scholarly journals Integrated analysis of the underlying immunomodulator and survival signature of STON1 gene in kidney renal clear cell carcinoma

2021 ◽  
Author(s):  
Axiu Zheng ◽  
Jianrong Bai ◽  
Yanping Ha ◽  
Bingshu Wang ◽  
Yuan Zou ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Clinicopathological Characteristics ◽  
Risk Scores ◽  
Integrated Analysis

Abstract Background Stonin 1 (STON1) is an endocytic protein but its role in cancer remains unclear. Here, we investigated the role of STON1 in kidney renal clear cell carcinoma (KIRC). Methods We undertook bioinformatics analyses of a series of public databases to determine the expression and clinical significance of STON1 in KIRC and focused on STON1-related immunomodulator and survival signatures. A nomogram model integrating clinicopathological characteristics and risk scores for KIRC was established and validated. Results Through TGCA and GEO databases, STON1 mRNA was found to be significantly downregulated in KIRC compared with normal controls, and decreased STON1 was related to grade, TNM stage, distant metastasis, and vital status of KIRC. Furthermore, OncoLnc, UALCAN, Kaplan–Meier, and GEPIA2 analyses supported that KIRC patients with high STON1 expression had better overall survival. STON1 was positively associated with mismatch proteins including MLH1, PMS2, MSH2, MSH6 and EpCAM, and was negatively correlated with tumor mutational burden. Interestingly, arm-level deletion of STON1 was clearly related to the abundance of immune cells and the infiltration abundance in the majority of 26 immune cell types that were positively related to STON1 mRNA level in the TIMER database. The TISIDB database revealed 21 immunostimulators and 10 immunoinhibitors that were obviously related to STON1 in KIRC. In univariate and multivariate Cox regression analyses, CTLA4 , KDR , LAG3 , PDCD1 , HHLA2 , TNFRSF25 , and TNFSF14 were screened to establish a risk score model, and the low-risk group had a better prognosis for KIRC. Furthermore, a nomogram integrating clinicopathological characteristics and risk score had better accuracy and practicability in predicating the survival of KIRC patients. Conclusions Decreased STON1 expression in KIRC leads to clinical progression and poor survival. Mechanically, loss of STON1 is associated with the aberrant immune microenvironment in KIRC. Integrated clinicopathological characteristics and risk score derived from STON1 -related immunomodulators can aid the prediction of KIRC survival.

scholarly journals Comprehensive Analysis of the Immune Infiltrates of Pyroptosis in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhuolun Sun ◽  
Changying Jing ◽  
Xudong Guo ◽  
Mingxiao Zhang ◽  
Feng Kong ◽  
...  
Keyword(s):  
High Risk ◽  
Cell Carcinoma ◽  
Poor Prognosis ◽  
Immune Cell ◽  
Risk Model ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores

Kidney renal clear cell carcinoma (KIRC) has long been identified as a highly immune-infiltrated tumor. However, the underlying role of pyroptosis in the tumor microenvironment (TME) of KIRC remains poorly described. Herein, we systematically analyzed the prognostic value, role in the TME, response to ICIs, and drug sensitivity of pyroptosis-related genes (PRGs) in KIRC patients based on The Cancer Genome Atlas (TCGA) database. Cluster 2, by consensus clustering for 24 PRGs, presented a poor prognosis, likely because malignancy-related hallmarks were remarkably enriched. Additionally, we constructed a prognostic prediction model that discriminated well between high- and low-risk patients and was further confirmed in external E-MTAB-1980 cohort and HSP cohort. By further analyzing the TME based on the risk model, higher immune cell infiltration and lower tumor purity were found in the high-risk group, which presented a poor prognosis. Patients with high risk scores also exhibited higher ICI expression, indicating that these patients may be more prone to profit from ICIs. The sensitivity to anticancer drugs that correlated with model-related genes was also identified. Collectively, the pyroptosis-related prognosis risk model may improve prognostic information and provide directions for current research investigations on immunotherapeutic strategies for KIRC patients.

scholarly journals Development and validation of ferroptosis-related lncRNAs prognosis signatures in kidney renal clear cell carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiao-Liang Xing ◽  
Zhi-Yong Yao ◽  
Jialan Ou ◽  
Chaoqun Xing ◽  
Feng Li
Keyword(s):  
Cell Carcinoma ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Assessment Model ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Risk Scores ◽  
Non Coding Rnas

Abstract Background Ferroptosis is a recently recognised new type of cell death which may be a potential target for cancer therapy. In the present study, we aimed to screen ferroptosis-related differentially expressed long non-coding RNAs as biomarkers to predict the outcome of kidney renal clear cell carcinoma. Methods RNAseq count data and corresponding clinical information were obtained from the Cancer Genome Atlas database. Lists of ferroptosis-related genes and long non-coding RNAs were obtained from the FerrDb and GENCODE databases, respectively. The candidate prognostic signatures were screened by Cox regression analyses and least absolute shrinkage and selection operator analyses. Results Three ferroptosis-related long non-coding RNAs (DUXAP8, LINC02609, and LUCAT1) were significantly correlated with the overall survival of kidney renal clear cell carcinoma independently. Kidney renal clear cell carcinoma patients with high-risk values displayed worse OS. Meanwhile, the expression of these three ferroptosis-related long non-coding RNAs and their risk scores were significantly correlated with clinicopathological features. Principal component analyses showed that patients with kidney renal clear cell carcinoma have differential risk values were well distinguished by the three ferroptosis-related long non-coding RNAs. Conclusions The present study suggests that the risk assessment model constructed by these three ferroptosis-related long non-coding RNAs could accurately predict the outcome of kidney renal clear cell carcinoma. We also provide a novel perspective for cancer prognosis screening.

scholarly journals LncRNA CDKN2B-AS1 stabilized by IGF2BP3 drives the malignancy of renal clear cell carcinoma through epigenetically activating NUF2 transcription

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Xina Xie ◽  
Jiatian Lin ◽  
Xiaoqin Fan ◽  
Yuantang Zhong ◽  
Yequn Chen ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Survival Data ◽  
Kinase Inhibitor ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Binding Protein ◽  
Renal Clear Cell Carcinoma ◽  
Receiver Operating Curve ◽  
Migration And Invasion ◽  
Integrated Analysis

AbstractBecause of the lack of sensitivity to radiotherapy and chemotherapy, therapeutic options for renal clear cell carcinoma (KIRC) are scarce. Long noncoding RNAs (lncRNAs) play crucial roles in the progression of cancer. However, their functional roles and upstream mechanisms in KIRC remain largely unknown. Exploring the functions of potential essential lncRNAs may lead to the discovery of novel targets for the diagnosis and treatment of KIRC. Here, according to the integrated analysis of RNA sequencing and survival data in TCGA-KIRC datasets, cyclin-dependent kinase inhibitor 2B antisense lncRNA (CDKN2B-AS1) was discovered to be the most upregulated among the 14 lncRNAs that were significantly overexpressed in KIRC and related to shorter survival. Functionally, CDKN2B-AS1 depletion suppressed cell proliferation, migration, and invasion both in vitro and in vivo. Mechanistically, CDKN2B-AS1 exerted its oncogenic activity by recruiting the CREB-binding protein and SET and MYND domain-containing 3 epigenetic-modifying complex to the promoter region of Ndc80 kinetochore complex component (NUF2), where it epigenetically activated NUF2 transcription by augmenting local H3K27ac and H3K4me3 modifications. Moreover, we also showed that CDKN2B-AS1 interacted with and was stabilized by insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3), an oncofetal protein showing increased levels in KIRC. The Kaplan–Meier method and receiver operating curve analysis revealed that patients whose IGF2BP3, CDKN2B-AS1 and NUF2 are all elevated showed the shortest survival time, and the combined panel (containing IGF2BP3, CDKN2B-AS1, and NUF2) possessed the highest accuracy in discriminating high-risk from low-risk KIRC patients. Thus, we conclude that the stabilization of CDKN2B-AS1 by IGF2BP3 drives the malignancy of KIRC through epigenetically activating NUF2 transcription and that the IGF2BP3/CDKN2B-AS1/NUF2 axis may be an ideal prognostic and diagnostic biomarker and therapeutic target for KIRC.

GALNT14 expression in renal clear cell carcinoma and its effect on prognosis

2021 ◽  
Author(s):  
Yuqin Wei ◽  
Fan Wu ◽  
Shengfeng Zhang ◽  
Yanlin Tan ◽  
Qunying Wu ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Regulatory Pathways ◽  
Cox Analysis ◽  
The Relationship

Abstract Background The expression of GALNT14 in kidney renal clear cell carcinoma (KIRC) and its clinical significance remains unknown. Methods The KIRC data expressed by GALNT14 was downloaded from The Cancer Genome Atlas (TCGA) database. The expression of GALNT14 was analyzed by R software, Perl software and online analysis database. The relationship between GALNT14 expression and clinicopathological features in KIRC was analyzed by univariate, multivariate Cox regression and some databases. Gene Expression Profling Interactive Analysis (GEPIA), Starbase v3.0, UALCAN, and Kaplan-Meier were used to analyze the relationship between GALNT14 expression and overall survival (OS) in KIRC. UALCAN detects the expression of GALNT14 methylation in KIRC. Linkedomics and Genemania were used to analyze the gene co-expression of GALNT14. Gene Set Enrichment Analysis (GSEA) was performed to search for potential regulatory pathways. Results We found that GALNT14 was overexpressed in KIRC (p=1.433e-25). Patients with high GALNT14 expression in KIRC had a better prognosis than patients with low GALNT14 expression (p=0.008). In addition, high GALNT14 expression in KIRC was significantly associated with low T stage and positive OS (p<0.05). Univariate Cox analysis showed that GALNT14 was positively correlated with OS (p<0.001). Multivariate Cox analysis showed that GALNT14 was associated with OS (p<0.001), age (p=0.01) and histological grade (p=0.02). GALNT14 methylation is low expressed in KIRC (p<0.001). GSEA analysis showed that GALNT14 was enriched in histidine metabolism, peroxisome, and renin-angiotensin system pathways. Conclusion GALNT14 can be used as an independent prognostic factor for renal clear cell carcinoma and a potential target for clinical diagnosis and treatment of KIRC.

scholarly journals Tumor antigens and immune subtypes guided mRNA vaccine development for kidney renal clear cell carcinoma

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Hang Xu ◽  
Xiaonan Zheng ◽  
Shiyu Zhang ◽  
Xianyanling Yi ◽  
Tianyi Zhang ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Topoisomerase Ii ◽  
Tumor Antigens ◽  
Immune Cell ◽  
Vaccine Development ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Survival Outcomes ◽  
Topoisomerase Ii Alpha

AbstractCurrent treatment strategy for kidney renal clear cell carcinoma (KIRC) is limited. Tumor-associated antigens, especially neoantigen-based personalized mRNA vaccines represent new strategies and manifest clinical benefits in solid tumors, but only a small proportion of patients could benefit from them, which prompts us to identify effective antigens and suitable populations to facilitate mRNA vaccines application in cancer therapy. Through performing expression, mutation, survival and correlation analyses in TCGA-KIRC dataset, we identified four genes including DNA topoisomerase II alpha (TOP2A), neutrophil cytosol factor 4 (NCF4), formin-like protein 1 (FMNL1) and docking protein 3 (DOK3) as potential KIRC-specific neoantigen candidates. These four genes were upregulated, mutated and positively associated with survival and antigen-presenting cells in TCGA-KIRC. Furthermore, we identified two immune subtypes, named renal cell carcinoma immune subtype 1 (RIS1) and RIS2, of KIRC. Distinct clinical, molecular and immune-related signatures were observed between RIS1 and RIS2. Patients of RIS2 had better survival outcomes than those of RIS1. Further comprehensive immune-related analyses indicated that RIS1 is immunologically “hot” and represent an immunosuppressive phenotype, whereas RIS2 represents an immunologically “cold” phenotype. RIS1 and RIS2 also showed differential features with regard to tumor infiltrating immune cells and immune checkpoint-related genes. Moreover, the immune landscape construction identified the immune cell components of each KIRC patient, predicted their survival outcomes, and assisted the development of personalized mRNA vaccines. In summary, our study identified TOP2A, NCF4, FMNL1 and DOK3 as potential effective neoantigens for KIRC mRNA vaccine development, and patients with RIS2 tumor might benefit more from mRNA vaccination.

scholarly journals Analyzing and Validating the Prognostic Value of a TNF-Related Signature in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Wenhao Zhang ◽  
Changjiu Li ◽  
Fanding Wu ◽  
Ning Li ◽  
Yuwei Wang ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Prognostic Value ◽  
T Regulatory Cells ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Risk Groups ◽  
Renal Clear Cell Carcinoma ◽  
Risk Scores ◽  
Immune Infiltration ◽  
Mutation Burden

Background: Kidney renal clear cell carcinoma (KIRC) has the highest incidence rate in renal cell carcinoma (RCC). Although bioinformatics is widely used in cancer, few reliable biomarkers of KIRC have been found. Therefore, continued efforts are required to elucidate the potential mechanism of the biogenesis and progression of KIRC.Methods: We evaluated the expression of tumor necrosis factor (TNF) family genes in KIRC, and constructed a prognostic signature. We validated the signature by another database and explored the relationship between the signature and progression of KIRC. We assessed the prognostic value, immune infiltration, and tumor mutation burden (TMB) of the signature in KIRC.Results: We selected four key genes (TNFSF14, TNFRSF19, TNFRSF21, and EDA) to construct the TNF-related signature. We divided the KIRC patients into high- and low-risk groups based on the signature. Patients with higher risk scores had shorter overall survival and worse prognosis. With another database, we validated the value of the signature. The signature was considered as an independent risk factor. A higher level of risk score was relevant to higher level of immune infiltration, especially T regulatory cells, CD8+ T cells, and macrophages. The signature was also associated with TMB scores, and it may have an effect on assessing the efficacy of immunotherapy.Conclusion: This is the first TNF-family-related signature of KIRC and we demonstrated its effectiveness. It played a significant role in predicting the prognosis of patients with KIRC. It also has the potential to become a powerful tool in guiding the immunotherapy of KIRC patients in clinical practice.

scholarly journals Identification of a Prognostic Risk Signature of Kidney Renal Clear Cell Carcinoma Based on Regulating the Immune Response Pathway Exploration

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Guangzhen Wu ◽  
Yingkun Xu ◽  
Chenglin Han ◽  
Zilong Wang ◽  
Jiayi Li ◽  
...  
Keyword(s):  
Immune Response ◽  
Cell Carcinoma ◽  
Sensitivity And Specificity ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Lasso Regression ◽  
Regression Curve ◽  
Cox Regression Analysis

Purpose. To construct a survival model for predicting the prognosis of patients with kidney renal clear cell carcinoma (KIRC) based on gene expression related to immune response regulation. Materials and Methods. KIRC mRNA sequencing data and patient clinical data were downloaded from the TCGA database. The pathways and genes involved in the regulation of the immune response were identified from the GSEA database. A single factor Cox analysis was used to determine the association of mRNA in relation to patient prognosis P < 0.05 . The prognostic risk model was further established using the LASSO regression curve. The survival prognosis model was constructed, and the sensitivity and specificity of the model were evaluated using the ROC curve. Results. Compared with normal kidney tissues, there were 28 dysregulated mRNA expressions in KIRC tissues P < 0.05 . Univariate Cox regression analysis revealed that 12 mRNAs were related to the prognosis of patients with renal cell carcinoma. The LASSO regression curve drew a risk signature consisting of six genes: TRAF6, FYN, IKBKG, LAT2, C2, IL4, EREG, TRAF2, and IL12A. The five-year ROC area analysis (AUC) showed that the model has good sensitivity and specificity (AUC >0.712). Conclusion. We constructed a risk prediction model based on the regulated immune response-related genes, which can effectively predict the survival of patients with KIRC.

scholarly journals Systematic Identification of Survival-Associated Alternative Splicing Events in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yubin Wei ◽  
Zheng Zhang ◽  
Rui Peng ◽  
Yan Sun ◽  
Luyu Zhang ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Cell Carcinoma ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Splicing Factors ◽  
Interactive Network ◽  
Highly Correlated

There is growing evidence that aberrant alternative splicing (AS) is highly correlated with driving tumorigenesis, but its function in kidney renal clear cell carcinoma (KIRC) remains to be discovered. In this study, we obtained the level-3 RNA sequencing and clinical data of KIRC from The Cancer Genome Atlas (TGCA). Combining with the splicing event detail information from TGCA SpliceSeq database, we established the independent prognosis signatures for KIRC with the univariate and multivariate Cox regression analyses. Then, we used the Kaplan-Meier analysis and receiver operating characteristic curves (ROCs) to assess the accuracy of prognosis signatures. We also constructed the regulatory network of splicing factors (SFs) and AS events. Our results showed that a total of 12029 survival-associated AS events of 5761 genes were found in 524 KIRC patients. All types of prognosis signatures displayed a satisfactory ability to reliably predict, especially in exon skip model which the area under curve of ROC was 0.802. Moreover, 18 splicing factors (SFs) highly correlated to AS events were identified. With the construction of the SF-AS interactive network, we found that SF powerfully promotes the occurrence of abnormal AS and may have a profound role in KIRC. Collectively, we screened survival-associated AS events and established prognosis signatures for KIRC, coupling with the SF-AS interactive network, which might provide a key perspective to clarify the potential mechanism of AS in KIRC.

scholarly journals Identification of miRNA signatures for kidney renal clear cell carcinoma using the tensor-decomposition method

2019 ◽  
Author(s):  
Ka-Lok Ng ◽  
Y-h Taguchi
Keyword(s):  
Feature Extraction ◽  
Cell Carcinoma ◽  
Complex Disease ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Expression Profiles ◽  
Tensor Decomposition ◽  
Renal Clear Cell Carcinoma ◽  
Integrated Analysis ◽  
Unsupervised Feature Extraction

AbstractCancer is a highly complex disease caused by multiple genetic factors. MicroRNA (miRNA) and mRNA expression profiles are useful for identifying prognostic biomarkers for cancer. Kidney renal clear cell carcinoma (KIRC), which accounts for more than 70% of all renal malignant tumour cases, was selected for our analysis.Traditional methods of identifying cancer prognostic markers may not be accurate. Tensor decomposition (TD) is a useful method uncovering the underlying low-dimensional structures in the tensor. The TD-based unsupervised feature extraction method was applied to analyse mRNA and miRNA expression profiles. Biological annotations of the prognostic miRNAs and mRNAs were examined utilizing the pathway and oncogenic signature databases DIANA-miRPath and MSigDB.TD identified the miRNA signatures and the associated genes. These genes were found to be involved in cancer-related pathways, and 23 genes were significantly correlated with the survival of KIRC patients. We demonstrated that the results are robust and not highly dependent upon the databases we selected. Compared with traditional supervised methods tested, TD achieves much better performance in selecting prognostic miRNAs and mRNAs.These results suggest that integrated analysis using the TD-based unsupervised feature extraction technique is an effective strategy for identifying prognostic signatures in cancer studies.

scholarly journals High PON1 Expression Predicts Poor Prognosis In Kidney Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Chenxia Jiang ◽  
Xinyu Zhang ◽  
Xiaoyan Li ◽  
Jia Li ◽  
Hua Huang
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Factor ◽  
Cell Carcinoma ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Independent Prognostic Factor ◽  
Cox Regression Analysis

Abstract Background: Relevant study had demonstrated that Paraoxonase-1 (PON1) had relationship with occurrence and development of tumors which suggested that PON1 was a key gene in promoting tumor progression. However, the relationship between PON1 and Kidney renal clear cell carcinoma (KIRC) is still unclear so far. Methods: We downloaded relevant data about KIRC from TCGA dataset and compared it with normal renal tissues. Immunohistochemistry (IHC) was applied to analyze the expression of PON1. Univariate cox regression analysis and multivariate cox regression analysis were also utilized to analyze independent factors associated with prognosis. Gene set enrichment analysis was conducted to find the signaling pathways of PON1 in KIRC. Finally, we also investigated whether PON1 had relationship with immunity. Results: As shown in results, PON1 expression was decreased in KIRC compared with adjacent paracancer tissues. Immunohistochemistry (IHC) was utilized to find the expression of PON1. After survival analysis, the high expression of PON1 was significantly related to overall survival (P<0.001). Univariate/Multivariate cox regression analysis both revealed that PON1 could serve as an independent prognostic factor. To analyze overall survival (OS) of patients with KIRC, nomogram was developed. GSEA revealed that PON1 was correlated with homologous recombination. Besides, PON1 had few relationships with immunity. Conclusions: Our results revealed that PON1 could serve as an independent prognostic factor for KIRC, providing a novel target for KIRC future treatments.

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