Liu Weihao, Liu Ying, Chen Shisheng, Hui Jialiang, He Shuhua
Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Department of Oncology, Huadu District People's Hospital of Guangzhou, Guangzhou, 510810, Guangdong, China.
Discov Oncol. 2024 Jul 16;15(1):286. doi: 10.1007/s12672-024-01141-7.
Chromatin regulators (CRs) are capable of causing epigenetic alterations, which are significant features of cancer. However, the function of CRs in controlling Clear Cell Renal Cell Carcinoma (ccRCC) is not well understood. This research aims to discover a CRs prognostic signature in ccRCC and to elucidate the roles of CRs-related genes in tumor microenvironment (TME).
Expression profiles and relevant clinical annotations were retrieved from the Cancer Genome Atlas (TCGA) and UCSC Xena platform for progression-free survival (PFS) data. The R package "limma" was used to identify differentially expressed CRs. A predictive model based on five CRs was developed using LASSO-Cox analysis. The model's predictive power and applicability were validated using K-M curves, ROC curves, nomograms, comparisons with other models, stratified survival analyses, and validation with the ICGC cohort. GO and GSEA analyses were performed to investigate mechanisms differentiating low and high riskScore groups. Immunogenicity was assessed using Tumor Mutational Burden (TMB), immune cell infiltrations were inferred, and immunotherapy was evaluated using immunophenogram analysis and the expression patterns of human leukocyte antigen (HLA) and checkpoint genes. Differentially expressed CRs (DECRs) between low and high riskScore groups were identified using log2|FC|> 1 and FDR < 0.05. AURKB, one of the high-risk DECRs and a component of our prognostic model, was selected for further analysis.
We constructed a 5 CRs signature, which demonstrated a strong capacity to predict survival and greater applicability in ccRCC. Elevated immunogenicity and immune infiltration in the high riskScore group were associated with poor prognosis. Immunotherapy was more effective in the high riskScore group, and certain chemotherapy medications, including cisplatin, docetaxel, bleomycin, and axitinib, had lower IC50 values. Our research shows that AURKB is critical for the immunogenicity and immune infiltration of the high riskScore group.
Our study produced a reliable prognostic prediction model using only 5 CRs. We found that AURKB promotes immunogenicity and immune infiltration. This research provides crucial support for the development of prognostic biomarkers and treatment strategies for ccRCC.
染色质调节因子(CRs)能够引起表观遗传改变,这是癌症的重要特征。然而,CRs在控制透明细胞肾细胞癌(ccRCC)中的功能尚不清楚。本研究旨在发现ccRCC中的CRs预后特征,并阐明CRs相关基因在肿瘤微环境(TME)中的作用。
从癌症基因组图谱(TCGA)和UCSC Xena平台检索表达谱及相关临床注释,以获取无进展生存期(PFS)数据。使用R包“limma”来识别差异表达的CRs。采用LASSO-Cox分析建立基于5个CRs的预测模型。使用K-M曲线、ROC曲线、列线图、与其他模型比较、分层生存分析以及ICGC队列验证等方法对模型的预测能力和适用性进行验证。进行GO和GSEA分析以研究区分低风险评分组和高风险评分组的机制。使用肿瘤突变负荷(TMB)评估免疫原性,推断免疫细胞浸润情况,并通过免疫表型分析以及人类白细胞抗原(HLA)和检查点基因的表达模式评估免疫治疗效果。使用log2|FC|>1和FDR<0.05来识别低风险评分组和高风险评分组之间的差异表达CRs(DECRs)。选择高风险DECRs之一且是我们预后模型组成部分的AURKB进行进一步分析。
我们构建了一个由5个CRs组成的特征,其在预测ccRCC生存方面显示出强大能力且具有更高的适用性。高风险评分组中免疫原性和免疫浸润的升高与预后不良相关。免疫治疗在高风险评分组中更有效,某些化疗药物,包括顺铂、多西他赛、博来霉素和阿昔替尼,具有较低的IC50值。我们研究表明AURKB对高风险评分组的免疫原性和免疫浸润至关重要。
我们的研究仅使用5个CRs就产生了一个可靠的预后预测模型。我们发现AURKB促进免疫原性和免疫浸润。本研究为ccRCC预后生物标志物的开发和治疗策略提供了关键支持。