Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma

Abstract

Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

Integrated proteomic and metabolomic analysis of plasma reveals regulatory pathways and key elements in thyroid cancer

“…On the other hand, LCAT was classified as a differentially expressed protein which has shown great robustness as a biomarker for ovarian cancer, 37 colorectal cancer, 38 breast cancer, 39 and hepatocellular carcinoma. 40 The current study was the first to identify LCAT as a potential diagnostic biomarker in thyroid cancer. Interestingly, as a validation, similar results were found in a comparative analysis of serum proteome profiles of thyroid cancer, which showed significant downregulation of LCAT in the thyroid cancer group compared to the benign thyroid nodule group.…”

Integrating the Epigenome and Transcriptome of Hepatocellular Carcinoma to Identify Systematic Enhancer Aberrations and Establish an Aberrant Enhancer-Related Prognostic Signature

“…Furthermore, the good predictive performance of our identified AE-DEG-based signature was assessed through comparisons with seven established similar prognostic models ( Long et al, 2019 ; Zhang et al, 2020b ; Ouyang et al, 2020 ; Tang et al, 2020 ; Zhu et al, 2020 ; He et al, 2021 ; Wang et al, 2021 ). Among all signatures, the hypoxia-related gene-based signature and our AE-DEG-based signature were the only two models in which all AUCs were higher than 0.7, which is a well-accepted criterion for high predictive accuracy.…”

Development and validation of a coagulation-related genes prognostic model for hepatocellular carcinoma

“…CYP2C9 is a member of the cytochrome P2C (CYP2C) family and is involved in clinical drug metabolism. The low expression of CYP2C9 and LCAT in HCC is associated with poor prognosis [20][21][22]. Nevertheless, the research on the expression of CENPE in HCC samples and its clinical relevance is very limited.…”