Dataset Information

Characterization of Hypoxia-Related Molecular Subtypes in Clear Cell Renal Cell Carcinoma to Aid Immunotherapy and Targeted Therapy via Multi-Omics Analysis.

ABSTRACT: Objective: Tumor hypoxia is a key factor in resistance to anti-cancer treatment. Herein, this study aimed to characterize hypoxia-related molecular subtypes and assess their correlations with immunotherapy and targeted therapy in clear cell renal cell carcinoma (ccRCC). Materials: We comprehensively analyzed copy number variation (CNV), somatic mutation, transcriptome expression profile and clinical information for ccRCC from TCGA and ICGC databases. Based on 98 prognosis-related hypoxia genes, samples were clustered using unsupervized non-negative matrix factorization (NMF) analysis. We characterized the differences between subtypes concerning prognosis, CNV, somatic mutations, pathways, immune cell infiltrations, stromal/immune scores, tumor purity, immune checkpoint inhibitors (ICI), response to immunotherapy and targeted therapy and CXC chemokines. Based on differentially expressed genes (DEGs) between subtypes, a prognostic signature was built by LASSO Cox regression analysis, followed by construction of a nomogram incorporating the signature and clinical features. Results: Two hypoxia-related molecular subtypes (C1 and C2) were constructed for ccRCC. Differential CNV, somatic mutations and pathways were found between subtypes. C2 exhibited poorer prognosis, higher immune/stromal scores, and lower tumor purity than C1. Furthermore, C2 had more sensitivity to immunotherapy and targeted therapy than C1. The levels of CXCL1/2/3/5/6/8 chemokines in C2 were distinctly higher than in C1. Consistently, DEGs between subtypes were significantly enriched in cytokine-cytokine receptor interaction and immune responses. This subtype-specific signature can independently predict patients' prognosis. Following verification, the nomogram could be utilized for personalized prediction of the survival probability. Conclusion: Our findings characterized two hypoxia-related molecular subtypes for ccRCC, which can assist in identifying high-risk patients with poor clinical outcomes and patients who can benefit from immunotherapy or targeted therapy.

SUBMITTER: Zhong W

PROVIDER: S-EPMC8267011 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Ubiquitin modification is the most common protein post-translational modification (PTM) process in organisms, and 1332 ubiquitin regulators have been identified in humans. Ubiquitin regulators, especially E3 ligases and deubiquitinases, are widely involved in immune processes. This study aims to explore the ubiquitin modification features of clear cell renal cell carcinoma (ccRCC) and to elucidate the role of such ubiquitin modifications in shaping anti-tumor immunity and individual benefits from immune checkpoint blockade (ICB). A comprehensive analysis was performed in the TCGA cohort (n = 530) and GEO cohort (n = 682). RNA sequencing data of 758 differentially expressed regulators, which was validated by the proteomics data, was used for k-means unsupervised consensus clustering and three ubiquitin patterns of ccRCC were identified. Then, we focused on the ubiquitin modification and tumor progression signatures, immune infiltration characteristics, and prognostic value. The three patterns with different ubiquitin modification signatures correspond to "immune desert phenotype," "immune resistance phenotype," and "immune-inflammatory phenotype," respectively. To facilitate clinical application, we constructed a ubiquitin score to evaluate individual patients' ubiquitination outcome, and it was demonstrated to be an independent risk factor for overall survival (OS) in multivariate Cox analysis. It was found that the high score group was correlated to higher immune cells infiltrating level and PD-1/PD-L1/CTLA-4 expression. More importantly, we found that the high score group was predicted to be sensitive to anti-PD-1 treatment, while the low-score group showed lower predicted IC50 values in treatment with Pazopanib and Axitinib. In summary, this study elucidated the potential link between ubiquitin modification and immune infiltration landscape of ccRCC for the first time and provided a new assessment protocol for the precise selection of treatment strategies for patients with advanced ccRCC.

Project description:BackgroundTumorigenesis is highly heterogeneous, and using clinicopathological signatures only is not enough to effectively distinguish clear cell renal cell carcinoma (ccRCC) and improve risk stratification of patients. DNA methylation (DNAm) with the stability and reversibility often occurs in the early stage of tumorigenesis. Disorders of transcription and metabolism are also an important molecular mechanisms of tumorigenesis. Therefore, it is necessary to identify effective biomarkers involved in tumorigenesis through multi-omics analysis, and these biomarkers also provide new potential therapeutic targets.MethodThe discovery stage involved 160 pairs of ccRCC and matched normal tissues for investigation of DNAm and biomarkers as well as 318 cases of ccRCC including clinical signatures. Correlation analysis of epigenetic, transcriptomic and metabolomic data revealed the connection and discordance among multi-omics and the deregulated functional modules. Diagnostic or prognostic biomarkers were obtained by the correlation analysis, the Least Absolute Shrinkage and Selection Operator (LASSO) and the LASSO-Cox methods. Two classifiers were established based on random forest (RF) and LASSO-Cox algorithms in training datasets. Seven independent datasets were used to evaluate robustness and universality. The molecular biological function of biomarkers were investigated using DAVID and GeneMANIA.ResultsBased on multi-omics analysis, the epigenetic measurements uniquely identified DNAm dysregulation of cellular mechanisms resulting in transcriptomic alterations, including cell proliferation, immune response and inflammation. Combination of the gene co-expression network and metabolic network identified 134 CpG sites (CpGs) as potential biomarkers. Based on the LASSO and RF algorithms, five CpGs were obtained to build a diagnostic classifierwith better classification performance (AUC > 99%). A eight-CpG-based prognostic classifier was obtained to improve risk stratification (hazard ratio (HR) > 4; log-rank test, p-value < 0.01). Based on independent datasets and seven additional cancers, the diagnostic and prognostic classifiers also had better robustness and stability. The molecular biological function of genes with abnormal methylation were significantly associated with glycolysis/gluconeogenesis and signal transduction.ConclusionThe present study provides a comprehensive analysis of ccRCC using multi-omics data. These findings indicated that multi-omics analysis could identify some novel epigenetic factors, which were the most important causes of advanced cancer and poor clinical prognosis. Diagnostic and prognostic biomarkers were identified, which provided a promising avenue to develop effective therapies for ccRCC.

Project description:BackgroundTumor angiogenesis, an essential process for cancer proliferation and metastasis, has a critical role in prognostic of kidney renal clear cell carcinoma (KIRC), as well as a target in guiding treatment with antiangiogenic agents. However, tumor angiogenesis subtypes and potential epigenetic regulation mechanisms in KIRC patient remains poorly characterized. System evaluation of angiogenesis subtypes in KIRC patient might help to reveal the mechanisms of KIRC and develop more target treatments for patients.MethodTen independent tumor angiogenesis signatures were obtained from molecular signatures database (MSigDB) and gene set variation analysis was performed to calculate the angiogenesis score in silico using the Cancer Genome Atlas (TCGA) KIRC dataset. Tumor angiogenesis subtypes in 539 TCGA-KIRC patients were identified using consensus clustering analysis. The potential regulation mechanisms was studied using gene mutation, copy number variation, and differential methylation analysis (DMA). The master transcription factors (MTF) that cause the difference in tumor angiogenesis signals were completed by transcription factor enrichment analysis.ResultsThe angiogenesis score of a prognosis related angiogenesis signature including 189 genes was significantly correlated with immune score, stroma score, hypoxia score, and vascular endothelial growth factor (VEGF) signal score in 539 TCGA KIRC patients. MMRN2, CLEC14A, ACVRL1, EFNB2, and TEK in candidate gene set showed highest correlation coefficient with angiogenesis score in TCGA-KIRC patients. In addition, all of them were associated with overall survival in both TCGA-KIRC and E-MTAB-1980 KIRC data. Clustering analysis based on 183 genes in angiogenesis signature identified two prognosis related angiogenesis subtypes in TCGA KIRC patients. Two clusters also showed different angiogenesis score, immune score, stroma score, hypoxia score, VEGF signal score, and microenvironment score. DMA identified 59,654 differential methylation sites between two clusters and part of these sites were correlated with tumor angiogenesis genes including CDH13, COL4A3, and RHOB. In addition, RFX2, SOX13, and THRA were identified as top three MTF in regulating angiogenesis signature in KIRC patients.ConclusionOur study indicate that evaluation the angiogenesis subtypes of KIRC based on angiogenesis signature with 183 genes and potential epigenetic mechanisms may help to develop more target treatments for KIRC patients. Video Abstract.

Dataset Information

Characterization of Hypoxia-Related Molecular Subtypes in Clear Cell Renal Cell Carcinoma to Aid Immunotherapy and Targeted Therapy via Multi-Omics Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure