Identification of a Necroptosis-Related Prognostic Index and Associated Regulatory Axis in Kidney Renal Clear Cell Carcinoma

Non-coding transcriptome profiles in clear-cell renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC) is a common urological malignancy with an increasing incidence. The development of molecular biomarkers that can predict the response to treatment and guide personalized therapy selection would substantially improve patient outcomes. Dysregulation of non-coding RNA (ncRNA) has been shown to have a role in the pathogenesis of ccRCC. Thus, an increasing number of studies are being carried out with a focus on the identification of ncRNA biomarkers in ccRCC tissue samples and the connection of these markers with patients' prognosis, pathological stage and grade (including metastatic potential), and therapy outcome. RNA sequencing analysis led to the identification of several ncRNA biomarkers that are dysregulated in ccRCC and might have a role in ccRCC development. These ncRNAs have the potential to be prognostic and predictive biomarkers for ccRCC, with prospective applications in personalized treatment selection. Research on ncRNA biomarkers in ccRCC is advancing, but clinical implementation remains preliminary owing to challenges in validation, standardization and reproducibility. Comprehensive studies and integration of ncRNAs into clinical trials are essential to accelerate the clinical use of these biomarkers.

Disulfidptosis-associated LncRNA signature predicts prognosis and immune response in kidney renal clear cell carcinoma

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.

The fate and function of non-coding RNAs during necroptosis

Necroptosis is a novel form of cell death which is activated when apoptotic cell death signals are disrupted. Accumulating body of observations suggests that noncoding RNAs, which are the lately discovered mystery of the human genome, are significantly associated with necroptotic signaling circuitry. The fate and function of miRNAs have been well documented in human disease, especially cancer. Recently, lncRNAs have gained much attention due to their diverse regulatory functions. Although available studies are currently based on bioinformatic analysis, predicted interactions desires further attention, as these hold significant promise and should not be overlooked. In the light of these, here we comprehensively review and discuss noncoding RNA molecules that play significant roles during execution of necroptotic cell death.

Identification and validation of prognostic and tumor microenvironment characteristics of necroptosis index and BIRC3 in clear cell renal cell carcinoma

Background

Necroptosis is a form of programmed cell death; it has an important role in tumorigenesis and metastasis. However, details of the regulation and function of necroptosis in clear cell renal cell carcinoma (ccRCC) remain unclear. It is necessary to explore the significance of necroptosis in ccRCC.

Methods

Necroptosis-related clusters were discerned through the application of Consensus Clustering. Based on the TCGA and GEO databases, we identified prognostic necroptosis-related genes (NRGs) with univariate COX regression analysis. The necroptosis-related model was constructed through the utilization of LASSO regression analysis, and the immune properties, tumor mutation burden, and immunotherapy characteristics of the model were assessed using multiple algorithms and datasets. Furthermore, we conducted comprehensive GO, KEGG, and GSVA analyses to probe into the functional aspects of biological pathways. To explore the expression and of hub gene (BIRC3) in different ccRCC cell types and cell lines, single-cell sequencing data was analysed and we performed Quantitative Real-time PCR to detect the expression of BIRC3 in ccRCC cell lines. Function of BIRC3 in ccRCC was assessed through Cell Counting Kit-8 (CCK8) assay (for proliferation), transwell and wound healing assays (for migration and invasion).

Results

Distinct necroptosis-related clusters exhibiting varying prognostic implications, and enrichment pathways were identified in ccRCC. A robust necroptosis-related model formulated based on the expression of six prognostic NRGs, presented substantial predictive capabilities of overall survival and was shown to be related with patients' immune profiles, tumor mutation burden, and response to immunotherapy. Notably, the hub gene BIRC3 was markedly upregulated in both ccRCC tissues and cell lines, and showed significant correlations with immunosuppressive cells, immune checkpoints, and oncogenic pathways. Downregulation of BIRC3 demonstrated a negative regulatory effect on ccRCC cell proliferation migration and invasion.

Conclusion

The necroptosis-related model assumed a pivotal role in determining the prognosis, tumor mutation burden, immunotherapy response, and immune cell infiltration characteristics among ccRCC patients. BIRC3 exhibited significant correlations with the immunosuppressive microenvironment, which highlighted its potential for informing the design of innovative immunotherapies for ccRCC patients.

Necroptosis-related genes are associated with prognostic features of kidney renal clear cell carcinoma

INTRODUCTION

Renal clear cell carcinoma is a common type of cancer in the adult urological system. It has a high mortality rate, with 30% of patients developing metastasis and 60% dying within 1-2 years of diagnosis. Recent advancements in tumor immunology and necroptosis have provided new insights into kidney cancer therapy. Therefore, it is crucial to identify potential targets for combining immunotherapy with necroptosis.

MATERIALS AND METHODS

Using the GSE168845 dataset and necroptosis-related genes, we identified genes that are differentially expressed in relation to necroptosis. We analyzed the prognostic value of these genes through differential expression analysis, prognostic analysis, and Cox regression analysis. The expression levels of the MYCN and CDKN2A genes were verified using the GSE53757 dataset. We also examined the association between the differentially expressed genes and clinicopathological features, as well as overall survival in our cohorts. In addition, we constructed a lasso Cox regression model to assess the correlation between these genes and immune score, ICP, and OCLR score. We conducted qRT-PCR to detect the expression of MYCN, CDKN2A, and ZBP1 in different samples of kidney renal clear cell carcinoma (KIRC). The expression levels of these genes were verified in a normal kidney cell line (HK-2 cells) and two KIRC cell lines (786-O, ACHN). The protein levels of MYCN and CDKN2A were detected using immunohistochemistry (IHC). SiRNA was used to silence the expression of MYCN and CDKN2A in the ACHN cell line, and wound healing assays were performed to measure cell migration.

RESULTS

MYCN, CDKN2A, and ZBP1 were identified as necroptosis-related genes with independent prognostic value, leading to the development of a risk prognostic model. The expression of the CDKN2A gene was significantly higher in KIRC tissues compared to normal tissues, while the expression of the MYCN gene was significantly lower in KIRC tissues. The expression of MYCN and CDKN2A was associated with tumor stage, metastasis, and overall survival in our cohort. Furthermore, MYCN, CDKN2A, and ZBP1 were significantly correlated with immune score, ICP, and OCLR score. The expression levels of CDKN2A and ZBP1 were higher in KIRC cells compared to normal kidney cells, while the expression of MYCN was lower in KIRC cells. The protein expression of MYCN and CDKN2A was also higher in KIRC tissues, as confirmed by IHC. The results of the wound healing assay indicated that silencing CDKN2A inhibited cell migration, while silencing MYCN enhanced cell migration.

CONCLUSIONS

MYCN and CDKN2A are potential targets and valuable prognostic biomarkers for combining immunotherapy with necroptosis in kidney renal clear cell carcinoma. CDKN2A promotes the migration of renal cancer cells, while MYCN inhibits their migration.

Qualification of Necroptosis-Related lncRNA to Forecast the Treatment Outcome, Immune Response, and Therapeutic Effect of Kidney Renal Clear Cell Carcinoma

Background

Kidney renal clear cell carcinoma (KIRC) is considered as a highly immune infiltrative tumor. Necroptosis is an inflammatory programmed cell death associated with a wide range of diseases. Long noncoding RNAs (lncRNAs) play important roles in gene regulation and immune function. lncRNA associated with necroptosis could systematically explore the prognostic value, regulate tumor microenvironment (TME), etc.

Method

The patients' data was collected from TCGA datasets. We used the univariate Cox regression (UCR) to select prediction lncRNAs that are related to necroptosis. Meanwhile, risk models were constructed using LASSO Cox regression (LCR). Kaplan–Meier (KM) analysis, accompanied with receiver operating characteristic (ROC) curves, was performed to assess the independent risk factors of different clinical characteristics. The evaluated factors are age, gender, disease staging, grade, and their related risk score. Databases such as Gene Ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSEA) were used to search the probable biological characteristics that could influence the risk groups, containing signaling pathway and immue-related pathways. The single-sample gene set enrichment analysis (ssGSEA) was chosen to perform gene set variation analysis (GSVA), and the GSEABase package was selected to detect the immune and inflammatory infiltration profiles. The TIDE and IC₅₀ evaluation were used to estimate the effectiveness of clinical treatment on KIRC.

Results

Based on the above analysis, we have got a conclusion that patients who show high risk had higher immune infiltration, immune checkpoint expression, and poorer prognosis. We identified 19 novel prognostic necroptosis-related lncRNAs, which could offer opinions for a deeper study of KIRC.

Conclusion

The risk model we constructed makes it possible to predict the prognosis of KIRC patients and offers directions for further research on the prognostication and treatment strategies for KIRC.

A novel marker based on necroptosis-related long non-coding RNA for forecasting prognostic in patients with clear cell renal cell carcinoma

Background: The incidence of clear cell renal cell carcinoma (ccRCC) is high and has increased gradually in recent years. At present, due to the lack of effective prognostic indicators, the prognosis of ccRCC patients is greatly affected.Necroptosis is a type of cell death, and along with cell necrosis is considered a new cancer treatment strategy. The aim of this study was to construct a new marker for predicting the prognosis of ccRCC patients based on long non-coding RNA (nrlncRNAs) associated with necroptosis.

Methods: RNA sequence data and clinical information of ccRCC patients from the Cancer Genome Atlas database (TCGA) were downloaded. NrlncRNA was identified by Pearson correlation study. The differentially expressed nrlncRNA and nrlncRNA pairs were identified by univariate Cox regression and Lasso-Cox regression. Finally, a Kaplan-Meier survival study, Cox regression, clinicopathological features correlation study, and receiver operating characteristic (ROC) spectrum were used to evaluate the prediction ability of 25-nrlncrnas for markers. In addition, correlations between the risk values and sensitivity to tumor-infiltrating immune cells, immune checkpoint inhibitors, and targeted drugs were also investigated.

Results: In the current research, a novel marker of 25-nrlncRNAs pairs was developed to improve prognostic prediction in patients with ccRCC. Compared with clinicopathological features, nrlncRNAs had a higher diagnostic validity for markers, with the 1-year, 3-years, and 5-years operating characteristic regions being 0.902, 0.835, and 0.856, respectively, and compared with the stage of 0.868, an increase of 0.034. Cox regression and stratified survival studies showed that this marker could be an independent predictor of ccRCC patients. In addition, patients with different risk scores had significant differences in tumor-infiltrating immune cells, immune checkpoint, and semi-inhibitory concentration of targeted drugs. The feature could be used to evaluate the clinical efficacy of immunotherapy and targeted drug therapy.

Conclusion: 25-nrlncRNAs pair markers may help to evaluate the prognosis and molecular characteristics of ccRCC patients, which improve treatment methods and can be more used in clinical practice.

Development and validation of novel inflammatory response-related gene signature to predict prostate cancer recurrence and response to immune checkpoint therapy

The aim of this study is to construct an inflammatory response-related genes (IRRGs) signature to monitor biochemical recurrence (BCR) and treatment effects in prostate cancer patients (PCa). A gene signature for inflammatory responses was constructed on the basis of the data from the Cancer Genome Atlas (TCGA) database, and validated in external datasets. It was analyzed using receiver operating characteristic curve, BCR-free survival, Cox regression, and nomogram. Distribution analysis and external model comparison were utilized. Then, enrichment analysis, tumor mutation burden, tumor immune microenvironment, and immune cell infiltration signatures were investigated. The role of the signature in immunotherapy was evaluated. The expression patterns of core genes were verified by RNA sequencing. We identified an IRRGs signature in the TCGA-PRAD cohort and verified it well in two other independent external datasets. The signature was a robust and independent prognostic index for predicting the BCR of PCa. The high-risk group of our signature predicted a shortened BCR time and an aggressive disease progression. A nomogram was constructed to predict BCR-free time in clinical practices. Neutrophils and CD8+ T cells were in higher abundance among the low-risk individuals. Immune functions varied significantly between the two groups and immune checkpoint therapy worked better for the low-risk patients. The expression of four IRRGs showed significant differences between PCa and surrounding benign tissues, and were validated in BPH-1 and DU145 cell lines by RNA sequencing. Our signature served as a reliable and promising biomarker for predicting the prognosis and evaluating the efficacy of immunotherapy, facilitating a better outcome for PCa patients.

Effects and Prognostic Values of Circadian Genes CSNK1E/GNA11/KLF9/THRAP3 in Kidney Renal Clear Cell Carcinoma via a Comprehensive Analysis

Kidney renal clear cell carcinoma (KIRC) is one of the most prevalent and deadly types of renal cancer in adults. Recent research has identified circadian genes as being involved in the development and progression of KIRC by altering their expression. This study aimed to identify circadian genes that are differentially expressed in KIRC and assess their role in KIRC progression. In KIRC, there were 553 differentially expressed rhythm genes (DERGs), with 300 up-regulated and 253 down-regulated DERGs. Functional enrichment analyses showed that DERGs were greatly enriched in the circadian rhythm and immune response pathways. Survival analyses indicated that higher expression levels of CSNK1E were related to shorter overall survival of KIRC patients, whereas lower expression levels of GNA11, KLF9, and THRAP3 were associated with shorter overall survival of KIRC patients. Through cell assay verification, the mRNA level of CSNK1E was significantly up-regulated, whereas the mRNA levels of GNA11, KLF9, and THRAP3 were dramatically down-regulated in KIRC cells, which were consistent with the bioinformatics analysis of KIRC patient samples. Age, grade, stage, TM classification, and CSNK1E expression were all shown to be high-risk variables, whereas GNA11, KLF9, and THRAP3 expression were found to be low-risk factors in univariate Cox analyses. Multivariate Cox analyses showed that CSNK1E and KLF9 were also independently related to overall survival. Immune infiltration analysis indicated that the proportion of immune cells varied greatly between KIRC tissues and normal tissue, whereas CSNK1E, GNA11, KLF9, and THRAP3 expression levels were substantially linked with the infiltration abundance of immune cells and immunological biomarkers. Moreover, interaction networks between CSNK1E/GNA11/KLF9/THRAP3 and immune genes were constructed to explore the stream connections. The findings could help us better understand the molecular mechanisms of KIRC progression, and CSNK1E/GNA11/KLF9/THRAP3 might be used as molecular targets for chronotherapy in KIRC patients in the near future.

Identification and validation of a signature involving voltage-gated chloride ion channel genes for prediction of prostate cancer recurrence

Voltage-gated chloride ion channels (CLCs) are transmembrane proteins that maintain chloride ion homeostasis in various cells. Accumulating studies indicated CLCs were related to cell growth, proliferation, and cell cycle. Nevertheless, the role of CLCs in prostate cancer (PCa) has not been systematically profiled. The purpose of this study was to investigate the expression profiles and biofunctions of CLCs genes, and construct a novel risk signature to predict biochemical recurrence (BCR) of PCa patients. We identified five differentially expressed CLCs genes in our cohort and then constructed a signature composed of CLCN2 and CLCN6 through Lasso-Cox regression analysis in the training cohort from the Cancer Genome Atlas (TCGA). The testing and entire cohorts from TCGA and the GSE21034 from the Gene Expression Omnibus (GEO) were used as internal and independent external validation datasets. This signature could divide PCa patients into the high and low risk groups with different prognoses, was apparently correlated with clinical features, and was an independent excellent prognostic indicator. Enrichment analysis indicated our signature was primarily concentrated in cellular process and metabolic process. The expression patterns of CLCN2 and CLCN6 were detected in our own cohort based immunohistochemistry staining, and we found CLCN2 and CLCN6 were highly expressed in PCa tissues compared with benign tissues and positively associated with higher Gleason score and shorter BCR-free time. Functional experiments revealed that CLCN2 and CLCN6 downregulation inhibited cell proliferation, colony formation, invasion, and migration, but prolonged cell cycle and promoted apoptosis. Furthermore, Seahorse assay showed that silencing CLCN2 or CLCN6 exerted potential inhibitory effects on energy metabolism in PCa. Collectively, our signature could provide a novel and robust strategy for the prognostic evaluation and improve treatment decision making for PCa patients.