A single-cell atlas of the multicellular ecosystem of primary and metastatic hepatocellular carcinoma

Calceolarioside B targets MMP12 in the tumor microenvironment to inhibit M2 macrophage polarization and suppress hepatocellular carcinoma progression

BACKGROUND

Tumor-associated macrophages (TAMs) are crucial in hepatocellular carcinoma (HCC) progression and prognosis, making them promising immunotherapy targets. In traditional Chinese medicine (TCM), qi stagnation and blood stasis are linked to the HCC tumor microenvironment (TME), but few studies explore the effects of related TCM herbs on the TME. Calceolarioside B, a key phenylethanoid glycoside in Akebiae Fructus, has not been well studied for its pharmacological activities or molecular targets, and its role in HCC remains unclear.

PURPOSE

This study aimed to investigate the effects of Calceolarioside B on TAMs in HCC and clarify its potential targets and regulatory mechanisms.

METHODS

Murine intrahepatic transplantation HCC models and macrophage-HCC cell co-culture systems were used to investigate the effects of Calceolarioside B on M2-like TAMs polarization and infiltration, and tumor growth. Cellular thermal shift assay, small molecular pull-down assay and surface plasmon resonance were utilized to identify the potential targets regulating M2-like TAMs. Single-cell RNA sequencing and TCGA dataset analyses clarified the differential expression, prognosis, and TAMs association of the potential targets in HCC.

RESULTS

Calceolarioside B reduces M2-like TAMs polarization and infiltration in the TME by binding to and inhibiting matrix metallopeptidase-12 (MMP12) form both macrophages and HCC cells, thereby preventing immunosuppressive effects. Public database analysis revealed that MMP12 overexpression promoted macrophage infiltration, with MMP12+ macrophages preferentially aggregating in primary and metastatic HCC tumors.

CONCLUSION

Calceolarioside B is identified as a novel MMP12 inhibitor modulating TAMs in the TME, offering a potential TAM-targeting strategy for HCC therapy.

Nicotinamide metabolism affects the prognosis of hepatocellular carcinoma by influencing the tumor microenvironment

In this study, we utilized the public database along with single-cell genomics techniques to systematically analyze the expression patterns and clinical significance of key genes in the nicotinamide metabolism pathway in liver cancer samples. The findings indicate that differential nicotinamide metabolism-related key genes are expressed in liver cancer samples. The liver cancer samples were put into separate subgroups using consistency clustering analysis based on differential gene expression levels observed. Additionally, immune infiltration and drug sensitivity analysis also revealed differences between the two subgroups. Survival analysis suggested that the key genes were associated with prognosis. Finally, a prognostic model was established using the key genes, offering a fresh viewpoint on the molecular mechanism investigating liver cancer. This study demonstrated the significant correlation between key genes in the nicotinamide metabolism pathway and the occurrence and progression of liver cancer and indicated that these key genes could serve as prognostic markers and tailored treatment targets for liver cancer.

Development of a circadian-related prognostic signature highlights RBM17 as a stemness regulator in liver cancer

The liver exhibits extensive circadian regulation among organs. Epidemiological studies have substantiated that disruptions in circadian rhythm constitute a risk factor for the oncogenesis of liver cancer. Nonetheless, the molecular underpinnings of how circadian dysregulation influences liver cancer progression remain elusive. Our research aims to elucidate these mechanisms and develop a predictive model for prognosis and treatment responsiveness. Our multi-omics analysis revealed extensive dysregulation of liver circadian genes (LCGs) in liver cancer. Employing machine learning algorithms, we pinpointed four pivotal dysregulated LCGs. Through the integration of single-cell, bulk, and spatial transcriptomics, we further elucidated the interconnections between LCGs dysregulation and the tumor microenvironment. In vivo and in vitro experiments demonstrated that RBM17, identified as a crucial dysregulated LCG, promotes the progression of liver cancer and cisplatin resistance by facilitating cancer stem cell phenotype. The circadian prognosis scores (CPS), based on these four genes, effectively reflected the prognosis of liver cancer patients and their responses to various therapeutic interventions. Mechanism of Action (MOA) analysis suggested that high CPS level may sensitize tumors to cell cycle-targeted therapies. Collectively, our findings provide new insights into the interplay between liver circadian gene regulation and liver cancer progression, and propose novel therapeutic targets for liver cancer.

TGF-β1 induces autophagy and mediates the effect on macrophages differentiation in primary liver cancer

BACKGROUND

Tumor-associated macrophages (TAMs) are closely associated with tumor development and patient outcomes due to their plasticity and polarization capacity. Several distinct TAMs have been proposed, but a complete understanding of heterogeneity and differentiation spectrum of macrophage in human primary liver cancer remains elusive.

METHODS

Deep single-cell RNA sequencing (scRNA-seq) data from 19 primary liver cancer patients were used to profile the transcriptomes of TAMs in liver cancer. Ingenuity pathway analysis (IPA) and in vitro experiments were used to explore possible mechanisms responsible for related signaling pathways altered at the transcriptional level. Finally, we analyzed the relationship between the abundance of the TAMs and the survival outcomes of the 428 patients in the Cancer Genome Atlas (TCGA).

RESULTS

Transcriptional profiles allowed us to identify four distinct TAMs cell subsets based on molecular and functional properties and to reconstruct their developmental trajectory. Specifically, TAM_c4 was preferentially enriched and potentially expanded in the advanced-stage patients or those receiving immune checkpoint blockade therapy (ICT). Gene pathway analysis revealed aberrant TGFB1 activation in TAM_c4, which was experimentally confirmed to drive TAM phenotypic transitions via autophagy signaling. High abundance of TAM_c4 is found to be related to a short survival time and low abundance of CD8+ T cells in primary liver cancers.

CONCLUSIONS

This integrated transcriptome compendium and experimental validation offer both mechanistic insights and a resource for understanding TAM heterogeneity in primary liver cancers.

A comprehensive review and in silico analysis of the role of survivin (BIRC5) in hepatocellular carcinoma hallmarks: A step toward precision

Hepatocellular carcinoma (HCC) is a complex malignancy driven by the dysregulation of multiple cellular pathways. Survivin, a key member of the inhibitor of apoptosis (IAP) family, plays a central role in HCC tumorigenesis and progression. Despite significant research, a comprehensive understanding of the contributions of survivin to the hallmarks of cancer, its molecular network, and its potential as a therapeutic target remains incomplete. In this review, we integrated bioinformatics analysis with an extensive literature review to provide deeper insights into the role of survivin in HCC. Using bioinformatics tools such as the Human Protein Atlas, GEPIA, STRING, TIMER, and Metascape, we analyzed survivin expression and its functional associations and identified the top 20 coexpressed genes in HCC. These include TK1, SPC25, SGO2, PTTG1, PRR11, PLK1, NCAPH, KPNA2, KIF2C, KIF11, HJURP, GTSE1, FOXM1, CEP55, CENPA, CDCA3, CDC45, CCNB2, CCNB1 and CTD-2510F5.4. Our findings also revealed significant protein-protein interactions among these genes, which were enriched in pathways associated with the FOXM1 oncogenic signaling cascade, and biological processes such as cell cycle regulation, mitotic checkpoints, and diseases such as liver neoplasms. We also discussed the involvement of survivin in key oncogenic pathways, including the PI3K/AKT, WNT/β-catenin, Hippo, and JAK/STAT3 pathways, and its role in modulating cell cycle checkpoints, apoptosis, and autophagy. Furthermore, we explored its interactions with the tumor microenvironment, particularly its impact on immune modulation through myeloid-derived suppressor cells (MDSCs), tumor-associated macrophages, and natural killer cell function in HCC. Additionally, we highlighted its involvement in alkylglycerone phosphate synthase (AGPS)-mediated lipid reprogramming and identified important gaps in the survivin network that warrant further investigation. This review also examined the role of survivin in cancer stemness, inflammation, and virally mediated hepatocarcinogenesis. We evaluated its potential as a diagnostic, prognostic, predictive, and pharmacodynamic biomarker in HCC, emphasizing its relevance in precision medicine. Finally, we summarized emerging survivin-targeted therapeutics and ongoing clinical trials, underscoring the need for novel strategies to effectively target survivin in HCC.

Integration of Bulk RNA and Single-Cell Analyses Reveal Distinct Expression Patterns of Anoikis-Related Genes and the Immunosuppressive Role of NQO1+ Macrophages in Hepatocellular Carcinoma

Anoikis resistance plays a crucial role in the proliferation, metastasis, and invasion of hepatocellular carcinoma (HCC). However, the key genes involved remain to be identified. This study aimed to investigate the prognostic value and impact of anoikis-related genes (ARGs) on the immunosuppressive microenvironment in HCC patients through the integration of bulk RNA and single-cell RNA sequencing (scRNA-seq) bioinformatic analysis. An anoikis-related gene risk score model (ARGRS) comprising 11 ARGs was established via machine learning. scRNA-seq was performed to assess the heterogeneity of ARGs in HCC. In vitro experiments were conducted to investigate the effects of NAD(P)H: quinone oxidoreductase 1 (NQO1) on the polarization, phenotype, and function of macrophages. Bioinformatics analysis demonstrated that ARGRS had perfect efficiency in predicting the prognosis of HCC patients and that ARGs potentially play a role in maintaining the invasion and metastasis of malignant cells. Notably, NQO1+ macrophages presented features consistent with alternatively activated macrophages (M2) and displayed a powerful immunosuppressive effect, particularly in close interaction with T cells within the tumor immune microenvironment. Moreover, inhibition of NQO1 expression via dicoumarol resulted in reduced expression of the M2-associated markers CD206 and CD163, as well as the immunosuppressive cytokines IL-10 and TGF-β. Strikingly, this treatment effectively mitigated the immunosuppressive impact of macrophages on T cells. Collectively, ARGs are closely associated with the poor prognosis of HCC patients, and NQO1+ macrophages may have an immunosuppressive effect on HCC, suggesting that intervention in anoikis may represent a potential strategy for HCC treatment.

Lipid Metabolism Reprogramming in Tumor-Associated Macrophages Modulates Their Function in Primary Liver Cancers

Lipids are a complex class of biomolecules with pivotal roles in the onset, progression, and maintenance of cancers. Lipids, derived from the tumor microenvironment (TME) or synthesized by cancer cells themselves, govern a large variety of pro-tumorigenic functions. In recent years, lipid metabolism and the reprogramming of liver cancer cells have received increasing attention, revealing that altered regulation of diverse lipid species, including triacylglycerols, phospholipids, sphingolipids, ceramides, fatty acids, and cholesterol, actively contributes to the initiation and progression of primary liver cancer. Lipid metabolic reprogramming also modifies the TME by influencing the recruitment, activation, and function of immune cells. Tumor-associated macrophages (TAM) are essential components of TME that sustain cancer growth, promoting invasion and mediating immune evasion. Macrophage polarization toward a tumor-supportive phenotype is associated with metabolic reprogramming. Indeed, lipid accumulation and enhanced fatty acid oxidation in TAM contribute to polarization to a M2 phenotype. In this review, we examine lipid metabolism in hepatocellular carcinoma and cholangiocarcinoma, focusing on TAM lipid metabolic reprogramming.

Gpr109A in TAMs promoted hepatocellular carcinoma via increasing PKA/PPARγ/MerTK/IL-10/TGFβ induced M2c polarization

To delineate Gpr109A's role and mechanisms in modulating the immune microenvironment of hepatocellular carcinoma. Employing Gpr109A-knockout mice and in vitro co-cultures of hepatocellular carcinoma cells with macrophages, this study utilized a suite of techniques, including lentiviral vectors for stable cell line establishment, Western blotting, cell scratch, CCK-8, transwell assays, flow cytometry, immunohistochemistry and phagocytosis assay to assess various cellular behaviors and interactions. Gpr109A deletion markedly reduced the oncogenic potential of H22 cells, both in vivo and when co-cultured with knockout macrophages, impairing their growth, invasion, and migration. In Gpr109A-knockout macrophages, an upregulation of MerTK and a reduction in immunosuppressive cytokine release were observed, indicating a shift towards an M2c macrophage phenotype. This shift is linked to Gpr109A's role in promoting protease overexpression and inhibiting SHP2 phosphorylation, crucial for enhancing cancer cell proliferation and invasiveness. Gpr109A significantly influences macrophage polarization to the M2c type, augmenting hepatocellular carcinoma cell aggressiveness.

Chaperonin containing TCP1 subunit 5 as a novel pan-cancer prognostic biomarker for tumor stemness and immunotherapy response: insights from multi-omics data, integrated machine learning, and experimental validation

BACKGROUND

Chaperonin containing TCP1 subunit 5 (CCT5), a vital component of the molecular chaperonin complex, has been implicated in tumorigenesis, cancer stemness maintenance, and therapeutic resistance. Nevertheless, its comprehensive roles in pan-cancer progression, underlying biological functions, and potential as a predictor of immunotherapy response remains poorly understood.

METHODS

We performed a comprehensive multi-omics pan-cancer analysis of CCT5 across 33 cancer types, integrating bulk RNA-seq, single-cell RNA-seq (scRNA-seq), and spatial transcriptomics data. CCT5 expression patterns, prognostic relevance, stemness association, and immune microenvironment relationships were evaluated. A novel CCT5-based signature (CCT5.Sig) was developed using machine learning on 23 immune checkpoint blockade (ICB) cohorts (n = 1394) spanning eight cancer types. Model performance was assessed using AUC metrics and survival analyses.

RESULTS

CCT5 was significantly overexpressed in tumor tissues and primarily localized to malignant and cycling cells. High CCT5 expression correlated with poor prognosis in multiple cancers and was enriched in oncogenic, cell cycle, and DNA damage repair pathways. CCT5 expression was positively associated with mRNAsi, mDNAsi, and CytoTRACE scores, indicating a role in stemness maintenance. Furthermore, CCT5-high tumors exhibited immune-cold phenotypes, with reduced TILs and CD8⁺ T cell activity. The CCT5.Sig model, based on genes co-expressed with CCT5, achieved superior predictive accuracy for ICB response (AUC = 0.82 in validation and 0.76 in independent testing), outperforming existing pan-cancer signatures.

CONCLUSION

This study reveals the multifaceted oncogenic roles of CCT5 and highlights its potential as a pan-cancer biomarker for prognosis and immunotherapy response. The machine learning-derived CCT5.Sig model provides a robust tool for patient stratification and may inform personalized immunotherapy strategies.

Research progress of CD73-adenosine signaling regulating hepatocellular carcinoma through tumor microenvironment

Adenosine signaling pathway is a kind of signal regulation hub widely existing in human body, which is involved in a series of physiological processes such as energy supply of body cells. CD73 is a highly concerned signaling protein in purine adenosine pathway, and its role in tumor development and prognosis has been paid more and more attention in recent years, especially in hepatocellular carcinoma (HCC). In this paper, the specific mechanism by which CD73-adenosine signaling regulates tumor microenvironment (TME) of liver cancer tumors was analyzed in detail, highlighting the importance of this pathway as a therapeutic target to combat tumor immunosuppression and enhance the anti-tumor immune response to prevent and treat hepatocellular carcinoma (HCC). In addition, a variety of current targeted therapeutic strategies for adenosine metabolic pathways are summarized, including the development of new drugs in the stage of preclinical research and clinical trials, and the mechanism of action, implementation possibility, and clinical effects of these therapies are discussed. By summarizing the latest scientific research results, in this review, we attempt to paint a panorama of the mechanism of adenosine action in tumor immunotherapy, with the aim to provide a solid theoretical basis and practical guidance for subsequent research and clinical application, ultimately promoting the development of more accurate and efficient tumor immunotherapy.

CellFM: a large-scale foundation model pre-trained on transcriptomics of 100 million human cells

Single-cell sequencing provides transcriptomic profiling at single-cell resolution, uncovering cellular heterogeneity with unprecedented precision. Yet, current single cell data analysis suffers from the inherent data noises, batch effects, and sparsity, highlighting the requirement of a unified model to represent cellular states. To circumvent this problem, many recent efforts focus on training single-cell foundation models based on large datasets. However, current human foundation models are still limited by the sizes of training data and model parameters. Here, we have collected a diverse dataset of 100 million human cells, on which we train a single-cell foundation model (CellFM) containing 800 million parameters. To balance efficiency and performance, the model is trained through a modified RetNet framework on the MindSpore. Extensive experiments have shown that CellFM outperforms existing models in cell annotation, perturbation prediction, gene function prediction, and gene-gene relationship capturing.

Exploring the potential function of high expression of ANAPC1 in regulating ubiquitination in hepatocellular carcinoma

BACKGROUND

ANAPC1, a key regulator of the ubiquitination in tumour development, has not been thoroughly studied in hepatocellular carcinoma (HCC).

AIM

To elucidate the expression of ANAPC1 in HCC and its potential regulatory mechanism related to ubiquitination.

METHODS

Bulk RNA (RNA sequencing and microarrays), immunohistochemistry (IHC) tissues, and single-cell RNA sequencing (scRNA-seq) data were integrated to comprehensively investigate ANAPC1 expression in HCC. Clustered regularly interspaced short palindromic repeats analysis was performed to assess growth in HCC cell lines following ANAPC1 knockout. Enrichment analyses were conducted to explore the functions of ANAPC1. ScRNA-seq data was used to examine the cell cycle and metabolic levels. CellChat analysis was applied to investigate the interactions between ANAPC1 and different cell types. The relationship between ANAPC1 expression and drug concentration was analyzed.

RESULTS

ANAPC1 messenger RNA was found to be upregulated in bulk RNA, IHC tissues samples and malignant hepatocytes. The proliferation of JHH2 cell lines was most significantly inhibited after ANAPC1 knockdown. In biological pathways, the development of HCC was found to be linked to the regulation of ubiquitin-mediated proteolysis. Additionally, scRNA-seq results indicated that highly expressed ANAPC1 was in the G2/M phase, with increased glycolysis/gluconeogenesis activity. A CellChat analysis showed that ANAPC1 was associated with the regulation of the migration inhibitory factor-(cluster of differentiation 74 + C-X-C chemokine receptor type 4) pathway. Higher ANAPC1 expression correlated with stronger effects of sorafenib, dasatinib, ibrutinib, lapatinib, nilotinib and afatinib.

CONCLUSION

The high expression level of ANAPC1 may regulate the cell cycle and metabolic levels of HCC through the ubiquitination-related pathway, thereby promoting disease progression.

Multi-omic analysis reveals a CAF-stemness-governed classification in HCC liver transplant recipients beyond the Milan criteria

In patients with hepatocellular carcinoma (HCC) meeting the Milan criteria, liver transplantation (LT) is an effective therapy. This study aims to define the survival-related molecular biological features helping precisely identifying the patients with HCC beyond the Milan criteria who have acceptable outcomes. In the derivation cohort, integrated analyses of tumor tissues are conducted using RNA sequencing (RNA-seq), proteomic landscape, and transposase-accessible chromatin sequencing (ATAC-seq). Based on transcriptomics, three subgroups that significantly differ in overall survival were identified in the derivation cohort, and these findings are validated in an independent cohort. In-depth bioinformatics analysis using RNA-seq and proteomics reveals that the promotion of cancer stemness by cancer-associated fibroblasts (CAFs) can be responsible for the negative biological characteristics observed in high-risk HCC patients. The ATAC-seq identifies key factors regulating transcription, which may bridge CAF infiltration and stemness. Finally, we demonstrate that the CAF-derived CXCL12 sustains the stemness of HCC cells by promoting XRCC5 through CXCR4.

Integration of single-cell and bulk RNA sequencing identifies and validates T cell-related prognostic model in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a lethal malignancy, and predicting patient prognosis remains a significant challenge in clinical treatment. T cells play a crucial role in the tumor microenvironment, influencing tumorigenesis and progression. In this study, we constructed a T cell-related prognostic model for HCC. Using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, we identified 6,281 T cells from 10 HCC patients and subsequently identified 855 T cell-related genes. Comprehensive analyses were conducted on T cells and their associated genes, including enrichment analysis, cell-cell communication, trajectory analysis, and transcription factor analysis. By integrating scRNA-seq and bulk RNA-seq data with prognostic information from The Cancer Genome Atlas (TCGA), we identified T cell-related prognostic genes and constructed a model using LASSO regression. The model, incorporating PTTG1, LMNB1, SLC38A1, and BATF, was externally validated using the International Cancer Genome Consortium (ICGC) database. It effectively stratified patients into high- and low-risk groups based on risk scores, revealing significant differences in immune cell infiltration between these groups. Differential expression levels of PTTG1 and BATF between HCC and adjacent non-tumor tissues were further validated by immunohistochemistry (IHC) in 25 patient tissue samples. Moreover, a Cox regression analysis was performed to integrate risk scores with clinical features, resulting in a nomogram capable of predicting patient survival probabilities. This study introduces a novel prognostic risk model for HCC patients, aimed at stratifying patients by risk, enhancing personalized treatment strategies, and offering new insights into the role of T cell-related genes in HCC progression.

Targeting HMGB2 acts as dual immunomodulator by bolstering CD8+ T cell function and inhibiting tumor growth in hepatocellular carcinoma

T cell exhaustion is a critical obstacle for durable treatment response in hepatocellular carcinoma (HCC). Developing drugs that control tumor growth and simultaneously bolster immune function is of great significance. Although high-mobility group box 2 (HMGB2) has been reported to be crucial to HCC prognosis, its role in the tumor microenvironment remains unclear. Here, we found HMGB2+ CD8+ T cells as being associated with immune exhaustion and resistance to anti-PD-1 treatment through single-cell RNA sequencing. Mechanistically, HMGB2 impaired the oxidative phosphorylation in CD8+ T cells and inactivated the interferon-γ response in tumor cells, reducing the antitumor effector function. Tannic acid, a specific inhibitor of HMGB2, synergized with PD-1 antibody to attenuate tumor growth and reverse T cell exhaustion. Our findings highlight the unique role of HMGB2 as an immune exhaustion associated molecule. Targeting HMGB2 on both CD8+ T cells and tumor cells contributed to promising treatment strategies for HCC.

Identification of a cancer stem cell-like subpopulation that promotes HCC metastasis

Background & Aims

Cancer stem cells (CSCs) are well-established drivers of tumorigenesis, but their role in regulating tumor metastasis remains poorly understood. Here, we report the identification and characterization of a cluster of metastasis-promoting CSC-like cells in hepatocellular carcinoma (HCC).

Methods

CSC-like cells in HCC were identified through the analysis of single cell RNA-sequencing data from 19 HCC samples. The stemness and invasive characteristics of these cells were evaluated using bioinformatical analyses of nine clinical cohorts and experimental validations. Spatial transcriptomics sequencing of 12 HCC samples revealed the cellular interactions between the CSC-like cells and tumor microenvironments, which were validated through gene co-expression analyses and immunohistochemistry. Finally, signaling pathway blockade was used to assess the potential clinical application of CSC-like cells.

Results

Through comprehensive analyses of single cell RNA-sequencing data from 19 patients with HCC and spatial transcriptomics data from 12 patients with HCC, a metastasis-promoting CSC-like subpopulation was identified. These CSC-like cells expressed high levels of epithelial-mesenchymal transition genes and were associated with poor prognosis of HCC. Histologically, CSC-like cells were enriched in highly aggressive tumors, especially in intrahepatic disseminated foci, where they interacted with immune cells. Functionally, CSC-like cells induced macrophage M2 polarization and T cell exhaustion through the ICAM1 signaling pathway, forming immunosuppressive microenvironments. Downregulation of ICAM1 expression in CSC-like cells suppressed macrophage M2-polarization and T cell exhaustion, thereby reversing antitumor immune effects.

Conclusions

Our study identified a metastasis-promoting CSC subpopulation, providing a potential perspective for CSC-targeted therapies in HCC.

Impact and implications

The heterogeneity of CSCs in HCC has been identified, yet the identification and characterization of metastasis-promoting CSC subpopulations remain unexplored. Here, we identified a CSC-like tumor cell subpopulation that promotes HCC metastasis by increasing cell invasiveness and suppressing antitumor immune responses via the ICAM1 signaling pathway. Our study uncovers novel mechanisms of HCC metastasis from the perspective of CSCs, and proposes potential tumor therapeutic strategies by inhibiting cellular interactions between CSC-like cells and immune cells.

SCGB3A1-Epi and KLK10-Epi Crosstalk With Fibroblasts Promotes Liver Metastasis of Breast Cancer and Pancreatic Ductal Adenocarcinoma

BACKGROUND

The liver often serves as the principal site for metastatic spread from a variety of solid tumors, and metastasis to the liver markedly diminishes patient survival. Single-cell RNA sequencing (scRNA-seq) has helped uncover the complexity of liver tumor metastasis. However, the key cellular subtypes of breast cancer and pancreatic ductal adenocarcinoma (PDAC) with liver metastasis and their mechanisms of action are unclear, making treatment difficult.

METHODS

We used integrated scRNA-seq data to dissect liver metastasis-specific epithelial cell subtypes in breast cancer and PDAC, and elucidated their mechanisms through functional analyses and intercellular interactions with fibroblasts.

RESULTS

Interestingly, our results show that SCGB3A1-Epi and KLK10-Epi are key drivers of liver metastasis in breast cancer and PDAC, respectively. These subtypes are associated with high malignancy rates and involved in oxidative phosphorylation and other critical pathways. Specific ligand-receptor interactions were observed between these epithelial subtypes and fibroblasts, with significant interactions between CD74-APP receptors in SCGB3A1-Epi and Fib-11 in breast cancer and between SPP1-CD44 receptors in KLK10-Epi and Fib-11 in PDAC. High expression levels of Fib-11 and CD74 were correlated with improved survival in breast cancer, whereas high SPP1 and CD44 expression predicted worse PDAC outcomes. Fib-11 is implicated in signaling pathways associated with tumor metastasis, particularly those involving cell adhesion molecules.

CONCLUSIONS

We revealed the cellular heterogeneity of liver metastasis and provided a crucial research foundation for developing novel therapeutic strategies to specifically target metastatic cell subtypes, thereby enhancing patient prognosis.

Multiple-omics analysis of aggrephagy-related cellular patterns and development of an aggrephagy-related signature for hepatocellular carcinoma

BACKGROUND

Protein aggrephagy, a selected autophagy process response for degrading protein aggregates, plays a critical role in various cancers. However, its regulatory mechanisms and clinical implications in hepatocellular carcinoma (HCC) remain largely unexplored.

METHODS

We integrated bulk RNA-seq data from TCGA and single-cell RNA sequencing (scRNA-seq) data from GEO databases to systematically analyze aggrephagy-related genes (AGGRGs) in HCC. Prognostic aggrephagy-related genes (AGGRGs) were identified through univariate Cox and LASSO regression analyses, followed by the construction of a risk prediction model. Patients were stratified into high- and low-risk groups based on the median risk score. Comparative analyses were performed to assess clinical outcomes, pathway enrichment, and drug sensitivity. Independent risk factors were incorporated a nomogram using univariate and multivariate Cox regression. At the single-cell level, the AGG scores were calculated using AUCell algorithm, and cell interactions and pseudotime trajectory analyses were conducted. Finally, protein levels of key AGGRG was assessed via tissue microarray.

RESULTS

Eight AGGRGs (PFKP, TPX2, UBE2S, GOT2, ST6GALNAC4, ADAM15, G6PD, and KPNA2) were identified as prognostic markers for HCC. The high-risk group exhibited significantly worse survival outcomes, heightened drug resistance, and enrichment in cell cycle, mTORC1 signaling, and reactive oxygen species pathways. Single-cell transcriptomic analysis revealed 11 distinct cell types within the HCC tumor microenvironment (TME), including hepatocytes, T cells, NK cells, macrophages, monocytes, dendritic cells, plasma B cells, mature B cells, mast cells, endothelial cells, and fibroblasts. Hepatocytes exhibited the highest AGGRG scores and were associated with metabolic reprograming, proliferation, and immune evasion. Further subclustering of malignant hepatocytes using inferCNV revealed eight functionally heterogeneous subpopulations with extensive intercellular crosstalk. Trajectory analysis showed G6PD- and CCNB1-expressing subpopulations in early-to-intermediate differentiation states, whereas C3 and ARGs marked terminal differentiation. Notably, G6PD was predominantly expressed in early and mid-stages, while KPNA2, PFKP, and TPX2 were upregulated in advanced tumor states. Immunohistochemical (IHC) validation confirmed significant overexpression of G6PD in HCC tissues compared to adjacent normal tissues.

CONCLUSION

These findings provide a molecular framework for targeting aggrephagy pathways in HCC treatment strategies.

LAMA4+ CD90+ eCAFs provide immunosuppressive microenvironment for liver cancer through induction of CD8+ T cell senescence

Despite significant advances in cancer biology research and treatment, clinical outcomes for patients with liver cancer remain unsatisfactory. The biological and molecular mechanisms underlying the bidirectional signaling between tumor cells and the tumor microenvironment (TME), which promotes tumor progression in the liver, remain to be elucidated. Fibroblasts are crucial regulators of tumor progression and response to therapy; however, our understanding of their roles remains limited. Here, we integrated single-cell RNA sequencing and spatial transcriptomic data of pan-liver cancers to characterize the different subtypes of cancer-associated fibroblasts (CAFs). siRNA transfection was used for knockdown the expression of LAMA4. Western blot assay was used for gene expression analysis. Flow cytometry was used to detect proliferation, toxicity and cytolytic capacity of CD8+ T cells. To establish a spontaneous murine hepatocellular carcinoma (HCC) model, a combined DEN and CCL4 approach was performed. Notably, we identified CD90+ extracellular matrix CAFs (eCAFs) associated with poor prognosis. These CD90+ eCAFs, located distal to the tumor nest, overlapped with the distribution of CD8+ T cells. Functional experiments demonstrated that CD90+ eCAFs recruited CD8+ T cells and inhibited their function through secretion of LAMA4. Further investigation revealed that LAMA4 induced the CD8+ T cell senescence through a DNA damage signaling pathway mediated by the receptor ITGA6. In a mouse model of spontaneous HCC, targeting LAMA4 can inhibit the progression of malignant transformation and synergize with anti-PD-1 therapy. Our study reveals the function of specific CAFs subtypes and highlights the importance of interactions with the immune system.

Macrophage-derived exosomes in cancer: a double-edged sword with therapeutic potential

Solid cancer contains a complicated communication network between cancer cells and components in the tumor microenvironment (TME), significantly influencing the progression of cancer. Exosomes function as key carriers of signaling molecules in these communications, including the intricate signalings of tumor-associated macrophages (TAMs) on cancer cells and the TME. With their natural lipid bilayer structures and biological activity that relates to their original cell, exosomes have emerged as efficient carriers in studies on cancer therapy. Intrigued by the heterogeneity and plasticity of both macrophages and exosomes, we regard macrophage-derived exosomes in cancer as a double-edged sword. For instance, TAM-derived exosomes, educated by the TME, can promote resistance to cancer therapies, while macrophage-derived exosomes generated in vitro have shown favorable potential in cancer therapy. Here, we depict the reasons for the heterogeneity of TAM-derived exosomes, as well as the manifold roles of TAM-derived exosomes in cancer progression, metastasis, and resistance to cancer therapy. In particular, we emphasize the recent advancements of modified macrophage-derived exosomes in diverse cancer therapies, arguing that these modified exosomes are endowed with unique advantages by their macrophage origin. We outline the challenges in translating these scientific discoveries into clinical cancer therapy, aiming to provide patients with safe and effective treatments.

Multiple machine learning algorithms identified SLC6A8 as a diagnostic biomarker of the late stage of Hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a chronic liver disease characterized by persistent tumor growth, contributing significantly to mortality rates worldwide. Consequently, there is an urgent need to develop effective diagnostic and treatment strategies for HCC. This study aims to identify crucial genes for early HCC diagnosis to mitigate disease progression and to investigate differences in immune cell infiltration between early-stage and late-stage HCC. We integrated two published datasets for a comprehensive analysis, identifying 575 DEGs subjected to GSEA to reveal pathways distinguishing early-stage from late-stage HCC. Notably, the gene SLC6A8 emerged as a potential diagnostic biomarker for late-stage HCC through machine learning (LASSO-LR/SVM-RFE/RF-Boruta). ROC curves for SLC6A8 were utilized to evaluate diagnostic accuracy. The ImmuCellAI algorithm assessed immune cell composition differences between early and late-stage HCC, revealing that SLC6A8 expression positively correlates with resting Tfh cells and Th2, while negatively correlating with B cells, indicating its association with immune cell infiltration patterns. To strengthen our results, we further analyzed SLC6A8 expression using single-cell transcriptome data, confirming notably overexpression in late-stage HCC, particularly in key liver cell types such as Hepatocyte cells. Overall, our study nominates SLC6A8 as a dual biomarker for HCC Staging and precision therapy.

Clinical potential and experimental validation of prognostic genes in hepatocellular carcinoma revealed by risk modeling utilizing single cell and transcriptome constructs

Background

Hepatocellular carcinoma (HCC) is the leading cause of tumor-related mortality worldwide. There is an urgent need for predictive biomarkers to guide treatment decisions. This study aimed to identify robust prognostic genes for HCC and to establish a theoretical foundation for clinical interventions.

Methods

The HCC datasets were obtained from public databases and then differential expression analysis were used to obtain significant gene expression profiles. Subsequently, univariate Cox regression analysis and PH assumption test were performed, and a risk model was developed using an optimal algorithm from 101 combinations on the TCGA-LIHC dataset to pinpoint prognostic genes. Immune infiltration and drug sensitivity analyses were conducted to assess the impact of these genes and to explore potential chemotherapeutic agents for HCC. Additionally, single-cell analysis was employed to identify key cellular players and their interactions within the tumor microenvironment. Finally, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was utilized to validate the roles of these prognostic genes in HCC.

Results

A total of eight prognostic genes were identified (MCM10, CEP55, KIF18A, ORC6, KIF23, CDC45, CDT1, and PLK4). The risk model, constructed based on these genes, was effective in predicting survival outcomes for HCC patients. CEP55 exhibited the strongest positive correlation with activated CD4 T cells. The top 10 drugs showed increased sensitivity in the low-risk group. B cells were identified as key cellular components with the highest interaction numbers and strengths with macrophages in both HCC and control groups. Prognostic genes were more highly expressed in the initial state of B cell differentiation. RT-qPCR confirmed significant upregulation of MCM10, KIF18A, CDC45, and PLK4 in HCC tissues (p< 0.05).

Conclusion

This study successfully identified eight prognostic genes (MCM10, CEP55, KIF18A, ORC6, KIF23, CDC45, CDT1, and PLK4), which provided new directions for exploring the potential pathogenesis and clinical treatment research of HCC.

Integrating bulk and single-cell RNA sequencing reveals SH3D21 promotes hepatocellular carcinoma progression by activating the PI3K/AKT/mTOR pathway

As a novel genetic biomarker, the potential role of SH3D21 in hepatocellular carcinoma remains unclear. Here, we decipher the expression and function of SH3D21 in human hepatocellular carcinoma. The expression level and clinical significance of SH3D21 in hepatocellular carcinoma patients, the relationship between SH3D21 and the features of tumor microenvironment (TME) and role of SH3D21 in promoting hepatocellular carcinoma progression were analyzed based on the bulk samples obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Single-cell sequencing samples from Gene Expression Omnibus (GEO) database were employed to verify the prediction mechanism. Additionally, different biological effects of SH3D21 on hepatocellular carcinoma cells were investigated by qRT-PCR, CCK-8 assay, colony forming assay and Western blot analysis. Bioinformatics analysis and in vitro experiments revealed that the expression level of SH3D21 was up-regulated in hepatocellular carcinoma and correlated with the poor prognosis in hepatocellular carcinoma patients. SH3D21 effectively promoted the proliferation, invasion, and migration as well as the formation of immunosuppressive microenvironment of hepatocellular carcinoma. In addition, SH3D21 can activate the PI3K/AKT/mTOR signaling pathway. SH3D21 stimulates the progression of hepatocellular carcinoma by activating the PI3K/AKT/mTOR signaling pathway, and SH3D21 can serve as a prognostic biomarker and therapeutic target for hepatocellular carcinoma.

Precision surgery for hepatocellular carcinoma

Hepatocellular carcinoma arises in the setting of cirrhosis in most cases, requiring multidisciplinary input to define resectability. In this regard, more precise surgical management considers patient factors and anatomical states, including resection margins, tumour biology, and perioperative therapy. Together with advances in surgical techniques, this integrated approach has resulted in considerable improvements in patient morbidity and oncological outcomes. Despite this, recurrence rates in hepatocellular carcinoma remain high. As the systemic treatment landscape in hepatocellular carcinoma continues to evolve and locoregional options are increasingly used, we review current and future opportunities to individualise the surgical management of patients with hepatocellular carcinoma.

FABP5+ lipid-loaded macrophages process tumour-derived unsaturated fatty acid signal to suppress T-cell antitumour immunity

BACKGROUND & AIMS

Tumour-associated macrophages (TAMs) contribute to hepatocellular carcinoma (HCC) progression. However, while the pro-tumour and immunosuppressive roles of lipid-loaded macrophages are well established, the mechanisms by which lipid metabolism enhances the tumour-promoting effects of TAMs remain unclear.

METHODS

Single-cell RNA sequencing was performed on mouse and human HCC tumour samples to elucidate the landscape of HCC TAMs. Macrophages were stimulated with various long-chain unsaturated fatty acids (UFAs) to assess immunosuppressive molecule expression in vitro. Additionally, in vivo and in vitro studies were conducted using mice with macrophage-specific deficiencies in fatty acid-binding protein 5 (FABP5) or peroxisome proliferator-activated receptor γ (PPARγ).

RESULTS

Single-cell RNA sequencing identified a subpopulation of FABP5+ lipid-loaded TAMs characterized by enhanced immune checkpoint blocker ligands and immunosuppressive molecules in an oncogene-mutant HCC mouse model and human HCC tumours. Mechanistically, long-chain UFAs released by tumour cells activate PPARγ via FABP5, resulting in immunosuppressive properties in TAMs. FABP5 deficiency in macrophages decreases immunosuppressive molecule expression, enhances T cell-dependent antitumour immunity, diminishes HCC growth, and improves immunotherapy efficacy.

CONCLUSIONS

This study demonstrates that UFAs promote tumourigenesis by enhancing the immunosuppressive tumour microenvironment via FABP5-PPARγ signalling and provides a proof-of-concept for targeting this pathway to improve the efficacy of tumour immunotherapy.

IMPACT AND IMPLICATIONS

Despite the role of tumour-associated macrophages (TAMs) in promoting tumour progression being well established, the mechanisms by which lipid metabolism enhances the tumour-promoting effects of TAMs remain unclear. Our study reveals that FABP5-mediated unsaturated fatty acid metabolism in TAMs is crucial for modulating antitumour T-cell immunity and influencing the efficacy of immunotherapy. This finding provides novel insights into the immunomodulatory roles of FABP5+ lipid-loaded TAMs in hepatocellular carcinoma and suggests that targeting FABP5 could offer a new approach to liver cancer treatment.

Deciphering the Oncogenic Landscape of Hepatocytes Through Integrated Single-Nucleus and Bulk RNA-Seq of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality, while the hepatocyte mechanisms driving oncogenesis remains poorly understood. In this study, single-nucleus RNA sequencing of samples from 22 HCC patients revealed 10 distinct hepatocyte subtypes, including beneficial Hep0, predominantly malignant Hep2, and immunosuppressive Hep9. These subtypes were strongly associated with patient prognosis, confirmed in TCGA-LIHC and Fudan HCC cohorts through hepatocyte composition deconvolution. A quantile-based scoring method is developed to integrate data from 29 public HCC datasets, creating a Quantile Distribution Model (QDM) with excellent diagnostic accuracy (Area Under the Curve, AUC = 0.968-0.982). QDM was employed to screen potential biomarkers, revealing that PDE7B functions as a key gene whose suppression promotes HCC progression. Guided by the genes specific to Hep0/2/9 subtypes, HCC is categorized into metabolic, inflammatory, and matrix classes, which are distinguishable in gene mutation frequencies, survival times, enriched pathways, and immune infiltration. Meanwhile, the sensitive drugs of the three HCC classes are identified, namely ouabain, teniposide, and TG-101348. This study presents the largest single-cell hepatocyte dataset to date, offering transformative insights into hepatocarcinogenesis and a comprehensive framework for advancing HCC diagnostics, prognostics, and personalized treatment strategies.

Unraveling the immune-activated tumor microenvironment correlated with clinical response to atezolizumab plus bevacizumab in advanced HCC

Background & Aims

Despite atezolizumab plus bevacizumab being a standard treatment for advanced hepatocellular carcinoma (HCC), a significant proportion of patients do not achieve durable benefit. This study aimed to identify predictive biomarkers for this therapy by investigating the role of immune activation within the tumor microenvironment (TME).

Methods

We characterized the intratumoral TME of patients with advanced HCC treated with atezolizumab plus bevacizumab using single cell transcriptomics on pretreatment tumor biopsies from 12 patients. To complement and support these findings, we integrated our single cell data with publicly available bulk RNA-sequencing data from independent clinical trial cohorts.

Results

Patients who responded to combination therapy with atezolizumab plus bevacizumab demonstrated an immune-activated TME, marked by enhanced cytotoxicity and a tumor-specific T cell response. These patients also exhibited an increased proportion of inflammatory cytokine-enriched tumor-associated macrophage clusters with stronger interactions with T cells, an increased population of conventional dendritic cells, and activated antigen-presenting function in tumor endothelial cells. When publicly available bulk RNA-sequencing data from independent clinical trial cohorts were analyzed, these immune activation features were associated with improved progression-free survival (median 10.8 months, 95% CI: 7.3-not reached versus 5.5 months, 95% CI: 4.0-6.7; p <0.001).

Conclusions

These findings suggest that the existence of an activated immune TME before treatment is crucial for a favorable clinical response in patients with HCC treated with atezolizumab plus bevacizumab.

Impact and implications

Only a subset of patients with HCC benefit from combination therapy with atezolizumab plus bevacizumab, limiting its clinical utility. In this study, we used single cell RNA analysis to identify TME features associated with a clinical response to this therapy. Our findings suggest that a pre-existing immune-activated TME is crucial for predicting the response to atezolizumab plus bevacizumab. Specifically, features such as enhanced T cell cytotoxicity, inflammatory cytokine-enriched macrophage clusters, active antigen presentation in endothelial cells, and an increased presence of dendritic cells may aid patient selection and inform therapeutic strategies.

The dual role of CXCL9/SPP1 polarized tumor-associated macrophages in modulating anti-tumor immunity in hepatocellular carcinoma

Introduction

The main challenge for cancer therapy lies in immuno-suppressive tumor micro-environment. Reprogramming tumor-associated macrophages (TAMs) into an anti-tumor phenotype is a promising strategy.

Methods

A comprehensive analysis by combing multi-regional single-cell, bulk and spatial transcriptome profiling with radiomics characterization was conducted to dissect the heterogeneity of TAMs and resolve the landscape of the CXCL9:SPP1 (CS) macrophage polarity in HCC.

Results

TAMs were particularly increased in HCC. SPP1+ TAMs and CXCL9+ TAMs were identified as the dominant subtypes with different evolutionary trajectories. SPP1+ TAMs, located in the tumor core, co-localized with cancer-associated fibroblasts to promote tumor growth and further contributed to worse prognosis. In contrast, CXCL9+ TAMs, located in the peritumoral region, synergized with CD8+ T cells to create an immunostimulatory micro-environment. For the first time, we explored the applicability of CS polarity in HCC tumors and revealed several key transcription factors involved in shaping this polarity. Moreover, CS polarity could serve as a potential indicator of prognostic and micro-environmental status for HCC patients. Based on medical imaging data, we developed a radiomics tool, RCSP (Radiogenomics-based CXCL9/SPP1 Polarity), to assist in non-invasively predicting the CS polarity in HCC patients.

Conclusion

Our research sheds light on the regulatory roles of SPP1+ TAMs and CXCL9+ TAMs in the micro-environment and provides new therapeutic targets or insights for the reprogramming of targeted macrophages in HCC.

scMalignantFinder distinguishes malignant cells in single-cell and spatial transcriptomics by leveraging cancer signatures

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing tumor heterogeneity, yet accurately identifying malignant cells remains challenging. Here, we propose scMalignantFinder, a machine learning tool specifically designed to distinguish malignant cells from their normal counterparts using a data- and knowledge-driven strategy. To develop the tool, multiple cancer datasets were collected, and the initially annotated malignant cells were calibrated using nine carefully curated pan-cancer gene signatures, resulting in over 400,000 single-cell transcriptomes for training. The union of differentially expressed genes across datasets was taken as the features for model construction to comprehensively capture tumor transcriptional diversity. scMalignantFinder outperformed existing automated methods across two gold-standard and eleven patient-derived scRNA-seq datasets. The capability to predict malignancy probability empowers scMalignantFinder to capture dynamic characteristics during tumor progression. Furthermore, scMalignantFinder holds the potential to annotate malignant regions in tumor spatial transcriptomics. Overall, we provide an efficient tool for detecting heterogeneous malignant cell populations.

Myeloid cell path to malignancy: insights into liver cancer

Clinically approved treatments for advanced liver cancer often lack potency because of the heterogeneous characteristics of hepatocellular carcinoma (HCC). This complexity is largely driven by context-dependent inflammatory responses brought on by diverse etiologies, such as metabolic dysfunction-associated steatohepatitis (MASH), the genetic makeup of cancer cells, and the versatile adaptability of immune cells, such as myeloid cells. In this review, we discuss the evolutionary dynamics of the immune landscape, particularly that of liver-resident Kupffer cells (KCs), TREM2+, and SPP1+ macrophages with an active role during liver disease progression, which eventually fuels hepatocarcinogenesis. We highlight exploitable immunomodulatory avenues amenable to mitigate both the inherent pathological characteristics of liver cancers and the associated external factors that favor malignancy, paving a roadmap toward improving the management and therapeutic outcome for patients with HCC.

iMetAct: An integrated systematic inference of metabolic activity for dissecting tumor metabolic preference and tumor-immune microenvironment

Metabolic enzymes play a central role in cancer metabolic reprogramming, and their dysregulation creates vulnerabilities that can be exploited for therapy. However, accurately measuring metabolic enzyme activity in a high-throughput manner remains challenging due to the complex, multi-layered regulatory mechanisms involved. Here, we present iMetAct, a framework that integrates metabolic-transcription networks with an information propagation strategy to infer enzyme activity from gene expression data. iMetAct outperforms expression-based methods in predicting metabolite conversion rates by accounting for the effects of post-translational modifications. With iMetAct, we identify clinically significant subtypes of hepatocellular carcinoma with distinct metabolic preferences driven by dysregulated enzymes and metabolic regulators acting at both the transcriptional and non-transcriptional levels. Moreover, applying iMetAct to single-cell RNA sequencing data allows for the exploration of cancer cell metabolism and its interplay with immune regulation in the tumor microenvironment. An accompanying online platform further facilitates tumor metabolic analysis, patient stratification, and immune microenvironment characterization.

Pyruvate metabolism enzyme DLAT promotes tumorigenesis by suppressing leucine catabolism

Pyruvate and branched-chain amino acid (BCAA) metabolism are pivotal pathways in tumor progression, yet the intricate interplay between them and its implications for tumor progression remain elusive. Our research reveals that dihydrolipoamide S-acetyltransferase (DLAT), a pyruvate metabolism enzyme, promotes leucine accumulation and sustains mammalian target of rapamycin (mTOR) complex activation in hepatocellular carcinoma (HCC). Mechanistically, DLAT directly acetylates the K109 residue of AU RNA-binding methylglutaconyl-coenzyme A (CoA) hydratase (AUH), a critical enzyme in leucine catabolism, inhibiting its activity and leading to leucine accumulation. Notably, DLAT upregulation correlates with poor prognosis in patients with HCC. Therefore, we developed an AUHK109R-mRNA lipid nanoparticles (LNPs) therapeutic strategy, which effectively inhibits tumor growth by restoring leucine catabolism and inhibiting mTOR activation in vivo. In summary, our findings uncover DLAT's unexpected role as an acetyltransferase for AUH, suppressing leucine catabolism. Restoring leucine catabolism with AUHK109R-mRNA LNP effectively inhibits HCC development, highlighting a novel direction for cancer research.

Tumor Prognostic Risk Model Related to Monocytes/Macrophages in Hepatocellular Carcinoma Based on Machine Learning and Multi-Omics

Tumor-associated macrophages (TAMs) are crucial in hepatocellular carcinoma (HCC) development and invasion. This study explores monocyte/ macrophage-associated gene expression profiles in HCC, constructs a prognostic model based on these genes, and examines its relationship with drug resistance and immune therapy responses. Single-cell RNA sequencing(scRNA-seq) data from 10 HCC tissue biopsy samples, totaling 24,597 cells, were obtained from the GEO database to identify monocyte/macrophage-associated genes. A prognostic model was constructed and validated using external datasets and Western blot. Relationships between the model, clinical correlates, drug sensitivity, and immune therapy responses were investigated. From scRNA-seq data, 2,799 monocyte/macrophage marker genes were identified. Using the TCGA dataset, a prognostic model based on the single-gene UQCRH was constructed, stratifying patients into high-risk and low-risk groups based on overall survival rates. High-risk group patients showed reduced survival rates and higher UQCRH expression in tumor tissues. Western blot analysis further confirmed the elevated expression of UQCRH in HCC cell lines. Spatial transcriptomics analysis revealed that high UQCRH expression co-localized with malignant cells in the tumor tissue. Drug sensitivity analysis revealed that the high-risk group had lower sensitivity to sorafenib and axitinib. Immune therapy response analysis indicated poorer outcomes in the high-risk group, with more pronounced APC inhibition and a weaker IFN-II response. Clinical indicator analysis showed a positive correlation between high UQCRH expression and tumor invasion. Enrichment analysis of UQCRH and associated molecules indicated involvement in oxidative phosphorylation and mitochondrial electron transport. This study introduces a prognostic model for HCC patients based on monocyte/macrophage marker genes. The single-gene model predicts HCC patient survival and treatment outcomes, identifying high-risk individuals with varying drug sensitivities and immune suppression states.

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.

Transfer learning radiomic model predicts intratumoral tertiary lymphoid structures in hepatocellular carcinoma: a multicenter study

BACKGROUND

Intratumoral tertiary lymphoid structures (iTLS) in hepatocellular carcinoma (HCC) are associated with improved survival and may influence treatment decisions. However, their non-invasive detection remains challenging in HCC. We aim to develop a non-invasive model using baseline contrast-enhanced MRI to predict the iTLS status.

METHODS

A total of 660 patients with HCC who underwent surgery were retrospectively recruited from four centers between October 2015 and January 2023 and divided into training, internal test, and external validation sets. After features dimensionality and selection, corresponding features were used to construct transfer learning radiomic (TLR) models for diagnosing iTLS, and model interpretability was explored with pathway analysis in The Cancer Genome Atlas-Liver HCC. The performances of models were assessed using the area under the receiver operating characteristic curve (AUC). The log-rank test was used to evaluate the prognostic value of the TLR model. The combination therapy set of 101 patients with advanced HCC treated with first-line anti-programmed death 1 or ligand 1 plus antiangiogenic treatment between January 2021 and January 2024 was used to investigate the value of the TLR model for evaluating the treatment response.

RESULTS

The presence of iTLS was identified in 46.0% (n=308) patients. The TLR model demonstrated excellent performance in predicting the presence of iTLS in training (AUC=0.91, 95% CI: 0.87, 0.94), internal test (AUC=0.85, 95% CI: 0.77, 0.93) and external validation set (AUC=0.85, 95% CI: 0.81, 0.90). The TLR model-predicted iTLS group has favorable overall survival (HR=0.66; 95% CI: 0.48, 0.90; p=0.007) and relapse-free survival (HR=0.64; 95% CI: 0.48, 0.85; p=0.001) in the external validation set. The model-predicted iTLS status was associated with inflammatory response and specific tumor-associated signaling activation (all p<0.001). The proportion of treatment responders was significantly higher in the model-predicted group with iTLS than in the group without iTLS (36% vs 13.73%, p=0.009).

CONCLUSION

The TLR model has indicated accurate prediction of iTLS status, which may assist in the risk stratification for patients with HCC in clinical practice.

Molecular and immune landscape of hepatocellular carcinoma for therapeutic development

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, with an estimated 750,000 deaths in 2022. Recent emergence of molecular targeted agents and immune checkpoint inhibitors and their combination therapies have been transforming HCC care, but their prognostic impact in advanced-stage disease remains unsatisfactory. In addition, their application to early-stage disease is still an unmet need. Omics profiling studies have elucidated recurrent and heterogeneously present molecular aberrations involved in pro-cancer tumor (immune) microenvironment that may guide therapeutic strategies. Recurrent aberrations such somatic mutations in TERT promoter and TP53 have been regarded undruggable, but recent studies have suggested that these may serve as new classes of therapeutic targets. HCC markers such as alpha-fetoprotein, glypican-3, and epithelial cell adhesion molecule have also been explored as therapeutic targets. These molecular features may be utilized as biomarkers to guide the application of new approaches as companion biomarkers to maximize therapeutic benefits in patients who are likely to benefit from the therapies, while minimizing unnecessary harm in patients who will not respond. The explosive number of new agents in the pipelines have posed challenges in their clinical testing. Novel clinical trial designs guided by predictive biomarkers have been proposed to enable their efficient and cost-effective evaluation. These new developments collectively facilitate clinical translation of personalized molecular-targeted therapies in HCC and substantially improve prognosis of HCC patients.

Chemokine (C-C Motif) Ligand 2/CCR2/Extracellular Signal-Regulated Kinase Signal Induced through Cancer Cell-Macrophage Interaction Contributes to Hepatocellular Carcinoma Progression

Tumor-infiltrating macrophages, known as tumor-associated macrophages, play a crucial role in the tumor microenvironment. Herein, immunohistochemistry revealed that intratumoral CD68-positive macrophages are associated with poor prognosis and clinicopathologic factors in patients with hepatocellular carcinoma (HCC). Subsequently, an indirect co-culture system involving HCC cells and peripheral blood-derived macrophages was developed. cDNA microarray analysis revealed that chemokine (C-C motif) ligand 2 (CCL2) was highly expressed in HCC cells co-cultured with macrophages. CCL2 neutralization suppressed proliferation, migration, and phosphorylation of extracellular signal-regulated kinase (Erk) in HCC cells and macrophages enhanced through co-culture. In contrast, recombinant human CCL2 (rhCCL2) addition facilitated these malignant phenotypes and increased Erk phosphorylation levels in HCC cells and macrophages. The primary CCL2 receptor, CCR2, was expressed in HCC cells and macrophages and was up-regulated in co-cultured HCC cells. CCR2 inhibition suppressed malignant phenotypes and reduced phosphorylated levels of Erk enhanced by rhCCL2. Additionally, the inhibition of Erk signal suppressed rhCCL2-enhanced malignant phenotypes. Moreover, serum CCL2 levels were higher in patients with HCC than those in healthy donors. On the basis of immunohistochemistry, CCL2-positive cases with high CCR2 expression and phosphorylated Erk-positive cases exhibited poor survival outcomes. Therefore, CCL2 up-regulation through interactions between HCC cells and macrophages contributed to HCC progression, making the CCL2/CCR2/Erk signal a potential target for HCC treatment.

Molecular and immune landscape of hepatocellular carcinoma to guide therapeutic decision-making

Liver cancer, primarily HCC, exhibits highly heterogeneous histological and molecular aberrations across tumors and within individual tumor nodules. Such intertumor and intratumor heterogeneities may lead to diversity in the natural history of disease progression and various clinical disparities across the patients. Recently developed multimodality, single-cell, and spatial omics profiling technologies have enabled interrogation of the intertumor/intratumor heterogeneity in the cancer cells and the tumor immune microenvironment. These features may influence the natural history and efficacy of emerging therapies targeting novel molecular and immune pathways, some of which had been deemed undruggable. Thus, comprehensive characterization of the heterogeneities at various levels may facilitate the discovery of biomarkers that enable personalized and rational treatment decisions, and optimize treatment efficacy while minimizing the risk of adverse effects. Such companion biomarkers will also refine HCC treatment algorithms across disease stages for cost-effective patient management by optimizing the allocation of limited medical resources. Despite this promise, the complexity of the intertumor/intratumor heterogeneity and ever-expanding inventory of therapeutic agents and regimens have made clinical evaluation and translation of biomarkers increasingly challenging. To address this issue, novel clinical trial designs have been proposed and incorporated into recent studies. In this review, we discuss the latest findings in the molecular and immune landscape of HCC for their potential and utility as biomarkers, the framework of evaluation and clinical application of predictive/prognostic biomarkers, and ongoing biomarker-guided therapeutic clinical trials. These new developments may revolutionize patient care and substantially impact the still dismal HCC mortality.

HBV-associated hepatocellular carcinomas inhibit antitumor CD8+ T cell via the long noncoding RNA HDAC2-AS2

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. Extracellular vesicles (EV) are critical mediators of intercellular communication within the tumor microenvironment, and cancer-cell-secreted EVs often facilitate cancer progression. Here we show that in HBV-associated HCC, tumor-cell-derived EVs contain a TGFβ-inducible long noncoding RNA, termed HDAC2-AS2. EVs enriched with HDAC2-AS2 facilitate cancer progression by suppressing cytotoxicity of intra-tumor CD8+ T cells. Mechanistically, in activated cytotoxic CD8+ T cells, translocation of the transcription factor cyclin-dependent kinase 9 (CDK9), to the cytoplasm is critical for functional integrity. HDAC2-AS2 targets and blocks cytosolic CDK9, and this results in exhaustion of PD-1+CD8+ T cells and suppression of IFN-γ+CD8+ T cell cytotoxicity. Notably, we demonstrate that low CDK9 and high HDAC2-AS2 expressions are associated with poor survival of HCC, which can be rescued by anti-PD-1 therapy. These findings emphasize the significance of tumor-derived EVs in suppressing antitumor CD8+ T cell immunity to promote tumorigenesis, and highlight extracellular HDAC2-AS2 as a promising biomarker and therapeutic target for HCC.

Hamid SS. Multiomics in silico analysis identifies TM4SF4 as a cell surface target in hepatocellular carcinoma

The clinical application of cellular immunotherapy in hepatocellular carcinoma (HCC) is impeded by the lack of a cell surface target frequently expressed in HCC cells and with minimal presence in normal tissues to reduce on-target, off-tumor toxicity. To address this, an in silico multomics analysis was conducted to identify an optimal therapeutic target in HCC. A longlist of genes (n = 12,948) expressed in HCCs according to The Human Protein Atlas database were examined. Eight genes were shortlisted to identify one with the highest expression in HCCs, without being shed into circulation, and with restrictive expression profile in other normal human tissues. A total of eight genes were shortlisted and subsequently ranked according to the combination of their transcript and protein expression levels in HCC cases (n = 791) derived from four independent datasets. TM4SF4 was the top-ranked target with the highest expression in HCCs. TM4SF4 showed more favorable expression profile with significantly lower expression in normal human tissues but more highly expressed in HCC compared with seven other common HCC therapeutic targets. Furthermore, scRNA-seq and immunohistochemistry datasets showed that TM4SF4 was absent in immune cell populations but highly expressed in the bile duct canaliculi of hepatocytes, regions inaccessible to immune cells. In scRNA-seq dataset of HCCs, TM4SF4 expression was positively associated with mitochondrial components and oxidative phosphorylation Gene Ontologies in HCC cells (n = 15,787 cells), suggesting its potential roles in mitochondrial-mediated oncogenic effects in HCC. Taken together, TM4SF4 is proposed as a promising cell surface target in HCC due to its high expression in HCC cells with restricted expression profile in non-cancerous tissues, and association with HCC oncogenic pathways.

Integration of single-cell and spatial transcriptomics reveals fibroblast subtypes in hepatocellular carcinoma: spatial distribution, differentiation trajectories, and therapeutic potential

BACKGROUND

Cancer-associated fibroblasts (CAFs) are key components of the hepatocellular carcinoma (HCC) tumor microenvironment (TME). regulating tumor proliferation, metastasis, therapy resistance, immune evasion via diverse mechanisms. A deeper understanding of the l diversity of CAFs is essential for predicting patient prognosis and guiding treatment strategies.

METHODS

We examined the diversity of CAFs in HCC by integrating single-cell, bulk, and spatial transcriptome analyses.

RESULTS

Using a training cohort of 88 HCC single-cell RNA sequencing (scRNA-seq) samples and a validation cohort of 94 samples, encompassing over 1.2 million cells, we classified three fibroblast subpopulations in HCC: HLA-DRB1 + CAF, MMP11 + CAF, and VEGFA + CAF based on highly expressed genes of which, which are primarily located in normal tissue, tumor boundaries, and tumor interiors, respectively. Cell trajectory analysis revealed that VEGFA + CAFs are at the terminal stage of differentiation, which, notably, is tumor-specific. VEGFA + CAFs were significantly associated with patient survival, and the hypoxic microenvironment was found to be a major factor inducing VEGFA + CAFs. Through cellular communication with capillary endothelial cells (CapECs), VEGFA + CAFs promoted intra-tumoral angiogenesis, facilitating tumor progression and metastasis. Additionally, a machine learning model developed using high-expression genes from VEGFA + CAFs demonstrated high accuracy in predicting prognosis and sorafenib response in HCC patients.

CONCLUSIONS

We characterized three fibroblast subpopulations in HCC and revealed their distinct spatial distributions within the tumor. VEGFA + CAFs, which was induced by hypoxic TME, were associated with poorer prognosis, as they promote tumor angiogenesis through cellular communication with CapECs. Our findings provide novel insights and pave the way for individualized therapy in HCC patients.

scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization

The rapid advancement of single-cell technologies has created an urgent need for effective methods to integrate and harmonize single-cell data. Technical and biological variations across studies complicate data integration, while conventional tools often struggle with reliance on gene expression distribution assumptions and over-correction. Here, we present scCobra, a deep generative neural network designed to overcome these challenges through contrastive learning with domain adaptation. scCobra effectively mitigates batch effects, minimizes over-correction, and ensures biologically meaningful data integration without assuming specific gene expression distributions. It enables online label transfer across datasets with batch effects, allowing continuous integration of new data without retraining. Additionally, scCobra supports batch effect simulation, advanced multi-omic integration, and scalable processing of large datasets. By integrating and harmonizing datasets from similar studies, scCobra expands the available data for investigating specific biological problems, improving cross-study comparability, and revealing insights that may be obscured in isolated datasets.

Metabolic reprogramming of glucose: the metabolic basis for the occurrence and development of hepatocellular carcinoma

Primary liver cancer is a common malignant tumor of the digestive system, with hepatocellular carcinoma (HCC) being the most prevalent type. It is characterized by high malignancy, insidious onset, and a lack of specific early diagnostic and therapeutic markers, posing a serious threat to human health. The occurrence and development of HCC are closely related to its metabolic processes. Similar to other malignant tumors, metabolic reprogramming occurs extensively in tumor cells, with glucose metabolism reprogramming being particularly prominent. This is characterized by abnormal activation of glycolysis and inhibition of oxidative phosphorylation and gluconeogenesis, among other changes. Glucose metabolism reprogramming provides intermediates and energy for HCC to meet its demands for rapid growth, proliferation, and metastasis. Additionally, various enzymes and signaling molecules involved in glucose metabolism reprogramming play irreplaceable roles. Therefore, regulating key metabolic enzymes and pathways in these processes is considered an important target for the diagnosis and treatment of HCC. This paper reviews the current status and progress of glucose metabolism reprogramming in HCC, aiming to provide new insights for the diagnosis, detection, and comprehensive treatment strategies of HCC involving combined glucose metabolism intervention in clinical settings.

Inflammation and Immunity in Liver Neoplasms: Implications for Future Therapeutic Strategies

Over the past two decades, the "hallmarks of cancer" have revolutionized cancer research and highlighted the crucial roles of inflammation and immunity. Protumorigenic inflammation promotes cancer development along with inhibition of antitumor immunity, shaping the tumor microenvironment (TME) toward a tumor-permissive state and further enhancing the malignant potential of cancer cells. This immunosuppressive TME allows tumors to evade immunosurveillance. Thus, understanding the complex interplay between tumors and the immune system within the TME has become pivotal, especially with the advent of immunotherapy. Although immunotherapy has achieved notable success in many malignancies, primary liver cancer, particularly hepatocellular carcinoma, presents unique challenges. The hepatic immunosuppressive environment poses obstacles to the effectiveness of immunotherapy, along with high mortality rates and limited treatment options for patients with liver cancer. In this review, we discuss current understanding of the complex immune-mediated mechanisms underlying liver neoplasms, focusing on hepatocellular carcinoma and liver metastases. We describe the molecular and cellular heterogeneity within the TME, highlighting how this presents unique challenges and opportunities for immunotherapy in liver cancers. By unraveling the immune landscape of liver neoplasms, this review aims to contribute to the development of more effective therapeutic interventions, ultimately improving clinical outcomes for patients with liver cancer.

A deep learning framework for in silico screening of anticancer drugs at the single-cell level

Tumor heterogeneity plays a pivotal role in tumor progression and resistance to clinical treatment. Single-cell RNA sequencing (scRNA-seq) enables us to explore heterogeneity within a cell population and identify rare cell types, thereby improving our design of targeted therapeutic strategies. Here, we use a pan-cancer and pan-tissue single-cell transcriptional landscape to reveal heterogeneous expression patterns within malignant cells, precancerous cells, as well as cancer-associated stromal and endothelial cells. We introduce a deep learning framework named Shennong for in silico screening of anticancer drugs for targeting each of the landscape cell clusters. Utilizing Shennong, we could predict individual cell responses to pharmacologic compounds, evaluate drug candidates' tissue damaging effects, and investigate their corresponding action mechanisms. Prioritized compounds in Shennong's prediction results include FDA-approved drugs currently undergoing clinical trials for new indications, as well as drug candidates reporting anti-tumor activity. Furthermore, the tissue damaging effect prediction aligns with documented injuries and terminated discovery events. This robust and explainable framework has the potential to accelerate the drug discovery process and enhance the accuracy and efficiency of drug screening.

Functional heterogeneity of fibroblasts in primary tumors and metastases

Cancer-associated fibroblasts (CAFs) are abundant components of the tumor microenvironment (TME) of most solid malignancies and have emerged as key regulators of cancer progression and therapy response. Although recent technological advances have uncovered substantial CAF molecular heterogeneity at the single-cell level, defining functional roles for most described CAF populations remains challenging. With the aim of bridging CAF molecular and functional heterogeneity, this review focuses on recently identified functional interactions of CAF subtypes with malignant cells, immune cells, and other stromal cells in primary tumors and metastases. Dissecting the heterogeneous functional crosstalk of specific CAF populations with other components is starting to uncover candidate combinatorial strategies for therapeutically targeting the TME and cancer progression.

Spatial‒temporal heterogeneities of liver cancer and the discovery of the invasive zone

Solid tumours are intricate and highly heterogeneous ecosystems, which grow in and invade normal organs. Their progression is mediated by cancer cells' interaction with different cell types, such as immune cells, stromal cells and endothelial cells, and with the extracellular matrix. Owing to its high incidence, aggressive growth and resistance to local and systemic treatments, liver cancer has particularly high mortality rates worldwide. In recent decades, spatial heterogeneity has garnered significant attention as an unfavourable biological characteristic of the tumour microenvironment, prompting extensive research into its role in liver tumour development. Advances in spatial omics have facilitated the detailed spatial analysis of cell types, states and cell‒cell interactions, allowing a thorough understanding of the spatial and temporal heterogeneities of tumour microenvironment and informing the development of novel therapeutic approaches. This review illustrates the latest discovery of the invasive zone, and systematically introduced specific macroscopic spatial heterogeneities, pathological spatial heterogeneities and tumour microenvironment heterogeneities of liver cancer.

Comprehensive Analysis of Clinical and Molecular Features in Cancer Patients Associated With Major Human Oncoviruses

Viral infections contribute to a higher incidence of cancer than any other individual risk factor. This study aimed to compare the clinical and molecular features of four viral-associated cancers: stomach adenocarcinoma (STAD), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), and cervical squamous cell carcinoma (CESC). Patients were categorized based on viral infection status, as provided in the clinical data, into virus-associated and non-virus-associated groups, followed by a comprehensive comparison of clinical and molecular features. Our analysis disclosed that viral infections confer unique clinical and molecular signatures to their associated tumors. Specifically, human papillomavirus-associated (HPV+) HNSC and hepatitis B virus-associated (HBV+) LIHC patients were predominantly male, younger, and exhibited better clinical prognoses. Virus-associated tumors displayed enhanced immune microenvironments and high DNA damage response scores, while non-virus-associated tumors were enriched in stromal signatures. HPV+ HNSC and Epstein-Barr virus-associated (EBV+) STAD showed similarities across multi-omics features, including better responses to immunotherapy, lower TP53 mutation rates, tumor mutation burden (TMB), and copy number alteration (CNA). Conversely, HBV+, Hepatitis C virus-associated (HCV+) LIHCs and HPV+ CESC were more genomically unstable due to high TP53 mutation rates, TMB, and CNA. At the protein level, Caspase-7 and Syk were upregulated in HPV+ HNSC and EBV+ STAD, and positively correlated with the enrichment levels of CD8 + T cell, PD-L1, and cytolytic activity. Patient stratification based on infection status has significant clinical implications, particularly for patient prognosis and drug response.

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.

Identification of a novel immunogenic cell death-related classifier to predict prognosis and optimize precision treatment in hepatocellular carcinoma

Accumulating studies have highlighted the biological significance of immunogenic cell death (ICD) in cancer immunity. However, the influence of ICD on tumor microenvironment (TME) formation and immune response in Hepatocellular carcinoma (HCC) remains largely unexplored. In this study, we systematically analyzed the mRNA profiles of ICD-related genes in 1847 HCC patients and identified three molecular subtypes with significantly different immune features and prognostic stratification. A reliable risk model named ICD score was constructed via machine learning algorithms to assess the immunological status, therapeutic responses, and clinical outcomes of individual HCC patients. High ICD score indicated an immune-excluded TME phenotype, with lower anticancer immunity and shorter survival time. In contrast, low ICD score corresponded to abundant immune cell infiltration, high sensitivity to immunotherapy and a positive prognosis, indicating an "immune-hot" phenotype. Pan-cancer analysis further validated a negative association between ICD score and the immune cell infiltration levels. In conclusion, our findings revealed that the ICD score could serve as a robust prognostic biomarker to predict the benefits of immunotherapy and optimize the clinical decision-making of HCC patients.