scholarly journals Construction of an Immune-related Gene Model for Predicting Prognosis and Immune Infiltration in Clear Cell Renal Cell Carcinoma

2022 ◽  
Author(s):  
Fengping Ji ◽  
Xin Liu ◽  
Yanping Zhang ◽  
Erpeng Liu ◽  
Jianguo Wen
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Immune Checkpoint ◽  
Renal Cell ◽  
Clear Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Renal Cell Carcinoma ◽  
Immune Infiltration

Abstract Background: Clear cell renal cell carcinoma (ccRCC) is a common pathological type of kidney cancer with high immune infiltration that has been proven to be treatable with immune checkpoint inhibitor (ICI) therapy. However, the role of immunity in ccRCC remains poorly understood. Therefore, this paper aimed to develop and validate a novel immune-related prognostic marker to predict both the overall survival rate (OS) of ccRCC patients and the response to ICI therapy.Methods: Based on the transcriptome and clinicopathological data of ccRCC from The Cancer Genome Atlas (TCGA) dataset and immune-related genes (IRGs) from immune datasets, IRGs related to prognosis were screened to construct an IRG prognostic index (IRGPI) via coexpression analysis and Cox regression. After verifying that IRGPI was a prognostic indicator independent of clinical parameters, a nomogram was established. In addition, functional enrichment analysis, the CIBERSORT algorithm and single-sample gene set enrichment analysis (ssGSEA) were performed to compare the molecular and immune characteristics of IRGPI-defined subgroups. Finally, the expression of immunosuppressive genes, tumor mutational burden (TMB) and the TIDE algorithm were used to predict the response of ICI therapy in different IRGPI subgroups. Results: A total of 11 IRGs (IFNG, XCL1, APOBEC3G, CD86, CXCR3, IL10RA, IL2RG, CD244, SH2D1A, CD3D and FCER1G) were included in the IRGPI module. IRGPIhigh patients had a worse OS and had poorer clinical pathological status than IRGPIlow patients. A nomogram containing clinical features and IRGPI scores may guide the clinical practice of ccRCC. Chemokine signaling pathways were mainly involved in functional enrichment analysis. Furthermore, the IRGPI could effectively reflect the immune characteristics and immune checkpoint gene expression of ccRCC and the response to ICI therapy.Conclusions: The IRGPI is a promising biomarker for determining prognosis and has the potential to be used to predict immunotherapy response in ccRCC.

scholarly journals A novel ferroptosis-related gene signature associated with cell cycle for prognosis prediction in patients with clear cell renal cell carcinoma

BMC Cancer ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Siteng Chen ◽  
Encheng Zhang ◽  
Tuanjie Guo ◽  
Jialiang Shao ◽  
Tao Wang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Renal Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Related Gene ◽  
Cell Renal Cell Carcinoma ◽  
Prognosis Model

Abstract Background It is of great urgency to explore useful prognostic markers for patients with clear cell renal cell carcinoma (ccRCC). Prognostic models based on ferroptosis-related gene (FRG) in ccRCC is poorly reported for now. Methods Comprehensive analysis of 22 FRGs were performed in 629 ccRCC samples from two independent patient cohorts. We carried out least absolute shrinkage and selection operator analysis to screen out prognosis-related FRGs and constructed prognosis model for patients with ccRCC. Weighted gene co-expression network analysis was also carried out for potential functional enrichment analysis. Results Based on the TCGA cohort, a total of 11 prognosis-associated FRGs were selected for the construction of the prognosis model. Significantly differential overall survival (hazard ratio = 3.61, 95% CI: 2.68–4.87, p < 0.0001) was observed between patients with high and low FRG score in the TCGA cohort, which was further verified in the CPTAC cohort with hazard ratio value of 5.13 (95% CI: 1.65–15.90, p = 0.019). Subgroup survival analysis revealed that our FRG score could significantly distinguish patients with high survival risk among different tumor stages and different tumor grades. Functional enrichment analysis illustrated that the process of cell cycle, including cell cycle-mitotic pathway, cytokinesis pathway and nuclear division pathway, might be involved in the regulation of ccRCC through ferroptosis. Conclusions We developed and verified a FRG signature for the prognosis prediction of patients with ccRCC, which could act as a risk factor and help to update the tumor staging system when integrated with clinicopathological characteristics. Cell cycle-related pathways might be involved in the regulation of ccRCC through ferroptosis.

scholarly journals Data Mining of Prognostic Microenvironment-Related Genes in Clear Cell Renal Cell Carcinoma: A Study with TCGA Database

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Bin Chen ◽  
Wei Chen ◽  
Jing Jin ◽  
Xueping Wang ◽  
Yifang Cao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Value ◽  
Clear Cell ◽  
Enrichment Analysis ◽  
Cancer Prognosis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent kidney malignancies. The tumor microenvironment (TME) is highly related to the oncogenesis, progress, and prognosis of ccRCC. The aim of this study was to infer the level of infiltrating stromal and immune cells and assess the prognostic value of them. The gene expression profile was obtained from TCGA and used for calculating the stromal and immune scores. Based on a cut-off value, patients were divided into low- and high-stromal/immune score groups. Survival analysis was performed to evaluate the prognostic value of stromal and immune scores. Moreover, differentially expressed genes (DEGs) that are highly related to TME were determined and applied for functional enrichment analysis and protein-protein interaction (PPI) network. The Kaplan-Meier plot demonstrated that patients with high-immune scores and stromal scores had poorer clinical outcome. In addition, a total of 89 DEGs were identified and mainly involved in 5 pathways. The top 5 degree genes were extracted from the PPI network; among them, IL10 and XCR1 were highly associated with prognosis of ccRCC. The results of the present study demonstrated that ESTIMATE algorithm-based stromal and immune scores may be a credible indicator of cancer prognosis and IL10 along with XCR1 may be a potential key regulator for the TME of ccRCC.

scholarly journals Comprehensive profiling of aberrant prognostic alternative splicing signatures in clear cell renal cell carcinoma: a retrospective study based on large-scale sequencing data

2020 ◽  
Author(s):  
Dong Zhang ◽  
Zhao Zhang ◽  
Yi Duan ◽  
Guangxu Ji ◽  
Hongliang Wu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Alternative Splicing ◽  
Renal Cell ◽  
Large Scale ◽  
Clear Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Sequencing Data ◽  
Cell Renal Cell Carcinoma

Abstract Backgroud: Clear cell renal cell carcinoma(ccRCC) is the most common type with poor prognosis in kidney tumor. Growing evidence has indicated that aberrant alternative splicing (AS) events are efficacious signatures for tumor prognosis predicting and therapeutic targets. Systematic and comprehensive analysis of AS in ccRCC is in urgent need.Methods: Level 3 RNA-seq data were acquired from TCGA data portal and the AS profiles were performed with assistance of SpliceSeq software. Univariate cox regression analysis was applied for screening prognosis-related AS events. Gene functional enrichment analysis revealed the pathways enriched by prognosis-related AS. The final AS panel was developed by LASSO-penalized method for predicting prognosis and compared with traditional clinical factors. The potential regulatory network was analyzed via Spearman correlation between splicing factors (SFs).Results: A total of 2100 survival-associated AS events were filtered from 1666 parent genes. Gene functional enrichment analysis suggested that the regulation of autophagy could be a potential mechanism of splicing regulatory in ccRCC. 17 aberrant AS events formed the final AS panel which can estimate OS probability in ccRCC patients. The AUC values of ROC curves for the final AS panel can keep above 0.7 spanning 1 year to 5 years.Conclusion: We developed a robust and individualized predictive model based on large-scale sequencing data. The identified vital AS events and splicing networks may be valuable in deciphering the potential mechanisms of AS on tumorigenesis of ccRCC.

scholarly journals SYNE1 mutation may enhance the response to immune checkpoint blockade therapy in clear cell renal cell carcinoma patients

Aging ◽  
2020 ◽  
Vol 12 (19) ◽  
pp. 19316-19324
Author(s):  
Pengju Li ◽  
Jeifei Xiao ◽  
Bangfen Zhou ◽  
Jinhuan Wei ◽  
Junhang Luo ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Immune Checkpoint ◽  
Renal Cell ◽  
Clear Cell ◽  
Immune Checkpoint Blockade ◽  
Checkpoint Blockade ◽  
Cell Renal Cell Carcinoma

scholarly journals Development and Interpretation of a Genomic Instability Derived lncRNAs Based Risk Signature as a Predictor of Prognosis for Clear Cell Renal Cell Carcinoma Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Huiying Yang ◽  
Xiaoling Xiong ◽  
Hua Li
Keyword(s):  
Renal Cell Carcinoma ◽  
High Risk ◽  
Cell Carcinoma ◽  
Genomic Instability ◽  
Renal Cell ◽  
Clear Cell ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Prognosis Prediction

BackgroundClear cell renal cell carcinoma (ccRCC) is a kind of frequently diagnosed cancer, leading to high death rate in patients. Genomic instability (GI) is regarded as playing indispensable roles in tumorigenesis and impacting the prognosis of patients. The aberrant regulation of long non-coding RNAs (lncRNAs) is a main cause of GI. We combined the somatic mutation profiles and expression profiles to identify GI derived lncRNAs (GID-lncRNAs) in ccRCC and developed a GID-lncRNAs based risk signature for prognosis prediction and medication guidance.MethodsWe decided cases with top 25% cumulative number of somatic mutations as genomically unstable (GU) group and last 25% as genomically stable (GS) group, and identified differentially expressed lncRNAs (GID-lncRNAs) between two groups. Then we developed the risk signature with all overall survival related GID-lncRNAs with least absolute shrinkage and selection operator (LASSO) Cox regression. The functions of the GID-lncRNAs were partly interpreted by enrichment analysis. We finally validated the effectiveness of the risk signature in prognosis prediction and medication guidance.ResultsWe developed a seven-lncRNAs (LINC00460, AL139351.1, AC156455.1, AL035446.1, LINC02471, AC022509.2, and LINC01606) risk signature and divided all samples into high-risk and low-risk groups. Patients in high-risk group were in more severe clinicopathologic status (higher tumor grade, pathological stage, T stage, and more metastasis) and were deemed to have less survival time and lower survival rate. The efficacy of prognosis prediction was validated by receiver operating characteristic analysis. Enrichment analysis revealed that the lncRNAs in the risk signature mainly participate in regulation of cell cycle, DNA replication, material metabolism, and other vital biological processes in the tumorigenesis of ccRCC. Moreover, the risk signature could help assess the possibility of response to precise treatments.ConclusionOur study combined the somatic mutation profiles and the expression profiles of ccRCC for the first time and developed a GID-lncRNAs based risk signature for prognosis predicting and therapeutic scheme deciding. We validated the efficacy of the risk signature and partly interpreted the roles of the seven lncRNAs composing the risk signature in ccRCC. Our study provides novel insights into the roles of genomic instability derived lncRNAs in ccRCC.

scholarly journals Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma

Science ◽  
2018 ◽  
Vol 359 (6377) ◽  
pp. 801-806 ◽  
Author(s):  
Diana Miao ◽  
Claire A. Margolis ◽  
Wenhua Gao ◽  
Martin H. Voss ◽  
Wei Li ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Immune Checkpoint ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma

Systematic Analyses of The Role of Prognostic And Immunological of EIF3A, A Reader Protein, In Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
Yi Zhang ◽  
Xiaoliang Hua ◽  
Haoqiang Shi ◽  
Li Zhang ◽  
HaiBing Xiao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
T Cells ◽  
Cell Carcinoma ◽  
Immune Cells ◽  
Renal Cell ◽  
Clear Cell ◽  
Multivariate Analyses ◽  
Enrichment Analysis ◽  
Marker Genes ◽  
Cell Renal Cell Carcinoma

Abstract Background: Eukaryotic initiation factor 3a, EIF3A, as a “reader” protein for RNA methylation, has been found to be related to promote tumorigenesis in different variety of cancers. The impaction of EIF3A in clear cell renal cell carcinoma (ccRCC) has yet to be expounded. This study aimed to identify the prognostic value of EIF3A in ccRCC and investigate the relationship between expression and immune infiltration.Methods: We collected 29 m6a related mRNA data and clinicopathological parameters from Cancer Genmoe Atlas (TCGA) database. Logistic regression analyses were used to analyze the correlation between EIF3A expression and clinical characteristics. Immunohistochemistry (IHC) were applied to examine EIF3A levels in normal and ccRCC tissues. Univariate and multivariate analyses were conducted to recognize forcefully independent factor in associated with overall survival (OS) and diseases free survival (DFS). Nomogram was aim at predicting the 1-, 3-and 5-year survival probabilities. Gene set enrichment analysis (GSEA) was carried out to the potential function and related signaling pathways of EIF3A expression. To investigate EIF3A of co-expressed genes, we used LinkedOmics and its result was undertaken enrichment analysis. Simultaneously, to employ LinkedOmics and STRING dataset drew a conclusion that EIF3A co-expressed genes and visualized via Cytoscape. Finally, we evaluated that EIF3A expression correlated between with infiltration of immune cells and the expression of marker genes in ccRCC by Tumor Immune Estimation Resource (TIMER) and Gene Expression Profiling Interactive Analysis (GEPIA).Result: EIF3A expression was significantly different between ccRCC tissues and normal tissues. EIF3A expression was connected with poor prognostic clinicopathological factors, and K–M analyses revealed that low EIF3A expression was correlated with poor prognosis. The result of univariate and multivariate analyses proved that EIF3A was a prognostic factor in ccRCC patients. GSEA results indicated that high expression was enriched in renal cell carcinoma pathway and so on. EIF3A expression was significantly positively correlated with B cells, CD8+T cells, CD4+T cells, neutrophils, macrophages, and dendritic cells. Furthermore, EIF3A expression was associated with most of marker genes of immune cells.Conclusions: EIF3A could serve as potential biomarkers for prognostic and diagnostic stratification factor for ccRCC and is related with immune cells infiltrates.

scholarly journals Prognostic implications of Aquaporin 9 expression in clear cell renal cell carcinoma

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Wen-Hao Xu ◽  
Shen-Nan Shi ◽  
Yue Xu ◽  
Jun Wang ◽  
Hong-Kai Wang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Value ◽  
Clear Cell ◽  
Roc Curves ◽  
Functional Enrichment ◽  
Clinicopathological Parameters ◽  
Cell Renal Cell Carcinoma ◽  
Aquaporin 9

Abstract Background Growing evidence has demonstrated immune reactivity as a confirmed important carcinogenesis and therapy efficacy for clear cell renal cell carcinoma (ccRCC). Aquaporin 9 (AQP9) is involved in many immune-related signals; however, its role in ccRCC remains to be elucidated. This study investigated AQP9 expression in tumor tissues and defined the prognostic value in ccRCC patients. Methods A total of 913 ccRCC patients with available RNA-sequence data from the Cancer Genome Atlas (TCGA) database and Fudan University Shanghai Cancer Center (FUSCC) were consecutively recruited in analyses. Differential transcriptional and proteome expression profiles were obtained and validated using multiple datasets. A partial likelihood test from Cox regression analysis was developed to address the influence of independent factors on progression-free survival (PFS) and overall survival (OS). The Kaplan–Meier method and log-rank test were performed to assess survival. Receiver operating characteristic (ROC) curves were used to describe binary classifier value of AQP9 using area under the curve (AUC) score. Functional enrichment analyses and immune infiltration analysis were used to describe significantly involved hallmark pathways of hub genes. Results Significantly elevated transcriptional and proteomic AQP9 expressions were found in ccRCC samples. Increased AQP9 mRNA expression was significantly associated with advanced clinicopathological parameters and correlated with shorter PFS and OS in TCGA and FUSCC cohorts (p < 0.001). ROC curves suggested the significant diagnostic and prognostic ability of AQP9 (PFS, AUC = 0.823; OS, AUC = 0.828). Functional annotations indicated that AQP9 is involved in the most significant hallmarks including complement, coagulation, IL6/JAK–STAT3, inflammatory response and TNF-alpha signaling pathways. Conclusion Our study revealed that elevated AQP9 expression was significantly correlated with aggressive progression, poor survival and immune infiltrations in ccRCC patients, and we validated its prognostic value in a real-world cohort. These data suggest that AQP9 may act as an oncogene and a promising prognostic marker in ccRCC.

scholarly journals A Novel Immune-Related lncRNA-Based Model for Survival Prediction in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-37
Author(s):  
Zedan Zhang ◽  
Yanlin Tang ◽  
Yanjun Liu ◽  
Hongkai Zhuang ◽  
Enyu Lin ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
T Cells ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Functional Enrichment ◽  
Clinical Parameters ◽  
Training Set ◽  
Cell Renal Cell Carcinoma

Background. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer whose incidence and mortality rate are increasing. Identifying immune-related lncRNAs and constructing a model would probably provide new insights into biomarkers and immunotherapy for ccRCC and aid in the prognosis prediction. Methods. The transcription profile and clinical information were obtained from The Cancer Genome Atlas (TCGA). Immune-related gene sets and transcription factor genes were downloaded from GSEA website and Cistrome database, respectively. Tumor samples were divided into the training set and the testing set. Immune-related differentially expressed lncRNAs (IDElncRNAs) were identified from the whole set. Univariate Cox regression, LASSO, and stepwise multivariate Cox regression were performed to screen out ideal prognostic IDElncRNAs (PIDElncRNAs) from the training set and develop a multi-lncRNA signature. Results. Consequently, AC012236.1, AC078778.1, AC078950.1, AC087318.1, and AC092535.4 were screened to be significantly related to the prognosis of ccRCC patients, which were used to establish the five-lncRNA signature. Its wide diagnostic capacity was revealed in different subgroups of clinical parameters. Then AJCC-stage, Fuhrman-grade, pharmaceutical, age, and risk score regarded as independent prognostic factors were integrated to construct a nomogram, whose good performance in predicting 3-, 5-, and 7-year overall survival of ccRCC patients was revealed by time-dependent ROC curves and verified by the testing sets and ICGC dataset. The calibration plots showed great agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis showed the signature and each lncRNA were mainly enriched in pathways associated with regulation of immune response. Several kinds of tumor-infiltrating immune cells like regulatory T cells, T follicular helper cells, CD8+ T cells, resting mast cells, and naïve B cells were significantly correlated with the signature. Conclusion. Therefore, we constructed a five-lncRNA model integrating clinical parameters to help predict the prognosis of ccRCC patients. The five immune-related lncRNAs could potentially be therapeutic targets for immunotherapy in ccRCC in the future.

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