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

Abstract

IntroductionHepatocellular 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.MethodsCox 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.ResultsTwelve 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.ConclusionA 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.

Tumor Mutational Burden for Predicting Prognosis and Therapy Outcome of Hepatocellular Carcinoma

“…Another study suggested that a complex network consisting of 3 lncRNAs, 12 miRNAs, and 21 mRNAs may help in classifying patients into two subgroups and predicting HCC progression independently from TMB status [ 142 ]. Another study investigated the connection among single nucleotide polymorphisms (SNPs), TMB, and the epithelial–mesenchymal transition EMT-related lncRNAs, observing a specific signature to predict HCC prognosis, with high TMB and TP53 mutation associated with worse prognosis [ 143 ].…”

Identification of lncRNAs involved in response to ionizing radiation in fibroblasts of long-term survivors of childhood cancer and cancer-free controls

“…They showed that CDKN1A , TP53I3 , and PPM1D were upregulated by AL158206.1. Contrasting these findings, another work examining lncRNAs involved in the epithelial-mesenchymal-transition, associated with metastasis, identified AL158206.1 as one of four lncRNAs having the strongest negative impact on the survival of patients undergoing drug treatment of hepatocellular carcinoma ( 61 ). Interestingly, we observed upregulation of AL158206.1 and thus induction of protective radiation response after LDIR only in N1.…”

Cancer-Derived Extracellular Vesicles as Biomarkers for Cutaneous Squamous Cell Carcinoma: A Systematic Review

“…EV characteristic profiles had been postulated to provide insights into tumor behaviors. Previous research showed that PD1 and PD-L1 expression on melanoma-derived EVs predicted resistance to checkpoint inhibitors [ 54 ]; EV mRNA could predict NSCLC survival and efficacy of different treatment modalities [ 53 ]; long noncoding RNA may serve as a prognostic and immunotherapeutic predictor for testicular germ cell tumor and HCC [ 55 , 56 ]. Besides, EV DNA reflected both chromosomal and mitochondrial DNA of the original tumor, which could be a powerful tool to assess tumor genome and predict prognosis [ 57 ].…”