Prognosis Of Kidney Renal Clear Cell Carcinoma Research Articles

PIEZO1 mediates matrix stiffness-induced tumor progression in kidney renal clear cell carcinoma by activating the Ca2+/Calpain/YAP pathway

ObjectiveThe significance of physical factors in the onset and progression of tumors has been increasingly substantiated by a multitude of studies. The extracellular matrix, a pivotal component of the tumor microenvironment, has been the subject of extensive investigation in connection with the advancement of KIRC (Kidney Renal Clear Cell Carcinoma) in recent years. PIEZO1, a mechanosensitive ion channel, has been recognized as a modulator of diverse physiological processes. Nonetheless, the precise function of PIEZO1 as a transducer of mechanical stimuli in KIRC remains poorly elucidated. MethodsA bioinformatics analysis was conducted using data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) to explore the correlation between matrix stiffness indicators, such as COL1A1 and LOX mRNA levels, and KIRC prognosis. Expression patterns of mechanosensitive ion channels, particularly PIEZO1, were examined. Collagen-coated polyacrylamide hydrogel models were utilized to simulate varying stiffness environments and study their effects on KIRC cell behavior in vitro. Functional experiments, including PIEZO1 knockdown and overexpression, were performed to investigate the molecular mechanisms underlying matrix stiffness-induced cellular changes. Interventions in the Ca2+/Calpain/YAP Pathway were conducted to evaluate their effects on cell growth, EMT, and stemness characteristics. ResultsOur findings indicate a significant correlation between matrix stiffness and the prognosis of KIRC patients. It is observed that higher mechanical stiffness can facilitate the growth and metastasis of KIRC cells. Notably, we have also observed that the deficiency of PIEZO1 hinders the proliferation, EMT, and stemness characteristics of KIRC cells induced by a stiff matrix. Our study suggests that PIEZO1 plays a crucial role in mediating KIRC growth and metastasis through the activation of the Ca2+/Calpain/YAP Pathway. ConclusionThis study elucidates a novel mechanism through which the activation of PIEZO1 leads to calcium influx, subsequent calpain activation, and YAP nuclear translocation, thereby contributing to the progression of KIRC driven by matrix stiffness.

The signature genes of cuproptosis associates with tumor immune microenvironment and predicts prognosis in kidney renal clear cell carcinoma.

Cuproptosis is a new form of cell death, which has great potential to be developed in tumors treatment. Our study aimed to explore the predictive value of cuproptosis-related genes (CRGs) in various cancers, with a focus on kidney renal clear cell carcinoma (KIRC). A total of 9502 pan-cancer patients from TCGA cohort were enrolled. The relationships between CRGs and overall survival (OS) or disease-free survival (DFS) were analyzed. Gene Set Variation Analysis (GSVA) enrichment analysis was performed to explore the expression differences of CRGs. Multivariate Cox regression analysis was used to evaluate the association between GSVA scores and patient survival. KEGG and GO analyses were employed to identify the biological functions and pathways. The expression and prognostic characteristics of FDX1 were examined to evaluate the correlation between FDX1 and KIRC. Cell experiments were conducted to verify whether FDX1 was involved in cuproptosis of Caki-1 cells induced by Elesclomol. Positive cuproptosis signature genes(pos.cu.sig) exhibited the correlation with prognosis in KIRC, and all of these genes showed differential expression between KIRC and normal tissues. The GSVA score of pos.cu.sig was associated with excellent survival (HR=0.61, P<0.05), which can also serve as an independent prognostic factor for KIRC. There was a close correlation between pos.cu.sig and the tumor immune microenvironment in KIRC by KEGG and GO analysis. FDX1 expression was correlated with KIRC grade and positively associated with prognosis in KIRC patients. Compared with the control group, cell proliferation and migration were significantly inhibited, FDX1 expression was up-regulated, and Fe-S cluster protein content was decreased of Caki-1 cells after Elesclomol treatment. This study provides compelling evidence that cuproptosis is closely linked to the prognosis of KIRC. FDX1 holds promise as a viable biomarker and therapeutic target for assessing the effectiveness of tumor immunotherapy in KIRC.

Identification and expression of prognostic-related genes in kidney renal clear cell carcinoma and their possible regulatory mechanisms.

Many factors affect the prognosis of kidney renal clear cell carcinoma (KIRC). Early diagnosis can significantly improve the prognosis of KIRC patients. Therefore, a method needs to be developed to diagnose KIRC early, predict patient prognosis, and improve personalized treatments. The objective of this study is to utilize bioinformatics tools and public database resources to identify differentially expressed genes (DEGs) between renal cancer tissues and adjacent normal tissues, and to further screen for prognostic-related genes (PRGs) of KIRC. KIRC was studied using R language and FunRich software and several databases, including the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), the University of Alabama at Birmingham cancer data analysis Portal (UALCAN), and Tumor Immune Estimation Resource (TIMER) databases. Moreover, quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the expression of multiple genes in KIRC and adjacent normal tissues. There were substantial differences in immune cell infiltration between the KIRC and adjacent normal tissues in the GSE40435 and GSE46699 datasets. In addition, we screened multiple PRGs of KIRC by combining the GEO and TCGA data. The UALCAN database verified that some representative PRGs were differently expressed depending on the lymph node metastasis status, grade, and stage of KIRC. The qRT-PCR results confirmed the expression of the PRGs in KIRC and adjacent normal tissues. Through the GO and KEGG analyses, interaction analysis, and TIMER database, we found that the prognosis of KIRC was closely related to immune microenvironment and vascular endothelial growth factor (VEGF)/VEGF receptor (VEGFR) signaling. Our findings could contribute to the prognosis prediction of KIRC, the selection of personalized treatments, and the early diagnosis of KIRC.

Research on molecular characteristics of ADME-related genes in kidney renal clear cell carcinoma

Genes involved in drug absorption, distribution, metabolism, and excretion (ADME) are named ADME genes. However, the comprehensive role of ADME genes in kidney renal clear cell carcinoma (KIRC) remains unclear. Using the clinical and gene expression data of KIRC patients downloaded from The Cancer Genome Atlas (TCGA), ArrayExpress, and the Gene Expression Omnibus (GEO) databases, we cluster patients into two patterns, and the population with a relatively poor prognosis demonstrated higher level of immunosuppressive cell infiltration and higher proportion of glycolytic subtypes. Then, 17 ADME genes combination identified through the least absolute shrinkage and selection operator algorithm (LASSO, 1000 times) was utilized to calculate the ADME score. The ADME score was found to be an independent predictor of prognosis in KIRC and to be tightly associated with the infiltration level of immune cells, metabolic properties, tumor-related signaling pathways, genetic variation, and responses to chemotherapeutics. Our work revealed the characteristics of ADME in KIRC. Assessing the ADME profiles of individual patients can deepen our comprehension of tumor microenvironment (TME) features in KIRC and can aid in developing more personalized and effective therapeutic strategies.

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.

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

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. 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. 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. 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.

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

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. 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. 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. The LMR lncRNAs signature has significant implications for prognostic assessment and clinical treatment guidance in KIRC.

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

BackgroundDespite 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.MethodsThe 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.ResultsThe 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.ConclusionADH5 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.

Abstract 3681: Transcribed ultraconserved regions are associated with survival in multiple tumor types

Abstract Ultraconserved regions (UCRs), encompassing 481 genomic segments identical across humans, mice, and rats, and conserved in other species, are distributed across all human chromosomes except 21 and Y. Over 90% of these regions, termed transcribed ultraconserved regions (T-UCRs) are expressed in at least one human tissue. Although extensively studied in the context of cancer since their discovery, the prognostic significance of T-UCRs in a pan-cancer context has not been explored. For this, TCGA data extracted from the TANRIC platform was utilized, differential expression and survival analysis was performed on different tumor types. Our analysis revealed that approximately 300 UCRs were expressed in the majority of the 20 tumor tissues. Additionally, it was noted that all examined tumor tissues expressed a higher number of UCRs compared to their non-tumor counterparts. Through t-distributed stochastic neighbor embedding (t-SNE) analysis, UCR expression patterns could distinguish primary tumor origins and types. Regarding the prognostic value, we identified 100 (20,8%) T-UCRs associated with disease-specific survival (DSS) and 102 (21.2%) T-UCRs associated with progression-free interval (PFI). Those T-UCRs are particularly important in the survival outcomes of kidney renal clear cell carcinoma (KIRC) and low-grade gliomas (LGC). In addition, we verified that uc.44, uc.48, uc.135, uc.144, uc.153, uc.217, uc.255, uc.256, uc.344, uc.357, uc.390, uc.427 and uc.436 were associated with survival of more than one tumor. Particularly, uc.135 high expression was associated with poor prognosis in Kidney Renal Papillary Cell Carcinoma (KIRP) and LGC, and good prognosis in KIRC. Uc.135 are mapped on MECOM gene, coding an oncoprotein acting as transcriptional regulator and involved in apoptosis, proliferation, and cell differentiation. Our findings suggest that analysis of T-UCRs expression have the potential to be used as predictive biomarkers and can help in studying the mechanisms and specific roles of the highlighted T-UCRs in cancer. Citation Format: Ana Carolina Rodrigues, Douglas Adamoski, Daniela F. Gradia, Jaqueline Carvalho de Oliveira. Transcribed ultraconserved regions are associated with survival in multiple tumor types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3681.

Comprehensive Analysis Reveals Deoxyribonuclease 1 as a Potential Prognostic and Diagnostic Biomarker in Human Cancers.

Deoxyribonuclease 1 (DNASE1) is an important gene associated with several cancers, including liver, bladder, and gastric cancer. It has been linked to autoimmune illnesses, including systemic lupus erythematosus, which may lead to cancer formation. However, the role of DNASE1 in cancer has not beenstudied. We performed a pan-cancer analysis using bioinformatics tools, including Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA), and University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) databases, Kaplan-Meier plotter, and cBioPortal, to investigate the expression of DNASE1 across various cancers as well as its association with immune infiltration and genetic alterations. Public datasets were used to validate DNASE1 expression in kidney renal clear cell carcinoma (KIRC) and kidney papillary renal cell carcinoma (KIRP) samples. DNASE1 was found to be highly expressed in many cancers, such as bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), head and neck squamous cell carcinoma (HNSC), and was lowly expressed in other cancers, including KIRC, KIRP, and thyroid carcinoma (THCA). Additionally, TIMER results showed an association of DNASE1 with immune cell infiltration in KIRC and KIRP. Survival analysis indicated that high DNASE1 expression was associated with poor prognosis in KIRC. We also discovered that altered DNASE1 expression was related to poor prognosis in The Cancer Genome Atlas (TCGA) tumors. DNASE1 could potentially be used as a prognostic and diagnostic biomarker for KIRC and as a diagnostic biomarker for KIRP.

Comprehensive analysis of pain genes in prognosis of kidney renal clear cell carcinoma and tumor immunotherapy: A comprehensive bioinformatic study.

The effect of pain genes (NAV1, EHMT2, SP1, SLC6A4, COMT, OPRM1, OPRD1, CYP2D6, and CYP3A4) have not been reported previously in kidney renal clear cell carcinoma (KIRC) patients and thus we made a comprehensive analysis of pain genes in the prognosis of KIRC and tumor immunotherapy. In this study, TCGA, Kaplan-Meier plotter, Metascape, STRING, Human Protein Atlas, Single Cell Expression Atlas database, LinkedOmics, cBioPortal, MethSurv, CancerSEA, COSMIC database and R package (ggplot2, version 3.3.3) were used for comprehensive analysis of pain genes in KIRC. Pearson and Spearman correlation coefficients were for co-expression analysis. Immunotherapy and TISIDB database were used for tumor Immunotherapy. Representative pain genes (SP1, SLC6A4, COMT, OPRD1, CYP2D6, and CYP3A4) were statistically significant (p < 0.0001) in the prognosis of KIRC. Immunotherapy (anti-PD-1 therapy, anti-PD-L1 therapy, and anti-CTLA4 therapy) and immunomodulator (immunoinhibitor, immunostimulator, and MHC molecule) in KIRC were significant associated with pain genes (SP1, SLC6A4, COMT, OPRD1, CYP2D6, and CYP3A4), which were the important addition to clinical decision making for patients. Our study uncovered a mechanism for the effect of pain genes on KIRC outcome via the modulation of associated co-expression gene networks, gene variation, and tumor Immunotherapy.

Unveiling the hidden role of disulfidptosis in kidney renal clear cell carcinoma: a prognostic signature for personalized treatment.

The role of disulfidptosis in kidney renal clear cell carcinoma (KIRC) remains unknown. This study investigated disulfidptosis-related biomarkers for KIRC prognosis prediction and individualized treatment. KIRC patients were clustered by disulfidptosis profiles. Differential expression analysis, survival models, and machine learning were used to construct the disulfidptosis-related prognostic signature (DRPS). Characterizations of the tumor immune microenvironment, genetic drivers, drug sensitivity, and immunotherapy response were explored according to the DRPS risk stratification. Markers included in the signature were validated using single-cell, spatial transcriptomics, quantitative RT-qPCR, and immunohistochemistry. In the discovery cohort, we unveiled two clusters of KIRC patients that differed significantly in disulfidptosis regulator expressions and overall survival (OS). After multiple feature selection steps, a DRPS prognostic model with four features (CHAC1, COL7A1, FOXM1, SHOX2) was constructed and validated. Combined with clinical factors, the model demonstrated robust performance in the discovery and external validation cohorts (5-year AUC = 0.793 and 0.846, respectively). KIRC patients with high-risk scores are characterized by inferior OS, less tumor purity, and increased infiltrations of fibroblasts, M1 macrophages, and B cells. High-risk patients also have higher frequencies of BAP1 and AHNAK2 mutation. Besides, the correlation between the DRPS score and the chemotherapy-response signature indicated the potential effect of Gefitinib for high-risk patients. Among the signature genes, FOXM1 is highly expressed in cycling tumor cells and exhibits spatial aggregation, while others are expressed sparsely within tumor samples. The DRPS model enables improved clinical management and personalized KIRC therapy. The identified biomarkers and immune characteristics offer new mechanistic insight into disulfidptosis in KIRC.

Development and Validation of Anoiki-Related Lncrna Signature Prediction Model for KIRC Prognosis.

Various cancer types have been studied and understood using long noncoding RNA (lncRNA). Despite this, only a few studies have examined anoikis-related lncRNAs in kidney renal clear cell carcinoma (KIRC). As a result, this study evaluated a powerful prognostic model for KIRC patients based on anoikis-lncRNAs and identified potential biological targets. Anoikis-related lncRNAs associated with patient prognosis were identified using Pearson correlation, variance, and univariate Cox regression analyses. A predictive model that incorporated 4 anoikis-related lncRNAs has been constructed using the least absolute shrinkage and selection operator (LASSO) regression algorithm. The prognostic performance of the proposed model has also been assessed utilizing Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analyses. An ESTIMATE analysis was carried out on the low- as well as high-risk subtypes to evaluate immune cell infiltration status. Furthermore, CIBERSORT, TIMER, and QUANTISEQ along with other algorithms were applied for determining the infiltration status of numerous immune cells across both groups. In addition, immune checkpoint gene expression in both groups was also determined. Finally, drug sensitivity assays and in vitro experiments were performed to validate the results. A total of sixty-three lncRNAs associated with anoikis and KIRC prognosis were identified via univariate cox analysis, and four lncRNAs (Z99289.2, AC084876.1, LINC00460, and AC090337.2.) were selected as hub lncRNAs. A prognostic signature has been developed based on the expression levels and coefficiency of these four lncRNAs while establishing its efficacy in part and whole TCGA KIRC cohort. Furthermore, by using this risk signature, high- as well as low-risk KIRC patients could be distinguished more precisely it can predict patient outcomes as well. The survival predictions by the nomogram exhibited an absolute degree of concordance with actual situations. In vitro experiments verified that LINC00460 downregulation contributed to the growth inhibition of KIRC cell lines and promoted apoptosis of cancer cells. This study suggests that anoikis-related lncRNAs could serve as valuable prognostic markers for KIRC. Additionally, they may provide insight into future KIRC treatment options by reflecting on the situation of the kidney immune microenvironment.

Rho GTPase-activating protein 4 is upregulated in Kidney Renal Clear Cell Carcinoma and associated with poor prognosis and immune infiltration.

Kidney Renal Clear Cell Carcinoma (KIRC) is a malignant tumor that seriously threatens human health. Rho GTPase-activating protein 4 (ARHGAP4) plays an important role in the occurrence and development of tumors. The purpose of this study was to explore the role of ARHGAP4 in the progression of KIRC and its diagnostic and prognostic value. Multiple analytical methods and in vitro cell assays were used to explore the expression of ARHGAP4 and its value in the progression, diagnosis and prognosis of KIRC. The biological function of ARHGAP4 was studied by GO analysis and KEGG pathway analysis, and then the relationship between ARHGAP4 and immune infiltration was analyzed. The expression of ARHGAP4 was significantly up-regulated in KIRC. We found that the high expression of ARHGAP4 was related to the progression of KIRC and suggested a poor prognosis. Compared with normal tissues, ARHGAP4 had a better diagnostic value in KIRC. The biological function of ARHGAP4 was related to immunity, and its expression was also closely related to tumor immune infiltration and immune checkpoints. Our study demonstrated that ARHGAP4 may be a biomarker, which is related to the progression, diagnosis and prognosis of KIRC. Its biological functions are related to tumor immune infiltration.

A comprehensive analysis of essential meiotic endonuclease 1 to prognosis and immune infiltration in kidney renal clear cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common type of cancer, and its molecular pathogenesis is unclear. In this study, we investigated the prognostic value of essential meiotic endonuclease 1 (EME1) in kidney renal clear cell carcinoma (KIRC). We downloaded the RNA-Seq expression of 526 KIRC tissues and 72 normal tissues from the TCGA database and the corresponding clinical data. The gene expression profiles associated with four clear cell renal cell carcinomas were downloaded from the GEO database for analysis. The expression of EME1 in clear renal cell carcinoma and its correlation with the clinical baseline data were analyzed. Kaplan-Meier survival curve analysis was performed to assess the relationship between EME1 and patient survival. Enrichment analysis was performed to elucidate the possible functions of EME1. We also analyzed the relationship between the EME1 expression and immune infiltration through TIMER2.0 and TISIDB online databases as well as the relationship between EME1 and common immune checkpoints. EME1 was identified as a risk factor for overall survival in clear cell renal cell carcinoma with a hazard ratio of 3.201 (95% confidence interval: 2.430-4.215; p < 0.001). EME1 was highly expressed in KIRC compared to that in normal tissues (p < 0.001) and in the worse TNM stages and late stages (stage 3/4) (p < 0.001). High EME1 expression was strongly associated with the advanced T stage (p = 0.003), advanced N stage (p = 0.002), and advanced M stage (p = 0.006). Research data on KIRC were simultaneously collected and analyzed from the GEO database, including GSE40435, GSE53000, GSE68417, and GSE53757. EME1 predicted the survival status in KIRC patients (AUC = 0.62). We further established a nomogram including the correlation between the high and low EME1 expression, and EME1 was found to contribute to the prediction of the probability of patient survival with a c-index = 0.796. Kaplan-Meier analysis revealed a lower likelihood of survival with a high EME1 expression (p < 0.001). In addition, further bioinformatics analysis suggested that EME1 may be associated with the extent of immune infiltration in KIRC. An increased expression of EME1 in KIRC is thus associated with advanced clinicopathological features, possibly acting as a potential biomarker of poor prognosis in KIRC.

An integrated bioinformatic investigation of kallikrein gene family members in kidney renel cell carcinoma.

KLKs have been proved to be key regulators of the tumor microenvironment. In this study, we explored the potential of Kallikrein-related peptidases (KLKs) as clinical diagnostic and prognostic markers in patients with kidney renal clear cell carcinoma (KIRC) as well as their relationship with common immuno-inhibitor and immune cell infiltration in the tumor microenvironment to provide new targets and novel ideas for KIRC therapy. Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), UCSC Xena, Genotype-Tissue Expression (GTEx), Kaplan-Meier plotter, cBioPortal, STRING, GeneMANIA, and TISIDB were used to analyze the differential expression, prognostic value, gene changes, molecular interaction, and immune infiltration of KLKs in patients with KIRC. From the gene expression level, it can be determined that KLK1, KLK6, and KLK7 are differentially expressed in KIRC and normal tissues. From the perspective of clinical prognosis, KLK1, KLK13, and KLK14 are highly correlated with the clinical prognosis of KIRC. The expression of KLKs is regulated by various immunosuppressive agents, with KDR, PVRL2, and VTCN1 being the most significant. The expression of KLKs is significantly correlated with the infiltration of various immune cells, of which Eosinophils and Neutrophils are the most significant. KLK1, KLK6, KLK7, KLK13, and KLK14 have potential as diagnostic and prognostic biomarkers, among which KLK1 is the most significant. This study may provide detailed immune information and promising targets for KIRC immunotherapy to assist in designing new immunotherapies.

Cuproptosis gene-related, neural network-based prognosis prediction and drug-target prediction for KIRC.

Kidney renal clear cell carcinoma (KIRC), as a common case in renal cell carcinoma (RCC), has the risk of postoperative recurrence, thus its prognosis is poor and its prognostic markers are usually based on imaging methods, which have the problem of low specificity. In addition, cuproptosis, as a novel mode of cell death, has been used as a biomarker to predict disease in many cancers in recent years, which also provides an important basis for prognostic prediction in KIRC. For postoperative patients with KIRC, an important means of preventing disease recurrence is pharmacological treatment, and thus matching the appropriate drug to the specific patient's target is also particularly important. With the development of neural networks, their predictive performance in the field of medical big data has surpassed that of traditional methods, and this also applies to the field of prognosis prediction and drug-target prediction. The purpose of this study is to screen for cuproptosis genes related to the prognosis of KIRC and to establish a deep neural network (DNN) model for patient risk prediction, while also developing a personalized nomogram model for predicting patient survival. In addition, sensitivity drugs for KIRC were screened, and a graph neural network (GNN) model was established to predict the targets of the drugs, in order to discover potential drug action sites and provide new treatment ideas for KIRC. We used the Cancer Genome Atlas (TCGA) database, International Cancer Genome Consortium (ICGC) database, and DrugBank database for our study. Differentially expressed genes (DEGs) were screened using TCGA data, and then a DNN-based risk prediction model was built and validated using ICGC data. Subsequently, the differences between high- and low-risk groups were analyzed and KIRC-sensitive drugs were screened, and finally a GNN model was trained using DrugBank data to predict the relevant targets of these drugs. A prognostic model was built by screening 10 significantly different cuproptosis-related genes, the model had an AUC of 0.739 on the training set (TCGA data) and an AUC of 0.707 on the validation set (ICGC data), which demonstrated a good predictive performance. Based on the prognostic model in this paper, patients were also classified into high- and low-risk groups, and functional analyses were performed. In addition, 251 drugs were screened for sensitivity, and four drugs were ultimately found to have high sensitivity, with 5-Fluorouracil having the best inhibitory effect, and subsequently their corresponding targets were also predicted by GraphSAGE, with the most prominent targets including Cytochrome P450 2D6, UDP-glucuronosyltransferase 1A, and Proto-oncogene tyrosine-protein kinase receptor Ret. Notably, the average accuracy of GraphSAGE was 0.817 ± 0.013, which was higher than that of GAT and GTN. Our KIRC risk prediction model, constructed using 10 cuproptosis-related genes, had good independent prognostic ability. In addition, we screened four highly sensitive drugs and predicted relevant targets for these four drugs that might treat KIRC. Finally, literature research revealed that four drug-target interactions have been demonstrated in previous studies and the remaining targets are potential sites of drug action for future research.

CD72, a new immune checkpoint molecule, is a novel prognostic biomarker for kidney renal clear cell carcinoma

BackgroundThe incidence and mortality of clear cell carcinoma of the kidney increases yearly. There are limited screening methods and advances in treating kidney renal clear cell carcinoma (KIRC). It is important to find new biomarkers to screen, diagnose and predict the prognosis of KIRC. Some studies have shown that CD72 influences the development and progression of colorectal cancer, nasopharyngeal cancer, and acute lymphoid leukemia. However, there is a lack of research on the role of CD72 in the pathogenesis of KIRC. This study aimed to determine whether CD72 is associated with the prognosis and immune infiltration of KIRC, providing an essential molecular basis for the early non-invasive diagnosis and immunotherapy of KIRC.MethodsUsing TCGA, GTE, GEO, and ImmPort databases, we obtained the differentially expressed mRNA (DEmRNA) associated with the prognosis and immunity of KIRC patients. We used the Kruskal–Wallis test to identify clinicopathological parameters associated with target gene expression. We performed univariate and multivariate COX regression analyses to determine the effect of target gene expression and clinicopathological parameters on survival. We analyzed the target genes' relevant functions and signaling pathways through enrichment analysis. Finally, the correlation of target genes with tumor immune infiltration was explored by ssGSEA and Spearman correlation analysis.ResultsThe results revealed that patients with KIRC with higher expression of CD72 have a poorer prognosis. CD72 was associated with the Pathologic T stage, Pathologic stage, Pathologic M stage, Pathologic N stage, Histologic grade in KIRC patients, Laterality, and OS event. It was an independent predictor of the overall survival of KIRC patients. Functional enrichment analysis showed that CD72 was significantly enriched in oncogenic and immune-related pathways. According to ssGSEA and Spearman correlation analysis, CD72 expression was significantly associated with tumor immune cells and immune checkpoints.ConclusionOur study suggests that CD72 is associated with tumor immunity and may be a biomarker relevant to the diagnosis and prognosis of KIRC patients.

Prognostic model of kidney renal clear cell carcinoma using aging-related long noncoding RNA signatures identifies THBS1-IT1 as a potential prognostic biomarker for multiple cancers.

Aging is responsible for the main intrinsic triggers of cancers; however, the studies of aging risk factors in cancer animal models and cancer patients are rare and insufficient to be represented in cancer clinical trials. For a better understanding of the complex regulatory networks of aging and cancers, 8 candidate aging related long noncoding RNAs (CarLncs) identified from the healthy aging models, centenarians and their offsprings, were selected and their association with kidney renal clear cell carcinoma (KIRC) was explored by series of cutting edge analyses such as support vector machine (SVM) and random forest (RF) algorithms. Using data downloaded from TCGA and GTEx databases, a regulatory network of CarLncs-miRNA-mRNA was constructed and five genes within the network were screened out as aging related feature genes for developing KIRC prognostic models. After a strict filtering pipeline for modeling, a formula using the transcript per million (TPM) values of feature genes "LncAging_score = 0.008* MMP11 + 0.066* THBS1-IT1 + (-0.014)* DYNLL2 + (-0.030)* RMND5A+ 0.008* PEG10" was developed. ROC analysis and nomogram suggest our model achieves a great performance in KIRC prognosis. Among the 8 CarLncs, we found that THBS1-IT1 was significantly dysregulated in 12 cancer types. A comprehensive pan-cancer analysis demonstrated that THBS1-IT1 is a potential prognostic biomarker in not only KIRC but also multiple cancers, such as LUSC, BLCA, GBM, LGG, MESO, PAAD, STAD and THCA, it was correlated with tumor microenvironment (TME) and tumor immune cell infiltration (TICI) and its high expression was related with poor survival.

Fatty acid metabolism-related genes as a novel module biomarker for kidney renal clear cell carcinoma: Bioinformatics modeling with experimental verification

BackgroundsLipid metabolism reprogramming is a hallmark of cancer, however, the associations between fatty acid metabolism (FAM) and kidney renal clear cell carcinoma (KIRC) prognosis are still less investigated. MethodsThe gene expression and clinical data of KIRC were obtained from TCGA. Using Cox regression and LASSO regression, a novel prognostic risk score model based on FAM-related genes was constructed, and a nomogram for prediction of overall survival rate of patients with KIRC was proposed. The correlation between risk score and the immune cell infiltration, immune-related function and tumor mutation burden (TMB) were explored. Finally, a hub gene was extracted from the model, and RT-qPCR, Western blot, Immunohistochemical, EdU, Scratch assay and Transwell experiments were conducted to validate and decipher the biomarker role of the hub gene in KIRC theranostics. ResultsIn this study, a novel risk score model and a nomogram were constructed based on 20 FAM-related genes to predict the prognosis of KIRC patients with AUC>0.7 at 1-, 3-, and 5-years. Patients in different subgroups showed different phenotypes in immune cell infiltration, immune-related function, TMB, and sensitivity to immunotherapy. In particular, the hub gene in the model, i.e., ACADM, was significantly down-expressed in human KIRC samples, and the knockdown of OCLN promoted proliferation, migration and invasion of KIRC cells in vitro. ConclusionsIn this study, a novel risk score model and a module biomarker based on FAM-related genes were screened for KIRC prognosis. More clinical carcinogenic validations will be performed for future translational applications of the findings.