Integrated omics landscape of hepatocellular carcinoma suggests proteomic subtypes for precision therapy

Disease classification, diagnostic challenges, and evolving clinical trial design in MASLD

Metabolic dysfunction-associated steatotic liver disease (MASLD) diagnosis and management have evolved rapidly alongside the increasing prevalence of obesity and related complications. Hepatology has expanded its focus beyond late-stage cirrhosis and portal hypertension to earlier, complex MASLD cases in younger patients, necessitating closer collaboration with endocrinology. The renaming of nonalcoholic fatty liver disease (NAFLD) to MASLD reflects its pathophysiology, reduces stigma, and has prompted new research directions. Noninvasive tests such as liver stiffness measurement now play a crucial role in diagnosis, reducing reliance on invasive liver biopsies. However, advanced omics technologies, despite their potential to enhance diagnostic precision and patient stratification, remain underutilized in routine clinical practice. Behavioral factors, including posttraumatic stress disorder (PTSD) and lifestyle choices, influence disease outcomes and must be integrated into patient management strategies. Primary care settings are critical for early screening to prevent progression to advanced disease, yet sizable challenges remain in implementing effective screening protocols. This Review explores these evolving aspects of MASLD diagnosis and management, emphasizing the need for improved diagnostic tools, multidisciplinary collaboration, and holistic care approaches to address existing gaps and ensure comprehensive patient care across all healthcare levels.

Amplifying X-ray-Induced Charge Transfer Facilitates Direct Sensitization of Photosensitizers in Radiotherapy

X-ray-induced photodynamic therapy offers substantial promise for treating deep-seated tumors, but it is still limited by highly inefficient energy transfer processes and the stringent requirements for scintillators with high luminescence quantum yield and significant singlet-triplet intersystem crossing ratios. Herein, we describe X-ray-induced electron-dynamic therapy (X-eDT), which obviates the need for intersystem crossing by exposing nonluminescent hafnium-silica nanoparticles to X-rays, to generate high-energy electrons that can sensitize lower-lying triplet states of various photosensitizers. Our approach strongly induced the production of singlet oxygen (6.18-fold) in vitro even at lower X-ray doses, and in mice it strongly inhibited the growth of xenografts derived from liver, breast, or colon cancer cell lines (CDX), and growth of patient-derived xenografts (PDX) of hepatocellular carcinoma. In these CDX preclinical systems, X-eDT was not only effective against the irradiated xenograft but also against untreated xenografts in the same animal, and these abscopal effects involved enhanced tumor infiltration by CD4+T cells, CD8+T cells, and IFN-γ-polarized M1 macrophages within the tumor microenvironment. X-eDT even stimulated the production of memory T cells that inhibited rechallenges after treatment. These findings suggest that X-eDT can be effective against primary and metastatic tumors as well as tumor recurrence, which makes it much more powerful than conventional X-PDT.

A novel prognostic framework for HBV-infected hepatocellular carcinoma: insights from ferroptosis and iron metabolism proteomics

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.

High-Throughput Proteomics Reveals a Novel Small Open Reading Frame-Encoded Peptide That Promotes Hepatocellular Carcinoma Invasion and Migration

Long noncoding RNAs (lncRNAs) are closely associated with tumor development, and increasing evidence suggests that small open reading frame (smORF) within lncRNAs also have the capability to encode smORF-encoded peptides (SEPs). Here, we thoroughly uncovered the SEP expression profile of hepatocellular carcinoma (HCC) from tumor and adjacent nontumor tissues of 154 HCC patients using high-throughput mass spectrometry (MS). A total of 208 SEPs were identified, with no significant difference in abundance and stability compared with coding region proteins. Notably, the peptide encoded by LINC01007 (LINC01007-33AA) was significantly upregulated in HCC tissues (p < 0.05) and could serve as an independent risk factor affecting prognosis (HR [95% CI]: 1.31[1.01-1.7]). This endogenous peptide was further confirmed at both the mRNA and protein levels, and its overexpression significantly enhances the invasion and migration of HCC cells. These findings highlight the potential of MS-based methods to identify novel noncoding sequence encoded functional peptides associated with tumor progression.

Complete remission in an advanced hepatocellular carcinoma patient with AXIN1 mutation after systemic therapy: A case report

Primary hepatocellular carcinoma (HCC) is a common malignancy with high morbidity and mortality. Despite progress in systemic therapies, survival in advanced HCC remains poor due to patient heterogeneity and individual differences, necessitating a personalized approach rather than relying solely on guidelines. Here, we present an exceptional case study in which a systematic regimen without immune checkpoint inhibitors was chosen based on the patient's specific genetic test results. Remarkably effective with long-term survival benefits were observed as a result. This case underscores the importance of incorporating tumor profiling and personalized treatment plans, in addition to adhering to guidelines and standards, for delivering more efficacious and well-tolerated therapeutic options to patients with liver cancer.

Artificial intelligence applied to 'omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment

Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.

Proteomic Characterization of Liver Cancer Cells Treated with Clinical Targeted Drugs for Hepatocellular Carcinoma

Background/Objectives: Hepatocellular carcinoma (HCC) remains a significant global health concern, primarily due to the limited efficacy of targeted therapies, which are often compromised by drug resistance and adverse side effects. Methods: In this study, we utilized a Tandem Mass Tag (TMT)-based quantitative proteomic approach to analyze global protein expression and serine/threonine/tyrosine (S/T/Y) phosphorylation modifications in HepG2 cells following treatment with three clinically relevant hepatocellular carcinoma-targeted agents: apatinib, regorafenib, and lenvatinib. Results: Utilizing KEGG pathway enrichment analysis, biological process enrichment analysis, and protein interaction network analysis, we elucidated the common and specific metabolic pathways, biological processes, and protein interaction regulatory networks influenced by three liver cancer therapeutics. The study additionally proposed potential combinational treatment strategies, highlighting a possible synergistic interaction between HCC-targeted drugs and the DNA methyltransferase inhibitor. Furthermore, through the integration of clinical phosphorylation site data, we identified several phosphorylation sites that exhibited higher abundance in tumor tissues compared to adjacent non-tumor tissues. These sites were associated with poor prognosis and elevated functional scores. Conclusions: In summary, this study conducted an in-depth analysis of the molecular alterations in proteins and phosphorylation modifications induced by clinical HCC-targeted drugs, predicting drug combination strategies and therapeutic targets.

The 2024 Report on the Human Proteome from the HUPO Human Proteome Project

The Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify at least one isoform of every protein-coding gene and (2) to make proteomics an integral part of multiomics studies of human health and disease. The past year has seen major transitions for the HPP. neXtProt was retired as the official HPP knowledge base, UniProtKB became the reference proteome knowledge base, and Ensembl-GENCODE provides the reference protein target list. A function evidence FE1-5 scoring system has been developed for functional annotation of proteins, parallel to the PE1-5 UniProtKB/neXtProt scheme for evidence of protein expression. This report includes updates from neXtProt (version 2023-09) and UniProtKB release 2024_04, with protein expression detected (PE1) for 18138 of the 19411 GENCODE protein-coding genes (93%). The number of non-PE1 proteins ("missing proteins") is now 1273. The transition to GENCODE is a net reduction of 367 proteins (19,411 PE1-5 instead of 19,778 PE1-4 last year in neXtProt). We include reports from the Biology and Disease-driven HPP, the Human Protein Atlas, and the HPP Grand Challenge Project. We expect the new Functional Evidence FE1-5 scheme to energize the Grand Challenge Project for functional annotation of human proteins throughout the global proteomics community, including π-HuB in China.

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Cancer metabolic reprogramming and precision medicine-current perspective

Despite the advanced technologies and global attention on cancer treatment strategies, cancer continues to claim lives and adversely affects socio-economic development. Although combination therapies were anticipated to eradicate this disease, the resilient and restorative nature of cancers allows them to proliferate at the expense of host immune cells energetically. This proliferation is driven by metabolic profiles specific to the cancer type and the patient. An emerging field is exploring the metabolic reprogramming (MR) of cancers to predict effective treatments. This mini-review discusses the recent advancements in cancer MR that have contributed to predictive, preventive, and precision medicine. Current perspectives on the mechanisms of various cancer types and prospects for MR and personalized cancer medicine are essential for optimizing metabolic outputs necessary for personalized treatments.

Integrated analysis of tumor and adjacent non-tumor proteomic data reveals SERPINH1 as a recurrence biomarker and drug target in hepatocellular carcinoma

The high rate of postoperative recurrence contributes to the poor outcome in hepatocellular carcinoma (HCC), and effective strategies for managing recurrence are currently lacking. Based on seven pairs of tumors and non-tumor adjacent tissues (NATs) proteomic datasets across five cancer types, this study systematically investigates the stratified and therapeutic value of tumors and NATs for tumor recurrence. NATs exhibited stable and irreplaceable independent prognostic capabilities for recurrence, complementing clinical indicators and tumor characteristics. In comparison to tumor tissues, NATs exhibit higher enrichment levels of recurrence-related proteins in pathways such as immunity, extracellular matrix, and angiogenesis. Taking HCC as an example, we identified SERPINH1 as a recurrent biomarker with drug-targeting potential that applied to both tumors and NATs and then validated them through independent immunohistochemistry cohorts and animal experiments. Patients with high SERPINH1 expression in both tumors and NATs have the highest 5-year recurrence rates, even among clinically low recurrence risk groups. Targeting SERPINH1 can effectively delay tumor occurrence and progression. This study highlights the significant importance of NATs in recurrence prediction and postoperative management, proposing a recurrence management strategy that focuses on both tumors and NATs. SERPINH1 emerges as a valuable biomarker and drug target for addressing postoperative recurrence in HCC.

Recent developments in molecular targeted therapies for hepatocellular carcinoma in the genomic era

INTRODUCTION

Primary liver cancer is a major health problem being the sixth most frequent cancer in the world and the third cause of cancer-related death in the world. The most common histological type of liver cancer is hepatocellular carcinoma (HCC, 75-80%).

AREAS COVERED

Based on primary literature, this review provides an updated analysis of studies of genetic characterization of HCC at the level of gene mutation profiling, copy number alterations, and gene expression, with the definition of molecular subgroups and the identification of some molecular biomarkers and therapeutic targets. Recent therapeutic developments are also highlighted.

EXPERT OPINION

Deepening the understanding of the molecular complexity of HCC is progressively paving the way for the development of more personalized treatment approaches. Two important strategies involve the definition and validation of molecularly defined therapeutic targets in a subset of HCC patients and the identification of suitable biomarkers for approved systematic therapies (multikinase inhibitors and immunotherapies). The extensive molecular characterization of patients at the genomic and transcriptomic levels and the inclusion of detailed and relevant translational studies in clinical trials will represent a fundamental tool for improving the benefit of systemic therapies in HCC.

Pharmacokinetics of aspirin: evaluating shortcomings in the literature

INTRODUCTION

Aspirin is known for its therapeutic benefits in preventing strokes and relieving pain. However, it is toxic to some individuals, and the biological mechanisms causing toxicity are unknown. Limited literature is available on the role of glycine conjugation as the principal pathway in aspirin detoxification. Previous studies have quantified this two-step enzyme reaction as a singular enzymatic process. Consequently, the individual contributions of these enzymes to the kinetics remain unclear.

AREAS COVERED

This review summarized the available information on the pharmacokinetics and detoxification of aspirin by the glycine conjugation pathway. Literature searches were conducted using Google Scholar and the academic journal databases accessible through the North-West University Library. Furthermore, the factors affecting interindividual variation in aspirin metabolism and what is known regarding aspirin toxicity were discussed.

EXPERT OPINION

The greatest drawback in understanding the pharmacokinetics of aspirin is the limited information available on the substrate preference of the xenobiotic ligase (ACSM) responsible for activating salicylate to salicyl-CoA. Furthermore, previous pharmacokinetic studies did not consider the contribution of other substrates from the diet or genetic variants, to the detoxification rate of glycine conjugation. Impaired glycine conjugation might contribute to adverse health effects seen in Reye's syndrome and cancer.

Multi Omics Applications in Biological Systems

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

Proteomics Identifies LUC7L3 as a Prognostic Biomarker for Hepatocellular Carcinoma

Alternative splicing has been shown to participate in tumor progression, including hepatocellular carcinoma. The poor prognosis of patients with HCC calls for molecular classification and biomarker identification to facilitate precision medicine. We performed ssGSEA analysis to quantify the pathway activity of RNA splicing in three HCC cohorts. Kaplan-Meier and Cox methods were used for survival analysis. GO and GSEA were performed to analyze pathway enrichment. We confirmed that RNA splicing is significantly correlated with prognosis, and identified an alternative splicing-associated protein LUC7L3 as a potential HCC prognostic biomarker. Further bioinformatics analysis revealed that high LUC7L3 expression indicated a more progressive HCC subtype and worse clinical features. Cell proliferation-related pathways were enriched in HCC patients with high LUC7L3 expression. Consistently, we proved that LUC7L3 knockdown could significantly inhibit cell proliferation and suppress the activation of associated signaling pathways in vitro. In this research, the relevance between RNA splicing and HCC patient prognosis was outlined. Our newly identified biomarker LUC7L3 could provide stratification for patient survival and recurrence risk, facilitating early medical intervention before recurrence or disease progression.

COP9 signalosome complex is a prognostic biomarker and corresponds with immune infiltration in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is among the most common deadly tumors but still lacks specific biomarkers for diagnosis, prognosis, and treatment guidance. The COP9 signalosome (COPS) is an essential regulator of the ubiquitin conjugation pathway upregulated in various cancers. We evaluated the contributions of COPS subunits to HCC tumorigenesis and their utility for prognosis. We comprehensively evaluated the tumor expression pattern and tumorigenic functions of COPS subunits using The Cancer Genome Atlas (TCGA), The Human Protein Atlas and immunohistochemistry. Kaplan-Meier, Cox regression, ROC curve, and nomogram analyses were used to assess the predictive values of COPS subunits for clinical outcome. Expression levels of COPS subunits were significantly upregulated in HCC tissues, which predicted shorter overall survival (OS). Further, Cox regression analysis identified COPS5, COPS7B, and COPS9 as independent prognostic biomarkers for OS. High mutation rates were also found in COPS subunits. Functional network analysis indicated that COPS and neighboring genes regulate 'protein neddylation', 'protein deneddylation', and 'protein ubiquitination'. The COPS PPI included strong interactions with p53, CUL1/2/3/4, and JUN. Moreover, the correlations between COPS subunit expression levels and tumor immune cell infiltration rates were examined using TIMER, TISIDB, ssGSEA, and ESTIMATE packages. COPS subunits expression levels were positively correlated with specific tumor immune cell infiltration rates, immunoregulator expression levels, and microsatellite instability in HCC. Finally, knockout of COPS6 and COPS9 in HCC cells reduced while overexpression enhanced proliferation rate and metastasis capacity. Our study revealed that COPS potential biomarker for unfavorable HCC prognosis and indicators of immune infiltration, tumorigenicity, and metastasis.

Proteomic Analysis Identifies GSN as a Noninvasive Circulating Serum Biomarker for Predicting Early Recurrence of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is susceptible to early recurrence, but it lacks effective predictive biomarkers. In this study, we retrospectively selected 179 individuals as a discovery cohort (126 HCC patients and 53 liver cirrhosis (LC) patients) for screening candidate serum biomarkers of early recurrence based on data independent acquisition-mass spectrometry strategy. And then, the candidate biomarkers were validated in an additional independent cohort with 192 individuals (142 HCC patients and 50 LC patients) using parallel reaction monitoring targeted quantitative techniques (PXD047852). Eventually, we validated that gelsolin (GSN) concentrations were significantly lower in HCC than in LC (p < 0.0001), patients with low GSN concentrations had a poor prognosis (p < 0.0001), and GSN concentrations were significantly lower in early recurrence HCC than in late recurrence HCC (p < 0.0001). These trends were also observed in alpha-fetoprotein (AFP)-negative HCC patients. The area under the curve of machine-learning-based predictive model (GSN and microvascular invasion) for predicting early recurrence risk reached 0.803 (95% confidence interval (CI): 0.786-0.820) and maintained the same efficacy in AFP-negative patients. In conclusion, GSN is a novel serum biomarker for early recurrence of HCC. The model could provide timely warning to HCC patients at high risk of recurrence.

Metabolic reprogramming in the tumor microenvironment of liver cancer

The liver is essential for metabolic homeostasis. The onset of liver cancer is often accompanied by dysregulated liver function, leading to metabolic rearrangements. Overwhelming evidence has illustrated that dysregulated cellular metabolism can, in turn, promote anabolic growth and tumor propagation in a hostile microenvironment. In addition to supporting continuous tumor growth and survival, disrupted metabolic process also creates obstacles for the anticancer immune response and restrains durable clinical remission following immunotherapy. In this review, we elucidate the metabolic communication between liver cancer cells and their surrounding immune cells and discuss how metabolic reprogramming of liver cancer impacts the immune microenvironment and the efficacy of anticancer immunotherapy. We also describe the crucial role of the gut-liver axis in remodeling the metabolic crosstalk of immune surveillance and escape, highlighting novel therapeutic opportunities.