Disulfidptosis-associated LncRNA signature predicts prognosis and immune response in kidney renal clear cell carcinoma
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ABSTRACT:
SUBMITTER: Xu K
PROVIDER: S-EPMC11340127 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:Clear cell Renal Cell Carcinoma (ccRCC), the most deadly and life-threatening tumor in the urinary system, has a dismal prognosis and a high risk of metastasizing. Regulation of ferroptosis is a prospective therapeutic target to eradicate malignant cells. Our objective was to seek ferroptosis-associated long non-coding RNAs (FALs) and developed a prediction signature for ccRCC. We extracted transcriptome data and clinical information from The Cancer Genome Atlas (TCGA) databases. Ferroptosis-associated genes (FAGs) were obtained from FerrDb database. A ferroptosis-associated lncRNA prognostic signature (FLPS) of ccRCC was generated utilizing univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression, sequentially, based on 8 lncRNAs (LINC00460, AC124854.1, AC084876.1, IGFL2-AS1, LINC00551, AC083967.1, AC073487.1, and LINC02446). The signature's independent predictive value for ccRCC was demonstrated using univariate and multivariate regression analysis (P < 0.05). Subsequently, by combining independent predictive factors, a prognostic nomogram was established. Immunity analysis proclaimed a striking difference in terms of cells, function, checkpoints, and ESTIMATE scores between low- and high-risk groups. Overall, the innovative signature of ferroptosis-associated signatures may have a considerable effect on the immune response and prognosis for ccRCC.
| S-EPMC9902540 | biostudies-literature
Project description:Kidney renal clear cell carcinoma (KIRC) is a heterogeneous malignant tumor with high incidence, metastasis, and mortality. The imbalance of copper homeostasis can produce cytotoxicity and cause cell damage. At the same time, copper can also induce tumor cell death and inhibit tumor transformation. The latest research found that this copper-induced cell death is different from the known cell death pathway, so it is defined as cuproptosis. We included 539 KIRC samples and 72 normal tissues from the Cancer Genome Atlas (TCGA) in our study. After identifying long non-coding RNAs (lncRNAs) significantly associated with cuproptosis, we clustered 526 KIRC samples based on the prognostic lncRNAs and obtained two different patterns (Cuproptosis.C1 and C2). C1 indicated an obviously worse prognostic outcome and possessed a higher immune score and immune cell infiltration level. Moreover, a prognosis signature (CRGscore) was constructed to effectively and accurately evaluate the overall survival (OS) of KIRC patients. There were significant differences in tumor immune microenvironment (TIME) and tumor mutation burden (TMB) between CRGscore-defined groups. CRGscore also has the potential to predict medicine efficacy.
| S-EPMC9515514 | biostudies-literature
Project description:Background: Oxidative stress is related to oncogenic transformation in kidney renal clear cell carcinoma (KIRC). We intended to identify a prognostic antioxidant gene signature and investigate its relationship with immune infiltration in KIRC. Methods: With the support of The Cancer Genome Atlas (TCGA) database, we researched the gene expression and clinical data of KIRC patients. Antioxidant related genes with significant differences in expression between KIRC and normal samples were then identified. Through univariate and multivariate Cox analysis, a prognostic gene model was established and all patients were divided into high- and low-risk subgroups. Single sample gene set enrichment analysis was adopted to analyze the immune infiltration, HLA expression, and immune checkpoint genes in different risk groups. Finally, the prognostic nomogram model was established and evaluated. Results: We identified six antioxidant genes significantly correlated with the outcome of KIRC patients as independent predictors, namely DPEP1 (HR = 0.97, P < 0.05), GSTM3 (HR = 0.97, P < 0.05), IYD (HR = 0.33, P < 0.05), KDM3B (HR = 0.96, P < 0.05), PRDX2 (HR = 0.99, P < 0.05), and PRXL2A (HR = 0.96, P < 0.05). The high- and low-risk subgroups of KIRC patients were grouped according to the six-gene signature. Patients with higher risk scores had poorer prognosis, more advanced grade and stage, and more abundance of M0 macrophages, regulatory T cells, and follicular helper T cells. There were statistically significant differences in HLA and checkpoint gene expression between the two risk subgroups. The performance of the nomogram was favorable (concordance index = 0.766) and reliably predicted the 3-year (AUC = 0.792) and 5-year (AUC = 0.766) survival of patients with KIRC. Conclusion: The novel six antioxidant related gene signature could effectively forecast the prognosis of patients with KIRC, supply insights into the interaction between cellular antioxidant mechanisms and cancer, and is an innovative tool for selecting potential patients and targets for immunotherapy.
| S-EPMC8416991 | biostudies-literature
Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a prevalent urogenital malignancy characterized by heterogeneous patterns. Stemness is a pivotal factor in tumor progression, recurrence, and metastasis. Nevertheless, the impact of stemness-related long non-coding RNAs (SRlncRNAs) on the prognosis of ccRCC remains elusive. In this study, we aimed to delve into the SRlncRNAs of ccRCC and develop a signature for risk stratification and prognosis prediction.MethodGene-expression and clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We calculated RNA stemness scores (RNAss) for the samples to evaluate their stemness. SRlncRNAs and stemness-related mRNAs (SRmRNAs) in ccRCC were identified through weighted correlation network analysis (WGCNA), which employed sophisticated statistical methodologies to identify interconnected modules of related genes. Enrichment analysis was performed to explore the potential functions of SRmRNAs. Multiple machine learning algorithms were employed to construct a prognostic signature. Samples from TCGA-KIRC and GSE29609 cohorts were designated as the training and validation cohorts, respectively. Based on their risk scores, samples were stratified into low- and high-risk groups. Prognosis analysis, immune infiltration assessment, drug sensitivity prediction, mutation landscape, and gene set enrichment analysis (GSEA) were conducted to investigate the distinct characteristics of the low- and high-risk groups. Additionally, a web-based calculator was developed to facilitate clinical application. Expression and effects of SRlncRNAs in ccRCC were further corroborated through the utilization of single-cell RNA-seq (scRNA-seq), as well as in vitro and in vivo experiments.ResultsSRlncRNAs and SRmRNAs were identified based on RNAss and WGCNA. The least absolute shrinkage and selection operator (LASSO) in combination with multivariate Cox regression was selected as the optimal approach. Six SRlncRNAs were used to construct the prognostic signature. Samples in the low- and high-risk groups exhibited distinct characteristics in terms of prognosis, GSEA pathways, immune infiltration profiles, drug sensitivity, and mutation status. A nomogram and a web-based calculator were developed to facilitate the clinical application of the model. ScRNA-seq and RT-qPCR demonstrated the differential expression of SRlncRNAs between ccRCC tumors and normal tissues. In vitro and in vivo experiments demonstrated that downregulation of EMX2OS and LINC00944 affected the proliferation, migration, invasion, apoptosis, and metastasis of ccRCC cells.ConclusionWe uncovered the crucial associations between SRlncRNAs and the prognosis of ccRCC. By leveraging these findings, we developed a novel SRlncRNA-related signature and a user-friendly web calculator. This signature holds great potential in facilitating risk stratification and guiding tailored treatment strategies for ccRCC patients. Both in vitro and in vivo experiments confirmed the role of SRlncRNAs in the progression of ccRCC.
| S-EPMC11869577 | biostudies-literature
Project description:Disulfidptosis a new cell death mode, which can cause the death of Hepatocellular Carcinoma (HCC) cells. However, the significance of disulfidptosis-related Long non-coding RNAs (DRLs) in the prognosis and immunotherapy of HCC remains unclear. Based on The Cancer Genome Atlas (TCGA) database, we used Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression model to construct DRL Prognostic Signature (DRLPS)-based risk scores and performed Gene Expression Omnibus outside validation. Survival analysis was performed and a nomogram was constructed. Moreover, we performed functional enrichment annotation, immune infiltration and drug sensitivity analyses. Five DRLs (AL590705.3, AC072054.1, AC069307.1, AC107959.3 and ZNF232-AS1) were identified to construct prognostic signature. DRLPS-based risk scores exhibited better predictive efficacy of survival than conventional clinical features. The nomogram showed high congruence between the predicted survival and observed survival. Gene set were mainly enriched in cell proliferation, differentiation and growth function related pathways. Immune cell infiltration in the low-risk group was significantly higher than that in the high-risk group. Additionally, the high-risk group exhibited higher sensitivity to Afatinib, Fulvestrant, Gefitinib, Osimertinib, Sapitinib, and Taselisib. In conclusion, our study highlighted the potential utility of the constructed DRLPS in the prognosis prediction of HCC patients, which demonstrated promising clinical application value.
| S-EPMC10883983 | biostudies-literature
Project description:Kidney renal clear cell carcinoma (KIRC) is one of the most common cancers with high mortality all over the world. Many studies have proposed that genes could be used to predict prognosis in KIRC. In this study, RNA expression data from next-generation sequencing and clinical information of 523 patients downloaded from The Cancer Genome Atlas (TCGA) dataset were analyzed in order to identify the relationship between gene expression level and the prognosis of KIRC patients. A set of five genes that significantly associated with overall survival time was identified and a model containing these five genes was constructed by Cox regression analysis. By Kaplan-Meier and Receiver Operating Characteristic (ROC) analysis, we confirmed that the model had good sensitivity and specificity. In summary, expression of the five-gene model is associated with the prognosis outcomes of KIRC patients, and it may have an important clinical significance.
| S-EPMC4619904 | biostudies-literature
Project description:Necroptosis is a new type of programmed cell death and involves the occurrence and development of various cancers. Moreover, the aberrantly expressed lncRNA can also affect tumorigenesis, migration, and invasion. However, there are few types of research on the necroptosis-related lncRNA (NRL), especially in kidney renal clear cell carcinoma (KIRC). In this study, we analyzed the sequencing data obtained from the TGCA-KIRC dataset, then applied the LASSO and COX analysis to identify 6 NRLs (AC124854.1, AL117336.1, DLGAP1-AS2, EPB41L4A-DT, HOXA-AS2, and LINC02100) to construct a risk model. Patients suffering from KIRC were divided into high- and low-risk groups according to the risk score, and the patients in the low-risk group had a longer OS. This signature can be used as an indicator to predict the prognosis of KIRC independent of other clinicopathological features. In addition, the gene set enrichment analysis showed that some tumor and immune-associated pathways were more enriched in a high-risk group. We also found significant differences between the high and low-risk groups in the infiltrating immune cells, immune functions, and expression of immune checkpoint molecules. Finally, we use the "pRRophetic" package to complete the drug sensitivity prediction, and the risk score could reflect patients' response to 8 small molecule compounds. In general, NRLs divided KIRC into two subtypes with different risk scores. Furthermore, this signature based on the 6 NRLs could provide a promising method to predict the prognosis and immune response of KIRC patients. To some extent, our findings helped give a reference for further research between NRLs and KIRC and find more effective therapeutic drugs for KIRC.
| S-EPMC9818734 | biostudies-literature
Project description:Bromodomain (BRD) proteins exhibit a variety of activities, such as histone modification, transcription factor recruitment, chromatin remodeling, and mediator or enhancer complex assembly, that affect transcription initiation and elongation. These proteins also participate in epigenetic regulation. Although specific epigenetic regulation plays an important role in the occurrence and development of cancer, the characteristics of the BRD family in renal clear cell carcinoma (KIRC) have not been determined. In this study, we investigated the expression of BRD family genes in KIRC at the transcriptome level and examined the relationship of the expression of these genes with patient overall survival. mRNA levels of tumor tissues and adjacent tissues were extracted from The Cancer Genome Atlas (TCGA) database. Seven BRD genes (KAT2A, KAT2B, SP140, BRD9, BRPF3, SMARCA2, and EP300) were searched by using LASSO Cox regression and the model with prognostic risk integration. The patients were divided into two groups: high risk and low risk. The combined analysis of these seven BRD genes showed a significant association with the high-risk groups and lower overall survival (OS). This analysis demonstrated that total survival could be predicted well in the low-risk group according to the time-dependent receiver operating characteristic (ROC) curve. The prognosis was determined to be consistent with that obtained using an independent dataset from TCGA. The relevant biological functions were identified using Gene Set Enrichment Analysis (GSEA). In summary, this study provides an optimized survival prediction model and promising data resources for further research investigating the role of the expression of BRD genes in KIRC.
| S-EPMC8206647 | biostudies-literature
Project description: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.
| S-EPMC10770353 | biostudies-literature
Project description: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 (IC50) in the high risk group whereas Sunitinib and Bosutinib had lower IC50. 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.
| S-EPMC10539335 | biostudies-literature