Prognosis Of ccRCC Research Articles

VDRA downregulate β-catenin/Smad3 and DNA damage and repair associated with improved prognosis in ccRCC patients

The clear cell renal cell carcinoma (ccRCC) spotlighted the poorest survival, while chromophobe renal cell carcinoma (chRCC) was associated with the best survival. Earlier studies corroborated vitamin D receptor (VDR) was a promising molecular for improving the prognosis of RCC. In contrast to VDRA, the one of VDR isoforms, VDRB1 (VDR isoform B1) has an N-terminal extension of 50 amino acids and is less ligand-dependent. However, the functional differences between VDRA and VDRB1, and their roles in the prognosis of ccRCC and chRCC, have not been investigated. In the present study, we uncovered that the transcripts related to vitamin D pathway and cellular calcium signaling were effectively decreased in the context of ccRCC, yet failed to exert a comparable effect within chRCC. Specially, minimally levels of VDRA wherein kidneys of patients suffering from ccRCC predict shorter survival time. In addition, the protein expressions for β-catenin/Smad3 pathway and DNA damage and repair pathways were obviously impeded in VDRA-overexpressed ccRCC cells, yet this inhibitory effect was conspicuously absent in enable VDRB1 cells. Our results provide a new idea to improve the prognosis of ccRCC via VDRA upregulation.

Comprehensive analysis of m6 A methylome and transcriptome by Nanopore sequencing in clear cell renal carcinoma.

N6 -methyladenosine (m6 A) is the most prevalent epigenetic modification on eukaryotic messenger RNAs. Recent studies have focused on elucidating the key role of m6 A modification patterns in tumor progression. However, the relationship between m6 A and transcriptional regulation remains elusive. Nanopore technology enables the quantification of m6 A levels at each genomic site. In this study, a pair of tumor tissues and adjacent normal tissues from clear cell renal cell carcinoma (ccRCC) surgical samples were collected for Nanopore direct RNA sequencing. We identified 9644 genes displaying anomalous m6 A modifications, with 5343 genes upregulated and 4301 genes downregulated. Among these, 5224 genes were regarded as dysregulated genes, encompassing abnormal regulation of both m6 A modification and RNA expression. Gene Set Enrichment Analysis revealed an enrichment of these genes in pathways related to renal system progress and fatty acid metabolic progress. Furthermore, the χ2 test demonstrated a significant association between the levels of m6 A in dysregulated genes and their transcriptional expression levels. Additionally, we identified four obesity-associated genes (FTO, LEPR, ADIPOR2, and NPY5R) among the dysregulated genes. Further analyses using public databases revealed that these fourgenes were all related to the prognosis and diagnosis of ccRCC. This study introduced the novel approach of employing conjoint analysis of m6 A modification and RNA expression based on Nanopore sequencing to explore potential disease-related genes. Our work demonstrates the feasibility of the application of Nanopore sequencing technology in RNA epigenetic regulation research and identifies new potential therapeutic targets for ccRCC.

Upregulation of CCNB2 and a novel lncRNAs-related risk model predict prognosis in clear cell renal cell carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is the main type of renal cell carcinoma. Cyclin B2 (CCNB2) is a subtype of B-type cyclin that is associated with the prognosis of several cancers. This study aimed to identify the relationship between CCNB2 and progression of ccRCC and construct a novel lncRNAs-related model to predict prognosis of ccRCC patients.MethodsThe data were obtained from public databases. We identified CCNB2 in ccRCC using Kaplan–Meier survival analysis, univariate and multivariate Cox regression, and Gene Ontology analysis. External validation was then performed. The risk model was constructed based on prognostic lncRNAs by the LASSO algorithm and multivariate Cox regression. Receiver operating characteristics (ROC) curves were used to evaluate the model. Consensus clustering analysis was performed to re-stratify the patients. Finally, we analyzed the tumor-immune microenvironment and performed screening of potential drugs.ResultsCCNB2 associated with late clinicopathological parameters and poor prognosis in ccRCC and was an independent predictor for disease-free survival. In addition, CCNB2 shared the same expression pattern with known suppressive immune checkpoints. A risk model dependent on the expression of three prognostic CCNB2-related lncRNAs (SNHG17, VPS9D1-AS1, and ZMIZ1-AS1) was constructed. The risk signature was an independent predictor of ccRCC. The area under the ROC (AUC) curve for overall survival at 1-, 3-, 5-, and 8-year was 0.704, 0.702, 0.741, and 0.763. The high-risk group and cluster 2 had stronger immunogenicity and were more sensitive to immunotherapy.ConclusionCCNB2 could be an important biomarker for predicting prognosis in ccRCC patients. Furthermore, we developed a novel lncRNAs-related risk model and identified two CCNB2-related molecular clusters. The risk model performed well in predicting overall survival and immunological microenvironment of ccRCC.

Multiomics combined with single-cell analysis shows that mitophagy-related genes could accurately predict the prognosis of patients with clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a heterogeneous tumor that accounts for a large proportion of kidney cancer, It is prone to recurrence and metastasis, and has a high mortality rate. Although mitophagy is important for metastasis and the recurrence of various tumors, its effect on renal clear cell carcinoma is poorly understood. Mitophagy-related genes were obtained through the GeneCards database. We normalised the data from different sources by removing the batch effect. Next, we conducted a preliminary screening of mitophagy-related genes and obtained prognosis-related genes from differentially expressed genes. We constructed a prognostic model using least absolute shrinkage and selection operator (LASSO) regression with data from The Cancer Genome Atlas (TCGA) and GSE29609 datasets and validated it internally. International Cancer Genome Consortium (ICGC) and E-MTAB-1980 cohorts also provided double external validation. In addition, we combined multi-omics and single-cell data to comprehensively analyse mitophagy-related gene model signature (MRGMS). Combined with the mitophagy-related gene model (MRGM) score, we constructed a nomogram. Finally, we performed pathway enrichment analysis using a variety of methods. Multiomics and single-cell data analysis showed that the MRGMS is important for patients with ccRCC and is expected to become a new biomarker. The construction of a nomogram was conducive to accurately predicting patient survival. Mitophagy-related genes are important for predicting the prognosis of ccRCC and are conducive to the development of more personalised treatment plans for patients.

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC). In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression. A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group. The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

The Expression of Alamandine Receptor MrgD in Clear Cell Renal Cell Carcinoma Is Associated with a Worse Prognosis and Unfavorable Response to Antiangiogenic Therapy.

Renal cell carcinoma (RCC) ranks among the most prevalent malignancies in Western countries, marked by its notable heterogeneity, which contributes to an unpredictable clinical trajectory. The insufficiency of dependable biomarkers adds complexity to assessing this tumor progression. Imbalances of several components of the intrarenal renin-angiotensin system (iRAS) significantly impact patient prognoses and responses to first-line immunotherapies. In this study, we analyzed the immunohistochemical expression of the Mas-related G-protein-coupled receptor D (MrgD), which recognizes the novel RAS peptide alamandine (ALA), in a series of 87 clear cell renal cell (CCRCCs), 19 papillary (PRCC), 7 chromophobe (ChRCC) renal cell carcinomas, and 11 renal oncocytomas (RO). MrgD was expressed in all the renal tumor subtypes, with a higher mean staining intensity in the PRCCs, ChRCCs, and ROs. A high expression of MrgD at the tumor center and at the infiltrative front of CCRCC tissues was significantly associated with a high histological grade, large tumor diameter, local invasion, and locoregional node and distant metastasis. Patients with worse 5-year cancer-specific survival and a poorer response to antiangiogenic tyrosine-kinase inhibitors (TKIs) showed higher MrgD expression at the center of their primary tumors. These findings suggest a possible role of MrgD in renal carcinogenetic processes. Further studies are necessary to unveil its potential as a novel biomarker for CCRCC prognosis and response to frontline therapies.

SPCS, a Novel Classifier System Based on Senescence Axis Regulators Reveals Tumor Microenvironment Heterogeneity and Guides Frontline Therapy for Clear Cell Renal Carcinoma

RationaleThe emerging evidence suggested that senescence regulator genes were involved in multi cancers, which may be utilized as new targets for cancers. However, the dysregulation and clinical impact of senescence regulator genes in clear cell renal cell cancer (ccRCC) were still in foggy. MethodsUsing multiomics data from TCGA-KIRC and other datasets, we comprehensively investigated the function of senescence regulator genes in ccRCC. ccRCC patients could be remodeled into 2 significant different groups basing on senescence regulators expression: senescence-pattern cancer subtype1 (SPCS1) and subtype2 (SPCS2). We further explored clinical characteristics, functional analysis, tumor immune microenvironment, immunotherapy response, genomic mutation and drug sensitivity between the 2 subtypes. Besides, senescence-pattern related risk model was established to determine the patient's prognosis of ccRCC. Finally, the overview of MECP2 function was investigated in multi cancers. ResultsccRCC patients could be divided into SPCS1 (normal aging group) and SPCS2 (Aging disorder group). The 2 subtypes showed significant different clinical characteristics and biological process in ccRCC. SPCS2, an aggressive subtype, comprised higher clinical stage and worse prognosis of ccRCC patients. SPCS2 subtype indicated activated oncogenic signaling pathway and metabolic signatures to prompt cancer expansion. SPCS2 subgroup owned immunocompromised status, which induced immune dysfunction and low ICI therapy response. The genome-copy numbers of SPCS2, including arm-gain and arm-loss was significantly more frequent than SPCS1. In addition, the 2 subtypes argue contrasting drug sensitivity profiles in clinical specimens and matched cell lines. Finally, we constructed a prognostic risk model consisted of each subtype's leading biomarkers, which exerted a satisfied performance for ccRCC patients. ConclusionSenescence regulator-related signature could modify functional pathways and tumor immune microenvironment by genome mutation and pathway interaction. Senescence regulator-related molecular subtype strengthen the understanding of ccRCC’ characterization and guide clinical treatment. Targeting senescence regulators may be regard as a proper way in ccRCC.

Multidimensional comprehensive and integrated analysis of the potential function of TMEM25 in renal clear cell carcinoma with low expression status.

Transmembrane 25(TMEM25) stands out as a potential prognostic biomarker and therapeutic target in the realm of cancer, yet its precise mechanism of action within clear cell renal cell carcinoma (ccRCC) remains unclear. Gene expression data and clinically relevant information extracted from The Cancer Genome Atlas (TCGA) and Gene expression omnibus (GEO) databases unveil the expression patterns of TMEM25 within renal clear cell carcinoma, which reveals its prognostic and diagnostic significance. The protein expression data is available via the Human Protein Atlas (HPA) database. Further, qPCR experiments conducted on cells and tissues provide strong evidence of the gene's expression status. Additionally, they explore the correlations between TMEM25 expression and DNA methylation, gene mutations, immune cell infiltration, and drug sensitivity within this specific tumor context. At both the RNA and protein levels, TMEM25 displays a noteworthy downregulation in expression, which is consistently linked to an unfavorable prognosis. Receiver Operating Characteristic (ROC) curve analysis, univariate and multivariate Cox regression analyses confirmed the ability of TMEM25 to diagnose and determine prognosis in ccRCC. Its expression related closely with various immune cell types, immune checkpoints, immune inhibitors, and MHC molecules. Within ccRCC tissues, TMEM25 DNA methylation levels are observed to be elevated, and this upregulation is observed across various conditions. TMEM25 mutations also have an impact on the prognosis of ccRCC patients and the results of drug sensitivity analyses are useful for clinical decision-making. TMEM25 in ccRCC could potentially function as a tumor suppressor gene, holding substantial promise as a novel biomarker for diagnosing, treating, and prognosticating ccRCC patients.

Comprehensive analysis of a tryptophan metabolism-related model in the prognostic prediction and immune status for clear cell renal carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is characterized as one of the most common types of urological cancer with high degrees of malignancy and mortality. Due to the limited effectiveness of existing traditional therapeutic methods and poor prognosis, the treatment and therapy of advanced ccRCC patients remain challenging. Tryptophan metabolism has been widely investigated because it significantly participates in the malignant traits of multiple cancers. The functions and prognostic values of tryptophan metabolism-related genes (TMR) in ccRCC remain virtually obscure.MethodsWe employed the expression levels of 40 TMR genes to identify the subtypes of ccRCC and explored the clinical characteristics, prognosis, immune features, and immunotherapy response in the subtypes. Then, a model was constructed for the prediction of prognosis based on the differentially expressed genes (DEGs) in the subtypes from the TCGA database and verified using the ICGC database. The prediction performance of this model was confirmed by the receiver operating characteristic (ROC) curves. The relationship of Risk Score with the infiltration of distinct tumor microenvironment cells, the expression profiles of immune checkpoint genes, and the treatment benefits of immunotherapy and chemotherapy drugs were also investigated.ResultsThe two subtypes revealed dramatic differences in terms of clinical characteristics, prognosis, immune features, and immunotherapy response. The constructed 6-gene-based model showed that the high Risk Score was significantly connected to poor overall survival (OS) and advanced tumor stages. Furthermore, increased expression of CYP1B1, KMO, and TDO2 was observed in ccRCC tissues at the translation levels, and an unfavorable prognosis for these patients was also found.ConclusionWe identified 2 molecular subtypes of ccRCC based on the expression of TMR genes and constructed a prognosis-related model that may be used as a powerful tool to guide the prediction of ccRCC prognosis and personalized therapy. In addition, CYP1B1, KMO, and TDO2 can be regarded as the risk prognostic genes for ccRCC.

Comprehensive analysis of ETS1 expression and its prognostic value in clear cell renal cell carcinoma.

ETS1, a member of the large ETS domain family of transcription factors, plays a role in the progression of many types of carcinoma. ETS1 expression has been linked to a more favorable prognosis in renal cell carcinoma. The objective of this study was to assess the predictive significance of ETS1 in individuals suffering from clear cell renal cell carcinoma (ccRCC). The correlation between ETS1 expression and ccRCC was analyzed. Data on ETS1 and clinical information for ccRCC patients were obtained from the Cancer Genome Atlas database and analyzed using R software. Then, we presented validation results using RT-qPCR (quantitative reverse transcription PCR). The receiver operator characteristic (ROC) curves were generated using the pROC software package to determine the cutoff values for ETS1. Additionally, the ImmuneScore, StromalScore, and ESTIMATEScore were calculated using the ESTIMATES algorithm. The connection between ccRCC and ETS1 was investigated using enrichment analysis based on Gene Oncology and the Kyoto Encyclopedia of Genes and Genomes. The tumor immunity estimation resource (TIMER) and the integrated repository portal for tumor-immune system interactions (TISIDB) databases were utilized to analyze the association between ETS1 expression and immune cell infiltration in ccRCC. The impact of ETS1 on the survival of ccRCC patients was evaluated using the PrognoScan database. We evaluated the Tumor Mutation Burden (TMB) value between the two sets of samples with high and low ETS1 expression, as well as the differences in gene mutations between the two groups. The mRNA expression of ETS1 in ccRCC was higher compared to normal tissues. Results showed a significant positive correlation between elevated ETS1 expression levels and improved overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS), with a P < 0.05. Furthermore, high ETS1 expression levels were closely linked to early tumor stage and prolonged survival time. TMB in the ETS1-high expression group was significantly less than that in the ETS1-low expression group. Downregulation of ETS1 expression correlated with poor prognosis and immune infiltration in ccRCC, further suggesting that ETS1 may be a biomarker for better prognosis in ccRCC patients.

Ferroptosis-associated genes and compounds in renal cell carcinoma.

As the main type of renal cell carcinoma (RCC), clear cell RCC (ccRCC) is often associated with the deletion or mutation of the von Hippel Lindau (VHL) gene, enhancement of glucose and lipid metabolism, and heterogeneity of the tumor microenvironment. VHL alterations in RCC cells lead to the activation of hypoxia-inducible factors and their downstream target vascular endothelial growth factor, and to the reprogramming of multiple cell death pathways and metabolic weakness, including ferroptosis, which are associated with targeted therapy or immunotherapy. The changes in biological metabolites (e.g., iron and lipids) support ferroptosis as a potential therapeutic strategy for RCC, while iron metabolism and ferroptosis regulation have been examined as anti-RCC agents in numerous studies, and various ferroptosis-related molecules have been shown to be related to the metastasis and prognosis of ccRCC. For example, glutathione peroxidase 4 and glutaminase inhibitors can inhibit pyrimidine synthesis and increase reactive oxygen species levels in VHL-deficient RCC cells. In addition, the release of damage-associated molecular patterns by tumor cells undergoing ferroptosis also mediates antitumor immunity, and immune therapy can synergize with targeted therapy or radiotherapy through ferroptosis. However, Inducing ferroptosis not only suppresses cancer, but also promotes cancer development due to its potential negative effects on anti-cancer immunity. Therefore, ferroptosis and various tumor microenviroment-related molecules may co-occur during the development and treatment of RCC, and further understanding of the interactions, core targets, and related drugs of ferroptosis may provide new combination drug strategies for RCC treatment. Here we summarize the key genes and compounds on ferroptosis and RCC in order to envision future treatment strategies and to provide sufficient information for overcoming RCC resistance through ferroptosis.

CircSP3 encodes SP3-461aa to promote ccRCC progression via stabilizing MYH9 and activating the PI3K-Akt signaling pathway.

Clear cell renal cell carcinoma (ccRCC) is a primary kidney cancer with high aggressive phenotype and extremely poor prognosis. Accumulating evidence suggests that circular RNAs (circRNAs) play pivotal roles in the occurrence and development of various human cancers. However, the expression, clinical significance and regulatory role of circRNAs in ccRCC remain largely unclear. Here we report that circSP3 to be increased in tissues from ccRCC patients and ccRCC cells, and to positively correlate with ccRCC malignant features. Knockdown of circSP3 inhibits proliferation, triggers apoptosis, and reduces migration and invasion in different ccRCC cells in vitro. Correspondingly, circSP3 overexpression Promote ccRCC tumorigenicity in a mouse xenograft model. Mechanistically, circSP3 could bind with the ribosome to initiate the translation process to encodes a novel 461-amino acid peptide referred to as SP3-461aa, which protects the MYH9 protein from proteasomal degradation. SP3-461aa played a pivotal role in mediating the oncogenic effects of circSP3 by interacting with the MYH9 protein and activating the PI3K-Akt signaling pathway. These findings suggested that circSP3 plays an important role in ccRCC development and could be a potential biomarker for the treatment and prognosis of ccRCC.

The Value of Dual-Energy Computed Tomography-Based Radiomics in the Evaluation of Interstitial Fibers of Clear Cell Renal Carcinoma.

We investigated the potential of dual-energy computed tomography (DECT) radiomics in assessing cancer-associated fibroblasts in clear cell renal carcinoma (ccRCC). A retrospective analysis was conducted on 132 patients with ccRCC. The arterial and venous phase iodine-based material decomposition images (IMDIs), virtual non-contrast images, 70 keV, 100 keV, and 150 keV virtual monoenergetic images, and mixed energy images (MEIs) were obtained from the DECT datasets. On the Radcloud platform, radiomics feature extraction, feature selection, and model establishment were performed. Seven radiomics models were established using the support vector machine. The predictive performance was evaluated by utilizing receiver operating characteristic and the area under the curve (AUC) was calculated. Nomograms were constructed. The combined model demonstrated high efficiency in evaluating pseudocapsule thickness with AUC, specificity, and sensitivity of 0.833, 0.870, and 0.750, respectively in the validation set, surpassing those of other models. The precision, F1-score, and Youden index were also higher for the combined model. For evaluating the number of collagen fibers, the combined model exhibited the highest AUC (0.741) among all models, with a specificity of 0.830 and a sensitivity of 0.330. The AUC in the 150 kv model and IMDI model were slightly lower than those in the combined model (0.728 and 0.710, respectively), with corresponding sensitivity and specificity of 0.560/0.780 and 0.670/0.830. The nomogram exhibited that Rad-score had good prediction efficiency. DECT radiomics features have significant value in evaluating the interstitial fibers of ccRCC. The combined model of IMDI + MEI exhibits superior performance in assessing the thickness of the pseudocapsule, while the combined, 150 keV, and IMDI models demonstrate higher efficacy in evaluating collagen fiber number. Radiomics, combined with imaging features and clinical features, has excellent predictive performance. These findings offer crucial support for the clinical diagnosis, treatment, and prognosis of ccRCC and provide valuable insights into the application of DECT.

Identification of prognostic RNA editing profiles for clear cell renal carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer and currently lacks effective biomarkers. This research aims to analyze and identify RNA editing profile associated with ccRCC prognosis through bioinformatics and in vitro experiments. Transcriptome data and clinical information for ccRCC were retrieved from the TCGA database, and RNA editing files were obtained from the Synapse database. Prognostic models were screened, developed, and assessed using consistency index analysis and independent prognostic analysis, etc. Internal validation models were also constructed for further evaluation. Differential genes were investigated using GO, KEGG, and GSEA enrichment analyses. Furthermore, qPCR was performed to determine gene expression in human renal tubular epithelial cells HK-2 and ccRCC cells A-498, 786-O, and Caki-2. An RNA editing-based risk score, that effectively distinguishes between high and low-risk populations, has been identified. It includes CHD3| chr17:7815229, MYO19| chr17:34853704, OIP5-AS1| chr15:41590962, MRI1| chr19:13883962, GBP4| chr1:89649327, APOL1| chr22:36662830, FCF1| chr14:75203040 edited sites or genes and could serve as an independent prognostic factor for ccRCC patients. qPCR results showed significant up-regulation of CHD3, MYO19, MRI1, APOL1, and FCF1 in A-498, 786-O, and Caki-2 cells, while the expression of OIP5-AS1 and GBP4 was significantly down-regulated. RNA editing site-based prognostic models are valuable in differentiating between high and low-risk populations. The seven identified RNA editing sites may be utilized as potential biomarkers for ccRCC.

A Model to Predict Prognosis of Renal Cell Clear Cell Carcinoma Based on 3 Angiogenesis-related Long Non-coding RNAs.

Background: Tumor angiogenesis is closely related to the progression of clear cell renal cell carcinoma (ccRCC). Long non-coding RNAs (lncRNAs) regulating angiogenesis could be potential biomarkers for predicting ccRCC prognosis. With this study, we aimed to construct a prognostic model based on lncRNAs and explore its underlying mechanisms. Methods: RNA data and clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Angiogenesis-related genes (ARGs) were extracted from the Molecular Signatures database. Pearson correlation and LASSO and COX regression analyses were performed to identify survival-related AR-lncRNAs (sAR-lncRNAs) and construct a prognostic model. The predictive power of the prognostic model was verified according to Kaplan‒Meier curve, receiver operating characteristic (ROC) curve and nomogram analyses. The correlation between the prognostic model and clinicopathological characteristics was assessed via univariate and multivariate analyses. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was subsequently performed to elucidate the mechanisms of the sAR-lncRNAs. In vitro qPCR, immunohistochemistry, migration and invasion assays were conducted to confirm the angiogenic function of sAR-lncRNAs. Results: Three sAR-lncRNAs were used to construct a prognostic model. The model was moderately accurate in predicting 1- , 3- and 5-year ccRCC prognosis, and the risk score according to this model was closely related to clinicopathological characteristics such as T grade and T stage. A nomogram was constructed to precisely estimate the overall survival of ccRCC patients. KEGG enrichment analysis indicated that the MAPK and Notch pathways were highly enriched in high-risk patients. Additionally, we found that the expression of the lncRNAs AC005324.4 and AC104964.4 in the prognostic model was lower in ccRCC cell lines and cancer tissues than in the HK-2 cell line and paracancerous tissues, while the expression of the lncRNA AC087482.1 showed the opposite trend. In a coculture model, knockdown of lncRNA AC005324.4 and lncRNA AC104964.4 significantly promoted the migration and invasion of human umbilical vein endothelial cells (HUVECs), but siR-AC087482.1 transfection alleviated these effects. Conclusions: We constructed a prognostic model based on 3 sAR-lncRNAs and validated its value in clinicopathological characteristics and prognostic prediction of ccRCC patients, providing a new perspective for ccRCC treatment decision making.

Prognostic Value and Clinical Significance of Vesicular Transport-Related Genes in Clear Cell Renal Cell Carcinoma

Introduction: Vesicular transport (VT) has a complex relationship with tumor progression and immunity. But prognostic significance of VT in clear cell renal cell carcinoma (ccRCC) is unclear. Thus, we aimed to establish a prognostic model according to VT to predict overall survival of ccRCC patients. Methods: We used patient data from TCGA database and built a prognostic model with 13 VT-related genes (VTRGs) by differential expression analysis, LASSO regression, and univariate/multivariate Cox analysis. The model was validated internally and externally, and survival analysis and ROC curves depicted excellent predictive ability. Furthermore, higher modeled riskscores corresponded to more advanced tumor progression. To further understand the potential reasons for different prognoses in patients, we did enrichment analysis on differentially expressed genes identified by the model in risk groups. The expression levels and roles of SAA1 and KIF18B in ccRCC were verified by qRT-PCR and cell function experiments. Results: Humoral immune response and cAMP signaling pathway may be the biological processes and pathways leading to poor prognosis. Further analysis of immune microenvironment presented that ccRCC patients with poor prognoses had highly immune-infiltrated characteristics. We compared the drug response data of samples from different prognostic patients in the GDSC database and identified drug sensitivity differences associated with prognosis. Finally, we demonstrated that SAA1 and KIF18B could increase the proliferation, migration, and invasion ability of ccRCC cells using cellular experiments. Conclusion: In summary, we further revealed the importance of VTRGs in ccRCC prognosis.

Epigenetic dysregulated long non-coding RNAs in renal cell carcinoma based on multi-omics data and their influence on target drugs sensibility.

Epigenetic modifications play a crucial role in cancer development, and our study utilized public data to analyze which leads to the discovery of significant epigenetic abnormalities in lncRNAs, offering valuable insights into prognosis and treatment strategies for renal carcinoma. Public data were obtained from the Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) database. The analysis of the online public data was all completed in R software. We discovered a great number of epigenetic abnormalities of lncRNA in renal cancer, which is achieved by comparing the following modification and methylation of histone region changes on the promoter and enhancer of lncRNA: H3K27ac, H3K4me1, H3K4me3. As a result, 12 specific epigenetic disorders of lncRNA genes in renal cancer were identified. Finally, based on this lncRNA, we investigated the prognosis of renal cancer samples, among which 8 lncRNA can be seen as markers of prognosis in renal cancer, which had great prediction ability for ccRCC prognosis. Meanwhile, high risk score may pose response better to axitinib and nilotinib, but not sorafenib or sunitinib. Beyond, we observed an elevated level of risk score in immunotherapy non-responders. Further, biological enrichment and immuno-infiltration analysis was conducted to investigate the fundamental differences between patients categorized as high or low risk. Our research improves the understanding in the function of epigenetic dysregulated long non-coding RNAs in renal carcinoma.

Activating transcription factor 3 is a new biomarker correlation with renal clear cell carcinoma progression.

Background: Clear cell renal cell carcinoma (ccRCC) is the most invasive type of cancer, with a high risk of metastasis and recurrence. Therefore, there is an urgent need to identify novel prognostic predictors and therapeutic targets of ccRCC. Activating transcription factor 3 (ATF3), a tumor oncogene or repressor, has rarely been examined in ccRCC. In the present study, we comprehensively elucidate the prognostic value and potential functions of ATF3 in ccRCC.Methods: Several TCGA-based online databases were used to analyze ATF3 expression in ccRCC and determine ccRCC prognosis. The upstream-binding micro (mi) RNAs of ATF3 and long non-coding (lnc)RNAs were predicted using the StarBase database.Results: Analysis of several TCGA-based online databases showed that ATF3 expression is decreased in ccRCC, suggesting a significant association with the prognosis of patients with ccRCC. Furthermore, we found hsa-miR-221-3p to be potential regulatory miRNA of ATF3 in ccRCC. Prediction and analysis of the upstream lncRNAs indicated that PAXIP1-AS2 and OIP5-AS1 were the most potent upstream lncRNAs of the hsa-miR-221-3p/ATF3 axis in ccRCC. The results of the GO and KEGG analyses implied that ATF3 is likely involved in the regulation of apoptotic signaling in response to endoplasmic reticulum (ER) stress in ccRCC. Correlation analysis revealed a positive relationship between ATF3 expression and ER stress.Conclusions: Our in silico findings highlighted that ATF3 expression was low in ccRCC and negatively correlated with poor prognosis. Furthermore, PAXIP1-AS2 and the OIP5-AS1/hsa-miR-221-3p/ATF3 axis were identified as significant potential regulators of ER stress-mediated apoptosis in ccRCC.

The categorizations of vasculogenic mimicry in clear cell renal cell carcinoma unveil inherent connections with clinical and immune features.

Background: Clear cell renal cell carcinoma (ccRCC) stands as the prevailing variant kidney cancer in humans. Unfortunately, patients with disseminated RCC at diagnosis often have a diminished prognosis. Rapid tumor growth necessitates efficient blood supply for oxygen and nutrients, involving the circulation of blood from vessels to tumor tissues, facilitating tumor cell entry into the extracellular matrix. Vasculogenic mimicry (VM) significantly contributes to tumor growth and metastasis. Within this investigation, we identified vasculogenic mimicry-related genes (VMRGs) by analyzing data from 607 cases of kidney renal clear cell carcinoma (KIRC) in The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). These findings offer insights into ccRCC progression and metastasis. Method: We identified VMRGs-related subtypes using consistent clustering methods. The signature of the VMRGs was created using univariate Cox regression and LASSO Cox regression analyses. To evaluate differences in immune cell infiltration, we employed ssGSEA. Afterwards, we created an innovative risk assessment model, known as the VM index, along with a nomogram to forecast the prognosis of ccRCC. Additionally, we verified the expression of an important gene related to VM, peroxiredoxin 2 (PRDX2), in tissue samples. Furthermore, we assessed the sensitivity to drugs in various groups by utilizing the pRRophetic R package. Results: Significant predictors of survival rates in both high- and low-risk groups of KIRC patients were identified as VMRGs. The independent prognostic factors for RCC were confirmed by both univariate and multivariate Cox regression analyses, validating VMRG risk signatures. Differences were observed in drug sensitivity, immune checkpoint expression, and responses to immune therapy between patients classified into high- and low-VMRG-risk groups. Our nomograms consistently demonstrated precise predictive capabilities. Finally, we experimentally verified PRDX2 expression levels and their impact on prognosis. Conclusion: The signature predicts patient prognosis and therapy response, laying the groundwork for future clinical strategies in treating ccRCC patients.

CHEK2 is a potential prognostic biomarker associated with immune infiltration in clear cell renal cell carcinoma

Checkpoint kinase 2 (CHEK2) plays a crucial role in responding to DNA damage and is linked to diverse cancer types. However, its significance in the prediction of prognosis and impacts on the immune status of clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to identify the role of CHEK2 in prognosis and immune microenvironment of ccRCC. We analyzed transcriptome and clinicopathological data from the cancer genome atlas (TCGA) database and conducted functional enrichment analysis to explore molecular mechanisms. The relationship between CHEK2 and immune infiltration was evaluated, and drug sensitivity analysis was performed using the CellMiner database. The results showed that CHEK2 was an independent predictor of ccRCC prognosis and was closely associated with immune-related processes. Additionally, high expression of CHEK2 was linked to resistance to certain targeted drugs. These findings suggest that CHEK2 could serve as a biomarker for ccRCC, providing insights into tumor immune microenvironment alterations and immunotherapeutic response. Further investigation is needed to fully understand the potential of CHEK2 as a prognostic predictor and therapeutic target for ccRCC.