Prognosis Of Renal Cell Carcinoma Research Articles

Obesity alters the fitness of peritumoral adipose tissue, exacerbating tumor invasiveness in renal cancer through the induction of ADAM12 and CYP1B1.

Obesity exacerbates the risk and aggressiveness of many types of cancer. Adipose tissue (AT) represents a prevalent component of the tumor microenvironment (TME) and contributes to cancer development and progression. Reciprocal communication between cancer and adipose cells leads to the generation of cancer-associated adipocytes (CAAs), which in turn foster tumor invasiveness by producing paracrine metabolites, adipocytokines, and growth factors. Interfering with the crosstalk between CAAs and cancer cells is of key relevance in the prevention of tumor progression. The present study aimed to analyze the contribution of peritumoral AT in renal cell carcinoma (RCC) progression in lean versus overweight or obese patients. By isolating human adipose-derived stromal/stem cells from the three groups of patients and performing conditioned medium studies with RCC cells along with in vivo xenograft experiments, we found that peritumoral adipocytes from the three groups show a distinct expression profile of genes. In particular, ADAM metallopeptidase domain 12 (ADAM12) and cytochrome P450 family 1 subfamily B member 1 (CYP1B1) were found to be upregulated in obesity and their silencing reduced RCC cell invasiveness. In conclusion, high ADAM12 and CYP1B1 expressions in the peritumoral adipocytes boost tumor invasiveness and may serve as an indicator of poor prognosis in RCC.

Development and validation of a prediction model for the prognosis of renal cell carcinoma with liver metastases: a population-based cohort study.

Current studies on the establishment of prognostic model for renal cell carcinoma (RCC) with liver metastases (LM) were scarce. This study aimed to develop nomograms to predict the prognosis of RCC with LM. Patients diagnosed with RCC between 2010 and 2021 from the Surveillance, Epidemiology, and End Results (SEER) database were selected. The eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning algorithms were used to screen for the most influential factors affecting prognosis, and the Venn diagram method was employed for further refinement. Subsequently, a nomogram related to brain metastases was constructed. The performance of the nomograms was evaluated through receiver operating characteristics (ROC) curves, calibration plots, C-index, time-dependent C-index, and decision curve analysis (DCA). Kaplan-Meier (K-M) survival curves were used to provide additional verification of the clinical efficacy of the nomogram. This research comprised 2,395 RCC patients with LM. The Venn diagram demonstrated that age, histological type, grade, AJCC T stage, AJCC N stage, surgery, chemotherapy, marital status, and lung metastasis were highly relevant variables to patients with LM. The AUC, C-index, calibration curves, and DCA curves showed excellent performance of the nomogram. Additionally, the prognostic nomogram accurately classified RCC with LM patients into low- and high-risk groups for mortality. This study developed a novel nomogram to predict the prognostic factors of RCC with LM, providing a valuable reference for making accurate clinical decisions.

Percutaneous irreversible electroporation of renal cell carcinomas in an acquired solitary kidney: A primary study.

To evaluate the postoperative complications and prognosis of renal cell carcinoma (RCC) in a solitary kidney after irreversible electroporation (IRE). A total of 8 patients with 9 RCCs in a solitary kidney treated with computed tomography (CT)-guided IRE from February 2017 to September 2020 were retrospectively analyzed. Follow-up included contrast-enhanced CT or magnetic resonance imaging examinations at 1 day, 1 week, 1 month, 3 months, 6 months, 12 months, and each year after IRE and the evaluation of the incidence of postoperative complications, renal function changes, local tumor recurrence, and metastasis. Technical success was achieved in all 8 patients treated with IRE. No serious complications were observed. Recurrence or metastasis occurred in two patients. The renal function and hemoglobin values of the 8 patients before treatment and at the last follow-up showed no significant difference. IRE is a relatively effective, safe, and feasible treatment for RCCs in a solitary kidney, which improved the effective survival and quality of life of these patients.

Tertiary lymphoid structures potentially promote immune checkpoint inhibitor response in SMARCB1-deficient medullary renal cell carcinoma

The WHO’s classification of renal cell carcinoma (RCC) has identified loss of SMARCB1 as one of the driven mutations. Despite intensive postoperative interventions, the prognosis for SMARCB1-deficient medullary RCC remains poor, indicating insufficiency in current therapy. Herein, we reported the treatment outcomes of five patients with metastatic SMARCB1-deficient medullary RCC and molecular correlates. Four patients were treated with first-line immune checkpoint inhibitors (ICI) plus tyrosine kinase inhibitors (TKI) combination therapy with a median PFS (mPFS) of 12.3 months. Transcriptomic analysis revealed enrichment of immune-related pathways in SMARCB1-deficient medullary RCC compared to clear-cell and papillary RCC. Multiple immunofluorescence (mIF) revealed the association between the formation of mature tertiary lymphoid structures (TLSs) and the favorable response to ICI-based combination therapy. In conclusion, ICI-based combination therapy showed promising anti-tumor activity in SMARCB1-deficient medullary RCC patients. The presence of mature tertiary TLSs may partially elucidate the mechanism underlying treatment response.

IKBKE regulates renal cell carcinoma progression and sunitinib resistance through the RRM2-AKT pathway.

Tyrosine kinase inhibitors (TKIs), such as sunitinib, have emerged as promising agents in renal cell carcinoma (RCC) treatment, particularly in patients at advanced/metastatic clinical stages. However, acquired resistance to sunitinib is common following prolonged clinical treatment in RCC. Increasing evidence has demonstrated a strong correlation between inhibitor of nuclear factor kappa B kinase subunit epsilon (IKBKE) and cancer progression as well as drug resistance. Here, we found that IKBKE is upregulated in RCC tissues and sunitinib-resistant RCC cells. High IKBKE expression is positively correlated with advanced clinical staging and a poor prognosis in RCC. Silencing IKBKE downregulates ribonucleotide reductase M2 (RRM2) and induces cell cycle arrest at G2/M phase, suppressing RCC progression and enhancing sunitinib sensitivity to RCC cells. Mechanistically, IKBKE interacts with and phosphorylates RRM2 to activate the AKT signaling pathway to promotes RCC progression and sunitinib resistance. Notably, the IKBKE inhibitor CYT387 restores sunitinib sensitivity in RCC cells by downregulating RRM2 expression. Collectively, these results indicate that inhibition of IKBKE restrains RCC progression and enhances sunitinib sensitivity by downregulating RRM2 through the RRM2-AKT pathway, suggesting that IKBKE may be a potential therapeutic target for RCC.

The prognostic value of visible hematuria is only significant in T1a renal cell carcinoma: a single-center retrospective study

ObjectivesTo investigate the prognostic value of visible hematuria in T1a renal cell carcinoma (RCC).Materials and methodsIn the RCC database of the Chinese People’s Liberation Army General Hospital Department of Urology, we assembled the records of patients with unilateral RCC over 18 years of age diagnosed between 2008 and 2019. The clinical stage was cT1, and the tumors ranged in size from 0 to 7 cm. The primary treatments were partial nephrectomy (PN) or radical nephrectomy (RN). Logistic regression analysis, Cox regression, interaction analysis, and Kaplan-Meier survival analysis were used to study the correlation between visible hematuria and progression-free survival (PFS), and cancer-specific survival (CSS).ResultsA total of 7,610 patients with cT1 RCC comprised the study population, including 505 RCC patients with visible hematuria. The average follow-up time was 64.6 months (range: 12–144 months). Visible hematuria was significantly associated with the prognosis (PFS, hazard ratio [HR] = 2.7, P < 0.001; CSS, HR = 4.2, P < 0.001) of T1a RCC, but was more significant for CSS in cases of a tumor size ≤ 2 cm (HR = 26.8, P = 0.026). This effect was not significant in T1b RCC (PFS, HR = 0.7, P = 0.153; CSS, HR = 1.1, P = 0.862). The interaction between visible hematuria and tumor size was significant (P = 0.001).ConclusionsThis study showed that visible hematuria was an independent risk factor for PFS and CSS in T1a RCC. The predictive value of visible hematuria for CSS was more significant in RCCs ≤ 2 cm, but did not reach statistical significance in T1b RCC. T1a RCC patients with visible hematuria should be intensively monitored during follow-up.

Niemann-Pick C1-like 1 as a Prognostic Marker in Renal Cell Carcinoma: A Retrospective Cohort Study.

Renal cell carcinoma (RCC) is a highly aggressive malignancy accounting for the majority of kidney cancers. Despite recent advancements in therapeutic options, the prognosis for advanced-stage RCC remains poor. Niemann-Pick C1-Like 1 (NPC1L1) plays a crucial role in cholesterol absorption and has been implicated in cancer progression across various cancers. However, its expression patterns and prognostic significance in RCC remain unclear. In this study, NPC1L1 expression in normal and RCC tissues, including subtypes, was compared using TCGA, GEPIA2, and The Human Protein Atlas. Clinical correlations were assessed, and the impact of NPC1L1 on overall survival (OS) and progression-free survival (PFS) was evaluated. Gene effect scores were analyzed using the DepMap tool to determine the involvement of NPC1L1 in RCC progression. NPC1L1 expression was significantly lower in RCC tissues compared to normal tissues, particularly in the clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) subtypes, but increased in advanced tumor stages. Higher NPC1L1 expression was associated with worse OS and PFS in RCC patients. Multivariable Cox regression confirmed NPC1L1 as an independent prognostic marker. Additionally, gene effect scores showed that NPC1L1 is essential for the survival of specific RCC cell lines. This study determines NPC1L1 as an independent prognostic indicator in RCC, with higher expression associated with poor survival outcomes. These findings suggest that NPC1L1 could serve as a valuable marker for identifying high-risk RCC patients. Further research is required to investigate the molecular mechanisms underlying the role of NPC1L1 in RCC progression.

Impact of neoadjuvant therapy on prognosis in renal cell carcinoma with inferior vena cava thrombus

BackgroundThe standard treatment for non-metastatic renal cell carcinoma (RCC) with inferior vena cava (IVC) thrombus is complete surgical resection; however, this procedure is complex and carries high complication rates and perioperative mortality. Previous studies have explored preoperative multimodal therapy to reduce surgical difficulty, but limited evidence prevents guideline recommendations. This study aimed to investigate the impact of neoadjuvant therapy on the prognosis of patients with RCC and IVC thrombus without distant metastasis. MethodsData from 2006 to 2024 on RCC patients with IVC thrombus undergoing radical nephrectomy plus IVC thrombus resection were collected. Patients received neoadjuvant therapy, including tyrosine kinase inhibitors or immune checkpoint inhibitors, followed by surgery. Tumor size and thrombus height were assessed by computed tomography. Disease-free survival (DFS) and overall survival (OS) were calculated using Kaplan-Meier curves. Multivariate analysis was used to identify factors predicting DFS. ResultsThirty-one patients who did not receive neoadjuvant chemotherapy therapy (NAC-Naive group) and 19 patients who received neoadjuvant chemotherapy therapy (NAC group) were analyzed. The NAC group showed significant reductions in primary renal tumor size and neutrophil-to-lymphocyte ratio compared to the NAC-Naive group just before nephrectomy. The NAC group had significantly improved DFS and OS. Median DFS and OS were not reached in the NAC group compared to 26.3 months and 73.5 months, respectively, in the NAC-Naive group. The NAC group had a 2-year recurrence-free survival rate of 70.9% compared to 50.6% in the NAC-Naive group. Multivariate analysis identified a preoperative tumor size of 10 cm or larger and lack of neoadjuvant therapy as poor prognostic factors for DFS. ConclusionNeoadjuvant therapy significantly improves the prognosis of RCC patients with IVC thrombus. This therapy reduces surgical invasiveness and has a mid- to long-term preventive effect on recurrence. These findings support the potential benefit of neoadjuvant systemic therapy in improving outcomes for this patient population.

Multinucleated tumor cells and micropapillary morphology appear to be predictors of poor prognosis in renal cell carcinoma with papillary and oncocytic features

Renal cell carcinoma with papillary and oncocytic features (RCC-PO) are poorly understood, partially due to conflicting results in multiple studies. The histological features that predict behavior of RCC-PO have not been elucidated. The aim is to review clinicopathologic features and to correlate clinical outcomes of patients with RCC-PO to further expand our knowledge on these heterogeneous tumors. An archival search was done for “RCC” and “papillary,” and tumors with >50% papillary and oncocytic features were included. Clinicopathologic data including tumor size, grade, stage, molecular and immunohistochemical testing when performed, and follow-up data were collected. Using multivariate analyses, correlation between histological features, tumor stage and prognosis were analyzed. Sixty-one patients with RCC-PO were identified of which 49 (80%) were male with a median age of 65 (range: 36–93) years, and a mean tumor size of 5.2 (range: 1–21.5) cm. Micropapillary features were seen in 4, bizarre nuclei (at least 3 times larger or with irregular shape) in 6, multinucleated tumor cells (MTC) in 15, single or small clusters (SSC) (made of 2–3 tumor cells) located away from areas of necrosis in 16, and striking eosinophilic cytoplasmic inclusions in 3 tumors, respectively. Thirty-six (59%) tumors were high-grade (WHO/ISUP grade 3–4), and 23 (38%) had a high stage (≥pT3 or pN1). Tumors were positive for AMACR (15/16) and CK7 (13/17), with preserved FH (7/7) staining and were all negative for CD117 (0/7), ALK, TFE3, cathepsin K, Melan A, and HMB45 (0/4, each). Three tumors underwent chromosomal microarray (CMA) plus gene fusion assay, and FISH and germline testing for FLCN and MET gene alterations by PCR were done on 1 each. Ten (16%) patients had a local recurrence (LR) or metastasis after nephrectomy; 4 died of disease (2 had tumors with micropapillary features), with a median follow-up of 7 (range: 0.01–19) years. Tumors with micropapillary features showed significantly higher RCC-PO-related mortality (50% vs. 3.5%, p < 0.001). In multivariable analysis, SSC correlated with a higher stage (HR: 11.95; p = 0.005); micropapillary features (HR: 18.42; p = 0.017) and MTC (HR: 180.22; p = 0.036) with presence of metastasis/LR; and micropapillary features with a higher RCC-PO-related mortality (HR: 60.35; p = 0.036). RCC-PO are cytogenetically heterogeneous with overlapping features of various renal neoplasms. Micropapillary features and MTC appear to be independent predictors of poor outcomes in these tumors.

Comprehensive assessment of the association between tumor-infiltrating immune cells and the prognosis of renal cell carcinoma.

According to current statistics, renal cancer accounts for 3% of all cancers worldwide. Renal cell carcinoma (RCC) is the most common solid lesion in the kidney and accounts for approximately 90% of all renal malignancies. Increasing evidence has shown an association between immune infiltration in RCC and clinical outcomes. To discover possible targets for the immune system, we investigated the link between tumor-infiltrating immune cells (TIICs) and the prognosis of RCC. To investigate the effects of 22 TIICs on the prognosis of RCC patients and identify potential therapeutic targets for RCC immunotherapy. The CIBERSORT algorithm partitioned the 22 TIICs from the Cancer Genome Atlas cohort into proportions. Cox regression analysis was employed to evaluate the impact of 22 TIICs on the probability of developing RCC. A predictive model for immunological risk was developed by analyzing the statistical relationship between the subpopulations of TIICs and survival outcomes. Furthermore, multivariate Cox regression analysis was used to investigate independent factors for the prognostic prediction of RCC. A value of P < 0.05 was regarded as statistically significant. Compared to normal tissues, RCC tissues exhibited a distinct infiltration of immune cells. An immune risk score model was established and univariate Cox regression analysis revealed a significant association between four immune cell types and the survival risk connected to RCC. High-risk individuals were correlated to poorer outcomes according to the Kaplan-Meier survival curve (P = 1E-05). The immunological risk score model was demonstrated to be a dependable predictor of survival risk (area under the curve = 0.747) via the receiver operating characteristic curve. According to multivariate Cox regression analysis, the immune risk score model independently predicted RCC patients' prognosis (hazard ratio = 1.550, 95%CI: 1.342-1.791; P < 0.001). Finally, we established a nomogram that accurately and comprehensively forecast the survival of patients with RCC. TIICs play various roles in RCC prognosis. The immunological risk score is an independent predictor of poor survival in kidney cancer cases.

PSMD2 overexpression as a biomarker for resistance and prognosis in renal cell carcinoma treated with immune checkpoint and tyrosine kinase inhibitors.

Integrated immune checkpoint inhibitors (ICIs) plus tyrosine kinase inhibitors (TKIs) are now the recommended first-line therapy to manage renal cell carcinoma (mRCC). Proteasome 26S subunit non-ATPase 2 (PSMD2) overexpression in tumors has been correlated with tumor progression. Currently, mRCC lacks an established biomarker for the combination of ICI+TKI. This study involved RNA sequencing of RCC patients from two cohorts treated with ICI+TKI (ZS-MRCC and JAVELIN-Renal-101). We utilized immunohistochemistry alongside flow cytometry, aiming at assessing immune cell infiltration and functionality in high-risk localized RCC samples. Response and progression-free survival (PFS) were evaluated relying upon RECIST criteria. PSMD2 was significantly overexpressed in advanced RCC and among non-responders to ICI+TKI therapy. Overexpressed PSMD2 was correlated with poor PFS in the ZS-MRCC and JAVELIN-101 cohorts. Multivariate Cox analysis validated PSMD2 as an independent PFS predictor. PSMD2 overexpression was related to a reduction in CD8+ T cells, especially GZMB+ CD8+ T cells, besides an increase in PD1+ CD4+ T cells. Additionally, tumors with high PSMD2 levels showed enhanced T cell exhaustion levels and a higher regulatory T cell presence. A Machine Learning (ML) model based on PSMD2 expression and other screened factors was subsequently developed to predict the effectiveness of ICI+TKI. Elevated PSMD2 expression is linked to resistance and decreased PFS in mRCC patients undergoing ICI+TKI therapy. High PSMD2 levels are also associated with impaired function and increased exhaustion of tumor-infiltrating lymphocytes. An ML model incorporating PSMD2 expression could potentially identify patients who may have a higher likelihood of benefiting from ICI+TKI.

1707P A plasma proteomic based algorithm is associated with prognosis in renal cell carcinoma

1707P A plasma proteomic based algorithm is associated with prognosis in renal cell carcinoma

Liquid biopsy in renal cell carcinoma.

This commentary focuses on the article by Correa et al on the association of circulating tumor DNA with patient prognosis in renal cell carcinoma.

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.

Tumor contour irregularity on preoperative CT predicts prognosis in renal cell carcinoma: a multi-institutional study

Radiology-based prognostic biomarkers play a crucial role in patient counseling, enhancing surveillance, and designing clinical trials effectively. This study aims to assess the predictive significance of preoperative CT-based tumor contour irregularity in determining clinical outcomes among patients with renal cell carcinoma (RCC). We conducted a retrospective multi-institutional review involving 2218 patients pathologically diagnosed with RCC. The training and internal validation sets included patients at Zhongshan Hospital between January 2009 and August 2019. The external test set comprised patients from the First Affiliated Hospital, Zhejiang University School of Medicine (January 2016 to January 2018), the Xiamen Branch of Zhongshan Hospital (November 2017 to June 2023), and the Cancer Imaging Archive. The contour irregularity degree (CID), quantified as the ratio of irregular cross-sections to the total tumor cross-sections, was analyzed for its prognostic relevance across different subgroups of RCC patients. A novel CID-based scoring system was developed, and its predictive efficacy was evaluated and compared with existing prognostic models. The CID exhibited significant discriminatory power in predicting overall survival (OS), recurrence-free survival (RFS), and disease-specific survival (DSS) among patients with RCC tumors measuring 3cm or larger (all p<0.001). Multivariate analyses confirmed the CID as an independent prognostic indicator. Notably, the CID augmented prognostic stratification among RCC patients within distinct risk subgroups delineated by SSIGN models and ISUP grades. The CID-based nomogram (C-Model) demonstrated robust predictive performance, with C-index values of 0.88 (95%CI: 0.84-0.92) in the training set, 0.92 (95%CI: 0.88-0.98) in the internal validation set, and 0.86 (95%CI: 0.81-0.90) in the external test set, surpassing existing prognostic models. Routine imaging-based assessment of the CID serves as an independent prognostic factor, offering incremental prognostic value to existing models in RCC patients with tumors measuring 3cm or larger. This study was funded by grants from National Natural Science Foundation of China; Shanghai Municipal Health Commission; China National Key R&D Program and Science and Technology Commission of Shanghai Municipality.

Prediction of clear cell renal cell carcinoma prognosis based on an immunogenomic landscape analysis

Immune cell infiltration and tumor-related immune molecules play key roles in tumorigenesis and tumor progression. The influence of immune interactions on the molecular characteristics and prognosis of clear cell renal cell carcinoma (ccRCC) remains unclear. A machine learning algorithm was applied to the transcriptome data from The Cancer Genome Atlas database to determine the immunophenotypic and immunological characteristics of ccRCC patients. These algorithms included single-sample gene set enrichment analyses and cell type identification. Using bioinformatics techniques, we examined the prognostic potential and regulatory networks of immune-related genes (IRGs) involved in ccRCC immune interactions. Fifteen IRGs (CCL7, CHGA, CMA1, CRABP2, IFNE, ISG15, NPR3, PDIA2, PGLYRP2, PLA2G2A, SAA1, TEK, TGFA, TNFSF14, and UCN2) were identified as prognostic IRGs associated with overall survival and were used to construct a prognostic model. The area under the receiver operating characteristic curve at 1 year was 0.927; 3 years, 0.822; and 5 years, 0.717, indicating good predictive accuracy. Molecular regulatory networks were found to govern immune interactions in ccRCC. Additionally, we developed a nomogram containing the model and clinical characteristics with high prognostic potential. By systematically examining the sophisticated regulatory mechanisms, molecular characteristics, and prognostic potential of ccRCC immune interactions, we provided an important framework for understanding the molecular mechanisms of ccRCC and identifying new prognostic markers and therapeutic targets for future research.

Prognostic Significance of VAV3 Gene Variants and Expression in Renal Cell Carcinoma.

Renal cell carcinoma (RCC) is characterized by high mortality and morbidity rates. Vav guanine nucleotide exchange factors (VAVs), crucial for signal transduction between cell membrane receptors and intracellular mediators, have been implicated in carcinogenesis. However, their potential prognostic value in RCC remains unclear. The impact of 150 common VAV polymorphisms on RCC risk and survival was investigated in a cohort of 630 individuals. Publicly available gene expression datasets were utilized to analyze VAV gene expression in relation to patient outcomes. The VAV3 rs17019888 polymorphism was significantly associated with RCC risk and overall survival after adjusting for false discovery rates. Expression quantitative trait loci analysis revealed that the risk allele of rs17019888 is linked to reduced VAV3 expression. Analysis of 19 kidney cancer gene expression datasets revealed lower VAV3 expression in RCC tissues compared to normal tissues, with higher expression correlating with better prognosis. Gene set enrichment analysis demonstrated that VAV3 negatively regulates the ubiquitin-proteasome system, extracellular matrix and membrane receptors, inflammatory responses, matrix metalloproteinases, and cell cycle pathways. Furthermore, elevated VAV3 expression was associated with increased infiltration of B cells, macrophages, and neutrophils into the RCC tumor microenvironment. Our findings suggest that VAV3 gene variants influence RCC risk and survival, contributing to a favorable prognosis in RCC.

DEPDC1 as a metabolic target regulates glycolysis in renal cell carcinoma through AKT/mTOR/HIF1α pathway

Renal cell carcinoma (RCC) is considered a “metabolic disease” characterized by elevated glycolysis in patients with advanced RCC. Tyrosine kinase inhibitor (TKI) therapy is currently an important treatment option for advanced RCC, but drug resistance may develop in some patients. Combining TKI with targeted metabolic therapy may provide a more effective approach for patients with advanced RCC. An analysis of 14 RCC patients (including three needle biopsy samples with TKI resistance) revealed by sing-cell RNA sequencing (scRNA-seq) that glycolysis played a crucial role in poor prognosis and drug resistance in RCC. TCGA-KIRC and glycolysis gene set analysis identified DEPDC1 as a target associated with malignant progression and drug resistance in KIRC. Subsequent experiments demonstrated that DEPDC1 promoted malignant progression and glycolysis of RCC, and knockdown DEPDC1 could reverse TKI resistance in RCC cell lines. Bulk RNA sequencing (RNA-seq) and non-targeted metabolomics sequencing suggested that DEPDC1 may regulate RCC glycolysis via AKT/mTOR/HIF1α pathway, a finding supported by protein-level analysis. Clinical tissue samples from 98 RCC patients demonstrated that DEPDC1 was associated with poor prognosis and predicted RCC metastasis. In conclusion, this multi-omics analysis suggests that DEPDC1 could serve as a novel target for TKI combined with targeted metabolic therapy in advanced RCC patients with TKI resistance.

Single-cell sequencing reveals novel proliferative cell type: a key player in renal cell carcinoma prognosis and therapeutic response

Renal cell carcinoma (RCC) is characterized by a variety of subtypes, each defined by unique genetic and morphological features. This study utilizes single-cell RNA sequencing to explore the molecular heterogeneity of RCC. A highly proliferative cell subset, termed as “Prol,” was discovered within RCC tumors, and its increased presence was linked to poorer patient outcomes. An artificial intelligence network, encompassing traditional regression, machine learning, and deep learning algorithms, was employed to develop a Prol signature capable of predicting prognosis. The signature demonstrated superior performance in predicting RCC prognosis compared to other signatures and exhibited pan-cancer prognostic capabilities. RCC patients with high Prol signature scores exhibited resistance to targeted therapies and immunotherapies. Furthermore, the key gene CEP55 from the Prol signature was validated by both proteinomics and quantitative real time polymerase chain reaction. Our findings may provide new insights into the molecular and cellular mechanisms of RCC and facilitate the development of novel biomarkers and therapeutic targets.

Multimodal Machine Learning for Prognosis and Survival Prediction in Renal Cell Carcinoma Patients: A Two-Stage Framework with Model Fusion and Interpretability Analysis

Current medical limitations in predicting cancer survival status and time necessitate advancements beyond traditional methods and physical indicators. This research introduces a novel two-stage prognostic framework for renal cell carcinoma, addressing the inadequacies of existing diagnostic approaches. In the first stage, the framework accurately predicts the survival status (alive or deceased) with metrics Accuracy, Precision, Recall, and F1 score to evaluate the effects of the classification results, while the second stage focuses on forecasting the future survival time of deceased patients with Root Mean Square Error and Mean Absolute Error to evaluate the regression results. Leveraging popular machine learning models, such as Adaptive Boosting, Extra Trees, Gradient Boosting, Random Forest, and Extreme Gradient Boosting, along with fusion models like Voting, Stacking, and Blending, our approach significantly improves prognostic accuracy as shown in our experiments. The novelty of our research lies in the integration of a logistic regression meta-model for interpreting the blending model’s predictions, enhancing transparency. By the SHapley Additive exPlanations’ interpretability, we provide insights into variable contributions, aiding understanding at both global and local levels. Through modal segmentation and multimodal fusion applied to raw data from the Surveillance, Epidemiology, and End Results program, we enhance the precision of renal cell carcinoma prognosis. Our proposed model provides an interpretable analysis of model predictions, highlighting key variables influencing classification and regression decisions in the two-stage renal cell carcinoma prognosis framework. By addressing the black-box problem inherent in machine learning, our proposed model helps healthcare practitioners with a more reliable and transparent basis for applying machine learning in cancer prognostication.