Interference with ENO2 promotes ferroptosis and inhibits glycolysis in clear cell renal cell carcinoma by regulating Hippo­YAP1 signaling.

Glycolytic enzyme enolase 2 (ENO2) is dysregulated in various cancer types. Nevertheless, the role and underlying mechanism of ENO2 in clear cell renal cell carcinoma (ccRCC) remain unclear. Therefore, the current study investigated the effect and mechanism of ENO2 in ccRCC. ENO2 expression in a ccRCC cell line was assessed using reverse transcription-quantitative PCR and western blotting. Analysis of glycolysis was performed by estimating the extracellular acidification rate, lactic acid concentration, glucose uptake and the expression of glucose transporter 1, pyruvate kinase muscle isozyme M2 and hexokinase 2. Moreover, ferroptosis was assessed by detecting the level of total iron, lipid peroxide, reactive oxygen species and the expression of ferroptosis-related protein. In addition, mitochondrial function was assessed using JC-1 staining and detection kits. The results indicated that ENO2 is expressed at high levels in ccRCC cell lines, and interference with ENO2 expression inhibits glycolysis, promotes ferroptosis and affects mitochondrial function in ccRCC cells. Further investigation demonstrated that interference with ENO2 expression affected ferroptosis levels in ccRCC cells by inhibiting the glycolysis process. Mechanistically, the present results indicated that ENO2 may affect ferroptosis, glycolysis and mitochondrial functions by regulating Hippo-yes-associated protein 1 (YAP1) signaling in ccRCC cells. In conclusion, the present study showed that ENO2 affects ferroptosis, glycolysis and mitochondrial functions in ccRCC cells by regulating Hippo-YAP1 signaling, hence demonstrating its potential as a therapeutic target in ccRCC.

Ultrasound-Based Radiomics for Predicting the WHO/ISUP Grading of Clear-Cell Renal Cell Carcinoma.

OBJECTIVE: To explore the performance of ultrasound image-based radiomics in predicting World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading of clear-cell renal cell carcinoma (ccRCC). METHODS: A retrospective study was conducted via histopathological examination on participants with ccRCC from January 2021 to August 2023. Participants were randomly allocated to a training set and a validation set in a 3:1 ratio. The maximum cross-sectional image of the lesion on the preoperative ultrasound image was obtained, with the region of interest (ROI) delineated manually. Radiomic features were computed from the ROIs and subsequently normalized using Z-scores. Wilcoxon test and least absolute shrinkage and selection operator (LASSO) regression were applied for feature reduction and model development. The performance of the model was estimated by indicators including area under the curve (AUC), sensitivity and specificity. RESULTS: A total of 336 participants (median age, 57 y; 106 women) with ccRCC were finally included, of whom 243 had low-grade tumors (grade 1-2) and 93 had high-grade tumors (grade 3-4). A total of 1163 radiomic features were extracted from the ROIs for model construction and 117 informative radiomics features selected by Wilcoxon test were submitted to LASSO. Our ultrasound-based radiomics model included 51 features and achieved AUCs of 0.90 and 0.79 for the training and validation sets, respectively. Within the training set, the sensitivity and specificity measured 0.75 and 0.92, respectively, whereas in the validation set, the sensitivity and specificity measured 0.65 and 0.84, respectively. In the subgroup analysis, for the training and validation sets Philips AUCs were 0.91 and 0.75, Toshiba AUCs were 0.82 and 0.90, and General Electric AUCs were 0.95 and 0.82, respectively. CONCLUSION: Ultrasound-based radiomics can effectively predict the WHO/ISUP grading of ccRCC.

Radiomics nomogram based on CT radiomics features and clinical factors for prediction of Ki-67 expression and prognosis in clear cell renal cell carcinoma: a two-center study.

OBJECTIVES: To develop and validate a radiomics nomogram combining radiomics features and clinical factors for preoperative evaluation of Ki-67 expression status and prognostic prediction in clear cell renal cell carcinoma (ccRCC). METHODS: Two medical centers of 185 ccRCC patients were included, and each of them formed a training group (n = 130) and a validation group (n = 55). The independent predictor of Ki-67 expression status was identified by univariate and multivariate regression, and radiomics features were extracted from the preoperative CT images. The maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) were used to identify the radiomics features that were most relevant for high Ki-67 expression. Subsequently, clinical model, radiomics signature (RS), and radiomics nomogram were established. The performance for prediction of Ki-67 expression status was validated using area under curve (AUC), calibration curve, Delong test, decision curve analysis (DCA). Prognostic prediction was assessed by survival curve and concordance index (C-index). RESULTS: Tumour size was the only independent predictor of Ki-67 expression status. Five radiomics features were finally identified to construct the RS (AUC: training group, 0.821; validation group, 0.799). The radiomics nomogram achieved a higher AUC (training group, 0.841; validation group, 0.814) and clinical net benefit. Besides, the radiomics nomogram provided a highest C-index (training group, 0.841; validation group, 0.820) in predicting prognosis for ccRCC patients. CONCLUSIONS: The radiomics nomogram can accurately predict the Ki-67 expression status and exhibit a great capacity for prognostic prediction in patients with ccRCC and may provide value for tailoring personalized treatment strategies and facilitating comprehensive clinical monitoring for ccRCC patients.

Prediction of clear cell renal cell carcinoma ≤ 4cm: visual assessment of ultrasound characteristics versus ultrasonographic radiomics analysis.

Objective: To investigate the diagnostic efficacy of the clinical ultrasound imaging model, ultrasonographic radiomics model, and comprehensive model based on ultrasonographic radiomics for the differentiation of small clear cell Renal Cell Carcinoma (ccRCC) and Renal Angiomyolipoma (RAML). Methods: The clinical, ultrasound, and contrast-enhanced CT(CECT) imaging data of 302 small renal tumors (maximum diameter ≤ 4cm) patients in Tianjin Medical University Cancer Institute and Hospital from June 2018 to June 2022 were retrospectively analyzed, with 182 patients of ccRCC and 120 patients of RAML. The ultrasound images of the largest diameter of renal tumors were manually segmented by ITK-SNAP software, and Pyradiomics (v3.0.1) module in Python 3.8.7 was applied to extract ultrasonographic radiomics features from ROI segmented images. The patients were randomly divided into training and internal validation cohorts in the ratio of 7:3. The Random Forest algorithm of the Sklearn module was applied to construct the clinical ultrasound imaging model, ultrasonographic radiomics model, and comprehensive model. The efficacy of the prediction models was verified in an independent external validation cohort consisting of 69 patients, from 230 small renal tumor patients in two different institutions. The Delong test compared the predictive ability of three models and CECT. Calibration Curve and clinical Decision Curve Analysis were applied to evaluate the model and determine the net benefit to patients. Results: 491 ultrasonographic radiomics features were extracted from 302 small renal tumor patients, and 9 ultrasonographic radiomics features were finally retained for modeling after regression and dimensionality reduction. In the internal validation cohort, the area under the curve (AUC), sensitivity, specificity, and accuracy of the clinical ultrasound imaging model, ultrasonographic radiomics model, comprehensive model, and CECT were 0.75, 76.7%, 60.0%, 70.0%; 0.80, 85.6%, 61.7%, 76.0%; 0.88, 90.6%, 76.7%, 85.0% and 0.90, 92.6%, 88.9%, 91.1%, respectively. In the external validation cohort, AUC, sensitivity, specificity, and accuracy of the three models and CECT were 0.73, 67.5%, 69.1%, 68.3%; 0.89, 86.7%, 80.0%, 83.5%; 0.90, 85.0%, 85.5%, 85.2% and 0.91, 94.6%, 88.3%, 91.3%, respectively. The DeLong test showed no significant difference between the clinical ultrasound imaging model and the ultrasonographic radiomics model (Z=-1.287, P=0.198). The comprehensive model showed superior diagnostic performance than the ultrasonographic radiomics model (Z=4. 394, P<0.001) and the clinical ultrasound imaging model (Z=4. 732, P<0.001). Moreover, there was no significant difference in AUC between the comprehensive model and CECT (Z=-0.252, P=0.801). Both in the internal and external validation cohort, the Calibration Curve and Decision Curve Analysis showed a better performance of the comprehensive model. Conclusion: It is feasible to construct an ultrasonographic radiomics model for distinguishing small ccRCC and RAML based on ultrasound images, and the diagnostic performance of the comprehensive model is superior to the clinical ultrasound imaging model and ultrasonographic radiomics model, similar to that of CECT.

Decoding PTEN regulation in clear cell renal cell carcinoma: Pathway for biomarker discovery and therapeutic insights.

Renal cell carcinoma is the most common adult renal solid tumor and the deadliest urological cancer, with clear cell renal cell carcinoma (ccRCC) being the predominant subtype. The PI3K/AKT signaling pathway assumes a central role in ccRCC tumorigenesis, wherein its abnormal activation confers a highly aggressive phenotype, leading to swift resistance against current therapies and distant metastasis. Thus, treatment resistance and disease progression remain a persistent clinical challenge in managing ccRCC effectively. PTEN, an antagonist of the PI3K/AKT signaling axis, emerges as a crucial factor in tumor progression, often experiencing loss or inactivation in ccRCC, thereby contributing to elevated mortality rates in patients. Therefore, understanding the molecular mechanisms underlying PTEN suppression in ccRCC tumors holds promise for the discovery of biomarkers and therapeutic targets, ultimately enhancing patient monitoring and treatment outcomes. The present review aims to summarize these mechanisms, emphasizing their potential prognostic, predictive, and therapeutic value in managing ccRCC.

Revealing the characteristics of SETD2-mutated clear cell renal cell carcinoma through tumor heterogeneity analysis.

Background: Renal cell carcinoma (RCC) is the most prevalent type of malignant kidney tumor in adults, with clear cell renal cell carcinoma (ccRCC) comprising about 75% of all cases. The SETD2 gene, which is involved in the modification of histone proteins, is often found to have alterations in ccRCC. Yet, our understanding of how these SETD2 mutations affect ccRCC characteristics and behavior within the tumor microenvironment is still not fully understood. Methods: We conducted a detailed analysis of single-cell RNA sequencing (scRNA-seq) data from ccRCC. First, the data was preprocessed using the Python package, "scanpy." High variability genes were pinpointed through Pearson's correlation coefficient. Dimensionality reduction and clustering identification were performed using Principal Component Analysis (PCA) and the Leiden algorithm. Malignant cell identification was conducted with the "InferCNV" R package, while cell trajectories and intercellular communication were depicted using the Python packages "VIA" and "cellphoneDB." We then employed the R package "Deseq2" to determine differentially expressed genes (DEGs) between groups. Using high-dimensional weighted gene correlation network analysis (hdWGCNA), co-expression modules were identified. We intersected these modules with DEGs to establish prognostic models through univariate Cox and the least absolute shrinkage and selection operator (LASSO) method. Results: We identified 69 and 53 distinctive cell clusters, respectively. These were classified further into 12 unique cell types. This analysis highlighted the presence of an abnormal tumor sub-cluster (MT + group), identified by high mitochondrial-encoded protein gene expression and an indication of unfavorable prognosis. Investigation of cellular interactions spotlighted significant interactions between the MT + group and endothelial cells, macrophaes. In addition, we developed a prognostic model based on six characteristic genes. Notably, risk scores derived from these genes correlated significantly with various clinical features. Finally, a nomogram model was established to facilitate more accurate outcome prediction, incorporating four independent risk factors. Conclusion: Our findings provide insight into the crucial transcriptomic characteristics of ccRCC associated with SETD2 mutation. We discovered that this mutation-induced subcluster could stimulate M2 polarization in macrophages, suggesting a heightened propensity for metastasis. Moreover, our prognostic model demonstrated effectiveness in forecasting overall survival for ccRCC patients, thus presenting a valuable clinical tool.

N-acetylgalactosaminyltransferase GALNT6 is a potential therapeutic target of clear cell renal cell carcinoma progression.

High expression of truncated O-glycans Tn antigen predicts adverse clinical outcome in patients with clear cell renal cell carcinoma (ccRCC). To understand the biosynthetic underpinnings of Tn antigen changes in ccRCC, we focused on N-acetylgalactosaminyltransferases (GALNTs, also known as GalNAcTs) known to be involved in Tn antigen synthesis. Data from GSE15641 profile and local cohort showed that GALNT6 was significantly upregulated in ccRCC tissues. The current study aimed to determine the role of GALNT6 in ccRCC, and whether GALNT6-mediated O-glycosylation aggravates malignant behaviors. Gain- and loss-of-function experiments showed that overexpression of GALNT6 accelerated ccRCC cell proliferation, migration, and invasion, as well as promoted ccRCC-derived xenograft tumor growth and lung metastasis. In line with this, silencing of GALNT6 yielded the opposite results. Mechanically, high expression of GALNT6 led to the accumulation of Tn antigen in ccRCC cells. By undertaking immunoprecipitation coupled with liquid chromatography/mass spectrometry, vicia villosa agglutinin blot, and site-directed mutagenesis assays, we found that O-glycosylation of prohibitin 2 (PHB2) at Ser161 was required for the GALNT6-induced ccRCC cell proliferation, migration, and invasion. Additionally, we identified lens epithelium-derived growth factor (LEDGF) as a key regulator of GALNT6 transcriptional induction in ccRCC growth and an upstream contributor to ccRCC aggressive behavior. Collectively, our findings indicate that GALNT6-mediated abnormal O-glycosylation promotes ccRCC progression, which provides a potential therapeutic target in ccRCC development.

Frequent gene mutations and the correlations with clinicopathological features in clear cell renal cell carcinoma: preliminary study based on Chinese population and TCGA database.

BACKGROUND: Large-scale sequencing plays important roles in revealing the genomic map of ccRCC and predicting prognosis and therapeutic response to targeted drugs. However, the relevant clinical data is still sparse in Chinese population. METHODS: Fresh tumor specimens were collected from 66 Chinese ccRCC patients, then the genomic RNAs were subjected to whole transcriptome sequencing (WTS). We comprehensively analyzed the frequently mutated genes from our hospital's cohort as well as TCGA-KIRC cohort. RESULTS: VHL gene is the most frequently mutated gene in ccRCC. In our cohort, BAP1 and PTEN are significantly associated with a higher tumor grade and DNM2 is significantly associated with a lower tumor grade. The mutant type (MT) groups of BAP1 or PTEN, BAP1 or SETD2, BAP1 or TP53, BAP1 or MTOR, BAP1 or FAT1 and BAP1 or AR had a significantly correlation with higher tumor grade in our cohort. Moreover, we identified HMCN1 was a hub mutant gene which was closely related to worse prognosis and may enhance anti-tumor immune responses. CONCLUSIONS: In this preliminary research, we comprehensively analyzed the frequently mutated genes in the Chinese population and TCGA database, which may bring new insights to the diagnosis and medical treatment of ccRCC.

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

OBJECTIVES: Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC). METHODS: In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression. RESULTS: A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group. CONCLUSION: The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

The Radiogenomic Landscape of Clear Cell Renal Cell Carcinoma: Insights into Lipid Metabolism through Evaluation of ADFP Expression.

This study aims to explore the relationship between radiological imaging and genomic characteristics in clear cell renal cell carcinoma (ccRCC), focusing on the expression of adipose differentiation-related protein (ADFP) detected through computed tomography (CT). The goal is to establish a radiogenomic lipid profile and understand its association with tumor characteristics. Data from The Cancer Genome Atlas (TCGA) and the Cancer Imaging Archive (TCIA) were utilized to correlate imaging features with adipose differentiation-related protein (ADFP) expression in ccRCC. CT scans assessed various tumor features, including size, composition, margin, necrosis, and growth pattern, alongside measurements of tumoral Hounsfield units (HU) and abdominal adipose tissue compartments. Statistical analyses compared demographics, clinical-pathological features, adipose tissue quantification, and tumoral HU between groups. Among 197 patients, 22.8% exhibited ADFP expression significantly associated with hydronephrosis. Low-grade ccRCC patients expressing ADFP had higher quantities of visceral and subcutaneous adipose tissue and lower tumoral HU values compared to their high-grade counterparts. Similar trends were observed in low-grade ccRCC patients without ADFP expression. ADFP expression in ccRCC correlates with specific imaging features such as hydronephrosis and altered adipose tissue distribution. Low-grade ccRCC patients with ADFP expression display a distinct lipid metabolic profile, emphasizing the relationship between radiological features, genomic expression, and tumor metabolism. These findings suggest potential for personalized diagnostic and therapeutic strategies targeting tumor lipid metabolism.

Construction of an autophagy-related genes risk model as predicting prognosis: BAG1 suppresses growth of clear cell renal cell carcinoma.

BACKGROUND: The incidence of clear cell renal cell carcinoma (ccRCC) is increasing annually. While the cure rate and prognosis of early ccRCC are promising, the 5-year survival rate of patients with metastatic ccRCC is below 12%. Autophagy disfunction is closely related to infection, cancer, neurodegeneration and aging. Nevertheless, there has been limited exploration of the association between autophagy and ccRCC through bioinformatics analysis. METHODS: A novel risk model of autophagy-related genes (ARGs) was constructed to predict the prognosis of patients with ccRCC and guide the individualized treatment to some extent. Relevant data samples were obtained from the TCGA database, and ccRCC-related ARGs were identified by Pearson correlation analysis, leading to the establishment of a risk model covering 10 ccRCC-related ARGs. Many indicators were used to assess the accuracy of the risk model. RESULTS: Receiver operating characteristic (ROC) curve analysis showed that the risk model had high accuracy, indicating that the risk model could predict the prognosis of ccRCC patients. Moreover, the findings revealed significant differences about immune and metabolic features in low- and high-risk groups. The study also found that BAG1 within the risk model was closely related to the prognosis of ccRCC and an independent risk factor. In vitro and in vivo experiments validated for the first time that BAG1 could suppress the proliferation, migration, and invasion of ccRCC. CONCLUSION: The construction of ARGs risk model, can well predict the prognosis of ccRCC patients, and provide guidance for individual therapy to patients. It was also found that BAG1 has significant prognostic value for ccRCC patients and acts as a tumor suppressor gene in ccRCC. These findings have crucial implications for the prognosis and treatment of ccRCC patients.

AUY922 improves sensitivity to sunitinib in clear cell renal cell carcinoma based on network pharmacology and in vitro experiments.

Clear Cell Renal Cell Carcinoma (ccRCC), the most prevalent form of renal cell carcinoma (RCC), poses a significant threat to human health due to its rising morbidity and mortality rates. Sunitinib, a pivotal targeted drug for the treatment of ccRCC, presents a significant challenge due to the high susceptibility of ccRCC to resistance. HSP90 inhibitor AUY922 has demonstrated anti-tumor activity in a range of cancer types. However, its efficacy in combination with sunitinib for ccRCC treatment has not been evaluated. In this study, we employed bioinformatics, network pharmacology, and in vitro assays to verify that AUY922 inhibits cell viability, proliferation, and migration of ccRCC cell lines 786-O and ACHN, with IC50s of 91.86 µM for 786-O and 115.5 µM for ACHN. The effect of AUY922 enhancing the inhibitory effect of sunitinib on ccRCC was further confirmed. The CCK-8 assay demonstrated that the IC50 of sunitinib was reduced from 15.10 µM to 11.91 µM for 786-O and from 17.65 µM to 13.66 µM for ACHN, after the combined application of AUY922. The EdU assay and wound healing assay indicated that AUY922 augmented the inhibitory impact of sunitinib on the proliferation and migration of ccRCC cells. Western blot and RT-PCR analyses demonstrated that AUY922 increased the sensitivity of ccRCC cells to sunitinib by targeting the HIF-1α/VEGFA/VEGFR pathway. Our study represents the first investigation into the role and mechanism of AUY922 in enhancing the sensitivity of ccRCC to sunitinib. In conclusion, the findings indicate the potential for AUY922 to enhance the therapeutic efficacy of sunitinib and overcome sunitinib resistance in ccRCC.

The role of BUD31 in clear cell renal cell carcinoma: prognostic significance, alternative splicing, and tumor immune environment.

BUD31, a splicing factor, is linked to various cancers. This study examines BUD31's expression, prognostic value, mutation profile, genomic instability, tumor immune environment, and role in clear cell renal cell carcinoma (ccRCC), focusing on cell cycle regulation via alternative splicing. BUD31 expression was analyzed using TCGA and GTEx databases across 33 cancers. Techniques included IHC staining, survival analysis, Cox regression, and nomogram construction. Mutation landscape, genomic instability, and tumor immune microenvironment were evaluated. Functional assays on ccRCC cell lines involved BUD31 knockdown, RNA sequencing, and alternative splicing analysis. BUD31 was upregulated in multiple tumors, including ccRCC. High BUD31 expression correlated with worse survival outcomes and was identified as an independent predictor of poor prognosis in ccRCC. High BUD31 expression also correlated with increased genomic instability and a less active immune microenvironment. BUD31 knockdown inhibited cell proliferation, migration, and invasion in vitro and reduced tumor growth in vivo. RNA sequencing identified 390 alternative splicing events regulated by BUD31, including 17 cell cycle-related genes. KEGG analysis highlighted pathways involved in cell cycle regulation, indicating BUD31's role in promoting cell cycle progression through alternative splicing. BUD31 is upregulated in various tumors and is associated with poor outcomes, increased genomic instability, and a suppressed immune microenvironment in ccRCC. BUD31 promotes cell cycle progression via alternative splicing, suggesting it as a prognostic biomarker and potential therapeutic target in ccRCC.

Clinical characteristics of renal cell carcinoma in the transplanted kidney in renal transplant recipients: a systematic scoping review.

Background: Renal transplant recipients confront a substantially elevated susceptibility to renal cell carcinoma (RCC), particularly in their native kidneys as opposed to allografts. Methods: In this systematic scoping review, exhaustive searches were conducted of the MEDLINE and EMBASE databases. Information was gathered on clinical manifestations, donor demographics, diagnostic intervals, tumor dimensions, histopathological characteristics, and therapeutic outcomes associated with RCC arising in allograft kidneys. Results: The searches yielded a corpus of 42 case reports and 11 retrospective cohorts, encompassing a cohort of 274 patients. The majority of cases (75.4%) were clinically latent, discerned primarily through imaging modalities. Symptomatic presentations encompassed manifestations such as hematuria, elevated serum creatinine levels, abdominal discomfort, and graft-related pain. The mean temporal interval between renal transplantation and RCC diagnosis was calculated at 11.6 years, albeit displaying considerable variance. Notably, papillary and clear cell RCC emerged as the prevailing histopathological subtypes. However, the paucity of longitudinal follow-up data represents a notable caveat. Conclusion: This investigation underscores the imperative of rigorous posttransplant surveillance regimes owing to the substantial prevalence of asymptomatic RCC instances. Future research should focus on clinical outcomes and cost-effectiveness of screening practices to develop preventive strategies.

SMEK1 promotes clear cell renal cell carcinoma progression via EGFR tyrosine-kinase dependent pathway.

Studying the mechanisms underlying clear cell renal cell carcinoma (ccRCC), the most common subtype of kidney cancer, may address an unmet need in ccRCC-targeted drug research. Growing evidences indicate that protein phosphatase 4 (PP4) plays an important role in cancer biology. Here, we characterized the upregulation of PP4 core component SMEK1 in ccRCC using tissue microarrays and revealed that its high expression is closely associated with reduced patient survival. We then conducted cell function experiments and animal experiments to prove the tumor-promoting effect of SMEK1. Next, RNA-seq was performed to explore its underlying mechanism, and the results revealed that SMEK1-regulated genes were extensively involved in cell motility, and the canonical tyrosine kinase receptor EGFR was one of its targets. Moreover, we verified the regulatory effect of SMEK1 on EGFR and its downstream MAPK and AKT pathway through molecular experiments, in which erlotinib, a tyrosine kinase inhibitor, can partially block this regulation, demonstrating that SMEK1 mediates its effects dependent on the tyrosine kinase activity of EGFR. Mechanistically, SMEK1 bond to PRMT5 and facilitated PRMT5-mediated histone methylation to promote the transcription of EGFR. Furthermore, we studied the upstream regulators of SMEK1 and demonstrated that the transcription factor E2F1 could directly bind to the SMEK1 promoter by chromatin immunoprecipitation. Functionally, E2F1 could also induce ccRCC progression by manipulating the expression of SMEK1. Collectively, our findings demonstrate the overexpression of SMEK1 in ccRCC, and reveal a novel E2F1/SMEK1/PRMT5/EGFR-tyrosine-kinase-dependent pathway for ccRCC progression.

Clinical outcomes and prognostic factors in metastatic nonclear cell renal cell carcinoma treated with immuno-oncology combination therapy.

BACKGROUND: Metastatic nonclear cell renal cell carcinoma (nccRCC) is a heterogeneous disease with poor prognosis. The clinical characteristics and prognostic factors of immuno-oncology (IO) combination therapy for nccRCC are not well known. This study analyzed patients with metastatic nccRCC treated with IO combination therapy. METHODS: We retrospectively collected data from 447 patients with metastatic RCC treated with IO-based combination therapy as first-line treatment between September 2018 and July 2023 in a Japanese multicenter study. The primary endpoints were objective response rate, progression-free survival (PFS), and overall survival (OS), comparing groups treated with IO-IO and IO-tyrosine kinase inhibitor (TKI) therapies. RESULTS: Seventy-five patients with metastatic nccRCC were eligible for analysis: 39 were classified into the IO-IO group and 36 into the IO-TKI group. Median PFS was 5.4 months (95% CI: 1.6-9.1) for the IO-IO group and 5.6 (95% CI: 3.4-12.0) for the IO + TKI group. Median OS was 24.2 months (95% CI: 7.5-NA) for the IO-IO group and 23.4 (95% CI: 18.8-NA) for the IO + TKI group, with no significant difference. In univariate analysis, International Metastatic Renal Cell Carcinoma Database Consortium scores, Karnofsky performance status, neutrophil-to-lymphocyte ratio, and the presence of liver metastases were significantly associated with OS, whereas in multivariate analysis, only the presence of liver metastases was significantly associated with OS (P = .035). CONCLUSIONS: There was no significant difference in OS or PFS between IO-IO and IO-TKI combination therapy as first-line treatment for patients with nccRCC. Liver metastasis is a poor prognostic factor for such patients.

Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self-supervised deep learning.

BACKGROUND: Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC), such as guiding adjuvant chemotherapy and improving clinical outcomes. However, the time-consuming and expensive high-throughput sequencing methods severely limit their clinical applicability. Predicting intratumoral heterogeneity poses significant challenges in biology and clinical settings. Our aimed to develop a self-supervised attention-based multiple instance learning (SSL-ABMIL) model to predict TMB and VHL mutation status from hematoxylin and eosin-stained histopathological images. METHODS: We obtained whole slide images (WSIs) and somatic mutation data of 350 ccRCC patients from The Cancer Genome Atlas for developing SSL-ABMIL model. In parallel, 163 ccRCC patients from Clinical Proteomic Tumor Analysis Consortium cohort was used as independent external validation set. We systematically compared three different models (Wang-ABMIL, Ciga-ABMIL, and ImageNet-MIL) for their ability to predict TMB and VHL alterations. RESULTS: We first identified two groups of populations with high- and low-TMB (cut-off point = 0.9). In two independent cohorts, the Wang-ABMIL model achieved the highest performance with decent generalization performance (AUROC = 0.83 ± 0.02 and 0.8 ± 0.04 in predicting TMB and VHL, respectively). Attention heatmaps revealed that the Wang-ABMIL model paid the highest attention to tumor regions in high-TMB patients, while in VHL mutation prediction, non-tumor regions were also assigned high attention, particularly the stromal regions infiltrated by lymphocytes. CONCLUSIONS: Our results indicated that SSL-ABMIL can effectively extract histological features for predicting TMB and VHL mutation, demonstrating promising results in linking tumor morphology and molecular biology.

Comprehensive multi-omics analysis reveals a combination of lncRNAs that synergistically regulate glycolysis and immunotherapeutic effects in renal clear cell carcinoma.

BACKGROUND: Clear cell renal carcinoma is a common urological malignancy with poor prognosis and treatment outcomes. lncRNAs are important in metabolic reprogramming and the tumor immune microenvironment, but their role in clear cell renal carcinoma is unclear. METHODS: Renal clear cell carcinoma sample data from The Cancer Genome Atlas was used to establish a new risk profile by glycolysis-associated lncRNAs via machine learning. Risk profile-associated column-line plots were constructed to provide a quantitative tool for clinical practice. Patients with renal clear cell carcinoma were divided into high- and low-risk groups. Clinical features, tumor immune microenvironments, and immunotherapy responses were systematically analyzed. We experimentally confirmed the role of LINC01138 and LINC01605 in renal clear cell carcinoma. RESULTS: The risk profile, consisting of LUCAT1, LINC01138, LINC01605, and HOTAIR, reliably predicted survival in patients with renal clear cell carcinoma and was validated in multiple external datasets. The high-risk group presented higher levels of immune cell infiltration and better immunotherapy responses than the low-risk group. LINC01138 and LINC01605 knockdown inhibited the proliferation of renal clear cell carcinoma. CONCLUSIONS: The identified risk profiles can accurately assess the prognosis of patients with clear cell renal carcinoma and identify patient populations that would benefit from immunotherapy, providing valuable insights and therapeutic targets for the clinical management of clear cell renal carcinoma.

Bioinformatics analysis to disclose shared molecular mechanisms between type-2 diabetes and clear-cell renal-cell carcinoma, and therapeutic indications.

Type 2 diabetes (T2D) and Clear-cell renal cell carcinoma (ccRCC) are both complicated diseases which incidence rates gradually increasing. Population based studies show that severity of ccRCC might be associated with T2D. However, so far, no researcher yet investigated about the molecular mechanisms of their association. This study explored T2D and ccRCC causing shared key genes (sKGs) from multiple transcriptomics profiles to investigate their common pathogenetic processes and associated drug molecules. We identified 259 shared differentially expressed genes (sDEGs) that can separate both T2D and ccRCC patients from control samples. Local correlation analysis based on the expressions of sDEGs indicated significant association between T2D and ccRCC. Then ten sDEGs (CDC42, SCARB1, GOT2, CXCL8, FN1, IL1B, JUN, TLR2, TLR4, and VIM) were selected as the sKGs through the protein-protein interaction (PPI) network analysis. These sKGs were found significantly associated with different CpG sites of DNA methylation that might be the cause of ccRCC. The sKGs-set enrichment analysis with Gene Ontology (GO) terms and KEGG pathways revealed some crucial shared molecular functions, biological process, cellular components and KEGG pathways that might be associated with development of both T2D and ccRCC. The regulatory network analysis of sKGs identified six post-transcriptional regulators (hsa-mir-93-5p, hsa-mir-203a-3p, hsa-mir-204-5p, hsa-mir-335-5p, hsa-mir-26b-5p, and hsa-mir-1-3p) and five transcriptional regulators (YY1, FOXL1, FOXC1, NR2F1 and GATA2) of sKGs. Finally, sKGs-guided top-ranked three repurposable drug molecules (Digoxin, Imatinib, and Dovitinib) were recommended as the common treatment for both T2D and ccRCC by molecular docking and ADME/T analysis. Therefore, the results of this study may be useful for diagnosis and therapies of ccRCC patients who are also suffering from T2D.

Predicting CTLA4 expression and prognosis in clear cell renal cell carcinoma using a pathomics signature of histopathological images and machine learning.

Background: CTLA4, an immune checkpoint, plays an important role in tumor immunotherapy. The purpose of this study was to develop a pathomics signature to evaluate CTLA4 expression and predict clinical outcomes in clear cell renal cell carcinoma (ccRCC) patients. Methods: A total of 354 patients from the TCGA-KIRC dataset were enrolled in this study. The patients were stratified into two groups based on the level of CTLA4 expression, and overall survival rates were analyzed between groups. Pathological features were identified using machine learning algorithms, and a gradient boosting machine (GBM) was employed to construct the pathomics signatures for predicting prognosis and CTLA4 expression. The predictive performance of the model was subsequently assessed. Enrichment analysis was performed on diferentially expressed genes related to the pathomics score (PS). Additionally, correlations between PS and TMB, as well as immune infiltration profiles associated with different PS values, were explored. In vitro experiments, CTLA4 knockdown was performed to investigate its impact on cell proliferation, migration, invasion, TGF-ß signaling pathway, and macrophage polarization. Results: High expression of CTLA4 was associated with an unfavorable prognosis in ccRCC patients. The pathomics signature displayed good performance in the validation set (AUC = 0.737; P < 0.001 in the log-rank test). The PS was positively correlated with CTLA4 expression. We next explored the underlying mechanism and found the associations between the pathomics signature and TGF-ß signaling pathways, TMB, and Tregs. Further in vitro experiments demonstrated that CTLA4 knockdown inhibited cell proliferation, migration, invasion, TGF-ß expression, and macrophage M2 polarization. Conclusion: High expression of CTLA4 was found to correlate with poor prognosis in ccRCC patients. The pathomics signature established by our group using machine learning effectively predicted both patient prognosis and CTLA4 expression levels in ccRCC cases.