Qingbo Pan1, Fanbo Qin2, Hanyu Yuan3, Baoning He4, Ni Yang4, Yitong Zhang4, Hong Ren1, Yi Zeng1. 1. Department of Infectious Diseases, The Key Laboratory of Molecular Biology for Infectious Diseases, Chinese Ministry of Education, Institute for Viral Hepatitis, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China. 2. Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China. 3. Caojie Community Medical Service Centre Hechuan, Chongqing, China. 4. Chongqing YuCai Secondary School, Chongqing, China.
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) is the most common malignant disease worldwide. Although the diagnosis and treatment of HCC have greatly improved in the recent years, there is still a lack of accurate methods to predict the prognosis of patients. Evidence has shown that Hippo signaling in tissues adjacent to HCC plays a significant role in HCC development. In the present study, we aimed to construct a model based on the expression of Hippo-related genes (HRGs) in tissues adjacent to HCC to predict the prognosis of HCC patients. METHODS: Gene expression data of paired normal tissues adjacent to HCC (PNTAH) and clinical information were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The HRG signature was constructed using four canonical Hippo-related pathways. Univariate Cox regression analysis was used to screen survival-related HRGs. LASSO and multivariate Cox regression analyses were used to construct the prognostic model. The true and false positive rates of the model were confirmed using receiver operating characteristic (ROC) analysis. RESULTS: The prognostic model was constructed based on the expression levels of five HRGs (NF2, MYC, BIRC3, CSNK1E, and MINK1) in PNTAH. The mortality rate of HCC patients increased as the risk score determined by the model increased. Furthermore, the risk score was found to be an independent risk factor for the survival of patients. ROC analysis showed that the prognostic model had a better predictive value than the other conventional clinical parameters. Moreover, the reliability of the prognostic model was confirmed in TCGA-LIHC cohort. A nomogram was generated to predict patient survival. An exploration of the predictive value of the model in HCC tissues indicated that the model is PNTAH-specific. CONCLUSIONS: We developed and validated a prognostic model based on the expression levels of five HRGs in PNTAH, and this model should be helpful in predicting the prognosis of patients with HCC.
BACKGROUND:Hepatocellular carcinoma (HCC) is the most common malignant disease worldwide. Although the diagnosis and treatment of HCC have greatly improved in the recent years, there is still a lack of accurate methods to predict the prognosis of patients. Evidence has shown that Hippo signaling in tissues adjacent to HCC plays a significant role in HCC development. In the present study, we aimed to construct a model based on the expression of Hippo-related genes (HRGs) in tissues adjacent to HCC to predict the prognosis of HCC patients. METHODS: Gene expression data of paired normal tissues adjacent to HCC (PNTAH) and clinical information were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The HRG signature was constructed using four canonical Hippo-related pathways. Univariate Cox regression analysis was used to screen survival-related HRGs. LASSO and multivariate Cox regression analyses were used to construct the prognostic model. The true and false positive rates of the model were confirmed using receiver operating characteristic (ROC) analysis. RESULTS: The prognostic model was constructed based on the expression levels of five HRGs (NF2, MYC, BIRC3, CSNK1E, and MINK1) in PNTAH. The mortality rate of HCC patients increased as the risk score determined by the model increased. Furthermore, the risk score was found to be an independent risk factor for the survival of patients. ROC analysis showed that the prognostic model had a better predictive value than the other conventional clinical parameters. Moreover, the reliability of the prognostic model was confirmed in TCGA-LIHC cohort. A nomogram was generated to predict patient survival. An exploration of the predictive value of the model in HCC tissues indicated that the model is PNTAH-specific. CONCLUSIONS: We developed and validated a prognostic model based on the expression levels of five HRGs in PNTAH, and this model should be helpful in predicting the prognosis of patients with HCC.
Authors: Eman A Elghoroury; Esmat E Abdelghaffar; Eman Awadallah; Solaf A Kamel; Dina Kandil; Eman M Hassan; Mirhane Hassan; Mahmoud M Kamel; Mohammed M Gomaa; Lamiaa A Fathalla Journal: Int J Immunopathol Pharmacol Date: 2022 Jan-Dec Impact factor: 3.298
Authors: Jesper Eisfeldt; Jakob Schuy; Eva-Lena Stattin; Malin Kvarnung; Anna Falk; Lars Feuk; Anna Lindstrand Journal: Int J Mol Sci Date: 2022-08-20 Impact factor: 6.208