Su Yao, Yang Jin
College of Pharmacy, Chengdu University, Chengdu, China.
Department of Urology, Affiliated Hospital & Clinical Medical College of Chengdu University, Chengdu, China.
Comb Chem High Throughput Screen. 2025;28(9):1524-1542. doi: 10.2174/0113862073271880231114100544.
Various cancer types have been studied and understood using long noncoding RNA (lncRNA). Despite this, only a few studies have examined anoikis-related lncRNAs in kidney renal clear cell carcinoma (KIRC). As a result, this study evaluated a powerful prognostic model for KIRC patients based on anoikis-lncRNAs and identified potential biological targets.
Anoikis-related lncRNAs associated with patient prognosis were identified using Pearson correlation, variance, and univariate Cox regression analyses. A predictive model that incorporated 4 anoikis-related lncRNAs has been constructed using the least absolute shrinkage and selection operator (LASSO) regression algorithm. The prognostic performance of the proposed model has also been assessed utilizing Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analyses. An ESTIMATE analysis was carried out on the low- as well as high-risk subtypes to evaluate immune cell infiltration status. Furthermore, CIBERSORT, TIMER, and QUANTISEQ along with other algorithms were applied for determining the infiltration status of numerous immune cells across both groups. In addition, immune checkpoint gene expression in both groups was also determined. Finally, drug sensitivity assays and experiments were performed to validate the results.
A total of sixty-three lncRNAs associated with anoikis and KIRC prognosis were identified via univariate cox analysis, and four lncRNAs (Z99289.2, AC084876.1, LINC00460, and AC090337.2.) were selected as hub lncRNAs. A prognostic signature has been developed based on the expression levels and coefficiency of these four lncRNAs while establishing its efficacy in part and whole TCGA KIRC cohort. Furthermore, by using this risk signature, high- as well as low-risk KIRC patients could be distinguished more precisely it can predict patient outcomes as well. The survival predictions by the nomogram exhibited an absolute degree of concordance with actual situations. experiments verified that LINC00460 downregulation contributed to the growth inhibition of KIRC cell lines and promoted apoptosis of cancer cells.
This study suggests that anoikis-related lncRNAs could serve as valuable prognostic markers for KIRC. Additionally, they may provide insight into future KIRC treatment options by reflecting on the situation of the kidney immune microenvironment.
人们已经利用长链非编码RNA(lncRNA)对多种癌症类型进行了研究和了解。尽管如此,只有少数研究探讨了肾透明细胞癌(KIRC)中与失巢凋亡相关的lncRNA。因此,本研究基于失巢凋亡相关lncRNA评估了一种针对KIRC患者的强大预后模型,并确定了潜在的生物学靶点。
使用Pearson相关性分析、方差分析和单因素Cox回归分析,鉴定与患者预后相关的失巢凋亡相关lncRNA。利用最小绝对收缩和选择算子(LASSO)回归算法构建了一个包含4个失巢凋亡相关lncRNA的预测模型。还利用Kaplan-Meier(KM)生存分析和受试者工作特征(ROC)曲线分析评估了所提出模型的预后性能。对低风险和高风险亚型进行ESTIMATE分析,以评估免疫细胞浸润状态。此外,应用CIBERSORT、TIMER和QUANTISEQ以及其他算法来确定两组中多种免疫细胞的浸润状态。另外,还测定了两组中免疫检查点基因的表达。最后,进行药物敏感性试验和实验以验证结果。
通过单因素cox分析共鉴定出63个与失巢凋亡和KIRC预后相关的lncRNA,其中4个lncRNA(Z99289.2、AC084876.1、LINC00460和AC090337.2)被选为核心lncRNA。基于这4个lncRNA的表达水平和系数开发了一种预后特征,并在部分和整个TCGA KIRC队列中确定了其有效性。此外,利用这种风险特征,可以更准确地区分高风险和低风险的KIRC患者,也能够预测患者的预后。列线图的生存预测与实际情况显示出高度一致性。实验证实,LINC00460的下调有助于抑制KIRC细胞系的生长并促进癌细胞凋亡。
这项研究表明,失巢凋亡相关lncRNA可作为KIRC有价值的预后标志物。此外,它们可能通过反映肾脏免疫微环境的情况,为未来KIRC的治疗方案提供思路。