Liver hepatocellular carcinoma (LIHC) is a molecularly heterogeneous malignancy for which additional genetically supported biomarkers and functional regulators remain to be identified. We aimed to identify candidate genes through integrative genetic and transcriptomic screening, then determine the cellular context and functional relevance of the leading candidate. We applied Summary-data-based Mendelian Randomization to FinnGen LIHC genome-wide association summary statistics and GTEx v8 liver cis-eQTL summary data. A 1000 Genomes European-ancestry panel provided the linkage disequilibrium reference. Candidate genes were cross-referenced with Gene Expression Omnibus differentially expressed genes and TCGA-LIHC prognostic genes. After ATP13A2 emerged from this screen, we used bulk, single-cell, and spatial transcriptomic analyses to characterize its expression and cellular context. HUVEC knockdown, migration, tube formation, qRT-PCR, and Western blotting were then used for functional assessment. ATP13A2 was the only gene shared by the SMR-prioritized, GEO differential-expression, and TCGA-LIHC prognostic sets. ATP13A2 expression was higher in LIHC tissues and independently associated with worse survival in the TCGA-LIHC cohort. Single-cell analysis subsequently localized ATP13A2 predominantly to tumor endothelial cells, and spatial and pathway analyses associated ATP13A2-positive endothelial cells with angiogenic programs and inferred PTN-NCL signaling. In HUVECs, ATP13A2 knockdown reduced migration and tube formation and was accompanied by reduced ERK1/2 and p38 MAPK phosphorylation. An unbiased integrative screen prioritized ATP13A2 as a genetically supported LIHC candidate, after which single-cell analysis identified its tumor-endothelial context. Functional knockdown data support an association with endothelial angiogenic phenotypes. Rescue, in vivo, and independent clinical validation remain required.

OBJECTIVE:To elucidate the therapeutic potential and mechanisms of Fuzi (Aconiti Lateralis Radix Praeparata) against intrahepatic cholangiocarcinoma (ICC). METHODS:Fuzi-related targets were obtained from TCMSP, HERB, and ETCM databases, and ICC-related differentially expressed genes were retrieved from Gene Expression Omnibus. Common hub targets were identified using network pharmacology and machine learning, followed by functional enrichment and immune infiltration analyses. Effects of aconitine, a major alkaloid of Fuzi, were validated in RBE cells using qPCR, Western blot, Transwell assays, and flow cytometry. RESULTS:Thirty-six common genes were identified, mainly enriched in metabolic and cancer-related pathways. Topoisomerase II alpha (TOP2A) and alpha1A-adrenergic receptor (ADRA1A) were identified as hub genes with high diagnostic value (area under curve>0.98). High TOP2A expression was linked to poor disease-free survival. Several immune cells, including regulatory T cells, showed altered infiltration in ICC and correlated with hub gene expression. Aconitine inhibited the proliferation, migration, and invasion of RBE cells in a dose- and time-dependent manner (P<0.05 or P<0.01). It also induced G1 phase arrest and apoptosis, accompanied by modulation of Bcl-2 and Bax expression, and downregulation of TOP2A. TOP2A knockdown similarly reduced migration and invasion and altered Bcl-2/Bax expression, but had limited effects on apoptosis rate or cell cycle distribution. CONCLUSION:TOP2A was identified as Fuzi's potential therapeutic target in ICC, where its compound aconitine exerts antitumor effects by downregulating TOP2A, providing novel mechanistic insights and targeted therapy potential.

Acute-on-chronic liver failure (ACLF) exhibits high mortality and heterogeneity, demanding precise risk stratification. We aimed to identify metabolic subtypes and explore their association with prognosis and real-world artificial liver support (ALS) responsiveness. This prospective, multicenter, observational cohort study enrolled 142 ACLF patients. Serum was collected at baseline, prior to ALS initiation. Patients were subtyped via metabolomics clustering. ALS exposure was analyzed using inverse probability of treatment weighting (IPTW) and Cox models to test for heterogeneity of treatment effect. Two subtypes were identified (Cluster 1/2). Cluster 2 showed significantly higher 90 day mortality (p = 0.032) and severe amino acid metabolic reprogramming, particularly in branched-chain amino acid and glutamine pathways. Exploratory analysis suggested a differential association between ALS and survival across subtypes (interaction p = 0.12). ALS showed a weaker survival association in Cluster 2 after IPTW adjustment. ACLF patients possess distinct amino acid-based metabolic subtypes that are potent prognostic indicators. Baseline metabolic profiling can serve as a stratification tool to identify patients who may derive the greatest clinical benefit from ALS therapy, guiding personalized treatment strategies.

Background:Gestational diabetes mellitus (GDM) increases the risk of cardiovascular abnormalities in offspring. The objective of this study is to assess changes in left ventricular myocardial work using the left ventricular pressure-strain loop (LVPSL) method in neonates born to mothers with GDM. The aim of the research is to examine early impairments in neonatal left ventricular systolic function and to investigate whether these impairments persist over time. Methods:In a prospective cohort study, we enrolled 61 neonates born to mothers with GDM and 30 healthy neonates born to mothers without pregnancy complications between August 2021 and March 2023 using a random method. The GDM group was further subdivided based on maternal hemoglobin A1c (HbA1c) levels into those with HbA1c ≤6.5% and those with HbA1c >6.5%. Echocardiographic assessments and left ventricular myocardial work parameters were measured and compared across the three groups using one-way analysis of variance (ANOVA) with multiple comparisons conducted using the Least Significant Difference t (LSD-T) test and multiple correction using the Bonferroni method in terms of data of normal distribution and homoscedasticity. Non-normally distributed data were presented as median (first quartile, third quartile) [M (Q1, Q3)] and compared using the Kruskal-Wallis H test. The correlation between myocardial work parameters in neonates born to mothers with GDM and the maternal HbA1c levels was also analyzed using Pearson correlation analysis or Spearman's rank correlation. Results:The enrolled 61 neonates born to women with GDM comprised 34 male and 27 female neonates, with a gestational age (GA) of 38.9±1.7 weeks. The control group 30 healthy neonates comprised 17 males and 13 females, with a GA of 39.1±1.8 weeks. Neonates in the HbA1c ≤6.5% and HbA1c >6.5% groups demonstrated increased interventricular septal thickness (IVSD) (P<0.05) compared to the control group. However, no significant differences in IVSD were observed among the groups after a 12-month follow-up (P>0.05). At birth and during the 12-month follow-up, global longitudinal strain (GLS), global work index (GWI), and global constructive work (GCW) values were lower in both HbA1c groups compared to the control group, with the HbA1c >6.5% group revealing significantly reduced GLS, GWI, and GCW (P<0.05). Neonatal GLS exhibited a positive correlation with maternal HbA1c, whereas GWI and GCW revealed negative correlations (r=0.683, r=-0.709, r=-0.688, P<0.001). Conclusions:The LVPSL method can examine early impairments in left ventricular systolic function in neonates born to mothers with GDM. More severe impairments are associated with poorer glycemic control during pregnancy, as indicated by higher maternal HbA1c levels. These functional impairments persist in the offspring 12 months postpartum.

OBJECTIVES:To explore the correlation of serum tryptophan level with 90-day mortality risk in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). METHODS:This retrospective study was conducted among 108 patients with HBV-ACLF, whose survival outcomes within 90 days after diagnosis were recorded. The correlation of baseline serum tryptophan levels measured by high-performance liquid chromatography with 90-day mortality of the patients was analyzed, and the predictive value of serum tryptophan for 90-day mortality was explored. RESULTS:Within 90 days after diagnosis, 53 (29.4%) of the patients died and 127 (70.6%) survived. The deceased patients had significantly lower baseline serum tryptophan levels than the survivors (7.31±3.73 pg/mL vs 13.32±7.15 pg/mL, P<0.001). Multivariate analysis suggested that serum tryptophan level was an independent factor correlated with mortality of HBV-ACLF after adjustment for confounding variables. The patients with serum tryptophan levels below the median level (10.14 pg/mL) at admission had significantly higher 90-day mortality risks than those with higher tryptophan levels (43.3% vs 15.6%, HR: 3.157, 95% CI: 1.713-5.817), and the complication by kidney dysfunction further increased the risk to 73.3% as compared with patients with higher serum tryptophan levels with normal kidney function (15.0%; HR: 7.558, 95% CI: 3.369-16.960). Serum tryptophan levels had an area under the receiver operating characteristic curve of 0.771 (95% CI: 0.699-0.844) for predicting 90-day mortality. CONCLUSIONS:Serum tryptophan level is closely correlated with the survival outcomes of patients with HBV-ACLF, and a decreased tryptophan level indicates a high 90-day mortality risk, which can be further increased by the complication by kidney dysfunction.

OBJECTIVES:To investigate the impact of hepatitis B virus (HBV) infection on oral microbiota and metabolites in patients with metabolic dysfunction-associated fatty liver disease (MAFLD) and the underlying mechanisms. METHODS:This prospective study was conducted in 47 MAFLD patients complicated with chronic hepatitis B (CHB) and 48 MAFLD patients without CHB enrolled from November, 2023 to January, 2024. Fasting tongue coating samples were collected from the patients for analyzing microbial community structures and metabolites using high-throughput 16S rDNA sequencing and non-targeted metabolomics techniques, and their associations with clinical indicators and biological pathways were explored using correlation analysis and functional annotation. RESULTS:The levels of fasting blood glucose, total cholesterol (TC), gamma-glutamyl transferase (GGT), and severity of fatty liver were all significantly lower in MAFLD+CHB group than in MAFLD group. Microbiota analysis showed that the abundances of Patescibacteria (at the phylum level), Hydrogenophaga, and Absconditabacteriales (at the genus level) were significantly increased, while the abundance of Megasphaera was decreased in MAFLD+CHB group. The differential microbiota were significantly correlated with TC, GGT and low-density lipoprotein (r=-0.68‒0.75). Metabolomics analysis revealed that 469 metabolites (including lipids and amino acids) were upregulated and 2306 (including organic oxygen-containing compounds and phenylpropanoids) were downregulated in MAFLD+CHB group, for which KEGG enrichment analysis suggested abnormal activation of the linoleic acid metabolism and glycerophospholipid metabolism pathways. Correlation analysis between microbiota and metabolites indicated that Patescibacteria and Megasphaera, which were positively correlated with lipid metabolites and negatively with fatty acid metabolites, respectively, jointly affected glycolipid metabolism and oxidative stress pathways. CONCLUSIONS:Compared to patients with MAFLD alone, MAFLD patients with concurrent chronic HBV infection showed lower levels in some lipid metabolism indicators and the degree of hepatic steatosis, accompanied by alterations in oral microbiota structure and metabolic profiles. The precise mechanisms involved require further investigation to be fully elucidated.

Background and aims: Metabolic dysfunction-associated fatty liver disease (MAFLD) is associated with coronary artery disease (CAD), but existing risk assessment tools lack precision and scalability. We developed an AI-driven multimodal framework integrating traditional Chinese medicine (TCM) tongue-based diagnosis with clinical biomarkers to stratify CAD risk in MAFLD. Methods: In this cross-sectional study with prospective data collection, which comprised 1073 MAFLD patients stratified by CAD status (MAFLD without CAD, n = 942; MAFLD with CAD, n = 131), baseline characteristics were compared using chi-square tests for categorical variables and Mann-Whitney U tests for continuous variables, with p < 0.05 considered significant. We developed two distinct deep learning models: Model-1 (non-invasive) using ResNet18 combined tongue image features with demographic and comorbidity data, and Model-2 (comprehensive) incorporated blood biomarkers alongside the features used in Model-1. Multimodal feature fusion was achieved through a dedicated cross-attention network. Model performance was rigorously evaluated using 10-fold cross-validation, with area under the curve (AUC), sensitivity, and specificity as primary metrics. Focal loss was employed to address class imbalance. Results: The coexistence of CAD was associated with significantly higher rates of hypertension, diabetes, and familial cardiovascular history in patients with MAFLD (p < 0.001), along with distinct metabolic profiles: elevated systemic inflammation (neutrophil-lymphocyte ratio), advanced fibrosis (FIB-4), elevated fasting glucose levels, and attenuated hepatic inflammation (ALT and AST). Model-1 achieved an AUC of 0.858 (sensitivity 0.778, specificity 0.908), while Model-2 demonstrated superior discrimination (AUC 0.933), particularly in tertiary care. Conclusion: Our AI-driven dual-model framework addresses the critical unmet need for CAD identification in MAFLD patients, providing a community-scalable screening tool (Model-1) and a precision clinical assessment model (Model-2), while offering empirical support for TCM tongue diagnosis. Pending external validation to confirm its generalizability, this approach may serve as a cost-efficient non-invasive screening strategy for this high-risk population.

Globally, pancreatic cancer is recognized as one of the deadliest malignancies that lacks effective targeted therapies. This study aims to explore the role of cyclin I-like protein (CCNI2), a homolog of cyclin I (CCNI), in the progression of pancreatic cancer, thereby providing a theoretical basis for its treatment. Firstly, the expression of CCNI2 in pancreatic cancer tissues was determined through immunohistochemical staining. The biological role of CCNI2 in pancreatic cancer cells was further assessed using both in vitro and in vivo loss/gain-of-function assays. Our data revealed that CCNI2 expression was abnormally elevated in pancreatic cancer, and clinically, increased CCNI2 expression generally correlated with reduced overall survival. Functionally, CCNI2 contributed to the malignant progression of pancreatic cancer by promoting the proliferation and migration of tumor cells. Consistently, in vivo experiments verified that CCNI2 knockdown impaired the tumorigenic ability of pancreatic cancer cells. Moreover, the addition of phosphatidylinositol 3-kinase (PI3K) inhibitors could partially reverse the promoting effect of CCNI2 on the malignant phenotypes of pancreatic cancer cells. CCNI2 promoted pancreatic cancer through PI3K/protein kinase B (AKT) signaling pathway, indicating its potential as a prognostic marker and therapeutic target for pancreatic cancer.

Despite the importance of spliceosome core components in cellular processes, their roles in cancer development, including hepatocellular carcinoma (HCC), remain poorly understood. In this study, we uncover a critical role for SmD2, a core component of the spliceosome machinery, in modulating DNA damage in HCC through its impact on BRCA1/FANC cassette exons and expression. Our findings reveal that SmD2 depletion sensitizes HCC cells to PARP inhibitors, expanding the potential therapeutic targets. We also demonstrate that SmD2 acetylation by p300 leads to its degradation, while HDAC2-mediated deacetylation stabilizes SmD2. Importantly, we show that the combination of Romidepsin and Olaparib exhibits significant therapeutic potential in multiple HCC models, highlighting the promise of targeting SmD2 acetylation and HDAC2 inhibition alongside PARP inhibitors for HCC treatment.

Supplementary Figure S1-S18

Background and aims Currently, there are still no definitive consensus in the treatment of intrahepatic cholangiocarcinoma (iCCA). This study aimed to build a clinical decision support tool based on machine learning using the Surveillance, Epidemiology, and End Results (SEER) database and the data from the Fifth Medical Center of the PLA General Hospital in China. Methods 4,398 eligible patients from the SEER database and 504 eligible patients from the hospital data, who presented with histologically proven iCCA, were enrolled for modeling by cross-validation based on machine learning. All the models were trained using the open-source Python library scikit-survival version 0.16.0. Shapley additive explanations method was used to help clinicians better understand the obtained results. Permutation importance was calculated using library ELI5. Results All involved treatment modalities could contribute to a better prognosis. Three models were derived and tested using different data sources, with concordance indices of 0.67, 0.69, and 0.73, respectively. The prediction results were consistent with those under actual situations involving randomly selected patients. Model 2, trained using the hospital data, was selected to develop an online tool, due to its advantage in predicting short-term prognosis. Conclusion The prediction model and tool established in this study can be applied to predict the prognosis of iCCA after treatment by inputting the patient's clinical parameters or TNM stages and treatment options, thus contributing to optimal clinical decisions. KEY MESSAGES A prognostic model related to disease staging and treatment mode was conducted using the method of machine learning, based on the big data of multi centers. The online calculator can predict the short-term survival prognosis of intrahepatic cholangiocarcinoma, thus, help to make the best clinical decision. The online calculator built to calculate the mortality risk and overall survival can be easily obtained and applied.

Abstract Background It is widely known that muscle mass influences the outcomes of many chronic diseases. Erector spine mass is a convenient parameter obtained from routine abdominal computed tomography (CT). The clinical application value of erector spine mass, and whether erector spine mass could predict the outcome of disease has not been studied. Aim To evaluate the role of the erector spine index (ESI) calculated based on abdominal CT imaging in the progression of acute-on-chronic liver failure related to the hepatitis B virus (HBV-ACLF). Methods We performed a retrospective study of 118 HBV-ACLF patients and calculated the ESI (the total erector spine area normalized for height2 in meters) for each patient through abdominal CT. The findings were analyzed regarding the progression of HBV-ACLF and the ESI at baseline, including mortality and the development of complications. Results The ESI level was associated with mortality and the development of complications. During the 90-day follow-up period, patients with a low ESI (<12.05 cm2/m2) had higher mortality than those with a high ESI (≥ 12.05 cm2/m2) (51.7% vs. 26.7%), and the cumulative survival rates were 71.0%±4.6 and 85.8%±3.9, respectively (log-rank P = 0.003). The hazard ratios (HRs) calculated using univariable and multivariable analyses were 2.23(95% confidence interval (CI): 1.25–4.21, P = 0.005) and 2.52 (95% CI: 1.34–9.24, P = 0.011), respectively. Patients with a low ESI (<12.05 cm2/m2) had higher incidences of kidney dysfunction (43.5% vs. 23.2%, P = 0.029; log-rank P = 0.017) and hepatic encephalopathy (39.6% vs. 14.0%, P = 0.003; log-rank P = 0.010) than those with a high ESI. A low ESI was an independent risk factor for kidney dysfunction (adjusted HR = 1.36, 95% CI: 1.05–2.93, P = 0.043) and the development of hepatic encephalopathy (adjusted HR = 2.26; 95% CI: 2.05–3.13, P = 0.036). In addition, the presence of hepatic encephalopathy (the odds ratio (OR) = 2.26, 95% CI: 2.05–3.18, P = 0.006), spontaneous bacterial peritonitis (OR = 3.95, 95% CI: 1.01–5.46, P = 0.037), and kidney dysfunction (OR = 4.47, 95% CI: 1.02–9.64, P = 0.032) was independently associated with a low ESI in patients. Conclusion A low ESI is an independent risk factor for mortality in patients with HBV-ACLF, as well as the development of kidney dysfunction and hepatic encephalopathy.

Information of PLCB1 associated RNA binding proteins

Univariate and multivariate analyses of factors associated with survival in CCA patients.

Clinical characteristics of 60 CCA patients depending on PLCB1 expression levels

Hepatocellular carcinoma（HCC）, one of the most common malignant tumors in China, severely threatens the life and health of patients. In recent years, precision medicine, clinical diagnoses, treatments, and innovative research have led to important breakthroughs in HCC care. The discovery of new biomarkers and the promotion of liquid biopsy technologies have greatly facilitated the early diagnosis and treatment of HCC. Progress in targeted therapy and immunotherapy has provided more choices for precise HCC treatment. Multiomics technologies, such as genomics, transcriptomics, and metabolomics, have enabled deeper understanding of the occurrence and development mechanisms, heterogeneity, and genetic mutation characteristics of HCC. The continued promotion and accurate typing of HCC, accurate guidance of treatment, and accurate prognostication have provided more treatment opportunities and prolonged survival timelines for patients with HCC. Innovative HCC research providing an in-depth understanding of the biological characteristics of HCC will be translated into accurate clinical practices for the diagnosis and treatment of HCC.

Abstract Background and aims To date, there is still a lack of consensus on the treatment of intrahepatic cholangiocarcinoma (iCCA). This study aims to build a clinical decision support tool based on machine learning of the Surveillance, Epidemiology, and End Results (SEER) database and the Fifth Medical Center of PLA General Hospital in China. Methods A total of 4,398 eligible patients with pathology-proven iCCA from the SEER database and 504 from the hospital data were enrolled for modeling by cross-validation based on the method of machine learning. All models were trained by the open-source Python library 4scikit-survival version 0.16.0 and explained by SHapley Additive exPlanations. Permutation importance was calculated using the library ELI5. Results All of the involved treatment modalities can contribute to a better prognosis. Three models were derived and tested among different data sources with the concordance index in the test datasets of 0.67, 0.69, and 0.73 respectively. The prediction was also consistent with the actual situation in randomly selected real patients. Model 2 trained by the hospital data was selected to develop an online tool because of the advantage of predicting the short-term prognosis.Conclusion The prediction model and tool of this study can be applied to predicting patients’ prognosis after treatment by inputting the patient’s clinical parameters or TNM stages and the treatment option and thus contribute to the optimal clinical decision.

Background and Purpose: Liver metastases are common in neuroendocrine neoplasm (NEN). Cystic NEN liver metastases (cNENLM) are rare, and the efficacy of transarterial embolization (TAE) has not been reported. This study summarized and analyzed the efficacy and safety of TAE for cNENLM. Methods: From January 2016 to April 2022, 10 patients with cNENLM were enrolled in this study among 440 patients under TAE treatment at The First Affiliated Hospital, Sun Yat-sen University and Fudan University Shanghai Cancer center. The efficacy was evaluated according to Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. Objective response rate (ORR), disease control rate (DCR) and progression-free survival (PFS) were analyzed as well. The common terminology criteria for adverse events (CTCAE) v5.0 was applied to evaluate the adverse effects. Results: With 80.0% ORR and 100.0% DCR according to RECIST 1.1, 3 cases achieved PFS. Among them, the longest one was 25.0 months. Disease progression was not observed in the remaining patients. The common complications were fever, hepatalgia and transient liver dysfunction, which could be alleviated by symptomatic treatment. No severe complication occurred. Conclusion: cNENLM are infrequent. TAE had significant curative effect on cNENLM, and complications were manageable.

Precision medicine in hepatocellular carcinoma (HCC) relies on validated biomarkers that help subgroup patients for targeted treatment. Here, we identified a novel candidate oncogene, ribosomal protein L22-like1 (RPL22L1), which was markedly elevated in HCC, contributed to HCC malignancy and adverse patient survival. Functional studies indicated RPL22L1 overexpression accelerated cell proliferation, migration, invasion and sorafenib resistance. Mechanism studies revealed that RPL22L1 activated ERK to induce atypical epithelial-to-mesenchymal transition (EMT) progress. Importantly, the ERK inhibitor (ERKi) could potentiate sorafenib efficiency in RPL22L1-high HCC cells. In summary, these data uncover RPL22L1 is a potential marker to guide precision therapy for utilizing ERKi to enhance the sorafenib efficacy in RPL22L1-high HCC patients.