• hepatocellular carcinoma
• overall survival
• prognosis
• score model

• ZDXK202239 Jiangsu Provincial Medical Key Discipline (Laboratory)
• YJXYY202201 Jiangsu Provincial Research Hospital
• BK20181492 Natural Science Foundation of Jiangsu Province

• Immune checkpoint inhibitors
• Local regional therapy
• Recurrence type
• Recurrent hepatocellular carcinoma
• Systemic therapy
• Tumor microenvironment

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with a rising incidence worldwide. The prognosis of HCC patients after radical resection remains poor. Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer, which can assist with cancer diagnosis, therapeutic decision-making and prognosis improvement.

To develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival (OS) of HCC patients after radical hepatectomy.

A total of 150 HCC patients were randomly divided into a training cohort (n = 107) and a validation cohort (n = 43). Radiomics features were extracted from the entire tumour lesion. The least absolute shrinkage and selection operator algorithm was applied for the selection of radiomics features and the construction of the radiomics signature. Univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors and develop the predictive nomogram, incorporating clinicopathological characteristics and the radiomics signature. The accuracy of the nomogram was assessed with the concordance index, receiver operating characteristic (ROC) curve and calibration curve. The clinical utility was evaluated by decision curve analysis (DCA). Kaplan–Meier methodology was used to compare the survival between the low- and high-risk subgroups.

In total, seven radiomics features were selected to construct the radiomics signature. According to the results of univariate and multivariate Cox regression analyses, alpha-fetoprotein (AFP), neutrophil-to-lymphocyte ratio (NLR) and radiomics signature were included to build the nomogram. The C-indices of the nomogram in the training and validation cohorts were 0.736 and 0.774, respectively. ROC curve analysis for predicting 1-, 3-, and 5-year OS confirmed satisfactory accuracy [training cohort, area under the curve (AUC) = 0.850, 0.791 and 0.823, respectively; validation cohort, AUC = 0.905, 0.884 and 0.911, respectively]. The calibration curve analysis indicated a good agreement between the nomogram-prediction and actual survival. DCA curves suggested that the nomogram had more benefit than traditional staging system models. Kaplan–Meier survival analysis indicated that patients in the low-risk group had longer OS and disease-free survival (all P < 0.0001).

The nomogram containing the radiomics signature, NLR and AFP is a reliable tool for predicting the OS of HCC patients.

• Hepatocellular carcinoma
• Radiomics
• Contrast-enhanced computed tomography
• Survival prediction

• Carcinoma, Hepatocellular/diagnostic imaging
• Carcinoma, Hepatocellular/surgery
• Humans
• Liver Neoplasms/diagnostic imaging
• Liver Neoplasms/epidemiology
• Liver Neoplasms/surgery
• Nomograms
• Retrospective Studies
• Tomography, X-Ray Computed/methods
• alpha-Fetoproteins

• hepatocellular carcinoma
• prognosis
• tumor-infiltrating lymphocytes

In hepatocellular carcinoma patients, due to the microenvironmental specificity of liver, the tumor microenvironment exhibits high immunosuppression and drug resistance, resulting in excessive or insufficient responses to immunotherapy. The dynamic interactions between tumor cells and immune modulators in the TME significantly impact the occurrence and development of tumors, efficacy, and drug resistance, which can create a much more positive response to immunotherapy. Moreover, with the wide application of single-cell sequencing technology in the TME, increasing evidence shows an interaction network among cells. Sequencing results suggest that specific tumor-associated macrophages are a hub node, connecting different cell populations in the cell interaction network, and can could regulate tumor generation and antitumor immunity. This review focused on therapeutic targets that could be targeted to remodel the tumor microenvironment and reprogram the tumor-associated macrophage phenotype in hepatocellular carcinoma patients, thereby improving immunotherapeutic efficacy.

• Antitumor immunity
• Hepatocellular carcinoma
• Immune checkpoint
• Immunotherapy
• Tumor microenvironment
• Tumor-associated macrophages

• cholangiocarcinoma
• hepatocellular carcinoma
• liver metastasis
• pathology
• tumor immunology
• tumor microenvironment

• hepatocellular carcinoma
• liver resection
• neutrophil-lymphocyte ratio
• postoperative
• preo-perative

Prediction of prognosis of hepatocellular carcinoma (HCC) has shown an important role in improving treatment outcomes and preventing disease progression, however, the prognostic indicator of HCC is still lacking. The purpose of this study is to investigate the predictive value of GLR (gamma-glutamyl transpeptidase to lymphocyte count ratio) in single HCC with a tumor size (TS) ≤ 5 cm. A retrospective analysis was performed on 272 patients with TS ≤ 5 cm who underwent radical resection. The Pearson χ² test was applied to discuss the relationship between HCC and GLR, alpha-fetoprotein (AFP). Then univariate and multivariate analysis was utilized to predict the risk factors for survival prognosis in patients. In this study, GLR showed a positive relation with tumor size, tumor-node-metastasis (TNM) stage, microvascular invasion, early recurrence, and serum aspartate aminotransferase (AST) level, while the AFP value only correlated with drinking. Elevated GLR value had poor overall survival (OS) and progression-free survival (PFS) of TS ≤ 5 cm HCC patients, GLR level and tumor size were closely related to the prognosis of small HCC patients compared with AFP. GLR may serve as a prognostic marker for dynamic monitoring of HCC patients with single TS ≤ 5 cm after radical resection.

• AFP
• GLR
• hepatocellular carcinoma
• prognosis
• survival

Hepatocellular carcinoma (HCC) has high recurrence rates even after curative hepatectomy. Drug therapy for recurrence of HCC is still limited; therefore, identifying new therapeutic targets is urgently needed. We searched for genes that would predict HCC recurrence from intrahepatic metastasis in an exhaustive DNA microarray database by searching genes associated with high early recurrence rate and having higher expression in the tumor area compared to background liver. We detected lysyl oxidase (LOX) and validated the clinical significance of LOX in 358 patients who underwent hepatectomy. Expression of LOX was evaluated by qRT‐ PCR, and immunohistochemical (IHC) staining. High LOX expression group had a significantly higher recurrence rate than the low LOX expression group (2‐year recurrence rate was 64.0% vs 24.2%, P < .0001 for IHC) and poorer survival rate (5‐year rate was 60.1% vs 86.2%, P < .0001 for IHC). Multivariate analysis showed that high LOX expression was an independent risk factor for early recurrence (IHC: HR, 2.52; P < .0001). Bioinformatic analysis showed that LOX expression was associated with hypoxia‐inducible factor‐1α (HIF‐1α) and the hypoxia cascade, suggesting that HIF‐1α or hypoxia regulates LOX expression and induces epithelial‐mesenchymal transition (EMT). In vitro, LOX and HIF‐1α were involved in migration and invasion capability. High LOX expression is associated with EMT markers and predicts early recurrence and poor survival in patients with HCC. These findings indicate that lysyl oxidase could be a potential therapeutic target for early recurrence of HCC.

• early recurrence
• epithelial-mesenchymal transition
• hepatocellular carcinoma
• intrahepatic metastasis
• lysyl oxidase