Individualized immune-related gene signature predicts immune status and oncologic outcomes in clear cell renal cell carcinoma patients

BackgroundAccumulating evidences indicate significant alterations in the aerobic glycolysis in clear cell renal cell carcinoma (ccRCC). We aim to develop and validate a glycolysis-related genes signature for predicting the clinical outcomes of patients with ccRCC.MethodsmRNA expression profiling of ccRCC was obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis and lasso Cox regression model were performed to identify and construct the prognostic gene signature. The protein expression levels of the core genes were obtained from the Human Protein Atlas database. We used four external independent data sets to verify the predictive power of the model for prognosis, tyrosine kinase inhibitor (TKI) therapy, and immunotherapy responses, respectively. Finally, we explored the potential mechanism of this signature through gene set enrichment analysis (GSEA).ResultsThrough the GSEA, glycolysis-related gene sets were significantly different between ccRCC tissues and normal tissues. Next, we identified and constructed a seven-mRNA signature (GALM, TGFA, RBCK1, CD44, HK3, KIF20A, and IDUA), which was significantly correlated with worse survival outcome and was an independent prognostic indicator for ccRCC patients. Furthermore, the expression levels of hub genes were validated based on the Human Protein Atlas databases. More importantly, the model can predict patients’ response to TKI therapy and immunotherapy. These findings were successfully validated in the external independent ccRCC cohorts. The mechanism exploration showed that the model may influence the prognosis by influencing tumor proliferation, base mismatch repair system and immune status of patients.ConclusionsOur study has built up a robust glycolysis-based molecular signature that predicts the prognosis and TKI therapy and immunotherapy responses of patients with ccRCC with high accuracy, which might provide important guidance for clinical assessment. Also, clinical investigations in large ccRCC cohorts are greatly needed to validate our findings.

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Development and validation of an individualized DNA repair-related gene signature in localized clear cell renal cell carcinoma

World Journal of Urology ◽

10.1007/s00345-020-03270-4 ◽

2020 ◽

Author(s):

Ying Xiong ◽

Yu Qi ◽

Wenyao Lin ◽

Qi Bai ◽

Li Liu ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Dna Repair ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Gene Signature ◽

Related Gene ◽

Cell Renal Cell Carcinoma ◽

Development And Validation

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The Immune-Related Gene HCST as a Novel Biomarker for the Diagnosis and Prognosis of Clear Cell Renal Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.630706 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yongying Zhou ◽

Xiao Wang ◽

Weibing Zhang ◽

Huiyong Liu ◽

Daoquan Liu ◽

...

Keyword(s):

Gene Expression ◽

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Immune Cells ◽

Renal Cell ◽

Clear Cell ◽

Signal Transducer ◽

Related Gene ◽

Cell Renal Cell Carcinoma ◽

Immune Related Gene

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney tumor worldwide. Analysis of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases showed that the immune-related gene (IRG) hematopoietic cell signal transducer (HCST) could provide guidance for the diagnosis, prognosis, and treatment of ccRCC. The RNA-seq data of ccRCC tissues were extracted from two databases: TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) and GEO (https://www.ncbi.nlm.nih.gov/geo/). Corresponding clinical information was downloaded from TCGA. Immune-related gene data were extracted from the IMMPORT website (https://www.immport.org/). Differential analysis with R software (https://www.r-project.org/) was used to obtain a prognosis model of ccRCC IRGs. The differences were combined with the clinical data to assess the usefulness of the HCST as a prognostic biomarker. Based on data obtained from the Oncomine (https://www.oncomine.org/), Human Protein Atlas (https://www.proteinatlas.org/), and PubMed (https://pubmed.ncbi.nlm.nih.gov/) databases, the expression levels of the HCST in ccRCC, clinical-pathological indicators of relevance, and influence on prognosis were analyzed. Regulation of the HCST gene in ccRCC was assessed by gene set enrichment analysis (GSEA). In TCGA/GEO databases, the high HCST expression in tumor tissues was significantly correlated to the TMN stage, tumor grade, invasion depth, and lymphatic metastasis (p < 0.05). The overall survival (OS) of patients with high HCST gene expression was significantly lower than that of patients with low HCST gene expression (p < 0.001). Multivariate Cox regression analysis suggested that the HCST expression level [hazard ratio (HR) = 1.630, 95% confidence interval (CI) = 1.042–2.552], tumor cell grade (HR = 1.829, 95% CI = 1.115–3.001), and distant metastasis (HR = 2.634, 95%, CI = 1.562–4.442) were independent risk factors affecting the OS of ccRCC patients (all, p < 0.05). The GSEA study showed that there was significant enrichment in cell adhesion, tumorigenesis, and immune and inflammatory responses in HCST high expression samples. Hematopoietic cell signal transducer expression was closely associated with the levels of infiltrating immune cells around ccRCC tissues, especially dendritic cells (DCs). In conclusion, the present study suggested that the HCST was interrelated to the clinicopathology and poor prognosis of ccRCC. High HCST expression was also closely correlated with the levels of tumor-infiltrating immune cells, especially DCs.

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Ferroptosis-Related Gene Signature Accurately Predicts Survival Outcomes in Patients With Clear-Cell Renal Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.649347 ◽

2021 ◽

Vol 11 ◽

Author(s):

Kaili Chang ◽

Chong Yuan ◽

Xueguang Liu

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Gene Signature ◽

Receptor Interaction ◽

Related Gene ◽

Prognostic Signature ◽

Cell Renal Cell Carcinoma ◽

Cox Regression Analysis

As a type of regulated cell death induced by Ras selective lethal (RSL) compounds such as erasti, ferroptosis is characterized by iron-dependent lipid peroxide accumulation to lethal levels. At present, little is known about the role of ferroptosis-related genes in clear-cell renal cell carcinoma (ccRCC). In the present study, the expression data of ferroptosis-related genes in ccRCC were obtained from the Cancer Genome Atlas (TCGA), and COX regression analysis was performed to construct a risk model of ferroptosis prognostic signature. The GEO database was used to verify the accuracy of the model. The following findings were made: the results reveal that the prognostic signature constructed by 11 ferroptosis genes (CARS, CD44, DPP4, GCLC, HMGCR, HSPB1, NCOA4, SAT1, PHKG2, GOT1, HMOX1) was significantly related to the overall survival (OS) of ccRCC patients based on the lowest Akaike information criterion (AIC); multivariate analysis indicates that ferroptosis-related gene prognostic signature was an independent prognostic factor in ccRCC patients; the calibration curve and c-index value (0.77) demonstrate that the nomogram with the signature could predict the survival of ccRCC patients; and enrichment analysis shows that the high-risk group were enriched in humoral immunity and receptor interaction pathways. The aforementioned findings indicate that the ferroptosis-related gene signature can accurately predict the prognosis of ccRCC patients and provide valuable insights for individualized treatment.

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Development of a novel gene signature to predict prognosis and response to PD-1 blockade in clear cell renal cell carcinoma

OncoImmunology ◽

10.1080/2162402x.2021.1933332 ◽

2021 ◽

Vol 10 (1) ◽

pp. 1933332

Author(s):

Xiaomao Yin ◽

Zaoyu Wang ◽

Jianfeng Wang ◽

Yunze Xu ◽

Wen Kong ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Gene Signature ◽

Cell Renal Cell Carcinoma ◽

Novel Gene

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A prognostic nomogram based on competing endogenous RNA network for clear-cell renal cell carcinoma

10.21203/rs.3.rs-19720/v1 ◽

2020 ◽

Author(s):

Yun Peng ◽

Shangrong Wu ◽

Zihan Xu ◽

Dingkun Hou ◽

Nan Li ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Rna Sequencing ◽

Renal Cell ◽

Cox Regression ◽

Clear Cell ◽

Gene Signature ◽

Expression Data ◽

Competing Endogenous Rna ◽

Cell Renal Cell Carcinoma

Abstract Backgroud Clear-cell renal cell carcinoma (ccRCC) is stubborn to traditional chemotherapy and radiation treatment, which makes its clinical management a major challenge. Recently, we have made efforts to understand the etiology of ccRCC. Increasing evidence revealed that the competing endogenous RNA (ceRNA) were involved in the development of various tumor. However, it’s scant for studying on ccRCC, and a comprehensive analysis of prognostic model based on lncRNA-miRNA-mRNA ceRNA regulatory network of ccRCC with large-scale sample size and RNA‐sequencing expression data is still limited. Methods RNA‐sequencing expression data were taken out from GTEx database and TCGA database, A total of 354 samples with ccRCC and 157 normal controlled samples were included in our study. The ccRCC-specific genes were obtained from WGCNA and differential expression analysis. Following, the communication between mRNAs and lncRNAs and target miRNAs were predicted by MiRcode, starBase, miRTarBase, and TargetScan. A gene signature of eight genes was constructed by univariate Cox regression, lasso methods and multivariate Cox regression analysis. Results A total of 2191 mRNAs and 1377 lncRNAs was identified, and a dys-regulated ceRNA network for ccRCC was established using 7 mRNAs, 363 lncRNAs, and 3 miRNAs. Further, a gene signature in cluding 8 genes based on this ceRNA was constructed, meanwhile, a nomogram predicting 1-, 3-, 5-year survival probability containing both clinical characteristics and ccRCC-specific gene signatures was developed. Conclusion It could contribute to a better understanding of ccRCC tumorigenesis mechanism and guide clinicians to make a more accurate treatment decision.

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A four-gene signature predicts survival in clear-cell renal-cell carcinoma

Oncotarget ◽

10.18632/oncotarget.12631 ◽

2016 ◽

Vol 7 (50) ◽

pp. 82712-82726 ◽

Cited By ~ 17

Author(s):

Jun Dai ◽

Yuchao Lu ◽

Jinyu Wang ◽

Lili Yang ◽

Yingyan Han ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Gene Signature ◽

Cell Renal Cell Carcinoma

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Unraveling the molecular profile underpinning pancreatic tropisms in metastatic clear cell renal cell carcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e16096 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e16096-e16096

Author(s):

Nirmish Singla ◽

Oreoluwa Onabolu ◽

Layton Woolford ◽

Christina Stevens ◽

Vanina Tcheuyap ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Cleveland Clinic ◽

Metastatic Tumor ◽

Prognostic Models ◽

Oncologic Outcomes ◽

Molecular Profile ◽

Cell Renal Cell Carcinoma

e16096 Background: The tropism of cancer metastases is poorly understood yet holds prognostic value. Clear cell renal cell carcinoma (ccRCC) exhibits a broad pattern of metastases, making it an optimal model to study organotropism. Notably, when ccRCC metastasizes to the pancreas (PM) independently of other sites, it is associated with favorable outcomes in patients for unclear reasons. Here, we comprehensively analyzed the clinical and molecular profile of patients with PM. Methods: RCC patients with PM from UTSW and Cleveland Clinic were identified. Clinicopathologic data and oncologic outcomes were analyzed. Whole exome sequencing (WES), RNAseq, and histologic assessment of primary and metastatic tumors from PM patients were conducted. Results: 31 RCC patients with PM were identified. We observed remarkably favorable outcomes in our PM cohort, with a median overall survival (OS) of 10.7 years from metastatic diagnosis and a long latency between initial diagnosis and development of metastasis (median 69 months in patients who were non-metastatic at diagnosis). OS was independent of both metastatic tumor burden and known IMDC prognostic factors. We discovered that tumors from PM patients were markedly uniform and clustered together by gene expression analysis. WES and DNA copy number analyses revealed a high frequency of VHL and PBRM1 mutations, 3p loss, and 5q amplification, along with a lower frequency of 9p, 14q and 4q losses and BAP1 mutations, characteristic of indolent ccRCC. Furthermore, the genomic and histologic features of tumors from patients with PM can be recapitulated in patient-derived xenograft models. Conclusions: To our knowledge, this is the first report to unravel molecular determinants of organotropism, and we highlight that organotropism can be an independent prognostic factor. Understanding tumor heterogeneity may help refine prognostic models for metastatic RCC and hold implications for improved personalization of therapy.

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