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基于血清长链非编码 RNA 的肝细胞癌淋巴结转移无创预测列线图

A Noninvasive Prediction Nomogram for Lymph Node Metastasis of Hepatocellular Carcinoma Based on Serum Long Noncoding RNAs.

机构信息

Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.

Department of Radiation Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.

出版信息

Biomed Res Int. 2019 Jul 1;2019:1710670. doi: 10.1155/2019/1710670. eCollection 2019.

DOI:10.1155/2019/1710670
PMID:31355249
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6634290/
Abstract

BACKGROUND AND OBJECTIVES

Lymph node metastasis (LNM) is common in hepatocellular carcinoma (HCC). In order to intervene HCC LNM in advance, we developed a prediction nomogram based on serum long noncoding RNA (lncRNA).

METHODS

Serum samples from 242 HCC patients were gathered and randomly enrolled into the training and validation cohorts. LncRNAs screened out from microarray were quantified with qRT-PCR. Univariate and multivariate analyses were applied for screening independent risk factors. A prediction nomogram was ultimately developed for HCC LNM. The nomogram was estimated by discrimination and calibration tests in the validation cohort. The effects of the candidate lncRNA on the malignant phenotypes of HCC cells were further explored by wound healing assay and colony formation assay.

RESULTS

ENST00000418803, lnc-ZNF35-4:1, lnc-EPS15L1-2:1, BCLC stage, and vascular invasion were selected as components of the nomogram according to the adjusted multivariate analysis. The nomogram effectively predicted the HCC LNM risk among the cohorts with suitable calibration fittings and displayed high discrimination with C-index of 0.89 and 0.85. Moreover, the abnormally high expression of lnc-EPS15L1-2:1 in HCC cell lines showed significant carcinogenic effects.

CONCLUSIONS

The noninvasive nomogram may provide more diagnostic basis for treatments of HCC. The biomarkers identified can bring new clues to basic researches.

摘要

背景与目的

淋巴结转移(LNM)在肝细胞癌(HCC)中很常见。为了提前干预 HCC 的 LNM,我们开发了一个基于血清长链非编码 RNA(lncRNA)的预测列线图。

方法

收集了 242 例 HCC 患者的血清样本,并将其随机纳入训练和验证队列。使用 qRT-PCR 定量筛选出的 lncRNA。采用单因素和多因素分析筛选独立的危险因素。最终为 HCC 的 LNM 开发了一个预测列线图。在验证队列中通过判别和校准试验对该列线图进行了评估。通过划痕愈合试验和集落形成试验进一步探索候选 lncRNA 对 HCC 细胞恶性表型的影响。

结果

根据调整后的多因素分析,ENST00000418803、lnc-ZNF35-4:1、lnc-EPS15L1-2:1、BCLC 分期和血管侵犯被选为列线图的组成部分。该列线图在两个队列中均能有效地预测 HCC 的 LNM 风险,具有合适的校准拟合,显示出较高的判别能力,C 指数为 0.89 和 0.85。此外,HCC 细胞系中 lnc-EPS15L1-2:1 的异常高表达显示出显著的致癌作用。

结论

这种非侵入性的列线图可能为 HCC 的治疗提供更多的诊断依据。所鉴定的生物标志物可为基础研究提供新线索。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/bb871562780f/BMRI2019-1710670.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/b789c3a00352/BMRI2019-1710670.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/6fe7f395f773/BMRI2019-1710670.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/12b1b08b1bd5/BMRI2019-1710670.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/31ea36d954ad/BMRI2019-1710670.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/6698acca9ae6/BMRI2019-1710670.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/a4aa09a6d3b2/BMRI2019-1710670.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/48d815caf849/BMRI2019-1710670.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/198ef44a051b/BMRI2019-1710670.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/bb871562780f/BMRI2019-1710670.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/b789c3a00352/BMRI2019-1710670.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/6fe7f395f773/BMRI2019-1710670.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/12b1b08b1bd5/BMRI2019-1710670.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/31ea36d954ad/BMRI2019-1710670.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/6698acca9ae6/BMRI2019-1710670.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/a4aa09a6d3b2/BMRI2019-1710670.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/48d815caf849/BMRI2019-1710670.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/198ef44a051b/BMRI2019-1710670.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1093/6634290/bb871562780f/BMRI2019-1710670.009.jpg

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