Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach.

Eskezeia Yihunie Dessie,Jeffrey J.P Tsai,Jan-Gowth Chang,Ya-Sian Chang,Hui-Shan Chiang,Siang-Jyun Tu,Ka-Lok Ng

doi:10.3390/ijms22041632

Abstract

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients. We used multivariate Cox regression model with three hybrid penalties approach including least absolute shrinkage and selection operator (Lasso), adaptive lasso and elastic net algorithms for informative prognostic-related genes selection. Then, the best subset regression was used to identify the best prognostic gene signature. The prognostic gene-based risk score was constructed using the Cox coefficient of the prognostic gene signature. The model was evaluated by Kaplan–Meier (KM) and receiver operating characteristic curve (ROC) analyses. A novel four-gene signature associated with prognosis was identified and the risk score was constructed based on the four-gene signature. The risk score efficiently distinguished the patients into a high-risk group with poor prognosis. The time-dependent ROC analysis revealed that the risk model had a good performance with an area under the curve (AUC) of 0.780, 0.732, 0.733 in 1-, 2- and 3-year prognosis prediction in The Cancer Genome Atlas (TCGA) dataset. Moreover, the risk score revealed a high diagnostic performance to classify HCC from normal samples. The prognosis and diagnosis prediction performances of risk scores were verified in external validation datasets. Functional enrichment analysis of the four-gene signature and its co-expressed genes involved in the metabolic and cell cycle pathways was constructed. Overall, we developed a novel-gene-based prognostic model to predict high-risk HCC patients and we hope that our findings can provide promising insight to explore the role of the four-gene signature in HCC patients and aid risk classification.

Highlights

Hepatocellular carcinoma (HCC) is one of the most frequent malignant cancers and the fourth leading cause of cancer-related deaths worldwide [1]
According to prognostic gene selection methods discussed in the Materials and Methods section, we identified a total of 75 prognosis-associated differentially expressed genes (DEGs) out of 339 DEGs satisfying the criteria of hazard ratio (HR) > 1 or HR < 1 and p-value < 0.05 based on univariate Cox regression analysis in The Cancer Genome Atlas (TCGA) dataset of HCC patients (n = 359)
Mmeetthhooddss. section, we identified a total of 75 prognosis-associated DEGs out of 339 DEGs satisfying the criteria of hazard ratio (HR) > 1 or HR < 1 and p-value < 0.05 based on univariaGteeCneoSxyrmegbroelssion analyEsliasstiinc NTCetGA dataset of LHaCssCo patients (n A= d3a5p9t)i.veToLafsusorther identifyFAinMfo83rmD ative prognostic0.0g9e1nes associated wi0th.10t4he prognosis of H0C.1C13patients, 0twh.0irte7he01,p0λo-fCUpLToEDuBPlCdlECXaT2=2cr2S20rfo0es.a0st3-uv9raelaisdneadleticoλtniown−e00a0r..l00ge.-014=o0i68mr1i0tp.h0lme0m4s3ei2nn)cteltudhdatiotngiddeee0rln.ia0v---ts5ietf8diyc tnheet,olpatsismo,aal nλdvaa000dl...a000u280pe444tsiv(λe lasso =

Summary

Introduction

Hepatocellular carcinoma (HCC) is one of the most frequent malignant cancers and the fourth leading cause of cancer-related deaths worldwide [1]. The majority of HCC patients are first diagnosed at late stage, and these patients experienced poor prognosis and high recurrence [3,4,5]. HCC patients associated with poor prognosis need to be monitored and treated effectively to improve their prognosis. Risk factors including tumor-node-metastasis (TNM) staging, vascular invasion, and other parameters are commonly used for risk assessment of HCC patients [6]. These clinicopathological risk factors are not sufficient to classify between patients who have a high or low risk and fail to predict which patients are more likely to benefit from adjuvant chemotherapy. In addition to clinicopathological risk factors, there is a strong demand to discover a novel and reliable signature to predict HCC patient prognosis and to identify the high-risk subgroup of HCC patients

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Methods

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Discussion

Conclusion