Identification of senescence-associated long non-coding RNAs to predict prognosis and immune microenvironment in patients with hepatocellular carcinoma.
Identification of senescence-associated long non-coding RNAs to predict prognosis and immune microenvironment in patients with hepatocellular carcinoma.
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DOI：
10.3389/fgene.2022.956094
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发表时间：
2022
影响因子：
3.7
通讯作者：
Zhou, Gangqiao
中科院分区：
文献类型：
--
作者：
Gao, Chengzhi;Zhou, Guangming;Cheng, Min;Feng, Lan;Cao, Pengbo;Zhou, Gangqiao
关键词：
背景：细胞衰老在肿瘤的发生发展中起着复杂而重要的作用，因为它对肿瘤的发生有不同的影响。然而，与肿瘤衰老相关的长非编码RNA(LncRNAs)及其在肝细胞癌(HCC)中的预后价值仍未被发现。方法：在肿瘤基因组图谱(TCGA)数据集中，通过基因集变异分析(GSVA)确定跨癌癌基因诱导衰老(OIS)的特征。通过相关分析确定了与OIS相关的lncRNAs。用COX回归分析筛选与预后相关的LncRNA，用最小绝对收缩和选择算子(LASSO)回归分析建立最优预测模型。通过Kaplan-Meier生存分析、诺模图、分层生存分析和受试者工作特征曲线(ROC)分析来评估模型的性能。基因集浓缩分析(GSEA)和通过估计RNA转录本相对亚集进行细胞类型鉴定(CiberSort)分别用于探讨功能相关性和免疫细胞渗透。结果：首先，我们检查了泛癌OIS信号，发现几种类型的癌症OIS与患者的生存密切相关，包括肝细胞癌。随后，基于OIS签名，我们确定了76个与OIS相关的LncRNAs，这些LncRNA在肝细胞癌中具有预后价值。通过Lasso-Cox回归分析，我们建立了基于11个(包括NRAV、AC015908.3、MIR100HG、AL365203.2、AC009005.1、SNHG3、LINC01138、AC090192.2、AC008622.2、AL139423.1和AC026356.1)的最优预后模型。随后证实，风险评分是总体生存(OS)的独立和潜在的风险指标(HR[95%CI]=4.9[2.74-8.70]，p<；0.001)，其表现优于传统的临床病理因素。此外，风险评分越高的患者，其促炎症衰老相关分泌表型(SASP)水平也越高，调节性T(Treg)细胞的浸润率越高，而幼稚B细胞的浸润率越低，提示OIS对免疫微环境的调节作用。此外，我们发现NRAV是一种具有代表性的OIS相关LncRNA，它在主要由DNA低甲基化驱动的肝细胞癌中过度表达。结论：基于11个OIS相关的LncRNAs，我们建立了一个有前景的预测肝癌患者预后的指标，并强调了OIS在肝细胞癌中潜在的免疫微环境调节作用，为肿瘤衰老提供了广阔的分子前景。
Background: Cellular senescence plays a complicated and vital role in cancer development because of its divergent effects on tumorigenicity. However, the long non-coding RNAs (lncRNAs) associated with tumor senescence and their prognostic value in hepatocellular carcinoma (HCC) remain unexplored. Methods: The trans-cancer oncogene-induced senescence (OIS) signature was determined by gene set variation analysis (GSVA) in the cancer genome atlas (TCGA) dataset. The OIS-related lncRNAs were identified by correlation analyses. Cox regression analyses were used to screen lncRNAs associated with prognosis, and an optimal predictive model was created by regression analysis of the least absolute shrinkage and selection operator (LASSO). The performance of the model was evaluated by Kaplan-Meier survival analyses, nomograms, stratified survival analyses, and receiver operating characteristic curve (ROC) analyses. Gene set enrichment analysis (GSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) were carried out to explore the functional relevance and immune cell infiltration, respectively. Results: Firstly, we examined the pan-cancer OIS signature, and found several types of cancer with OIS strongly associated with the survival of patients, including HCC. Subsequently, based on the OIS signature, we identified 76 OIS-related lncRNAs with prognostic values in HCC. We then established an optimal prognostic model based on 11 (including NRAV, AC015908.3, MIR100HG, AL365203.2, AC009005.1, SNHG3, LINC01138, AC090192.2, AC008622.2, AL139423.1, and AC026356.1) of these lncRNAs by LASSO-Cox regression analysis. It was then confirmed that the risk score was an independent and potential risk indicator for overall survival (OS) (HR [95% CI] = 4.90 [2.74–8.70], p < 0.001), which outperforms those traditional clinicopathological factors. Furthermore, patients with higher risk scores also showed more advanced levels of a proinflammatory senescence-associated secretory phenotype (SASP), higher infiltration of regulatory T (Treg) cells and lower infiltration of naïve B cells, suggesting the regulatory effects of OIS on immune microenvironment. Additionally, we identified NRAV as a representative OIS-related lncRNA, which is over-expressed in HCC tumors mainly driven by DNA hypomethylation. Conclusion: Based on 11 OIS-related lncRNAs, we established a promising prognostic predictor for HCC patients, and highlighted the potential immune microenvironment-modulatory roles of OIS in HCC, providing a broad molecular perspective of tumor senescence.
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影响因子：
14.5
作者：
Chibaya, Loretah;Snyder, Jarin;Ruscetti, Marcus
通讯作者：
Ruscetti, Marcus
DOI：
10.1146/annurev-pathol-121808-102144
发表时间：
2010
期刊：
Annual review of pathology
影响因子：
--
作者：
Coppé JP;Desprez PY;Krtolica A;Campisi J
通讯作者：
Campisi J
影响因子：
6.4
作者：
Farrington, SM;McKinley, AJ;Dunlop, MG
通讯作者：
Dunlop, MG
影响因子：
21.3
作者：
Glück S;Guey B;Gulen MF;Wolter K;Kang TW;Schmacke NA;Bridgeman A;Rehwinkel J;Zender L;Ablasser A
通讯作者：
Ablasser A
影响因子：
4
作者：
Black AP;Mehta AS
通讯作者：
Mehta AS