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Page 4 of 12 Yu et al. Hepatoma Res. 2025;11:29 | https://dx.doi.org/10.20517/2394-5079.2025.47

lymphocytes in the sirAEs group and control group was 37.43 ± 16.39 and 25.50 ± 11.72, respectively. The
number of lymphocytes in sirAEs group was significantly higher than that in the control group (P < 0.05).
The level of creatinine in the sirAEs group was 58.23 ± 25.86, which was significantly higher than that in the
control group (44.18 ± 31.87, P < 0.05). No significant differences were observed between the two groups
with regard to the remaining clinical indicators (P ≥ 0.05) [Table 1].

In the sirAEs group, patients were treated with tislelizumab (n = 14), pembrolizumab (n = 10),
camrelizumab (n = 10), sintilimab (n = 6), and toripalimab (n = 6). In the control group, treatments
included tislelizumab (n = 25), pembrolizumab (n = 20), camrelizumab (n = 13), sintilimab (n = 12), and
toripalimab (n = 14).

Types of sirAEs
In the sirAEs group, complications included hand-foot skin rash (n = 7), hypertension (n = 5), proteinuria
(n = 3), diarrhea (n = 7), hypothyroidism (n = 8), and gastrointestinal bleeding (n = 4). Furthermore, five
patients showed increased aspartate aminotransferase (AST), 5 showed increased alanine aminotransferase
(ALT), and 8 showed increased total bilirubin. Among these, five patients demonstrated elevated AST and
ALT levels simultaneously, one of whom also presented with concomitant hyperbilirubinemia.

Predictors of sirAEs
Univariate binary logistic regression analysis showed that Child-Pugh score, liver cirrhosis, NLR, Tregs,
lymphocytes, and creatinine were significantly correlated with sirAEs (P < 0.05) [Table 2]. These six
significant factors were subsequently included in a multivariate binary logistic regression analysis. The
results identified liver cirrhosis, NLR, Tregs, lymphocytes, and creatinine as independent influencing
factors for sirAEs (P < 0.05) [Table 3].

Construction and verification of nomogram prediction model
A nomogram prediction model was constructed based on the five independent influencing factors obtained
from the multivariate binary logistic regression analysis including liver cirrhosis, NLR, Treg, lymphocytes,
and creatinine [Figure 1]. Figure 2 showed that the area under the ROC curve (AUC) values of the
prediction model were significantly higher than those of the other five independent predictors. The AUC of
the model was 0.885 (95% confidence interval: 0.820-0.951). The model’s sensitivity and specificity were
81.2% and 88.9%, respectively. The cut-off value of the model was 137.786 [Table 4]. The total score of the
nomogram ranges from 80 to 160 points, corresponding to a predicted probability of 0.1 to 0.9. When the
total score is ≥ 137.786, the model demonstrates a sensitivity of 81.2% and a specificity of 88.9% in the
training set, with a corresponding predicted probability of approximately 0.52. This indicates that the
patient's risk of developing sirAEs is significantly elevated. The calibration curve in Figure 3 shows that
there is no significant difference between the fitted curve of the nomogram model and the ideal curve, and
the agreement is high, suggesting the good predictive effect of the model.

DISCUSSION
Immunotherapy has become an important treatment modality for HCC. However, while it enhances
antitumor immune activity, it may also excessively amplify normal immune responses, leading to immune
dysregulation and the subsequent development of sirAEs . As a pivotal locoregional therapy, TACE
[9]
markedly improves survival in HCC when combined with immunotherapy. Yet it simultaneously remodels
the tumor immune microenvironment into a more intricate landscape, making the onset of sirAEs
increasingly unpredictable for clinicians. Hence, an effective and readily applicable predictive model for
sirAEs is urgently required.