Prognostic and predictive significance of the tumor microenvironment in hepatocellular carcinoma

Expression and clinical significance of NLRC5 in hepatocellular carcinoma

NLRC5, the largest member of the nucleotide-binding and oligomerization domain (NOD)-like receptor (NLR) family, has been reported to participate in the regulation of immune function and is associated with chronic inflammatory diseases. However, the biological function of NLRC5 in hepatocellular carcinoma (HCC) has not been fully demonstrated. The aim of this study is to evaluate NLRC5 expression in the tumor tissues of HCC patients undergoing surgical treatment, assess its prognostic value, and explore its relationship with critical immune-related molecules within the tumor microenvironment. A total of 100 patients with hepatitis B virus-associated HCC receiving surgical treatment were enrolled in the study. Immunohistochemical results were obtained by scoring the intensity of cellular staining and the percentage of positive cells in the tissue sections. The association between NLRC5 expression levels and the main clinicopathological factors was analyzed by Chi-square test method. The prognostic values were analyzed by COX regression model and the Kaplan-Meier survival curve. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive performance of NLRC5 in postoperative patients with HCC. IHC showed that high expression of NLRC5 was observed in 67% of HCC tissue samples. Chi-square test showed that NLRC5 was a risk factor associated with tumor number, satellite nodule, and envelope invasion. Kaplan-Meier survival curves and COX survival analysis showed that high expression of NLRC5 was significantly associated with decreased overall survival (OS) in HCC patients (HR = 1.79, 95% CI 1.03-3.12, p = .041). However, univariate logistic regression analysis revealed that NLRC5 showed positive relationship with GZMB and CD8α suggesting its role in immune escape of HCC. ROC curve analysis showed that the combination of tumor number, envelope invasion, and NLRC5 expression (area under the curve = 0.824, sensitivity = 77.30%, specificity = 82.4%) can more accurately evaluate the prognosis of HCC patients compared to the combination of only tumor number and envelope invasion (area under the curve = 0.690, sensitivity = 43.9%, specificity = 94.1%).NLRC5 plays a crucial role in progression of HCC and can be considered as a potential prognostic and predictive biomarker. Targeting NLRC5 may provide an attractive therapeutic approach for HCC.

A multi-parametric prognostic model based on clinicopathologic features: vessels encapsulating tumor clusters and hepatic plates predict overall survival in hepatocellular carcinoma patients

BACKGROUND

Vessels encapsulating tumor clusters (VETC) is a newly described vascular pattern that is distinct from microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Despite its importance, the current pathological diagnosis report does not include information on VETC and hepatic plates (HP). We aimed to evaluate the prognostic value of integrating VETC and HP (VETC-HP model) in the assessment of HCC.

METHODS

A total of 1255 HCC patients who underwent radical surgery were classified into training (879 patients) and validation (376 patients) cohorts. Additionally, 37 patients treated with lenvatinib were studied, included 31 patients in high-risk group and 6 patients in low-risk group. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to establish a prognostic model for the training set. Harrell's concordance index (C-index), time-dependent receiver operating characteristics curve (tdROC), and decision curve analysis were utilized to evaluate our model's performance by comparing it to traditional tumor node metastasis (TNM) staging for individualized prognosis.

RESULTS

A prognostic model, VETC-HP model, based on risk scores for overall survival (OS) was established. The VETC-HP model demonstrated robust performance, with area under the curve (AUC) values of 0.832 and 0.780 for predicting 3- and 5-year OS in the training cohort, and 0.805 and 0.750 in the validation cohort, respectively. The model showed superior prediction accuracy and discrimination power compared to TNM staging, with C-index values of 0.753 and 0.672 for OS and disease-free survival (DFS) in the training cohort, and 0.728 and 0.615 in the validation cohort, respectively, compared to 0.626 and 0.573 for TNM staging in the training cohort, and 0.629 and 0.511 in the validation cohort. Thus, VETC-HP model had higher C-index than TNM stage system(p < 0.01).Furthermore, in the high-risk group, lenvatinib alone appeared to offer less clinical benefit but better disease-free survival time.

CONCLUSIONS

The VETC-HP model enhances DFS and OS prediction in HCC compared to traditional TNM staging systems. This model enables personalized temporal survival estimation, potentially improving clinical decision-making in surveillance management and treatment strategies.

The correlation between tumor-associated macrophages and the prognosis of east Asian hepatocellular carcinoma patients: A systematic review and meta-analysis

BACKGROUND

Previous related studies have found that the levels of tumor-associated macrophages (TAMs) were correlated with prognoses in hepatocellular carcinoma. However, the prognostic value of TAMs for East Asian HCC patients remains inconclusive.

METHODS

Our objectives were to systematically review the performance and explore the prognostic and clinical value of TAMs in patients with HCC. A total of 23 relevant studies of 4389 patients were included into our meta-analysis. And the work has been reported in line with PRISMA guidelines.

RESULTS

The results demonstrated that increased expression level of peritumoral infiltrated CD68+ macrophages had a poor prognostic value on overall survival (OS), disease free survival (DFS) and recurrence-free survival (RFS). However, there was no correlation between disease-free survival (DFS) and the abundance of CD68+ TAMs both in intratumoral regions. Additionally, low density of CD169+, high density of CD206, and high density of CD204+ TAMs had a worse prognostic value on OS while the CD163+ TAMs had no diagnostic value on OS. The densities of CD68+ TAMs exhibited significantly correlation with AFP level and vascular invasion. The levels of CD169+ TAMs showed apparent relation to vascular invasion and TNM stages.

CONCLUSION

These findings indicate that TAMs may accomplish as significant prognostic biomarkers for East Asian HCC patients. However, further researches should be performed to estimate the clinical value of TAMs in HCC.

A Noninvasive Approach to Evaluate Tumor Immune Microenvironment and Predict Outcomes in Hepatocellular Carcinoma

It is widely recognized that tumor immune microenvironment (TIME) plays a crucial role in tumor progression, metastasis, and therapeutic response. Despite several noninvasive strategies have emerged for cancer diagnosis and prognosis, there are still lack of effective radiomic-based model to evaluate TIME status, let alone predict clinical outcome and immune checkpoint inhibitor (ICIs) response for hepatocellular carcinoma (HCC). In this study, we developed a radiomic model to evaluate TIME status within the tumor and predict prognosis and immunotherapy response. A total of 301 patients who underwent magnetic resonance imaging (MRI) examinations were enrolled in our study. The intra-tumoral expression of 17 immune-related molecules were evaluated using co-detection by indexing (CODEX) technology, and we construct Immunoscore (IS) with the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression method to evaluate TIME. Of 6115 features extracted from MRI, five core features were filtered out, and the Radiomic Immunoscore (RIS) showed high accuracy in predicting TIME status in testing cohort (area under the curve = 0.753). More importantly, RIS model showed the capability of predicting therapeutic response to anti-programmed cell death 1 (PD-1) immunotherapy in an independent cohort with advanced HCC patients (area under the curve = 0.731). In comparison with previously radiomic-based models, our integrated RIS model exhibits not only higher accuracy in predicting prognosis but also the potential guiding significance to HCC immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00136-8.

A meta-analysis and of clinical values of 11 blood biomarkers, such as AFP, DCP, and GP73 for diagnosis of hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma lacks ideal diagnostic biomarkers. There is a lack of scientific evaluation of relevant promising biomarkers as well. Therefore this study reanalyzes the related studies of 11 blood biomarkers of HCC, and compares the diagnostic value of these biomarkers for HCC systematically.

METHODS

The relevant literatures on the diagnostic value in HCC of 11 blood indexes in recent 5 years were searched in PubMed, Embase, and Cochrane libraries. Data were extracted and analyzed.

RESULTS

Finally, 83 literature studies were brought into meta-analysis. The pooled sensitivity and specificity of AFP were 0.61 and 0.87, respectively. The AUC of AFP were 0.78. The AUC and sum of sensitivity and specificity of the combination of AFP and other biomarkers were all significantly higher than that of AFP, including AFP + AFP-L3 + DCP, AFP + DCP, AFP/DCP, AFP + GPC3. Among other biomarkers, the AUC and sum of sensitivity and specificity of biomarkers including DCP, GPC3, GP73, Hsp90alpha, midkine, and OPN were significantly higher than that of AFP. In this study, GP73 had the highest sum of sensitivity and specificity (1.78) and AUC (0.95).

CONCLUSIONS

The pooled sensitivity and specificity of AFP were 0.61 and 0.87, respectively. The AUC of AFP were 0.78. The combination of AFP and other biomarkers improved the diagnostic efficiency. The diagnostic value of biomarkers including DCP, GPC3, GP73, Hsp90alpha, midkine, and OPN was higher than that of AFP. GP73 had the best diagnostic value for HCC with the highest sum of sensitivity and specificity (1.78) and AUC (0.95).KEY MESSAGESThe pooled sensitivity and specificity of AFP were 0.61 and 0.87, respectively. The AUC of AFP were 0.78. The combination of AFP and other biomarkers improved the diagnostic efficiency of HCC.The diagnostic value of biomarkers including DCP, GPC3, GP73, Hsp90alpha, midkine, and OPN was higher than that of AFP.GP73 had the best diagnostic value for HCC.

Role of NCF2 as a potential prognostic factor and immune infiltration indicator in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the major causes of cancer-related deaths globally. The tumor microenvironment (TME) plays a crucial role in the prognosis and treatment of HCC. Hence, it is important to exploit new biomarkers for survival surveillance and TME estimation of HCC.

METHODS

HCC samples data was collected from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database, and clinical samples were collected from our center. The TME of HCC were explored with ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data), ssGSEA (single sample Gene Sets Enrichment Analysis) and CIBERSORT algorithm. Differentially expressed genes were analyzed with functional enrichment analysis. Immunohistochemistry was implemented to validate the results.

RESULTS

Based on TCGA database, we found that Neutrophil Cytosolic Factor 2 (NCF2) was significantly associated with the prognosis of HCC patients, involved in immune-related biological processes of HCC and closely associated with some types of immunocompetent cells. The survival analysis based on NCF2 expression assessed by immunohistochemistry also confirmed that NCF2-positive group had a shorter relapse free survival (RFS) and overall survival (OS) than NCF2-negative group. Multivariate Cox regression revealed NCF2 expression level and lymphovascular space invasion (LVSI) were independent risk factors for HCC patients. Receiver operating characteristic curves showed that the combination of NCF2 and LVSI had higher predictive efficacy on the 1-year RFS rate and 5-year OS rate than each of them alone. Besides, the expression level of NCF2 was positively associated with M0 and M2 macrophages infiltration. Furthermore, NCF2 expression was positively correlated with CSF1, IL4, IL10, CD206, CD163, CSF1R and TGFβ1.

CONCLUSION

We proposed that higher NCF2 expression predicted an adverse prognosis and more M2 macrophages infiltration in HCC patients.

The Prognostic Value of Natural Killer Cells and Their Receptors/Ligands in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Background

Natural killer (NK) cells play major roles in eliminating tumor cells. Preliminary studies have shown that NK cells and their receptors/ligands have prognostic value in malignant tumors. However, the relevance of NK cells and their receptors/ligands level to the prognosis of hepatocellular carcinoma (HCC) remains unclear.

Methods

Several electronic databases were searched from database inception to November 8, 2021. Random effects were introduced to this meta-analysis. The relevance of NK cells and their receptors/ligands level to the prognosis of HCC was evaluated using hazard ratios (HRs) with 95% confidence interval (95%CI).

Results

26 studies were included in the analysis. The pooled results showed that high NK cells levels were associated with better overall survival (HR=0.70, 95%CI 0.57–0.86, P=0.001) and disease-free survival (HR=0.61, 95%CI 0.40-0.93, P=0.022) of HCC patients. In subgroup analysis for overall survival, CD57⁺ NK cells (HR=0.70, 95%CI 0.55-0.89, P=0.004) had better prognostic value over CD56⁺ NK cells (HR=0.69, 95%CI 0.38-1.25, P=0.224), and intratumor NK cells had better prognostic value (HR=0.71, 95%CI 0.55-0.90, P=0.005) over peripheral NK cells (HR=0.66, 95%CI 0.41-1.06, P=0.088). In addition, high level of NK cell inhibitory receptors predicted increased recurrence of HCC, while the prognostic role of NK cell activating receptors remained unclear.

Conclusion

NK cells and their inhibitory receptors have prognostic value for HCC. The prognostic role of NK cell activating receptors is unclear and more high-quality prospective studies are essential to evaluate the prognostic value of NK cells and their receptors/ligands for HCC.

A Promising Preoperative Prediction Model for Microvascular Invasion in Hepatocellular Carcinoma Based on an Extreme Gradient Boosting Algorithm

Background

The non-invasive preoperative diagnosis of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is vital for precise surgical decision-making and patient prognosis. Herein, we aimed to develop an MVI prediction model with valid performance and clinical interpretability.

Methods

A total of 2160 patients with HCC without macroscopic invasion who underwent hepatectomy for the first time in West China Hospital from January 2015 to June 2019 were retrospectively included, and randomly divided into training and a validation cohort at a ratio of 8:2. Preoperative demographic features, imaging characteristics, and laboratory indexes of the patients were collected. Five machine learning algorithms were used: logistic regression, random forest, support vector machine, extreme gradient boosting (XGBoost), and multilayer perception. Performance was evaluated using the area under the receiver operating characteristic curve (AUC). We also determined the Shapley Additive exPlanation value to explain the influence of each feature on the MVI prediction model.

Results

The top six important preoperative factors associated with MVI were the maximum image diameter, protein induced by vitamin K absence or antagonist-II, α-fetoprotein level, satellite nodules, alanine aminotransferase (AST)/aspartate aminotransferase (ALT) ratio, and AST level, according to the XGBoost model. The XGBoost model for preoperative prediction of MVI exhibited a better AUC (0.8, 95% confidence interval: 0.74–0.83) than the other prediction models. Furthermore, to facilitate use of the model in clinical settings, we developed a user-friendly online calculator for MVI risk prediction based on the XGBoost model.

Conclusions

The XGBoost model achieved outstanding performance for non-invasive preoperative prediction of MVI based on big data. Moreover, the MVI risk calculator would assist clinicians in conveniently determining the optimal therapeutic remedy and ameliorating the prognosis of patients with HCC.