Intratumoral heterogeneity and clonal evolution in liver cancer

The landscape of malignant transition: Unraveling cancer cell-of-origin and heterogeneous tissue microenvironment

Understanding disease progression and sophisticated tumor ecosystems is imperative for investigating tumorigenesis mechanisms and developing novel prevention strategies. Here, we dissected heterogeneous microenvironments during malignant transitions by leveraging data from 1396 samples spanning 13 major tissues. Within transitional stem-like subpopulations highly enriched in precancers and cancers, we identified 30 recurring cellular states strongly linked to malignancy, including hypoxia and epithelial senescence, revealing a high degree of plasticity in epithelial stem cells. By characterizing dynamics in stem-cell crosstalk with the microenvironment along the pseudotime axis, we found differential roles of ANXA1 at different stages of tumor development. In precancerous stages, reduced ANXA1 levels promoted monocyte differentiation toward M1 macrophages and inflammatory responses, whereas during malignant progression, upregulated ANXA1 fostered M2 macrophage polarization and cancer-associated fibroblast transformation by increasing TGF-β production. Our spatiotemporal analysis further provided insights into mechanisms responsible for immunosuppression and a potential target to control evolution of precancer and mitigate the risk for cancer development.

High-order interaction modeling of tumor-microenvironment crosstalk for tumor growth

Signaling interactions between cancer cells and nonmalignant cells in the tumor microenvironment (TME) are believed to influence tumor progression and drug resistance. However, the genomic machineries mediating such an influence remain elusive, making it difficult to determine therapeutic targets on the tumor and its microenvironment. Here, we argue that a computational model, derived from the integration of evolutionary game theory and ecosystem theory through allometric scaling law, can chart the genomic atlas of high-order interaction networks involving tumor cells, TME, and tumor mass. We assess the application of this model to identify the causal influence of gene-induced tumor-TME crosstalk on tumor growth. The findings demonstrate that cooperation and competition between tumor cells and their infiltrating microenvironment promote or inhibit tumor growth in diverse ways. We identify specific genes that govern this promotion or inhibition, which can be used as genetic targets to alter tumor growth. This model opens up a new avenue to precisely infer the genomic underpinnings of tumor-TME interactions and their impact on tumor progression from any omics data.

Noninvasive prognostic classification of ITH in HCC with multi-omics insights and therapeutic implications

Intratumoral heterogeneity (ITH) is a critical factor associated with treatment failure and disease relapse in hepatocellular carcinoma (HCC). However, decoding ITH in a noninvasive and comprehensive manner remains a notable challenge. In this study involving 851 patients from five centers, we developed a noninvasive prognostic classification for ITH using radiomics based on multisequence MRI, termed radiomics ITH (RITH) phenotypes. The RITH phenotypes highly correlated with prognosis and pathological ITH. In addition, through an integrated multi-omics analysis, we uncovered the molecular mechanisms underlying RITH, notably enhancing its biological interpretability. Specifically, high-RITH tumors demonstrated an enrichment of cancer-associated fibroblasts and activation of extracellular matrix remodeling. Our approach facilitates the noninvasive refined classification of ITH using radiomics and multi-omics, paving the way for tailored treatment strategies in HCC. Extracellular matrix-receptor interaction could be a potential therapeutic target in patients with high-RITH tumors. Given the routine use of radiologic imaging in oncology, our methodology ignites versatile framework for broader application to other solid tumors.

Halting hepatocellular carcinoma: Identifying intercellular crosstalk in HBV-driven disease

Hepatitis B infection can lead to liver fibrosis and hepatocellular carcinoma (HCC). Despite antiviral therapies, some patients still develop HCC. This study investigates hepatitis B virus (HBV)-induced hepatocyte-hepatic stellate cell (HSC) crosstalk and its role in liver fibrosis and HCC. Using MYC-driven liver cancer stem cell organoids, HCC-patient-derived xenograft (PDX) models, and HBV replication models, this study reveals that HBV transcription affected hepatocyte development, activated the DNA repair pathway, and promoted glycolysis. HBV activated nicotinamide phosphoribosyltransferase (NAMPT) through DNA damage receptor ATR. NAMPT-insulin receptor (INSR)-mediated hepatocyte-HSC crosstalk caused HSCs to develop a myofibroblast phenotype and activated telomere maintenance mechanisms via PARP1 multisite lactylation. Inhibition of the ATR-NAMPT-INSR-PARP1 pathway effectively blocks HBV-induced liver fibrosis and HCC progression. Targeting this pathway could be a promising strategy for chronic HBV infection management.

Tumor Heterogeneity and the Immune Response in Non-Small Cell Lung Cancer: Emerging Insights and Implications for Immunotherapy

Resistance to immune checkpoint inhibitors (ICIs) represents a major challenge for the effective treatment of non-small cell lung cancer (NSCLC). Tumor heterogeneity has been identified as an important mechanism of treatment resistance in cancer and has been increasingly implicated in ICI resistance. The diversity and clonality of tumor neoantigens, which represent the target epitopes for tumor-specific immune cells, have been shown to impact the efficacy of immunotherapy. Advances in genomic techniques have further enhanced our understanding of clonal landscapes within NSCLC and their evolution in response to therapy. In this review, we examine the role of tumor heterogeneity during immune surveillance in NSCLC and highlight its spatial and temporal evolution as revealed by modern technologies. We explore additional sources of heterogeneity, including epigenetic and metabolic factors, that have come under greater scrutiny as potential mediators of the immune response. We finally discuss the implications of tumor heterogeneity on the efficacy of ICIs and highlight potential strategies for overcoming therapeutic resistance.

Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma

OBJECTIVES

The potential of medical imaging to non-invasively assess intratumoral heterogeneity (ITH) is increasingly being recognized. This study aimed to investigate the value of the ITH-based deep learning model for preoperative prediction of histopathologic grade in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

A total of 858 patients from primary cohort and two external cohorts were included. 3.0T or 1.5T axial portal venous phase MRI images were collected. We conducted radiomics feature-driven K-means clustering for automatic partition to reveal ITH. 2.5D and 3D deep learning models based on ResNet architecture were trained to extract deep learning hidden features of each subregion. The selected features were used to train Random Forest classifier, which constructed the feature-fusion model.

RESULTS

The extracted voxel-level radiomics features were unsupervised clustered by K-means to generate three subregions. In the 2.5D deep learning, the feature-fusion model based on ITH had superior predictive efficacy than the whole-tumor model (AUC: 0.82 vs. 0.72; p = 0.004). Even in the validation and external test sets, this model maintained a high AUC of 0.78-0.83, and net reclassification indices indicated that it could improve prediction by 25-28%. Regarding the prognostic value, overall survival (OS) and recurrence-free survival (RFS) could be significantly stratified by the 2.5D feature-fusion model, and multivariable Cox regressions indicated its signature was identified as a risk predictor for OS and RFS (p < 0.05).

CONCLUSION

The ITH-based feature-fusion model provided a non-invasive method for classifying tumor differentiation in HCC, which may serve as a promising strategy for stratification management.

Recombinant α-Toxin BmK-M9 Inhibits Breast Cancer Progression by Regulating β-Catenin In Vivo

Screening bioactive compounds from natural sources, including animals and plants, is a valuable strategy for identifying novel anti-tumor agents. α-Toxin BmK-M9, a key component of scorpion venom, has received limited attention regarding its potential anti-cancer effects and underlying mechanisms in breast cancer. This study investigates the effects and mechanisms of BmK-M9 in breast cancer using in vitro experiments and a nude mouse model. mRNA sequencing was performed to identify affected signaling pathways, while Western blotting and immunohistochemistry were utilized to analyze the Wnt/β-catenin signaling pathway. The results demonstrated that BmK-M9 significantly inhibited breast cancer cell invasion and migration in vitro and suppressed tumor growth in vivo. Transcriptomic analysis revealed that BmK-M9 influenced cellular processes related to proliferation, apoptosis, motility, and metabolism. Furthermore, BmK-M9 markedly downregulated β-catenin expression in the Wnt/β-catenin pathway. These findings suggest that BmK-M9 exerts anti-tumor effects in breast cancer by modulating Wnt/β-catenin signaling, highlighting its potential as a promising therapeutic candidate.

LiverSCA 2.0: An Enhanced Comprehensive Cell Atlas for Human Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are two distinct types of primary liver cancer (PLC) characterized by considerable extents of cellular and molecular heterogeneities. We recently developed a web-based cell atlas called LiverSCA that possesses a user-friendly interface and comprehensive functionalities. It facilitates the exploration of gene expression patterns, cellular compositions, and intercellular communication within the microenvironments of liver and PLC tumors.

METHODS

To further enhance the documentation of data pinpointing different phenotypes/subtypes of liver and PLC, we extended the catalog of LiverSCA with additional datasets, e.g., ICC and metabolic dysfunction-associated steatotic liver disease/steatosis (MASLD/MASH).

RESULTS

The current enhanced version of the LiverSCA cell atlas encompasses six phenotypes (normal, HBV-HCC, HCV-HCC, non-viral HCC, ICC, and MASH), 63 patients, and over 248,000 cells. Furthermore, we have incorporated comparative visualization methods that allow users to simultaneously examine and compare gene expression levels between two different phenotypes.

CONCLUSIONS

We are committed to the continuous development of LiverSCA and envision that it will serve as a valuable resource to support researchers in convenient investigations into the cellular and molecular landscapes of liver and PLC.

Multiomics analysis unveils the cellular ecosystem with clinical relevance in aldosterone-producing adenomas with KCNJ5 mutations

Aldosterone-producing adenomas (APA), a major endocrine tumor and leading subtype of primary aldosteronism, cause secondary hypertension with high cardiometabolic risks. Despite potentially producing multiple steroid hormones, detailed cellular mechanisms in APA remain insufficiently studied. Our multiomics analysis focusing on APA with KCNJ5 mutations, which represent the most common genetic form, revealed marked cellular heterogeneity. Tumor cell reprogramming initiated from stress-responsive cells to aldosterone-producing or cortisol-producing cells, with the latter progressing to proliferative stromal-like cells. These cell subtypes showed spatial segregation, and APA exhibited genomic intratumor heterogeneity. Among the nonparenchymal cells, lipid-associated macrophages, which were abundant in APA, might promote the progression of cortisol-producing and stromal-like cells, suggesting their role in the tumor microenvironment. Intratumor cortisol synthesis was correlated with increased blood cortisol levels, which were associated with the development of vertebral fractures, a hallmark of osteoporosis. This study unveils the complex cellular ecosystem with clinical relevance in APA with KCNJ5 mutations, providing insights into tumor biology that could inform future clinical approaches.

Precision models in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) represents a global health challenge, and ranks among one of the most prevalent and deadliest cancers worldwide. Therapeutic advances have expanded the treatment armamentarium for patients with advanced HCC, but obstacles remain. Precision oncology, which aims to match specific therapies to patients who have tumours with particular features, holds great promise. However, its implementation has been hindered by the existence of numerous 'HCC influencers' that contribute to the high inter-patient heterogeneity. HCC influencers include tumour-related characteristics, such as genetic alterations, immune infiltration, stromal composition and aetiology, and patient-specific factors, such as sex, age, germline variants and the microbiome. This Review delves into the intricate world of HCC, describing the most innovative model systems that can be harnessed to identify precision and/or personalized therapies. We provide examples of how different models have been used to nominate candidate biomarkers, their limitations and strategies to optimize such models. We also highlight the importance of reproducing distinct HCC influencers in a flexible and modular way, with the aim of dissecting their relative contribution to therapy response. Next-generation HCC models will pave the way for faster discovery of precision therapies for patients with advanced HCC.

Unveiling expression patterns, mechanisms, and therapeutic opportunities of transmembrane protein 106C: From pan-cancers to hepatocellular carcinoma

BACKGROUND

Although transmembrane protein 106C (TMEM106C) has been elucidated to be overexpressed in cancers, its underlying mechanisms have not yet been fully understood.

AIM

To investigate the expression levels and molecular mechanisms of TMEM106C across 34 different cancer types, including liver hepatocellular carcinoma (LIHC).

METHODS

We analyzed TMEM106C expression patterns in pan-cancers using microenvironment cell populations counter to evaluate its association with the tumor microenvironment. Gene set enrichment analysis was conducted to identify molecular pathways related to TMEM106C. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis was conducted to identify upstream transcriptional regulators of TMEM106C. In LIHC, we examined mRNA profiles, performed in-house quantitative polymerase chain reaction, immunohistochemistry, and constructed a co-expression gene network. Functional assays, including cell counting kit-8, cell cycle, apoptosis, migration, and invasion, were conducted. The effect of nitidine chloride (NC) on LIHC xenograft was evaluated through RNA sequencing and molecular docking. Finally, potential therapeutic agents targeting TMEM106C were predicted.

RESULTS

TMEM106C was significantly overexpressed in 27 different cancer types and presaged poor prognosis in four of these types, including LIHC. Across pan-cancers, TMEM106C was inversely correlated to the abundances of immune and stromal cells. Furthermore, TMEM106C was significantly linked to cell cycle and DNA replication pathways in pan-cancers. ChIP-seq analysis predicted CCCTC-binding factor as a pivotal transcriptional factor targeting the TMEM106C gene in pan-cancers. Integrated analysis showed that TMEM106C was upregulated in 4657 LIHC compared with 3652 normal liver tissue [combined standardized mean difference = 1.31 (1.09, 1.52)]. In-house LIHC samples verified the expression status of TMEM106C. Higher TMEM106C expression signified worse survival conditions in LIHC patients treated with sorafenib, a tyrosine kinase inhibitor (TKI). Co-expressed analysis revealed that TMEM106C were significantly enriched in the cell cycle pathway. Knockout experiments demonstrated that TMEM106C plays a crucial role in LIHC cell proliferation, migration, and invasion, with cell cycle arrest occurring at the DNA synthesis phase, and increased apoptosis. Notably, TMEM106C upregulation was attenuated by NC treatment. Finally, TMEM106C expression levels were significantly correlated with the drug sensitivity of anti-hepatocellular carcinoma agents, including JNJ-42756493, a TKI agent.

CONCLUSION

Overexpressed TMEM106C was predicted as an oncogene in pan-cancers, which may serve as a promising therapeutic target for various cancers, including LIHC. Targeting TMEM106C could potentially offer a novel direction in overcoming TKI resistance specifically in LIHC. Future research directions include in-depth experimental validation and exploration of TMEM106C's role in other cancer types.

Lactate-Fueled Theranostic Nanoplatforms for Enhanced MRI-Guided Ferroptosis Synergistic with Immunotherapy of Hepatocellular Carcinoma

Treatment for hepatocellular carcinoma (HCC) may be improved with ferroptosis, a regulated form of cell death. However, the sensitivity of HCC to ferroptosis was strongly limited by lactic acid. In this study, a platelet membrane (PM)-engineered nanoparticle loaded with erastin, superparamagnetic iron oxide nanoparticles (SPIO) and lactate oxidase (LOX) (termed PM@ESL NPs) was designed for magnetic resonance imaging (MRI)-guided enhanced ferroptosis-immunotherapy of HCC. It was found that PM@ESL NPs could actively accumulate into the tumor due to the tumor-homing ability of PM. Subsequently, PM@ESL NPs could effectively enhance the sensitivity of HCC to ferroptosis by removing the lactic acid in the tumor. The removal of lactic acid also produces hydrogen peroxide (H2O2), which therefore converted into the cytotoxic hydroxyl radicals by the reaction of H2O2 with Fe2+/Fe3+ released from SPIO. Due to the combined ferroptosis and chemodynamic therapy (CDT), PM@ESL NPS showed a strong ability to induce immunogenic cell death (ICD), which could effectively suppress the growth and metastasis of HCC when combined with αPD-L1 immunotherapy. Furthermore, the incorporation of SPIO endows PM@ESL NPs with an outstanding MRI-T2 monitoring capability for HCC treatment. In conclusion, this study introduces a pioneering MRI-guided approach that enhances ferroptosis in tumors and synergistically improves immunotherapy.

Personalized treatment approaches in hepatocellular carcinoma

Personalized medicine is an emerging field that provides novel approaches to disease's early diagnosis, prevention, treatment, and prognosis based on the patient's criteria in gene expression, environmental factors, lifestyle, and diet. To date, hepatocellular carcinoma (HCC) is a significant global health burden, with an increasing incidence and significant death rates, despite advancements in surveillance, diagnosis, and therapeutic approaches. The majority of HCC lesions develop in patients with liver cirrhosis, carrying the risks of mortality associated with both the tumor burden and the cirrhosis. New therapeutic agents involving immune checkpoint inhibitors and targeted agents have been developed for sequential or concomitant application for advanced HCC but only a tiny percentage of patients benefit from each approach. Moreover, clinicians encounter difficulties determining the most appropriate regimen for each patient. This emphasizes the need for a personalized treatment approach. In other words, patients should no longer undergo treatment based on their tumor's histology but depending on the distinct molecular targets specific to their tumor biology. However, the utilization of precision medicine in managing HCC is still challenging. This review aims to discuss the role of personalized medicine in diagnosing, managing, and defining the prognosis of HCC. We also discuss the role of liquid biopsy and their clinical applications for immunotherapies in HCC. More clinical studies are still necessary to improve the precision of biomarkers used in the treatment decision for patients with HCC.

EASL Clinical Practice Guidelines on the management of hepatocellular carcinoma

Liver cancer is the third leading cause of cancer-related deaths worldwide, with hepatocellular carcinoma (HCC) accounting for approximately 90% of primary liver cancers. Advances in diagnostic and therapeutic tools, along with improved understanding of their application, are transforming patient treatment. Integrating these innovations into clinical practice presents challenges and necessitates guidance. These clinical practice guidelines offer updated advice for managing patients with HCC and provide a comprehensive review of pertinent data. Key updates from the 2018 EASL guidelines include personalised surveillance based on individual risk assessment and the use of new tools, standardisation of liver imaging procedures and diagnostic criteria, use of minimally invasive surgery in complex cases together with updates on the integrated role of liver transplantation, transitions between surgical, locoregional, and systemic therapies, the role of radiation therapies, and the use of combination immunotherapies at various stages of disease. Above all, there is an absolute need for a multiparametric assessment of individual risks and benefits, considering the patient's perspective, by a multidisciplinary team encompassing various specialties.

A prognostic model of 8-T/B cell receptor-related signatures for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide. The T cell receptor (TCR) and B cell receptor (BCR) are the receptors on the surface of T or B cell, which are crucial for recognizing tumor antigens. It is profound to establish a practical TCR/BCR-related gene signature prognostic model for the further diagnosis and treatment among HCC patients.

METHODS

In this study, we categorized gene expression data of HCC patients from The Cancer Genome Altas and identified TCR related genes by the Least Absolute Shrinkage and Selection Operator and multivariate Cox regression analysis. Both the CIBERSORT algorithm and the TB tools were used to analyze the features and heterogeneity of the tumor microenvironment.

RESULTS

Finally, an 8-gene prognostic model was successfully established and achieved the validation in both the International Cancer Genome Consortium and Nanfang Hospital cohorts. Patients were divided into high-risk and low-risk groups based on the median of the risk scores. We observed that tumor differentiation was worse while the fibrinogen concentration was higher in the high-risk group of patients. Both the number of unique TCR and BCR clonotypes and the expanded clones were higher in the low-risk group than in the high-risk group.

CONCLUSIONS

Together, our study screened a TCR/BCR-related signature prognostic model, which might turn into a beneficial and practical tool to solve the perplexities of the treatment, prognosis prediction and management for HCC patients.

Deciphering the heterogeneity and plasticity of the tumor microenvironment in liver cancer provides insights for prognosis

Liver cancer exhibits diverse molecular characteristics and distinct immune cell infiltration patterns, which significantly influence patient outcomes. In this study, we thoroughly examined the liver cancer tumor environment by analyzing data from 419,866 individual cells across nine datasets involving 99 patients. By categorizing patients into different groups based on their immune cell profiles, including immune deficiency, B cells-enriched, T cells-enriched and macrophages-enriched, we better understood how these cells change in various patient subgroups. Our investigation of liver metastases from intestinal cancer uncovered a group of mast cells that might promote metastasis through pathways like inositol phosphate metabolism. Using genomic and clinical data from The Cancer Genome Atlas, we identified specific cell components linked to tumor characteristics and genetics. Our detailed study of cancer-associated fibroblasts (CAFs) revealed how they adapt and acquire new functions in the tissue environment, highlighting their flexibility. Additionally, we found a significant connection between CAF-related genes and the prognosis of hepatocellular carcinoma patients. This research provides valuable insights into the makeup of the liver cancer tumor environment and its profound impact on patient outcomes, offering fresh perspectives for managing this challenging disease.

uniLIVER: a human liver cell atlas for data-driven cellular state mapping

The liver performs several vital functions such as metabolism, toxin removal, and glucose storage through the coordination of various cell types. With the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of the liver at single-cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER (http://lifeome.net/database/uniliver) by integrative analysis of a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Moreover, we introduce LiverCT, a novel machine learning based method for mapping any query dataset to the liver reference map by introducing the definition of "variant" cellular states analogy to the sequence variants in genomic analysis. Applying LiverCT on liver cancer datasets, we find that the "deviated" states of T cells are highly correlated with the stress pathway activities in hepatocellular carcinoma, and the enrichments of tumor cells with the hepatocyte-cholangiocyte "intermediate" states significantly indicate poor prognosis. Besides, we find that the tumor cells of different patients have different zonation tendencies and this zonation tendency is also significantly associated with the prognosis. This reference atlas mapping framework can also be extended to any other tissues.

Unraveling the potential mechanism and prognostic value of pentose phosphate pathway in hepatocellular carcinoma: a comprehensive analysis integrating bulk transcriptomics and single-cell sequencing data

Hepatocellular carcinoma (HCC) remains a malignant and life-threatening tumor with an extremely poor prognosis, posing a significant global health challenge. Despite the continuous emergence of novel therapeutic agents, patients exhibit substantial heterogeneity in their responses to anti-tumor drugs and overall prognosis. The pentose phosphate pathway (PPP) is highly activated in various tumor cells and plays a pivotal role in tumor metabolic reprogramming. This study aimed to construct a model based on PPP-related Genes for risk assessment and prognosis prediction in HCC patients. We integrated RNA-seq and microarray data from TCGA, GEO, and ICGC databases, along with single-cell RNA sequencing (scRNA-seq) data obtained from HCC patients via GEO. Based on the "Seurat" R package, we identified distinct gene clusters related to the PPP within the scRNA-seq data. Using a penalized Cox regression model with least absolute shrinkage and selection operator (LASSO) penalties, we constructed a risk prognosis model. The validity of our risk prognosis model was further confirmed in external cohorts. Additionally, we developed a nomogram capable of accurately predicting overall survival in HCC patients. Furthermore, we explored the predictive potential of our risk model within the immune microenvironment and assessed its relevance to biological function, particularly in the context of immunotherapy. Subsequently, we performed in vitro functional validation of the key genes (ATAD2 and SPP1) in our model. A ten-gene signature associated with the PPP was formulated to enhance the prediction of HCC prognosis and anti-tumor treatment response. Following this, the ROC curve, nomogram, and calibration curve outcomes corroborated the model's robust clinical predictive capability. Functional enrichment analysis unveiled the engagement of the immune system and notable variances in the immune infiltration landscape across the high and low-risk groups. Additionally, tumor mutation frequencies were observed to be elevated in the high-risk group. Based on our analyses, the IC50 values of most identified anticancer agents demonstrated a correlation with the RiskScore. Additionally, the high-risk and low-risk groups exhibited differential sensitivity to various drugs. Cytological experiments revealed that silencing ATAD2 or SPP1 suppresses malignant phenotypes, including viability and migration, in liver cancer cells. In this study, a novel gene signature related to the PPP was developed, demonstrating favorable predictive performance. This signature holds significant guiding value for assessing the prognosis of HCC patients and directing individualized treatment strategies.

Facilitation of Tumor Stroma-Targeted Therapy: Model Difficulty and Co-Culture Organoid Method

Background: Tumors, as intricate ecosystems, comprise oncocytes and the highly dynamic tumor stroma. Tumor stroma, representing the non-cancerous and non-cellular composition of the tumor microenvironment (TME), plays a crucial role in oncogenesis and progression, through its interactions with biological, chemical, and mechanical signals. This review aims to analyze the challenges of stroma mimicry models, and highlight advanced personalized co-culture approaches for recapitulating tumor stroma using patient-derived tumor organoids (PDTOs). Methods: This review synthesizes findings from recent studies on tumor stroma composition, stromal remodeling, and the spatiotemporal heterogeneities of the TME. It explores popular stroma-related models, co-culture systems integrating PDTOs with stromal elements, and advanced techniques to improve stroma mimicry. Results: Stroma remodeling, driven by stromal cells, highlights the dynamism and heterogeneity of the TME. PDTOs, derived from tumor tissues or cancer-specific stem cells, accurately mimic the tissue-specific and genetic features of primary tumors, making them valuable for drug screening. Co-culture models combining PDTOs with stromal elements effectively recreate the dynamic TME, showing promise in personalized anti-cancer therapy. Advanced co-culture techniques and flexible combinations enhance the precision of tumor-stroma recapitulation. Conclusions: PDTO-based co-culture systems offer a promising platform for stroma mimicry and personalized anti-cancer therapy development. This review underscores the importance of refining these models to advance precision medicine and improve therapeutic outcomes.

Machine learning identification of NK cell immune characteristics in hepatocellular carcinoma based on single-cell sequencing and bulk RNA sequencing

BACKGROUND

Hepatocellular carcinoma (HCC) is a highly malignant tumor; however, its immune microenvironment and mechanisms remain elusive. Single-cell sequencing allows for the exploration of immune characteristics within tumor at the cellular level. However, current knowledge regarding the roles of different immune cell populations in liver cancer progression is limited.

OBJECTIVE

The main objective of this study is to identify molecular markers with NK cell immune characteristics in hepatocellular carcinoma using various machine learning methods based on Single-Cell Sequencing and Bulk RNA Sequencing.

METHODS

We collected samples from eight normal liver tissues and eight HCC tumor tissues and performed single-cell RNA sequencing for immune cell clustering and expression profile analysis. Using various bioinformatic approaches, we investigated the immune phenotype associated with natural killer (NK) cells expressing high CD7 level. In addition, we verified the role of CD7 in the growth of HCC after NK cell and HCC cells cocultured by RT-qPCR, MTS and Flow cytometer experiments. Finally, we constructed a machine learning model to develop a prognostic prediction system for HCC based on NK cell-related genes.

RESULTS

Through single-cell typing, we found that the proportions of hepatocytes and NK cells were significantly elevated in the tumor samples. Moreover, we found that the expression of CD7 was high in HCC and correlated with prognosis. More importantly, Overexpression of CD7 in NK cells significantly inhibited the activity of MHCC97 cells and increased the number of apoptosis of HCC cells (p < 0.05). Furthermore, we observed that NK cells with high CD7 expression were associated with an activated immune phenotype.

CONCLUSION

Our study found that CD7 is an important biomarker for assessing immune status and predicting survival of HCC patients; hence, it is a potential target for immune therapy against HCC.

Integrative multi-omics analysis reveals a novel subtype of hepatocellular carcinoma with biological and clinical relevance

Background

Hepatocellular carcinoma (HCC) is a highly heterogeneous tumor, and the development of accurate predictive models for prognosis and drug sensitivity remains challenging.

Methods

We integrated laboratory data and public cohorts to conduct a multi-omics analysis of HCC, which included bulk RNA sequencing, proteomic analysis, single-cell RNA sequencing (scRNA-seq), spatial transcriptomics sequencing (ST-seq), and genome sequencing. We constructed a tumor purity (TP) and tumor microenvironment (TME) prognostic risk model. Proteomic analysis validated the TP-TME-related signatures. Joint analysis of scRNA-seq and ST-seq revealed characteristic clusters associated with TP high-risk subtypes, and immunohistochemistry confirmed the expression of key genes. We conducted functional enrichment analysis, transcription factor activity inference, cell-cell interaction, drug efficacy analysis, and mutation information analysis to identify a novel subtype of HCC.

Results

Our analyses constructed a robust HCC prognostic risk prediction model. The patients with TP-TME high-risk subtypes predominantly exhibit hypoxia and activation of the Wnt/beta-catenin, Notch, and TGF-beta signaling pathways. Furthermore, we identified a novel subtype, XPO1+Epithelial. This subtype expresses signatures of the TP risk subtype and aligns with the biological behavior of high-risk patients. Additional analyses revealed that XPO1+Epithelial is influenced primarily by fibroblasts via ligand-receptor interactions, such as FN1-(ITGAV+ITGB1), and constitute a significant component of the TP-TME subtype. Moreover, XPO1+Epithelial interact with monocytes/macrophages, T/NK cells, and endothelial cells through ligand-receptor pairs, including MIF-(CD74+CXCR4), MIF-(CD74+CD44), and VEGFA-VEGFR1R2, respectively, thereby promoting the recruitment of immune-suppressive cells and angiogenesis. The ST-seq cohort treated with Tyrosine Kinase Inhibitors (TKIs) and Programmed Cell Death Protein 1 (PD-1) presented elevated levels of TP and TME risk subtype signature genes, as well as XPO1+Epithelial, T-cell, and endothelial cell infiltration in the treatment response group. Drug sensitivity analyses indicated that TP-TME high-risk subtypes, including sorafenib and pembrolizumab, were associated with sensitivity to multiple drugs. Further exploratory analyses revealed that CTLA4, PDCD1, and the cancer antigens MSLN, MUC1, EPCAM, and PROM1 presented significantly increase expression levels in the high-risk subtype group.

Conclusions

This study constructed a robust prognostic model for HCC and identified novel subgroups at the single-cell level, potentially assisting in the assessment of prognostic risk for HCC patients and facilitating personalized drug therapy.

Loss of LRP1 Promotes Hepatocellular Carcinoma Progression via UFL1-Mediated Activation of NF-κB Signaling

Low-density lipoprotein receptor-related protein-1 (LRP1) is thought to be correlated with hepatocellular carcinoma (HCC) invasion and metastasis. However, the precise mechanism through which LRP1 contributes to HCC progression remains unclear. Here, lower LRP1 levels are associated with malignant progression, and poor prognosis in patients with HCC is shown. LRP1 knockdown enhances the tumorigenicity of HCC cells in vitro and in vivo, whereas overexpression of either LRP1 or its β-chain has the opposite effect. Mechanistically, LRP1 knockdown promotes the binding of ubiquitin-like modifier 1 ligating enzyme 1 (UFL1) to OGA and accelerates ubiquitin-mediated OGA degradation, leading to increased O-GlcNAcylation of nuclear factor-kappa B (NF-κB) and subsequent inhibition of pro-apoptotic gene expression. Conversely, exogenously expressed truncated β-chain (β∆) stabilizes OGA by disrupting the association between UFL1 and OGA, consequently abolishing the anti-apoptotic effects of O-GlcNAcylated NF-κB. The findings identify LRP1, particularly its β-chain, as a novel upstream control factor that facilitates the stabilization of the OGA protein, thereby suppressing NF-κB signaling and attenuating HCC progression, thus suggesting a novel therapeutic strategy for HCC.

Molecular methods in cancer diagnostics: a short review

BACKGROUND

One of the ailments with the greatest fatality rates in the 21st century is cancer. Globally, molecular methods are widely employed to treat cancer-related disorders, and the body of research on this subject is growing yearly. A thorough and critical summary of the data supporting molecular methods for illnesses linked to cancer is required.

OBJECTIVE

In order to guide clinical practice and future research, it is important to examine and summarize the systematic reviews (SRs) that evaluate the efficacy and safety of molecular methods for disorders associated to cancer.

METHODS

We developed a comprehensive search strategy to find relevant articles from electronic databases like PubMed, Google Scholar, Web of Science (WoS), or Scopus. We looked through the literature and determined which diagnostic methods in cancer genetics were particularly reliable. We used phrases like 'cancer genetics', genetic susceptibility, Hereditary cancer, cancer risk assessment, 'cancer diagnostic tools', cancer screening', biomarkers, and molecular diagnostics, reviews and meta-analyses evaluating the efficacy and safety of molecular therapies for cancer-related disorders. Research that only consider treatment modalities that don't necessitate genetic or molecular diagnostics fall under the exclusion criteria.

RESULTS

The results of this comprehensive review clearly demonstrate the transformative impact of molecular methods in the realm of cancer genetics.This review underscores how these technologies have empowered researchers and clinicians to identify and understand key genetic alterations that drive malignancy, ranging from point mutations to structural variations. Such insights are instrumental in pinpointing critical oncogenic drivers and potential therapeutic targets, thus opening the door for methods in precision medicine that can significantly improve patient outcomes.

LIMITATION

The search does not specify a timeframe for publication inclusion, it may have missed recent advancements or changes in the field's landscape of molecular methods for cancer. As a result, it may not have included the most recent developments in the field.

CONCLUSION

After conducting an in-depth study on the molecular methods in cancer genetics, it is evident that these cutting-edge technologies have revolutionized the field of oncology, providing researchers and clinicians with powerful tools to unravel the complexities of cancer at the genetic level. The integration of molecular methods techniques has not only enhanced our understanding of cancer etiology, progression, and treatment response but has also opened new avenues for personalized medicine and targeted therapies, leading to improved patient outcomes.

LiverSCA: A comprehensive and user-friendly cell atlas in human hepatocellular carcinoma

We developed a cell atlas named LiverSCA on human liver cancer single-cell RNA sequencing data. It has a user-friendly web interface and comprehensive functionalities aiming to help researchers to make easy access to cellular and molecular landscapes of the tumor microenvironment in liver cancer. LiverSCA includes a complete analytical pipeline that allow mechanistic exploration on a wide variety of functionalities, such as cell clustering, cell annotation, identification of differentially expressed genes, functional enrichment analysis, analysis of cellular crosstalk, and pseudo-time trajectory analysis. Notably, our intuitive web interface allows users, particularly wet-lab researchers, to easily explore and undertake data discovery, without the need to handle any of the raw data.

Multi-omics Insights into PDHA1 as a Predictive Biomarker for Prognosis, Immunotherapy Efficacy, and Drug Sensitivity in Hepatocellular Carcinoma

PDHA1 was associated with metabolic reprogramming in tumor progression. However, the clinical value of PDHA1, especially for prediction of drug sensitivity in hepatocellular carcinoma (HCC), has not been fully investigated. In this study, we found that PDHA1 expression was higher in HCC tissues compared to normal tissues and was correlated with poor prognosis in HCC patients. PDHA1 expression was mainly positively associated with immune cell infiltration using the TIMER, XCell, MCPCOUNTER, CIBERSORT, EPIC, and QUANTISEQ algorithms, which was validated by single-cell RNA-sequencing analysis. We also discovered that PDHA1 expression was correlated with six immune checkpoint-related genes. Univariate and multivariate Cox regression analyses revealed that PDHA1 expression was an independent prognostic indicator for HCC patients, and the nomogram incorporating PDHA1 expression exhibited excellent predictive capacity. Furthermore, PDHA1 expression was positively linked to the sensitivity of 5-fluorouracil, gemcitabine, paclitaxel, and sorafenib, and the molecular docking analysis demonstrated their excellent binding affinity.

scRNA-Seq Analysis Revealed CAFs Regulating HCC Cells via PTN Signaling

Background

Cancer-associated fibroblasts (CAFs) play a pivotal role in shaping the microenvironment of hepatocellular carcinoma (HCC). However, the mechanisms through which CAFs influence the progression of HCC remain incompletely understood.

Methods

Single-cell RNA sequencing datasets (GSE158723 and GSE112271) were retrieved from the Gene Expression Omnibus (GEO) database at the National Center for Biotechnology Information (NCBI) and analyzed using R software. Our analysis suggested that CAFs may promote liver cancer cell development, possibly through the interaction of pleiotrophin (PTN) and syndecan-2 (SDC2). Clinical samples from HCC patients were collected and processed into frozen sections and single-cell suspensions for Masson staining, immunofluorescence staining, and flow cytometry. Additionally, Huh7 liver cancer cells and LO2 normal liver cells were cultured and subjected to immunofluorescence assays using cell slides.

Results

The proportion of CAFs in cancerous tissues was higher than in adjacent non-cancerous tissues, and pleiotrophin (PTN) expression was elevated in cancer tissues compared to adjacent tissues. These findings aligned with the results of the single-cell RNA sequencing (scRNA-seq) analysis. Furthermore, SDC2 expression was significantly upregulated in Huh7 liver cancer cells compared to LO2 normal liver cells.

Discussion

This study suggests that CAFs may contribute to HCC progression via the PTN/SDC2 signaling pathway. Our findings provide deeper insights into the interactions between CAFs and HCC cells within the tumor microenvironment (TME).

Network pharmacology and bioinformatic integrative analysis reveals candidate gene targets and potential therapeutic of East Kalimantan propolis against hepatocellular carcinoma

Introduction

Hepatocellular Carcinoma (HCC) is commonly treated with surgery, liver transplantation, and chemotherapy, but recurrence and metastasis remain challenges. Natural complementary therapies like propolis, known for its hepatoprotective properties, are gaining interest due to limited efficacy and toxicity of conventional chemotherapy. This study aims to identify core targets for HCC, assess the therapeutic potential of East Kalimantan propolis (EKP) from stingless bees, and analyze the molecular interactions.

Methods

EKP compounds were analyzed using target prediction tools related to HCC, alongside clinical data from the Gene Expression Omnibus (GEO) database, to identify overlapping genes with clinical relevance. The selected genes were then subjected to protein-protein interaction (PPI), GO and KEGG enrichment, immunohistochemical comparison and survival analysis to identify potential core targets and related pathways for HCC therapy. Furthermore, molecular docking and dynamics were conducted to verify the molecular interactions and stability of EKP compounds with targets.

Results

108 genes have been selected as HCC potential targets, which mostly associated with MicroRNAs in cancer, chemical carcinogenesis, and viral carcinogenesis pathways. These targets were obtained by overlapping genes from GEO clinical databases and target predictors. PPI network analysis revealed 4 main targets of propolis in HCC. Furthermore, differential expression genes, survival analysis, and Immunohistochemical analysis from databases suggested that AKR1C3 and MAPK1 promote HCC progression and shorten survival rate of HCC patients. Molecular docking and dynamic studies confirmed strong binding affinity and stability of Baicalein, Chrysin, Quercetin, and Myricetin with receptor targets within simulation time.

Conclusions

This study provides insight into the mechanism of action of EKP on HCC and identifies AKR1C3 and MAPK1 as candidate target treatments for future drug development.

Single-cell RNA sequencing reveals the landscape of the cellular ecosystem of primary hepatocellular carcinoma

Hepatocellular carcinoma (HCC) cells, along with multiple nonmalignant stromal cells, such as fibroblasts, endothelial cells and immune cells, comprise an intricate cellular ecosystem, undergo dynamic phenotypic changes and present complicated cellular interactions, thus synergistically facilitating HCC initiation and progression and leading to treatment resistance. Clarifying the heterogeneity, cell plasticity and complexity of the cellular ecosystem of HCC will be highly beneficial for understanding HCC development and identifying novel therapeutic targets. Single-cell RNA sequencing (scRNA-seq) refers to profiling the transcriptome at single-cell resolution, and the development of scRNA-seq technology and analysis algorithms has greatly promoted the analysis of cell composition, cell subpopulation heterogeneity, development trajectory and cell-to-cell interactions in cell populations. In this review, we systematically summarized and discussed scRNA-seq in treatment-naive primary HCC and revealed the global cell composition of HCC; the widespread molecular heterogeneity of HCC cells; the molecular subtypes of fibroblasts; the cell composition, functional states and development trajectory of immune cells; and the frequent interactions between different cell types to systematically draw the landscape of the cellular ecosystem of primary HCC.

The integrated molecular and histological analysis defines subtypes of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is highly heterogeneous. Our understanding of full molecular and immune landscape of ESCC remains limited, hindering the development of personalised therapeutic strategies. To address this, we perform genomic-transcriptomic characterizations and AI-aided histopathological image analysis of 120 Chinese ESCC patients. Here we show that ESCC can be categorized into differentiated, metabolic, immunogenic and stemness subtypes based on bulk and single-cell RNA-seq, each exhibiting specific molecular and histopathological features based on an amalgamated deep-learning model. The stemness subgroup with signature genes, such as WFDC2, SFRP1, LGR6 and VWA2, has the poorest prognosis and is associated with downregulated immune activities, a high frequency of EP300 mutation/activation, functional mutation enrichment in Wnt signalling and the highest level of intratumoural heterogeneity. The immune profiling by transcriptomics and immunohistochemistry reveals ESCC cells overexpress natural killer cell markers XCL1 and CD160 as immune evasion. Strikingly, XCL1 expression also affects the sensitivity of ESCC cells to common chemotherapy drugs. This study opens avenues for ESCC treatment and provides a valuable public resource to better understand ESCC.

CBX1 is involved in hepatocellular carcinoma progression and resistance to sorafenib and lenvatinib via IGF-1R/AKT/SNAIL signaling pathway

BACKGROUND

Chromobox Homolog 1 (CBX1) plays a crucial role in the pathogenesis of numerous diseases, including the evolution and advancement of diverse cancers. The role of CBX1 in pan-cancer and its mechanism in hepatocellular carcinoma (HCC), however, remains to be further investigated.

METHODS

Bioinformatics approaches were harnessed to scrutinize CBX1's expression profile, its association with tumor staging, and its potential impact on patient outcomes across various cancers. Single-cell RNA sequencing data facilitated the investigation of CBX1 expression patterns at the individual cell level. The CBX1 expression levels in HCC and adjacent non-tumor tissues were quantified through Real-Time Polymerase Chain Reaction (RT-PCR), Western Blotting (WB), and Immunohistochemical analyses. A tissue microarray was employed to explore the relationship between CBX1 levels, patient prognosis, and clinicopathological characteristics in HCC. Various in vitro assays-including CCK-8, colony formation, Transwell invasion, and scratch tests-were conducted to assess the proliferative and motility properties of HCC cells upon modulation of CBX1 expression. Moreover, the functional impact of CBX1 on HCC was further discerned through xenograft studies in nude mice.

RESULTS

CBX1 was found to be upregulated in most cancer forms, with heightened expression correlating with adverse patient prognoses. Within the context of HCC, elevated levels of CBX1 were consistently indicative of poorer clinical outcomes. Suppression of CBX1 through knockdown methodologies markedly diminished HCC cell proliferation, invasive capabilities, migratory activity, Epithelial-mesenchymal transition (EMT) processes, and resistance to Tyrosine kinase inhibitors (TKIs). Contrastingly, CBX1 augmentation facilitated the opposite effects. Subsequent investigative efforts revealed CBX1 to be a promoter of EMT and a contributor to increased TKI resistance within HCC cells, mediated via the IGF-1R/AKT/SNAIL signaling axis. The oncogenic activities of CBX1 proved to be attenuable either by AKT pathway inhibition or by targeted silencing of IGF-1R.

CONCLUSIONS

The broad overexpression of CBX1 in pan-cancer and specifically in HCC positions it as a putative oncogenic entity. It is implicated in forwarding HCC progression and exacerbating TKI resistance through its interaction with the IGF-1R/AKT/SNAIL signaling cascade.

NCAPD2 augments the tumorigenesis and progression of human liver cancer via the PI3K‑Akt‑mTOR signaling pathway

Non‑SMC condensin I complex subunit D2 (NCAPD2) is a newly identified oncogene; however, the specific biological function and molecular mechanism of NCAPD2 in liver cancer progression remain unknown. In the present study, the aberrant expression of NCAPD2 in liver cancer was investigated using public tumor databases, including TNMplot, The Cancer Genome Atlas and the International Cancer Genome Consortium based on bioinformatics analyses, and it was validated using a clinical cohort. It was revealed that NCAPD2 was significantly upregulated in liver cancer tissues compared with in control liver tissues, and NCAPD2 served as an independent prognostic factor and predicted poor prognosis in liver cancer. In addition, the expression of NCAPD2 was positively correlated with the percentage of Ki67+ cells. Finally, single‑cell sequencing data, gene‑set enrichment analyses and in vitro investigations, including cell proliferation assay, Transwell assay, wound healing assay, cell cycle experiments, cell apoptosis assay and western blotting, were carried out in human liver cancer cell lines to assess the biological mechanisms of NCAPD2 in patients with liver cancer. The results revealed that the upregulation of NCAPD2 enhanced tumor cell proliferation, invasion and cell cycle progression at the G2/M‑phase transition, and inhibited apoptosis in liver cancer cells. Furthermore, NCAPD2 overexpression was closely associated with the phosphatidylinositol 3‑kinase (PI3K)‑Akt‑mammalian target of rapamycin (mTOR)/c‑Myc signaling pathway and epithelial‑mesenchymal transition (EMT) progression in HepG2 and Huh7 cells. In addition, upregulated NCAPD2 was shown to have adverse effects on overall survival and disease‑specific survival in liver cancer. In conclusion, the overexpression of NCAPD2 was shown to lead to cell cycle progression at the G2/M‑phase transition, activation of the PI3K‑Akt‑mTOR/c‑Myc signaling pathway and EMT progression in human liver cancer cells.

Biomarkers for Immunotherapy Efficacy in Advanced Hepatocellular Carcinoma: A Comprehensive Review

Hepatocellular carcinoma (HCC), the most common primary liver malignancy and the sixth most common cancer globally, remains fatal for many patients with inappropriate responses to treatment. Recent advancements in immunotherapy have transformed the treatment landscape for advanced HCC. However, variability in patient responses to immunotherapy highlights the need for biomarkers that can predict treatment outcomes. This manuscript comprehensively reviews the evolving role of biomarkers in immunotherapy efficacy, spanning from blood-derived indicators-alpha-fetoprotein, inflammatory markers, cytokines, circulating tumor cells, and their DNA-to tissue-derived indicators-programmed cell death ligand 1 expression, tumor mutational burden, microsatellite instability, and tumor-infiltrating lymphocytes. The current body of evidence suggests that these biomarkers hold promise for improving patient selection and predicting immunotherapy outcomes. Each biomarker offers unique insights into disease biology and the immune landscape of HCC, potentially enhancing the precision of treatment strategies. However, challenges such as methodological variability, high costs, inconsistent findings, and the need for large-scale validation in well-powered two-arm trial studies persist, making them currently unsuitable for integration into standard care. Addressing these challenges through standardized techniques and implementation of further studies will be critical for the future incorporation of these biomarkers into clinical practice for advanced HCC.

Immunotherapy in liver cancer: overcoming the tolerogenic liver microenvironment

Liver cancer is a major global health concern, ranking among the top causes of cancer-related deaths worldwide. Despite advances in medical research, the prognosis for liver cancer remains poor, largely due to the inherent limitations of current therapies. Traditional treatments like surgery, radiation, and chemotherapy often fail to provide long-term remission and are associated with significant side effects. Immunotherapy has emerged as a promising avenue for cancer treatment, leveraging the body's immune system to target and destroy cancer cells. However, its application in liver cancer has been limited. One of the primary challenges is the liver's unique immune microenvironment, which can inhibit the effectiveness of immunotherapeutic agents. This immune microenvironment creates a barrier, leading to drug resistance and reducing the overall efficacy of treatment. Recent studies have focused on understanding the immunological landscape of liver cancer to develop strategies that can overcome these obstacles. By identifying the specific factors within the liver that contribute to immune suppression and drug resistance, researchers aim to enhance the effectiveness of immunotherapy. Prospective strategies include combining immunotherapy with other treatments, using targeted therapies to modulate the immune microenvironment, and developing new agents that can bypass or counteract the inhibitory mechanisms in the liver. These advancements hold promise for improving outcomes in liver cancer treatment.

Serum S-adenosylhomocysteine, rather than homocysteine, is associated with hepatocellular carcinoma survival: a prospective cohort study

BACKGROUND

Emerging evidence suggested that S-adenosylhomocysteine (SAH) may be a better serum biomarker for cardiovascular disease than homocysteine (Hcy). However, the role of SAH in hepatocellular carcinoma (HCC) prognosis remains unclear.

OBJECTIVES

We aimed to prospectively explore the relationships between serum SAH and related metabolites [Hcy, S-adenosylmethionine (SAM)] with HCC survival, and to evaluate the effect modifications by gene polymorphisms in one-carbon metabolism key enzymes.

METHODS

We included 1080 newly diagnosed patients with HCC from the Guangdong Liver Cancer Cohort. Serum SAH, Hcy, and SAM were measured utilizing high-performance liquid chromatography-tandem mass spectrometry. Gene polymorphisms in one-carbon metabolism key enzymes were identified using kompetitive allele-specific polymerase chain reaction. Primary outcomes were liver cancer-specific survival (LCSS) and overall survival (OS). Hazard ratios (HRs) and 95% confidence intervals (CIs) were computed using multivariate Cox proportional hazards models.

RESULTS

After a median follow-up of 3.6 y, 601 deaths occurred, with 552 (92%) attributed to HCC. Multivariable analysis revealed that patients in the highest quartile of serum SAH concentrations were significantly associated with worse survival compared with those in the lowest quartile, with HRs of 1.58 (95% CI: 1.19, 2.10; P-trend = 0.002) for LCSS and 1.54 (95% CI: 1.18, 2.02; P-trend = 0.001) for OS. There were no significant interactions between serum SAH concentrations and genetic variants of one-carbon metabolism key enzymes. No significant associations were found between serum Hcy, SAM concentrations, and SAM/SAH ratio with LCSS or OS.

CONCLUSIONS

Higher serum SAH concentrations, rather than Hcy, were independently associated with worse survival in patients with HCC, regardless of the genetic variants of one-carbon metabolism key enzymes. These findings suggest that SAH may be a novel metabolism-related prognostic biomarker for HCC.

Prognostic Hypoxia-Angiogenesis-Related Gene Signature in Hepatocellular Carcinoma, in Which HILPDA Contributes to Tumor Progression

Objective

Hepatocellular carcinoma (HCC) is the predominant form of liver cancer. Hypoxia can be involved in HCC tumor growth, invasion and metastasis through inducing angiogenesis. Nevertheless, the assessment of the impact of hypoxia and angiogenesis on the prognosis of HCC remains inadequate.

Methods

According to hypoxia-angiogenesis-related genes (HARGs) expression information and clinical data from patients within the Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, we constructed a prognostic model (HARG-score) using bioinformatic tools. In addition to assessing the predictive ability of this prognostic model in both Liver Cancer-Riken-Japan (LIRI-JP) and GSE14520 cohorts, we analyzed the correlation between HARG-score and clinical characteristics, immune infiltration and immunotherapy efficacy. Moreover, we investigated the exact role and underlying mechanism of key HARGs through molecular experiments.

Results

We constructed a 5-gene prognostic model HARG-score consisting of hypoxia-inducible lipid droplet-associated (HILPDA), erythropoietin (EPO), solute carrier family 2 member 1 (SLC2A1), proteasome subunit alpha type 7 (PSMA7) and cAMP responsive element-binding protein 1 (CREB1) through differentially expressed HARGs. The findings demonstrated that HARG-score was a good predictor of the prognosis of HCC patients from distinct cohorts and was correlated with clinical characteristics and immune infiltration. Furthermore, the HARG-score was identified as an independent prognostic factor. Lower HARG-score implied greater immunotherapy efficacy and better response. The expression and prognostic significance of these 5 genes were additionally validated in clinical data. In addition, experimental data revealed that the key gene HILPDA contributes to the progression of HCC through facilitating angiogenesis and affecting the expression of cytotoxic T-lymphocyte-associated protein 4 (CTLA4).

Conclusion

HARG-score has promising applications in prognosis prediction of HCC patients, in which HILPDA may be a latent prognostic biomarker and therapeutic target, providing a foundation for further research and treatment of HCC.

Spatial multiomics reveals a subpopulation of fibroblasts associated with cancer stemness in human hepatocellular carcinoma

BACKGROUND

Cancer-associated fibroblasts (CAFs) are the prominent cell type in the tumor microenvironment (TME), and CAF subsets have been identified in various tumors. However, how CAFs spatially coordinate other cell populations within the liver TME to promote cancer progression remains unclear.

METHODS

We combined multi-region proteomics (6 patients, 24 samples), 10X Genomics Visium spatial transcriptomics (11 patients, 25 samples), and multiplexed imaging (92 patients, 264 samples) technologies to decipher the expression heterogeneity, functional diversity, spatial distribution, colocalization, and interaction of fibroblasts. The newly identified CAF subpopulation was validated by cells isolated from 5 liver cancer patients and in vitro functional assays.

RESULTS

We identified a liver CAF subpopulation, marked by the expression of COL1A2, COL4A1, COL4A2, CTGF, and FSTL1, and named F5-CAF. F5-CAF is preferentially located within and around tumor nests and colocalizes with cancer cells with higher stemness in hepatocellular carcinoma (HCC). Multiplexed staining of 92 patients and the bulk transcriptome of 371 patients demonstrated that the abundance of F5-CAFs in HCC was associated with a worse prognosis. Further in vitro experiments showed that F5-CAFs isolated from liver cancer patients can promote the proliferation and stemness of HCC cells.

CONCLUSIONS

We identified a CAF subpopulation F5-CAF in liver cancer, which is associated with cancer stemness and unfavorable prognosis. Our results provide potential mechanisms by which the CAF subset in the TME promotes the development of liver cancer by supporting the survival of cancer stem cells.

The association between tumour heterogeneity and immune evasion mechanisms in hepatocellular carcinoma and its clinical implications

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide. The emergence of combination therapy, atezolizumab (anti-PDL1, immune checkpoint inhibitor) and bevacizumab (anti-VEGF) has revolutionised the management of HCC. Despite this breakthrough, the best overall response rate with first-line systemic therapy is only about 30%, owing to intra-tumoural heterogeneity, complex tumour microenvironment and the lack of predictive biomarkers. Many groups have attempted to classify HCC based on the immune microenvironment and have consistently observed better outcomes in immunologically "hot" HCC. We summarised possible mechanisms of tumour immune evasion based on the latest literature and the rationale for combination/sequential therapy to improve treatment response. Lastly, we proposed future strategies and therapies to overcome HCC immune evasion to further improve treatment outcomes of HCC.

Establishment of diagnostic model and identification of diagnostic markers between liver cancer and cirrhosis based on multi-chip and machine learning

OBJECTIVE

Most cases of hepatocellular carcinoma (HCC) arise as a consequence of cirrhosis. In this study, our objective is to construct a comprehensive diagnostic model that investigates the diagnostic markers distinguishing between cirrhosis and HCC.

METHODS

Based on multiple GEO datasets containing cirrhosis and HCC samples, we used lasso regression, random forest (RF)-recursive feature elimination (RFE) and receiver operator characteristic analysis to screen for characteristic genes. Subsequently, we integrated these genes into a multivariable logistic regression model and validated the linear prediction scores in both training and validation cohorts. The ssGSEA algorithm was used to estimate the fraction of infiltrating immune cells in the samples. Finally, molecular typing for patients with cirrhosis was performed using the CCP algorithm.

RESULTS

The study identified 137 differentially expressed genes (DEGs) and selected five significant genes (CXCL14, CAP2, FCN2, CCBE1 and UBE2C) to construct a diagnostic model. In both the training and validation cohorts, the model exhibited an area under the curve (AUC) greater than 0.9 and a kappa value of approximately 0.9. Additionally, the calibration curve demonstrated excellent concordance between observed and predicted incidence rates. Comparatively, HCC displayed overall downregulation of infiltrating immune cells compared to cirrhosis. Notably, CCBE1 showed strong correlations with the tumour immune microenvironment as well as genes associated with cell death and cellular ageing processes. Furthermore, cirrhosis subtypes with high linear predictive scores were enriched in multiple cancer-related pathways.

CONCLUSION

In conclusion, we successfully identified diagnostic markers distinguishing between cirrhosis and hepatocellular carcinoma and developed a novel diagnostic model for discriminating the two conditions. CCBE1 might exert a pivotal role in regulating the tumour microenvironment, cell death and senescence.

Cell atlas of the regenerating human liver after portal vein embolization

The liver has the remarkable capacity to regenerate. In the clinic, regeneration is induced by portal vein embolization, which redirects portal blood flow, resulting in liver hypertrophy in locations with increased blood supply, and atrophy of embolized segments. Here, we apply single-cell and single-nucleus transcriptomics on healthy, hypertrophied, and atrophied patient-derived liver samples to explore cell states in the regenerating liver. Our data unveils pervasive upregulation of genes associated with developmental processes, cellular adhesion, and inflammation in post-portal vein embolization liver, disrupted portal-central hepatocyte zonation, and altered cell subtype composition of endothelial and immune cells. Interlineage crosstalk analysis reveals mesenchymal cells as an interaction hub between immune and endothelial cells, and highlights the importance of extracellular matrix proteins in liver regeneration. Moreover, we establish tissue-scale iterative indirect immunofluorescence imaging for high-dimensional spatial analysis of perivascular microenvironments, uncovering changes to tissue architecture in regenerating liver lobules. Altogether, our data is a rich resource revealing cellular and histological changes in human liver regeneration.

DPF2 overexpression correlates with immune infiltration and dismal prognosis in hepatocellular carcinoma

Background: Double plant homeodomain finger 2 (DPF2), belonging to the d4 family of structural domains, has been associated with various human malignancies. However, its impact on hepatocellular carcinoma (HCC) remains unclear. The objective of this study is to elucidate the role of DPF2 in the diagnosis and prognosis of HCC. Methods: DPF2 gene expression in HCC and adjacent tissues was analyzed using Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, validated by immunohistochemical staining of Guangxi specimens and data from the Human Protein Atlas (HPA). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genome (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to identify DPF2's potential pathways and functions in HCC. DPF2's mutation and methylation statuses were assessed via cBioPortal and MethSurv. The association between DPF2 and immune infiltration was investigated by TIMER. The prognostic value of DPF2 in HCC was established through Kaplan-Meier and Cox regression analyses. Results: DPF2 levels were significantly higher in HCC than normal tissues (p<0.001), correlating with more severe HCC features (p<0.05). Higher DPF2 expression predicted poorer overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). DPF2 involvement was noted in critical signaling pathways including the cell cycle and Wnt. It also correlated with T helper cells, Th2 cells, and immune checkpoints like CTLA-4, PD-1, and PD-L1. Conclusion: High DPF2 expression, associated with poor HCC prognosis, may disrupt tumor immune balance and promote immune evasion. DPF2 could potentially be utilized as a biomarker for diagnosing and prognosticating hepatocellular carcinoma.

Applications of single-cell multi-omics in liver cancer

Primary liver cancer, more specifically hepatocellular carcinoma (HCC), remains a significant global health problem associated with increasing incidence and mortality. Clinical, biological, and molecular heterogeneity are well-known hallmarks of cancer and HCC is considered one of the most heterogeneous tumour types, displaying substantial inter-patient, intertumoural and intratumoural variability. This heterogeneity plays a pivotal role in hepatocarcinogenesis, metastasis, relapse and drug response or resistance. Unimodal single-cell sequencing techniques have already revolutionised our understanding of the different layers of molecular hierarchy in the tumour microenvironment of HCC. By highlighting the cellular heterogeneity and the intricate interactions among cancer, immune and stromal cells before and during treatment, these techniques have contributed to a deeper comprehension of tumour clonality, hematogenous spreading and the mechanisms of action of immune checkpoint inhibitors. However, major questions remain to be elucidated, with the identification of biomarkers predicting response or resistance to immunotherapy-based regimens representing an important unmet clinical need. Although the application of single-cell multi-omics in liver cancer research has been limited thus far, a revolution of individualised care for patients with HCC will only be possible by integrating various unimodal methods into multi-omics methodologies at the single-cell resolution. In this review, we will highlight the different established single-cell sequencing techniques and explore their biological and clinical impact on liver cancer research, while casting a glance at the future role of multi-omics in this dynamic and rapidly evolving field.

Research progress and application of liver organoids for disease modeling and regenerative therapy

The liver is a major metabolic organ of the human body and has a high incidence of diseases. In recent years, the annual incidence of liver disease has increased, seriously endangering human life and health. The study of the occurrence and development mechanism of liver diseases, discovery of new therapeutic targets, and establishment of new methods of medical treatment are major issues related to the national economy and people's livelihood. The development of stable and effective research models is expected to provide new insights into the pathogenesis of liver diseases and the search for more effective treatment options. Organoid technology is a new in vitro culture system, and organoids constructed by human cells can simulate the morphological structure, gene expression, and glucose and lipid metabolism of organs in vivo, providing a new model for related research on liver diseases. This paper reviews the latest research progress on liver organoids from the establishment of cell sources and application of liver organoids and discusses their application potential in the field of liver disease research.

Multifunctional and stimuli-responsive liposomes in hepatocellular carcinoma diagnosis and therapy

Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer, mainly occurring in Asian countries with an increased incidence rate globally. Currently, several kinds of therapies have been deployed for HCC therapy including surgical resection, chemotherapy, radiotherapy and immunotherapy. However, this tumor is still incurable, requiring novel strategies for its treatment. The nanomedicine has provided the new insights regarding the treatment of cancer that liposomes as lipid-based nanoparticles, have been widely applied in cancer therapy due to their biocompaitiblity, high drug loading and ease of synthesis and modification. The current review evaluates the application of liposomes for the HCC therapy. The drugs and genes lack targeting ability into tumor tissues and cells. Therefore, loading drugs or genes on liposomes can increase their accumulation in tumor site for HCC suppression. Moreover, the stimuli-responsive liposomes including pH-, redox- and light-sensitive liposomes are able to deliver drug into tumor microenvironment to improve therapeutic index. Since a number of receptors upregulate on HCC cells, the functionalization of liposomes with lactoferrin and peptides can promote the targeting ability towards HCC cells. Moreover, phototherapy can be induced by liposomes through loading phtoosensitizers to stimulate photothermal- and photodynamic-driven ablation of HCC cells. Overall, the findings are in line with the fact that liposomes are promising nanocarriers for the treatment of HCC.

Molecular Mechanisms of the Therapeutic Effect of Selenium Nanoparticles in Hepatocellular Carcinoma

This review describes and summarizes, for the first time, the molecular mechanisms of the cytotoxic effect of selenium nanoparticles of various origins on hepatocellular carcinoma cells. The text provides information from recent years indicating the regulation of various signaling pathways and endoplasmic reticulum stress by selenium nanoparticles; the pathways of cell death of liver cancer cells as a result of exposure to selenium nanoparticles are considered. Particular attention is paid to the participation of selenoproteins and selenium-containing thioredoxin reductases and glutathione peroxidases in these processes. Previously, there were no reviews that fully reflected the cytotoxic effects of selenium nanoparticles specifically in hepatocellular carcinoma, despite the fact that many reviews and experimental articles have been devoted to the causes of this disease and the molecular mechanisms of regulation of cytotoxic effects by other agents. The relevance of this review is primarily explained by the fact that despite the development of various drugs and approaches for the treatment and prevention of hepatocellular carcinoma, this disease is still the fourth leading cause of death in the world. For this reason, a complete understanding of the latest trends in the treatment of oncology of various etiologies, especially hepatocellular carcinoma, is extremely important.

TMEM147: A Promising Cancer Biomarker Associated with Immune Cell Infiltration and Prognosis in LIHC-Insights from a Comprehensive Pan-Cancer Genomic Analysis

Previous studies have demonstrated the regulatory roles of Transmembrane protein 147 (TMEM147) in various diseases, including cancer. However, systematic pan-cancer analyses investigating the role of TMEM147 in diagnosis, prognosis, and immunological prediction are lacking. An analysis of data from The Cancer Genome Atlas (TCGA) revealed differential TMEM147 expression across various types of cancer as well as within immune and molecular cancer subtypes. Moreover, high TMEM147 expression was associated with poor disease-specific survival (DSS), overall survival (OS), and progression-free interval (PFI) across cancers, suggesting its potential as a prognostic biomarker. Our study further revealed a significant correlation between TMEM147 expression and T helper cell and Tcm cell infiltration in most cancer types. In the case of liver hepatocellular carcinoma (LIHC), the effect of TMEM147 on prognosis varied among different clinical subtypes. Additionally, functional enrichment analysis revealed an association between TMEM147 and metabolic pathways. Finally, experiments on the MIHA cell line and four LIHC cell lines confirmed the role of TMEM147 in promoting liver cancer cell proliferation, further confirming the clinical value of TMEM147 in liver cancer diagnosis. Our findings suggest that TMEM147 may serve as a diagnostic and prognostic biomarker across cancers while also playing a significant role in LIHC.

Liver cancer from the perspective of single-cell sequencing: a review combined with bibliometric analysis

BACKGROUND

Liver cancer (LC) is a prevalent malignancy and a leading cause of cancer-related mortality worldwide. Extensive research has been conducted to enhance patient outcomes and develop effective prevention strategies, ranging from molecular mechanisms to clinical interventions. Single-cell sequencing, as a novel bioanalysis technology, has significantly contributed to the understanding of the global cognition and dynamic changes in liver cancer. However, there is a lack of bibliometric analysis in this specific research area. Therefore, the objective of this study is to provide a comprehensive overview of the knowledge structure and research hotspots in the field of single-cell sequencing in liver cancer research through the use of bibliometrics.

METHOD

Publications related to the application of single-cell sequencing technology to liver cancer research as of December 31, 2023, were searched on the web of science core collection (WoSCC) database. VOSviewers, CiteSpace, and R package "bibliometrix" were used to conduct this bibliometric analysis.

RESULTS

A total of 331 publications from 34 countries, primarily led by China and the United States, were included in this study. The research focuses on the application of single cell sequencing technology to liver cancer, and the number of related publications has been increasing year by year. The main research institutions involved in this field are Fudan University, Sun Yat-Sen University, and the Chinese Academy of Sciences. Frontiers in Immunology and Nature Communications is the most popular journal in this field, while Cell is the most frequently co-cited journal. These publications are authored by 2799 individuals, with Fan Jia and Zhou Jian having the most published papers, and Llovet Jm being the most frequently co-cited author. The use of single cell sequencing to explore the immune microenvironment of liver cancer, as well as its implications in immunotherapy and chemotherapy, remains the central focus of this field. The emerging research hotspots are characterized by keywords such as 'Gene-Expression', 'Prognosis', 'Tumor Heterogeneity', 'Immunoregulation', and 'Tumor Immune Microenvironment'.

CONCLUSION

This is the first bibliometric study that comprehensively summarizes the research trends and developments on the application of single cell sequencing in liver cancer. The study identifies recent research frontiers and hot directions, providing a valuable reference for researchers exploring the landscape of liver cancer, understanding the composition of the immune microenvironment, and utilizing single-cell sequencing technology to guide and enhance the prognosis of liver cancer patients.

Advances in spatial transcriptomics and its applications in cancer research

Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular mechanisms in tumors. In recent years, the spatial transcriptomics sequencing technologies have been developed rapidly and allow the quantification and illustration of gene expression in the spatial context of tissues. Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the development of spatial transcriptomics technologies, spatial transcriptomics tools and its application in cancer research. We also discuss the limitations and challenges of current spatial transcriptomics approaches, as well as future development and prospects.

Molecular Mechanisms in Tumorigenesis of Hepatocellular Carcinoma and in Target Treatments-An Overview

Hepatocellular carcinoma is the most common primary malignancy of the liver, with hepatocellular differentiation. It is ranked sixth among the most common cancers worldwide and is the third leading cause of cancer-related deaths. The most important etiological factors discussed here are viral infection (HBV, HCV), exposure to aflatoxin B1, metabolic syndrome, and obesity (as an independent factor). Directly or indirectly, they induce chromosomal aberrations, mutations, and epigenetic changes in specific genes involved in intracellular signaling pathways, responsible for synthesis of growth factors, cell proliferation, differentiation, survival, the metastasis process (including the epithelial-mesenchymal transition and the expression of adhesion molecules), and angiogenesis. All these disrupted molecular mechanisms contribute to hepatocarcinogenesis. Furthermore, equally important is the interaction between tumor cells and the components of the tumor microenvironment: inflammatory cells and macrophages-predominantly with a pro-tumoral role-hepatic stellate cells, tumor-associated fibroblasts, cancer stem cells, extracellular vesicles, and the extracellular matrix. In this paper, we reviewed the molecular biology of hepatocellular carcinoma and the intricate mechanisms involved in hepatocarcinogenesis, and we highlighted how certain signaling pathways can be pharmacologically influenced at various levels with specific molecules. Additionally, we mentioned several examples of recent clinical trials and briefly described the current treatment protocol according to the NCCN guidelines.

Pathologic assessment of hepatocellular carcinoma in the era of immunotherapy: a narrative review

Background and Objective

Immune checkpoint inhibitor (ICI)-based therapy has achieved impressive success in various cancer types. Several ICIs have been unprecedentedly approved as the treatment regimens for advanced hepatocellular carcinoma (HCC) in recent decade. Meanwhile, numerous clinical trials are being performed to exploit more ICIs into initially unresectable HCC and postoperative HCC to expectantly induce adequate tumor downstaging for further resection or implement adjuvant treatment for relapse-free survival, respectively. In this review, we aim to summarize some pragmatic histomorphologic, immunohistochemical, and molecular pathologic parameters which promisingly indicate the response of neoadjuvant/conversion ICI-related therapy and predict the efficacy of adjuvant/therapeutic ICI-related therapy for HCC.

Methods

We searched PubMed using the terms hepatocellular carcinoma, immunotherapy, immune checkpoint inhibitor, immune checkpoint blockade, conversion therapy, neoadjuvant therapy, adjuvant therapy, biomarker, pathologic evaluation, pathologic assessment till February 2023.

Key Content and Findings

Although there is no consensus regarding the pathologic evaluation of relevant HCC specimens, it is encouraging that a few of studies have concentrated on this field, and moreover, the methods and parameters noted on other cancer types are also worthy of reference. For the pathologic assessment of HCC specimens underwent immunotherapy, a suitable sampling scheme, identifying immunotherapy-related pathologic response, and quantification of pathologic response rate should be emphasized. For the patients of HCC who are scheduled to receive immunotherapy, tumor-infiltrating lymphocyte, intratumoral tertiary lymphoid structure, programmed cell death ligand 1, Wnt/β-catenin, microsatellite instability and mismatch repair, tumor mutational burden and tumor neoantigen, as well as some other signaling pathways are the potential predictive biomarkers of treatment response of ICI.

Conclusions

The management of HCC in the era of immunotherapy arises a brand-new pathological challenge that is to provide an immunotherapy-related diagnostic report. Albeit many related researches are preclinical or insufficient, they may tremendously alter the immunotherapy strategy of HCC in future.