A Five-Gene Signature for Recurrence Prediction of Hepatocellular Carcinoma Patients

Abstract

Background. Hepatocellular carcinoma (HCC) is one of the most aggressive malignancies with poor prognosis. There are many selectable treatments with good prognosis in Barcelona Clinic Liver Cancer- (BCLC-) 0, A, and B HCC patients, but the most crucial factor affecting survival is the high recurrence rate after treatments. Therefore, it is of great significance to predict the recurrence of BCLC-0, BCLC-A, and BCLC-B HCC patients. Aim. To develop a gene signature to enhance the prediction of recurrence among HCC patients. Materials and Methods. The RNA expression data and clinical data of HCC patients were obtained from the Gene Expression Omnibus (GEO) database. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to screen primarily prognostic biomarkers in GSE14520. Multivariate Cox regression analysis was introduced to verify the prognostic role of these genes. Ultimately, 5 genes were demonstrated to be related with the recurrence of HCC patients and a gene signature was established. GSE76427 was adopted to further verify the accuracy of gene signature. Subsequently, a nomogram based on gene signature was performed to predict recurrence. Gene functional enrichment analysis was conducted to investigate the potential biological processes and pathways. Results. We identified a five-gene signature which performs a powerful predictive ability in HCC patients. In the training set of GSE14520, area under the curve (AUC) for the five-gene predictive signature of 1, 2, and 3 years were 0.813, 0.786, and 0.766. Then, the relative operating characteristic (ROC) curves of five-gene predictive signature were verified in the GSE14520 validation set, the whole GSE14520, and GSE76427, showed good performance. A nomogram comprising the five-gene signature was built so as to show a good accuracy for predicting recurrence-free survival of HCC patients. Conclusion. The novel five-gene signature showed potential feasibility of recurrence prediction for early-stage HCC.

Identification of molecular subtypes and prognostic signature for hepatocellular carcinoma based on genes associated with homologous recombination deficiency

“…The joint analysis of Arjun Sarathi and Ashok Palaniappan showed that there were different stage-specific genes in different AJCC stages of HCC, specifically, they identified 2 genes specific to stage I and II, 10 specific to stage III, and 35 specific to stage IV 25 . Recently, a five-gene predictive signature was developed and highlighted potential prediction feasibility of recurrence of early-stage HCC 26 . In our work, 3623 DEGs between C1 and C2 subtypes were identified, and a risk score was constructed using univariate Cox analysis and LASSO-Cox regression analysis.…”

Recurrence Risk of Liver Cancer Post-hepatectomy Using Machine Learning and Study of Correlation With Immune Infiltration

“…In previous studies, machine learning methods have also been used to predict liver cancer recurrence. Wang et al( Wang et al, 2020 ) used lasso and Cox regressions to screen five mRNAs to predict HCC recurrence, and the predicted AUC values for 1-year, 2-year, and 3-year RFS rates from the independent validation data were 0.752, 0.651, and 0.677, respectively. Iizuka et al( Iizuka et al, 2003 ) used Fisher’s linear classifier algorithm to predict intrahepatic recurrence in hepatocellular carcinoma patients within 1 year after resection, using 18 mRNAs.…”

A novel mitochondria-related gene signature in esophageal carcinoma: prognostic, immune, and therapeutic features

“…The Kaplan-Meier (K-M) survival analysis was conducted using the R package "survival". The timedependent relative operating characteristic (ROC) curve was used to assess the accuracy of the established model, calculated with the R package "survival ROC" [7] . Concordance index (C-index) values were calculated based on Harrell's C-index [17] , to evaluate the difference between predicted value and actual value of Cox model in survival analysis and judge the prediction accuracy of the prognostic model of tumor patients.…”

“…The mitochondria produce adenosine triphosphate (ATP) for cells. Recent studies have shown that mitochondria regulate various physiological and pathological functions, such as the initiation, progression and metastasis of tumor cells [6][7] . The mitochondria produce reactive oxygen species (ROS) in the cell.…”