Identification of an Epithelial-Mesenchymal Transition-Related Long Non-coding RNA Prognostic Signature to Determine the Prognosis and Drug Treatment of Hepatocellular Carcinoma Patients

Shenglan Huang; Dan Li; Lingling Zhuang; Jian Zhang; Jianbing Wu

doi:10.3389/fmed.2022.850343

Identification of an Epithelial-Mesenchymal Transition-Related Long Non-coding RNA Prognostic Signature to Determine the Prognosis and Drug Treatment of Hepatocellular Carcinoma Patients

Front Med (Lausanne). 2022 May 24:9:850343. doi: 10.3389/fmed.2022.850343. eCollection 2022.

Authors

Shenglan Huang^{1

2}, Dan Li^{1

2}, Lingling Zhuang^{2

3}, Jian Zhang^{1

2}, Jianbing Wu^{1

2}

Affiliations

¹ Department of Oncology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
² Jiangxi Key Laboratory of Clinical and Translational Cancer Research, Nanchang, China.
³ Department of Gynaecology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.

Abstract

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Epithelial-mesenchymal transition (EMT) is crucial for cancer progression and metastasis. Thus, we aimed to construct an EMT-related lncRNA signature for predicting the prognosis of HCC patients.

Methods: Cox regression analysis and LASSO regression method were used to build an EMT-related lncRNAs risk signature based on TCGA database. Kaplan-Meier survival analysis was conducted to compare the overall survival (OS) in different risk groups. ROC curves and Cox proportional-hazards analysis were performed to evaluate the performance of the risk signature. RT-qPCR was conducted in HCC cell lines and tissue samples to detect the expression of some lncRNAs in this risk model. Furthermore, a nomogram involving the risk score and clinicopathological features was built and validated with calibration curves and ROC curves. In addition, we explored the association between risk signature and tumor immunity, somatic mutations status, and drugs sensitivity.

Results: Twelve EMT-related lncRNAs were obtained to construct the prognostic risk signature for patients with HCC. The Kaplan-Meier curve analysis revealed that patients in the high-risk group had worse overall survival (OS) than those in low-risk group. ROC curves and Cox regression analysis suggested the risk signature could predict HCC survival exactly and independently. The prognostic value of the risk model was confirmed in the testing and entire groups. We also found AC099850.3 and AC092171.2 were highly expressed in HCC cells and HCC tissues. The nomogram could accurately predict survival probability of HCC patients. Gene set enrichment analysis (GSEA) and gene ontology (GO) analysis showed that cancer-related pathways and cell division activity were enriched in high-risk group. The SNPs showed that the prevalence of TP53 mutations was significantly different between high- and low-risk groups; the TP53 mutations and the high TMB were both associated with a worse prognosis in patients with HCC. We also observed widely associations between risk signature and drugs sensitivity in HCC.

Conclusion: A novel EMT-related lncRNAs risk signature, including 12 lncRNAs, was established and identified in patients with HCC, which can accurately predict the prognosis of HCC patients and may be used to guide individualized treatment in the clinical practice.

Keywords: epithelial-mesenchymal transition; hepatocellular carcinoma; long non-coding RNA; nomogram; prognostic signature.