different survival outcomes. While pathway enrichment highlighted an aggressive, inflammation-driven profile for subgroup 2, subgroup 1 was characterized by metabolic prominence. Furthermore, subgroup 2 presented an intensified inflammatory response, hinting at potential immune exhaustion. Capitalizing on machine learning, we developed a risk model using the TCGA-KIRC dataset, efficiently categorizing ccRCC patients into high- and low-risk clusters. Notably, those in the low-risk group indicated a more favorable survival trajectory. Clinical evaluations further corroborated these findings, linking better outcomes with reduced risk scores. Additionally, observed mutation patterns allude to a potential association between elevated risk scores and cytokine storms. TIDE analysis illuminated possible immunotherapeutic benefits for the low-risk group, underscored by an evident rise in microsatellite instability. Finally, our drug sensitivity evaluations revealed distinct therapeutic responses between the groups. In summary, this research underscores the pivotal role of lactylation modifications in ccRCC and introduces a promising prognostic model. These revelations pave the way for enhanced prognostic precision, presenting a promising path toward personalized treatment strategies and enriching our comprehension of the multifaceted molecular landscape of the disease.

Development and validation of a novel anoikis-related gene signature in clear cell renal cell carcinoma.

Despite numerous treatments available, clear cell renal cell carcinoma (ccRCC) remains a deadly and invasive cancer. Anoikis-related genes (ARGs) are essential regulators of tumor metastasis and development. However, the potential roles of ARGs in ccRCC remain unclear. Based on the TCGA-KIRC cohort and GeneCards database, we identified differentially expressed ARGs in ccRCC. Then a 4 ARGs risk model was created by Cox regression and LASSO. The Kaplan-Meier and receiver operating characteristic (ROC) curves were utilized to verify the predictive efficacy of the prognostic signature. Subsequently, the possible molecular mechanism of ARGs was investigated by functional enrichment analysis. To assess the immune infiltration, immune checkpoint genes, and immune function in various risk groups, single sample gene set enrichment (ssGSEA) algorithm was employed. Furthermore, the low-risk and high-risk groups were compared in terms of tumor mutation burden (TMB). Ultimately, we analyzed the protein expression of these four ARGs utilizing the western blot test. Four genes were utilized to create a risk signature that may predict prognosis, enabling the classification of KIRC patients into groups with low or high risk. The reliability of the signature was examined utilizing survival analysis and ROC analysis. According to the multivariate Cox regression result, the risk score was a reliable independent prognostic predictor for KIRC patients. The novel risk model could differentiate between KIRC patients with various clinical outcomes and represent KIRC's specific immune status. An analysis of the correlation of TMB and risk score indicated a positive correlation between them, with high TMB being potentially linked to worse outcomes. Based on our findings, the prognostic signature of ARGs may be employed as an independent prognostic factor for ccRCC patients. It may introduce alternative perspectives on prognosis evaluation and serve as a prominent reference for personalized and precise therapy in KIRC.

Identification of disulfidptosis-related subtypes and development of a prognosis model based on stacking framework in renal clear cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor with an unsatisfactory prognosis. This study aims to identify the expression patterns of disulfidptosis-related genes (DRGs), develop a prognostic model, and predict immunological profiles. First, we identified differentially expressed DRGs in TCGA-KIRC cohort and analyzed their mutational profiles, methylation levels, and interaction networks. Subsequently, we identified disulfidptosis-associated molecular subtypes and investigated their prognostic and immunological characteristics. Simultaneously, a disulfidptosis-related prognostic signature (DRPS) was developed using a two-stage stacking framework consisting of 5 machine learning models. The effect of DRPS on immune cell infiltration levels was explored using seven different algorithms, and the status and function of T cells for distinct risk-score groups were evaluated based on T cell exhaustion and dysfunction scores. Additionally, the study also examined differences in clinical characteristics and therapy efficacy between high- and low-risk groups. We found two disulfidptosis-associated clusters, one of which had a poor prognosis and was linked to high immune cell infiltration but impaired T cell function. DRPS showed excellent predictive performance in all four cohorts and could accurately identified disulfidptosis-related molecular subtypes. The DRPS-based risk score was positively associated with poor prognosis, malignant pathological features, high immune cell infiltration levels, and T cell exhaustion or dysfunction, and better respond to immunotherapy and targeted therapy.Additionally, we have identified a close association between ISG20 and disulfidptosis as well as tumor immunity. Our study identified distinct disulfidptosis-related subtypes in ccRCC patients, and constructed the highly accurate and robust DRPS based on an ensemble learning framework, which has critical reference value in clinical decision-making and individualized treatment.And this workalso revealedISG20 exhibits promising potential as a therapeutic target for ccRCC.

TRAF2 promotes M2-polarized tumor-associated macrophage infiltration, angiogenesis and cancer progression by inhibiting autophagy in clear cell renal cell carcinoma

BackgroundThe management of advanced clear cell renal cell carcinoma (ccRCC) remains a major challenge in clinical practice, and the construction of more reliable prognostic prediction models and the further elucidation of key molecular mechanisms of tumor progression are topics in urgent need of in-depth investigation.MethodsWe used CIBERSORT to estimate the proportion of 22 tumor-infiltrating immune cell types in the TCGA-KIRC cohort. Weighted gene co-expression network analysis, least absolute shrinkage and selection operator regression analysis were used to build risk prediction models. Expression patterns and clinical significance of TRAF2 were determined through bioinformatics analysis, real-time qPCR, Western Blot, immunohistochemistry. GSEA analysis, transmission electron microscopy, 2D/3D colony formation assay, cell migration and invasion assay, and tube-formation assay were used to investigate the underlying function and mechanism of the TRAF2/M2 macrophage/autophagy axis.ResultsWe constructed a novel prognostic prediction model based on M2 macrophage-related genes, which was identified as an accurate, independent and specific prognostic risk model for ccRCC patients. A reliable nomogram was constructed to predict 1-, 3-, and 5-year overall survival for patients with ccRCC. As one of the constituent genes of the risk model, TRAF2 was determined to be upregulated in ccRCC and associated with poor clinical prognosis. We found that TRAF2 promotes malignant progression of ccRCC by regulating macrophage polarization, migration and angiogenesis. Mechanistically, we found that TRAF2 promotes the polarization of M2 macrophages, and this chemotaxis is achieved in an autophagy-dependent pathway. Orthotopic tumor growth assay results revealed that TRAF2 plays a key role as a promotor of ccRCC growth and metastasis.ConclusionsIn conclusion, this risk model is highly predictive of prognostic in ccRCC patients, which is expected to promote improved treatment evaluation and comprehensive management of ccRCC. Moreover, our findings reveal that the TRAF2/M2 macrophage/autophagy axis plays a key regulatory role in the malignant progression of ccRCC, and suggest that TRAF2 is a potential novel therapeutic target for advanced ccRCC.

A novel risk model of three SUMOylation genes based on RNA expression for potential prognosis and treatment sensitivity prediction in kidney cancer.

Introduction: Kidney cancer is one of the most common and lethal urological malignancies. Discovering a biomarker that can predict prognosis and potential drug treatment sensitivity is necessary for managing patients with kidney cancer. SUMOylation is a type of posttranslational modification that could impact many tumor-related pathways through the mediation of SUMOylation substrates. In addition, enzymes that participate in the process of SUMOylation can also influence tumorigenesis and development. Methods: We analyzed the clinical and molecular data which were obtanied from three databases, The Cancer Genome Atlas (TCGA), the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC), and ArrayExpress. Results: Through analysis of differentially expressed RNA based on the total TCGA-KIRC cohort, it was found that 29 SUMOylation genes were abnormally expressed, of which 17 genes were upregulated and 12 genes were downregulated in kidney cancer tissues. A SUMOylation risk model was built based on the discovery TCGA cohort and then validated successfully in the validation TCGA cohort, total TCGA cohort, CPTAC cohort, and E-TMAB-1980 cohort. Furthermore, the SUMOylation risk score was analyzed as an independent risk factor in all five cohorts, and a nomogram was constructed. Tumor tissues in different SUMOylation risk groups showed different immune statuses and varying sensitivity to the targeted drug treatment. Discussion: In conclusion, we examined the RNA expression status of SUMOylation genes in kidney cancer tissues and developed and validated a prognostic model for predicting kidney cancer outcomes using three databases and five cohorts. Furthermore, the SUMOylation model can serve as a biomarker for selecting appropriate therapeutic drugs for kidney cancer patients based on their RNA expression.

Construction of an interferon regulatory factors-related risk model for predicting prognosis, immune microenvironment and immunotherapy in clear cell renal cell carcinoma.

Interferon regulatory factors (IRFs) played complex and essential roles in progression, prognosis, and immune microenvironment in clear cell renal cell carcinoma (ccRCC). The purpose of this study was to construct a novel IRFs-related risk model to predict prognosis, tumor microenvironment (TME) and immunotherapy response in ccRCC. Multi-omics analysis of IRFs in ccRCC was performed based on bulk RNA sequencing and single cell RNA sequencing data. According to the expression profiles of IRFs, the ccRCC samples were clustered by non-negative matrix factorization (NMF) algorithm. Then, least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were applied to construct a risk model to predict prognosis, immune cells infiltration, immunotherapy response and targeted drug sensitivity in ccRCC. Furthermore, a nomogram comprising the risk model and clinical characteristics was established. Two molecular subtypes with different prognosis, clinical characteristics and infiltration levels of immune cells were identified in ccRCC. The IRFs-related risk model was developed as an independent prognostic indicator in the TCGA-KIRC cohort and validated in the E-MTAB-1980 cohort. The overall survival of patients in the low-risk group was better than that in the high-risk group. The risk model was superior to clinical characteristics and the ClearCode34 model in predicting the prognosis. In addition, a nomogram was developed to improve the clinical utility of the risk model. Moreover, the high-risk group had higher infiltration levels of CD8+ T cell, macrophages, T follicular helper cells and T helper (Th1) cells and activity score of type I IFN response but lower infiltration levels of mast cells and activity score of type II IFN response. Cancer immunity cycle showed that the immune activity score of most steps was remarkably higher in the high-risk group. TIDE scores indicated that patients in the low-risk group were more likely responsive to immunotherapy. Patients in different risk groups showed diverse drug sensitivity to axitinib, sorafenib, gefitinib, erlotinib, dasatinib and rapamycin. In brief, a robust and effective risk model was developed to predict prognosis, TME characteristics and responses to immunotherapy and targeted drugs in ccRCC, which might provide new insights into personalized and precise therapeutic strategies.

POLD1 as a Prognostic Biomarker Correlated with Cell Proliferation and Immune Infiltration in Clear Cell Renal Cell Carcinoma.

DNA polymerase delta 1 catalytic subunit (POLD1) plays a vital role in genomic copy with high fidelity and DNA damage repair processes. However, the prognostic value of POLD1 and its relationship with tumor immunity in clear cell renal cell carcinoma (ccRCC) remains to be further explored. Transcriptional data sets and clinical information were obtained from the TCGA, ICGC, and GEO databases. Differentially expressed genes (DEGs) were derived from the comparison between the low and high POLD1 expression groups in the TCGA-KIRC cohort. KEGG and gene ontology (GO) analyses were performed for those DEGs to explore the potential influence of POLD1 on the biological behaviors of ccRCC. The prognostic clinical value and mutational characteristics of patients were described and analyzed according to the POLD1 expression levels. TIMER and TISIDB databases were utilized to comprehensively investigate the potential relevance between the POLD1 levels and the status of the immune cells, as well as the tumor infiltration of immune cells. In addition, RT-qPCR, Western blot, immunohistochemistry and several functional and animal experiments were performed for clinical, in vitro and in vivo validation. POLD1 was highly expressed in a variety of tumors including ccRCC, and further verified in a validation cohort of 60 ccRCC samples and in vitro cell line experiments. POLD1 expression levels in the ccRCC samples were associated with various clinical characteristics including pathologic tumor stage and histologic grade. ccRCC patients with high POLD1 expression have poor clinical outcomes and exhibit a higher rate of somatic mutations than those with low POLD1 expression. Cox regression analysis also showed that POLD1 could act as a potential independent prognostic biomarker. The DEGs associated with POLD1 were significantly enriched in the immunity-related pathways. Moreover, further immune infiltration analysis indicated that high POLD1 expression was associated with high NK CD56bright cells, Treg cells, and myeloid-derived suppressor cells' (MDSCs) infiltration scores, as well as their marker gene sets of immune cell status. Meanwhile, POLD1 exhibited resistance to various drugs when highly expressed. Finally, the knockdown of POLD1 inhibited the proliferation and migration, and promoted the apoptosis of ccRCC cells in vitro and in vivo, as well as influenced the activation of oncogenic signaling. Our current study demonstrated that POLD1 is a potential prognostic biomarker for ccRCC patients. It might create a tumor immunosuppressive microenvironment and inhibit the susceptibility to ferroptosis leading to a poor prognosis.

MP41-13 QUANTITATIVE ASSESSMENT OF MESENCHYMAL DIFFERENTIATION FOR METASTATIC RISK PREDICTION OF CLEAR CELL RENAL CARCINOMA (CCRCC)

You have accessJournal of UrologyCME1 Apr 2023MP41-13 QUANTITATIVE ASSESSMENT OF MESENCHYMAL DIFFERENTIATION FOR METASTATIC RISK PREDICTION OF CLEAR CELL RENAL CARCINOMA (CCRCC) Daniel Vilchez, Bodong Zhang, Alejandro Sanchez, Deepika Sirohi, Tolga Tasdizen, and Beatrice Knudsen Daniel VilchezDaniel Vilchez More articles by this author , Bodong ZhangBodong Zhang More articles by this author , Alejandro SanchezAlejandro Sanchez More articles by this author , Deepika SirohiDeepika Sirohi More articles by this author , Tolga TasdizenTolga Tasdizen More articles by this author , and Beatrice KnudsenBeatrice Knudsen More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003279.13AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Epithelial-to-mesenchymal transition (EMT) results in poor prognosis sarcomatoid and rhabdoid histologic growth patterns (HGP) of ccRCC. However, only ∼15% of metastatic cases reveal areas of sarcomatoid/rhabdoid differentiation in the primary tumor. We questioned whether a computer can learn salient features of EMT that are associated with risk of metastatic progression across all HGPs of ccRCC. METHODS: The project uses 410 digital H&E slides of ccRCC. We incorporated a novel co-training approach into a machine learning model (MLM) and trained the model to distinguish epithelial (nested) and mesenchymal (sarcomatoid, rhabdoid) HGPs in a Utah cohort. The trained MLM is applied to the TCGA KIRC cohort (n=360) with annotated regions of nested, tubulo-papillary, low grade-spindle, sarcomatoid and rhabdoid HGPs for which the hazard ration of metastatic progression was published. For each case, the MLM outputs a mesenchymal-differentiation (DP-MD) score on a scale of 0 to 1 where closer to 1 is higher likelihood of EMT. RESULTS: A unique machine learning / AI model was developed to quantify sarcomatoid differentiation in ccRCC with no obvious histologic signs of mesenchymal morphology. H&E stains were used as separate inputs of a contrastive co-training model (H/E-CCTM) to force the learning of mesenchymal features. The inclusion of a semi-supervised learning component improved the accuracy of predicting mesenchymal differentiation by >4% over other state-of-the-art co-training algorithms and performed equally well when trained on 10% versus 100% of labeled cancer data. Sarcomatoid, low grade spindle and rhabdoid cases received average DP-MD scores of 0.91, 0.97 and 0.83, respectively in a held-out test set, while cases with nested morphology, including expansile, and high nuclear to cytoplasmic ratio averaged DP-MD scores of 0.08. Tubulo-papillary HGPs without obvious mesenchymal features obtained an average DP-MD score of 0.49. CONCLUSIONS: Computer-assisted morphometric analysis can be used to quantify mesenchymal differentiation and has the potential to improve the prediction of metastatic risk in ccRCC and the selection of patients for adjuvant treatment after nephrectomy. Source of Funding: Huntsman Cancer Foundation, NIH P30CA042014 © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e560 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Daniel Vilchez More articles by this author Bodong Zhang More articles by this author Alejandro Sanchez More articles by this author Deepika Sirohi More articles by this author Tolga Tasdizen More articles by this author Beatrice Knudsen More articles by this author Expand All Advertisement PDF downloadLoading ...

Autophagy-related gene LAPTM4B promotes the progression of renal clear cell carcinoma and is associated with immunity.

Renal cell carcinoma (RCC) is a common urologic disease. Currently, surgery is the primary treatment for renal cancer; immunotherapy is not as effective a treatment strategy as expected. Hence, understanding the mechanism in the tumor immune microenvironment (TME) and exploring novel immunotherapeutic targets are considered important. Recent studies have demonstrated that autophagy could affect the immune environment of renal cell carcinoma and induce proliferation and apoptosis of cancer cells. By comparing lysosomal genes and regulating autophagy genes, we identified the LAPTM4B gene to be related to RCC autophagy. By analyzing the TCGA-KIRC cohort using bioinformatics, we found M2 macrophages associated with tumor metastasis to be significantly increased in the immune microenvironment of patients with high expression of LAPTM4B. GO/KEGG/GSEA/GSVA results showed significant differences in tumor autophagy- and metastasis-related pathways. Single-cell sequencing was used to compare the expression of LAPTM4B in different cell types and obtain the differences in lysosomal and autophagy pathway activities in different ccRCC cells. Subsequently, we confirmed the differential expression of LAPTM4B in renal cell carcinoma of different Fuhrman grades using western blotting. Downregulation of LAPTM4B expression significantly reduced the proliferation of renal cell carcinoma cells and promoted cell apoptosis through cell experiments. Overall, our study demonstrated that the autophagy-related gene LAPTM4B plays a critical role in the TME of RCC, and suggested that LAPTM4B is a potential therapeutic target for RCC immunotherapy.

Prognostic Role of Long-Chain Acyl-Coenzyme A Synthetase Family Genes in Patients with Clear Cell Renal Cell Carcinoma: A Comprehensive Bioinformatics Analysis Confirmed with External Validation Cohorts

We aimed to determine the prognostic role of long-chain acyl-CoA synthetases (ACSLs) as a disease marker for kidney clear cell carcinoma (KIRC). The Cancer Genome Atlas (TCGA) data were accessed via open access LinkedOmics database for KIRC. Provisional datasets were used for analysis as previously described and gene expression quantification data were downloaded. The corresponding clinical information of patients also were obtained from the database. Five ACSL family members, ACSL1, ACSL3, ACSL4, ACSL5, and ACSL6, were investigated in the TCGA-KIRC cohort. Xena browser, cBioPortal and UALCAN, and Cancer Cell Line Encyclopedia (CCLE) databases were also used to confirm the results. External validation was performed using patient cohorts from the Gene Expression Omnibus (GEO-NCBI) database. Finally, the protein-protein interaction (PPI) was constructed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape software. Pathological T3-T4 stage tumors had significantly lower ACSL1 mRNA expression (P=.009). Patients with pathologically confirmed metastasis exhibited significantly lower expression, as well (P=.02). ACSL1 mRNA expression was associated with overall survival (OS) and negatively correlated with OS time. Univariate and multivariate analyses showed that lower ACSL1 mRNA expression level was associated with mortality. Moreover, ACSL1 mRNA expression was exhibited significant difference in some VHL gene region mutations and PBRM1_p.R1010 mutation, and negatively correlated with HIF1-alpha mRNA expression (P < .001). Confirmatory analyses and external validation also revealed similar findings. Lowered ACSL1 mRNA expression is associated with worse tumor histopathology and poor overall survival in KIRC. It may be used for prognostic marker for KIRC.

High SPATA18 Expression and its Diagnostic and Prognostic Value in Clear Cell Renal Cell Carcinoma.

BACKGROUND SPATA18 (spermatogenesis-associated 18, also called Mieap) encodes a protein that can induce lysosome-like organelles within mitochondria, which plays an important role in tumor growth. We measured the expression of SPATA18 in ccRCC, and assessed its diagnostic and prognostic clinical value in patients with clear cell renal cell carcinoma (ccRCC). MATERIAL AND METHODS We analyzed SPATA18 expression using data from the TCGA-KIRC cohort, GEO database, and UALCAN database. Immunohistochemistry was carried out to verify the expression in the ccRCC patients. The diagnostic value of SPATA18expression was evaluated by a receiver operating characteristic (ROC) curve. The correlation between clinical characteristics and SPATA18 expression was calculated by chi-square test. The prognostic value of SPATA18 expression was assessed by Kaplan-Meier analysis and Cox analysis. We conducted gene set enrichment analysis (GSEA) using TCGA database. RESULTS SPATA18 gene exhibited a higher expression in ccRCC tissues than in normal tissues. SPATA18 showed a substantial diagnostic value in ccRCC. SPATA18 expression was correlated with histological grade, clinical stage, T classification, and distant metastasis of ccRCC. Furthermore, high SPATA18 expression was associated with favorable overall survival. Multivariate analysis showed that SPATA18 was an independent risk factor for ccRCC. Gene set enrichment analysis (GSEA) showed that B cell receptors, WNT targets, extracellular matrix, oxidative phosphorylation, calcium metabolism, iron uptake and transport, potassium channels, and insulin receptor were differently enriched in the phenotype that was negatively correlated with SPATA18. CONCLUSIONS Our study indicated that high SPATA18 expression in ccRCC was associated with a good prognosis, and it could be a positive prognostic biomarker for ccRCC.

Identification and Validation of Cuproptosis-Related LncRNA Signatures in the Prognosis and Immunotherapy of Clear Cell Renal Cell Carcinoma Using Machine Learning.

(1) Objective: We aimed to mine cuproptosis-related LncRNAs with prognostic value and construct a corresponding prognostic model using machine learning. External validation of the model was performed in the ICGC database and in multiple renal cancer cell lines via qPCR. (2) Methods: TCGA and ICGC cohorts related to renal clear cell carcinoma were included. GO and KEGG analyses were conducted to determine the biological significance of differentially expressed cuproptosis-related LncRNAs (CRLRs). Machine learning (LASSO), Kaplan-Meier, and Cox analyses were conducted to determine the prognostic genes. The tumor microenvironment and tumor mutation load were further studied. TIDE and IC50 were used to evaluate the response to immunotherapy, a risk model of LncRNAs related to the cuproptosis genes was established, and the ability of this model was verified in an external independent ICGC cohort. LncRNAs were identified in normal HK-2 cells and verified in four renal cell lines via qPCR. (3) Results: We obtained 280 CRLRs and identified 66 LncRNAs included in the TCGA-KIRC cohort. Then, three hub LncRNAs (AC026401.3, FOXD2-AS1, and LASTR), which were over-expressed in the four ccRCC cell lines compared with the human renal cortex proximal tubule epithelial cell line HK-2, were identified. In the ICGC database, the expression of FOXD2-AS1 and LASTR was consistent with the qPCR and TCGA-KIRC. The results also indicated that patients with low-risk ccRCC-stratified by tumor-node metastasis stage, sex, and tumor grade-had significantly better overall survival than those with high-risk ccRCC. The predictive algorithm showed that, according to the three CRLR models, the low-risk group was more sensitive to nine target drugs (A.443654, A.770041, ABT.888, AG.014699, AMG.706, ATRA, AP.24534, axitinib, and AZ628), based on the estimated half-maximal inhibitory concentrations. In contrast, the high-risk group was more sensitive to ABT.263 and AKT inhibitors VIII and AS601245. Using the CRLR models, the correlation between the tumor immune microenvironment and cancer immunotherapy response revealed that high-risk patients are more likely to respond to immunotherapy than low-risk patients. In terms of immune marker levels, there were significant differences between the high- and low-risk groups. A high TMB score in the high-risk CRLR group was associated with worse survival, which could be a prognostic factor for KIRC. (4) Conclusions: This study elucidates the core cuproptosis-related LncRNAs, FOXD2-AS1, AC026401.3, and LASTR, in terms of potential predictive value, immunotherapeutic strategy, and outcome of ccRCC.

A metabolic reprogramming-related prognostic risk model for clear cell renal cell carcinoma: From construction to preliminary application.

Metabolic reprogramming is one of the characteristics of clear cell renal cell carcinoma (ccRCC). Although some treatments associated with the metabolic reprogramming for ccRCC have been identified, remain still lacking. In this study, we identified the differentially expressed genes (DEGs) associated with clinical traits with a total of 965 samples via DEG analysis and weighted correlation network analysis (WGCNA), screened the prognostic metabolism-related genes, and constructed the risk score prognostic models. We took the intersection of DEGs with significant difference coexpression modules and received two groups of intersection genes that were connected with metabolism via functional enrichment analysis. Then we respectively screened prognostic metabolic-related genes from the genes of the two intersection groups and constructed the risk score prognostic models. Compared with the predicted effect of clinical grade and stage for ccRCC patients, finally, we selected the model constructed with genes of ABAT, ALDH6A1, CHDH, EPHX2, ETNK2, and FBP1. The risk scores of the prognostic model were significantly related to overall survival (OS) and could serve as an independent prognostic factor. The Kaplan-Meier analysis and ROC curves revealed that the model efficiently predicts prognosis in the TCGA-KIRC cohort and the validation cohort. Then we investigated the potential underlying mechanism and sensitive drugs between high- and low-risk groups. The six key genes were significantly linked with worse OS and were downregulated in ccRCC, we confirmed the results in clinical samples. These results demonstrated the efficacy and robustness of the risk score prognostic model, based on the characteristics of metabolic reprogramming in ccRCC, and the key genes used in constructing the model also could develop into targets of molecular therapy for ccRCC.