Qu Junchi, Tao Di, Huang Wei, Lu Liting, Fan Junming, Zhang Shineng, Huang Fengting
Department of Gastroenterology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China.
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
Heliyon. 2023 Jul 20;9(8):e18493. doi: 10.1016/j.heliyon.2023.e18493. eCollection 2023 Aug.
Hepatocellular carcinoma (HCC) is characterized by extensive risk factors, high morbidity and mortality. Clinical prognostic evaluation assay assumes a nonspecific quality. Better HCC prognostics are urgently needed. Long noncoding RNAs (lncRNAs) exerts a crucial role in tumorigenesis and development. Excavating specific lncRNAs signature to ameliorate the high-risk survival prediction in HCC patients is worthwhile.
Differentially expressed lncRNAs (DElncRNAs) profile was acquired from The Cancer Genome Atlas database (TCGA). Then, the lncRNAs high-risk survival prognostic model was established using the least absolute shrinkage and selection operator (LASSO)-Cox regression algorithm. The lncRNAs were evaluated in clinical specimen by PCR. The receiver operating characteristic curve (ROC) analysis was further conducted to assess the potential prognostic value of the model. Moreover, a visible nomogram containing clinicopathological features and prognostic model was developed for prediction of survival property. Potential molecular mechanism was assessed by GO, KEGG, GSEA enrichment analysis and CIBERSORT immune infiltration analysis.
A novel 7-lncRNA risk model (AL161937.2, LINC01063, AC145207.5, POLH-AS1, LNCSRLR, MKLN1-AS, AC105345.1) was constructed and validated for HCC prognosis prediction. Kaplan-Meier analysis revealed that patients in the high-risk group suffered a poor prognosis ( = 1.813 × 10). These genes were detected by PCR, and the expression trend was in accordance with TCGA database. Interestingly, the risk score served as an independent risk factor for HCC patients (HR: 1.166, 95% CI:1.119-1.214, < 0.001). The nomogram was established, and the predictive accuracy in the nomogram was prior to the TNM stage according to the ROC curve analysis. Cell proliferation related pathway, decreased CD4 T cell, CD8 T cell, NK cell and elevated Neutrophil, Macrophage M0 were observed in high-risk group. Besides, suppression of MKLN1-AS expression inhibited cell proliferation of HCC cells by CCK8 assay
The 7-lncRNA signature may exert a particular prognostic prediction role in HCC and provide new insight in HCC carcinogenesis.
肝细胞癌（HCC）具有广泛的危险因素、高发病率和死亡率。临床预后评估检测具有非特异性。迫切需要更好的HCC预后指标。长链非编码RNA（lncRNAs）在肿瘤发生和发展中发挥关键作用。挖掘特定的lncRNAs特征以改善HCC患者的高危生存预测是值得的。
从癌症基因组图谱数据库（TCGA）获取差异表达lncRNAs（DElncRNAs）谱。然后，使用最小绝对收缩和选择算子（LASSO）-Cox回归算法建立lncRNAs高危生存预后模型。通过PCR在临床标本中评估lncRNAs。进一步进行受试者工作特征曲线（ROC）分析以评估模型的潜在预后价值。此外，开发了一个包含临床病理特征和预后模型的可视化列线图用于预测生存情况。通过GO、KEGG、GSEA富集分析和CIBERSORT免疫浸润分析评估潜在的分子机制。
构建了一种新的7-lncRNA风险模型（AL161937.2、LINC01063、AC145207.5、POLH-AS1、LNCSRLR、MKLN1-AS、AC105345.1）并验证其用于HCC预后预测。Kaplan-Meier分析显示高危组患者预后较差（P = 1.813×10）。通过PCR检测这些基因，其表达趋势与TCGA数据库一致。有趣的是，风险评分是HCC患者的独立危险因素（HR：1.166，95%CI：1.119 - 1.214，P < 0.001）。建立了列线图，根据ROC曲线分析，列线图的预测准确性优于TNM分期。在高危组中观察到细胞增殖相关途径、CD4 T细胞、CD8 T细胞、NK细胞减少以及中性粒细胞、巨噬细胞M0升高。此外，通过CCK8实验，抑制MKLN1-AS表达可抑制HCC细胞的增殖。
7-lncRNA特征可能在HCC中发挥特定的预后预测作用，并为HCC致癌机制提供新的见解。
