Zhang Jun-Song, Pan Run-Sang, Tian Xiao-Bin
School of Clinical Medicine, Guizhou Medical University, Guiyang, China.
School of Basic Medicine, Guizhou Medical University, Guiyang, China.
Front Oncol. 2023 Mar 23;13:1156663. doi: 10.3389/fonc.2023.1156663. eCollection 2023.
Anoikis is a specialized form of programmed apoptosis that occurs in two model epithelial cell lines and plays an important role in tumors. However, the prognostic value of anoikis-related lncRNA (ARLncs) in osteosarcoma (OS) has not been reported.
Based on GTEx and TARGET RNA sequencing data, we carried out a thorough bioinformatics analysis. The 27 anoikis-related genes were obtained from the Gene Set Enrichment Analysis (GSEA). Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis were successively used to screen for prognostic-related ARLncs. To create the prognostic signature of ARLncs, we performed multivariate Cox regression analysis. We calculated the risk score based on the risk coefficient, dividing OS patients into high- and low-risk subgroups. Additionally, the relationship between the OS immune microenvironment and risk prognostic models was investigated using function enrichment, including Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), single-sample gene set enrichment analysis (ssGSEA), and GSEA analysis. Finally, the potential effective drugs in OS were found by immune checkpoint and drug sensitivity screening.
A prognostic signature consisting of four ARLncs (AC079612.1, MEF2C-AS1, SNHG6, and TBX2-AS1) was constructed. To assess the regulation patterns of anoikis-related lncRNA genes, we created a risk score model. According to a survival analysis, high-risk patients have a poor prognosis as they progress. By using immune functional analysis, the lower-risk group demonstrated the opposite effects compared with the higher-risk group. GO and KEGG analysis showed that the ARLncs pathways and immune-related pathways were enriched. Immune checkpoints and drug sensitivity analysis might be used to determine the better effects of the higher group.
We identified a novel prognostic model based on a four-ARLncs signature that might serve as potential prognostic indicators that can be used to predict the prognosis of OS patients, and immunotherapy and drugs that may contribute to improving the overall survival of OS patients and advance our understanding of OS.
失巢凋亡是程序性凋亡的一种特殊形式，发生于两种模型上皮细胞系中，在肿瘤中起重要作用。然而，失巢凋亡相关长链非编码RNA（ARLncs）在骨肉瘤（OS）中的预后价值尚未见报道。
基于GTEx和TARGET RNA测序数据，我们进行了全面的生物信息学分析。从基因集富集分析（GSEA）中获得27个失巢凋亡相关基因。先后使用单因素Cox回归和最小绝对收缩和选择算子（LASSO）分析来筛选与预后相关的ARLncs。为创建ARLncs的预后特征，我们进行了多因素Cox回归分析。我们根据风险系数计算风险评分，将OS患者分为高风险和低风险亚组。此外，使用功能富集研究OS免疫微环境与风险预后模型之间的关系，包括基因本体（GO）、京都基因与基因组百科全书（KEGG）、单样本基因集富集分析（ssGSEA）和GSEA分析。最后，通过免疫检查点和药物敏感性筛选发现OS中的潜在有效药物。
构建了一个由四个ARLncs（AC079612.1、MEF2C-AS1、SNHG6和TBX2-AS1）组成的预后特征。为评估失巢凋亡相关lncRNA基因的调控模式，我们创建了一个风险评分模型。根据生存分析，高风险患者病情进展时预后较差。通过免疫功能分析，低风险组与高风险组表现出相反的效果。GO和KEGG分析表明，ARLncs途径和免疫相关途径得到富集。免疫检查点和药物敏感性分析可用于确定高风险组的更好疗效。
我们基于四个ARLncs特征鉴定了一种新型预后模型，该模型可能作为潜在的预后指标，用于预测OS患者的预后，以及可能有助于改善OS患者总生存期并推进我们对OS理解的免疫疗法和药物。
