Construction and Validation of TACE Therapeutic Efficacy by ALR Score and Nomogram: A Large, Multicenter Study

Macroscopic Characterization of Hepatocellular Carcinoma: An Underexploited Source of Prognostic Factors

The macroscopic appearance of a tumor such as hepatocellular carcinoma (HCC) may be defined as its phenotype which is de facto dictated by its genotype. Therefore, macroscopic characteristics of HCC are unlikely random but rather reflect genomic traits of cancer, presumably acting as a valuable source of information that can be retrieved and exploited to infer prognosis. This review aims to provide a comprehensive overview of the available data on the prognostic value of macroscopic characterization in HCC. A total of 57 studies meeting eligible criteria were identified, including patients undergoing liver resection (LR; 47 studies, 83%) or liver transplant (LT; 9 studies, 16%). The following macroscopic variables were investigated: tumor size (n = 42 studies), number of nodules (n = 28), vascular invasion (n = 24), bile duct invasion (n = 6), growth pattern (n = 15), resection margin (n = 11), tumor location (n = 6), capsule (n = 2) and satellite (n = 1). Although the selected studies provided insightful data with notable prognostic performances, a lack of standardization and substantial gaps were noted in the report and the analysis of gross findings. This topic remains incompletely covered. While the available studies underscored the value of macroscopic variables in HCC prognostication, important lacks were also observed. Macroscopic characterization of HCC is likely an underexploited source of prognostic factors that must be actively explored by future multidisciplinary research.

The role of gut microbiota in the occurrence and progression of non-alcoholic fatty liver disease

Background

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent cause of chronic liver disease worldwide, and gut microbes are associated with the development and progression of NAFLD. Despite numerous studies exploring the changes in gut microbes associated with NAFLD, there was no consistent pattern of changes.

Method

We retrieved studies on the human fecal microbiota sequenced by 16S rRNA gene amplification associated with NAFLD from the NCBI database up to April 2023, and re-analyzed them using bioinformatic methods.

Results

We finally screened 12 relevant studies related to NAFLD, which included a total of 1,189 study subjects (NAFLD, n = 654; healthy control, n = 398; obesity, n = 137). Our results revealed a significant decrease in gut microbial diversity with the occurrence and progression of NAFLD (SMD = -0.32; 95% CI -0.42 to -0.21; p < 0.001). Alpha diversity and the increased abundance of several crucial genera, including Desulfovibrio, Negativibacillus, and Prevotella, can serve as an indication of their predictive risk ability for the occurrence and progression of NAFLD (all AUC > 0.7). The occurrence and progression of NAFLD are significantly associated with higher levels of LPS biosynthesis, tryptophan metabolism, glutathione metabolism, and lipid metabolism.

Conclusion

This study elucidated gut microbes relevance to disease development and identified potential risk-associated microbes and functional pathways associated with NAFLD occurrence and progression.

Postoperative adjuvant immunotherapy for high-risk hepatocellular carcinoma patients

Background and aim

Standardized approach to postoperative adjuvant therapy for hepatocellular carcinoma (HCC) remains elusive. This study endeavors to examine the effects of postoperative PD-1 adjuvant therapy on the short-term and long-term prognosis of patients at a heightened risk of post-surgical recurrence.

Methods

The data of HCC patients who underwent hepatectomy at our center from June 2018 to March 2023 were collected from the hospital database. Propensity score matching (PSM) was employed to perform a 1:1 match between the postoperative anti-PD-1 antibody group and the postoperative non-anti-PD-1 antibody group. Kaplan-Meier method was utilized to compare the overall survival (OS) and recurrence-free survival (RFS) between the two groups. Cox regression analysis was conducted to identify the prognostic factors affecting patient outcomes. Subgroup analyses were performed for different high-risk factors.

Results

Among the 446 patients included in the study, 122 patients received adjuvant therapy with postoperative anti-PD-1 antibodies. After PSM, the PD-1 group had postoperative 1-year, 2-year, 3-year, and 4-year OS rates of 93.1%, 86.8%, 78.2%, and 51.1%, respectively, while the non-PD-1 group had rates of 85.3%, 70.2%, 47.7%, and 30.0%. The PD-1 group had postoperative 1-year, 2-year, 3-year, and 4-year RFS rates of 81.7%, 77.0%, 52.3%, and 23.1%, respectively, whereas the non-PD-1 group had rates of 68.4%, 47.7%, and 25.8% in 1-year, 2-year, 3-year. A multifactorial Cox regression analysis revealed that postoperative PD-1 use was a prognostic protective factor associated with OS and RFS. Subgroup analysis results indicated that HCC patients with high recurrence risks significantly benefited from postoperative anti-PD-1 antibody treatment in terms of OS and RFS.

Conclusion

For HCC patients with high-risk recurrence factors and undergoing hepatectomy, postoperative adjuvant therapy with anti-PD-1 antibodies can effectively improve their survival prognosis.

Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay

HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels HCC progression. Regrettably, inconspicuous early HCC symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage HCC patients relish augmented five-year survival rates. In contrast, advanced HCC exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading to diminished survival rates. This investigation endeavors to unearth diagnostic hallmark genes for HBV-HCC leveraging a bioinformatics framework, thus refining early HBV-HCC detection. Candidate genes were sieved via differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed three feature genes (HHIP, CXCL14, and CDHR2). Melding these genes yielded an innovative Artificial Neural Network (ANN) diagnostic blueprint, portending to alleviate patient encumbrance and elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage in HBV-HCC patients relative to solitary HCC. Through consensus clustering, HBV-HCC was stratified into two subtypes (C1 and C2), the latter potentially indicating milder immune impairment. The diagnostic model grounded in these feature genes showcased robust and transferrable prognostic potentialities, introducing a novel outlook for early HBV-HCC diagnosis. This exhaustive immunological odyssey stands poised to expedite immunotherapeutic curatives' emergence for HBV-HCC.

Tenofovir versus Entecavir on Outcomes of Hepatitis B Virus-Related Hepatocellular Carcinoma After FOLFOX-Hepatic Arterial Infusion Chemotherapy

Purpose

The efficacy of entecavir (ETV) versus tenofovir (TDF) on the prognosis of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients who underwent FOLFOX-hepatic arterial infusion chemotherapy (HAIC) remains unclear. In this study, we compared the outcomes between ETV and TDF in HBV-related advanced HCC patients who underwent FOLFOX-HAIC.

Methods

A total of 683 patients diagnosed with HBV-related HCC who underwent FOLFOX-HAIC and received TDF or ETV between January 2016 and December 2021 were included. Overall survival (OS), progression-free survival (PFS), HBV reactivation, and liver function of patients were compared between the ETV and TDF groups by propensity score matching (PSM).

Results

In the PSM cohort, for all patients and patients with ≥ 4 cycles of FOLFOX-HAIC, the median OS in the ETV group (15.2 months, 95% CI: 13.0-17.4 months; 16.6 months, 95% CI: 14.8-18.5 months; respectively) was shorter than that in the TDF group (23.0 months, 95% CI: 10.3-35.6 months; 27.3 months, 95% CI: 16.5-NA months; p=0.024, p=0.028; respectively). The median PFS in the ETV group (8.7 months, 95% CI: 7.9-9.5 months; 8.9 months, 95% CI: 8.0-9.8 months; respectively) was also shorter than that in the TDF group (11.8 months, 95% CI: 8.0-15.6 months; 12.7 months, 95% CI: 10.8-14.6 months; p=0.036, p=0.025; respectively). The rate of HBV reactivation in the ETV group was higher than that in the TDF group (12.3% vs 6.3%, p=0.040; 16.5% vs 6.2%, p=0.037, respectively). For liver function, the rate of ALBI grade that remained stable or improved in the ETV group was lower than that in the TDF group (44.6% vs 57.6%, p=0.006; 37.2% vs 53.8%, p=0.019, respectively).

Conclusion

Compared with ETV, TDF was associated with a better prognosis, lower proportion of HBV reactivation, and better preservation of liver function in advanced HBV-HCC patients who underwent FOLFOX-HAIC, especially those who received ≥ 4 cycles.

Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation

Purpose

To develop and compare various machine learning (ML) classifiers that employ radiomics extracted from contrast-enhanced magnetic resonance imaging (CEMRI) for diagnosing pathological differentiation of hepatocellular carcinoma (HCC), and validate the performance of the best model.

Methods

A total of 251 patients with HCCs (n = 262) were assigned to a training (n = 200) cohort and a validation (n = 62) cohort. A collection of 5502 radiomics signatures were extracted from the CEMRI images for each HCC nodule. To reduce redundancy and dimensionality, Spearman rank correlation, minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) approach were employed. Eight ML classifiers were trained to obtain the best radiomics model. The performance of each model was evaluated based on the area under the receiver operating characteristic curve (AUC). The radiomics model was integrated with liver imaging reporting and data system (LI-RADS) features to design a combined model.

Results

The eXtreme Gradient Boosting (XGBoost)-based radiomics model outperformed other ML classifiers in evaluating pHCC, achieving an AUC of 1.00 and accuracy of 1.00 in the training cohort. The LI-RADS model demonstrated an AUC value of 0.77 and 0.82 in the training and validation cohorts. The combined model exhibited best performance in both the training and validation cohorts, with AUCs of 1.00 and 0.86 for evaluating HCC differentiation, respectively.

Conclusion

CEMRI radiomics integrating LI-RADS features demonstrated excellent performance in evaluating HCC differentiation, suggesting an optimal clinical decision tool for individualized diagnosis of HCC differentiation.

Identification of M5c regulator-medicated methylation modification patterns for prognosis and immune microenvironment in glioma

Glioma is a common intracranial tumor and is generally associated with poor prognosis. Recently, numerous studies illustrated the importance of 5-methylcytosine (m5C) RNA modification to tumorigenesis. However, the prognostic value and immune correlation of m5C in glioma remain unclear. We obtained RNA expression and clinical information from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) datasets to analyze. Nonnegative matrix factorization (NMF) was used to classify patients into two subgroups and compare these patients in survival and clinicopathological characteristics. CIBERSORT and single-sample gene-set algorithm (ssGSEA) methods were used to investigate the relationship between m5C and the immune environment. The Weighted correlation network analysis (WGCNA) and univariate Cox proportional hazard model (CoxPH) were used to construct a m5C-related signature. Most of m5C RNA methylation regulators presented differential expression and prognostic values. There were obvious relationships between immune infiltration cells and m5C regulators, especially NSUN7. In the m5C-related module from WGCNA, we found SEPT3, CHI3L1, PLBD1, PHYHIPL, SAMD8, RAP1B, B3GNT5, RER1, PTPN7, SLC39A1, and MXI1 were prognostic factors for glioma, and they were used to construct the signature. The great significance of m5C-related signature in predicting the survival of patients with glioma was confirmed in the validation sets and CGGA cohort.

Development and Validation of a Novel Prognosis Model Based on a Panel of Three Immunogenic Cell Death-Related Genes for Non-Cirrhotic Hepatocellular Carcinoma

Purpose

The accurate prediction of non-cirrhotic hepatocellular carcinoma (NCHCC) risk facilitates improved surveillance strategy and decreases cancer-related mortality. This study aimed to explore the correlation between immunogenic cell death (ICD) and NCHCC prognosis using The Cancer Genome Atlas (TCGA) datasets, and the potential prognostic value of ICD-related genes in NCHCC.

Methods

Clinical and transcriptomic data of patients with NCHCC patients were retrieved from TCGA database. Weighted gene co-expression network analysis was performed to obtain the NCHCC phenotype-related module genes. Consensus clustering analysis was performed to classify the patients into two clusters based on intersection genes among differentially expressed genes (DEGs) between cancer and adjacent tissues, NCHCC phenotype-related genes, and ICD-related genes. NCHCC-derived tissue microarray was used to evaluate the correlation of the expression levels of key genes with NCHCC prognosis using immunohistochemical staining.

Results

Cox regression analyses were performed to construct a prognostic risk score model comprising three genes (TMC7, GRAMD1C, and GNPDA1) based on DEGs between two clusters. The model stratified patients with NCHCC into two risk groups. The overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group. Univariable and multivariable Cox regression analyses revealed that these signature genes are independent predictors of OS. Functional analysis revealed differential immune status between the two risk groups. Next, a nomogram was constructed, which demonstrated the potent distinguishing ability of the developed model based on receiver operating characteristic curves. In vitro functional validation revealed that the migration and invasion abilities of HepG2 and Huh7 cells were upregulated upon GRAMD1C knockdown but downregulated upon TMC7 knockdown.

Conclusion

This study developed a prognostic model comprising three genes, which can aid in predicting the survival of patients with NCHCC and guide the selection of drugs and molecular markers for NCHCC.

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient's quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients. Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach. Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis. Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC.

A Nomogram Prognostic Model for Advanced Hepatocellular Carcinoma Based on the Interaction Between CD8+T Cell Counts and Age

Objective

CD8+T cells are essential components of the adaptive immune system and are crucial in the body's immune system. This study aimed to investigate how the prognosis of patients with advanced hepatocellular carcinoma (HCC) was affected by their CD8+ T cell counts and age and established an effective nomogram model to predict the overall survival (OS).

Methods

A total of 427 patients with advanced HCC from Beijing Ditan Hospital, Capital Medical University, were enrolled in this study and randomly divided into training and validation groups, with 300 and 127 individuals in each group, respectively. Cox regression analysis was used to screen for independent risk factors for advanced HCC, and the interactive relationship between CD8+T cells and patient age was examined to establish a nomogram prediction model.

Results

Cox multivariate regression and interaction analyses indicated that tumor number, tumor size, aspartate aminotransferase (AST), C-reactive protein (CRP), relationship of CD8+T cell counts and age were independent predictors of 6-month OS in patients with advanced HCC, and the nomogram model was established based on these factors. The area under the receiver operating characteristic curve (AUC) of the nomogram model for predicting the 3-month, 6-month, and 12-month OS rates were 0.821, 0.802, and 0.756, respectively. Moreover, in clinical practice, patients with true-positive survival benefit more than true-positive death, therefore, we selected 25% as the clinical decision threshold probability based on probability density functions (PDFs) and clinical utility curves (CUCs), which can distinguish approximately 92% of patients who died and 37% of patients who survived.

Conclusion

The nomogram model based on CD8+T cell counts and age accurately assessed the prognosis of patients with advanced HCC and suggested that high CD8+T cell levels are beneficial to the survival of patients with advanced HCC.