Abstract
BackgroundThe initiation and progression of tumors were due to variations of gene sets rather than individual genes. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma.MethodsThe activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package. Unsupervised clustering was conducted to determine subtypes with the ConsensusClusterPlus R package in the TCGA-LIHC (n = 329) and LIRI-JP (n = 232) cohorts. Differentially expressed genes among subtypes were identified as initial variables. Then, we used TCGA-LIHC as the training set and LIRI-JP as the validation set. A six-gene model calculating the risk scores of patients was integrated with the least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were performed to assess predictive performances. Multivariate Cox regression analyses were implemented to select independent prognostic factors, and a prognostic nomogram was integrated. Moreover, the diagnostic values of six genes were explored with the ROC curves and immunohistochemistry.ResultsPatients could be separated into two subtypes with different prognoses in both cohorts based on the identified differential hallmark pathways. Six prognostic genes (ASF1A, CENPA, LDHA, PSMB2, SRPRB, UCK2) were included in the risk score signature, which was demonstrated to be an independent prognostic factor. A nomogram including 540 patients was further integrated and well-calibrated. ROC analyses in the five cohorts and immunohistochemistry experiments in solid tissues indicated that CENPA and UCK2 exhibited high and robust diagnostic values.ConclusionsOur study explored a promising prognostic nomogram and diagnostic biomarkers in hepatocellular carcinoma.
Highlights
Hepatocellular carcinoma (HCC) is the most common liver cancer and the fourth leading cause of tumor-induced death worldwide [1]
This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma
The activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package
Summary
Hepatocellular carcinoma (HCC) is the most common liver cancer and the fourth leading cause of tumor-induced death worldwide [1]. Due to the insidious onset of HCC and the need for viable treatment strategies, the prognosis of HCC remains very poor, and the 5year relative survival rate is no more than 10% [3]. In this manner, there is an urgent need to recognize robust and accurate biomarkers for HCC. In one investigation on an expansive breast cancer meta-dataset, straightforward multigene models reliably outflanked singlegene biomarkers in all segments [4]. In another survey on classifiers to predict breast cancer recurrences, integrated classifiers were much better than routine biomarkers (ER, PR, HER2, Ki67) [5]. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma
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