Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images.

There is an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal cell carcinoma (RCC). We proposed deep learning-based artificial intelligence strategies. The study included 1752 whole slide images from multiple centres. Based on the pixel-level of RCC segmentation, the diagnosis diagnostic model achieved an area under the receiver operating characteristic curve (AUC) of 0.977 (95% CI 0.969-0.984) in the external validation cohort. In addition, our diagnostic model exhibited excellent performance in the differential diagnosis of RCC from renal oncocytoma, which achieved an AUC of 0.951 (0.922-0.972). The graderisk for the recognition of high-grade tumour achieved AUCs of 0.840 (0.805-0.871) in the Cancer Genome Atlas (TCGA) cohort, 0.857 (0.813-0.894) in the Shanghai General Hospital (General) cohort, and 0.894 (0.842-0.933) in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohort, for the recognition of high-grade tumour. The OSrisk for predicting 5-year survival status achieved an AUC of 0.784 (0.746-0.819) in the TCGA cohort, which was further verified in the independent general cohort and the CPTAC cohort, with AUCs of 0.774 (0.723-0.820) and 0.702 (0.632-0.765), respectively. Moreover, the competing-risk nomogram (CRN) showed its potential to be a prognostic indicator, with a hazard ratio (HR) of 5.664 (3.893-8.239, p<0.0001), outperforming other traditional clinical prognostic indicators. Kaplan-Meier survival analysis further illustrated that our CRN could significantly distinguish patients with high survival risk. Deep learning-based artificial intelligence could be a useful tool for clinicians to diagnose and predict the prognosis of RCC patients, thus improving the process of individualised treatment.

Serum and Urine Metabolic Fingerprints Characterize Renal Cell Carcinoma for Classification, Early Diagnosis, and Prognosis.

Renal cell carcinoma (RCC) is a substantial pathology of the urinary system with a growing prevalence rate. However, current clinical methods have limitations for managing RCC due to the heterogeneity manifestations of the disease. Metabolic analyses are regarded as a preferred noninvasive approach in clinics, which can substantially benefit the characterization of RCC. This study constructs a nanoparticle-enhanced laser desorption ionization mass spectrometry (NELDI MS) to analyze metabolic fingerprints of renal tumors (n = 456) and healthy controls (n = 200). The classification models yielded the areas under curves (AUC) of 0.938 (95% confidence interval (CI), 0.884-0.967) for distinguishing renal tumors from healthy controls, 0.850 for differentiating malignant from benign tumors (95% CI, 0.821-0.915), and 0.925-0.932 for classifying subtypes of RCC (95% CI, 0.821-0.915). For the early stage of RCC subtypes, the averaged diagnostic sensitivity of 90.5% and specificity of 91.3% in the test set is achieved. Metabolic biomarkers are identified as the potential indicator for subtype diagnosis (p < 0.05). To validate the prognostic performance, a predictive model for RCC participants and achieve the prediction of disease (p = 0.003) is constructed. The study provides a promising prospect for applying metabolic analytical tools for RCC characterization.

ScRNA-seq revealed the tumor microenvironment heterogeneity related to the occurrence and metastasis in upper urinary tract urothelial carcinoma.

Metastasis is the greatest clinical challenge for UTUCs, which may have distinct molecular and cellular characteristics from earlier cancers. Herein, we provide single-cell transcriptome profiles of UTUC para cancer normal tissue, primary tumor lesions, and lymphatic metastases to explore possible mechanisms associated with UTUC occurrence and metastasis. From 28,315 cells obtained from normal and tumor tissues of 3 high-grade UTUC patients, we revealed the origin of UTUC tumor cells and the homology between metastatic and primary tumor cells. Unlike the immunomicroenvironment suppression of other tumors, we found no immunosuppression in the tumor microenvironment of UTUC. Moreover, it is imperative to note that stromal cells are pivotal in the advancement of UTUC. This comprehensive single-cell exploration enhances our comprehension of the molecular and cellular dynamics of metastatic UTUCs and discloses promising diagnostic and therapeutic targets in cancer-microenvironment interactions.

Well-Paired-Seq2: High-Throughput and High-Sensitivity Strategy for Characterizing Low RNA-Content Cell/Nucleus Transcriptomes.

Single-cell RNA sequencing (scRNA-seq) is a transformative technology that unravels the intricate cellular state heterogeneity. However, the Poisson-dependent cell capture and low sensitivity in scRNA-seq methods pose challenges for throughput and samples with a low RNA-content. Herein, to address these challenges, we present Well-Paired-Seq2 (WPS2), harnessing size-exclusion and quasi-static hydrodynamics for efficient cell capture. WPS2 exploits molecular crowding effect, tailing activity enhancement in reverse transcription, and homogeneous enzymatic reaction in the initial bead-based amplification to achieve 3116 genes and 8447 transcripts with an average of ∼20000 reads per cell. WPS2 detected 1420 more genes and 4864 more transcripts than our previous Well-Paired-Seq. It sensitively characterizes transcriptomes of low RNA-content single cells and nuclei, overcoming the Poisson limit for cell and barcoded bead capture. WPS2 also profiles transcriptomes from frozen clinical samples, revealing heterogeneous tumor copy number variations and intercellular crosstalk in clear cell renal cell carcinomas. Additionally, we provide the first single-cell-level characterization of rare metanephric adenoma (MA) and uncover potential specific markers. With the advantages of high sensitivity and high throughput, WPS2 holds promise for diverse basic and clinical research.

AURKA promotes renal cell carcinoma progression via regulation of CCNB1 transcription.

AURKA is a member of the serine/threonine kinase family and its kinase activity is crucial for the progression of mitosis. Recent studies have highlighted the therapeutic significance of AURKA inhibition in multiple cancer types. However, the specific mechanisms by which AURKA contributes to the progression of renal cell carcinoma (RCC) have not been fully elucidated. In this study, AURKA expression level was identified in human RCC tissues by immunohistochemical (IHC) staining. The function of AURKA on cell malignant phenotypes was evaluated in vitro after AURKA inhibition. The subcutaneous xenograft was conducted to confirm the in vivo effect of AURKA knockdown on growth of RCC cells. Finally, Co-IP, luciferase assay and ChIP experiments were performed to reveal the regulatory mechanism of AURKA on CCNB1. Our results showed a significant upregulation of AURKA in RCC tissues and cell lines, and a high AURKA expression was associated with poor prognosis. AURKA knockdown inhibited RCC cell proliferation and migration, induced cell apoptosis, and led to G1/G2 phase arrest. This effect was further confirmed by the use of an AURKA inhibitor. Mechanistically, AURKA interacted with E2F1, and subsequently recruited it to the promoter region of CCNB1. CCNB1 expression was essential for AURKA-induced RCC progression. Collectively, our results suggested that AURKA plays an important role in development of RCC via regulating CCNB1 transcription.

Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study.

BACKGROUND: Although separate analysis of individual factor can somewhat improve the prognostic performance, integration of multimodal information into a single signature is necessary to stratify patients with clear cell renal cell carcinoma (ccRCC) for adjuvant therapy after surgery. METHODS: A total of 414 patients with whole slide images, computed tomography images, and clinical data from three patient cohorts were retrospectively analyzed. The authors performed deep learning and machine learning algorithm to construct three single-modality prediction models for disease-free survival of ccRCC based on whole slide images, cell segmentation, and computed tomography images, respectively. A multimodel prediction signature (MMPS) for disease-free survival were further developed by combining three single-modality prediction models and tumor stage/grade system. Prognostic performance of the prognostic model was also verified in two independent validation cohorts. RESULTS: Single-modality prediction models performed well in predicting the disease-free survival status of ccRCC. The MMPS achieved higher area under the curve value of 0.742, 0.917, and 0.900 in three independent patient cohorts, respectively. MMPS could distinguish patients with worse disease-free survival, with HR of 12.90 (95% CI: 2.443-68.120, P <0.0001), 11.10 (95% CI: 5.467-22.520, P <0.0001), and 8.27 (95% CI: 1.482-46.130, P <0.0001) in three different patient cohorts. In addition, MMPS outperformed single-modality prediction models and current clinical prognostic factors, which could also provide complements to current risk stratification for adjuvant therapy of ccRCC. CONCLUSION: Our novel multimodel prediction analysis for disease-free survival exhibited significant improvements in prognostic prediction for patients with ccRCC. After further validation in multiple centers and regions, the multimodal system could be a potential practical tool for clinicians in the treatment for ccRCC patients.

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.

Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing.

Spatial transcriptomics technologies with high resolution often lack high sensitivity in mRNA detection. Here we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq), which offers both high sensitivity and high resolution. Decoder-seq combines dendrimeric nanosubstrates with microfluidic coordinate barcoding to generate spatial arrays with a DNA density approximately ten times higher than previously reported methods while maintaining flexibility in resolution. We show that the high RNA capture efficiency of Decoder-seq improved the detection of lowly expressed olfactory receptor (Olfr) genes in mouse olfactory bulbs and contributed to the discovery of a unique layer enrichment pattern for two Olfr genes. The near-cellular resolution provided by Decoder-seq has enabled the construction of a spatial single-cell atlas of the mouse hippocampus, revealing dendrite-enriched mRNAs in neurons. When applying Decoder-seq to human renal cell carcinomas, we dissected the heterogeneous tumor microenvironment across different cancer subtypes and identified spatial gradient-expressed genes related to epithelial-mesenchymal transition with the potential to predict tumor prognosis and progression.

Altered NCR3 Splice Variants May Result in Deficient NK Cell Function in Renal Cell Carcinoma Patients.

BACKGROUND/AIM: The natural killer (NK) cell function of patients with malignant tumours may be suppressed by deficiency, and the poor prognosis of renal cell carcinoma (RCC) patients may be due to escape from NK cell cytotoxicity, especially with respect to natural cytotoxicity receptors (NCRs) on the NK cell surface. However, the specific mechanism remains unclear. Therefore, in this study, we sought to explore the role of NCR, especially NCR3 splice variants, in the process of NK cell deficiency in RCC patients. MATERIALS AND METHODS: We used flow cytometry to analyse the phenotype of NK cells from the peripheral blood and kidney tumour tissue of RCC patients. The NKp30-mediated NK cell killing function was measured by antibody-dependent cell-mediated cytotoxicity (ADCC) in NK and RCC cell coincubation. We extracted RNA from the peripheral blood mononuclear cells (PBMCs) of RCC patients and renal carcinoma tissue and carried out real-time quantitative PCR to detect the mRNA levels of NKp30a, NKp30b and NKp30c. mRNA expression levels of cytokines (IL-6, IL-8, IL-10, IL-18 and TGF-ß) based on RNA extracted from renal carcinoma tissue and adjacent normal kidney tissues were also measured by real-time quantitative PCR. RESULTS: Regarding the phenotype of NK cells in RCC patients, the proportion of NK cells in tumour tissue was significantly reduced, with changes in the NK cell proportion being most obvious in NKp30+ NK cells. Furthermore, the results of the ADCC function assay showed limited NKp30+ NK cell-mediated cytotoxicity in RCC patients. Through real-time quantitative PCR, we found lower expression of NKp30a and NKp30b, the immunostimulatory splice variants of NCR3 encoding NKp30, in RCC patients. Moreover, expression of activating cytokines (IL-6 and IL-8) in renal cancer tissue was decreased, though inhibitory cytokine (TGF-ß) expression remained unchanged, which may result in an immunosuppressive cytokine microenvironment. CONCLUSION: Decreased expression of immunostimulatory NCR3 splice variants and the inhibitory cytokine microenvironment in RCC patients may contribute to deficient NK cell cytotoxicity and renal carcinoma cell immune escape from NK cell killing, which may provide a theoretical basis for finding new immunotherapeutic targets for RCC.

Genome-Wide Association Studies of Biluochun Tea Plant Populations in Dongting Mountain and Comprehensive Identification of Candidate Genes Associated with Core Agronomic Traits by Four Analysis Models.

The elite germplasm resources are key to the beautiful appearance and pleasant flavor of Biluochun tea. We collected and measured the agronomic traits of 95 tea plants to reveal the trait diversity and breeding value of Biluochun tea plant populations. The results revealed that the agronomic traits of Biluochun tea plant populations were diverse and had high breeding value. Additionally, we resequenced these tea plant populations to reveal genetic diversity, population structure, and selection pressure. The Biluochun tea plant populations contained two groups and were least affected by natural selection based on the results of population structure and selection pressure. More importantly, four non-synonymous single nucleotide polymorphisms (nsSNPs) and candidate genes associated with (-)-gallocatechin gallate (GCG), (-)-gallocatechin (GC), and caffeine (CAF) were detected using at least two GWAS models. The results will promote the development and application of molecular markers and the utilization of elite germplasm from Biluochun populations.

Artificial intelligence-based non-invasive tumor segmentation, grade stratification and prognosis prediction for clear-cell renal-cell carcinoma.

Due to the complicated histopathological characteristics of clear-cell renal-cell carcinoma (ccRCC), non-invasive prognosis before operative treatment is crucial in selecting the appropriate treatment. A total of 126 345 computerized tomography (CT) images from four independent patient cohorts were included for analysis in this study. We propose a V Bottleneck multi-resolution and focus-organ network (VB-MrFo-Net) using a cascade framework for deep learning analysis. The VB-MrFo-Net achieved better performance than VB-Net in tumor segmentation, with a Dice score of 0.87. The nuclear-grade prediction model performed best in the logistic regression classifier, with area under curve values from 0.782 to 0.746. Survival analysis revealed that our prediction model could significantly distinguish patients with high survival risk, with a hazard ratio (HR) of 2.49 [95% confidence interval (CI): 1.13-5.45, P = 0.023] in the General cohort. Excellent performance had also been verified in the Cancer Genome Atlas cohort, the Clinical Proteomic Tumor Analysis Consortium cohort, and the Kidney Tumor Segmentation Challenge cohort, with HRs of 2.77 (95%CI: 1.58-4.84, P = 0.0019), 3.83 (95%CI: 1.22-11.96, P = 0.029), and 2.80 (95%CI: 1.05-7.47, P = 0.025), respectively. In conclusion, we propose a novel VB-MrFo-Net for the renal tumor segmentation and automatic diagnosis of ccRCC. The risk stratification model could accurately distinguish patients with high tumor grade and high survival risk based on non-invasive CT images before surgical treatments, which could provide practical advice for deciding treatment options.

The microbiota and renal cell carcinoma.

Renal cell carcinoma (RCC) accounts for about 2% of cancer diagnoses and deaths worldwide. Recent studies emphasized the critical involvement of microbial populations in RCC from oncogenesis, tumor growth, and response to anticancer therapy. Microorganisms have been shown to be involved in various renal physiological and pathological processes by influencing the immune system function, metabolism of the host and pharmaceutical reactions. These findings have extended our understanding and provided more possibilities for the diagnostic or therapeutic development of microbiota, which could function as screening, prognostic, and predictive biomarkers, or be manipulated to prevent RCC progression, boost anticancer drug efficacy and lessen the side effects of therapy. This review aims to present an overview of the roles of microbiota in RCC, including pertinent mechanisms in microbiota-related carcinogenesis, the potential use of the microbiota as RCC biomarkers, and the possibility of modifying the microbiota for RCC prevention or treatment. According to these scientific findings, the clinical translation of microbiota is expected to improve the diagnosis and treatment of RCC.

Clinical values of circulating tumor cells count in localized renal cell carcinoma.

Background: Renal cancer is one of the most common malignant tumors of the urinary system, with distant metastasis occurring 30% of patients. Therefore, early detection and monitoring of tumor progression are of great significance in the diagnosis and treatment of renal cancer. However, current biomarkers used to diagnose, monitor recurrence and assess prognosis of renal cancer are still uncertain. Circulating tumor cells (CTCs) are tumor cells detached from the primary tumor or metastasis, invaded and existing in the peripheral blood, and are one of the most promising liquid biopsy targets because they can provide complete cell biological information. Microfluidic chip has advantages of miniaturization, high integration, and fast analysis, which has advantages in CTC separation and enrichment. Methods: In this study, 1 mL peripheral blood of each 30 patients with early localized renal cancer was collected before and 1 day after surgery. CTC enrichment was performed by microfluidic chip and CTCs were identified by immunofluorescence staining. All patients were followed up for a median of 17 months. Results: The number of CTCs before surgery was higher than that after surgery (P<0.001), and the number was positively correlated with tumor-node-metastasis (TNM) stage and International Society of Urological Pathology (ISUP) grade. Patients in group CTC ≤2 had a longer progression-free survival (PFS) than those in group CTC ≥3 (P<0.05). Conclusions: Surgical treatment can remarkably reduce the number of CTCs in patients, and CTC counts can also play a role in monitoring tumor load and predicting prognosis in renal cancer.

Spatio-temporal heterogeneity in cancer evolution and tumor microenvironment of renal cell carcinoma with tumor thrombus.

Metastasis is the most fatal aspect of cancer, often preceded by a tumor thrombus (TT) which forms within the vascular system. Renal cell carcinoma (RCC), the predominant form of kidney cancer, witnesses a venous system invasion in 4-10% of cases, resulting in venous tumor thrombus (RCC-TT). This variant represents a formidable clinical challenge due to its escalated surgical complexity, heightened risk of progression and metastasis, and an adverse prognosis. However, recent trials addressing RCC-TT face significant barriers stemming from the profound inter- and intra-tumoral heterogeneity, patient-specific treatment variations, and distinct therapeutic resistance patterns between the primary tumor (PT) and the TT. This review delves into the unique evolutionary pathway of RCC-TT, the relationship between the staging and grading of RCC-TT invasion patterns, and the spatial molecular profiling of RCC-TT. Additionally, we assess the temporal heterogeneity among TT, PT, and distant metastases, as well as the functional phenotypes of TME components. An outlook for future research on RCC-TT is also provided.

Humanistic care relieves mental distress of inpatients in the shelter hospital during COVID-19 pandemic in Shanghai: a cross-sectional observational study.

Objective: The prevalence of mental distress has been noted in shelter hospitals set up for COVID-19. Potential risk demographic and hospitalization factors were screened. We also aimed to determine whether humanistic care established in the shelter hospital was effective in ameliorating mental distress. Methods: A cross-sectional observational survey-based single-centered study was conducted from 28th April to 5th May 2022 during the COVID-19 pandemic in Shanghai. Asymptomatic adult inpatients and those with mild symptoms were recruited for this study, and humanistic care measures were carried out by the administrative office according to the Work Program on Psychological Assistance and Social Work Services at the Shelter Hospital launched on 5th March 2020. Symptoms of mental distress, such as reported stress, anxiety, depression, and insomnia were measured using the Chinese Stress Response Questionnaire-28, the Chinese version of Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, and Insomnia Severity Index-7, respectively. Results: In total, 1,246 out of 9,519 inpatients, including 565 (45.35%) women and 681 (54.65%) men, with a median age of 36 years responded to the survey. The overall prevalence of stress, anxiety, depression, and insomnia in inpatients was 94 (7.54%), 109 (8.75%), 141 (11.32%), and 144 (11.56%), respectively. Mental distress was aggravated by COVID-19-related symptoms, comorbidities, and prolonged hospital stays. A stable internet connection was the most effective measure to reduce stress and depression. Offering inpatient with study or work facilitations, and mental health education help to ameliorate anxiety and depression. Organizing volunteering was a potential protective factor against stress. Conclusion: Humanistic care is crucial and effective for protecting against mental distress, which should be emphasized in shelter hospitals.

Development and verification of a deep learning-based m6A modification model for clinical prognosis prediction of renal cell carcinoma.

BACKGROUND: The deep learning-based m6A modification model for clinical prognosis prediction of patients with renal cell carcinoma (RCC) had not been reported for now. In addition, the important roles of methyltransferase-like 14 (METTL14) in RCC have never been fully explored. METHODS: A high-level neural network based on deep learning algorithm was applied to construct the m6A-prognosis model. Western blotting, quantitative real-time PCR, immunohistochemistry and RNA immunoprecipitation were used for biological experimental verifications. RESULTS: The deep learning-based model performs well in predicting the survival status in 5-year follow-up, which also could significantly distinguish the patients with high overall survival risk in two independent patient cohort and a pan-cancer patient cohort. METTL14 deficiency could promote the migration and proliferation of renal cancer cells. In addition, our study also illustrated that METTL14 might participate in the regulation of circRNA in RCC. CONCLUSIONS: In summary, we developed and verified a deep learning-based m6A-prognosis model for patients with RCC. We proved that METTL14 deficiency could promote the migration and proliferation of renal cancer cells, which might throw light on the cancer prevention by targeting the METTL14 pathway.

CD44 Is a Prognostic Biomarker Correlated With Immune Infiltrates and Metastasis in Clear Cell Renal Cell Carcinoma.

BACKGROUND/AIM: CD44 is a critical cell-surface glycoprotein. However, its prognostic significance and correlation with tumor-infiltrating lymphocytes in clear cell renal cell carcinoma (ccRCC) are not well-understood. MATERIALS AND METHODS: The mRNA and protein levels of CD44 in ccRCC were assessed. The prognostic value of CD44 was analyzed in the TCGA and PrognoScan databases. The functional enrichment and immune infiltrates analyses were conducted. The STRING database was used to analyze the protein interactions of CD44. Tissue array, western blot, qRT-PCR, and transwell assay were performed to determine the expression and biological function of CD44 in ccRCC cells. RESULTS: CD44 was highly expressed in ccRCC and correlated with poor prognosis. CD44 mRNA and protein expression was associated with TNM stage, pathologic stage, and histologic grade. Functional enrichment analyses revealed CD44 is involved in extracellular matrix organization, metastasis, IL6/JAK/STAT3 signaling and so on. Moreover, CD44 expression was positively correlated with infiltrating levels of macrophages, Th2 cells and Th1 cells in ccRCC. Combining the immune infiltration analysis and immunohistochemistry, the SPP1/CD44 axis might participate in immune escape through regulating PD-L2 expression. Experiments indicated that CD44 was increased in ccRCC and inhibition of CD44 could suppress the migration of ccRCC cells. CONCLUSION: High expression of CD44 in ccRCC was associated with metastasis, poor prognosis, and high infiltrating levels of macrophages. The SPP1/CD44 axis potentially contributes to the regulation of PD-L2. These results demonstrated that targeting the SPP1/CD44 axis or inhibiting CD44 expression may be a new therapy to suppress ccRCC progression.

Loss of MIR503HG facilitates papillary renal cell carcinoma associated lymphatic metastasis by triggering NOTCH1/VEGFC signaling.

Clinical lymphatic metastasis indicates an extremely poor prognosis. Patients with papillary renal cell carcinoma (pRCC) have a high probability of progressing to lymphatic metastasis. However, the molecular mechanism of pRCC-associated lymphatic metastasis has not been elucidated. In this study, we found a downregulated long non-coding RNA (lncRNA) MIR503HG in pRCC primary tumor tissues due to hypermethylation at the CpG islands within its transcriptional start site. Decreased MIR503HG expression could stimulate tube formation and migration of human lymphatic endothelial cell (HLEC) and play a central role to promote lymphatic metastasis in vivo by enhancing tumor lymphangiogenesis. MIR503HG, located in the nucleus, bound with histone variant H2A.Z and affected the recruitment of histone variant H2A.Z to chromatin. Subsequently, increasing the H3K27 trimethylation caused by MIR503HG-overexpression epigenetically downregulated the NOTCH1 expression, which ultimately resulted in decreasing VEGFC secretion and lymphangiogenesis. Additionally, downregulated MIR503HG facilitated the HNRNPC expression, which ultimately promoted the maturation of NOTCH1 mRNA. Notably, upregulating MIR503HG expression might decrease pRCC resistance to the mTOR inhibitor. Together, these findings highlighted a VEGFC-independent mechanism of MIR503HG-mediated lymphatic metastasis. MIR503HG, identified as a novel pRCC-suppressor, would serve as the potentially biomarker for lymphatic metastasis.

piRNA-1742 promotes renal cell carcinoma malignancy by regulating USP8 stability through binding to hnRNPU and thereby inhibiting MUC12 ubiquitination.

Accumulating studies have confirmed that PIWI-interacting RNAs (piRNAs) are considered epigenetic effectors in cancer. We performed piRNA microarray expression analysis on renal cell carcinoma (RCC) tumor tissues and paired normal tissues and performed a series of in vivo and in vitro experiments to explore piRNAs associated with RCC progression and investigate their functional mechanisms. We found that piR-1742 was highly expressed in RCC tumors and that patients with high piR-1742 expression had a poor prognosis. Inhibition of piR-1742 significantly reduced tumor growth in RCC xenograft and organoid models. Mechanistically, piRNA-1742 regulates the stability of USP8 mRNA by binding directly to hnRNPU, which acts as a deubiquitinating enzyme that inhibits the ubiquitination of MUC12 and promotes the development of malignant RCC. Subsequently, nanotherapeutic systems loaded with piRNA-1742 inhibitors were found to effectively inhibit the metastasis and growth of RCC in vivo. Therefore, this study highlights the functional importance of piRNA-related ubiquitination in RCC and demonstrates the development of a related nanotherapeutic system, possibly contributing to the development of therapeutic approaches for RCC.