Background: The application of molecular targeting therapy and immunotherapy has notably prolonged the survival of patients with hepatocellular carcinoma (HCC). However, multidrug resistance and high molecular heterogeneity of HCC still prevent the further improvement of clinical benefits. Dysfunction of tumor-infiltrating natural killer (NK) cells was strongly related to HCC progression and survival benefits of HCC patients. Hence, an NK cell-related prognostic signature was built up to predict HCC patients’ prognosis and immunotherapeutic response. Methods: NK cell markers were selected from scRNA-Seq data obtained from GSE162616 data set. A consensus machine learning framework including a total of 77 algorithms was developed to establish the gene signature in TCGA–LIHC data set, GSE14520 data set, GSE76427 data set and ICGC–LIRI–JP data set. Moreover, the predictive efficacy on ICI response was externally validated by GSE91061 data set and PRJEB23709 data set. Results: With the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group. The prognostic signature displayed a good predictive performance for overall survival rate, moderate to high predictive accuracy and was an independent risk factor for HCC patients’ prognosis in TCGA, GEO and ICGC cohorts. Compared with high-risk group, low-risk patients showed higher IPS–PD1 blocker, IPS–CTLA4 blocker, common immune checkpoints expression but lower TIDE score, which indicated low-risk patients might be prone to benefiting from ICI treatment. Moreover, a real-world cohort, PRJEB23709, also revealed better immunotherapeutic response in low-risk group. Conclusions: Overall, the present study developed a gene signature based on NK cell-related genes, which offered a novel platform for prognosis and immunotherapeutic response evaluation of HCC patients.

Background: Hepatocellular carcinoma is one of the most common malignancies worldwide, representing a big health-care challenge globally. M2-like macrophages are significantly correlated with tumor progression, metastasis and treatment resistance. Methods: Integrative 10 machine learning algorithms were performed to developed a M2-like macrophage related prognostic signature (MRPS). Single-cell RNA-sequencing analysis was performed to dissect the ecosystem of HCC. Several approaches, including TIDE score, immunophenoscore, TMB score and tumor escape score were used to evaluate the predictive role of MRPS in immunology response. Results: The optimal MRPS constructed by the combination of stepCox + superPC algorithm served as an independent risk factor and showed stable and powerful performances in predicting the overall survival rate of HCC patients with 2-, 3-, and 4-year AUCs of 0. 763, 0.751, and 0.699 in TCGA cohort. HCC patients with low risk score possessed a more interaction of immunoactivated cells, including NK, CD8⁺ cytotoxic T, and activated B, and a less interaction of immunosuppressive cells, including Treg, CD4⁺ exhauster T, and M2-like macrophage. Low risk score indicated a higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score and lower tumor escape score in HCC, suggesting a better immunotherapy response. The IC50 value of docetaxel, gemcitabine, crizotinib and Osimertinib in HCC with high risk score were lower versus that with low risk score. HCC patients with high risk score had a higher score of cancer-related hallmarks, including angiogenesis, DNA repair, EMT, glycolysis, and NOTCH signaling. Conclusion: Our study proposed a novel MRPS for predicting the prognosis, ecosystem and immunotherapy response in HCC.

Background The feasibility and safety of laparoscopic major hepatectomy (LMH) are still uncertain. The purpose of the present study is to compare the short- and long-term outcomes of LMH with those of open major hepatectomy (OMH) for hepatocellular carcinoma (HCC). Method Between January 2012 and December 2018, a total of 26 patients received laparoscopic major hepatectomy in our center. To minimize any confounding factors, a 1:3 case-matched analysis was conducted based on the demographics and extent of liver resection. Data of demographics, perioperative outcomes, and long-term oncologic outcomes were reviewed. Results Intraoperative blood loss ( P = 0.007) was significantly lower in the LMH group. In addition, the LMH group exhibited a lower overall complication rate ( P = 0.039) and shorter postoperative hospital stay ( P = 0.024). However, no statistically significant difference was found between LMH and OMH regarding operation time ( P = 0.215) and operative cost ( P = 0.860). Two laparoscopic cases were converted to open liver resection. In regard to long-term outcomes, there was no significant difference between LMH and OMH regarding disease-free survival (DFS) ( P = 0.079) and overall survival (OS) ( P = 0.172). Conclusion LMH can be an effective and safe alternative to OMH for selected patients with liver cancer in short- and long-term outcomes.