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©The Author(s) 2024.
World J Clin Cases. Aug 26, 2024; 12(24): 5568-5582
Published online Aug 26, 2024. doi: 10.12998/wjcc.v12.i24.5568
Published online Aug 26, 2024. doi: 10.12998/wjcc.v12.i24.5568
Table 1 Correlation between key genes and prognosis of hepatocellular carcinoma patients
Characteristic | Total, n | Univariate analysis | Multivariate analysis | ||
Hazard ratio (95%CI) | P value | Hazard ratio (95%CI) | P value | ||
ATIC | 339 | 1.042 (1.027-1.058) | < 0.001 | 1.011 (0.989-1.033) | 0.348 |
EZH2 | 339 | 1.196 (1.128-1.269) | < 0.001 | 1.087 (1.006-1.174) | 0.035 |
HDGF | 339 | 1.010 (1.006-1.014) | < 0.001 | 1.004 (0.999-1.026) | 0.104 |
HEXB | 339 | 1.027 (1.014-1.039) | < 0.001 | 1.013 (0.999-1.026) | 0.060 |
HSPA4 | 339 | 1.040 (1.023-1.058) | < 0.001 | 1.017 (0.996-1.039) | 0.117 |
NRAS | 339 | 1.066 (1.042-1.091) | < 0.001 | 1.031 (1.003-1.059) | 0.032 |
PPT1 | 339 | 1.030 (1.019-1.041) | < 0.001 | 1.004 (0.988-1.020) | 0.665 |
Table 2 Relationship between risk score and clinicopathological parameters in hepatocellular carcinoma patients
Variables | Total, n = 339 | Risk score-low, n = 170 | Risk score-high, n = 169 | P value |
Age | 61.000 (51.000, 68.000) | 62.000 (52.000, 69.000) | 59.000 (51.000, 67.000) | 0.083 |
Sex | 0.310 | |||
Female | 107 (31.6%) | 58 (34.1%) | 49 (29.0%) | |
Male | 232 (68.4%) | 112 (65.9%) | 112 (71.0%) | |
Stage | 0.876 | |||
S1+S2 | 252 (74.3%) | 127 (74.7%) | 125 (74.0%) | |
S3+S4 | 87 (25.7%) | 43 (25.3%) | 44 (26.0%) | |
Grade | 0.457 | |||
G1+G2 | 212 (62.5%) | 103 (60.6%) | 109 (64.5%) | |
G3+G4 | 127 (35.5%) | 67 (39.4%) | 60 (35.5%) |
Table 3 Correlation between risk scores in clinical features and hepatocellular carcinoma prognosis
Characteristic | Total, n | Multivariate analysis | |
Hazard ratio (95%CI) | P value | ||
Risk score | 339 | 5.339 (3.139-9.078) | < 0.001 |
Age | 339 | 1.018 (1.002-1.034) | 0.028 |
Sex | 339 | 1.010 (1.006-1.014) | 0.172 |
Grade | 339 | 1.113 (0.744-1.664) | 0.603 |
Stage | 339 | 0.888 (0.579-1.363) | 0.588 |
Table 4 Binding energy between EZH2 and four chemotherapy drugs in molecular docking
Medicine | Hub targets (PDB ID) | Binding energy in kcal/mol |
Belinostat | EZH2 (5h14) | -4.58 |
BRD-K34222889 | EZH2 (5h14) | -4.23 |
Ciclopirox | EZH2 (5h14) | -4.07 |
Cytarabine hydrochloride | EZH2 (5h14) | -1.75 |
Table 5 Difference in the area under the curves of EZH2, NRAS, and the risk score in diagnosing hepatocellular carcinoma
Name | EZH2 | NRAS | Risk score |
EZH2 | / | < 0.05 | < 0.05 |
NRAS | < 0.05 | / | < 0.05 |
Risk score | < 0.05 | < 0.05 | / |
Table 6 Results of predicting EZH2 in the training set and validation set based on the logistic and random forest classifier algorithm
Classification model | AUC | Accuracy | Sensitivity | Specificity | F1 score | |
Validation | Logistic | 0.792 | 0.667 | 0.800 | 0.833 | 0.833 |
Random Forest | 0.812 | 0.750 | 1.000 | 0.750 | 0.833 | |
Training | Logistic | 0.787 | 0.750 | 0.679 | 0.881 | 0.744 |
Random Forest | 1.000 | 0.938 | 1.000 | 1.000 | 1.000 |
- Citation: Yu TY, Zhan ZJ, Lin Q, Huang ZH. Computed tomography-based radiomics predicts the fibroblast-related gene EZH2 expression level and survival of hepatocellular carcinoma. World J Clin Cases 2024; 12(24): 5568-5582
- URL: https://www.wjgnet.com/2307-8960/full/v12/i24/5568.htm
- DOI: https://dx.doi.org/10.12998/wjcc.v12.i24.5568