Systematic analysis of the expression profiles and prognostic significance of the MED gene family in renal clear cell carcinoma.

The mediator complex (MED) family is a contributing factor in the regulation of transcription and proliferation of cells, and is closely associated with the development of various types of cancer. However, the significance of the expression levels and prognostic value of MED genes in kidney renal clear cell carcinoma (KIRC) have rarely been reported. The present study analyzed the expression and prognostic potential of MED genes in KIRC. The Search Tool for the Retrieval of Interacting Genes/Proteins was used to construct the protein-protein interaction network (PPI), the Assistant for Clinical Bioinformatics database was used to perform correlation analysis, GEPIA 2 was utilized to draw the Kaplan-Meier plot and analyze prognostic significance and the Tumor Immune Estimation Resource was used to assess the association of MED genes with the infiltration of immune cells in patients with KIRC. A total of 30 MED genes were identified, and among these genes, 11 were selected for the creation of a prognostic gene signature based on the results of a LASSO Cox regression analysis. Furthermore, according to univariate and multivariate analyses, MED7, MED16, MED21, MED25 and MED29 may be valuable independent predictive biomarkers for the prognosis of individuals with KIRC. Furthermore, there were significant differences in the expression levels of MED7, MED21 and MED25 in KIRC among different tumor grades. Additionally, patients with KIRC with high transcription levels of MED7, MED21 and MED29 had considerably longer overall survival times. The expression levels of MED genes were also linked to the infiltration of several immune cells. Overall, MED genes may have potential significance in predicting the prognosis of patients with KIRC.

A CAIX Dual-Targeting Small-Molecule Probe for Noninvasive Imaging of ccRCC.

Carbonic anhydrase IX (CAIX), a zinc metal transmembrane protein, is highly expressed in 95% of clear cell renal cell carcinomas (ccRCCs). A positron emission tomography (PET) probe designed to target CAIX in nuclear medicine imaging technology can achieve precise positioning, is noninvasive, and can be used to monitor CAIX expression in lesions in real time. In this study, we constructed a novel acetazolamide dual-targeted small-molecule probe [68Ga]Ga-LF-4, which targets CAIX by binding to a specific amino acid sequence. After attenuation correction, the radiolabeling yield reached 66.95 ± 0.57% (n = 5) after 15 min of reaction and the radiochemical purity reached 99% (n = 5). [68Ga]Ga-LF-4 has good in vitro and in vivo stability, and in vivo safety and high affinity for CAIX, with a Kd value of 6.62 nM. Moreover, [68Ga]Ga-LF-4 could be quickly cleared from the blood in vivo. The biodistribution study revealed that the [68Ga]Ga-LF-4 signal was concentrated in the heart, lung, and kidney after administration, which was the same as that observed in the micro-PET/CT study. In a ccRCC patient-derived xenograft (PDX) model, the signal significantly accumulated in the tumor after administration, where it was retained for up to 4 h. After competitive blockade with LF-4, uptake at the tumor site was significantly reduced. The SUVmax of the probe [68Ga]Ga-LF-4 at the ccRCC tumor site was three times greater than that in the PC3 group with low CAIX expression at 30 min (ccRCC vs PC3:1.86 ± 0.03 vs 0.62 ± 0.01, t = 48.2, P < 0.0001). These results indicate that [68Ga]Ga-LF-4 is a novel small-molecule probe that targets CAIX and can be used to image localized and metastatic ccRCC lesions.

Landscape analysis of alternative splicing in kidney renal clear cell carcinoma and their clinical significance.

A growing number of studies reveal that alternative splicing (AS) is associated with tumorigenesis, progression, and metastasis. Systematic analysis of alternative splicing signatures in renal cancer is lacking. In our study, we investigated the AS landscape of kidney renal clear cell carcinoma (KIRC) and identified AS predictive model to improve the prognostic prediction of KIRC. We obtained clinical data and gene expression profiles of KIRC patients from the TCGA database to evaluate AS events. The calculation results for seven types of AS events indicated that 46276 AS events from 10577 genes were identified. Next, we applied Cox regression analysis to identify 5864 prognostic-associated AS events. We used the Metascape database to verify the potential pathways of prognostic-associated AS. Moreover, we constructed KIRC prediction systems with prognostic-associated AS events by the LASSO Cox regression model. AUCs demonstrated that these prediction systems had excellent prognostic accuracy simultaneously. We identified 34 prognostic associated splicing factors (SFs) and constructed homologous regulatory networks. Furthermore, in vitro experiments were performed to validate the favorable effect of SFs FMR1 in KIRC. In conclusion, we overviewed AS events in KIRC and identified AS-based prognostic models to assist the survival prediction of KIRC patients. Our study may provide a novel predictive signature to improve the prognostic prediction of KIRC, which might facilitate KIRC patient counseling and individualized management.

Identification of a signature of histone modifiers in kidney renal clear cell carcinoma.

Kidney renal clear cell carcinoma (KIRC) is a cancer that is closely associated with epigenetic alterations, and histone modifiers (HMs) are closely related to epigenetic regulation. Therefore, this study aimed to comprehensively explore the function and prognostic value of HMs-based signature in KIRC. HMs were first obtained from top journal. Then, the mRNA expression profiles and clinical information in KIRC samples were downloaded from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (Lasso) analysis were implemented to find prognosis-related HMs and construct a risk model related to the prognosis in KIRC. Kaplan-Meier analysis was used to determine prognostic differences between high- and low-risk groups. Immune infiltration and drug sensitivity analysis were also performed between high- and low-risk groups. Eventually, 8 HMs were successfully identified for the construction of a risk model in KIRC. The results of the correlation analysis between risk signature and the prognosis showed HMs-based signature has good prognostic value in KIRC. Results of immune analysis of risk models showed there were significant differences in the level of immune cell infiltration and expression of immune checkpoints between high- and low-risk groups. The results of the drug sensitivity analysis showed that the high-risk group was more sensitive to several chemotherapeutic agents such as Sunitinib, Tipifarnib, Nilotinib and Bosutinib than the low-risk group. In conclusion, we successfully constructed HMs-based prognostic signature that can predict the prognosis of KIRC.

Ultrasound contrast-enhanced radiomics model for preoperative prediction of the tumor grade of clear cell renal cell carcinoma: an exploratory study.

BACKGROUND: This study aims to explore machine learning(ML) methods for non-invasive assessment of WHO/ISUP nuclear grading in clear cell renal cell carcinoma(ccRCC) using contrast-enhanced ultrasound(CEUS) radiomics. METHODS: This retrospective study included 122 patients diagnosed as ccRCC after surgical resection. They were divided into a training set (n = 86) and a testing set(n = 36). CEUS radiographic features were extracted from CEUS images, and XGBoost ML models (US, CP, and MP model) with independent features at different phases were established. Multivariate regression analysis was performed on the characteristics of different radiomics phases to determine the indicators used for developing the prediction model of the combined CEUS model and establishing the XGBoost model. The training set was used to train the above four kinds of radiomics models, which were then tested in the testing set. Radiologists evaluated tumor characteristics, established a CEUS reading model, and compared the diagnostic efficacy of CEUS reading model with independent characteristics and combined CEUS model prediction models. RESULTS: The combined CEUS radiomics model demonstrated the best performance in the training set, with an area under the curve (AUC) of 0.84, accuracy of 0.779, sensitivity of 0.717, specificity of 0.879, positive predictive value (PPV) of 0.905, and negative predictive value (NPV) of0.659. In the testing set, the AUC was 0.811, with an accuracy of 0.784, sensitivity of 0.783, specificity of 0.786, PPV of 0.857, and NPV of 0.688. CONCLUSIONS: The radiomics model based on CEUS exhibits high accuracy in non-invasive prediction of ccRCC. This model can be utilized for non-invasive detection of WHO/ISUP nuclear grading of ccRCC and can serve as an effective tool to assist clinical decision-making processes.

ZEB family is a prognostic biomarker and correlates with anoikis and immune infiltration in kidney renal clear cell carcinoma.

BACKGROUND: Zinc finger E-box binding homEeobox 1 (ZEB1) and ZEB2 are two anoikis-related transcription factors. The mRNA expressions of these two genes are significantly increased in kidney renal clear cell carcinoma (KIRC), which are associated with poor survival. Meanwhile, the mechanisms and clinical significance of ZEB1 and ZEB2 upregulation in KIRC remain unknown. METHODS: Through the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, expression profiles, prognostic value and receiver operating characteristic curves (ROCs) of ZEB1 and ZEB2 were evaluated. The correlations of ZEB1 and ZEB2 with anoikis were further assessed in TCGA-KIRC database. Next, miRTarBase, miRDB, and TargetScan were used to predict microRNAs targeting ZEB1 and ZEB2, and TCGA-KIRC database was utilized to discern differences in microRNAs and establish the association between microRNAs and ZEBs. TCGA, TIMER, TISIDB, and TISCH were used to analyze tumor immune infiltration. RESULTS: It was found that ZEB1 and ZEB2 expression were related with histologic grade in KIRC patient. Kaplan-Meier survival analyses showed that KIRC patients with low ZEB1 or ZEB2 levels had a significantly lower survival rate. Meanwhile, ZEB1 and ZEB2 are closely related to anoikis and are regulated by microRNAs. We constructed a risk model using univariate Cox and LASSO regression analyses to identify two microRNAs (hsa-miR-130b-3p and hsa-miR-138-5p). Furthermore, ZEB1 and ZEB2 regulate immune cell invasion in KIRC tumor microenvironments. CONCLUSIONS: Anoikis, cytotoxic immune cell infiltration, and patient survival outcomes were correlated with ZEB1 and ZEB2 mRNA upregulation in KIRC. ZEB1 and ZEB2 are regulated by microRNAs.

An analysis of AURKB's prognostic and immunological roles across various cancers.

Aurora kinase B (AURKB), an essential regulator in the process of mitosis, has been revealed through various studies to have a significant role in cancer development and progression. However, the specific mechanisms remain poorly understood. This study, therefore, seeks to elucidate the multifaceted role of AURKB in diverse cancer types. This study utilized bioinformatics techniques to examine the transcript, protein, promoter methylation and mutation levels of AURKB. The study further analysed associations between AURKB and factors such as prognosis, pathological stage, biological function, immune infiltration, tumour mutational burden (TMB) and microsatellite instability (MSI). In addition, immunohistochemical staining data of 50 cases of renal clear cell carcinoma and its adjacent normal tissues were collected to verify the difference in protein expression of AURKB in the two tissues. The results show that AURKB is highly expressed in most cancers, and the protein level of AURKB and the methylation level of its promoter vary among cancer types. Survival analysis showed that AURKB was associated with overall survival in 12 cancer types and progression-free survival in 11 cancer types. Elevated levels of AURKB were detected in the advanced stages of 10 different cancers. AURKB has a potential impact on cancer progression through its effects on cell cycle regulation as well as inflammatory and immune-related pathways. We observed a strong association between AURKB and immune cell infiltration, immunomodulatory factors, TMB and MSI. Importantly, we confirmed that the AURKB protein is highly expressed in kidney renal clear cell carcinoma (KIRC). Our study reveals that AURKB may be a potential biomarker for pan-cancer and KIRC.

Identification of a lactate metabolism-related lncRNAs signature for predicting the prognosis in patients with kidney renal clear cell carcinoma.

Background: Lactate metabolism-related (LMR) long noncoding RNAs (lncRNAs) play significant roles in various cancers, but their impact on kidney renal clear cell carcinoma (KIRC) remains unclear. This study aimed to explore the value of LMR lncRNA and develop a risk model for KIRC. Methods: Data on KIRC patients were downloaded from The Cancer Genome Atlas (TCGA) database. LMR lncRNAs were identified by co-expression, univariate and multivariate analyses, and least absolute shrinkage selection operator (LASSO) regression analysis. Subsequently, a prognostic signature was constructed and its accuracy was verified. To predict the prognosis of KIRC effectively, we established a nomogram based on this information. Enrichment analysis, tumor mutational burden (TMB) analysis, immune status and the therapeutic sensitivities of KIRC patients were also investigated. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect the expression of lncRNAs. Results: We constructed and verified a predictive signature based on six LMR lncRNA (LINC00944, AC090772.3, Z83745.1, AP001267.3, AC092296.1, and AL162377.1) to assess the patient prognoses of KIRC. Survival analyses showed a more unfavorable outcome in high-risk patients (P<0.001). Enrichment analysis demonstrated that immune-related pathways were enriched in the high-risk group. Besides, patients classified by risk scores had distinguishable immune status, TMB, response to immunotherapy, and sensitivity to chemotherapy and targeted drugs. Conclusions: The LMR lncRNAs signature has significant implications for prognostic assessment and clinical treatment guidance in KIRC.

Comprehensive Pan-Cancer Analysis of Connexin 43 as a Potential Biomarker and Therapeutic Target in Human Kidney Renal Clear Cell Carcinoma (KIRC).

Background and Objectives: Connexin 43 (Cx43) is involved in the transfer of small signaling molecules between neighboring cells, thereby exerting a major influence on the initiation and progression of tumorigenesis. However, there is a lack of systematic research on Cx43 expression and its predictive role in clinical diagnosis and prognosis in pan-cancer. Materials and Methods: Several biological databases were used to evaluate the expression levels of GJA1 (encoding Cx43) and its diagnostic and prognostic significance in pan-cancer. We targeted kidney renal clear cell carcinoma (KIRC) and investigated the relationship between GJA1 expression and different clinical features of KIRC patients. Then, we performed cell-based experiments to partially confirm our results and predicted several proteins that were functionally related to Cx43. Results: The expression of GJA1 has a high level of accuracy in predicting KIRC. High GJA1 expression was remarkably correlated with a favorable prognosis, and this expression was reduced in groups with poor clinical features in KIRC. Cell experiments confirmed the inhibitory effects of increased GJA1 expression on the migratory capacity of human renal cancer (RCC) cell lines, and protein-protein interaction (PPI) analysis predicted that CDH1 and CTNNB1 were closely related to Cx43. Conclusions: GJA1 could be a promising independent favorable prognostic factor for KIRC, and upregulation of GJA1 expression could inhibit the migratory capacity of renal cancer cells.

Evaluating the Role of Neddylation Modifications in Kidney Renal Clear Cell Carcinoma: An Integrated Approach Using Bioinformatics, MLN4924 Dosing Experiments, and RNA Sequencing.

BACKGROUND: Neddylation, a post-translational modification process, plays a crucial role in various human neoplasms. However, its connection with kidney renal clear cell carcinoma (KIRC) remains under-researched. METHODS: We validated the Gene Set Cancer Analysis Lite (GSCALite) platform against The Cancer Genome Atlas (TCGA) database, analyzing 33 cancer types and their link with 17 neddylation-related genes. This included examining copy number variations (CNVs), single nucleotide variations (SNVs), mRNA expression, cellular pathway involvement, and methylation. Using Gene Set Variation Analysis (GSVA), we categorized these genes into three clusters and examined their impact on KIRC patient prognosis, drug responses, immune infiltration, and oncogenic pathways. Afterward, our objective is to identify genes that exhibit overexpression in KIRC and are associated with an adverse prognosis. After pinpointing the specific target gene, we used the specific inhibitor MLN4924 to inhibit the neddylation pathway to conduct RNA sequencing and related in vitro experiments to verify and study the specificity and potential mechanisms related to the target. This approach is geared towards enhancing our understanding of the prognostic importance of neddylation modification in KIRC. RESULTS: We identified significant CNV, SNV, and methylation events in neddylation-related genes across various cancers, with notably higher expression levels observed in KIRC. Cluster analysis revealed a potential trade-off in the interactions among neddylation-related genes, where both high and low levels of gene expression are linked to adverse prognoses. This association is particularly pronounced concerning lymph node involvement, T stage classification, and Fustat score. Simultaneously, our research discovered that PSMB10 exhibits overexpression in KIRC when compared to normal tissues, negatively impacting patient prognosis. Through RNA sequencing and in vitro assays, we confirmed that the inhibition of neddylation modification could play a role in the regulation of various signaling pathways, thereby influencing the prognosis of KIRC. Moreover, our results underscore PSMB10 as a viable target for therapeutic intervention in KIRC, opening up novel pathways for the development of targeted treatment strategies. CONCLUSION: This study underscores the regulatory function and potential mechanism of neddylation modification on the phenotype of KIRC, identifying PSMB10 as a key regulatory target with a significant role in influencing the prognosis of KIRC.

LASSO regression and WGCNA-based telomerase-associated lncRNA signaling predicts clear cell renal cell carcinoma prognosis and immunotherapy response.

OBJECTIVE: To investigate whether telomerase-associated lncRNA expression affects the prognosis and anti-tumor immunity of patients with renal clear cell carcinoma (ccRCC). METHODS: A series of analyses were performed to establish a prognostic risk model and validate its accuracy. Immune-related analyses were performed to assess further the association between immune status, tumor microenvironment, and prognostic risk models. RESULTS: Eight telomerase-associated lncRNAs associated with prognosis were identified and applied to establish a prognostic risk model. Overall survival was higher in the low-risk group. CONCLUSION: The established prognostic risk model has a good predictive ability for the prognosis of ccRCC patients and provides a new possible therapeutic target for ccRCC.

Clinical Implications of a Six-Protein Signature in Bone Metastasis of Renal Cell Carcinoma.

Bone metastases is prevalent from renal cell carcinoma (RCC) with poor quality of life and prognosis. Our previous proteomics analysis identified dysregulated proteins in the bone-tropism RCC cells. In this study, we further examined the clinical implications of these proteins using multiple clinical cohorts. We identified 6 proteins with significant upregulation in RCC tumor tissue in comparing to tumor adjacent normal tissue (p<0.05). High expression of these 6 protein-encoding genes significantly correlates with a poor survival in the TCGA-KIRC (Kidney renal clear cell carcinoma) cohort (log-rank test p=2.7e-05), and they all individually had a reverse-correlation with the gene expression of VHL and PBRM1 (p<0.001), and positive-correlation with the expression of VEGFA (p<0.001). Further gene set variation analysis (GSVA) revealed positive correlation with Th17 cells enrichment and negative CD8 T cell infiltration in the RCC tumor microenvironment. High expression of these 6 genes in pretreatment tumors favors longer overall survival (OS)(p=0.027) in anti-PDL1 treated patients (n=428). We treated one humeral metastases RCC patient with the anti-PDL1 antibody drug atezolizumab after examined the elevated expression of the 6 proteins in his nephrectomy tumor tissue, the tumor at the fracture site shrunk remarkably after four courses of treatment. These results altogether suggest a clinical implication of the 6-protein signature in RCC bone metastasis prognosis and response to immune-checkpoint inhibitor treatment.

Exploring the role of coagulation-related genes in renal cell carcinoma: Implications for tumor microenvironment and prognostic biomarkers.

PURPOSE: Clear cell renal cell carcinoma (ccRCC) frequently progresses to advanced stages due to tumor thrombus (TTs) formation. In this study, we aimed to investigate the role of coagulation-related pathway activation in the progression of ccRCC. METHODS: Consensus clustering was used to identify coagulation-related molecular clusters of ccRCC patients from The Cancer Genome Atlas Program (TCGA) database. The function of coagulation and its correlation with the immune microenvironment were investigated. Protein-protein interactions and differential expression analysis were used to identify the key gene, which was verified by external experiments. The coagulation-associated risk score was constructed by cox proportional hazards regression. RESULTS: Notable disparities were detected in immune characteristics, prognostic differentiation and drug sensitivity between two coagulation-related clusters. Through the integration of clinical stage significance and protein-protein interactions, the key gene MMP9 was screened and it was significantly correlated with CD4+T cells, CD8+T cells and Treg cells. A coagulation-related risk score prognostic model was developed in the Cancer Genome Atlas (TCGA) cohort for risk stratification and prognosis prediction. The prognostic predictive values of the coagulation-related risk score were further authenticated in both TCGA-KIRC and E-MTAB-1980 cohorts. CONCLUSION: There is an obvious correlation between the coagulation and the tumor microenvironment in ccRCC. As a key coagulation-related gene, MMP9 may promote the progression of renal cell carcinoma by influencing immune infiltration of CD8+T cells and Treg cells. Additionally, the risk score could be used as a durable prognostic biomarker, which could assist in clinical decision making for ccRCC patients.

zDHHC3-mediated S-palmitoylation of SLC9A2 regulates apoptosis in kidney clear cell carcinoma.

PURPOSE: Kidney clear cell carcinoma (KIRC) has a poor prognosis, high morbidity and mortality rates, and high invasion and metastasis rate, and effective therapeutic targets are lacking. zDHHC3 has been implicated in various cancers, but its specific role in KIRC remains unclear. METHODS: In this study, we performed a pan-cancer analysis, bioinformatics analysis, and cell experiment to detect the role of zDHHC3 in KIRC. RESULTS: zDHHC3 was significantly down-regulated in KIRC, and that its high expression was associated with favorable patient outcomes. We identified 202 hub genes that were most relevant to high zDHHC3 expression and KIRC, and found that they were involved mainly in ion transport and renal cell carcinoma. Among these hub genes, SLC9A2 was identified as a downstream gene of zDHHC3. zDHHC3 suppression led to decreased expression and S-palmitoylation of SLC9A2, which further inhibited the apoptosis of Caki-2 cells. CONCLUSION: Our findings suggest that zDHHC3 plays an important role in KIRC, due partly to its regulation of SLC9A2 S-palmitoylation. The targeting of the zDHHC3-SLC9A2 axis may provide a new option for the clinical treatment of KIRC.

Alternative Polyadenylation Regulatory Factors Signature for Survival Prediction in Kidney Renal Cell Carcinoma.

Background: Alternative polyadenylation (APA) plays a vital regulatory role in various diseases. It is widely accepted that APA is regulated by APA regulatory factors. Objective: Whether APA regulatory factors affect the prognosis of renal cell carcinoma remains unclear, and this is the main topic of this study. Methods: We downloaded the transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database. We used the Lasso regression system to construct an APA model for analyzing the relationship between common APA regulatory factors and renal cell carcinoma. We also validated our APA model using independent GEO datasets (GSE29609, GSE76207). Results: It was found that the expression levels of 5 APA regulatory factors (CPSF1, CPSF2, CSTF2, PABPC1, and PABPC4) were significantly associated with tumor gene mutation burden (TMB) score in renal clear cell carcinoma, and the risk score constructed using the expression level of 5 key APA regulatory factors could be used to predict the outcome of renal clear cell carcinoma. The TMB score is associated with the remodeling of the immune microenvironment. Conclusions: By identifying key APA regulatory factors in renal cell carcinoma and constructing risk scores for key APA regulatory factors, we showed that key APA regulators affect prognosis of renal clear cell carcinoma patients. In addition, the risk score level is associated with TMB, indicating that APA may affect the efficacy of immunotherapy through immune microenvironment-related genes. This helps us better understand the mRNA processing mechanism of renal clear cell carcinoma.

Elevated ADH5 expression suggested better prognosis in kidney renal clear cell carcinoma (KIRC) and related to immunity through single-cell and bulk RNA-sequencing.

BACKGROUND: Despite the rapid advances in modern medical technology, kidney renal clear cell carcinoma (KIRC) remains a challenging clinical problem in urology. Researchers urgently search for useful markers to break through the therapeutic conundrum due to its high lethality. Therefore, the study explores the value of ADH5 on overall survival (OS) and the immunology of KIRC. METHODS: The gene expression matrix and clinical information on ADH5 in the TCGA database were validated using external databases and qRT-PCR. To confirm the correlation between ADH5 and KIRC prognosis, univariate/multivariate Cox regression analysis was used. We also explored the signaling pathways associated with ADH5 in KIRC and investigated its association with immunity. RESULTS: The mRNA and protein levels showed an apparent downregulation of ADH5 in KIRC. Correlation analysis revealed that ADH5 was directly related to histological grade, clinical stage, and TMN stage (p < 0.05). Univariate and multivariate Cox regression analysis identified ADH5 as an independent factor affecting the prognosis of KIRC. Enrichment analysis looked into five ADH5-related signaling pathways. The results showed no correlation between ADH5 and TMB, TNB, and MSI. From an immunological perspective, ADH5 was found to be associated with the tumor microenvironment, immune cell infiltration, and immune checkpoints. Lower ADH5 expression was associated with greater responsiveness to immunotherapy. Single-cell sequencing revealed that ADH5 is highly expressed in immune cells. CONCLUSION: ADH5 could be a promising prognostic biomarker and a potential therapeutic target for KIRC. Besides, it was found that KIRC patients with low ADH5 expression were more sensitive to immunotherapy.

Neurogenesis-Associated Protein, a Potential Prognostic Biomarker in Anti-PD-1 Based Kidney Renal Clear Cell Carcinoma Patient Therapeutics.

The transketolase 1 gene (TKTL1) is an essential factor that contributes to brain development. Some studies have shown the influence of TKTL1 in cancers, but it has been rarely reported in kidney cancer. Furthermore, the role of TKTL1 in the prognosis and tumor infiltration of immune cells in various cancers, particularly kidney cancer, remains unknown. In this study, TKTL1 expression and its clinical characteristics were investigated using a variety of databases. TIMER was used to investigate the relationship between TKTL1 and immune infiltrates in various types of cancer. We also studied the relationship between TKTL1 expression and response to PD-1 blocker immunotherapy in renal cancer. We conducted TKTL1 agonists virtual screening from 13,633 natural compounds (L6020), implemented secondary library construction according to the types of top results, and then conducted secondary virtual screening for 367 alkaloids. Finally, in vitro assays of cell viability assays and colony formation assays were performed to demonstrate the pharmacological potency of the screening of TKTL1 agonists. Using these methods, we determined that TKTL1 significantly affects the prognostic potential in different types of kidney cancer patients. The underlying mechanism might be that the TKTL1 expression level was positively associated with devious immunocytes in kidney renal clear cell carcinoma (KIRC) rather than in kidney renal papillary cell carcinoma (KIRP) and kidney chromophobe (KICH). This recruitment may result from the up-regulation of the mTOR signaling pathway affecting T cell metabolism. We also found that TKTL1 may act as an immunomodulator in KIRC patients' response to anti-PD-1 therapy. Moreover, we also found that piperine and glibenclamide are potent agonists of TKTL1. We have demonstrated, in vitro, that piperine and glibenclamide can inhibit the proliferation and clone formation of Caki-2 cell lines by agonizing the expression of TKTL1. In summary, our discovery implies that TKTL1 may be a promising prognostic biomarker for KIRC patients who respond to anti-PD-1 therapy. Piperine and glibenclamide may be effective therapeutic TKTL1 agonists, providing a theoretical basis for the clinical treatment of kidney cancer.

PTK6: An emerging biomarker for prognosis and immunotherapeutic response in clear cell renal carcinoma (KIRC).

Kidney renal clear cell carcinoma (KIRC), one of the most prevalent form of kidney carcinoma, is highly aggressive cancer known for significant immune infiltration and high mortality rates. The absence of sensitivity to traditional therapy has spurred the search for new treatments. Protein Tyrosine Kinase 6 (PTK6) is implicated in promoting cancer growth, spread, and metastasis. Our review of The Cancer Genome Atlas database revealed PTK6 overexpression in KIRC, though its specific role in this cancer type was unclear. We investigated PTK6's cancer-promoting roles in KIRC using the database and confirmed our findings with patient-derived tissues. Our analysis showed that elevated PTK6 expression is linked to worse outcomes and higher levels of immune infiltration. It also correlates positively with neo-antigens (NEO) and DNA ploidy changes in KIRC. This research delves into PTK6's role in KIRC development, suggesting PTK6 as a possible biomarker for prognosis and treatment in KIRC.

Prediction of Fuhrman pathological grade of renal clear cell carcinoma based on CT texture analysis.

OBJECTIVE: To study the predictive performance of the imaging model based on the texture analysis of CT plain scan in distinguishing between low (grade I and II) and high (grade III and IV) of Fuhrman pathological grade of renal clear cell carcinoma. METHODS: The clinical data of 94 patients with ccRCC who underwent CT scan and were confirmed by biopsy or surgery in TCGA-KIRC public database were retrospectively analyzed. There were 32 cases of low-grade ccRCC and 62 cases of high-grade ccRCC. The patients were randomly divided into training set and verification set according to the proportion of 7:3 by stratified sampling method. The imaging characteristics of ccRCC were calculated in the plain CT images. Lasso regression was used to reduce the dimensionality of the imaging characteristics of the training set, and binary logistic regression was used to construct the prediction model. Bootstrap method was used to verify the training set model and the validation set model, and the area under the receiver operating characteristic (ROC) curve (AUC) was calculated respectively. RESULTS: Binary logistic regression showed that only imaging features were independent risk factors for predicting the Furhman classification of ccRCC. The predictive model was y = 1/[1 + exp (-z)], z = 1.274 × imaging risk score + 0.072. The results of bootstrap internal validation showed that the AUC of the training group was 0.961 (95% CI: 0.900-0.913). The Hosmer-Lemeshow goodness of fit test showed that the prediction model had a good calibration in the training group (P = 0.416). The AUC of prediction model in validation group was 0.731 (95% CI: 0.500-1.000). The Hosmer-Lemeshow goodness of fit test results showed that the prediction model had a good calibration in the validation group (P = 0.592). CONCLUSION: The model based on CT texture analysis has a good predictive effect in differentiating low-grade and high-grade ccRCC and can provide reference for the treatment and prognosis of patients.