Prognosis and therapeutic benefits prediction based on NK cell marker genes through single-cell RNA-seq with integrated bulk RNA-seq analysis for hepatocellular carcinoma.

Tumor-infiltrating immune cells greatly participate in regulating tumorigenesis and metastasis of hepatocellular carcinoma (HCC). Natural killer cell, as an important role of innate immunity, plays an indispensable role in antitumor immunity and regulate tumor development. In this study, we firstly identified 251 NK cell marker genes of HCC based on single-cell RNA sequencing data. Subsequently, an NK cell marker genes-related prognostic signature (NKPS) was developed in the cancer genome atlas (TCGA) cohort for risk stratification and prognosis prediction. The predictive value of the NKPS in prognosis was well validated in different clinical subgroups and three external datasets (ICGC-LIHC cohort, GSE14520 cohort and Guilin cohort). Moreover, multivariate analysis revealed the independent prognostic value of NKPS for OS in HCC. Further functional analysis indicated the NKPS was associated with basic cellular processes, that may contribute to the development and progression of HCC. Thereafter, immune characteristics as well as the therapeutic benefits in NKPS risk score-defined subgroups were analyzed. Patients with low-risk score exhibited immune-active status, manifested as higher immune scores, more infiltration of CD8+ T cells and macrophage M1, and higher T-cell receptor (TCR) richness and diversity. Remarkably, the NKPS was negatively correlated with immunotherapy response-related signatures. In addition, the low-risk group exhibited significantly improved therapeutic benefits, either from immunotherapy or traditional chemotherapy and target therapy. Overall, the NKPS showed an excellent predictive value for prognosis and therapeutic responses for HCC, which might also provide novel insights into better HCC management strategies.

The predictive value of PD-L1 expression in patients with advanced hepatocellular carcinoma treated with PD-1/PD-L1 inhibitors: A systematic review and meta-analysis.

BACKGROUND AND AIM: Programmed death 1 (PD-1) and programmed death ligand 1 (PD-L1) inhibitors have transformed the treatment landscape of advanced hepatocellular carcinoma (HCC), but consistent responses are not observed in all patients, and prognostic biomarkers to guide treatment decisions are lacking. We aimed to evaluate the predictive value of PD-L1 expression in advanced HCC patients treated with PD-1/PD-L1 inhibitors. METHODS: A comprehensive search of PubMed, Embase, Web of Science, and the Cochrane Library was conducted. Studies comparing the objective response rate (ORR) and/or disease control rate (DCR) based on the tumor PD-L1 status of HCC were included. RESULTS: Eleven studies with 1,330 HCC patients treated with PD-1/PD-L1 inhibitors were included. Pooled odds ratio (OR) analysis demonstrated a significantly improved ORR in PD-L1-positive patients compared with PD-L1-negative patients (OR, 1.86, 95% CI, 1.35-2.55). Similar results were observed in the anti-PD-1 treatment (p < 0.001) and anti-PD-1/PD-L1 monotherapy (p < 0.001) subgroups. The pooled ORRs in the PD-L1-positive and PD-L1-negative groups were 26% (95% CI, 20%-32%) and 18% (95% CI, 13%-22%), respectively. For DCR, the pooled OR analysis showed no significant difference between PD-L1-positive patients and PD-L1-negative patients (66% [95% CI, 55%-76%] vs. 69% [95% CI, 62%-76%]; OR, 0.92, 95% CI, 0.59-1.44). The results were consistent across the drug target and combination treatment subgroups. CONCLUSION: Positive PD-L1 expression is associated with a better ORR in advanced HCC patients treated with anti-PD-1/PD-L1-based therapies. This feature can help to identify HCC patients who will benefit most from PD-1/PD-L1 inhibitors.

Novel index for the prediction of significant liver fibrosis and cirrhosis in chronic hepatitis B patients in China.

BACKGROUND: Noninvasive, practical, and convenient means of detection for the prediction of liver fibrosis and cirrhosis in China are greatly needed. AIM: To develop a precise noninvasive test to stage liver fibrosis and cirrhosis. METHODS: With liver biopsy as the gold standard, we established a new index, [alkaline phosphatase (U/L) + gamma-glutamyl transpeptidase (U/L)/platelet (109/L) (AGPR)], to predict liver fibrosis and cirrhosis. In addition, we compared the area under the receiver operating characteristic curve (AUROC) of AGPR, gamma-glutamyl transpeptidase to platelet ratio, aspartate transaminase to platelet ratio index, and FIB-4 and evaluated the accuracy of these routine laboratory indices in predicting liver fibrosis and cirrhosis. RESULTS: Correlation analysis revealed a significant positive correlation between AGPR and liver fibrosis stage (P < 0.001). In the training cohort, the AUROC of AGPR was 0.83 (95%CI: 0.78-0.87) for predicting fibrosis (≥ F2), 0.84 (95%CI: 0.79-0.88) for predicting extensive fibrosis (≥ F3), and 0.87 (95%CI: 0.83-0.91) for predicting cirrhosis (F4). In the validation cohort, the AUROCs of AGPR to predict ≥ F2, ≥ F3 and F4 were 0.83 (95%CI: 0.77-0.88), 0.83 (95%CI: 0.77-0.89), and 0.84 (95%CI: 0.78-0.89), respectively. CONCLUSION: The AGPR index should become a new, simple, accurate, and noninvasive marker to predict liver fibrosis and cirrhosis in chronic hepatitis B patients.

Preoperative contrast-enhanced computed tomography-based radiomics model for overall survival prediction in hepatocellular carcinoma.

BACKGROUND: 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. AIM: To develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival (OS) of HCC patients after radical hepatectomy. METHODS: 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. RESULTS: 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). CONCLUSION: The nomogram containing the radiomics signature, NLR and AFP is a reliable tool for predicting the OS of HCC patients.

Preoperative Radiomics Analysis of Contrast-Enhanced CT for Microvascular Invasion and Prognosis Stratification in Hepatocellular Carcinoma.

Purpose: Microvascular invasion (MVI) impairs long-term prognosis of patients with hepatocellular carcinoma (HCC). We aimed to develop a novel nomogram to predict MVI and patients' prognosis based on radiomic features of contrast-enhanced CT (CECT). Patients and Methods: HCC patients who underwent curative resection were enrolled. The radiomic features were extracted from the region of tumor, and the optimal MVI-related radiomic features were selected and applied to construct radiomic signature (Rad-score). The prediction models were created according to the logistic regression and evaluated. Biomarkers were analyzed via q-PCR from randomly selected HCC patients. Correlations between biomarkers and radiomic signature were analyzed. Results: A total of 421 HCC patients were enrolled. A total of 1962 radiomic features were extracted from the region of tumor, and the 11 optimal MVI-related radiomic features showed a favor predictive ability with area under the curves (AUCs) of 0.796 and 0.810 in training and validation cohorts, respectively. Aspartate aminotransferase (AST), tumor number, alpha-fetoprotein (AFP) level, and radiomics signature were independent risk factors of MVI. The four factors were integrated into the novel nomogram, named as CRM, with AUCs of 0.767 in training cohort and 0.793 in validation cohort for predicting MVI, best among radiomics signature alone and clinical model. The nomogram was well-calibrated with favorable clinical value demonstrated by decision curve analysis and can divide patients into high- or low-risk subgroups of recurrence and mortality. In addition, gene BCAT1, DTGCU2, DOCK3 were analyzed via q-PCR and serum AFP were identified as having significant association with radiomics signature. Conclusion: The novel nomogram demonstrated good performance in preoperatively predicting the probability of MVI, which might guide clinical decision.