Fatty acid oxidation (FAO) denotes the mitochondrial aerobic process responsible for breaking down fatty acids (FAs) into acetyl-CoA units. This process holds a central position in the cancer metabolic landscape, with certain tumor cells relying primarily on FAO for energy production. Over the past decade, mounting evidence has underscored the critical role of FAO in various cellular processes such as cell growth, epigenetic modifications, tissue-immune homeostasis, cell signal transduction, and more. FAO is tightly regulated by multiple evolutionarily conserved mechanisms, and any dysregulation can predispose to cancer development. In this view, we summarize recent findings to provide an updated understanding of the multifaceted roles of FAO in tumor development, metastasis, and the response to cancer therapy. Additionally, we explore the regulatory mechanisms of FAO, laying the groundwork for potential therapeutic interventions targeting FAO in cancers within the metabolic landscape.

The growing interest in post-translational protein modification, particularly in SUMOylation, is driven by its crucial role in cell cycle regulation. SUMOylation affects various cell cycle regulators, including oncogenes, suggesting its relevance in cancer. SUMO E3 ligases are pivotal in this process, exhibiting diverse functionalities through structural domains and subcellular localizations. A less-explored SUMO E3 ligase, RANBP2, a component of the vertebrate nuclear pore complex, emerges as a central player in cellular cycle processes, as well as in tumorigenesis. The current studies illuminate the importance of RANBP2 and underscore the need for more extensive studies to validate its clinical applicability in neoplastic interventions. Our review elucidates the significance of RANBP2 across various types of malignancies. Additionally, it delves into exploring RANBP2 as a prospective therapeutic target for cancer treatment, offering insights into the avenues that scholars should pursue in their subsequent research endeavors. Thus, further investigation into RANBP2's role in solid tumorigenesis is eagerly awaited.

Several prognostic gene signatures have been proposed as predictors of the prognosis of hepatocellular carcinoma (HCC), yet none are implemented in the clinical setting. We aimed to validate two gene scores previously derived from European cohorts.

The patients who underwent liver resection for HCC at Copenhagen University Hospital, Rigshospitalet from 2014 to 2018 were included. RNA sequencing determined the expression of genes in the '5-gene score' (HN1, RAN, RAMP3, KRT19, TAF9B) and 'HepatoPredict' (CLU, DPT, SPRY2, CAPSN1). Univariable Cox regression assessed associations with overall and disease-free survival. These parameters were also analyzed in the The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) (n = 359) and National Institute of Health (NIH) (n = 178) cohorts.

Among 51 patients (88% male), 59% had no underlying liver disease and 25% had cirrhosis. No individual genes were significantly associated with overall survival in the Danish cohort. In the TCGA-LIHC cohort, CLU was linked to better overall survival, and in the NIH cohort, high expression of SPRY2 was associated with poorer overall survival. In the TCGA-LIHC cohort, HN1, RAN, and TAF9B were associated with poorer overall survival, while RAMP3 was linked to better overall survival. No genes were associated with disease-free survival.

Few individual genes significantly predicted survival in the larger cohorts, and none in the Danish cohort. However, the clinical implication of this needs further investigation.

With the increasing prevalence of environmental pollutants, there is growing concern about the potential effects of these substances in major diseases such as liver cancer. Previous studies have suggested that various chemicals, such as benzo[a]pyrene(BaP), produced by burning carbon containing fuels, may negatively affect liver health, but the exact mechanisms remain unclear. This study aimed to explore the potential molecular mechanisms of BaP in the progression of liver cancer. Through an exhaustive study of databases such as ChEMBL, SwissTargetPrediction, STITCH and TCGA, we identified 169 potential targets that are closely related to BaP and liver cancer. Next, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using the clusterProfiler package to study the biological functions and important pathways of potential targets induced by BaP, which showed that these targets were associated with mitochondrial function, cellular energy metabolism and REDOX reactions. The protein interaction (PPI) network was constructed using the STRING database and Cytoscape software to identify the core targets UBA52, NDUFS8, CYP1A2, NDUFS1 and CYP3A4. The interaction between BaP and these core proteins was further analyzed via molecular docking using the CB-Dock2 database, demonstrating high binding stability, which suggests their critical role in BaP-induced hepatocellular carcinoma (HCC) toxicity. Subsequently, we found significant differences in the expression of five core genes (UBA52, NDUFS8, CYP1A2, NDUFS1, CYP3A4) in HCC, and significant correlation between UBA52, NDUFS8 and CYP3A4 and survival of HCC patients. Single-cell sequencing analysis showed that the expression of UBA52 gene was particularly pronounced in the three immune cells.

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) shares various features with small duct type intrahepatic cholangiocarcinoma (SmD-iCCA) and sometimes histological diagnosis may be difficult.

We examined genetic alterations such as hTERT promoter (hTERT), p53, and fibroblast growth factor receptor 2 (FGFR2) in 103 PLCs diagnosed as cHCC-CCA or SmD-iCCA. A cluster analysis was performed on the R software for re-classification of PLCs including cHCC-CCA and SmD-iCCA.

The primary liver carcinomas (PLCs) were divided into 5 clusters; 19 tumors (18 %) in Cluster-1 (with alterations in hTERT and/or p53), 24 (23 %) in Cluster-2 (FGFR2 and/or p53), 13 (13 %) in Cluster-3 (IDH2 or null), 19 (18 %) in Cluster-4 (MTAP and/or FGFR2), 28 (27 %) in Cluster-5 (ARID1A and/or PBRM1), being based on genetic alterations. Cluster-1 and Clusters-2 to- 5 formed distinct 2 groups. Cluster-1 was characterized by significantly bigger size, rich and higher histological grade of HCC component, significantly less cholangiolocellular carcinoma (CLC)-component, ductal plate malformation pattern and bile duct adenoma in the background livers. No SmD-iCCA was included in Cluster-1, whereas SmD-iCCA distributed evenly in Clusters 2-5. Cluster-4 was characterized by higher prevalence of hepatitis B and higher histological diversity scores.

PLCs diagnosed as cHCC-CCA or SmD-iCCAs could be divided into 5 clusters based on genetic alterations. Cluster-1 was HCC-like cluster characterized by hTERT alteration, rich and higher grade of HCC and bigger size. Clusters-2-5 may be iCCA-like clusters characterized by different genetic alterations. cHCC-CCA in Cluster-1 and Clusters-2-5 may be handled separately for further analysis and treatment.

Fat mass and obesity-associated protein (FTO) has been linked to various cancers, though its role in hepatocellular carcinoma (HCC) remains unclear. This study aimed to investigate FTO expression, its clinical relevance, functional role in HCC progression, and the underlying molecular mechanisms.

Quantitative reverse-transcription polymerase chain reaction and immunohistochemical analysis were used to assess FTO expression in HCC. Functional assays, including proliferation, invasion, and epithelial-mesenchymal transition studies, were conducted using HCC cell lines with FTO knockdown. N6-methyladenosine (m6A) RNA immunoprecipitation and RNA stability assays further elucidated the role of FTO in BUB1 mRNA methylation and stability. Co-immunoprecipitation studies were employed to confirm the interaction between BUB1 and TGF-βR1. In vivo studies in nude mice were conducted to evaluate tumor growth following FTO knockdown.

FTO was significantly upregulated in HCC tissues compared to normal liver tissues, with higher expression observed in advanced tumor-node-metastasis stages and metastatic HCC. Elevated FTO correlated with poor overall survival in patients. Silencing FTO decreased HCC cell proliferation, colony formation, invasion, epithelial-mesenchymal transition, and tumor growth in nude mice. Mechanistically, FTO downregulation led to increased m6A modification of BUB1 mRNA, thereby promoting its degradation via the YTH domain family 2-dependent pathway and reducing BUB1 protein levels. Additionally, BUB1 physically interacted with TGF-βR1, activating downstream TGF-β signaling.

FTO is overexpressed in HCC and is associated with poor clinical outcomes. Mechanistically, FTO promotes HCC progression by stabilizing BUB1 mRNA through an m6A-YTH domain family 2-dependent pathway, which activates TGF-β signaling. Targeting the FTO-BUB1-TGF-βR1 regulatory network may offer a promising therapeutic strategy for HCC.

Cancers are shaped by somatic mutations, microenvironment, and patient background, each altering gene expression and regulation in complex ways, resulting in heterogeneous cellular states and dynamics. Inferring gene regulatory networks (GRNs) from expression data can help characterize this regulation-driven heterogeneity, but network inference requires many statistical samples, limiting GRNs to cluster-level analyses that ignore intracluster heterogeneity. We propose to move beyond coarse analyses of predefined subgroups by using contextualized learning, a multitask learning paradigm that uses multiview contexts including phenotypic, molecular, and environmental information to infer personalized models. With sample-specific contexts, contextualization enables sample-specific models and even generalizes at test time to predict network models for entirely unseen contexts. We unify three network model classes (Correlation, Markov, and Neighborhood Selection) and estimate context-specific GRNs for 7,997 tumors across 25 tumor types, using copy number and driver mutation profiles, tumor microenvironment, and patient demographics as model context. Our generative modeling approach allows us to predict GRNs for unseen tumor types based on a pan-cancer model of how somatic mutations affect gene regulation. Finally, contextualized networks enable GRN-based precision oncology by providing a structured view of expression dynamics at sample-specific resolution, explaining known biomarkers in terms of network-mediated effects and leading to subtypings that improve survival prognosis. We provide a SKLearn-style Python package https://contextualized.ml for learning and analyzing contextualized models, as well as interactive plotting tools for pan-cancer data exploration at https://github.com/cnellington/CancerContextualized.

The mechanism of hepatocarcinogenesis after sustained virological response (SVR) in hepatitis C virus (HCV) patients is unclear. We compared gene profiles of hepatocellular carcinoma (HCC) between HCV-SVR, steatotic liver disease (SLD), and HCV-non-SVR patients.

This study analyzed 126 resected HCCs from patients with HCV and SLD, classifying them as HCV-SVR (n = 22), HCV-non-SVR (n = 56), and SLD (n = 48). Deep sequencing of 2910 hotspots in 55 cancer-related genes was conducted to examine mutations and copy number variations in both cancerous and background liver tissues.

The HCV-SVR group comprised more patients who consumed alcohol (45.5% vs. 15.7%, p = 0.008), were obese (54.5% vs. 17.9%, p = 0.002), and had dyslipidemia (18.2% vs. 3.6%, p = 0.029) and hyperuricemia (18.2% vs. 3.6%, p = 0.029) than the HCV-non-SVR group. Mutational profiling of the HCV-SVR HCC showed significantly lower alteration rates of AXIN1 (13.6% vs. 42.9%, p = 0.016), ARID2 (9.1% vs. 39.3%, p = 0.013), and TP53 (9.1% vs. 32.1%, p = 0.030) than HCV-non-SVR patients. Compared with HCV-non-SVR-HCC, SLD-HCCs showed significantly lower rates of TERT promoter mutations (62.5% vs. 85.7%, p = 0.004), ARID2 alterations (12.5% vs. 39.3%, p = 0.003), and AXIN1 alterations (12.5% vs. 42.9%, p = 0.002). HCV-SVR/MASH/MASLD/ALD-HCC had significantly lower alteration rates of the Wnt/β-catenin (41.4% vs. 60.7%, p = 0.048) and chromatin remodeling pathways (27.1% vs. 48.2%, p = 0.026) than HCV-non-SVR-HCC.

HCV-SVR HCC is linked to alcohol use and metabolic diseases, showing a mutational profile similar to SLD-HCC.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths globally, with its development closely related to complex molecular mechanisms such as gene mutations and abnormal signaling pathways. However, the specific roles of many key genes remain unclear. UBA52 and BARD1 are important genes associated with protein degradation, DNA repair, and cell cycle regulation, but their mechanisms in liver cancer are not well understood.

This study integrated HCC datasets (GSE135631, GSE184733, GSE202853) from the gene expression omnibus (GEO) database to screen for differentially expressed genes (DEGs), perform functional enrichment analysis, weighted gene co-expression network analysis (WGCNA), construct protein-protein interaction (PPI) networks, and conduct survival analysis. Western Blot (WB) and RT-qPCR experiments were used to verify the expression of UBA52 and BARD1 in liver cancer cells and their association with the PI3K/AKT signaling pathway.

Bioinformatics analysis identified UBA52 and BARD1 as core genes, showing high expression in HCC tissues and correlation with poor prognosis. Western Blot and RT-qPCR results further confirmed the high expression of UBA52 and BARD1 in HCC cell lines (HepG2 and Hep3b). PI3K inhibitors significantly downregulated the expression of UBA52 and BARD1, restored the levels of apoptosis-related factors (Fas, BAX, Caspase-3), and inhibited the expression of cell cycle-related proteins (Cyclin-D1, c-Myc). These findings suggest that UBA52 and BARD1 may regulate HCC cell proliferation, apoptosis, and metastasis through the PI3K/AKT signaling pathway. Furthermore, the molecular mechanism of hepatocellular carcinoma can be modulated by knocking out BARD1 or UBA52.

UBA52 and BARD1 are highly expressed in HCC, and their abnormal expression may promote the occurrence and development of liver cancer by regulating the PI3K/AKT signaling pathway and mechanisms related to apoptosis and cell cycle. The high expression of UBA52 and BARD1 is closely associated with poor prognosis, indicating their potential value as early diagnostic and targeted therapeutic biomarkers for HCC.

There is limited literature on sarcoid like granuloma (SLG) associated with hepatocellular carcinoma (HCC). Here in, we studied clinicopathological characteristics, and explored the potential significance of SLG in HCC. We termed these tumors as granuloma rich HCC (GrHCC). We reviewed clinicopathologic features in 30 GrHCC tumors that were diagnosed in 21 patients during a period of 68-month at a single institution. The study included 17 males and 4 females, with ages ranging from 43 to 71 years in males and 20 to 69 years in females. Tumor downstaging was done in 4 patients. Tumor sizes ranged from 0.6 to 23 cm, with a mean size of 2.41 cm. Majority of tumors showed well to moderate cellular differentiation. A solitary well-formed epithelioid granuloma sufficed to classify the tumor as GrHCC. The intratumoral granulomas were compact, well-formed, and discrete, consisting of collections of epithelioid histiocytes and multinucleate histiocytic giant cells. Mild lymphocytic inflammation was also noted. Single to several granulomas were identified in the tumor. Size of granuloma ranged from 170 to 650 μm. Only one tumor showed necrotizing granulomas. Genomic analysis of 4 tumors revealed TP53 mutation. Two tumors also exhibited a TERT promoter mutation. All patients were alive till last follow-up, except for one, who died due to septic shock, unrelated to the HCC. This study provides valuable insights into the clinical findings, histopathological features and molecular characteristics of GrHCC.

Basal cell adenoma (BCA) and basal cell adenocarcinoma (BCAC) of the salivary gland are rare tumours that can be difficult to distinguish from each other and other salivary gland tumour subtypes. Using next-generation sequencing, we identify a recurrent FBXW11 missense mutation (p.F517S) in BCA that is mutually exclusive with the previously reported CTNNB1 p.I35T gain-of-function (GoF) mutation with these mutations collectively accounting for 94% of BCAs. In vitro, mutant FBXW11 is characterised by defective binding to β-catenin and higher protein levels within the nucleus. This is consistent with the increased nuclear expression of β-catenin and activation of the Wnt/β-catenin pathway. The genomic profiles of BCAC are distinct from BCA, with hotspot DICER1 and HRAS mutations and putative driver mutations affecting PI3K/AKT and NF-κB signalling pathway genes. These findings have important implications for the diagnosis and treatment of BCA and BCAC, which, despite histopathologic overlap, may be unrelated entities.

The year 2024 marks the 60th anniversary of the discovery of the Epstein-Barr virus (EBV), the first virus confirmed to cause human cancer. Viral infections significantly contribute to the global cancer burden, with seven known Group 1 oncogenic viruses, including hepatitis B virus (HBV), human papillomavirus (HPV), EBV, Kaposi sarcoma-associated herpesvirus (KSHV), hepatitis C virus (HCV), human T-cell leukemia virus type 1 (HTLV-1), and human immunodeficiency virus (HIV). These oncogenic viruses induce cellular transformation and cancer development by altering various biological processes within host cells, particularly under immunosuppression or co-carcinogenic exposures. These viruses are primarily associated with hepatocellular carcinoma, gastric cancer, cervical cancer, nasopharyngeal carcinoma, Kaposi sarcoma, lymphoma, and adult T-cell leukemia/lymphoma. Understanding the mechanisms of viral oncogenesis is crucial for identifying and characterizing the early biological processes of virus-related cancers, providing new targets and strategies for treatment or prevention. This review first outlines the global epidemiology of virus-related tumors, milestone events in research, and the process by which oncogenic viruses infect target cells. It then focuses on the molecular mechanisms by which these viruses induce tumors directly or indirectly, including the regulation of oncogenes or tumor suppressor genes, induction of genomic instability, disruption of regular life cycle of cells, immune suppression, chronic inflammation, and inducing angiogenesis. Finally, current therapeutic strategies for virus-related tumors and recent advances in preclinical and clinical research are discussed.

Hepatocellular carcinoma (HCC) remains one of the major threats to human health worldwide. The emergence of systemic therapeutic options has greatly improved the prognosis of patients with HCC, particularly those with advanced stages of the disease. In this review, we discussed the pathogenesis of HCC, genetic alterations associated with the development of HCC, and alterations in the tumor immune microenvironment. Then, important indicators and emerging technologies related to the diagnosis of HCC are summarized. Also, we reviewed the major advances in treatments for HCC, offering insights into future prospects for next-generation managements.

Early detection of hepatocellular carcinoma (HCC) in patients with liver cirrhosis (LC) and/or hepatitis virus B/C infection (HVI) improves survival, highlighting the need for accurate, affordable diagnostic tools. Here, 11 methylated DNA markers (MDMs) are identified during marker discovery. In phase I, each selected MDM is validated in 175 plasma samples (HCC, n = 85; LC/HVI, n = 72) by the CO-methylation aMplification rEal-Time PCR (COMET) assay. Of these, 8 MDMs are qualified for phase II study, where a logistic regression model (COMET-LR) is trained and validated with 336 plasma samples (HCC, n = 211; LC/HVI, n = 113; training vs validation, 2:1). In the validation, the COMET-LR achieved 90.0% sensitivity at 97.4% specificity. Notably, sensitivity in patients with TNM stage I, diameter<3 cm, AFP-negative (<20 ng mL-1), PIVKA-II-negative (<40 mAU mL-1) is 82.4%, 77.8%, 88.6%, and 85.7%, respectively. The COMET-LR outperformed multiple protein markers (AFP, AFP-L3, and PIVKA-II) and published scores for HCC screening (GALAD, Doylestown, and ASAP), in terms of both sensitivity and specificity. The assay represents a significant advancement in addressing the unmet need for accurate, non-invasive, accessible, and cost-effective early detection tools for LC/HVI individuals. Further validation in a prospective cohort is warranted.

Genome conformation underlies transcriptional regulation by distal enhancers, and genomic rearrangements in cancer can alter critical regulatory interactions. Here we profiled the three-dimensional genome architecture and enhancer connectome of 69 tumor samples spanning 15 primary human cancer types from The Cancer Genome Atlas. We discovered the following three archetypes of enhancer usage for over 100 oncogenes across human cancers: static, selective gain or dynamic rewiring. Integrative analyses revealed the enhancer landscape of noncancer cells in the tumor microenvironment for genes related to immune escape. Deep whole-genome sequencing and enhancer connectome mapping provided accurate detection and validation of diverse structural variants across cancer genomes and revealed distinct enhancer rewiring consequences from noncoding point mutations, genomic inversions, translocations and focal amplifications. Extrachromosomal DNA promoted more extensive enhancer rewiring among several types of focal amplification mechanisms. These results suggest a systematic approach to understanding genome topology in cancer etiology and therapy.

Obesity is a leading risk factor for development of hepatocellular carcinoma (HCC). High-fat intake produces cytotoxic effects in liver cells, such as excessive reactive oxygen species (ROS) accumulation and apoptosis. How HCC cells regulate ROS level and escape the cytotoxic effects of high fat diet (HFD) stress remains unclear. Herein, this work reports a critical anti-ROS/apoptotic role of the ubiquitin-like protein interferon stimulated gene 15 (ISG15) in HFD-promoted HCC. In mouse models and clinical HCC samples, upregulation of ISG15 is associated with hepatic steatosis. Notably, upregulated ISG15 elevates cellular glutathione levels, which subsequently reduces ROS accumulation and confers resistance to apoptosis in HCC cells. In diethylnitrosamine-induced HCC mouse model, HFD-feeding promotes HCC progression in wildtype mice, while tumor growth is significantly suppressed accompanied by apoptosis of HCC cells in Isg15-KO mice. Mechanistically, ISG15 promotes the activity of γ-glutamate cysteine ligase (γ-GCL), a rate-limiting heterodimeric holoenzyme of glutathione synthesis consisting of glutamate-cysteine ligase catalytic subunit (GCLC) and glutamate-cysteine ligase modifier subunit (GCLM). Independent of ISGylation, ISG15 forms an ISG15/GCLM/GCLC complex that promotes GCLM-GCLC interaction, increases glutathione generation and inhibits HFD-induced apoptosis in HCC cells. Together, an anti-apoptotic ISG15-γ-GCL-glutothione axis is suggested in HFD-promoted HCC.

Effective classification methods and prognostic models enable more accurate classification and treatment of hepatocellular carcinoma (HCC) patients. However, the weak correlation between RNA and protein data has limited the clinical utility of previous RNA-based prognostic models for HCC. In this work, we constructed a novel prognostic framework for HCC patients using seven differentially expressed proteins associated with ferroptosis and iron metabolism. Furthermore, this prognostic model robustly classifies HCC patients into three clinically relevant risk groups. Significant differences in overall survival, age, tumor differentiation, microvascular invasion, distant metastasis, and alpha-fetoprotein levels were observed among the risk groups. Based on the prognostic model and known biological pathways, we explored the potential mechanisms underlying the inconsistent differential expression patterns of FTH1 (Ferritin heavy chain 1) mRNA and protein. Our findings demonstrated that tumor tissues in HCC patients promote liver cancer progression by downregulating FTH1 protein expression, rather than upregulating FTH1 mRNA expression, ultimately leading to poor prognosis. Subsequently, based on risk score and tumor size, we developed a nomogram for predicting the prognosis of HCC patients, which demonstrated superior predictive performance in both the training and validation cohorts (C-index: 0.774; AUC for 1-5 years: 0.783-0.964). Additionally, our findings demonstrated that the adverse prognosis of high-risk HCC patients was closely correlated with ferroptosis in liver cancer tissues, alterations in iron metabolism, and changes in the tumor immune microenvironment. In conclusion, our prognostic model and predictive nomogram offer novel insights and tools for the effective classification of HCC patients, potentially enhancing clinical decision-making and outcomes.

Prediction of prognosis in patients with hepatocellular carcinoma (HCC) by single-omics profiling has been widely studied. However, the prognosis related to biomarkers of multiple omics has not been investigated. We aimed to establish and validate a prediction model for prognosis prediction of resectable HCC combining multi-omics and clinicopathological factors.

The training cohort involved multi-omics data of 330 patients with resectable HCC (stage I-IIIA) at mutational, copy number variation (CNV), transcriptional, and methylation levels from The Cancer Genome Atlas (TCGA) database, along with clinicopathological information. The validation cohort involved samples from 40 HCC patients of Beijing Youan Hospital. Univariate and multivariate analyses were performed in single-omics with clinicopathological variables regarding patient prognosis, and independent risk factors were combined to establish the multi-omics model. The predictive accuracy was assessed by the receiver operating characteristic (ROC) method.

The mutational, copy number, transcriptional, and methylation alterations in HCC were characterized. TP53, CTNNB1, and TTN were among the genes with the top mutational frequency, and FBN1 and MAP1B mutations were independent risk factors for patient overall survival (OS). 1q21.3 and 1q23.3 ranked the highest in copy number amplifications, and 8p12 and 8p23.3 ranked the highest in deletions, and CSMD1, TP53, and RB1 were genes with the most frequent CNVs. AFP, GPC3, and TERT were among genes with the most significant aberrant transcription, and the transcription of CCNJL, FRMD1, and GRPEL2 were independent risk factors for OS. Both hypermethylation and hypomethylation can be observed. The aberrant methylation of CXorf15, DACT2, GP6, KIAA1522, and PDIA3 were independent risk factors. Single-omics models were established with independent risk factors, and were validated by internal and external datasets. A prognostic model for OS with multi-omics independent risk factors and clinicopathlogical information was established. Internal and external validation achieved an optimal maximal area under the curve (AUC) of 0.98 at 1 year and 0.88 at 2 years, respectively.

A multi-omics model combining molecular aberrancies and clinicopathological information was established and proved to be optimal for prognosis prediction of resectable HCC. This model may be helpful for therapeutic strategy selection and survival assessment.

Triple negative breast cancer (TNBC) is a more aggressive subtype of breast cancer that usually progresses rapidly, develops drug resistance, metastasis, and relapses, and remains a challenge for clinicians to treat. Programmed cell death (PCD), a conserved mechanism of cell suicide controlled by various pathways, contributed to carcinogenesis and cancer progression. Nevertheless, the prognostic significance of PCD-related genes in TNBC remains largely unclear, and more accurate prognostic models are urgently needed.

Gene expression profiles and clinical information of TNBC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to establish the PCD-related gene signature. Kaplan-Meier plotter, receiver operating characteristic curves, and nomogram were applied to validate the prognostic value of the gene signature. Gene set enrichment analysis was carried out to investigate the pathways and molecular functions.

Five PCD-related genes including SEPTIN3, SCARB1, CHML, SYNM, and COL5A3 were identified to establish the PCD-related risk score for TNBC patients. Patients stratified into high-risk or low-risk group showed significantly different survival outcome, immune infiltration, and drug susceptibility. Kaplan-Meier and receiver operating characteristic curves showed a good performance for survival prediction in different cohorts. Gene set enrichment analysis revealed that the five-gene signature was associated with tumor metabolism, cancer cell proliferation, invasion and metastasis, and tumor microenvironment. Nomogram including the five-gene signature was established.

A novel five PCD-related gene signature and nomogram could be used for prognostic prediction in TNBC. The present work might offer useful insights in digging sensitive and effective biomarkers for TNBC prognosis prediction and establishing accurate prognostic model in clinical management.

High c-Myc protein accumulation contributes to the proliferation, invasion, and drug resistance in multiple cancer cells, but the underlying mechanism about c-Myc accumulation remains not to be elucidated. Here, we demonstrate that TTC36 promotes c-Myc protein accumulation in hepatocellular carcinoma cells, thereby driving the proliferation and sorafenib resistance in hepatocellular carcinoma cells. Ttc36 depletion disrupts the interaction between SET and PPP2R1A, consequently activating PP2A. Activated PP2A directly dephosphorylates p-c-MycS62 and activates GSK3β, relying on AKT, leading increased phosphorylation of p-c-MycT58, finally promotes FBXW7-mediated polyubiquitination and degradation of c-Myc. Inhibitors targeting GSK3β and PP2A effectively reverse the sorafenib resistance promoted by TTC36. These findings highlight the crucial role of TTC36 in c-Myc accumulation-caused proliferation and sorafenib resistance in HCC, providing a promising combination strategy for treating patients with c-Myc protein accumulation in advanced HCC.

Hepatocellular carcinoma (HCC) is the third major cause of cancer death worldwide, with more than a doubling of incidence over the past two decades in the United States. Yet, the survival rate remains less than 20%, often due to late diagnosis at advanced stages. Current HCC screening approaches are serum alpha-fetoprotein (AFP) testing and ultrasound (US) of cirrhotic patients. However, these remain suboptimal, particularly in the setting of underlying obesity and metabolic dysfunction-associated steatotic liver disease/steatohepatitis (MASLD/MASH), which are also rising in incidence. Therefore, there is an urgent need for novel biomarkers that can stratify risk and predict early diagnosis of HCC, which is curable. Advances in liver cancer biology, multi-omics technologies, artificial intelligence, and precision algorithms have facilitated the development of promising candidates, with several emerging from completed phase 2 and 3 clinical trials. This review highlights the performance of these novel biomarkers and algorithms from a mechanistic perspective and provides new insight into how pathological processes can be detected through blood-based biomarkers. Through human studies compiled with animal models and mechanistic insight in pathways such as the TGF-β pathway, the biological progression from chronic liver disease to cirrhosis and HCC can be delineated. This integrated approach with new biomarkers merit further validation to refine HCC screening and improve early detection and risk stratification.

Pharmacogenetic (PGx) testing can be used to help guide drug therapy and decrease or avoid the risk of adverse drug reactions. CYP2D6 is an important pharmacogene in pharmacogenomics testing panels. However, phenoconversion, whereby an individual's ability to metabolize a drug does not match the genotype-predicted metabolizer status, is a confounding factor to the accurate application of PGx testing results to patient care. To address this issue, CYP2D6 expression between and within genotype-predicted CYP2D6 metabolizer phenotype groups was compared using WGS and RNA-Seq data from 134 normal liver tissue donors obtained from the GTEx program. Wide variability in CYP2D6 mRNA levels was observed within metabolizer phenotype groups. The median expression level for ultrarapid metabolizers (UMs) was 738.9 TPM (transcripts per million; 196.8-778.9 TPM), 212.5 TPM (32.1-666.5 TPM) for normal metabolizers (NMs), 219.6 TPM for intermediate metabolizers (IMs) (22-389.8 TPM), and 121.2 TPM for poor metabolizers (PMs) (9.3-298.2 TPM). The PM and UM phenotypes were significant predictors of CYP2D6 expression (p = 0.0004 and p = 0.019, respectively). Interestingly, expression of the gene encoding human serum albumin (ALB) was also a significant predictor of CYP2D6 expression (p = 0.0003). Data from 50 patients with hepatocellular carcinoma obtained from the TCGA program showed no significant difference in expression between tumor tissue (median = 119.7 TPM, range 0.16-817.7 TPM) and normal matched tissue (median = 143.3 TPM, range 26.2-810.7 TPM). Transcriptional regulation of CYP2D6 expression may contribute to differences in drug response and risk for CYP2D6 phenoconversion. Efforts to understand the role of gene expression to predict CYP2D6 phenoconversion may inform the use of PGx testing in the clinical setting.

Metabolic reprogramming is a hallmark of malignant transformation. While initial studies in the field of cancer metabolism focused on central carbon metabolism, the field has expanded to metabolism beyond glucose and glutamine and uncovered the important role of amino acids in tumorigenesis and tumor immunity as energy sources, signaling molecules, and precursors for (epi)genetic modification. As a result of the development and application of new technologies, a multifaceted picture has emerged, showing that context-dependent heterogeneity in amino acid metabolism exists between tumors and even within distinct regions of solid tumors. Understanding the complexity and flexibility of amino acid metabolism in cancer is critical because it can influence therapeutic responses and predict clinical outcomes. This overview discusses the current findings on the heterogeneity in amino acid metabolism in cancer and how understanding the metabolic diversity of amino acids can be translated into more clinically relevant therapeutic interventions.

Hepatocellular carcinoma (HCC) is the primary form of liver cancer that poses a significant global health concern due to its increasing incidence rates and diverse etiology. Chronic infection induced by hepatitis B virus (HBV) is a prominent etiological factor influencing the development of HCC. Although recent advances in multi-omics approaches have facilitated extensive exploration of HCC molecular characteristics, translating the characteristics of subtypes into clinical applications has been challenging due to parameters like limited sample size and complex classifiers for early detection. In the present study, we performed transcriptomics profiling of HBV-infected HCC patient tissue data to gather comprehensive insights into the intricate molecular mechanisms underlying HBV-associated HCC, specifically, viral protein interactions that influence the expression of oncogenes. The 1059 differentially expressed genes (DEGs) identified across two GEO data sets revealed upregulation of cell cycle and mitosis-related genes, alongside downregulation of genes involved in fatty acid degradation and cytochrome P450 activity. CDK1 and CDC20 which are part of the top cluster and hub gene from interactome analysis were identified as potential markers for HBV-positive HCC through gene expression pattern and overall survival analysis. Additionally, 19 DEGs showing significance in HCC development were identified as interacting partners with HBV proteins. Among them, the interaction of HBsAg with ALB and SHBG and their downregulation correlates to the lower testosterone levels identified in HBV and HCC patients. Together, the study enhances the understanding of the heterogeneity and molecular pathogenesis of HBV-positive HCC.

Proliferating hepatocytes often undergo ductal metaplasia to balance the energy trade-off between cellular functions and replication, hindering the expansion of human adult hepatocytes with functional competency1. Here we demonstrate that the combined activation of Wnt and STAT3 signalling enables long-term self-renewal of human adult hepatocyte organoids. YAP activation facilitates hepatocyte proliferation but commits it towards the biliary duct lineage. By contrast, STAT3 activation by oncostatin M induces hepatocyte proliferation while counteracting ductal metaplasia and maintaining the hepatic identity. Xenotransplanted hepatocyte organoids repopulate the recipient mouse liver and reconstitute the metabolic zonation structure. Upon niche factor removal and hormone supplementation, hepatocyte organoids form cord-like structures with bile canalicular networks and exhibit major liver metabolic functions comparable to those of in vivo hepatocytes. Hepatocyte organoids are amenable to gene editing, prompting functional modelling of inherent metabolic liver diseases. The new culture system offers a promising avenue for developing therapeutic strategies against human liver diseases.

Hepatocellular carcinoma (HCC), the predominant form of primary liver cancer worldwide, continues to pose a substantial health challenge with limited treatment options for advanced stages. Despite progress in therapies such as surgery, transplantation, and targeted treatments, prognosis remains bleak for many patients. The advent of immunotherapy has revolutionized the landscape of advanced HCC treatment, offering hope for improved outcomes. However, its efficacy is limited, with a modest response rate of approximately 20% as a single-agent therapy, underscoring the urgent need to decipher mechanisms of immunotherapy resistance. Tumor protein 53 gene (TP53), a pivotal tumor suppressor gene, and Programmed death ligand 1 (PD-L1), a crucial immune checkpoint ligand, play central roles in HCC's evasion of immune responses. Understanding how tumor protein 53 (p53) influences PD-L1 expression and immune system interactions is essential for unraveling the complexities of immunotherapy resistance mechanisms. Elucidating these molecular interactions not only enhances our understanding of HCC's underlying mechanisms but also lays the foundation for developing targeted treatments that may improve outcomes for patients with advanced-stage liver cancer. Ultimately, deciphering the nexus of p53 and PD-L1 in immunotherapy resistance promises to advance treatment strategies and outcomes in the challenging landscape of HCC. This review delves into the intricate relationship between p53 and PD-L1 concerning immunotherapy resistance in HCC, offering insights that could pave the way for novel therapeutic strategies aimed at enhancing treatment efficacy and overcoming resistance in advanced stages of the disease.

Ubiquitination, a critical post-translational modification, plays a pivotal role in regulating protein stability and activity, influencing various aspects of cancer development, including metabolic reprogramming, immune evasion, and tumor progression. However, the specific role of ubiquitination in hepatocellular carcinoma (HCC), particularly in relation to the tumor microenvironment (TME), remains poorly understood. This study aims to systematically explore the role of ubiquitination in shaping the TME of HCC, with a focus on its impact on cancer progression and immune modulation.

We performed bioinformatics analysis by integrating multiple publicly available HCC datasets to assess the ubiquitination status across various cell types in the TME, including plasma cells, fibroblasts, endothelial cells, and epithelial-mesenchymal transition (EMT) cells. Ubiquitination scores were calculated to categorize these cell types, and survival data, along with spatial transcriptomics, were employed to evaluate how different levels of ubiquitination influence HCC progression. In vitro experiments, such as transwell, CCK8, and wound healing assays, were used to further investigate the role of the key ubiquitination gene UBE2C in HCC phenotypes.

Our study revealed that ubiquitination-related genes are significantly upregulated in HCC tissues, with high expression levels correlating with poor prognosis in patients. Pathway analysis showed that these genes are enriched in key processes such as cell cycle regulation, DNA repair, metabolic reprogramming, and p53 signaling. These pathways contribute to the TME by promoting tumor cell proliferation, facilitating matrix remodeling, and enhancing angiogenesis. Notably, UBE2C, a critical ubiquitination enzyme, appears to play a key role in immune evasion, potentially by inhibiting anti-tumor immune responses and reducing the immune system's ability to recognize and eliminate tumor cells. Furthermore, experimental data confirmed that UBE2C overexpression promotes HCC cell proliferation, invasion, and metastasis, further supporting its role in tumor progression and TME remodeling.

This study reveals the multifaceted regulatory roles of ubiquitination in HCC. Ubiquitination not only supports proliferation and anti-apoptotic functions within tumor cells but also promotes tumor progression by modulating the activity of immune and stromal cells. Among all ubiquitination-related genes, UBE2C emerges as a potential prognostic biomarker and therapeutic target in HCC, offering new directions for precision treatment of HCC in the future.

Using millions of methylation segments, we developed DiffuCpG, a generative artificial intelligence (AI) diffusion model designed to solve the critical challenge of missing data in high-throughput methylation technologies. DiffuCpG goes beyond conventional methods by leveraging both short-range interactions including nearby CpGs from both latitude and longitude of the dataset, local DNA sequences, and long-range interactions, including three-dimensional genome architecture and long-distance correlations, to comprehensively model the methylome. Compared to previous methods, through extensive independent validations across different tissue types, cancers, and technologies (whole-genome bisulfite sequencing, enhanced reduced representation bisulfite sequencing, single-cell bisulfite sequencing, and methylation arrays), DiffuCpG has demonstrated superior performance in accuracy, scalability, and versatility. On average, bisulfite sequencing dataset, DiffuCpG can extend the original dataset by millions of additional CpGs. As an alternative application of generative AI, DiffuCpG addresses a key bottleneck in epigenetic research and will substantially benefit studies relying on high-throughput methylation data.

Liver cancer is a significant global health concern, with projections indicating that the incidence of morbidity may surpass one million cases by 2025. Hepatocellular carcinoma (HCC) is the predominant subtype of liver cancer, constituting approximately 90% of all liver cancer diagnoses. Infections caused by the hepatitis B virus (HBV) and hepatitis C virus (HCV) are recognized as primary risk factors for the development of HCC. However, non-alcoholic steatohepatitis (NASH), which is often linked to metabolic syndrome or diabetes, is increasingly being recognized as a prevalent risk factor in Western populations. Furthermore, HCC associated with NASH exhibits distinct molecular pathogenesis. Patients diagnosed with HCC have access to a range of therapeutic interventions, including liver transplantation, surgical resection, percutaneous ablation, radiation therapy, and transarterial and systemic therapies. Consequently, effective clinical decision-making requires a multidisciplinary approach to adapt individualized treatment plans based on the patient's tumor stage, liver function, and overall performance status. The approval of new first- and second-line pharmacological agents, along with the establishment of immune checkpoint inhibitor therapies as standard treatment modalities, has contributed to an improved prognosis for patients with HCC. Nevertheless, the optimal sequencing of these therapeutic agents remains to be elucidated, highlighting the urgent need for predictive biomarkers to inform treatment selections. Traditional Chinese Medicine (TCM) has demonstrated potential as a complementary and alternative therapeutic approach for liver cancer, warranting further investigation. This review aimed to examine the comprehensive treatment of HCC through the lens of TCM, informed by the current understanding of its epidemiology, diagnosis, and pathophysiology.

Mutations in circulating nucleic acids can be used as biomarkers for the early detection and management of hepatocellular carcinoma (HCC). However, while circulating tumor DNA and microRNA have been extensively explored, circulating tumor mRNA and circulating mRNA mutants (ctmutRNA), which may provide advantages over other analytes, remain less well described. We previously reported the identification of 288 HCC selective ctmutRNA variants, called "candidates," from a small cohort of HCC patients using total RNAseq. The objective of the current study was to use targeted RNAseq to validate the specificity and sensitivity of these HCC selective variants in an independent cohort of patients with liver cirrhosis (LC).

Several methods to isolate small extracellular vesicles and amplify mRNA from the circulation were compared. RNA was isolated, and the primers and probes selective for the 288 regions of interest were used with RNA from HCC (N = 50) and LC and no HCC (N = 35) patients. HCC tumor tissues (N = 11), a normal liver tissue and 3 cell lines were also studied. cDNA synthesis was followed by library construction using QIAseq RNA Fusion XP panel. QC analysis was carried out with an Agilent Bioanalyzer before sequencing on a NextSeq 550 instrument. A GATK HaplotypeCaller was used for variant calling and annotation carried out using snpEff.

Among the test panel of 288 ctmutRNA candidates in the original cohort, 75 were detected in the new cohort of plasma samples. Moreover, 388 other variants in proximity to the original lesions were also found in multiple HCC but not LC plasma samples. A subset of 36 HCC selective variants was able to identify all HCC patients. The most common tumor specific variants were Indels and SNPs. Novel mRNA fusion variants, corresponding to SENP7, HYI, SAR1A, RASA2, TUBA transcripts, etc., were identified in HCC and LC patients.

Circulating RNA could be a robust analyte for noninvasive early detection of HCC and circulating RNA panels could be powerful tools in the entire spectrum of clinical management.

Most molecular classifications of cancer are based on bulk-tissue profiles that measure an average over many distinct cell types. As such, cancer subtypes inferred from transcriptomic or epigenetic data are strongly influenced by cell-type composition and do not necessarily reflect subtypes defined by cell-type-specific cancer-associated alterations, which could lead to suboptimal cancer classifications.

To address this problem, we here propose the novel concept of cell-type-specific combinatorial clustering (CELTYC), which aims to group cancer samples by the molecular alterations they display in specific cell types. We illustrate this concept in the context of DNA methylation data of liver and kidney cancer, deriving in each case novel cancer subtypes and assessing their prognostic relevance against current state-of-the-art prognostic models.

In both liver and kidney cancer, we reveal improved cell-type-specific prognostic models, not discoverable using standard methods. In the case of kidney cancer, we show how combinatorial indexing of epithelial and immune-cell clusters define improved prognostic models driven by synergy of high mitotic age and altered cytokine signaling. We validate the improved prognostic models in independent datasets and identify underlying cytokine-immune-cell signatures driving poor outcome.

In summary, cell-type-specific combinatorial clustering is a valuable strategy to help dissect and improve current prognostic classifications of cancer in terms of the underlying cell-type-specific epigenetic and transcriptomic alterations.

Hepatocellular carcinoma (HCC) with MYC oncogene amplification remains a serious challenge in clinical practice. Recent advances in comprehensive treatment strategies, particularly the combination of radiotherapy and immunotherapy, offer new hope. To further improve efficacy while lowering radiation doses, nanopharmaceuticals based on high-Z elements have been extensively studied in radio-immunotherapy. In this work, a hafnium-based metal-organic framework (Hf-MOF), UiO-66-Hf(2OH)-CB-839/BSO@HA (UiO-66-Hf(2OH)-C/B@HA), was designed to codeliver telaglenastat (CB-839) and buthionine sulfoximine (BSO), which synergistically inhibited glutamine metabolism and alleviated tumor hypoxia. Further modification with hyaluronic acid (HA) enhanced tumor targeting, ultimately strengthening the efficacy of radiotherapy in MYC-amplified HCC. Beyond increasing reactive oxygen species (ROS) generation, promoting DNA damage, and inducing tumor apoptosis, more importantly, UiO66-Hf(2OH)-C/B@HA triggered immunogenic cell death (ICD), driving the antitumor immune response. Combination with immune checkpoint blockade (ICB) further enhanced the efficacy, accompanied by increased infiltration of T cells with high granzyme B expression (GZMB+ T cells) within the tumor microenvironment (TME). In the orthotopic HCC model, established with MYC-amplified tumor cells, intravenous administration of UiO66-Hf(2OH)-C/B@HA significantly potentiated the efficacy of radio-immunotherapy, resulting in superior tumor regression. In summary, our study provides insights into the design of Hf-MOF for radio-immunotherapy and proposes a promising therapeutic approach for MYC-amplified HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality. Natural Killer (NK) cells play a crucial role in immune defense against HCC, but their activity is often impaired by the tumor microenvironment (TME). This study aims to integrate radiomics and transcriptomics to develop a prognostic model linking NK cell characteristics to clinical outcomes in HCC.

Transcriptomic data from five cohorts (734 HCC patients) from the Gene Expression Omnibus and The Cancer Genome Atlas databases were analyzed using the Microenvironment Cell Populations-counter algorithm. NK cell-related prognostic biomarkers were identified via weighted gene co-expression network analysis and LASSO-Cox regression. Radiomics models were established using CT imaging features from 239 patients in three datasets from The Cancer Imaging Archive and Shanghai East Hospital. HCC radiogenomic subtypes were proposed by integrating genetic biomarkers and radiomics models.

CD2 expression was identified as an independent NK cell-related prognostic biomarker, with a positive impact on prognosis and a strong correlation with NK cell-associated biological processes in HCC. A robust radiomics model was constructed, and the integration of CD2 expression with radioscore identified potential radiogenomic subtypes of HCC.

Radiomics has potential to link TME immune phenotypes with HCC prognosis. CD2 is a key biomarker connecting NK cells with radiomic features, offering a new classification of HCC into radiogenomic subtypes. This approach supports the use of radiogenomics in personalized HCC treatment.