Abstract
Objective: The aim of the present study was to construct a prognostic model based on the peptidyl prolyl cis–trans isomerase gene signature and explore the prognostic value of this model in patients with hepatocellular carcinoma. Methods: The transcriptome and clinical data of hepatocellular carcinoma patients were downloaded from The Cancer Genome Atlas and the International Cancer Genome Consortium database as the training set and validation set, respectively. Peptidyl prolyl cis–trans isomerase gene sets were obtained from the Molecular Signatures Database. The differential expression of peptidyl prolyl cis–trans isomerase genes was analyzed by R software. A prognostic model based on the peptidyl prolyl cis–trans isomerase signature was established by Cox, Lasso, and stepwise regression methods. Kaplan–Meier survival analysis was used to evaluate the prognostic value of the model and validate it with an independent external data. Finally, nomogram and calibration curves were developed in combination with clinical staging and risk score. Results: Differential gene expression analysis of hepatocellular carcinoma and adjacent tissues showed that there were 16 upregulated genes. A prognostic model of hepatocellular carcinoma was constructed based on three gene signatures by Cox, Lasso, and stepwise regression analysis. The Kaplan–Meier curve showed that hepatocellular carcinoma patients in high-risk score group had a worse prognosis (p < 0.05). The receiver operating characteristic curve revealed that the area under curve values of predicting the survival rate at 1, 2, 3, 4, and 5 years were 0.725, 0.680, 0.644, 0.630, and 0.639, respectively. In addition, the evaluation results of the model by the validation set were basically consistent with those of the training set. A nomogram incorporating clinical stage and risk score was established, and the calibration curve matched well with the diagonal. Conclusion: A prognostic model based on 3 peptidyl prolyl cis–trans isomerase gene signatures is expected to provide reference for prognostic risk stratification in patients with hepatocellular carcinoma.
Highlights
The 2020 edition GLOBOCAN released by the World Health Organization shows that liver cancer ranks sixth in the number of new cases of malignant tumors worldwide and is the third leading cause of cancer death in the world (Sung et al, 2021)
A prognostic model of hepatocellular carcinoma was constructed based on three gene signatures by Cox, Lasso, and stepwise regression analysis
A prognostic model based on 3 peptidyl prolyl cis–trans isomerase gene signatures is expected to provide reference for prognostic risk stratification in patients with hepatocellular carcinoma
Summary
The 2020 edition GLOBOCAN released by the World Health Organization shows that liver cancer ranks sixth in the number of new cases of malignant tumors worldwide and is the third leading cause of cancer death in the world (Sung et al, 2021). Current treatment options for HCC include radical hepatectomy, liver transplantation, arterial catheterization, radiotherapy, and chemotherapy. Surgical resection and liver transplantation are not appropriate for all HCC patients because most HCC patients are diagnosed as advanced or multifocal tumors, and the 5-year overall survival of HCC patients is less than 20% (Forner et al, 2018; Vibert et al, 2020; Yang and Heimbach, 2020). The TNM Classification of Malignant Tumors staging is one of the main reference indicators for prognosis assessment of HCC. TNM staging is insufficient in the assessment of prognosis due to the heterogeneity of tumors. The prognosis of HCC patients with the same TNM stage may vary, and even among HCC patients diagnosed with the same TNM stage and receiving similar clinical treatment, survival outcomes are various (Bruix et al, 2014; Dhir et al, 2016). It is necessary to find more effective prognostic biomarkers in order to more accurately evaluate the prognosis and develop individualized treatment strategies
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