Papillary Renal Cell Carcinoma Research Articles

Diagnostic Value of Clear Cell Likelihood Score v1.0 and v2.0 for Common Subtypes of Small Renal Masses: A Multicenter Comparative Study.

Clear cell likelihood score (ccLS) is reliable for diagnosing small renal masses (SRMs). However, the diagnostic value of Clear cell likelihood score version 1.0 (ccLS v1.0) and v2.0 for common subtypes of SRMs might be a potential score extension. To compare the diagnostic performance and interobserver agreement of ccLS v1.0 and v2.0 for characterizing five common subtypes of SRMs. Retrospective. 797 patients (563 males, 234 females; mean age, 53 ± 12 years) with 867 histologically proven renal masses. 3.0 and 1.5 T/T2 weighted imaging, T1 weighted imaging, diffusion-weighted imaging, a dual-echo chemical shift (in- and opposed-phase) T1 weighted imaging, multiphase dynamic contrast-enhanced imaging. Six abdominal radiologists were trained in the ccLS algorithm and independently scored each SRM using ccLS v1.0 and v2.0, respectively. All SRMs had definite pathological results. The pooled area under curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the diagnostic performance of ccLS v1.0 and v2.0 for characterizing common subtypes of SRMs. The average κ values were calculated to evaluate the interobserver agreement of the two scoring versions. Random-effects logistic regression; Receiver operating characteristic analysis; DeLong test; Weighted Kappa test; Z test. The statistical significance level was P < 0.05. The pooled AUCs of clear cell likelihood score version 2.0 (ccLS v2.0) were statistically superior to those of ccLS v1.0 for diagnosing clear cell renal cell carcinoma (ccRCC) (0.907 vs. 0.851), papillary renal cell carcinoma (pRCC) (0.926 vs. 0.888), renal oncocytoma (RO) (0.745 vs. 0.679), and angiomyolipoma without visible fat (AMLwvf) (0.826 vs. 0.766). Interobserver agreement for SRMs between ccLS v1.0 and v2.0 is comparable and was not statistically significant (P = 0.993). The diagnostic performance of ccLS v2.0 surpasses that of ccLS v1.0 for characterizing ccRCC, pRCC, RO, and AMLwvf. Especially, the standardized algorithm has optimal performance for ccRCC and pRCC. ccLS has potential as a supportive clinical tool. 4. Stage 2.

A rare case of TFEB/6p21/VEGFA-amplified renal cell carcinoma diagnosed by whole-exome sequencing: clinicopathological and genetic feature report and literature review

BackgroundTFEB/6p21/VEGFA-amplified renal cell carcinoma (RCC) is rare and difficult to diagnose, with diverse histological patterns and immunohistochemical and poorly defined molecular genetic characteristics.Case presentationWe report a case of a 63-year-old male admitted in 2017 with complex histomorphology, three morphological features of clear cell, eosinophilic and papillary RCC and resembling areas of glomerular and tubular formation. The immunophenotype also showed a mixture of CD10 and P504s. RCC with a high suspicion of collision tumors was indicated according to the 2014 WHO classification system; no precise diagnosis was possible. The patient was diagnosed at a different hospital with poorly differentiated lung squamous cell carcinoma one year after RCC surgery. We exploited molecular technology advances to retrospectively investigate the patient’s molecular genetic alterations by whole-exome sequencing. The results revealed a 6p21 amplification in VEGFA and TFEB gene acquisition absent in other RCC subtypes. Clear cell, papillary, chromophobe, TFE3-translocation, eosinophilic solid and cystic RCC were excluded. Strong TFEB and Melan-A protein positivity prompted rediagnosis as TFEB/6p21/VEGFA-amplified RCC as per 2022 WHO classification. TMB-L (low tumor mutational load), CCND3 gene acquisition and MRE11A and ATM gene deletion mutations indicated sensitivity to PD-1/PD-L1 inhibitor combinations and the FDA-approved targeted agents Niraparib (Grade C), Olaparib (Grade C), Rucaparib (Grade C) and Talazoparib (Class C). GO (Gene Ontology) and KEGG enrichment analyses revealed major mutations and abnormal CNVs in genes involved in biological processes such as the TGF-β, Hippo, E-cadherin, lysosomal biogenesis and autophagy signaling pathways, biofilm synthesis cell adhesion substance metabolism regulation and others. We compared TFEB/6p21/VEGFA-amplified with TFEB-translocated RCC; significant differences in disease onset age, histological patterns, pathological stages, clinical prognoses, and genetic characteristics were revealed.ConclusionWe clarified the patient’s challenging diagnosis and discussed the clinicopathology, immunophenotype, differential diagnosis, and molecular genetic information regarding TFEB/6p21/VEGFA-amplified RCC via exome analysis and a literature review.

Exploring genes within the glutathione peroxidase family as potential predictors of prognosis in papillary renal cell carcinoma

Abstract Objectives This research aims to explore the relationship between glutathione peroxidase (GPX) expression variations in papillary renal cell carcinoma (pRCC) and patient survival, while also developing and evaluating a customized survival prediction model based on GPX. Methods The transcriptomic dataset, including clinical parameters and GPX expression levels, is sourced from The Cancer Genome Atlas (TCGA) database, comprising 290 individuals diagnosed with pRCC. We utilized a univariate Cox regression model to select differentially expressed genes. Subsequently, we calculated risk scores through the least absolute shrinkage and selection operator (LASSO) regression. Based on the median risk score, patients were categorized into high and low-risk groups, establishing a prognostic risk model. Following this, the relationship between the risk model and the survival of pRCC patients was revealed through Kaplan–Meier survival curve analysis. The sensitivity and specificity of the predictive model were evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, chemotherapy drug sensitivity analysis was conducted on patients in the high and low-risk groups. Results A risk-scoring model was built by selecting GPX7 and GPX8. Compared to the low-risk group, individuals in the high-risk category showed significantly reduced overall survival rates (p=0.018). Additionally, validation results demonstrated the model’s good predictive accuracy. Conclusions The risk-scoring model constructed based on GPX family genes provides an innovative biomarker for forecasting the prognosis of pRCC and serves as a reference for individualized therapy in pRCC.

Construction and validation of a mutation-related model in papillary renal cell carcinoma and associated immune infiltration.

To improve the clinical evaluation of the prognosis of papillary renal cell carcinoma (PRCC), we screened a model to predict the survival of patients with mutations in related genes. We downloaded RNA sequencing information from all patients with PRCC in TCGA. We first analyzed the differences in genes and the enrichment of these differences. Then, by selecting mutant genes, constructing a protein-protein interaction network, lasso regression, and multivariable Cox regression, a prognosis model was constructed. Additionally, the model was validated using external data sets. We analyzed the immune infiltration of PRCC and the correlation between the model and popular targets. Finally, we performed tissue microarray analysis and immunohistochemistry to verify the expression levels of the three genes. We constructed a three-gene (NEK2, CENPA, and GINS2) model. The verification results indicated that the model had a good prediction effect. We also developed a visual nomogram. Enrichment analysis revealed the major pathways involved in muscle system processes. Immunoassays showed that the expression level of CENPA was positively correlated with PD-1 and CTLA4 expression levels. Immunohistochemical and tissue microarray results showed that these three genes were highly expressed in PRCC, which was consistent with the predicted results in the database. We constructed and verified a three-gene model to predict the patient survival. The results show that the model has a good prediction effect.

Papillary Renal Cell Carcinoma: Current Evidence and Future Directions

Papillary renal cell carcinoma (pRCC) comprises 15-20% of all patients with renal cell carcinoma (RCC). Although in the localized setting where pRCC appears to have better outcomes than clear cell RCC (ccRCC), patients with metastatic pRCC have significantly worse outcomes than patients with metastatic ccRCC. Because of the overall rarity of pRCC, there have been less research and clinical trials devoted to this subtype. Therefore, treatment of pRCC has generally been extrapolated from approved therapies for ccRCC. Recent data shows promise with newer tyrosine kinase inhibitors, and there is emerging evidence on their combination with immune checkpoint inhibitors. However, more dedicated clinical trials to pRCC are urgently needed, as response rates and outcomes still lag behind ccRCC. This review summarizes the pathophysiology, genetic features, the evolution of treatment approaches since the systemic cytokine era, and current challenges of managing pRCC.

Fumarate Hydratase-Deficient Renal Cell Carcinoma—A Clinicopathological Study of a Series of 11 Cases

Abstract Introduction Fumarate hydratase (FH)-deficient renal cell carcinoma (RCC) is a rare, molecularly defined renal tumor with aggressive behavior. The diagnosis of these tumors is challenging because of varied morphology and limited access to molecular testing and immunohistochemistry (IHC) for FH and 2-succinocysteine. We aim to elucidate the histomorphology, clinical presentation, and follow-up of this tumor in this first series of cases of FH-deficient RCCs from India. Objectives This article aims to understand and elucidate the clinical presentation, pathologic findings, treatment options, and outcomes of FH-deficient RCC. Materials and Methods Diagnosed cases of FH-deficient RCC between January 2021 and January 2023 including clinical details were retrieved from the electronic medical record database. Histopathological and immunohistochemical slides were reviewed. Results Out of 11 cases of FH-deficient RCC, 36% had been referred with a diagnosis of type 2 papillary RCC. One patient presented with metastatic disease. All had mixed histologic patterns with the predominant pattern being papillary and showed FH loss on IHC. The classically described inclusion like nucleoli was present only focally in most cases. A subset of tumors had low-grade solid-nested morphology and these patients presented at an earlier stage (T2a). Two patients on multikinase inhibitors are alive with disease at 14 months' follow-up. Conclusion FH-deficient RCCs can have varied histologic patterns within the same tumor and show loss of FH expression by IHC. A subset has low grade morphology and tends to have a more indolent course. It is important to have a high index of suspicion for this diagnosis due to its varied histological appearance and aggressive behavior.

The cuproptosis-related gene UBE2D2 functions as an immunotherapeutic and prognostic biomarker in pan-cancer.

Cuproptosis, as a unique modality of regulated cell death, requires the involvement of ubiquitin-binding enzyme UBE2D2. However, the prognostic and immunotherapeutic values of UBE2D2 in pan-cancer remain largely unknown. Using UCSC Xena, TIMER, Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Human Protein Atlas (HPA) databases, we aimed to explore the differential expression pattern of UBE2D2 across multiple cancer types and to evaluate its association with patient prognosis, clinical features, and genetic variations. The association between UBE2D2 and immunotherapy response was assessed by gene set enrichment analysis, tumor microenvironment, immune gene co-expression and drug half maximal inhibitory concentration (IC50) analysis. The mRNA and protein levels of UBE2D2 were markedly elevated in most cancer types, and UBE2D2 exhibited prognostic significance in liver hepatocellular carcinoma (LIHC), kidney chromophobe (KICH), uveal melanomas (UVM), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), and kidney renal papillary cell carcinoma (KIRP). UBE2D2 expression was correlated with clinical features, tumor mutation burden, microsatellite instability, and anti-tumor drug resistance in several tumor types. Gene enrichment analysis showed that UBE2D2 was significantly associated with immune-related pathways. The expression level of UBE2D2 was correlated with immune cell infiltration, including CD4 + T cells、Macrophages M2、CD8 + T cells in pan-cancer. PDCD1, CD274 and CTLA4 expression levels were positively correlated with UBE2D2 level in multiple cancers. We comprehensively investigated the potential value of UBE2D2 as a prognostic and immunotherapeutic predictor for pan-cancer, providing a novel insight for cancer immunotherapy.

Identification of prognostic stemness-related genes in kidney renal papillary cell carcinoma

BackgroundKidney renal papillary cell carcinoma (KIRP) is the second most prevalent malignant cancer originating from the renal epithelium. Nowadays, cancer stem cells and stemness-related genes (SRGs) are revealed to play important roles in the carcinogenesis and metastasis of various tumors. Consequently, we aim to investigate the underlying mechanisms of SRGs in KIRP.MethodsRNA-seq profiles of 141 KIRP samples were downloaded from the TCGA database, based on which we calculated the mRNA expression-based stemness index (mRNAsi). Next, we selected the differentially expressed genes (DEGs) between low- and high-mRNAsi groups. Then, we utilized weighted gene correlation network analysis (WGCNA) and univariate Cox analysis to identify prognostic SRGs. Afterwards, SRGs were included in the multivariate Cox regression analysis to establish a prognostic model. In addition, a regulatory network was constructed by Pearson correlation analysis, incorporating key genes, upstream transcription factors (TFs), and downstream signaling pathways. Finally, we used Connectivity map analysis to identify the potential inhibitors.ResultsIn total, 1124 genes were characterized as DEGs between low- and high-RNAsi groups. Based on six prognostic SRGs (CCKBR, GPR50, GDNF, SPOCK3, KC877982.1, and MYO15A), a prediction model was established with an area under curve of 0.861. Furthermore, among the TFs, genes, and signaling pathways that had significant correlations, the CBX2-ASPH-Notch signaling pathway was the most significantly correlated. Finally, resveratrol might be a potential inhibitor for KIRP.ConclusionsWe suggested that CBX2 could regulate ASPH through activation of the Notch signaling pathway, which might be correlated with the carcinogenesis, development, and unfavorable prognosis of KIRP.

Plasma Kidney Injury Molecule-1 for Preoperative Prediction of Renal Cell Carcinoma Versus Benign Renal Masses, and Association With Clinical Outcomes.

Both clear cell and papillary renal cell carcinomas (RCCs) overexpress kidney injury molecule-1 (KIM-1). We investigated whether plasma KIM-1 (pKIM-1) may be a useful risk stratification tool among patients with suspicious renal masses. Prenephrectomy pKIM-1 was measured in two independent cohorts of patients with renal masses. Cohort 1, from the prospective K2 trial, included 162 patients found to have clear cell RCC (cases) and 162 patients with benign renal masses (controls). Cohort 2 included 247 patients with small (cT1a) renal masses from an academic biorepository, of whom 184 had RCC. We assessed the relationship between pKIM-1, surgical pathology, and clinical outcomes. In Cohort 1, pKIM-1 distinguished RCC versus benign masses with area under the receiver operating curve (AUC-ROC, 0.81 [95% CI, 0.76 to 0.86]). In Cohort 2 (cT1a only), pKIM-1 distinguished RCC versus benign masses (AUC-ROC, 0.74 [95% CI, 0.67 to 0.80]) and the addition of pKIM-1 to an established nomogram for predicting malignancy improved the model AUC-ROC (0.65 [95% CI, 0.57 to 0.74] v 0.78 [95% CI, 0.72 to 0.85]). A pKIM-1 cutpoint identified using Cohort 2 demonstrated sensitivity of 92.5% and specificity of 60% for identifying RCC in Cohort 1. In long-term follow-up of RCC cases (Cohort 1), higher prenephrectomy pKIM-1 was associated with worse metastasis-free survival (multivariable MFS hazard ratio [HR] 1.29 per unit increase in log pKIM-1, 95% CI, 1.10 to 1.53) and overall survival (multivariable OS HR 1.31 per unit increase in log pKIM-1, 95% CI, 1.10 to 1.54). In long-term follow-up of Cohort 2, no metastatic events occurred, consistent with the favorable prognosis of resected cT1a RCC. Among patients with renal masses, pKIM-1 is associated with malignant pathology, worse MFS, and risk of death. pKIM-1 may be useful for selecting patients with renal masses for intervention versus surveillance.

Comprehensive proteogenomic characterization of rare kidney tumors.

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC.

Multi-ancestry genome-wide association study of kidney cancer identifies 63 susceptibility regions.

Here, in a multi-ancestry genome-wide association study meta-analysis of kidney cancer (29,020 cases and 835,670 controls), we identified 63 susceptibility regions (50 novel) containing 108 independent risk loci. In analyses stratified by subtype, 52 regions (78 loci) were associated with clear cell renal cell carcinoma (RCC) and 6 regions (7 loci) with papillary RCC. Notably, we report a variant common in African ancestry individuals ( rs7629500 ) in the 3' untranslated region of VHL, nearly tripling clear cell RCC risk (odds ratio 2.72, 95% confidence interval 2.23-3.30). In cis-expression quantitative trait locus analyses, 48 variants from 34 regions point toward 83 candidate genes. Enrichment of hypoxia-inducible factor-binding sites underscores the importance of hypoxia-related mechanisms in kidney cancer. Our results advance understanding of the genetic architecture of kidney cancer, provide clues for functional investigation and enable generation of a validated polygenic risk score with an estimated area under the curve of 0.65 (0.74 including risk factors) among European ancestry individuals.

Integrated Analysis of Clinical Outcome of Mesenchymal Stem Cellrelated Genes in Pan-cancer

Background: Although the application of mesenchymal stem cells (MSCs) in engineered medicine, such as tissue regeneration, is well known, new evidence is emerging that shows that MSCs can also promote cancer progression, metastasis, and drug resistance. However, no large-scale cohort analysis of MSCs has been conducted to reveal their impact on the prognosis of cancer patients. Objective: We propose the MSC score as a novel surrogate for poor prognosis in pan-cancer Methods: We used single sample gene set enrichment analysis to quantify MSC-related genes into a signature score and identify the signature score as a potential independent prognostic marker for cancer using multivariate Cox regression analysis. TIDE algorithm and neural network were utilized to assess the predictive accuracy of MSC-related genes for immunotherapy. Results: MSC-related gene expression significantly differed between normal and tumor samples across the 33 cancer types. Cox regression analysis suggested the MSC score as an independent prognostic marker for kidney renal papillary cell carcinoma, mesothelioma, glioma, and stomach adenocarcinoma. The abundance of fibroblasts was also more representative of the MSC score than the stromal score. Our findings supported the combined use of the TIDE algorithm and neural network to predict the accuracy of MSC-related genes for immunotherapy. Conclusion: We comprehensively characterized the transcriptome, genome, and epigenetics of MSCs in pan-cancer and revealed the crosstalk of MSCs in the tumor microenvironment, especially with cancer-related fibroblasts. It is suggested that this may be one of the key sources of resistance to cancer immunotherapy.

Frequency of Androgen Receptor Positivity in Tumors: A Study Evaluating More Than 18,000 Tumors

Androgen receptor (AR) is a transcription factor expressed in various normal tissues and is a therapeutic target for prostate and possibly other cancers. A TMA containing 18,234 samples from 141 different tumor types/subtypes and 608 samples of 76 different normal tissue types was analyzed by immunohistochemistry. AR positivity was found in 116 tumor types including 66 tumor types (46.8%) with ≥1 strongly positive tumor. Moderate/strong AR positivity was detected in testicular sex cord-stromal tumors (93.3–100%) and neoplasms of the prostate (79.3–98.7%), breast (25.0–75.5%), other gynecological tumors (0.9–100%), kidney (5.0–44.1%), and urinary bladder (5.4–24.2%). Low AR staining was associated with advanced tumor stage (pTa versus pT2-4; p &lt; 0.0001) in urothelial carcinoma; advanced pT (p &lt; 0.0001), high tumor grade (p &lt; 0.0001), nodal metastasis (p &lt; 0.0001), and reduced survival (p = 0.0024) in invasive breast carcinoma; high pT (p &lt; 0.0001) and grade (p &lt; 0.0001) in clear cell renal cell carcinoma (RCC); and high pT (p = 0.0055) as well as high grade (p &lt; 0.05) in papillary RCC. AR staining was unrelated to histopathological/clinical features in 157 endometrial carcinomas and in 221 ovarian carcinomas. Our data suggest a limited role of AR immunohistochemistry for tumor distinction and a prognostic role in breast and clear cell RCC and highlight tumor entities that might benefit from AR-targeted therapy.

Integrated Bioinformatics and Experimental Validation to Identify a Disulfidptosis-Related lncRNA Model for Prognostic Prediction in Papillary Renal Cell Carcinoma.

This study aimed to construct a prognostic model for papillary renal cell carcinoma (pRCC) utilizing disulfidptosis-associated long non-coding RNAs (lncRNAs). Additionally, it investigated the potential of these lncRNAs in predicting immune responses and drug sensitivity in pRCC. LncRNAs have been implicated in the progression and prognosis of pRCC. Recently, disulfidptosis, an emerging form of regulated cell death, has shown potential as a therapeutic approach for cancer. However, the potential association between disulfidptosis-related lncRNAs and pRCC remains unclear. We analyzed transcriptome profiling and clinical data of pRCC patients from The Cancer Genome Atlas database. Using Pearson correlation analysis, we identified lncRNAs associated with disulfidptosis. Based on the identified disulfidptosis-related lncRNAs that were correlated with overall survival (OS), we constructed a novel prediction model using the least absolute shrinkage and selection operator, univariable Cox regression, and multivariable Cox regression analyses. The model's utility was assessed through Kaplan-Meier survival, receiver operating characteristics, and principal component analyses. Moreover, functional analysis helped identify potential prognostic mechanisms, and the prediction of chemical drugs for pRCC was also performed. Finally, qRT-PCR validated the expression of prognostic lncRNAs in pRCC cells and patient samples. Our prediction model was based on nine disulfidptosis-related lncRNAs. Evaluation and validation analyses demonstrated that the model had excellent, consistent, and independent prognostic value for pRCC patients, with area under the curve (AUC) values of 0.954, 0.910, and 0.830 for 1-, 3-, and 5-year OS, respectively. Through functional analysis, we discovered a significant correlation between the identified prognostic signature and immunity. Additionally, in terms of chemotherapy sensitivity, our analysis indicated that the low-risk group exhibited higher sensitivity to sunitinib and pazopanib. Furthermore, the expression patterns of the identified lncRNAs were validated in samples obtained from pRCC cells and patients. This study successfully established and validated a novel disulfidptosis-related prediction model. The findings suggest the potential involvement of immune-related pathways in lncRNA signature-associated survival. This model holds promise for differentiating prognosis and improving personalized therapeutic strategies for pRCC in clinical practice.

Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting.

To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT). Patients who underwent surgical treatment for renal tumors at two tertiary centers from 2012 to 2022 were included retrospectively. Preoperative arterial (corticomedullary) and venous (nephrogenic) phase CT scans from these centers, as well as from external imaging facilities, were manually segmented, and standardized radiomic features were extracted. Following preprocessing and addressing the class imbalance, a ML algorithm based on extreme gradient boosting trees (XGB) was employed to predict renal tumor subtypes using 10-fold cross-validation. The evaluation was conducted using the multiclass area under the receiver operating characteristic curve (AUC). Algorithms were trained on data from one center and independently tested on data from the other center. The training cohort comprised n = 297 patients (64.3% clear cell renal cell cancer [RCC], 13.5% papillary renal cell carcinoma (pRCC), 7.4% chromophobe RCC, 9.4% oncocytomas, and 5.4% angiomyolipomas (AML)), and the testing cohort n = 121 patients (56.2%/16.5%/3.3%/21.5%/2.5%). The XGB algorithm demonstrated a diagnostic performance of AUC = 0.81/0.64/0.8 for venous/arterial/combined contrast phase CT in the training cohort, and AUC = 0.75/0.67/0.75 in the independent testing cohort. In pairwise comparisons, the lowest diagnostic accuracy was evident for the identification of oncocytomas (AUC = 0.57-0.69), and the highest for the identification of AMLs (AUC = 0.9-0.94) CONCLUSION: Radiomic feature analyses can distinguish renal tumor subtypes on routinely acquired CTs, with oncocytomas being the hardest subtype to identify. Radiomic feature analyses yield robust results for renal tumor assessment on routine CTs. Although radiologists routinely rely on arterial phase CT for renal tumor assessment and operative planning, radiomic features derived from arterial phase did not improve the accuracy of renal tumor subtype identification in our cohort.

Multi-omics and immunogenomics analysis revealed PFKFB3 as a targetable hallmark and mediates sunitinib resistance in papillary renal cell carcinoma: in silico study with laboratory verification

Glycolysis-related metabolic reprogramming is a central hallmark of human cancers, especially in renal cell carcinoma. However, the regulatory function of glycolytic signature in papillary RCC has not been well elucidated. In the present study, the glycolysis-immune predictive signature was constructed and validated using WGCNA, glycolysis-immune clustering analysis. PPI network of DEGs was constructed and visualized. Functional enrichments and patients’ overall survival were analyzed. QRT-PCR experiments were performed to detect hub genes’ expression and distribution, siRNA technology was used to silence targeted genes; cell proliferation and migration assays were applied to evaluate the biological function. Glucose concentration, lactate secretion, and ATP production were measured. Glycolysis-Immune Related Prognostic Index (GIRPI) was constructed and combined analyzed with single-cell RNA-seq. High-GIRPI signature predicted significantly poorer outcomes and relevant clinical features of pRCC patients. Moreover, GIRPI also participated in several pathways, which affected tumor immune microenvironment and provided potential therapeutic strategy. As a key glycolysis regulator, PFKFB3 could promote renal cancer cell proliferation and migration in vitro. Blocking of PFKFB3 by selective inhibitor PFK-015 or glycolytic inhibitor 2-DG significantly restrained renal cancer cells’ neoplastic potential. PFK-015 and sunitinib could synergistically inhibit pRCC cells proliferation. Glycolysis-Immune Risk Signature is closely associated with pRCC prognosis, progression, immune infiltration, and therapeutic response. PFKFB3 may serve as a pivotal glycolysis regulator and mediates Sunitinib resistance in pRCC patients.

ESCO2’s oncogenic role in human tumors: a pan-cancer analysis and experimental validation

PurposeEstablishment of sister chromatid cohesion N-acetyltransferase 2 (ESCO2) is involved in the mitotic S-phase adhesins acetylation and is responsible for bridging two sister chromatids. However, present ESCO2 cancer research is limited to a few cancers. No systematic pan-cancer analysis has been conducted to investigate its role in diagnosis, prognosis, and effector function.MethodsWe thoroughly examined the ESCO2 carcinogenesis in pan-cancer by combining public databases such as The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), UALCAN and Tumor Immune Single-cell Hub (TISCH). The analysis includes differential expression analysis, survival analysis, cellular effector function, gene mutation, single cell analysis, and tumor immune cell infiltration. Furthermore, we confirmed ESCO2’s impacts on clear cell renal cell carcinoma (ccRCC) cells’ proliferative and invasive capacities in vitro.ResultsIn our study, 30 of 33 cancer types exhibited considerably greater levels of ESCO2 expression in tumor tissue using TCGA and GTEx databases, whereas acute myeloid leukemia (LAML) exhibited significantly lower levels. Kaplan-Meier survival analyses in adrenocortical carcinoma (ACC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), mesothelioma (MESO), and pancreatic adenocarcinoma (PAAD) demonstrated that tumor patients with high ESCO2 expression have short survival periods. However, in thymoma (THYM), colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ), ESCO2 was a favorable prognostic factor. Moreover, ESCO2 expression positively correlates with tumor stage and tumor size in several cancers, including LIHC, KIRC, KIRP and LUAD. Function analysis revealed that ESCO2 participates in mitosis, cell cycle, DNA damage repair, and other processes. CDK1 was identified as a downstream gene regulated by ESCO2. Furthermore, ESCO2 might also be implicated in immune cell infiltration. Finally, ESCO2’S knockdown significantly inhibited the A498 and T24 cells’ proliferation, invasion, and migration.ConclusionsIn conclusion, ESCO2 is a possible pan-cancer biomarker and oncogene that can reliably predict the prognosis of cancer patients. ESCO2 was also implicated in the cell cycle and proliferation regulation. In a nutshell, ESCO2 is a therapeutically viable and dependable target.

The Necroptotic Process-Related Signature Predicts Immune Infiltration and Drug Sensitivity in Kidney Renal Papillary Cell Carcinoma.

It remains controversial whether the current subtypes of kidney renal papillary cell carcinoma (KIRP) can be used to predict the prognosis independently. This observational study aimed to identify a risk signature based on necroptotic pro-cess-related genes (NPRGs) in KIRP. In the training cohort, LASSO regression was applied to construct the risk signature from 158 NPRGs, followed by the analysis of Overall Survival (OS) using the Kaplan-Meier method. The signature accuracy was evaluated by the Receiver Operating Characteristic (ROC) curve, which was further validated by the test cohort. Wilcoxon test was used to compare the expressions of immune-related genes, neoantigen genes, and immune infiltration between differ-ent risk groups, while the correlation test was performed between NPRGs expressions and drug sensitivity. Gene set enrichment analysis was used to investigate the NPRGs' signature's biologi-cal functions. We finally screened out 4-NPRGs (BIRC3, CAMK2B, PYGM, and TRADD) for con-structing the risk signature with the area under the ROC curve (AUC) reaching about 0.8. The risk score could be used as an independent OS predictor. Consistent with the enriched signaling, the NPRGs signature was found to be closely associated with neoantigen, immune cell infiltration, and immune-related functions. Based on NPRGs expressions, we also predicted multiple drugs potentially sensitive or resistant to treatment. The novel 4-NPRGs risk signature can predict the prognosis, immune infiltration, and therapeutic sensitivity of KIRP.

Activating point mutations in the MET kinase domain represent a unique molecular subset of lung cancer and other malignancies targetable with MET inhibitors.

Activating point mutations in the MET tyrosine kinase domain (TKD) are oncogenic in a subset of papillary renal cell carcinomas (PRCC). Here, using comprehensive genomic profiling among >600,000 patients, we identify activating MET TKD point mutations as putative oncogenic driver across diverse cancers, with a frequency of ~0.5%. The most common mutations in the MET TKD defined as oncogenic or likely oncogenic according to OncoKB resulted in amino acid substitutions at positions H1094, L1195, F1200, D1228, Y1230, M1250, and others. Preclinical modeling of these alterations confirmed their oncogenic potential, and also demonstrated differential patterns of sensitivity to type I and type II MET inhibitors. Two patients with metastatic lung adenocarcinoma harboring MET TKD mutations (H1094Y, F1200I) and no other known oncogenic drivers achieved confirmed partial responses to a type I MET inhibitor. Activating MET TKD mutations occur in multiple malignancies and may confer clinical sensitivity to currently available MET inhibitors.

Prognostic significance of pT3a staging subclassifications in renal cell carcinoma: Not all pT3a are equal

ObjectiveTo examine the prognostic significance of perinephric fat, renal sinus fat, and renal vein invasion in patients with pT3a renal cell carcinoma (RCC) by histologic type. MethodsA population-based retrospective cohort study of patients with pT3aN0M0 RCC was performed using Surveillance, Epidemiology, and End Results (SEER) data for the years 2010 through 2019. Cox proportional hazards models were used to examine the relationship between pT3a subclassification groups and cancer-specific survival (CSS) by histological subtype (clear cell, papillary, chromophobe, and other). ResultsThe cohort consisted of 10,170 patients with pT3a RCC, including 8,446 (83.0%) with clear cell RCC and 1,724 (17.0%) with nonclear cell RCC (nccRCC). Median follow up was 36 months. Differences in CSS by pT3a subclassification groups were observed in all histological subtypes but were most pronounced in nccRCC, specifically papillary RCC. Compared to perinephric fat (PF) invasion only, renal vein (RV) invasion (HR = 4.9, 95%CI: 2.5–9.3, P < 0.01), renal sinus fat invasion (HR = 3.0, 95%CI: 1.4–6.2), RV and PF invasion (HR = 7.5, 95%CI: 3.5–16.0), and combination of all three characteristics (HR = 4.4, 95%CI: 1.2–15.5) were associated with worse CSS in patients with papillary RCC. ConclusionWe examined the prognostic role of pT3a staging subclassifications in RCC by histologic subtype and observed survival differences, particularly in papillary RCC. Our findings highlight the need to refine pT3a staging criteria to help guide individualized, multimodal treatment strategies for locally advanced RCC.