Development and validation of a novel necroptosis-related score to improve the outcomes of clear cell renal cell carcinoma.
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SUBMITTER: Chen J
PROVIDER: S-EPMC9510770 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
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Development and validation of a novel necroptosis-related score to improve the outcomes of clear cell renal cell carcinoma.
Chen Ji J Tao Qiqi Q Lang Zhichao Z Jin Yan Y Chen Guanqi G Li Xinling X Yu Zhixian Z Li Yeping Y
Frontiers in genetics 20220912
Necroptosis has been indicated as a key regulator of tumor progression. However, the prognostic regulatory role of necroptosis in clear cell renal cell carcinoma (ccRCC) needs to be further investigated. In this study, necroptosis-related subtypes were identified by mining the public cohort (<i>n</i> = 530) obtained from The Cancer Genome Atlas. By applying Principal Component Analysis (PCA), the necroptosis-related scores (N-Score) were developed to assess the prognosis procession of ccRCC. The ...[more]
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Project description:BackgroundNecroptosis is progressively becoming an important focus of research because of its role in the pathogenesis of cancer and other inflammatory diseases. Our study is designed to anticipate the survival time of kidney renal clear cell carcinoma (KIRC) by constructing a prognostic signature of necroptosis-related genes.MaterialsClinical information and RNA-seq data were acquired from Renal Cell Cancer-European Union (RECA-EU) and The Cancer Genome Atlas- (TCGA-) KIRC, respectively. ConsensusClusterPlus was used to identify molecular subtypes, and the distribution of immune cell infiltration, anticancer drug sensitivity, and somatic gene mutations was studied in these subtypes. Subsequently, LASSO-Cox regression and univariate Cox regression were also carried out to construct a necroptosis-related signature. Cox regression, survival analysis, clinicopathological characteristic correlation analysis, nomogram, cancer stem cell analysis, and receiver operating characteristic (ROC) curve were some tools employed to study the prognostic power of the signature.ResultsBased on the expression patterns of 66 survival-related necroptosis genes, we classified the KIRC into three subtypes (C1, C2, and C3) that are associated with necroptosis, which had significantly different tumor stem cell components. Among these, C2 patients had a longer survival time and enhanced immune status and were more sensitive to conventional chemotherapeutic drugs. Moreover, in order to predict the prognosis of KIRC patients, five genes (BMP8A, TLCD1, CLGN, GDF7, and RARB) were used to develop a necroptosis-related prognostic signature, which had an acceptable predictive potency. The results from Cox regression and stratified survival analysis revealed that the signature was an independent prognostic factor, whereas the nomogram and calibration curve demonstrated satisfactory survival time prediction based on the risk score.ConclusionsThree molecular subtypes and five necroptosis-related genes were discovered in KIRC using data from TCGA-KIRC and RECA-EU. Thus, a new biomarker and a potentially effective therapeutic approach for KIRC patients were provided in the current study.
| S-EPMC10005877 | biostudies-literature
Project description:Clear cell renal cell carcinoma (ccRCC) is the most common kind of kidney cancer with poor prognosis. Necroptosis is a newly observed type of programmed cell death in recent years. However, the effects of necroptosis-related lncRNAs (NRlncRNAs) on ccRCC have not been widely explored. The transcription profile and clinical information were obtained from The Cancer Genome Atlas. Necroptosis-related lncRNAs were identified by utilizing a co-expression network of necroptosis-related genes and lncRNAs. Univariate Cox regression, least absolute shrinkage, and selection operator regression and multivariate Cox regression were performed to screen out ideal prognostic necroptosis-related lncRNAss and develop a multi-lncRNA signature. Finally, 6 necroptosis-related lncRNA markers were established. Patients were separated into high- and low-risk groups based on the performance value of the median risk score. Kaplan-Meier analysis identified that high-risk patients had poorer prognosis than low-risk patients. Furthermore, the area under time-dependent receiver operating characteristic curve reached 0.743 at 1 year, 0.719 at 3 years, and 0.742 at 5 years, which indicating that they can be used to predict ccRCC prognosis. In addition, the proposed signature was related to immunocyte infiltration. A nomogram model was also established to provide a more beneficial prognostic indicator for the clinic. Altogether, in the present study, the 6-lncRNA prognostic risk signature are trustworthy and effective indicators for predicting the prognosis of ccRCC.
| S-EPMC9524942 | biostudies-literature
Project description:Necroptosis is a type of caspase-independent cell death, and it plays a critical role in regulating the development of cancer. To date, little is known about the role of necroptosis-related genes (NRGs) in clear cell renal cell carcinoma (ccRCC). In this study, we downloaded data regarding the expression of NRGs and overall survival (OS) from The Cancer Genome Atlas (TCGA) database and constructed a risk model to determine the prognostic features of necroptosis using COX regression analysis. Patients with ccRCC were divided into low-risk and high-risk groups based on their risk scores. Thereafter, Kaplan-Meier curves were used to evaluate OS, and receiver operating characteristic (ROC) curves were used to determine the accuracy of prediction. Stratified analyses were performed according to different clinical variables. Furthermore, we assessed the correlation between clinical variables and risk scores; the NRGs with differential expression were mainly enriched in positive regulation of intracellular transport and platinum resistance pathways. We constructed prognostic signatures for OS based on four NRGs and showed that the survival time was significantly longer in the low-risk groups than in the high-risk groups (p < 0.001). The area of the ROC curve for OS was 0.717, indicating excellent predictive accuracy of the established model. Therefore, a predictive model based on NRGs was constructed, which can predict the prognosis of patients and provides insights into the biological mechanisms underlying necroptosis in patients with ccRCC.
| S-EPMC9395686 | biostudies-literature
Project description:IntroductionRenal 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 methodsUsing 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.ResultsMYCN, 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.ConclusionsMYCN 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.
| S-EPMC10600093 | biostudies-literature
Project description:Clear cell renal cell carcinoma (ccRCC) is the most common histological and devastating subtype of renal cell carcinoma. Necroptosis is a form of programmed cell death that causes prominent inflammatory responses. miRNAs play a significant role in cancer progression through necroptosis. However, the prognostic value of necroptosis-related miRNAs remains ambiguous. In this study, 39 necroptosis-related miRNAs (NRMs) were extracted and 17 differentially expressed NRMs between normal and tumor samples were identified using data form The Cancer Genome Atlas (TCGA). After applying univariate Cox proportional hazard regression analysis and LASSO Cox regression model, six necroptosis-related miRNA signatures were identified in the training cohort and their expression levels were verified by qRT-PCR. Using the expression levels of these miRNAs, all patients were divided into the high- and low-risk groups. Patients in the high-risk group showed poor overall survival (P < 0.0001). Time-dependent ROC curves confirmed the good performance of our signature. The results were verified in the testing cohort and the entire TCGA cohort. Univariate and multivariate Cox regression models demonstrated that the risk score was an independent prognostic factor. Additionally, a predictive nomogram with good performance was constructed to enhance the implementation of the constructed signature in a clinical setting. We then employed miRBD, miRTarBase, and TargetScan to predict the target genes of six necroptosis-related miRNAs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses indicated that 392 potential target genes were enriched in cell proliferation-related biological processes. Six miRNAs and 59 differentially expressed target genes were used to construct an miRNA-mRNA interaction network, and 11 hub genes were selected for survival and tumor infiltration analysis. Drug sensitivity analysis revealed potential drugs that may contribute to cancer management. Hence, necroptosis-related genes play an important role in cancer biology. We developed, for the first time, a necroptosis-related miRNA signature to predict ccRCC prognosis.
| S-EPMC9061065 | biostudies-literature
Project description:BackgroundKidney renal clear cell carcinoma (KIRC) is one of the most common aggressive malignancies in the genitourinary system with the high degree of immune infiltration. However, the role of necroptosis-related genes in the immune infiltration of KIRC and the impact on overall survival have not been adequately studied.MethodsDifferentially expressed necroptosis-related genes were identified based on The Cancer Genome Atlas (TCGA). Then, we constructed a necroptosis-related prognostic index (NRPI) through Lasso Cox regression analysis. The KIRC patients were divided into NRPI-high and NRPI-low groups by the median. Univariate and multivariate Cox regression analyses were used to determine NRPI as an independent prognostic factor. The role of NRPI was assessed through nomogram, GO/KEGG enrichment analyses, and immune cells infiltration. The efficacy of immunotherapy in KIRC patients was evaluated by TIDE. The immunohistochemistry was performed to verify the difference in protein expression between tumor samples and normal tissues from our hospital.ResultsWe found that NRPI-high patients had higher mortality. The multivariate Cox regression between the signature and multiple clinicopathological characteristics proved that NRPI could effectively and independently predict the prognosis of KIRC. The protein expression of three necroptosis-related genes constituting NRPI was significantly different between tumor and normal tissues. NRPI was closely related to immunologically relevant pathways and functions. The tumor microenvironment and immune infiltrating cells showed clear distinctions in NRPI-high and NRPI-low patients. The analysis of clinical treatments found that NRPI-low patients responded better to immunotherapy, while NRPI-high patients were more sensitive to targeted therapy. Furthermore, we identified a lncRNAs/miRNA/mRNA regulatory axis for KIRC.ConclusionIn general, NRPI was a promising biomarker in predicting the prognosis and responses to treatments in KIRC.
| S-EPMC9173730 | biostudies-literature
Project description:BackgroundNecroptosis 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.MethodsNecroptosis-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).ResultsDistinct 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.ConclusionThe 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.
| S-EPMC10734432 | biostudies-literature
Project description:Objective: To construct a prognostic evaluation model for clear cell renal cell carcinoma (ccRCC) patients using bioinformatics method and to screen potential drugs for ccRCC. Methods: ccRCC RNA sequencing data, clinical data, and protein expression data were downloaded from the TCGA database. Univariate Cox and Lasso regression analyses were performed on the combined data to screen out the proteins related to the prognosis, and they were included in a multivariate Cox proportional hazard model. The patients were divided into high and low-risk groups for a survival difference analysis. The predictive power of the model was evaluated on the basis of overall survival, progression-free survival, independent prognostic, clinically relevant receiver operating characteristic (ROC) curve, C-index, principal component, and clinical data statistics analyses. GSEA enrichment and immune function correlation analyses were performed. The samples were divided into different subtypes based on the expression of the risk proteins, and survival analysis of the subtypes was performed. The risk-related protein and RNA sequencing data were analyzed to screen out sensitive drugs with significant differences between the high and low-risk groups. Results: A total of 469 ccRCC-related proteins were screened, of which 13 proteins with independent prognostic significance were screened by univariate Cox, Lasso, and multivariate Cox regression analyses to construct the prognostic model. The sensitivity and accuracy of the model in predicting the survival of patients with ccRCC were high (1 year: 0.811, 3 years: 0.783, 5 years: 0.777). The 13 proteins were closely related to immunity, and the model proteins were different between kidney and tumor tissues according to the HPA database. The samples were divided into three subtypes, and there were obvious clinical characteristics of the three subtypes in the grade and T, N and M stages. According to the IC50 values, CGP-60474, vinorelbine, doxorubicin, etoposide, FTI-277, JQ12, OSU-03012, pyrimethamine, and other drugs were more sensitive in the high-risk group. Conclusions: A prognostic model of protein expression in ccRCC was successfully constructed, which had good predictive ability for the prognosis of ccRCC patients. The ccRCC-related proteins in the model can be used as targets for studying the pathogenesis and targeted therapy.
| S-EPMC10088890 | biostudies-literature
Project description:BackgroundAs the main histological subtype of renal cell carcinoma, clear cell renal cell carcinoma (ccRCC) places a heavy burden on health worldwide. Autophagy-related long non-coding RNAs (ARlncRs) have shown tremendous potential as prognostic signatures in several studies, but the relationship between them and ccRCC still has to be demonstrated.MethodsThe RNA-sequencing and clinical characteristics of 483 ccRCC patients were downloaded download from the Cancer Genome Atlas and International Cancer Genome Consortium. ARlncRs were determined by Pearson correlation analysis. Univariate and multivariate Cox regression analyses were applied to establish a risk score model. A nomogram was constructed considering independent prognostic factors. The Harrell concordance index calibration curve and the receiver operating characteristic analysis were utilized to evaluate the nomogram. Furthermore, functional enrichment analysis was used for differentially expressed genes between the two groups of high- and low-risk scores.ResultsA total of 9 SARlncRs were established as a risk score model. The Kaplan-Meier survival curve, principal component analysis, and subgroup analysis showed that low overall survival of patients was associated with high-risk scores. Age, M stage, and risk score were identified as independent prognostic factors to establish a nomogram, whose concordance index in the training cohort, internal validation, and external ICGC cohort was 0.793, 0.671, and 0.668 respectively. The area under the curve for 5-year OS prediction in the training cohort, internal validation, and external ICGC cohort was 0.840, 0.706, and 0.708, respectively. GO analysis and KEGG analysis of DEGs demonstrated that immune- and inflammatory-related pathways are likely to be critically involved in the progress of ccRCC.ConclusionsWe established and validated a novel ARlncRs prognostic risk model which is valuable as a potential therapeutic target and prognosis indicator for ccRCC. A nomogram including the risk model is a promising clinical tool for outcomes prediction of ccRCC patients and further formulation of individualized strategy.
| S-EPMC9741795 | biostudies-literature
Project description:BackgroundDespite progression in its treatment, the clinical outcome of patients with clear cell renal cell carcinoma (ccRCC) remains not ideal. Anoikis is a unique form of programmed apoptosis, owing to insufficient cell-matrix interactions. Anoikis plays a crucial role in tumor migration and invasion, and tumor cells could protect themselves through the capacity of anoikis resistance.MethodsAnoikis-related genes (ARGs) were obtained from Genecards and Harmonizome portals. The ARGs related to ccRCC prognosis were identified through univariate Cox regression analysis, then we utilized these ARGs to construct a novel prognostic model for ccRCC patients. Moreover, we explored the expression profile of ARGs in ccRCC using the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. We also conducted Real-Time Polymerase Chain Reaction (RT-PCR) to probe ARGs expression of the risk score. Finally, we performed correlation analysis between ARGs and tumor immune microenvironment.ResultsWe identified 17 ARGs associated with ccRCC survival, from which 7 genes were chosen to construct a prognostic model. The prognostic model was verified as an independent prognostic indicator. The expression of most ARGs was higher in ccRCC samples. These ARGs were closely correlated with immune cell infiltration and immune checkpoint members, and had independent prognostic value respectively. Functional enrichment analysis demonstrated that these ARGs were significantly associated with multiple types of malignances.ConclusionThe prognostic signature was identified to be highly efficient in predicting ccRCC prognosis, and these ARGs were closely related to tumor microenvironment.
| S-EPMC10086373 | biostudies-literature