Clear Cell Carcinoma Patients Research Articles

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

BackgroundCTLA4, 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. MethodsA 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-β signaling pathway, and macrophage polarization. ResultsHigh 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-β signaling pathways, TMB, and Tregs. Further in vitro experiments demonstrated that CTLA4 knockdown inhibited cell proliferation, migration, invasion, TGF-β expression, and macrophage M2 polarization. ConclusionHigh 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.

Identification of miRNAs and Their Target Genes Associated with Sunitinib Resistance in Clear Cell Renal Cell Carcinoma Patients.

Sunitinib has greatly improved the survival of clear cell renal cell carcinoma (ccRCC) patients in recent years. However, 20-30% of treated patients do not respond. To identify miRNAs and genes associated with a response, comparisons were made between biopsies from responder and non-responder ccRCC patients. Using integrated transcriptomic analyses, we identified 37 miRNAs and 60 respective target genes, which were significantly associated with the NF-kappa B, PI3K-Akt and MAPK pathways. We validated expression of the miRNAs (miR-223, miR-155, miR-200b, miR-130b) and target genes (FLT1, PRDM1 and SAV1) in 35 ccRCC patients. High levels of miR-223 and low levels of FLT1, SAV1 and PRDM1 were associated with worse overall survival (OS), and combined miR-223 + SAV1 levels distinguished responders from non-responders (AUC = 0.92). Using immunohistochemical staining of 170 ccRCC patients, VEGFR1 (FLT1) expression was associated with treatment response, histological grade and RECIST (Response Evaluation Criteria in Solid Tumors) score, whereas SAV1 and BLIMP1 (PRDM1) were associated with metachronous metastatic disease. Using in situ hybridisation (ISH) to detect miR-155 we observed higher tumoural cell expression in non-responders, and non-tumoural cell expression with increased histological grade. In summary, our preliminary analysis using integrated miRNA-target gene analyses identified several novel biomarkers in ccRCC patients that surely warrant further investigation.

Identification of tumor-antigen signatures and immune subtypes for messenger RNA vaccine selection in advanced clear cell renal cell carcinoma

BackgroundAdvanced clear cell renal cell carcinoma still lacks reliable diagnostic and prognostic biomarkers. Recently, tumor vaccines targeting specific molecules have been proposed as a promising treatment in mitigating tumor progression, which was rekindled under the background of the COVID-19 pandemic. However, the application of messenger RNA vaccine against advanced clear cell renal cell carcinoma antigens remains stagnant, and no subgroup of patients deemed suitable for vaccination has been extensively studied or validated. Our study aims to explore novel advanced clear cell renal cell carcinoma antigen signatures to select suitable patients for vaccination. MethodsGene expression profiles of advanced clear cell renal cell carcinoma samples and their corresponding clinical data were retrieved from The Cancer Genome Atlas. The least absolute shrinkage and selection operator model was applied to develop signatures to stratify patients with advanced clear cell renal cell carcinoma. Receiver operating characteristic analysis was used to compare the prognostic accuracy of each factor. Tumor Immune Estimation Resource was used to visualize the relationship between the proportion of antigen-presenting cells and the expression of filtered genes. The "CIBERSORT" and "WGCNA" R Packages were employed to ascertain disparities in immune infiltration levels between advanced clear cell renal cell carcinoma subgroups. The Search Tools for the Retrieval of Interacting Genes database and Cytoscape were used to construct the protein-protein interaction network. CCK-8 and colony formation assays were included in the invitro experiment. ResultsIn total, 244 potential tumor antigens were identified. Using the least absolute shrinkage and selection operator Cox regression, 21 tumor antigens were selected for developing a risk evaluation signature. The risk score signature can be a useful tool to predict the outcome of advanced clear cell renal cell carcinoma patients. According to the differential clinical, molecular, and immune-related genes, we divided advanced clear cell renal cell carcinoma patients into the immune “cold” subtype and immune “hot” subtype. By developing a logistic score, the immune subtype signature can better distinguish a patient more likely to be immune “cold” subtype or immune “hot” subtype.Interestingly, patients with high risk scores had a higher proportion of immune “hot” subtype than those with a low risk score. Furthermore, the prognostic value was significantly improved when combining risk score and immune subtype. Distinct immune landscapes and signal pathways were observed between different tumor subtypes. Finally, novel tumor antigens related to oxidative stress were identified. ConclusionThe tumor-antigens–based risk score and immune subtype signatures identified potentially effective neo-antigens for advanced clear cell renal cell carcinoma messenger RNA vaccine development, and patients with low risk scores and immune “cold” subtype tumors are more prone to benefit from messenger RNA vaccination. Furthermore, our study highlights the significant role of oxidative stress in determining the efficacy of the messenger RNA vaccine.

Bioinformatics analysis and experimental validation of m6A and cuproptosis-related lncRNA NFE4 in clear cell renal cell carcinoma

PurposeThis study aimed to construct an m6A and cuproptosis-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using the information acquired from The Cancer Genome Atlas (TCGA) database.MethodsFirst, the co-expression analysis was performed to identify lncRNAs linked with N6-methyladenosine (m6A) and cuproptosis in ccRCC. Then, a model encompassing four candidate lncRNAs was constructed via univariate, least absolute shrinkage together with selection operator (LASSO), and multivariate regression analyses. Furthermore, Kaplan–Meier, principal component, functional enrichment annotation, and nomogram analyses were performed to develop a risk model that could effectively assess medical outcomes for ccRCC cases. Moreover, the cellular function of NFE4 in Caki-1/OS-RC-2 cultures was elucidated through CCK-8/EdU assessments and Transwell experiments. Dataset outcomes indicated that NFE4 can have possible implications in m6A and cuproptosis, and may promote ccRCC progression.ResultsWe constructed a panel of m6A and cuproptosis-related lncRNAs to construct a prognostic prediction model. The Kaplan–Meier and ROC curves showed that the feature had acceptable predictive validity in the TCGA training, test, and complete groups. Furthermore, the m6A and cuproptosis-related lncRNA model indicated higher diagnostic efficiency than other clinical features. Moreover, the NFE4 function analysis indicated a gene associated with m6A and cuproptosis-related lncRNAs in ccRCC. It was also revealed that the proliferation and migration of Caki-1 /OS-RC-2 cells were inhibited in the NFE4 knockdown group.ConclusionOverall, this study indicated that NFE4 and our constructed risk signature could predict outcomes and have potential clinical value.

Identifying genes associated with disease outcomes using joint sparse canonical correlation analysis-An application in renal clear cell carcinoma.

Somatic changes like copy number aberrations (CNAs) and epigenetic alterations like methylation have pivotal effects on disease outcomes and prognosis in cancer, by regulating gene expressions, that drive critical biological processes. To identify potential biomarkers and molecular targets and understand how they impact disease outcomes, it is important to identify key groups of CNAs, the associated methylation, and the gene expressions they impact, through a joint integrative analysis. Here, we propose a novel analysis pipeline, the joint sparse canonical correlation analysis (jsCCA), an extension of sCCA, to effectively identify an ensemble of CNAs, methylation sites and gene (expression) components in the context of disease endpoints, especially tumor characteristics. Our approach detects potentially orthogonal gene components that are highly correlated with sets of methylation sites which in turn are correlated with sets of CNA sites. It then identifies the genes within these components that are associated with the outcome. Further, we aggregate the effect of each gene expression set on tumor stage by constructing "gene component scores" and test its interaction with traditional risk factors. Analyzing clinical and genomic data on 515 renal clear cell carcinoma (ccRCC) patients from the TCGA-KIRC, we found eight gene components to be associated with methylation sites, regulated by groups of proximally located CNA sites. Association analysis with tumor stage at diagnosis identified a novel association of expression of ASAH1 gene trans-regulated by methylation of several genes including SIX5 and by CNAs in the 10q25 region including TCF7L2. Further analysis to quantify the overall effect of gene sets on tumor stage, revealed that two of the eight gene components have significant interaction with smoking in relation to tumor stage. These gene components represent distinct biological functions including immune function, inflammatory responses, and hypoxia-regulated pathways. Our findings suggest that jsCCA analysis can identify interpretable and important genes, regulatory structures, and clinically consequential pathways. Such methods are warranted for comprehensive analysis of multimodal data especially in cancer genomics.

First-Line Immune Combination Therapies for Nonclear Cell Versus Clear Cell Metastatic Renal Cell Carcinoma: Real-World Multicenter Data From Germany

IntroductionThe aim was to compare treatment outcomes of clear cell metastatic renal cell carcinoma (ccmRCC) versus non-ccmRCC (nccmRCC) patients who received first-line immune combination therapies. Materials and MethodsWithin our retrospective multi-institutional consecutive database of eight tertiary-care centers, we identified mRCC patients treated with first-line immune combination therapies between 11/2017 and 12/2022. Using log-rank analysis and multivariable Cox regression, we tested for differences in overall survival (OS) and progression-free survival (PFS) of nccmRCC versus ccmRCC patients. Covariables consisted of age at diagnosis, sex, International Metastatic Renal Cell Carcinoma Database Consortium risk groups, Eastern Cooperative Oncology Group status, and sarcomatoid feature. ResultsOf 289 study patients, 39 (13%) patients harbored nccmRCC. Median OS was 37 months versus not reached for ccmRCC versus nccmRCC patients (P = .6). Median PFS was 13 versus 15 months (P = .9). Multivariable Cox regression models did not identify nccmRCC as an independent predictor of higher overall mortality in mRCC patients (hazard ratio [HR]: 1.23; P = .6) or a higher progression rate (HR: 1.0; P = 1.0). ConclusionIn our real-world multi-institutional study, no differences in OS and PFS between ccmRCC and nccmRCC patients receiving first-line immune combination treatment were observed, even after adjustment for important patient and tumor characteristics. More prospective trials in nccmRCC patients are needed.

Crosstalk of disulfidptosis-related subtypes identifying a prognostic signature to improve prognosis and immunotherapy responses of clear cell renal cell carcinoma patients

BackgroundDisulfidptosis is a novel form of programmed cell death induced by high SLC7A11 expression under glucose starvation conditions, unlike other known forms of cell death. However, the roles of disulfidptosis in cancers have yet to be comprehensively well-studied, particularly in ccRCC.MethodsThe expression profiles and somatic mutation of DGs from the TCGA database were investigated. Two DGs clusters were identified by unsupervised consensus clustering analysis, and a disulfidptosis-related prognostic signature (DR score) was constructed. Furthermore, the predictive capacity of the DR score in prognosis was validated by several clinical cohorts. We also developed a nomogram based on the DR score and clinical features. Then, we investigated the differences in the clinicopathological information, TMB, tumor immune landscapes, and biological characteristics between the high- and low-risk groups. We evaluated whether the DR score is a robust tool for predicting immunotherapy response by the TIDE algorithm, immune checkpoint genes, submap analysis, and CheckMate immunotherapy cohort.ResultsWe identified two DGs clusters with significant differences in prognosis, tumor immune landscapes, and clinical features. The DR score has been demonstrated as an independent risk factor by several clinical cohorts. The high-risk group patients had a more complicated tumor immune microenvironment and suffered from more tumor immune evasion in immunotherapy. Moreover, patients in the low-risk group had better prognosis and response to immunotherapy, particularly in anti-PD1 and anti-CTLA-4 inhibitors, which were verified in the CheckMate immunotherapy cohort.ConclusionThe DR score can accurately predict the prognosis and immunotherapy response and assist clinicians in providing a personalized treatment regime for ccRCC patients.

WITHDRAWN: Prognostic Values of COL4As Transcriptional Expressions in Clear Cell Renal Cell Carcinoma Patients

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Semaphorin 3A causes immune suppression by inducing cytoskeletal paralysis in tumour-specific CD8+ T cells

Semaphorin-3A (SEMA3A) functions as a chemorepulsive signal during development and can affect T cells by altering their filamentous actin (F-actin) cytoskeleton. The exact extent of these effects on tumour-specific T cells are not completely understood. Here we demonstrate that Neuropilin-1 (NRP1) and Plexin-A1 and Plexin-A4 are upregulated on stimulated CD8+ T cells, allowing tumour-derived SEMA3A to inhibit T cell migration and assembly of the immunological synapse. Deletion of NRP1 in both CD4+ and CD8+ T cells enhance CD8+ T-cell infiltration into tumours and restricted tumour growth in animal models. Conversely, over-expression of SEMA3A inhibit CD8+ T-cell infiltration. We further show that SEMA3A affects CD8+ T cell F-actin, leading to inhibition of immune synapse formation and motility. Examining a clear cell renal cell carcinoma patient cohort, we find that SEMA3A expression is associated with reduced survival, and that T-cells appear trapped in SEMA3A rich regions. Our study establishes SEMA3A as an inhibitor of effector CD8+ T cell tumour infiltration, suggesting that blocking NRP1 could improve T cell function in tumours.

Abstract 3512: Joint probabilistic modeling of multimodal metabolic data with UnitedMet

Abstract Metabolites play a crucial role in the functioning of biological systems. Measurements of metabolite abundances by metabolomics and metabolic pathway activity by isotope-tracing are powerful approaches to study metabolic phenotypes. However, large-scale measurements of metabolite levels and pathway activity remain extremely scarce due to the technical challenges. Motivated by the strength of RNA-metabolite covariation, we present UnitedMet, a Bayesian probabilistic method for end-to-end joint modeling of RNA-sequencing and metabolic datasets that is capable of dimensionality reduction, data integration and metabolic modality prediction. UnitedMet demonstrates high accuracy predicting metabolite levels and isotopologue distributions from gene expression data in human tumor samples. To evaluate UnitedMet’s efficacy in clinical applications, we inferred metabolomic profiles and isotope labeling patterns from the RNA sequencing data of clear cell renal cell carcinoma (ccRCC) patients enrolled in TCGA and clinical trials. Correlation analysis between genetic mutations and predicted metabolic phenotypes revealed that BAP1 mutations were associated with increased contribution of glucose to TCA cycle in ccRCC. Increased TCA cycle activity was found to be associated with disease progression and poorer clinical outcome. UnitedMet, therefore, provides a solution that could be broadly applicable to enhance multimodal metabolomic datasets and facilitates comprehensive study of interplays between metabolic profiles, molecular alterations and human phenotypes. Citation Format: Amy X. Xie, Casey Bradshaw, Christopher Tosh, Wesley Tansey, Ed Reznik. Joint probabilistic modeling of multimodal metabolic data with UnitedMet [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3512.

Construction and validation of log odds of positive lymph nodes (LODDS)-based nomograms for predicting overall survival and cancer-specific survival in ovarian clear cell carcinoma patients.

Ovarian clear cell carcinoma (OCCC) is one of the special histologic subtypes of ovarian cancer. This study aimed to construct and validate log odds of positive lymph nodes (LODDS)-based nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Patients who underwent surgical treatment between 2010 and 2016 were extracted from the Surveillance Epidemiology and End Results (SEER) database and the data of OCCC patients from the First Affiliated Hospital of Dalian Medical University were used as the external validation group to test the validity of the prognostic model. The best-fitting models were selected by stepwise Cox regression analysis. Survival probability was calculated by the Kaplan-Meier method, and the differences in survival time between subgroups were compared using the log-rank test. Each nomogram's performance was assessed by the calibration plots, decision curve analysis (DCA), and receiver operating characteristics (ROC) curves. T stage, distant metastasis, marital status, and LODDS were identified as significant risk factors for OS. A model with four risk factors (age, T stage, stage, and LODDS value) was obtained for CSS. Nomograms were constructed by incorporating the prognostic factors to predict 1-, 3- and 5-year OS and CSS for OCCC patients, respectively. The area under the curve (AUC) range of our nomogram model for OS and CSS prediction ranged from 0.738-0.771 and 0.769-0.794, respectively, in the training cohort. The performance of this model was verified in the internal and external validation cohorts. Calibration plots illustrated nomograms have good prognostic reliability. Predictive nomograms were constructed and validated to evaluate the OS and CSS of OCCC patients. These nomograms may provide valuable prognostic information and guide postoperative personalized care in OCCC.

Affinity fine-tuning anti-CAIX CAR-T cells mitigate on-target off-tumor side effects

One of the major hurdles that has hindered the success of chimeric antigen receptor (CAR) T cell therapies against solid tumors is on-target off-tumor (OTOT) toxicity due to sharing of the same epitopes on normal tissues. To elevate the safety profile of CAR-T cells, an affinity/avidity fine-tuned CAR was designed enabling CAR-T cell activation only in the presence of a highly expressed tumor associated antigen (TAA) but not when recognizing the same antigen at a physiological level on healthy cells. Using direct stochastic optical reconstruction microscopy (dSTORM) which provides single-molecule resolution, and flow cytometry, we identified high carbonic anhydrase IX (CAIX) density on clear cell renal cell carcinoma (ccRCC) patient samples and low-density expression on healthy bile duct tissues. A Tet-On doxycycline-inducible CAIX expressing cell line was established to mimic various CAIX densities, providing coverage from CAIX-high skrc-59 tumor cells to CAIX-low MMNK-1 cholangiocytes. Assessing the killing of CAR-T cells, we demonstrated that low-affinity/high-avidity fine-tuned G9 CAR-T has a wider therapeutic window compared to high-affinity/high-avidity G250 that was used in the first anti-CAIX CAR-T clinical trial but displayed serious OTOT effects. To assess the therapeutic effect of G9 on patient samples, we generated ccRCC patient derived organotypic tumor spheroid (PDOTS) ex vivo cultures and demonstrated that G9 CAR-T cells exhibited superior efficacy, migration and cytokine release in these miniature tumors. Moreover, in an RCC orthotopic mouse model, G9 CAR-T cells showed enhanced tumor control compared to G250. In summary, G9 has successfully mitigated OTOT side effects and in doing so has made CAIX a druggable immunotherapeutic target.

Trends in survival of ovarian clear cell carcinoma patients from 2000 to 2015.

To analyze changes in survival outcomes in patients with ovarian clear cell carcinoma (OCCC) treated consecutively over a 16-year period using a population-based cohort. We conducted a retrospective analysis of OCCC from 2000 to 2015 using data from the Surveillance, Epidemiology, and End Results (SEER) program. The ovarian cancer-specific survival (OCSS) and overall survival (OS) were analyzed according to the year of diagnosis. Joinpoint Regression Program, Kaplan-Meier analysis, and multivariate Cox regression analyses were used for statistical analysis. We included 4257 patients in the analysis. The analysis of annual percentage change in OCSS (P=0.014) and OS (P=0.006) showed that patients diagnosed in later years had significantly better outcomes compared to those diagnosed in early years. The results of the multivariate Cox regression analyses showed that the year of diagnosis was the independent prognostic factor associated with OCSS (P=0.004) and had a borderline effect on OS (P=0.060). Regarding the SEER staging, the OCSS (P=0.017) and OS (P=0.004) of patients with distant stage showed a significant trend toward increased, while no significant trends were found in the survival of patients with localized or regional stage diseases. Similar trends were found in those aged <65 years or those treated with surgery and chemotherapy. However, no statistically significant changes in the survival rate were found in those aged ≥65 years or those receiving surgery alone regardless of SEER stage during the study period. Our study observed a significant increase in the survival outcomes in OCCC from 2000 to 2015, and patients aged <65 years and those with distant stage experienced a greater improvement in survival.

The malignant transformation of endometriosis: Is there a left lateral predisposition of ovarian clear cell and endometrioid carcinomas?

IntroductionEndometriosis affects 10% of women of reproductive age. There is evidence for a left lateral predisposition of endometriotic lesions and a 1.9-fold greater risk of ovarian cancer in endometriosis. The aim of this study is to determine whether a left lateral predisposition of ovarian clear-cell carcinoma (CCC) and endometrioid carcinoma (EC) exists. Materials and methodsA retrospective cohort study of all EC and CCC patients in Northern Ireland between March-2011 and June-2018. ANOVA was used to analyse preoperative prediction of stage, chi-squared (χ2) was used to compare left- and right-sided masses. Survival was estimated using Kaplan-Meier and log-rank test. A p-value <0.05 was considered significant. Results158 patients were identified (95 EC, 55 CCC, 8 mixed). Mean age was 57.65 years with 69% presenting at stage 1. The mean CA125 was 559 U/mL (p = 0.850) and mean abdominal mass size was 14.12 cm (p = 0.732). The most common presenting symptom was an abdominal mass (37%). Despite 67% of patients having endometriosis on final pathology, only 8.9% had a known history pre-operatively. 51% of tumours were located on the left (p = 0.036). For unilateral tumours this was significant for EC (P = 0.002) but not for CCC (P = 0.555). The 1-, 3- and 5-year overall survival for all types/stages was 85%, 78% and 71% respectively. ConclusionWhile CCC and EC are associated with endometriosis, only EC exhibits a left lateral predisposition. There is no association between preoperative CA125 or abdominal mass size and stage of disease.

Identification and validation of m6A-associated ferroptosis genes in renal clear cell carcinoma.

Urinary cancer is synonymous with clear cell renal cell carcinoma (ccRCC). Unfortunately, existing treatments for this illness are ineffective and unpromising. Finding novel ccRCC biomarkers is crucial to creating successful treatments. The Cancer Genome Atlas provided clear cell renal cell carcinoma transcriptome data. Functional enrichment analysis was performed on ccRCC and control samples' differentially expressed N6-methyladenosine RNA methylation and ferroptosis-related genes (DEMFRGs). Machine learning was used to find and model ccRCC patients' predicted genes. A nomogram was created for clear cell renal cell carcinoma patients. Prognostic genes were enriched. We examined patients' immune profiles by risk score. Our prognostic genes predicted ccRCC treatment drugs. We found 37 DEMFRGs by comparing 1913 differentially expressed ccRCC genes to 202 m6A RNA methylation FRGs. Functional enrichment analysis showed that hypoxia-induced cell death and metabolism pathways were the most differentially expressed methylation functional regulating genes. Five prognostic genes were found by machine learning: TRIB3, CHAC1, NNMT, EGFR, and SLC1A4. An advanced renal cell carcinoma nomogram with age and risk score accurately predicted the outcome. These five prognostic genes were linked to various cancers. Immunological cell number and checkpoint expression differed between high- and low-risk groups. The risk model successfully predicted immunotherapy outcome, showing high-risk individuals had poor results. NIACIN, TAE-684, ROCILETINIB, and others treat ccRCC. We found ccRCC prognostic genes that work. This discovery may lead to new ccRCC treatments.

Domain generalization enables general cancer cell annotation in single-cell and spatial transcriptomics

Single-cell and spatial transcriptome sequencing, two recently optimized transcriptome sequencing methods, are increasingly used to study cancer and related diseases. Cell annotation, particularly for malignant cell annotation, is essential and crucial for in-depth analyses in these studies. However, current algorithms lack accuracy and generalization, making it difficult to consistently and rapidly infer malignant cells from pan-cancer data. To address this issue, we present Cancer-Finder, a domain generalization-based deep-learning algorithm that can rapidly identify malignant cells in single-cell data with an average accuracy of 95.16%. More importantly, by replacing the single-cell training data with spatial transcriptomic datasets, Cancer-Finder can accurately identify malignant spots on spatial slides. Applying Cancer-Finder to 5 clear cell renal cell carcinoma spatial transcriptomic samples, Cancer-Finder demonstrates a good ability to identify malignant spots and identifies a gene signature consisting of 10 genes that are significantly co-localized and enriched at the tumor-normal interface and have a strong correlation with the prognosis of clear cell renal cell carcinoma patients. In conclusion, Cancer-Finder is an efficient and extensible tool for malignant cell annotation.

Quantification of eosinophilic area and its potential molecular feature in clear cell renal cell carcinoma.

Previous studies have acknowledged the presence of eosinophilic cytoplasm in clear cell renal cell carcinoma, yet the precise quantification method and potential molecular attributes in clear cell renal cell carcinoma remain elusive. This study endeavours to precisely quantify the eosinophilic attribute and probe into the molecular mechanisms governing its presence in clear cell renal cell carcinoma. Data from cohorts of clear cell renal cell carcinoma patients who underwent nephrectomy, comprising The Cancer Genome Atlas cohort (n=475) and Sun Yat-sen University Cancer Center cohort (n=480), were aggregated to assess the eosinophilic attribute. Additionally, Omics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) (n=58) were leveraged to explore the potential molecular features associated with eosinophilic clear cell renal cell carcinoma. Employing receiver operating characteristic curve analysis, the proportion of tumour cells with eosinophilic cytoplasm was determined, leading to the classification of each cohort into distinct groups: a clear group (<5%) and an eosinophilic group (≥5%). In both cohorts, the eosinophilic feature consistently correlated with higher International Society of Urological Pathology (ISUP) grade, elevated tumor stage, and the presence of necrosis. Furthermore, the Kaplan-Meier method demonstrated that patients in the eosinophilic group exhibited shorter overall survival or disease-free survival compared with those in the clear group, a pattern reaffirmed in various stratified survival analyses. Intriguingly, within The Cancer Genome Atlas cohort, the pathological characterization of cell cytoplasm (eosinophilic vs. clear) emerged as an independent risk factor for overall survival (hazard ratio = 2.507 [95% confidence interval: 1.328-4.733], P = 0.005) or disease-free survival (hazard ratio = 1.730 [95% confidence interval: 1.062-2.818], P = 0.028) via Cox regression analysis. Moreover, multi-Omics data unveiled frequent BAP1 mutations and down-regulation of Erythroblast Transformation-Specific-Related Gene associated with the eosinophilic feature in clear cell renal cell carcinoma. Additionally, patients with low expression of Erythroblast Transformation-Specific-Related Gene showed worse overall survival (P < 0.001). The quantification of the eosinophilic feature serves as a robust predictor of clinical prognosis in clear cell renal cell carcinoma. Furthermore, the manifestation of this feature may be linked to BAP1 mutations and the down-regulation of Erythroblast Transformation-Specific-Related Gene in clear cell renal cell carcinoma. Significantly, the expression levels of Erythroblast Transformation-Specific-Related Gene manifest as an exemplary prognostic marker, providing exceptional predictive accuracy for the clinical prognosis in clear cell renal cell carcinoma.

Nonlinear Associations of Adipose Tissue Metrics with Short-Term Postoperative Complications in Clear Cell Renal Cell Carcinoma Patients.

Nonlinear Associations of Adipose Tissue Metrics with Short-Term Postoperative Complications in Clear Cell Renal Cell Carcinoma Patients.

ASO Author Reflections: Clinical Stage I Clear Cell Renal Cell Carcinoma Patients with Gross Hematuria: Radical Nephrectomy or Partial Nephrectomy?

ASO Author Reflections: Clinical Stage I Clear Cell Renal Cell Carcinoma Patients with Gross Hematuria: Radical Nephrectomy or Partial Nephrectomy?

Married Status Affects Rates of Treatment and Mortality in Male and Female Renal Cell Carcinoma Patients Across all Stages

IntroductionThe association between treatment rates and cancer specific mortality (CSM) according to married status in male and female clear cell renal cell carcinoma (ccRCC) patients across all stages is unknown. Patient and MethodsUsing the Surveillance, Epidemiology, and End Results database (2004-2020), ccRCC patients were stratified according to married status (married vs. unmarried). Logistic regression models addressed treatment rates; Cox regression models addressed CSM rates. ResultsOf 98,142 patients, 43,999 (72%) males and 20,287 (55%) females were married. In stage-specific analyses, married status independently predicted higher nephrectomy rates in males and females (all P ≤ .03). In stage IV, married status predicted higher systemic therapy rate in males (P < .001), but not in females. In survival analyses, married males exhibited lower CSM rates relative to unmarried males (all P ≤ .02). Conversely, married females exhibited lower CSM rates only in stages I and III (all P ≤ .02), but not in stages II and IV. In subgroup analyses of T1aN0M0 patients, married status was associated with higher partial nephrectomy rates in both males and females (all P ≤ .005). ConclusionIn ccRCC, married status invariably predicts higher rates of guideline recommended surgical management (nephrectomy and partial nephrectomy). Moreover, even after adjustment for treatment type, married status independently predicted lower CSM rates in males across all stages. However, the effect of married status in females is only operational in stages I and III. Lack of association between married status in stages II and IV may potentially be explained by stronger association with treatment assignment which reduces the residual effect on survival.