Identification of PI3K-AKT Pathway-Related Genes and Construction of Prognostic Prediction Model for ccRCC.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC), the predominate histological type of renal cell carcinoma (RCC), has been extensively studied, with poor prognosis as the stage increases. Research findings consistently indicated that the PI3K-Akt pathway is commonly dysregulated across various cancer types, including ccRCC. Targeting the PI3K-Akt pathway held promise as a potential therapeutic approach for treating ccRCC. Development and validation of PI3K-Akt pathway-related genes related biomarkers can enhance healthcare management of patients with ccRCC. PURPOSE: This study aimed to identify the key genes in the PI3K-Akt pathway associated with the diagnosis and prognosis of CCRCC using data mining from the Cancer Genome Atlas (TCGA) and Gene Expression Synthesis (GEO) datasets. METHODS: The purpose of this study is to use bioinformatics methods to screen data sets and clinicopathological characteristics associated with ccRCC patients. The exhibited significantly differential expressed genes (DEGs) associated with the PI3K-Akt pathway were examined by KEGG. In addition, Kaplan-Meier (KM) analysis used to estimate the survival function of the differential genes by using the UALCAN database and graphPad Prism 9.0. And exploring the association between the expression levels of the selected genes and the survival status and time of patients with ccRCC based on SPSS22.0. Finally, a multigene prognostic model was constructed to assess the prognostic risk of ccRCC patients. RESULTS: A total of 911 genes with common highly expressed were selected based on the GEO and TCGA databases. According to the KEGG pathway analysis, there were 42 genes enriched in PI3K-Akt signalling pathway. And seven of highly expressed genes were linked to a poor prognosis in ccRCC. And a multigene prognostic model was established based on IL2RG, EFNA3, and MTCP1 synergistic expression might be utilized to predict the survival of ccRCC patients. CONCLUSIONS: Three PI3K-Akt pathway-related genes may be helpful to identify the prognosis and molecular characteristics of ccRCC patients and to improve therapeutic regimens, and these risk characteristics might be further applied in the clinic.

NOP2-mediated 5-methylcytosine modification of APOL1 messenger RNA activates PI3K-Akt and facilitates clear cell renal cell carcinoma progression.

Background: By regulating the functions of multiple RNAs, 5-methylcytosine (m5C) RNA methylation, particularly mediated by NOP2, is involved in tumorigenesis and developments. However, the specific functions and potential mechanisms of m5C, especially involving NOP2, in clear-cell renal cell carcinoma (ccRCC), remain unclear. Methods: NOP2 expression in cell lines and patient tissues was detected using western blotting, quantitative real-time polymerase chain reaction (RT-qPCR), and immunohistochemistry. The biological effects of NOP2 on ccRCC cells were investigated through a series of in vitro and in vivo experiments. To explore the potential regulatory mechanisms by which NOP2 affects ccRCC progression, m5C bisulfite sequencing, RNA-sequencing, RNA immunoprecipitation and methylated RNA immunoprecipitation (RIP/MeRIP) RT-qPCR assay, luciferase reporter assay, RNA stability assay, and bioinformatic analysis were performed. Results: NOP2 expression was significantly upregulated in ccRCC tissues and was associated with poor prognosis. Moreover, loss-of-function and gain-of-function assays demonstrated that NOP2 altered ccRCC cell proliferation, migration, and invasion. Mechanistically, NOP2 stimulated m5C modification of apolipoprotein L1 (APOL1) mRNA, and m5C reader YBX1 stabilized APOL1 mRNA through recognizing and binding to m5C site in the 3'-untranslated regions. Silencing APOL1 expression inhibited ccRCC cell proliferation in vitro and tumor formation in vivo. Furthermore, NOP2/APOL1 affected ccRCC progression via the PI3K-Akt signaling pathway. Conclusion: NOP2 functions as an oncogene in ccRCC by promoting tumor progression through the m5C-dependent stabilization of APOL1, which in turn regulates the PI3K-Akt signaling pathway, suggesting a potential therapeutic target for ccRCC.

Integrating Bulk and Single-Cell RNA-Seq Data to Identify Prognostic Features Related to Activated Dendritic Cells in Clear-Cell Renal-Cell Carcinoma.

Dendritic cells (DCs) serve as key regulators in tumor immunity, with activated DCs potentiating antitumor responses through the secretion of pro-inflammatory cytokines and the expression of co-stimulatory molecules. Most current studies focus on the relationship between DC subgroups and clear-cell renal-cell carcinoma (ccRCC), but there is limited research on the connection between DCs and ccRCC from the perspective of immune activation. In this study, activated DC genes were identified in both bulk and single-cell RNA-seq data. A prognostic model related to activated DCs was constructed using univariate, multivariate Cox regression and LASSO regression. The prognostic model was validated in three external validation sets: GSE167573, ICGC, and E-MTAB-1980. The prognostic model consists of five genes, PLCB2, XCR1, IFNG, HLA-DQB2, and SMIM24. The expression of these genes was validated in tissue samples using qRT-PCR. Stratified analysis revealed that the prognostic model was able to better predict outcomes in advanced ccRCC patients. The risk scores were associated with tumor progression, tumor mutation burden, immune cell infiltration, and adverse outcomes of immunotherapy. Notably, there was a strong correlation between the expression of the five genes and the sensitivity to JQ1, a BET inhibitor. Molecular docking indicated high-affinity binding of the proteins encoded by these genes with JQ1. In conclusion, our study reveals the crucial role of activated DCs in ccRCC, offering new insights into predicting immune response, targeted therapy effectiveness, and prognosis for ccRCC patients.

Identification of HDAC10 as a candidate oncogene in clear cell renal carcinoma that facilitates tumor proliferation and metastasis.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) remains one of the most lethal urological malignancies even though a great number of improvements in diagnosis and management have achieved over the past few decades. Accumulated evidence revealed that histone deacetylases (HDACs) play vital role in cell proliferation, differentiation and apoptosis. Nevertheless, the biological functions of histone deacetylation modification related genes in ccRCC remains poorly understood. METHOD: Bulk transcriptomic data and clinical information of ccRCC patients were obtained from the TCGA database and collected from the Chinese PLA General Hospital. A total of 36 histone deacetylation genes were selected and studied in our research. Univariate cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, random forest (RF) analysis, and protein-protein interaction (PPI) network analysis were applied to identify key genes affecting the prognosis of ccRCC. The 'oncoPredict' algorithm was utilized for drug-sensitive analysis. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to explore the potential biological function. The ssGSEA algorithm was used for tumor immune microenvironment analysis. The expression levels of HDAC10 were validated by RT-PCR and immunohistochemistry (IHC). 5-ethynyl-2'-deoxyuridine (EdU assay), CCK-8 assay, cell transwell migration and invasion assay and colony formation assay were performed to detect the proliferation and invasion ability of ccRCC cells. A nomogram incorporating HDAC10 and clinicopathological characteristics was established to predict the prognosis of ccRCC patients. RESULT: Two machine learning algorithms and PPI analysis identified four histone deacetylation genes that have a significant association with the prognosis of ccRCC, with HDAC10 being the key gene among them. HDAC10 is highly expressed in ccRCC and its high expression is associated with poor prognosis for ccRCC patients. Pathway enrichment and the experiments of EdU staining, CCK-8 assay, cell transwell migration and invasion assay and colony formation assay demonstrated that HDAC10 mediated the proliferation and metastasis of ccRCC cells and involved in reshaping the tumor microenvironment (TME) of ccRCC. A clinically reliable prognostic predictive model was established by incorporating HDAC10 and other clinicopathological characteristics ( https://nomogramhdac10.shinyapps.io/HDAC10_Nomogram/ ). CONCLUSION: Our study found the increased expression of HDAC10 was closely associated with poor prognosis of ccRCC patients. HDAC10 showed a pro-tumorigenic effect on ccRCC and promote the proliferation and metastasis of ccRCC, which may provide new light on targeted therapy for ccRCC.

Comprehensive analysis of LD-related genes signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma.

BACKGROUND: Lipid droplets (LD) in renal clear cell carcinoma (ccRCC)play a crucial role in lipid metabolism and immune response modulation. The purpose of this study was to create a LD-related signature to predict prognosis and guide the immunotherapy and targeted therapy in ccRCC patients. METHODS: We conducted a comprehensive analysis using transcriptional profiles and clinical data obtained from The Cancer Genome Atlas (TCGA). LD-related genes were identified from existing literature and the GeneCards database, and differentially expressed genes were determined. Sequentially, we conducted Cox regression analysis and Lasso regression analysis, to establish a prognostic risk model. The performance of the risk model was evaluated using Kaplan-Meier (KM) analysis and time-dependent receiver operating characteristic (ROC) analysis. Additionally, gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, and immunophenoscore (IPS) algorithm were used to assess the tumor microenvironment (TME) and treatment response. RESULTS: We constructed a risk signature with four LD-related genes in the TCGA dataset, which could be an independent prognostic factor in ccRCC patients. Then, patients were classified into two risk groups and exhibited notable differences in overall survival (OS), progression-free survival (PFS), and TME characteristics. Furthermore, we developed a comprehensive nomogram based on clinical features, which demonstrated good prognostic predictive value. According to the results of GSEA analysis, immune-related pathways were found to be significantly enriched in the high-risk group. Additionally, the high-risk group displayed high levels of immune cell infiltration, TMB and IPS scores, indicating better efficacy of immune checkpoint inhibitors (ICIs). Finally, high-risk demonstrated reduced IC50 values compared to the low-risk counterpart for specific targeted and chemotherapeutic drugs, suggesting that the patients receiving these targeted drugs in high-risk group had better treatment outcomes. CONCLUSIONS: Our findings suggested that the LD-related gene signature could potentially predict the prognosis of ccRCC patients. Additionally, it showed promise for predicting responses to immunotherapy and targeted therapy in ccRCC patients. These insights might potentially have guided the clinical management of these patients, but further validation and broader data analysis are needed to confirm these preliminary observations.

Radiomics predicts the prognosis of patients with clear cell renal cell carcinoma by reflecting the tumor heterogeneity and microenvironment.

PURPOSE: We aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment. METHODS: The clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What's more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results. RESULTS: At multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868-0.931), 0.854(0.819-0.899) and 0.831(0.813-0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05). CONCLUSION: The combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment.

TUBA1C orchestrates the immunosuppressive tumor microenvironment and resistance to immune checkpoint blockade in clear cell renal cell carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) poses substantial treatment challenges, especially in advanced stages where the efficacy of immune checkpoint blockade (ICB) therapy varies significantly. Elevated expression of the oncogene TUBA1C has been correlated with poor prognosis in various cancers, however, its role in ccRCC is unclear, especially concerning ICB resistance. Methods: Single-cell analysis was used to examine gene expression variations in malignant cells post-ICB therapy. This included investigating TUBA1C expression across different ICB response groups and its relationship with CD274. A general module of action was identified through pan-cancer and pan-tissue analysis. TUBA1C expression and its association with clinical characteristics and prognosis was further validated. Multiple algorithms were employed to explore immune cell infiltration levels, and the DepMap database was utilized to assess gene dependency and mutation status in kidney cancer cell lines. The in silico knockout of TUBA1C was performed using deep learning model, complemented by immunohistochemical assays, clinical cohort and functional assays validations. Results: TUBA1C expression is elevated in malignant cells following ICB therapy and is correlated with ICB resistance in ccRCC. High TUBA1C expression activates PI3K/AKT pathway and is associated with increased infiltration of regulatory T cells and myeloid-derived suppressor cells, which contributes to an immunosuppressive microenvironment in ccRCC. Patients with high TUBA1C expression exhibit a greater tumor mutation burden and increased genetic variation, which causes a worse prognosis. Additionally, TUBA1C dependency and its effects were evident in kidney cancer cell lines, where mutations conferred resistance to anti-PD-L1 therapy. In silico knockout analyses indicated that treatment targeting TUBA1C shifted malignant cells to a state responsive to ICB therapy. Immunohistochemistry, RT-qPCR and clinical cohort validation further confirmed that TUBA1C expression was upregulated and contributed to poorer outcome in ccRCC. Finaly, wound healing and CCK-8 assays demonstrated the potent oncogenic function of TUBA1C. Conclusions: TUBA1C is a pivotal regulator in ccRCC, affecting both disease progression and the effectiveness of ICB therapy by fostering an immunosuppressive microenvironment mediated by the PI3K/AKT pathway. Additionally, TUBA1C holds promise, both as a prognostic biomarker and a therapeutic target, for enhancing responsiveness to ICB.

A systematic investigation of clear cell renal cell carcinoma using meta-analysis and systems biology approaches.

Renal cell carcinoma with clear cells (ccRCC) is the most frequent kind; it accounts for almost 70% of all kidney cancers. A primary objective of current research was to find genes that may be used in ccRCC gene therapy to understand better the molecular pathways underlying the disease. Based on PubMed microarray searches and meta-analyses, we compared overall survival and recurrence-free survival rates in ccRCC patients with those in healthy samples. The technique was followed by a KEGG pathway and Gene Ontology (GO) function analyses, both performed in conjunction with the approach. Tumor immune estimate and multi-gene biomarkers validation for clinical outcomes were performed at the molecular and clinical cohort levels. Our analysis included fourteen GEO datasets based on inclusion and exclusion criteria. A meta-analysis procedure, network construction using PPIs, and four significant gene identification standard algorithms indicated that 11 genes had the most important differences. Ten genes were upregulated, and one was downregulated in the study. In order to analyze RFS and OS survival rates, 11 genes expressed in the GEPIA2 database were examined. Nearly nine of eleven significant genes have been found to beinvolved in tumor immunity. Furthermore, it was found that mRNA expression levels of these genes were significantly correlated with experimental literature studies on ccRCCs, which explained these findings. This study identified eleven gene panels associated with ccRCC growth and metastasis, as well as their immune system infiltration.

N6-methyladenosine enhances the expression of TGF-ß-SMAD signaling family to inhibit cell growth and promote cell metastasis.

TGF-ß-SMAD signaling pathway plays an important role in the progression of various cancers. However, posttranscriptional regulation such as N6-methyladenosine (m6A) of TGF-ß-SMAD signaling axis remains incompletely understood. Here, we reveal that insulin like growth factor 2 mRNA binding protein 2 (IGF2BP2) is low expression as well as associated with poor prognosis in clear cell renal cell carcinoma (ccRCC) patients and inhibits proliferation as well as promotes metastasis of ccRCC cells. Mechanistically, IGF2BP2 systematically regulates TGF-ß-SMAD signaling family, including TGF-ß1/2, TGF-ßR1/2 and SMAD2/3/4, through mediating their mRNA stability in an m6A-dependent manner. Furthermore, the functional effects of IGF2BP2 on ccRCC cells is mediated by TGF-ß-SMAD signaling downstream effector SMAD4, which is identified three m6A sites in 5'UTR and CDS. Our study establishes IGF2BP2-TGF-ß-SMAD axis as a new regulatory effector in ccRCC, providing new insights for developing novel therapeutic strategies.

PRMT1 inhibition perturbs RNA metabolism and induces DNA damage in clear cell renal cell carcinoma.

In addition to the ubiquitous loss of the VHL gene in clear cell renal cell carcinoma (ccRCC), co-deletions of chromatin-regulating genes are common drivers of tumorigenesis, suggesting potential vulnerability to epigenetic manipulation. A library of chemical probes targeting a spectrum of epigenetic regulators is screened using a panel of ccRCC models. MS023, a type I protein arginine methyltransferase (PRMT) inhibitor, is identified as an antitumorigenic agent. Individual knockdowns indicate PRMT1 as the specific critical dependency for cancer growth. Further analyses demonstrate impairments to cell cycle and DNA damage repair pathways upon MS023 treatment or PRMT1 knockdown. PRMT1-specific proteomics reveals an interactome rich in RNA binding proteins and further investigation indicates significant widespread disruptions in mRNA metabolism with both MS023 treatment and PRMT1 knockdown, resulting in R-loop accumulation and DNA damage over time. Our data supports PRMT1 as a target in ccRCC and informs a mechanism-based strategy for translational development.

APOBEC family reshapes the immune microenvironment and therapy sensitivity in clear cell renal cell carcinoma.

Emerging evidence suggests that the APOBEC family is implicated in multiple cancers and might be utilized as a new target for cancer detection and treatment. However, the dysregulation and clinical implication of the APOBEC family in clear cell renal cell cancer (ccRCC) remain elusive. TCGA multiomics data facilitated a comprehensive exploration of the APOBEC family across cancers, including ccRCC. Remodeling analysis classified ccRCC patients into two distinct subgroups: APOBEC family pattern cancer subtype 1 (APCS1) and subtype 2 (APCS2). The study investigated differences in clinical parameters, tumor immune microenvironment, therapeutic responsiveness, and genomic mutation landscapes between these subtypes. An APOBEC family-related risk model was developed and validated for predicting ccRCC patient prognosis, demonstrating good sensitivity and specificity. Finally, the overview of APOBEC3B function was investigated in multiple cancers and verified in clinical samples. APCS1 and APCS2 demonstrated considerably distinct clinical features and biological processes in ccRCC. APCS1, an aggressive subtype, has advanced clinical stage and a poor prognosis. APCS1 exhibited an oncogenic and metabolically active phenotype. APCS1 also exhibited a greater tumor mutation load and immunocompromised condition, resulting in immunological dysfunction and immune checkpoint treatment resistance. The genomic copy number variation of APCS1, including arm gain and loss, was much more than that of APCS2, which may help explain the tired immune system. Furthermore, the two subtypes have distinct drug sensitivity patterns in clinical specimens and matching cell lines. Finally, we developed a predictive risk model based on subtype biomarkers that performed well for ccRCC patients and validated the clinical impact of APOBEC3B. Aberrant APOBEC family expression patterns might modify the tumor immune microenvironment by increasing the genome mutation frequency, thus inducing an immune-exhausted phenotype. APOBEC family-based molecular subtypes could strengthen the understanding of ccRCC characterization and guide clinical treatment. Targeting APOBEC3B may be regarded as a new therapeutic target for ccRCC.

[Renal eosinophilic vacuolated tumor: a clinicopathological analysis of seven cases].

Objective: To investigate the clinicopathological features and differential diagnosis of eosinophilic vacuolated tumor (EVT). Methods: Seven cases of EVT with characteristic morphology and unequivocal diagnosis from the Affiliated Hospital of Qingdao University (6 cases), Qingdao, China and the 971 Hospital of PLA Navy (1 case), Qingdao, China between January 2010 and December 2021 were subject to morphological and immunohistochemical analyses. Additionally, whole exome sequencing (WES) was performed in two cases. Twenty-two cases of renal oncocytoma (RO) and 17 cases of eosinophilic chromophobe renal cell carcinoma (eChRCC) diagnosed at the same time were used as controls. Results: Four males and three females with a mean age of 42 years (range: 29-61 years) were included in the study. The tumors were nodular and well-circumscribed, with sizes ranging from 1.5 to 4.5 cm. On cross-section, they appeared gray-red or gray-white, solid, and soft. Tumor cells were arranged in nests, solid sheets, and acinar or small vesicular structures. These cells exhibited eosinophilic cytoplasm with large, prominent clear vacuoles and round nuclei with prominent nucleoli. Perinuclear halos were focally present in four cases, while small tumor cells with sparse cytoplasm and hyperchromatic nuclei were seen in one case. No necrosis or mitosis was noted. Edematous stroma was detected in three cases. All tumors were positive for CD117 and Cathepsin K, but negative for vimentin and CK7. CK20 was positive in scattered individual cells, and Ki-67 positivity ranged from 1% to 4%. Point mutations in MTOR were identified in both patients who were subject to the molecular analysis. Statistical differences in the expression of Cathepsin K, CD10, S-100A1, and Cyclin D1 between EVT and RO (P<0.05) were significant, so were the differences in the expression of Cathepsin K, CD10, CK7 and claudin 7 between EVT and eChRCC (P<0.001). Seven patients were followed up for 4 to 96 months (mean, 50 months), with no recurrences or metastases. Conclusions: EVT is a rare renal tumor that shares morphological and immunophenotypic features with RO and eChRCC, and it is closely linked to the TSC/MTOR pathway. The presence of large prominent transparent vacuoles in eosinophilic cytoplasm along with conspicuous nucleoli is its key morphological characteristics. The use of combined immunohistochemical stains greatly aids in its diagnosis. Typically, the tumor exhibits indolent biological behaviors with a favorable prognosis.

Integration of proteomics and transcriptomics to construct a prognostic signature of renal clear cell carcinoma.

Background: Protein information is often replaced by RNA data in studies to understand cancer-related biological processes or molecular functions, and proteins of prognostic significance in Kidney clear cell carcinoma (KIRC) remain to be mined. Methods: The cancer genome atlas program (TCGA) data was utilized to screen for proteins that are prognostically significant in KIRC. Machine learning algorithms were employed to develop protein prognostic models. Additionally, immune infiltration abundance, somatic mutation differences, and immunotherapeutic responses were analyzed in various protein risk subgroups. Ultimately, the validation of protein-coding genes was confirmed by utilizing an online database and implementing quantitative real-time PCR (qRT-PCR). Results: The patients were divided into two risk categories based on prognostic proteins, and notable disparities in both overall survival (OS) and progression free interval (PFI) were observed between the two groups. The OS was more unfavorable in the high-risk group, and there was a noteworthy disparity in the level of immune infiltration observed between the two groups. In addition, the nomogram showed high accuracy in predicting survival in KIRC patients. Conclusion: In this research, we elucidated the core proteins associated with prognosis in terms of survival prediction, immunotherapeutic response, somatic mutation, and immune microenvironment. Additionally, we have developed a reliable prognostic model with excellent predictive capabilities.

Identification and Validation of Cytotoxicity-Related Features to Predict Prognostic and Immunotherapy Response in Patients with Clear Cell Renal Cell Carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) is a renal cortical malignancy with a complex pathogenesis. Identifying ideal biomarkers to establish more accurate promising prognostic models is crucial for the survival of kidney cancer patients. Methods: Seurat R package was used for single-cell RNA-sequencing (scRNA-seq) data filtering, dimensionality reduction, clustering, and differentially expressed genes analysis. Gene coexpression network analysis (WGCNA) was performed to identify the cytotoxicity-related module. The independent cytotoxicity-related risk model was established by the survival R package, and Kaplan-Meier (KM) survival analysis and timeROC with area under the curve (AUC) were employed to confirm the prognosis and effectiveness of the risk model. The risk and prognosis in patients suffering from ccRCC were predicted by establishing a nomogram. A comparison of the level of immune infiltration in different risk groups and subtypes using the CIBERSORT, MCP-counter, and TIMER methods, as well as assessment of drug sensitivity to conventional chemotherapeutic agents in risk groups using the pRRophetic package, was made. Results: Eleven ccRCC subpopulations were identified by single-cell sequencing data from the GSE224630 dataset. The identified cytotoxicity-related T-cell cluster and module genes defined three cytotoxicity-related molecular subtypes. Six key genes (SOWAHB, SLC16A12, IL20RB, SLC12A8, PLG, and HHLA2) affecting prognosis risk genes were selected for developing a risk model. A nomogram containing the RiskScore and stage revealed that the RiskScore contributed the most and exhibited excellent predicted performance for prognosis in the calibration plots and decision curve analysis (DCA). Notably, high-risk patients with ccRCC demonstrate a poorer prognosis with higher immune infiltration characteristics and TIDE scores, whereas low-risk patients are more likely to benefit from immunotherapy. Conclusions: A ccRCC survival prognostic model was produced based on the cytotoxicity-related signature, which had important clinical significance and may provide guidance for ccRCC treatment.

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

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

Single-cell RNA-seq reveals heterogeneity in metastatic renal cell carcinoma and effect of anti-angiogenesis therapy in the pancreas metastatic lesion.

Metastatic clear cell renal cell carcinoma has heterogenous tumor microenvironment (TME). Among the metastatic lesions, pancreas metastasis is rare and controversy in treatment approaches. Here, extensive primary and metastatic lesion samples were included by single-cell RNA-seq to decipher the distinct metastasis TME. The hypoxic and inflammatory TME of pancreas metastasis was decoded in this study, and the activation of PAX8-myc signaling, and metabolic reprogramming were observed. The active components including endothelial cells, fibroblasts and T cells were profiled. Meanwhile, we also evaluated the effect of anti-angiogenesis treatment in the pancreas metastasis patient. The potential mechanisms of pancreatic tropism, instability of genome, and the response of immunotherapy were also discussed in this work. Taken together, our findings suggest a clue to the heterogeneity in metastasis TME and provide evidence for the treatment of pancreas metastasis in renal cell carcinoma patients.

NMRK2 is an efficient diagnostic indicator for Xp11.2 translocation renal cell carcinoma.

Xp11.2 translocation renal cell carcinomas (tRCC) are a rare and highly malignant type of renal cancer, lacking efficient diagnostic indicators and therapeutic targets. Through the analysis of public databases and our cohort, we identified NMRK2 as a potential diagnostic marker for distinguishing Xp11.2 tRCC from kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) due to its specific upregulation in Xp11.2 tRCC tissues. Mechanistically, we discovered that TFE3 fusion protein binds to the promoter of the NMRK2 gene, leading to its upregulation. Importantly, we established RNA- and protein-based diagnostic methods for identifying Xp11.2 tRCC based on NMRK2 expression levels, and the diagnostic performance of our methods was comparable to a dual-color break-apart fluorescence in situ hybridization assay. Moreover, we successfully identified fresh Xp11.2 tRCC tissues after surgical excision using our diagnostic methods and established an immortalized Xp11.2 tRCC cell line for further research purposes. Functional studies revealed that NMRK2 promotes the progression of Xp11.2 tRCC by upregulating the NAD+/NADH ratio, and supplementation with ß-nicotinamide mononucleotide (NMN) or nicotinamide riboside chloride (NR), effectively rescued the phenotypes induced by the knockdown of NMRK2 in Xp11.2 tRCC. Taken together, these data introduce a new diagnostic indicator capable of accurately distinguishing Xp11.2 tRCC and highlight the possibility of developing novel targeted therapeutics. © 2024 The Pathological Society of Great Britain and Ireland.

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

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