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Background: Renal cell carcinoma (RCC) is one of the most frequent urological cancers globally that has a good prognosis in the early tumor stages. However, there is a poor prognosis in metastatic RCC patients. Therefore, it is needed to evaluate the molecular biology of RCC progression to introduce the efficient diagnostic and therapeutic markers in these patients. Long non-coding RNAs (lncRNAs) have key roles in regulation of molecular mechanisms during RCC progression. In the present study, we assessed the levels of LINC00365 expressions in RCC patients to suggest that as a tumor marker in these patients., Methods: Fifty fresh RCC tumor tissues and their normal margins were collected to assess the levels of LINC00365 expressions and probable correlations with clinicopathological features of RCC patients., Results: There was significant LINC00365 up regulation in females compared with males (p=0.050). Among the RCC patients with total nephrectomy, there was a significant LINC00365 up regulation in advanced stage compared with primary stage tumors (p=0.035). RCC patients older than 60 years old who were undergone the total nephrectomy had also significant LINC00365 up regulation compared with RCC patients younger than 60 years old (p=0.039)., Conclusions: given the significant increase in LINC00365 expression in advanced stage RCC tumors and patients over 60 years old who had total nephrectomy; it could serve as a useful diagnostic marker in screening programs for old high-risk individuals. It was also noticed that female RCC patients had elevated levels of LINC00365 expressions in their tumor samples, suggesting its potential use as a gender-specific diagnostic marker for high-risk females. Nevertheless, evaluating the levels of LINC00365 in serum samples of RCC patients is necessary to suggest that as a reliable diagnostic marker in clinical settings., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier GmbH. All rights reserved.)

Aggressive clear cell renal cell carcinoma (ccRCC) has a bad prognosis. We seek new ccRCC biomarkers for diagnosis and treatment. We used exoRBase and The Cancer Genome Atlas Database to compare DEmRNAs, DEmiRNAs, DElncRNAs, and DEcircRNAs in ccRCC and normal renal tissues. CircRNAs and circRNAs targeting microRNAs (miRNAs) were anticipated and taken intersections, and several databases assessed the targeted link between common miRNAs and messenger RNAs (mRNAs). The Cancer Genome Atlas database was used to create a predictive mRNA signature that was validated in E-MTAB-1980. Finally, we examined competing endogenous RNA network miRNAs and long noncoding RNAs for ccRCC predictive biomarkers using overall survival analysis. We built the first competing endogenous RNA regulation network of circRNA-lncRNA-miRNA-mRNA and found that it substantially correlates with ccRCC prognosis. We unveiled ccRCC's posttranscriptional regulation mechanism in greater detail. Our findings identified novel biomarkers for ccRCC diagnosis, therapy, and prognosis., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

The methyltransferase Wilms' tumor 1-associated protein (WTAP) has been reported to be dysregulated in various tumors. However, its role in renal cell carcinoma (RCC) remains elusive. Here, we explored whether WTAP was upregulated in RCC specimens compared to normal tissues. Functionally, WTAP promoted RCC cell proliferation and metastasis in vivo and in vitro. Mechanistically, WTAP act as an N6-methyladenosine transferase to regulate the m 6 A modification of long noncoding RNA TEX41. Then, the upregulated m 6 A modification destabilized TEX41 in a YTHDF2-dependent manner. Furthermore, TEX41 interacted with the SUZ12 protein and increased the histone methyltransferase activity of SUZ12, resulting in HDAC1 silencing. Totally, our study demonstrated the oncogenic the role of WTAP/TEX41/SUZ12/HDAC1 axis in RCC progression., (© 2024. The Author(s).)

Competing Interests: Disclosure The authors report no financial or any other conflicts of interest in this work.

Long noncoding RNAs (lncRNAs) serve pivotal roles in cancer biology. The present study investigated the oncogenic roles of lncRNAs in renal cell carcinoma (RCC) and their potential as prognostic biomarkers. The lncRNA leucine‑rich repeat containing 75 A‑antisense RNA1 (LRRC75A‑AS1) was identified through lncRNA microarray as a potential lncRNA that may predict the efficacy of immune checkpoint inhibitor therapy and cancer progression in RCC. The present study subsequently assessed the expression of LRRC75A‑AS1 in 212 patients with clear cell RCC (ccRCC) who underwent nephrectomy, and performed in vitro functional analysis of LRRC75A‑AS1 in RCC cell lines. Additionally, the interactions between LRRC75A‑AS1, microRNA (miR)‑370‑5p and ADAMTS5 were explored. LRRC75A‑AS1 was revealed to be significantly upregulated in ccRCC tissues compared with in adjacent normal tissues, and high LRRC75A‑AS1 expression was associated with poor prognosis, including lower progression‑free survival, in patients with RCC. The knockdown of LRRC75A‑AS1 in RCC cell lines resulted in reduced cell proliferation and invasion, highlighting its role in promoting tumorigenesis. Furthermore, the interaction among LRRC75A‑AS1, miR‑370‑5p and ADAMTS5 was suggested as a regulatory mechanism underlying RCC progression. These findings indicated that LRRC75A‑AS1 may function as an oncogene in RCC, promoting cell proliferation and invasion. Its significant upregulation in ccRCC tissues and association with poor prognosis underscore its potential as a prognostic biomarker for RCC. Understanding the regulatory interactions among LRRC75A‑AS1, miR‑370‑5p and ADAMTS5 may provide new insights into the molecular mechanisms underlying RCC and facilitate the identification of novel therapeutic targets.

Research works suggested the role of long non-coding RNAs (lncRNAs) in pathogenesis of clear cell renal cell carcinoma (ccRCC). lncRNA HAGLR is studied in several malignancies, but not in ccRCC. From The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset, we analyzed molecular alterations of HAGLR and constructed a competitive endogenous RNA (ceRNA) network with related miRNAs and mRNAs. Gene Ontology analysis was done to identify important pathways enriched with HAGLR recovered mRNAs. Clinical importance of HAGLR and related mRNAs was assessed and, the impact of selected mRNA-encoding genes on tumor immune infiltration was studied using TIMER. HAGLR expression was reduced in ccRCC than in normal kidneys, and correlated significantly with gene promoter methylation. Low HAGLR level in tumors showed diagnostic potency, and was associated with clinicopathological parameters (stage/grade/metastasis) and poor patient survival. The HAGLR-associated ceRNA network constituted 13 miRNAs and 23 mRNAs differentially expressed in the TCGA-KIRC dataset. From HAGLR recovered mRNA-encoding genes, we developed a 5-gene (PAQR5, ARHGAP24, HABP4, PDLIM5, and RPS6KA2) prognostic signature in the training dataset and validated it in testing as well as entire datasets. The expression level of signature genes showed negative correlation with tumor infiltration of immune cells having adverse impact on ccRCC prognosis and also with tumor derived chemokines facilitating the infiltration. In conclusion, HAGLR seemed to play a tumor suppressive role in ccRCC. HAGLR and associated gene signature may have implementation in improving existing prognostic measure and developing effective immunotherapeutic strategies for ccRCC., Competing Interests: Declarations Conflict of interest The authors declare that they have no competing or financial interests. Ethics approval and consent to participate Not applicable., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Renal cell carcinoma (RCC) ranks among the leading causes of cancer-related mortality. Despite extensive research, the precise etiology and progression of RCC remain incompletely elucidated. Long noncoding RNA (lncRNA) has been identified as competitive endogenous RNA (ceRNA) capable of binding to microRNA (miRNA) sites, thereby modulating the expression of messenger RNAs (mRNA) and target genes. This regulatory network is known to exert a pivotal influence on cancer initiation and progression. However, the specific role and functional significance of the lncRNA-miRNA-mRNA ceRNA network in RCC remain poorly understood. The RCC transcriptome data was obtained from the gene expression omnibus database. The identification of differentially expressed long noncoding RNAs (DElncRNAs), differentially expressed miRNAs, and differentially expressed mRNAs (DEmRNAs) between RCC and corresponding paracancer tissues was performed using the "Limma" package in R 4.3.1 software. We employed a weighted gene co-expression network analysis to identify the key DElncRNAs that are most relevant to RCC. Subsequently, we utilized the encyclopedia of RNA interactomes database to predict the interactions between these DElncRNAs and miRNAs, and the miRDB database to predict the interactions between miRNAs and mRNAs. Therefore, key DElncRNAs were obtained to verify the expression of their related genes in the The Cancer Genome Atlas database and to analyze the prognosis. The construction of RCC-specific lncRNA-miRNA-mRNA ceRNA network was carried out using Cytoscape 3.7.0. A total of 286 DElncRNAs, 56 differentially expressed miRNAs, and 2065 DEmRNAs were identified in RCC. Seven key DElncRNAs (GAS6 antisense RNA 1, myocardial infarction associated transcript, long intergenic nonprotein coding RNA 921, MMP25 antisense RNA 1, Chromosome 22 Open Reading Frame 34, MIR34A host gene, MIR4435-2 host gene) were identified using weighted gene co-expression network analysis and encyclopedia of RNA interactomes databases. Subsequently, a network diagram comprising 217 nodes and 463 edges was constructed based on these key DElncRNAs. The functional analysis of DEmRNAs in the ceRNA network was conducted using Kyoto Encyclopedia of Genes and Genomes and gene ontology. We constructed RCC-specific ceRNA networks and identified the crucial lncRNAs associated with RCC using bioinformatics analysis, which will help us further understand the pathogenesis of this disease., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Kidney renal clear cell carcinoma (KIRC) represents a significant proportion of renal cell carcinomas and is characterized by high aggressiveness and poor prognosis despite advancements in immunotherapy. Disulfidptosis, a novel cell death pathway, has emerged as a critical mechanism in various cellular processes, including cancer. This study leverages machine learning to identify disulfidptosis-related long noncoding RNAs (DRlncRNAs) as potential prognostic biomarkers in KIRC, offering new insights into tumor pathogenesis and treatment avenues., Results: Our analysis of data from The Cancer Genome Atlas (TCGA) led to the identification of 431 DRlncRNAs correlated with disulfidptosis-related genes. Five key DRlncRNAs (SPINT1-AS1, AL161782.1, OVCH1-AS1, AC131009.3, and AC108673.3) were used to develop a prognostic model that effectively distinguished between low- and high-risk patients with significant differences in overall survival and progression-free survival. The low-risk group had a favorable prognosis associated with a protective immune microenvironment and a better response to targeted drugs. Conversely, the high-risk group displayed aggressive tumor features and poor immunotherapy outcomes. Validation through qRT‒PCR confirmed the differential expression of these DRlncRNAs in KIRC cells compared to normal kidney cells, underscoring their potential functional significance in tumor biology., Conclusions: This study established a robust link between disulfidptosis-related lncRNAs and patient prognosis in KIRC, underscoring their potential as prognostic biomarkers and therapeutic targets. The differential expression of these lncRNAs in tumor versus normal tissue further highlights their relevance in KIRC pathogenesis. The predictive model not only enhances our understanding of KIRC biology but also provides a novel stratification tool for precision medicine approaches, improving treatment personalization and outcomes in KIRC patients., (© 2024. The Author(s).)

Background: Clear cell renal carcinoma is a common urological malignancy with poor prognosis and treatment outcomes. lncRNAs are important in metabolic reprogramming and the tumor immune microenvironment, but their role in clear cell renal carcinoma is unclear., Methods: Renal clear cell carcinoma sample data from The Cancer Genome Atlas was used to establish a new risk profile by glycolysis-associated lncRNAs via machine learning. Risk profile-associated column-line plots were constructed to provide a quantitative tool for clinical practice. Patients with renal clear cell carcinoma were divided into high- and low-risk groups. Clinical features, tumor immune microenvironments, and immunotherapy responses were systematically analyzed. We experimentally confirmed the role of LINC01138 and LINC01605 in renal clear cell carcinoma., Results: The risk profile, consisting of LUCAT1, LINC01138, LINC01605, and HOTAIR, reliably predicted survival in patients with renal clear cell carcinoma and was validated in multiple external datasets. The high-risk group presented higher levels of immune cell infiltration and better immunotherapy responses than the low-risk group. LINC01138 and LINC01605 knockdown inhibited the proliferation of renal clear cell carcinoma., Conclusions: The identified risk profiles can accurately assess the prognosis of patients with clear cell renal carcinoma and identify patient populations that would benefit from immunotherapy, providing valuable insights and therapeutic targets for the clinical management of clear cell renal carcinoma.

Introduction Background: Disulfidptosis is a prevalent apoptotic mechanism, intrinsically linked to cancer prognosis. However, the specific involvement of disulfidptosis-related long non-coding RNA (DRLncRNAs) in Kidney renal clear cell carcinoma (KIRC) remains incompletely understood. This study aims to elucidate the potential prognostic significance of disulfidptosis-related LncRNAs in KIRC., Materials and Methods: Expression profiles and clinical data of KIRC patients were retrieved from the TCGA database to discern differentially expressed DRLncRNAs correlated with overall survival. Cox univariate analysis, Lasso Regression, and Cox multivariate analysis were used to construct a clinical prediction model., Results: Six signatures, namely FAM83C.AS1, AC136475.2, AC121338.2, AC026401.3, AC254562.3, and AC000050.2, were established to evaluate overall survival (OS) in the context of Kidney renal clear cell carcinoma (KIRC) in this study. Survival analysis and ROC curves demonstrated the strong predictive performance of the associated signature. The nomogram exhibited accurate prognostic predictions for overall patient survival, offering substantial clinical utility. Gene set enrichment analysis revealed that risk signals were enriched in various immune-related pathways. Furthermore, the risk features exhibited significant correlations with immune cells, immune function, immune cell infiltration, and immune checkpoints., Conclusion: This study has unveiled, for the first time, six disulfdptosis-related LncRNA signatures, laying a solid foundation for enhanced and precise prognostic predictions in KIRC., Competing Interests: Disclosure The authors have stated that they have no conflicts of interest., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

HOXA transcript at the distal tip (HOTTIP), a lncRNA, induces cell proliferation and cancer progression. However, the expression and function of HOTTIP in renal cell carcinoma (RCC) were rarely reported. The role of the HOTTIP in RCC was explored in this study. HOTTIP expresses higher in RCC tissues than in normal tissues and indicates poor prognosis based on the TCGA database. The over- and low-expression HOTTIP cell line was established in this research to assess the oncogenic function of HOTTIP in RCC progression. Mechanistic analyses revealed that HOTTIP functioned as a competing endogenous RNA (ceRNA) for miR-506. RIP experiment and luciferase assay were performed to explore the mechanisms of the sponge between HOTTIP and miR-506. HOTTIP down-regulation attenuated cell proliferation, migration, and invasion, which could be rescued by miR-506 down-regulation. On the whole, this study revealed that the HOTTIP/miR-506 axis has a dominant impact on RCC progression and potentially provides a novel strategy for RCC diagnosis and therapy.

Despite the dominant roles of cancer-associated fibroblasts (CAFs) have attached much attention in tumorigenesis, the CAFs-derived molecular determinants that regulate renal cell carcinoma (RCC) development remains elusive. Our previous study uncovered an oncogenic SNHG1 in the immune escape of RCC, whereas CAFs-derived exosomes could be a source accounting for increasing SNHG1 in RCC cells, this is still a mystery. The obtained CAFs and normal fibroblast (NFs) from fresh RCC and adjacent tissues were firstly identified using western blot and immunofluorescent staining. The enrichment of SNHG1 was validated by RT-qPCR. CAFs-derived exosomes were isolated from conditioned medium using ultracentrifugation method and ExoQuick-TC system. The internalization of exosomes, transfer of SNHG1, was measured by immunofluorescence. Regulation of conditioned medium or exosomal SNHG1 from CAFs on RCC biological functions was evaluated by CCK-8, EdU incorporation, colony formation, and transwell assays to assess the RCC cell proliferation, migration, and invasion. SNHG1 was significantly upregulated in CAFs isolated from RCC stroma. Exosomes derived from CAFs transferred SNHG1 to RCC cells and resulted in an increased SNHG1 expression in RCC cells. The exosomes excreted by CAFs promoted RCC cell proliferation, migration, and invasion, whereas the promotion effect of CAFs-exosomes on RCC progression was attenuated by SNHG1 knockdown. The present study revealed a new mechanism of exosomal SNHG1 extracted from CAFs enhanced RCC progression and may provide a potential target for the treatment of RCC., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Renal cell carcinoma (RCC) is a common malignancy. Local anesthetics were displayed powerful effects against various cancers. This study aims to probe the functions and molecular mechanism of ropivacaine in RCC., Methods: Different concentrations of ropivacaine were performed to administrate RCC cells including 786-O and Caki-1 cells. Cell viability and cell apoptosis were examined using CCK-8 and flow cytometry, respectively. Cell migration and invasion were determined by transwell assay. RMRP and CCDC65 expression was firstly predicted using TCGA dataset and further validated in RCC cells using qRT-PCR and western blot. The interactions among RMRP, EZH2 and CCDC65 were verified by RNA immunoprecipitation (RIP) and chromatin immunoprecipitation (ChIP) assays., Results: Ropivacaine effectively suppressed RCC cell viability, migration and invasion and enhanced cell apoptosis rate. Aberrantly elevated RMRP expression in RCC tissues was predicted by TCGA database. Interestingly, overexpressed RMRP observed in RCC cells could be also blocked upon the administration of ropivacaine. Likewise, RMRP knockdown further strengthened ropivacaine-mediated tumor suppressive effects on RCC cells. In terms of mechanism, RMRP directly interacted with EZH2, thereby modulating the histone methylation of CCDC65 to silence its expression. Moreover, ropivacaine inhibited tumor growth in mice bearing RCC tumor through regulating RMRP/EZH2/CCDC65 axis., Conclusion: In sum up, our work revealed that ropivacaine suppressed capacities of RCC cell viability, migration and invasion through modulating the RMRP/EZH2/CCDC65 axis, which laid the experimental foundation of ropivacaine for clinical application in the future., (© 2023. The Author(s), under exclusive licence to Tehran University of Medical Sciences.)

Objective: The objective of this study was to investigate the involvement of the long non-coding RNA (lncRNA) BRE-AS1 in clear cell renal cell carcinoma (ccRCC) and to explore its potential therapeutic role., Methods: The expression of BRE-AS1 and miR-106b-5p was determined by qRT-PCR. Overexpression of BRE-AS1 and miR-106b-5p were performed to study their relationship. Transwell assays were used to evaluate cell movement. Methylation-specific PCR (MSP) was performed to explore the role of BRE-AS1 in the methylation of the miR-106b-5p gene., Results: The results showed that the expression levels of BRE-AS1 were decreased, while those of miR-106b-5p were increased in ccRCC tissues. BRE-AS1 was found to be closely associated with the prognosis of patients with ccRCC. The expression of BRE-AS1 was inversely correlated with that of miR-106b-5p in tumor tissues. Overexpression of BRE-AS1 led to decreased expression levels of miR-106b-5p and increased methylation of the miR-106b-5p gene, whereas miR-106b-5p did not affect the expression of BRE-AS1. BRE-AS1 inhibited the movement and proliferation of ccRCC cell lines, while miR-106b-5p suppressed the role of BRE-AS1., Conclusion: BRE-AS1 may suppress ccRCC by downregulating the expression of miR-106b-5p., (©The Author(s) 2024. Open Access. This article is licensed under a Creative Commons CC-BY International License.)

Background: Exosomes play a crucial role in intercellular communication in clear cell renal cell carcinoma (ccRCC), while the long non-coding RNAs (lncRNAs) are implicated in tumorigenesis and progression., Aims: The purpose of this study is to construction a exosomes-related lncRNA score and a ceRNA network to predict the response to immunotherapy and potential targeted drug in ccRCC., Methods: Data of ccRCC patients were obtained from the TCGA database. Pearson correlation analysis was used to identify eExosomes-related lncRNAs (ERLRs) from Top10 exosomes-related genes that have been screened. The entire cohort was randomly divided into a training cohort and a validation cohort in equal scale. LASSO regression and multivariate cox regression was used to construct the ERLRs-based score. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and drug susceptibility between the high- and low-risk groups were also investigated. Finally, the relevant ceRNA network was constructed by machine learning to analyze their potential targets in immunotherapy and drug use of ccRCC patients., Results: A score consisting of 4ERLRs was identified, and patients with higher ERLRs-based score tended to have a worse prognosis than those with lower ERLRs-based score. ROC curves and multivariate Cox regression analysis demonstrated that the score could be considered as a risk factor for prognosis in both training and validation cohorts. Moreover, patients with high scores are predisposed to experience poor overall survival, a larger prevalence of advanced stage (III-IV), a greater tumor mutational burden, a higher infiltration of immunosuppressive cells, and a greater likelihood of responding favorably to immunotherapy. The importance of EMX2OS was determined by mechanical learning, and the ceRNA network was constructed, and EMX2OS may be a potential therapeutic target, possibly exerting its function through the EMX2OS/hsa-miR-31-5p/TLN2 axis., Conclusions: Based on machine learning, a novel ERLRs-based score was constructed for predicting the survival of ccRCC patients. The ERLRs-based score is a promising potential independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics. Meanwhile, we screened out key lncRNAEMX2OS and identified the EMX2OS/hsa-miR-31-5p/TLN2 axis, which may provide new clues for the targeted therapy of ccRCC., (© 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd.)

Long non-coding RNAs (lncRNAs) and cancer stem cells (CSCs) are crucial for the growth, migration, recurrence, and medication resistance of tumors. However, the impact of lncRNAs related to stemness on the outcome and tumor immune microenvironment (TIME) in clear cell renal cell carcinoma (ccRCC) is still unclear. In this study, we aimed to predict the outcome and TIME of ccRCC by constructing a stem related lncRNAs (SRlncRNAs) signature. We firstly downloaded ccRCC patients' clinical data and RNA sequencing data from UCSC and TCGA databases, and abtained the differentially expressed lncRNAs highly correlated with stem index in ccRCC through gene expression differential analysis and Pearson correlation analysis. Then, we selected suitable SRlncRNAs for constructing a prognostic signature of ccRCC patients by LASSO Cox regression. Further, we used nomogram and Kaplan Meier curves to evaluate the SRlncRNA signature for the prognose in ccRCC. At last, we used ssGSEA and GSVA to evaluate the correlation between the SRlncRNAs signature and TIME in ccRCC. Finally, We obtained a signtaure based on six SRlncRNAs, which are correlated with TIME and can effectively predict the ccRCC patients' prognosis. The SRlncRNAs signature may be a noval prognostic indicator in ccRCC., (© 2024. The Author(s).)

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.

Background: PCD-related long non-coding RNAs (PRLs) are rarely investigated in relation to clear cell renal carcinoma (ccRCC). As part of this study, we evaluated the immunological potential of PRL signatures as a biomarker for ccRCC prognosis and immunological function., Materials and Methods: Data were downloaded from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases. A Pearson correlation analysis was conducted on the 27 PCD-associated genes to determine whether lncRNAs were significantly associated with PCD. Kaplan-Meier analysis, biological function identification, immune infiltration analysis, estimation of efficacy of immunotherapy and targeted drug screening, and exploration of the landscape of mutation status were conducted by analyzing the risk scores., Results: Seven PRLs, LINC02747, AP001636.3, AC022126.1, LINC02657, LINC02609, LINC02154, and ZNNT1, were used to divide patients with ccRCC into groups with high and low risk. High-risk patients had a worse prognosis than low-risk patients, according to the results, and the PRL signature showed promising predictive ability. More immune cells were clustered in the high-risk group, whereas the immune cell function of the low-risk group was significantly suppressed. The high-risk group was less sensitive to immunotherapy, whereas the low-risk group had positive responses to most drugs., Conclusions: Collectively, we established and verified a PRL signature that could competently guide the prognostic survival and immunotherapy of ccRCC. In addition, molecular subtypes were determined for ccRCC based on PRL expression, which may help elucidate the underlying molecular mechanism of ccRCC and develop targeted treatments., (© 2024. The Author(s).)

A growing body of evidence shows that vasculogenic mimicry (VM) is closely related to the invasion and metastasis of many tumor cells. Although the estrogen receptor (ER) can promote initiation and progression of renal cell carcinoma (RCC), how the downstream biomolecules are involved, and the detailed mechanisms of how ER expression is elevated in RCC remain to be further elucidated. Here, we discovered that long noncoding RNA (LncRNA)-SERB is highly expressed in tumor cells of RCC patients. We used multiple RCC cells and an in vivo mouse model for our study, and results indicated that LncRNA-SERB could boost RCC VM formation and cell invasion in vitro and in vivo. Although a previous report showed that ERβ can affect the VM formation in RCC, it is unclear which factor could upregulate ERβ. This is the first study to show LncRNA-SERB can be the upstream regulator of ERβ to control RCC progression. Mechanistically, LncRNA-SERB may increase ERβ via binding to the promoter area, and ERβ functions through transcriptional regulation of zinc finger E-box binding homeobox 1 (ZEB1) to regulate VM formation. These results suggest that LncRNA-SERB promotes RCC cell VM formation and invasion by upregulating the ERβ/ZEB1 axis and that therapeutic targeting of this newly identified pathway may better inhibit RCC progression., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

This study aims to develop a prognostic signature based on m6A-related lncRNAs for clear cell renal cell carcinoma (ccRCC). Differential expression analysis and Pearson correlation analysis were used to identify m6A-related lncRNAs associated with patient outcomes in The Cancer Genome Atlas (TCGA) database. Our approach led to the development of an m6A-related lncRNA risk score (MRLrisk), formulated using six identified lncRNAs: NFE4, AL008729.2, AL139123.1, LINC02154, AC124854.1 and ARHGAP31-AS1. Higher MRLrisk was identified as a risk factor for patients' prognosis in ccRCC. Furthermore, an MRLrisk-based nomogram was developed and demonstrated as a reliable tool for prognosis prediction in ccRCC. Enrichment analysis and tumor mutation signature studies were conducted to investigate MRLrisk-related biological phenotypes. The tumor immune dysfunction and exclusion (TIDE) score was employed to infer patients' response to immunotherapy, indicating a negative correlation between high MRLrisk and immunotherapy response. Our focus then shifted to LINC02154 for deeper exploration. We assessed LINC02154 expression in 28 ccRCC/normal tissue pairs and 3 ccRCC cell lines through quantitative real-time polymerase chain reaction (qRT-PCR). Functional experiments, including EdU incorporation, flow cytometry and transwell assays, were performed to assess the role of LINC02154 in ccRCC cell functions, discovering that its downregulation hinders cancer cell proliferation and migration. Furthermore, the influence of LINC02154 on ccRCC cells' sensitivity to Sunitinib was explored using CCK-8 assays, demonstrating that decreased LINC02154 expression increases Sunitinib sensitivity. In summary, this study successfully developed an MRLrisk model with significant prognostic value for ccRCC and established LINC02154 as a critical biomarker and prospective therapeutic target in ccRCC management.

Cuproptosis and disulfidptosis, recently discovered mechanisms of cell death, have demonstrated that differential expression of key genes and long non-coding RNAs (lncRNAs) profoundly influences tumor development and affects their drug sensitivity. Clear cell renal cell carcinoma (ccRCC), the most common subtype of kidney cancer, presently lacks research utilizing cuproptosis and disulfidptosis-related lncRNAs (CDRLRs) as prognostic markers. In this study, we analyzed RNA-seq data, clinical information, and mutation data from The Cancer Genome Atlas (TCGA) on ccRCC and cross-referenced it with known cuproptosis and disulfidptosis-related genes (CDRGs). Using the LASSO machine learning algorithm, we identified four CDRLRs-ACVR2B-AS1, AC095055.1, AL161782.1, and MANEA-DT-that are strongly associated with prognosis and used them to construct a prognostic risk model. To verify the model's reliability and validate these four CDRLRs as significant prognostic factors, we performed dataset grouping validation, followed by RT-qPCR and external database validation for differential expression and prognosis of CDRLRs in ccRCC. Gene function and pathway analysis were conducted using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) for high- and low-risk groups. Additionally, we have analyzed the tumor mutation burden (TMB) and the immune microenvironment (TME), employing the oncoPredict and Immunophenoscore (IPS) algorithms to assess the sensitivity of diverse risk categories to targeted therapeutics and immunosuppressants. Our predominant objective is to refine prognostic predictions for patients with ccRCC and inform treatment decisions by conducting an exhaustive study on cuproptosis and disulfidptosis., (© 2024. The Author(s).)

Background: As one of the most common tumors, the pathogenesis and progression of clear cell renal cell carcinoma (ccRCC) in the immune microenvironment are still unknown., Methods: The differentially expressed immune-related lncRNA (DEirlncRNA) was screened through co-expression analysis and the limma package of R, which based on the ccRCC project of the TCGA database. Then, we designed the risk model by irlncRNA pairs. In RCC patients, we have compared the area under the curve, calculated the Akaike Information Criterion (AIC) value of the 5-year receiver operating characteristic curve, determined the cut-off point, and established the optimal model for distinguishing the high-risk group from the low-risk group. We used the model for immune system assessment, immune point detection and drug sensitivity analysis after verifying the feasibility of the above model through clinical features., Results: In our study, 1541 irlncRNAs were included. 739 irlncRNAs were identified as DEirlncRNAs to construct irlncRNA pairs. Then, 38 candidate DEirlncRNA pairs were included in the best risk assessment model through improved LASSO regression analysis. As a result, we found that in addition to age and gender, T stage, M stage, N stage, grade and clinical stage are significantly related to risk. Moreover, univariate and multivariate Cox regression analysis results reveals that in addition to gender, age, grade, clinical stage and risk score are independent prognostic factors. The results show that patients in the high-risk group are positively correlated with tumor infiltrating immune cells when the above model is applied to the immune system. But they are negatively correlated with endothelial cells, macrophages M2, mast cell activation, and neutrophils. In addition, the risk model was positively correlated with overexpressed genes (CTLA, LAG3 and SETD2, P<0.05). Finally, risk models can also play as an important role in predicting the sensitivity of targeted drugs., Conclusions: The new risk model may be a new method to predict the prognosis and immune status of ccRCC.

Metabolic reprogramming of energy is a newly recognized characteristic of cancer. In our current investigation, we examined the possible predictive importance of long noncoding RNAs (lncRNAs) associated to fatty acid metabolism in clear cell renal cell carcinoma (ccRCC). We conducted an analysis of the gene expression data obtained from patients diagnosed with ccRCC using the Cancer Genome Atlas (TCGA) database and the ArrayExpress database. We performed a screening to identify lncRNAs that are differentially expressed in fatty acid metabolism. Based on these findings, we developed a prognostic risk score model using these fatty acid metabolism-related lncRNAs. We then validated this model using Cox regression analysis, Kaplan-Meier survival analysis, and principal-component analysis (PCA). Furthermore, the prognostic risk score model was successfully validated using both the TCGA cohort and the E-MTAB-1980 cohort. We utilized gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) to determine the correlation between fatty acid metabolism and the PPAR signaling pathway in patients with ccRCC at various clinical stages and prognoses. We have discovered compelling evidence of the interaction between immune cells in the tumor microenvironment and tumor cells, which leads to immune evasion and resistance to drugs. This was achieved by the utilization of advanced techniques such as the CIBERSORT method, ESTIMATE R package, ssGSEA algorithm, and TIMER database exploration. Ultimately, we have established a network of competing endogenous RNA (ceRNA) that is related to fatty acid metabolism. The findings of our study suggest that medicines focused on fatty acid metabolism could be clinically significant for individuals with ccRCC. The utilization of this risk model, which is centered around the lncRNAs associated with fatty acid metabolism, could potentially provide valuable prognostic information and hold immunotherapeutic implications for patients with ccRCC., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Clear cell carcinoma (ccRCC) usually has a high metastasis rate and high mortality rate. To enable precise risk stratification, there is a need for novel biomarkers. As one form of apoptosis, anoikis results from the disruption of cell-cell connection or cell-ECM attachment. However, the impact of anoikis-related lncRNAs on ccRCC has not yet received adequate attention., Methods: The study utilized univariate Cox regression analysis in order to identify the overall survival (OS) associated anoikis-related lncRNAs (ARLs), followed by the LASSO algorithm for selection. On this basis, a risk model was subsequently established using five anoikis-related lncRNAs. To dig the inner molecular mechanism, KEGG, GO, and GSVA analyses were conducted. Additionally, the immune infiltration landscape was estimated using the ESTIMATE, CIBERSORT, and ssGSEA algorithms., Results: The study constructed a novel risk model based on five ARLs (AC092611.2, AC027601.2, AC103809.1, AL133215.2, and AL162586.1). Patients categorized as low-risk exhibited significantly better OS. Notably, the study observed marked different immune infiltration landscapes and drug sensitivity by risk stratification. Additionally, the study preliminarily explored potential signal pathways associated with risk stratification., Conclusion: The study exhibited the crucial role of ARLs in the carcinogenesis of ccRCC, potentially through differential immune infiltration. Furthermore, the established risk model could serve as a valuable stratification factor for predicting OS prognosis.

Fumarate hydratase (FH) deficient renal cell carcinoma (RCC) is a type of tumor with definite metabolic disorder, but the mechanism of metabolic remodeling is still unclear. LncRNA was reported to closely correlate with cancer metabolism, however the biological role of LncRNA in the development of progression of FH-deficent RCC was not well studied either. FH-deficient RCC samples were collected in my hospital and used for RNA-sequencing and Mass spectrometry analysis. FH-deficient RCC cell line UOK262 and control pFH cells were used for in vitro experiments, including proliferation assay, transwell assay, western-blot, mass spectrometry and so on. PDX mouse model was used for further drug inhibition experiments in vivo. In this study, we analyzed the profiles of LncRNA and mRNA in FH-deficienct RCC samples, and we found that the LncRNA-MIR4435-2GH was specifically highly expressed in FH-deficient RCC compared with ccRCC. In vitro experiments demonstrated that MIR4435-2HG was regulated by Fumarate through histone demethylation, and the deletion of this gene could inhibit glutamine metabolism. RNA-pulldown experiments showed that MIR4435-2HG specifically binds to STAT1, which can transcriptionally activate GLS1. GLS1 inhibitor CB-839 could significantly suppress tumor growth in PDX tumor models. This study analyzed the molecular mechanism of MIR4435-2HG in regulating metabolic remodeling of FH-deficient RCC in clinical samples, cells and animal models by combining transcriptional and metabolic methods. We found that that GLS1 was a therapeutic target for this tumor, and MIR4435-2HG can be used as a drug sensitivity marker., (© 2024. The Author(s).)

Purpose: Investigating the role of lncRNAs associated with the latest cell death mode (Disulfideptosis) in renal clear cell carcinoma, as well as their correlation with tumor prognosis, immune escape, immune checkpoints, tumor mutational burden, and malignant tumor progression. Searching for potential biomarkers and targets for renal clear cell carcinoma., Methods: Downloaded the expression profile data and clinical data of 533 cases of renal clear cell carcinoma from the TCGA database, and randomly divided them into a test set (267 cases) and a validation set (266 cases). Based on previous research, 13 genes associated with Disulfideptosis were obtained. Using R software, lncRNAs with a differential expression that is related to the prognosis of renal clear cell carcinoma and associated with Disulfideptosis were screened out. After univariate Cox regression analysis, Lasso regression analysis, and multivariate Cox regression analysis, lncRNAs with independent predictive ability were obtained. A predictive risk model was established based on the risk scores. Verification was carried out between the obtained high-risk and low-risk groups and their subgroups (including Age, Gender, tumor mutational burden (TMB), tumor grading, and staging). Subsequently, a nomogram was established, and a calibration curve was generated for verification. Performed GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) functional enrichment analyses. Downloaded the values of Tumor Immune Dysfunction and Exclusion (TIDE) for all samples and calculated the difference between the high and low-risk groups. Selected human renal tumor cell lines (786-O, OS-RC-2, A-498, ACHN) and human renal cortex proximal tubule epithelial cell line (HK-2). The RNA expression levels of the above lncRNAs in each cell line were analyzed using RT-qPCR (Real-time Quantitative PCR Detecting System). Used siRNA (small interfering RNA) to knock down FAM225B in 786-O and OS-RC-2 cell lines, and then performed in vitro cell experiments to validate the functional characteristics of FAM225B., Results: Our constructed predictive model includes 5 lncRNAs with an independent predictive ability (FAM225B, ZNF503-AS1, SPINT1-AS1, WWC2-AS2, LINC01338), which can effectively distinguish between patients in high and low-risk groups and their subgroups. The 1, 3, and 5-year AUC (Area Under the ROC Curve) values of the established nomogram are 0.756, 0.752, and 0.781, respectively. The 5-year AUC value is higher compared to other clinical characteristics (Age: 0.598, Gender: 0.488, Grade: 0.680, Stage: 0.717). After the knockdown of FAM225B, the proliferation, migration, and invasion abilities of renal cancer cell lines OS-RC-2 and 786-O all decreased., Conclusion: We have constructed and validated a prognostic model based on Disulfideptosis-associated lncRNAs. This model can effectively predict the high or low risk of patient prognosis and can distinguish the tumor cell mutational burden and immune escape capabilities among high-risk and low-risk patients. This predictive model can serve as an independent prognostic factor for renal clear cell carcinoma, providing a new direction for personalized treatment of patients with renal clear cell carcinoma.

Background: Long non-coding RNAs (lncRNAs) have been extensively investigated in the field of cancer, among which, lncRNA ladybird homeobox 2-antisense RNA 1 (LBX2-AS1) has been demonstrated to exert carcinogenic effects on a variety of malignancies. However, the biological functions of LBX2-AS1 in clear cell renal cell carcinoma (ccRCC) have not been explicitly elucidated., Methods: Arraystar lncRNA chip and qRT-PCR verify the expression of LncRNA LBX2-AS1 in ccRCC. CCK-8 assay and cell cloning assay were used to assess the proliferative capacity of ccRCC cells. Migration abilities were quantified by scratch assay and transwell assay. Potential molecular signaling pathways were determined by high-throughput whole transcriptomics analysis. WB analysis was performed to validate the relationship between LBX2-AS1 and key molecules of mitophagy pathway. The effect of LBX2-AS1 on mitophagy was observed by laser confocal microscopy. Rescue experiments further validated the role of downstream gene FOXO3A in the LBX2-AS1 signaling pathway. Finally, the authentic effect of LBX2-AS1 was verified in vivo., Results: LncRNA LBX2-AS1 was over expressed in ccRCC tissues and could enhance the proliferation and migration of ccRCC cells. Autophagic pathway was identified as a possible mechanism involved in the oncogenic effect of LBX2-AS1. Mitophagy levels were observed in LBX2-AS1 low-expressing cells through laser confocal microscopy. Knockdown of LBX2-AS1 significantly elevated mitophagy levels as observed using laser confocal microscopy and led to FOXOA3 decreasing in and BNIP3L and LC3 enrichment. Meanwhile, LBX2-AS1 knocking down dampened the proliferation of ccRCC cells in vivo., (© 2024. The Author(s).)

Background: Clear 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., Methods: The 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., Results: CCNB2 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., Conclusion: CCNB2 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., (© 2024. The Author(s).)

Disulfidptosis, a novel form of regulated cell death, occurs due to the aberrant accumulation of intracellular cystine and other disulfides. Moreover, targeting disulfidptosis could identify promising approaches for cancer treatment. Long non-coding RNAs (lncRNAs) are known to be critically implicated in clear cell renal cell carcinoma (ccRCC) development. Currently, the involvement of disulfidptosis-related lncRNAs in ccRCC is yet to be elucidated. This study primarily dealt with identifying and validating a disulfidptosis-related lncRNAs-based signature for predicting the prognosis and immune landscape of individuals with ccRCC. Clinical and RNA sequencing data of ccRCC samples were accessed from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was conducted for the identification of the disulfidptosis-related lncRNAs. Additionally, univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator Cox regression, and stepwise multivariate Cox analysis were executed to develop a novel risk prognostic model. The prognosis-predictive capacity of the model was then assessed using an integrated method. Variation in biological function was noted using GO, KEGG, and GSEA. Additionally, immune cell infiltration, the tumor mutational burden (TMB), and tumor immune dysfunction and exclusion (TIDE) scores were calculated to investigate differences in the immune landscape. Finally, the expression of hub disulfidptosis-related lncRNAs was validated using qPCR. We established a novel signature comprised of eight lncRNAs that were associated with disulfidptosis (SPINT1-AS1, AL121944.1, AC131009.3, AC104088.3, AL035071.1, LINC00886, AL035587.2, and AC007743.1). Kaplan-Meier and receiver operating characteristic curves demonstrated the acceptable predictive potency of the model. The nomogram and C-index confirmed the strong correlation between the risk signature and clinical decision-making. Furthermore, immune cell infiltration analysis and ssGSEA revealed significantly different immune statuses among risk groups. TMB analysis revealed the link between the high-risk group and high TMB. It is worth noting that the cumulative effect of the patients belonging to the high-risk group and having elevated TMB led to decreased patient survival times. The high-risk group depicted greater TIDE scores in contrast with the low-risk group, indicating greater potential for immune escape. Finally, qPCR validated the hub disulfidptosis-related lncRNAs in cell lines. The established novel signature holds potential regarding the prognosis prediction of individuals with ccRCC as well as predicting their responses to immunotherapy., (© 2024. The Author(s).)

Revealing the role of non-coding RNAs (ncRNAs) in inducing dysregulated pathological responses to external signals may identify therapeutic targets for inhibiting the progression of clear cell renal cell carcinoma (ccRCC). Non-coding RNAs belong to a class of RNA molecules that do not encode proteins but possess diverse biological functions, playing essential roles in the occurrence and development of metastatic and proliferative tumors. To investigate the impact of the upstream interaction between miR-142-3p and lncRNA MAGI2-AS3 on the tumor-suppressive activity of the STAM gene, we firstly conducted bioinformatics analysis to predict the upstream miRNAs of STAM and the upstream lncRNAs of the miRNAs through online databases (miRanda, miRDB, TargetScan, LncBase v2), which were further validated by the starBasev2.0 database. Subsequently, multiple experimental techniques were employed to validate these findings, including RT-qPCR, Western blotting, measurement of cellular functional activity, and luciferase reporter assays. Through these experimental methods, we provided compelling evidence regarding the role of miR-142-3p and MAGI2-AS3 in regulating STAM gene expression and functionality, revealing their potential significance in tumor suppression. Our research demonstrates the importance of the MAGI2-AS3/miR-142-3p/STAM signaling pathway axis in ccRCC. MAGI2-AS3 competes for binding with miR-142-3p, resulting in upregulated STAM gene expression. This upregulation inhibits tumor proliferation and metastasis in ccRCC cells. Conversely, overexpression of miR-142-3p or silencing of MAGI2-AS3 promotes tumor behavior, while downregulation of miR-142-3p inhibits the development of ccRCC. Targeting the MAGI2-AS3/miR-142-3p/STAM axis holds promise as a therapeutic strategy for ccRCC treatment., Competing Interests: Declaration of Competing Interest The authors declare that they have no competing interests., (Copyright © 2023. Published by Elsevier Inc.)

Introduction: Renal cell carcinoma (RCC) is a grave malignancy that poses a significant global health burden with over 400,000 new cases annually. Disulfidptosis, a newly discovered programmed cell death process, is linked to the actin cytoskeleton, which plays a vital role in maintaining cell shape and survival. The role of disulfidptosis is poorly depicted in the clear cell histologic variant of RCC (ccRCC)., Methods: Three sets of ccRCC cohorts, ICGC&#95;RECA-EU (n = 91), GSE76207 (n = 32) and TCGA-KIRC (n = 607), were included in our study, the batch effect of which was removed using the "combat" function. Correlation was calculated using the "rcorr" function of the "Hmisc" package for Pearson analysis, which was visualized using the "pheatmap" package. Principal component analysis was performed by the "vegan" package, visualized using the "scatterplot3d" package. Long non-coding RNAs (lncRNAs) associated with disulfidptosis were screened out using least absolute shrinkage and selection operator (LASSO) and COX analysis. Tumor mutation, immune landscaping and immunotherapy prediction were performed for further characterization of two risk groups., Results: A total of 1822 disulfidptosis-related lncRNAs was selected, among which 308 lncRNAs were found to be significantly associated with the clinical outcome of ccRCC patients. We retained 11 disulfidptosis-related lncRNAs, namely, AP000439.3, RP11-417E7.1, RP11-119D9.1, LINC01510, SNHG3, AC156455.1, RP11-291B21.2, EMX2OS, AC093850.2, HAGLR and RP11-389C8.2, through LASSO and COX analysis for prognosis model construction, which displayed satisfactory accuracy (area under the curve, AUC, values all above 0.6 in multiple cohorts) in stratification of ccRCC prognosis. A nomogram model was constructed by integrating clinical factors with risk score, which further enhanced the prediction efficacy (AUC values all above 0.7 in multiple cohorts). We found that patients of male gender, higher clinical stages and advanced pathological T stage were inclined to have higher risk score values. Dactinomycin&#95;1911, Vinblastine&#95;1004, Daporinad&#95;1248 and Vinorelbine&#95;2048 were identified as promising candidate drugs for treating ccRCC patients of higher risk score value. Moreover, patients of higher risk value were prone to be resistant to immunotherapy., Conclusion: We developed a prognosis predicting model based on 11 selected disulfidptosis-related lncRNAs, the efficacy of which was verified in different cohorts. Furthermore, we delineated an intricate portrait of tumor mutation, immune topography and pharmacosensitivity evaluations within disparate risk stratifications., (© 2023 John Wiley & Sons Ltd.)

Background: The most common urologic tumor in humans with the highest incidence rate is clear cell renal cell carcinoma (ccRCC). Long non-coding RNAs (lncRNAs) act as regulatory factors in several tumors. Here, we studied ccRCC regulated by hypoxia-inducible factor 1α (HIF1α)-antisense RNA 2 (AS2) or HIF1A-AS2., Methods: We performed wound-healing, transwell, and CCK-8 assays by decreasing or increasing the HIF1A-AS2 expression in RCC cell lines. Western blotting and qRT-PCR were used to identify the expression of downstream genes of the HIF1A-AS2 pathway. Gli1 and HIF1A-AS2 relationship was assessed using RIP and RNA pull-down assays. Lastly, transcriptome sequencing was performed on kidney cancer cells that had been knocked down to find possible regulatory mechanisms., Results: Our results suggest that high expression of HIF1A-AS2 may promote RCC cell proliferation and Gli1 expression as a downstream factor. Furthermore, they have physical binding sites and together regulate HIF1α to encourage the development of ccRCC. HIF1A-AS2 lncRNA may offer a new molecular target for ccRCC treatment., Conclusion: lncRNA HIF1A-AS2 affects ccRCC development by regulating HIF1a expression through Gli1., Competing Interests: Declaration of Competing Interest The authors declare that they have no competing interests., (Copyright © 2023 Elsevier GmbH. All rights reserved.)

Background: Renal cell carcinoma has several subtypes, with kidney renal clear cell carcinoma (KIRC) being the most common and heterogeneous. Purine metabolism is associated with cancer progression. However, the role of purine metabolism-related long non-coding RNAs (lncRNAs) in KIRC remains unknown., Methods: KIRC were grouped into Cluster-1 and Cluster-2 based on purine genes. Limma package was used to identify differentially expressed lncRNAs between two classes of purine genes. Single-factor screening was used followed by random forest dimensionality reduction and Lasso method to screen lncRNAs. A risk score model (Purine Score) containing the 3 lncRNAs was developed using the Lasso method., Results: A total of 22 differentially expressed lncRNAs were identified. These were reduced to a final set of three ( LINC01671 , ARAP1-AS1 and LINC02747 ). Age and metastasis (M) were identified as independent prognostic factors for KIRC using univariate and multivariate Cox analysis. An abnormal immune cell response was also associated with patient survival. The Purine Score correlated with abnormal expression of immune checkpoint genes. Genetic analysis of KIRC found somatic mutations in TP53 , TRIOBP , PBRM1 , PKHD1 , VHL , NPHP3 , TLN2 , CABIN1 , ABCC6 , XIRP2 , and CHD4 . In vitro cell experiments showed that knockdown of LINC01671 promoted the proliferation and migration of 786-O cells, while inhibiting apoptosis. Overexpression of LINC01671 inhibited the proliferation and migration of CAKI-1 cells, while promoting apoptosis. Gene Set Enrichment Analysis (GSEA) analysis revealed that LINC01671 was significantly enriched in the MAPK, NF-kappa B, mTOR, PI3K-Akt, and Wnt signaling pathways., Conclusions: LINC01671 may be a novel prognostic marker with important therapeutic value for KIRC., Competing Interests: The authors declare no conflict of interest., (© 2023 The Author(s). Published by IMR Press.)

Renal cell carcinoma (RCC), a prevalent form of renal malignancy, is distinguished by its proclivity for robust tumor proliferation and metastatic dissemination. Long non-coding RNAs (lncRNAs) have emerged as pivotal modulators of gene expression, exerting substantial influence over diverse biological processes, encompassing the intricate landscape of cancer development. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT-1), an exemplar among lncRNAs, has been discovered to assume functional responsibilities within the context of RCC. The conspicuous expression of MALAT-1 in RCC cells has been closely linked to the advancement of tumors and an unfavorable prognosis. Experimental evidence has demonstrated the pronounced ability of MALAT-1 to stimulate RCC cell proliferation, migration, and invasion, thereby underscoring its active participation in facilitating the metastatic cascade. Furthermore, MALAT-1 has been implicated in orchestrating angiogenesis, an indispensable process for tumor expansion and metastatic dissemination, through its regulatory influence on pro-angiogenic factor expression. MALAT-1 has also been linked to the evasion of immune surveillance in RCC, as it can regulate the expression of immune checkpoint molecules and modulate the tumor microenvironment. Hence, the potential utility of MALAT-1 as a diagnostic and prognostic biomarker in RCC emerges, warranting further investigation and validation of its clinical significance. This comprehensive review provides an overview of the diverse functional roles exhibited by MALAT-1 in RCC., (© 2023. The Author(s).)

Targeted therapy is pivotal in renal carcinoma treatment, and the dual-inhibitor NVP-BEZ235 has emerged as a promising candidate in preliminary studies. Its effectiveness against renal carcinoma and the mechanisms underlying potential resistance, however, warrant further exploration. This study aims to elucidate these aspects, enhancing our understanding of NVP-BEZ235's future clinical utility. To investigate resistance mechanisms, renal cancer cell lines were exposed to progressively increasing concentrations of NVP-BEZ235, leading to the development of stable resistance. These resistant cells underwent extensive RNA-sequencing analysis. We implemented gene interference techniques using plasmid vectors and lentivirus and conducted regular IC50 assessments. To pinpoint the role of LncRNAs, we utilized FISH and immunofluorescence staining assays, supplemented by RNA pull-down and RIP assays to delineate interactions between LncRNA and its RNA-binding protein (RBP). Further, Western blotting and qRT-PCR were employed to examine alterations in signaling pathways, with an animal model providing additional validation. Our results show a marked increase in the IC50 of NVP-BEZ235 in resistant cell lines compared to their parental counterparts. A significant revelation was the role of LncRNA-CHKB-AS1 in mediating drug resistance. We observed dysregulated expression of CHKB-AS1 in both clinical samples of clear cell renal cell carcinoma (ccRCC) and cell lines. In vivo experiments further substantiated our findings, showing that CHKB-AS1 overexpression significantly enhanced tumor growth and resistance to NVP-BEZ235 in a subcutaneous tumorigenesis model, as evidenced by increased tumor volume and weight, whereas CHKB-AS1 knockdown led to a marked reduction in these parameters. Critically, CHKB-AS1 was identified to interact with MAP4, a key regulator in the phosphorylation of the PI3k/Akt/mTOR pathway. This interaction contributes to a diminished antitumor effect of NVP-BEZ235, highlighting the intricate mechanism through which CHKB-AS1 modulates drug resistance pathways, potentially impacting therapeutic strategies against renal carcinoma., (© 2023. The Author(s).)

Increasing evidence has confirmed the vital role of Notch signaling in the tumorigenesis of clear cell renal cell carcinoma (ccRCC). The underlying function of long non-coding RNA (lncRNA) related to Notch signaling in ccRCC remains unclear. In present study, the prognostic value and therapeutic strategy of Notch signaling-related lncRNA are comprehensively explored in ccRCC. In total, we acquired 1422 NSRlncRNAs, of which 41 lncRNAs were identified the key NSRlncRNAs associated with the occurrence of ccRCC. The prognostic signature containing five NSRlncRNAs (AC092611.2, NNT-AS1, AGAP2-AS1, AC147651.3, and AC007406.3) was established and validated, and the ccRCC patients were clustered into the high- and low-risk groups. The overall survival of patients in the low-risk group were much more favorable than those in the high-risk group. Multivariate Cox regression analysis indicated that the risk score was an independent prognostic biomarker. Based on the risk score and clinical variables, a nomogram for predicting prognosis of ccRCC patients was constructed, and the calibration curves and DCA curves showed the superior predictive ability of nomogram. The risk score was correlated with immune cell infiltration, targeted therapy or chemotherapy sensitivity, and multiple oncogenic pathways. Additionally, consensus clustering analysis stratified the ccRCC patients into four clusters with obvious different outcomes, immune microenvironments, and expression of immune checkpoints. The constructed NSRlncRNA-based signature might serve as a potential biomarker for predicting prognosis and response to immunotherapy or targeted therapy in patients with ccRCC., (© 2023. The Author(s).)

Background: Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. This study aims to develop new biomarkers for KIRC and explore the impact of biomarkers on the immunotherapeutic efficacy for KIRC, providing a theoretical basis for the treatment of KIRC patients., Methods: Transcriptome data for KIRC was obtained from the The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Weighted gene co-expression network analysis identified KIRC-related modules of long noncoding RNAs (lncRNAs). Intersection analysis was performed differentially expressed lncRNAs between KIRC and normal control samples, and lncRNAs associated with N(7)-methylguanosine (m7G), resulting in differentially expressed m7G-associated lncRNAs in KIRC patients (DE-m7G-lncRNAs). Machine Learning was employed to select biomarkers for KIRC. The prognostic value of biomarkers and clinical features was evaluated using Kaplan-Meier (K-M) survival analysis, univariate and multivariate Cox regression analysis. A nomogram was constructed based on biomarkers and clinical features, and its efficacy was evaluated using calibration curves and decision curves. Functional enrichment analysis was performed to investigate the functional enrichment of biomarkers. Correlation analysis was conducted to explore the relationship between biomarkers and immune cell infiltration levels and common immune checkpoint in KIRC samples., Results: By intersecting 575 KIRC-related module lncRNAs, 1773 differentially expressed lncRNAs, and 62 m7G-related lncRNAs, we identified 42 DE-m7G-lncRNAs. Using XGBoost and Boruta algorithms, 8 biomarkers for KIRC were selected. Kaplan-Meier survival analysis showed significant survival differences in KIRC patients with high and low expression of the PTCSC3 and RP11-321G12.1. Univariate and multivariate Cox regression analyses showed that AP000696.2, PTCSC3 and clinical characteristics were independent prognostic factors for patients with KIRC. A nomogram based on these prognostic factors accurately predicted the prognosis of KIRC patients. The biomarkers showed associations with clinical features of KIRC patients, mainly localized in the cytoplasm and related to cytokine-mediated immune response. Furthermore, immune feature analysis demonstrated a significant decrease in immune cell infiltration levels in KIRC samples compared to normal samples, with a negative correlation observed between the biomarkers and most differentially infiltrating immune cells and common immune checkpoints., Conclusion: In summary, this study discovered eight prognostic biomarkers associated with KIRC patients. These biomarkers showed significant correlations with clinical features, immune cell infiltration, and immune checkpoint expression in KIRC patients, laying a theoretical foundation for the diagnosis and treatment of KIRC., (© 2023. The Author(s).)

Ferroptosis is a novel kind of iron- and reactive oxygen-induced cell death, investigation into ferroptosis-associated long noncoding RNAs (FALs) in clear cell renal cell carcinoma (ccRCC) is scarce. The goal of the research was to look at FALs' possible predictive significance, as well as their interaction with the immune microenvironment and therapeutic responsiveness of ccRCC. The Cancer Genome Atlas database was employed to retrieve RNA sequencing data from 530 individuals with ccRCC. Patients with ccRCC were randomly assigned to one of two groups: training or testing. Pearson's correlation analysis through the identified ferroptosis-related genes was implemented to screen for FALs. Finally, a FALs signature composed of eight lncRNAs was discovered for predicting survival outcomes in ccRCC patients. ccRCC patients in the training, testing, and overall cohorts were separated into low-risk and high-risk groups based on their risk score. The FALs signature was identified to be an independent factor for overall survival in the multivariate Cox analysis (hazard ratio = 1.013, 95% confidence interval = 1.008-1.018, p < 0.001). A clinically prognostic nomogram was created depending on the FALs signature and clinical characteristics. The nomogram provides greater clinical practicability and may reliably estimate patients' overall survival. The FALs signature may additionally precisely represent ccRCC's immunological environment, immunotherapy reaction, and drug sensitivity. The eight FALs and their signature provide precise and reliable methods for evaluating the clinical effects of in ccRCC patients, and they could be biological markers and targets for therapy., (© 2023 Wiley Periodicals LLC.)

Proven by publications, long non-coding RNAs (lncRNAs) play critical roles in the development of clear cell renal cell carcinoma (ccRCC). Although lncRNA LINC00565 has been implicated in the progression of various cancers, its biological effects on ccRCC remain unknown. This study aimed to investigate the biological functions of LINC00565, as well as its potential mechanism in ccRCC. Here, the expression data of mature microRNAs (miRNAs) (normal: 71, tumor: 545), messenger RNAs (mRNAs), and lncRNAs (normal: 72, tumor: 539) of ccRCC were acquired from The Cancer Genome Atlas (TCGA) database and subjected to differential expression analysis. Quantitative reverse transcriptase polymerase chain reaction analyzed the expression levels of LINC00565, miR-532-3p, and ADAM19 mRNA. TCGA database, dual-luciferase report detection, and Argonaute 2 RNA immunoprecipitation were utilized to confirm the relationships between LINC00565 and miR-532-3p and between miR-532-3p and ADAM19, respectively. The progression of ccRCC cells was determined via CCK-8, colony formation, scratch healing, and transwell assays. Western blot was applied to detect the protein levels of epithelial-mesenchymal transition markers and ADAM19. We herein suggested that LINC00565 was prominently upregulated in ccRCC tissues and cells. Knockdown of LINC00565 repressed cell progression. We further predicted and validated miR-532-3p as a target of LINC00565, and miR-532-3p could target ADAM19. Knockdown of LINC00565 resulted in ADAM19 level downregulation in ccRCC cells and suppressed miR-532-3p could restore ADAM19 level. Thus, the three RNAs constructed a ceRNA network. Overexpressed ADAM19 could eliminate the anticancer effects caused by knocking down LINC00565 on ccRCC cells. In conclusion, LINC00565 upregulated ADAM19 via absorbing miR-532-3p, thereby facilitating the progression of ccRCC cells., Competing Interests: None

MicroRNA-3613 (hsa-miR-3613-5p), a biomarker with a dual role as an oncogenic or tumor suppressor, is associated with different types of cancer. This study aimed to determine the correlation between the hsa-miR-3613-5p gene expression and Kidney renal clear cell carcinoma (KIRC). Utilizing several bioinformatics tools, we examined the expression level and clinicopathological value of hsa-miR-3613-5p in patients with KIRC compared to normal tissues. Other bioinformatic measures, including survival analysis, diagnostic merit of hsa-miR-3613-5p, downstream target prediction, potential upstream lncRNAs, network construction, and functional enrichment analysis of hsa-miR-3613-5p, were performed. We observed that overexpression of hsa-miR-3613-5p in KIRC tissues had valuable diagnostic merit and was significantly correlated with the poor overall survival of KIRC patients. We also realized a correlation between abnormal expression of hsa-miR-3613-5p and several clinical parameters such as pathological stage, race, age, and histological grades in patients with KIRC. Moreover, we constructed the most potential regulatory network of hsa-miR-3613-5p in KIRC with 17 different axes, including four pseudogenes, two lncRNAs, and three mRNAs. Besides, we uncovered six variants in the mature form of hsa-miR-3613-5p. Finally, pathway enrichment analysis demonstrated that the top-ranked pathways for hsa-miR-3613-5p are cell cycle, cell adhesion molecules (CAMs), and hepatocellular carcinoma pathways. The present report suggests that the higher expression of hsa-miR-3613-5p is associated with the progression of KIRC. Therefore, it may be considered a valuable indicator for the early detection, risk stratification, and targeted treatment of patients with KIRC., Competing Interests: Declaration of Competing Interest Authors declare no conflicts of interests., (Copyright © 2023 Elsevier GmbH. All rights reserved.)

Background: Renal cell carcinoma (RCC) accounts for approximately 2-3% of all adult malignancies. Clear cell renal cell carcinoma (ccRCC), which comprises 70-80% of all RCC cases, is the most common histological subtype., Methods: ccRCC transcriptome data and clinical information were downloaded from the TCGA database. We used the TCGA and GEPIA databases to analyze relative expression of BMP1 in various types of human cancer. GEPIA was used to perform survival analysis for BMP1 in various cancer types. Upstream binding miRNAs of BMP1 were obtained through several important target gene prediction tools. StarBase was used to predict candidate miRNAs that may bind to BMP1 and candidate lncRNAs that may bind to hsa-miR-532-3p. We analyzed the association between expression of BMP1 and immune cell infiltration levels in ccRCC using the TIMER website. The relationship between BMP1 expression levels and immune checkpoint expression levels was also investigated., Results: BMP1 was upregulated in GBM, HNSC, KIRC, KIRP and STAD and downregulated in KICH and PRAD. Combined with OS and DFS, BMP1 can be used as a biomarker for poor prognosis among patients with KIRC. Through expression analysis, survival analysis and correlation analysis, LINC00685, SLC16A1-AS1, PVT1, VPS9D1-AS1, SNHG15 and the CCDC18-AS1/hsa-miR-532-3p/BMP1 axis were established as the most potential upstream ncRNA-related pathways of BMP1 in ccRCC. Furthermore, we found that BMP1 levels correlated significantly positively with tumor immune cell infiltration, biomarkers of immune cells, and immune checkpoint expression., Conclusion: Our results demonstrate that ncRNA-mediated high expression of BMP1 is associated with poor prognosis and tumor immune infiltration in ccRCC., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Kidney renal clear cell carcinoma (KIRC) is one of the most prevalent malignant tumors of the urinary system, with a high recurrence and metastasis rate. Telomeres and long non-coding RNAs (lncRNAs) have been documented playing critical roles in cancer progression. However, the prognostic significance of telomere-related lncRNA (TRLs) in KIRC is less well-defined. The Cancer Genome Atlas database was applied to retrieve the expression profiles and corresponding clinical information of KIRC patients. To create the TRLs prognostic signature, univariate Cox regression, least absolute shrinkage and selection operator analyses were performed. The prognostic signature, comprised of nine prognostic TRLs, was developed and demonstrated superior prognostic ability for KIRC patients. Additionally, the risk score acted as an independent prognostic indicator. A nomogram incorporating age, grade, stage, and signature-based risk scores was also developed and exhibited excellent predictive accuracy. Several immune activities were associated with the signature, as determined by gene function analysis. Further analysis revealed differences in the status of immunity and the tumor microenvironment between low- and high-risk groups. Notably, KIRC patients with high-risk scores were more responsive to immunotherapy and chemotherapy. To summarize, our study developed a new prognostic signature consisting of nine telomere-related lncRNA that can precisely predict the prognosis of KIRC patients. The signature was shown to be of substantial value for the tumor microenvironment and tumor mutation burden, thereby contributing to a framework for the individualized treatment of patients.

Background: Long noncoding RNAs (lncRNAs) have been reported to play an important role in tumor immune modification. Nonetheless, the clinical implication of immune-associated lncRNAs in renal cell carcinoma (RCC) remains to be further explored., Methods: 76 combinations of machine learning algorithms were integrated to develop and validate a machine learning-derived immune-related lncRNA signature (MDILS) in five independent cohorts (n = 801). We collected 28 published signatures and collated clinical variables for comparison with MDILS to verify its efficacy. Subsequently, molecular mechanisms, immune status, mutation landscape, and pharmacological profile were further investigated in different stratified patients., Results: Patients with high MDILS displayed worse overall survival than those with low MDILS. The MDILS could independently predict overall survival and convey robust performance across five cohorts. MDILS has a significantly better performance compared with traditional clinical variables and 28 published signatures. Patients with low MDILS exhibited more abundant immune infiltration and higher potency of immunotherapeutic response, while patients with high MDILS might be more sensitive to multiple chemotherapeutic drugs (e.g., sunitinib and axitinib)., Conclusion: MDILS is a robust and promising tool to facilitate clinical decision-making and precision treatment of RCC., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Renal cell cancer is associated with the coagulation system. Long non-coding RNA (lncRNA) expression is closely associated with the development of clear cell renal cell carcinoma (ccRCC). The aim of this study was to build a novel lncRNA model to predict the prognosis and immunological state of ccRCC. The transcriptomic data and clinical data of ccRCC were retrieved from TCGA database, subsequently, the lasso regression and lambda spectra were used to filter prognostic lncRNAs. ROC curves and the C-index were used to confirm the predictive effectiveness of this model. We also explored the difference in immune infiltration, immune checkpoints, tumor mutation burden (TMB) and drug sensitivity between the high- and low-risk groups. We created an 8 lncRNA model for predicting the outcome of ccRCC. Multivariate Cox regression analysis showed that age, tumor grade, and risk score are independent prognostic factors for ccRCC patients. ROC curve and C-index revealed the model had a good performance in predicting prognosis of ccRCC. GO and KEGG analysis showed that coagulation related genes were related to immune response. In addition, high risk group had greater TMB level and higher immune checkpoints expression. Sorafenib, Imatinib, Pazopanib, and etoposide had higher half maximal inhibitory concentration (IC 50) in the high risk group whereas Sunitinib and Bosutinib had lower IC 50 . This novel coagulation-related long noncoding RNAs model could predict the prognosis of patients with ccRCC, and coagulation-related lncRNA may be connected to the tumor microenvironment and gene mutation of ccRCC., (© 2023. Springer Nature Limited.)

Long noncoding RNAs metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) can regulate tumorigenesis and progression of various cancers. However, there is little known about the tumor biology and regulatory mechanism of MALAT1 in clear cell renal cell carcinoma (ccRCC). The objective of this study was to evaluate the prognostic value and potential functions of MALAT1 in ccRCC based on the cancer genome atlas. Through bioinformatics research, we analyzed the expression of MALAT1 in ccRCC, and the relationship with clinicopathological features, overall survival and infiltration of immune cells, and established the prognostic models. The results showed that MALAT1 was highly expressed in ccRCC tissues and predicted poor ccRCC patient outcome. The expression level of MALAT1 was significantly correlated with histologic grade, pathologic grade, T stage, M stage. ROC curve showed that MALAT1 had a good diagnostic accuracy, area under the curve of 0.752. The univariate and multivariate cox regression analysis showed that high MALAT1 expression was an independent prognostic factor for overall survival in the cancer genome atlas (hazard ratio = 2.271, 95% confidence interval: 1.435-3.593, P < .001). Gene set enrichment analysis revealed that MALAT1 expression was associated with the DNA methylation, epigenetic regulation of gene expression signaling pathway. In addition, the prognostic models were established to predict 1-, 3- and 5-year survival. This study showed that high expression of MALAT1 might be a potential diagnostic and prognostic biomarker., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.)

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&#95;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.

Background: Renal cell carcinoma (RCC) is a malignant tumour that may develop in the kidney. RCC is one of the most common kinds of tumours of this sort, and its most common pathological subtype is kidney renal clear cell carcinoma (KIRC). However, the aetiology and pathogenesis of RCC still need to be clarified. Exploring the internal mechanism of RCC contributes to diagnosing and treating this disease. Pyroptosis is a critical process related to cell death. Recent research has shown that pyroptosis is a critical factor in the initiation and progression of tumour formation. Thus far, researchers have progressively uncovered evidence of the regulatory influence that long noncoding RNAs (lncRNAs) have on pyroptosis., Methods: In this work, a comprehensive bioinformatics approach was used to produce a predictive model according to pyroptosis-interrelated lncRNAs for the purpose of predicting the overall survival and molecular immune specialties of patients diagnosed with KIRC. This model was verified from multiple perspectives., Results: First, we discovered pyroptosis-associated lncRNAs in KIRC patients using the TCGA database and a Sankey diagram. Then, we developed and validated a KIRC patient risk model based on pyroptosis-related lncRNAs. We demonstrated the grouping power of PLnRM through PCA and used PLnRM to assess the tumour immune microenvironment and response to immunotherapy. Immunological and molecular traits of diverse PLnRM subgroups were evaluated, as were clinical KIRC patient characteristics and predictive risk models. On this basis, a predictive nomogram was developed and analyzed, and novel PLnRM candidate compounds were identified. Finally, we investigated possible medications used by KIRC patients., Conclusions: The results demonstrate that the model generated has significant value for KIRC in clinical practice., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Renal cancer stem cells (RCSCs) derived from clear cell renal cell carcinoma (ccRCC) tissues with higher microvessel density (MVD) have strong stemness and endothelial progenitor cells-like (EPCs-like) characteristics. A high level of lncRNA PVT1 expression is essential for simultaneously retaining strong RCSC stemness and EPCs-like characteristics. PVT1 binds with TAZ protein and prevents its phosphorylation, which promotes RCSC stemness. Moreover, RCSCs support endothelial differentiation and angiogenesis, which are mediated via the PVT1/miR-15b/KDR axis. This report provides insight into the determinants of RCSC impact on stemness and highlights the critical role of RCSC in angiogenesis. The presented findings suggest that targeting RCSC through PVT1 expression may be a new treatment strategy for ccRCC., (© 2023 The Authors. The FASEB Journal published by Wiley Periodicals LLC on behalf of Federation of American Societies for Experimental Biology.)

Background: Necroptosis is an immune-related cell death pathway involved in the regulation of the tumor microenvironment (TME). Here, we aimed to explore the role of necroptosis in clear cell renal cell carcinoma (ccRCC) and construct a necroptosis-related lncRNA (NRL) model to assess its potential association with clinical characteristics and immune status., Methods: Gene expression profiles and clinical data for ccRCC patients were obtained from the Cancer Genome Atlas (TCGA). Pearson's correlation, univariate Cox, and least absolute shrinkage and selection operator analyses were used to develop an NRL model. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curve analyses were used to determine the prognostic value of the NRL model. The clinical information was used to assess the diagnostic value of the NRL model. The TME, immune function, immune cell infiltration, and immune checkpoints associated with the NRL model risk score were studied using the ESTIMATE, GSEA, ssGSEA, and CIBERSORT algorithms. The immunophenoscore (IPS) and half-maximal inhibitory concentration (IC50) were used to compare the efficacies of immunotherapy and chemotherapy based on the NRL model. Finally, in vitro assays were performed to confirm the biological roles of NRLs., Results: A total of 18 necroptosis-related genes and 285 NRLs in ccRCC were identified. A four-NRL model was constructed and showed good performance in the diagnosis and prognosis of ccRCC patients. The ESTIMATE scores, tumor mutation burden, and tumor stemness indices were significantly correlated with NRL model risk score. Immune functions such as chemokine receptors and immune receptor activity showed differences between different risk groups. The infiltration of immunosuppressive cells such as Tregs was higher in high-risk patients than in low-risk patients. High-risk patients were more sensitive to immunotherapy and some chemotherapy drugs, such as sunitinib and temsirolimus. Finally, the expression of NRLs included in the model was verified, and knocking down these NRLs in tumor cells affected cell proliferation, migration, and invasion., Conclusion: Necroptosis plays an important role in the progression of ccRCC. The NRL model we constructed can be used to predict the clinical characteristics and immune features of ccRCC patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhang, Chen, Hu, Wu, Ye, Wang and Bai.)