Comprehensive analysis of spatial architecture in primary liver cancer

Integrated single-cell and spatial transcriptomic profiling reveals that CD177+ Tregs enhance immunosuppression through apoptosis and resistance to immunotherapy in hepatocellular carcinoma

Regulatory T cells (Tregs), an immunosuppressive subpopulation of CD4+ T cells, are prevalent in tumor tissues, where they impede effective antitumor immune responses and represent potential targets for immunotherapy. However, targeting tumor-infiltrating Treg cells (TiTregs) remains challenging. In this study, we identified CD177 as a biomarker specifically expressed in TiTregs but not in adjacent or peripheral Treg cells through single-cell transcriptome sequencing combined with a stringent screening strategy. These CD177+ TiTregs exhibited distinct transcriptional profiles characterized by enhanced immunosuppressive capabilities and were correlated with poor patient prognosis. Mechanistically, the apoptosis-related transcription factor REL drove the differentiation of CD177+ TiTregs, accompanied by apoptosis and enhanced immunosuppression. Furthermore, using a CD177 Treg conditional knockout mouse model, we demonstrated that inhibiting CD177 in Tregs significantly impaired their immunosuppressive function and inhibited the progression of hepatocellular carcinoma (HCC) in vitro. Our results underscore the critical role of CD177+ TiTregs in cancer immunology and highlight their potential as novel therapeutic targets in HCC.

The epigenetic basis of hepatocellular carcinoma - mechanisms and potential directions for biomarkers and therapeutics

Hepatocellular carcinoma (HCC) is the sixth leading cancer worldwide and has complex pathogenesis due to its heterogeneity, along with poor prognoses. Diagnosis is often late as current screening methods have limited sensitivity for early HCC. Moreover, current treatment regimens for intermediate-to-advanced HCC have high resistance rates, no robust predictive biomarkers, and limited survival benefits. A deeper understanding of the molecular biology of HCC may enhance tumor characterization and targeting of key carcinogenic signatures. The epigenetic landscape of HCC includes complex hallmarks of 1) global DNA hypomethylation of oncogenes and hypermethylation of tumor suppressors; 2) histone modifications, altering chromatin accessibility to upregulate oncogene expression, and/or suppress tumor suppressor gene expression; 3) genome-wide rearrangement of chromatin loops facilitating distal enhancer-promoter oncogenic interactions; and 4) RNA regulation via translational repression by microRNAs (miRNAs) and RNA modifications. Additionally, it is useful to consider etiology-specific epigenetic aberrancies, especially in viral hepatitis and metabolic dysfunction-associated steatotic liver disease (MASLD), which are the main risk factors of HCC. This article comprehensively explores the epigenetic signatures in HCC, highlighting their potential as biomarkers and therapeutic targets. Additionally, we examine how etiology-specific epigenetic patterns and the integration of epigenetic therapies with immunotherapy could advance personalized HCC treatment strategies.

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.

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.

FMO2+ cancer-associated fibroblasts sensitize anti-PD-1 therapy in patients with hepatocellular carcinoma

BACKGROUND

The efficacy of immune checkpoint inhibitors (ICIs) for hepatocellular carcinoma (HCC) is limited by heterogeneity in individual responses to therapy. The heterogeneous phenotypes and crucial roles of cancer-associated fibroblasts (CAFs) in immunotherapy resistance remain largely unclear.

METHODS

A specific CAF subset was identified by integrating comprehensive single-cell RNA sequencing, spatial transcriptomics and transcriptome profiling of patients with HCC with different responses to antiprogrammed cell death protein 1 (anti-PD-1) therapy. Mouse orthotopic HCC models and a coculture system were constructed, and cytometry by time-of-flight analysis was performed to investigate the functions and mechanisms of specific CAFs in the immune context of HCC.

RESULTS

We identified a distinct flavin-containing monooxygenase 2 (FMO2)+ CAF subset associated with a favorable response to anti-PD-1 therapy and better clinical outcomes. FMO2+ CAFs increase anti-PD-1 treatment efficacy by promoting tertiary lymphoid structure formation and increasing the infiltration of CD8+ T cells and M1-like macrophages through the C-C motif chemokine ligand 19 (CCL19)-C-C motif chemokine receptor 7 axis. Mechanistically, FMO2 promotes nuclear factor kappa B/p65-mediated CCL19 expression by competitively binding to glycogen synthase 1 (GYS1) with praja ring finger ubiquitin ligase 1 (PJA1), thereby suppressing the PJA1-mediated proteasomal degradation of GYS1. CCL19 treatment potentiated the therapeutic efficacy of anti-PD-1 therapy in mouse orthotopic HCC models. A favorable immunotherapy response was observed in patients with HCC with high serum levels of CCL19.

CONCLUSIONS

We identified a novel FMO2+ CAF subset that serves as a critical regulator of microenvironmental immune properties and a predictive biomarker of the immunotherapy response in patients with HCC. CCL19 in combination with anti-PD-1 therapy may constitute a novel therapeutic strategy for HCC.

Decoding per- and polyfluoroalkyl substances (PFAS) in hepatocellular carcinoma: a multi-omics and computational toxicology approach

BACKGROUND

Per- and polyfluoroalkyl substances (PFAS), particularly perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), are synthetic chemicals known for their widespread use and environmental persistence. These compounds have been increasingly linked to hepatotoxicity and the development of hepatocellular carcinoma (HCC). However, the molecular mechanisms by which PFAS contribute to HCC remain underexplored.

METHODS

This study employs a multi-omics approach that combines network toxicology, integrated machine learning, single-cell RNA sequencing, spatial transcriptomics, experimental validation, and molecular docking simulations to uncover the mechanisms through which PFAS exposure drives HCC. We analyzed publicly available transcriptomic data from several HCC cohorts and used differential gene expression analysis to identify targets associated with both PFAS exposure and HCC. We constructed a protein-protein interaction (PPI) network and a survival risk model, the PFAS-related HCC signature (PFASRHSig), based on integrated machine learning to identify prognostic biomarkers, with the goal of identifying core targets of PFAS in HCC progression and prognosis. RT-qPCR and immunohistochemical (IHC) staining were used to validate the expression levels of the targets in both tumor and normal tissues. Molecular docking simulations were conducted to assess the binding affinities between PFAS compounds and selected target proteins.

RESULTS

Functional enrichment studies revealed that PFAS targets were associated with metabolic signaling pathways, which are actively involved in lipid, glucose, drug metabolism, etc. Through integrated machine learning and PPI network analysis, we identified six genes, APOA1, ESR1, IGF1, PPARGC1A, SERPINE1, and PON1, that serve as core targets of PFAS in both HCC progression and prognosis. These targets were further validated via bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics, which revealed differential expression patterns across various cell types in the HCC tumor microenvironment. The results of RT-qPCR and IHC staining were consistent with the in silico findings. Molecular docking simulations revealed strong binding affinities between PFAS compounds and these core targets, supporting their potential roles in PFAS-induced hepatocarcinogenesis.

CONCLUSIONS

Our study highlights key molecular targets and pathways involved in PFAS-induced liver carcinogenesis and proposes a robust survival risk model (PFASRHSig) for HCC. These findings provide new insights into PFAS toxicity mechanisms and offer potential therapeutic targets for mitigating the health risks associated with PFAS exposure. Collectively, our findings help in advancing clinical applications by providing insights into disease mechanisms and potential therapeutic interventions.

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.

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.

Multi-omics analyses of the gut microbiota and metabolites in children with metabolic dysfunction-associated steatotic liver disease

The development and severity of metabolic dysfunction-associated steatotic liver disease (MASLD) in children are closely related to alterations of gut microbiota. This study aims to investigate changes in the gut microbiota signature and microbial metabolites in children with MASLD. We collected fecal samples from children and adolescents aged 6-16 years, and the presence of MASLD was diagnosed by ultrasound. We performed 16S ribosomal DNA sequencing and targeted metabolomics in 36 and 25 subjects, consisting of healthy controls, children with obesity, and children with MASLD. The α-diversity was significantly lower in children with obesity and MASLD compared with healthy controls. Linear discriminant analysis of effect size analysis identified Anaerostipes and A. hadrus as the top biomarkers differentiating the obesity group from the MASLD group. In MASLD patients with high alanine aminotransferase values (≥50 U/L for boys and 44 U/L for girls), we observed a decrease in the gut microbiota health index. MASLD patients with high shear wave elastography (E) values (≥6.2 kPa) showed an increased abundance of Ruminococcus torques, which was positively correlated with the levels of deoxycholic acid (DCA) and E values. Importantly, the mediation analysis identified positive associations between R. torques and clinical indicators of MASLD that were mediated by DCA. Overall, our study suggests that gut microbiota and metabolites are significantly altered in children with MASLD, and targeting R. torques may offer potential benefits for disease management.IMPORTANCEThis study investigated alterations in the gut microbiota signature and microbial metabolites in children with metabolic dysfunction-associated steatotic liver disease (MASLD). We found that an increased abundance of Ruminococcus torques was associated with increased levels of deoxycholic acid and the progression of MASLD, suggesting that R. torques may serve as a novel clinical target in pediatric MASLD.

A Novel Signature Composed of Hypoxia, Glycolysis, Lactylation Related Genes to Predict Prognosis and Immunotherapy in Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide. The hypoxic microenvironment in HCC enhances glycolysis and co-directed lactate accumulation, which leads to increased lactylation. However, the exact biological pattern remains to be elucidated. Therefore, we sought to identify hypoxia-glycolysis-lactylation (HGL) prognosis-related signatures and validate this in vitro.

METHODS

Transcriptomic data of patients with HCC were collected from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. Differentially expressed HGL genes between HCC and normal tissues were obtained by DEseq2. The consensus clustering algorithm was employed to stratify patients into two distinct clusters. Subsequently, the single sample Gene Set Enrichment Analysis (ssGSEA), Tumor Immune Estimation Resource (TIMER) and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to assess immune infiltration and immune evasion. Least Absolute Shrinkage and Selection Operator (LASSO) and COX regression analysis were used to identify an HGL prognosis-related signature. Based on spatial transcriptome and histological data, we analyzed the expression of these genes in HCC and explored the function of Homer Scaffold Protein 1 (HOMER1) in HCC cells.

RESULTS

We identified 72 differentially expressed HGL genes and two HGL clusters. Cluster2, with better survival (p < 0.001), was significantly enriched in metabolic-related pathways. The HGL prognosis-related signature exhibited great predictive efficacy for patients in TCGA, ICGC, and GSE148355 databases (3-year area under the curve (AUC) = 0.822, 0.738, and 0.707, respectively). The elevated expression of HOMER1 in HCC was revealed by the combination of spatial transcriptome and histological data. Knocking down HOMER1 significantly inhibited the malignant progression of HCC cells.

CONCLUSIONS

We identified a signature with great predictive efficacy and discovered a gene, HOMER1, that influences the malignant progression of HCC with the potential to become a novel therapeutic target.

METTL16/IGF2BP2 axis enhances malignant progression and DDP resistance through up-regulating COL4A1 by mediating the m6A methylation modification of LAMA4 in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the third most common malignant tumor after gastric cancer and esophageal cancer, which is a serious threat to human health. Methyltransferase-like protein 16 (METTL16) regulates the occurrence and development of various cancers, but its molecular mechanism in HCC has not been fully investigated.

METHODS

A series of databases were used to predict gene expression, methylation sites, correlation analysis, and protein interaction analysis. Gene expression levels were detected by quantitative real-time polymerase chain reaction (qRT-PCR), western blot, and immunohistochemistry (IHC). What's more, drug-resistant cell lines were established for drug resistance analysis. Cell proliferation was measured by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and 5-ethynyl-2'-deoxyuridine (EdU) staining. Flow cytometry, transwell and wound healing assays were used for apoptosis, invasion and migration, respectively. In addition, the regulatory mechanism of METTL16 in HCC was investigated by methylated RNA immunoprecipitation (MeRIP), RNA immunoprecipitation (RIP) and co-immunoprecipitation (Co-IP). Finally, constructing subcutaneous transplanted tumor in nude mice confirmed the effect of METTL16 in vivo.

RESULTS

METTL16 was up-regulated in HCC drug-resistant tissues and cells. Knockdown of METTL16 inhibited Cisplatin (DDP) resistance, proliferation, invasion and migration of HCC cells, but promoted apoptosis. Besides, laminin subunit alpha 4 (LAMA4), which was overexpressed in HCC drug-resistant tissues and cells, was selected as the target of METTL16. Mechanistically, METTL16 and m6A reader insulin like growth factor 2 mRNA binding protein 2 (IGF2BP2) co-regulated the m6A modification and mRNA stability of LAMA4, and LAMA4 weakened the effects of METTL16 knockdown on HCC drug-resistance. Meanwhile, LAMA4 bound to collagen type IV alpha 1 chain (COL4A1) and facilitated DDP resistance and HCC progression via COL4A1. Similarly, in vivo, METTL16 induced tumor growth, as well as LAMA4 and COL4A1 expression, and increased DDP resistance.

CONCLUSION

METTL16 and IGF2BP2 jointly mediated the m6A methylation modification of LAMA4, thereby promoting DDP resistance and malignant progression of HCC through regulation of COL4A1.

A spatially resolved transcriptome landscape during thyroid cancer progression

Tumor microenvironment (TME) remodeling plays a pivotal role in thyroid cancer progression, yet its spatial dynamics remain unclear. In this study, we integrate spatial transcriptomics and single-cell RNA sequencing to map the TME architecture across para-tumor thyroid (PT) tissue, papillary thyroid cancer (PTC), locally advanced PTC (LPTC), and anaplastic thyroid carcinoma (ATC). Our integrative analysis reveals extensive molecular and cellular heterogeneity during thyroid cancer progression, enabling the identification of three distinct thyrocyte meta-clusters, including TG+IYG+ subpopulation in PT, HLA-DRB1+HLA-DRA+ subpopulation in early cancerous stages, and APOE+APOC1+ subpopulation in late-stage progression. We reveal stage-specific tumor leading edge remodeling and establish high-confidence cell-cell interactions, such as COL8A1-ITHB1 in PTC, LAMB2-ITGB4 in LPTC, and SERPINE1-PLAUR in ATC. Notably, both SERPINE1 expression level and SERPINE1+ fibroblast abundance correlate with malignant progression and prognosis. These findings provide a spatially resolved framework of TME remodeling, offering insights for thyroid cancer diagnosis and treatment.

The glycolytic characteristics of hepatocellular carcinoma and its interaction with the microenvironment: a comprehensive omics study

BACKGROUND

Hepatocellular carcinoma (HCC) is a common malignant tumor characterized by a high recurrence rate and poor prognosis. This study aimed to identify glycolysis-related prognostic markers and immunological abnormalities in patients with HCC.

METHODS

We collected samples from cancerous and adjacent non-cancerous tissues for transcriptomic, metabolomic, and 16 S rRNA sequencing analyses. Glycolysis-related prognostic markers were identified by integrating public data from The Cancer Genome Atlas, GSE14520, and GSE76427 datasets. Additionally, single-cell sequencing data (GSE202642) were used to analyze the significantly infiltrated cellular subpopulations in HCC and investigate the expression of prognostic markers across different cell types. Spatial transcriptomics and mass cytometry (CyTOF) data were used to examine the expression differences in immune cells across tumor, peritumoral, and control tissues. Key prognostic markers were validated using reverse transcription-quantitative polymerase chain reaction, western blotting, and immunohistochemistry.

RESULTS

Differentially expressed genes (DEGs) between HCC and control tissues were primarily clustered in cell cycle and metabolic pathways, particularly in the glycolysis pathway. Metabolomic analysis identified 175 differentially expressed metabolites that were mainly enriched in digestive and amino acid metabolism pathways. 16 S rRNA analysis revealed a significant increase in the abundance of Aenigmarchaeota and a decrease in the abundance of Proteobacteria in HCC tissues. The former was positively associated with glycolysis, whereas the latter showed a negative association. Through public data integration, 17 glycolysis-related DEGs were identified and 101 predictive models were constructed using machine learning. The StepCox[both] + random survival forest model using AGL, G6PD, GOT2, and KIF20A exhibited the best diagnostic performance among the three datasets. Single-cell RNA sequencing indicated significant infiltration of CD8 + Tex, CD8 + T, CD8 + Trm, and epithelial cells in HCC tissues. AGL, G6PD, GOT2, and KIF20A were highly expressed in CD8 + Tex cells, CD8 + Trm cells, macrophages, and monocytes, respectively. Spatial transcriptomics and CyTOF analyses showed greater infiltration of CD8 + Tex and CD8 + Trm cells in tumor tissues than in controls. Molecular assays further confirmed that G6PD and KIF20A expression levels were significantly higher, whereas AGL and GOT2 expression levels were lower, in HCC tissues than in control tissues.

CONCLUSION

Through integrative multi-omics analysis, we identified glycolysis-related prognostic markers with distinct expression profiles across immune cell subsets in HCC. Our findings identify potential biomarkers and therapeutic targets for the diagnosis and treatment of HCC.

Cancer-associated fibroblasts: multidimensional players in liver cancer

Cancer-associated fibroblasts (CAFs), the most abundant stromal cells in the tumor microenvironment (TME), control tumor growth through production and organization of the extracellular matrix (ECM) for a long time. However, the results from different studies that have focused on targeting CAFs to disturb tumor progression are extremely controversial. Recent studies using advanced single-cell RNA sequencing technology (scRNAseq) combined with multiple genetically engineered mouse models have identified diverse CAF subpopulations in the premalignant liver microenvironment (PME) of hepatocellular carcinoma (HCC) and TME of intrahepatic cholangiocarcinoma (ICC), providing a deeper understanding of the exact roles of each CAF subpopulation in cancer development. This review focuses on the specific protein markers, signaling pathways, and functions of various emerging CAF subclusters that contribute to the development of ICC and HCC. Elucidating the role and regulation of CAF subpopulations under different pathophysiological conditions will facilitate the discovery of new therapeutics that modulate CAF activity.

Spatial immune scoring system predicts hepatocellular carcinoma recurrence

Given the high recurrence rates of hepatocellular carcinoma (HCC) post-resection1-3, improved early identification of patients at high risk for post-resection recurrence would help to improve patient outcomes and prioritize healthcare resources4-6. Here we observed a spatial and HCC recurrence-associated distribution of natural killer (NK) cells in the invasive front and tumour centre from 61 patients. Using extreme gradient boosting and inverse-variance weighting, we developed the tumour immune microenvironment spatial (TIMES) score based on the spatial expression patterns of five biomarkers (SPON2, ZFP36L2, ZFP36, VIM and HLA-DRB1) to predict HCC recurrence risk. The TIMES score (hazard ratio = 88.2, P < 0.001) outperformed current standard tools for patient risk stratification including the TNM and BCLC systems. We validated the model in 231 patients from five multicentred cohorts, achieving a real-world accuracy of 82.2% and specificity of 85.7%. The predictive power of these biomarkers emerged through the integration of their spatial distributions, rather than individual marker expression levels alone. In vivo models, including NK cell-specific Spon2-knockout mice, revealed that SPON2 enhances IFNγ secretion and NK cell infiltration at the invasive front. Our study introduces TIMES, a publicly accessible tool for predicting HCC recurrence risk, offering insights into its potential to inform treatment decisions for early-stage HCC.

Spatial transcriptomics reveals tryptophan metabolism restricting maturation of intratumoral tertiary lymphoid structures

Tertiary lymphoid structures (TLSs) are ectopic lymphoid aggregates found in numerous cancers, often linked to enhanced immunotherapy responses and better clinical outcomes. However, the factors driving TLS maturation are not fully understood. Using near single-cell spatial transcriptomic mapping, we comprehensively profile TLSs under various maturation stages and their microenvironment in hepatocellular carcinoma (HCC). Based on their developmental trajectories, we classify immature TLSs into two groups: conforming and deviating TLSs. Our findings indicate that conforming TLSs, similar to mature TLSs, possess a niche function for immunotherapy responses, while deviating TLSs do not. We discover that the tryptophan-enriched metabolic microenvironment shaped by malignant cells contributes to the deviation of TLS maturation. Inhibiting tryptophan metabolism promotes intratumoral TLS maturation and enhances tumor control, synergizing with anti-PD-1 treatments. Therefore, promoting TLS maturation represents a potential strategy to improve antitumor responses and immunotherapy outcomes.

Single-cell sequencing and spatial transcriptomics reveal the evolution of glucose metabolism in hepatocellular carcinoma and identify G6PD as a potential therapeutic target

Background

Glucose metabolism reprogramming provides significant insights into the development and progression of malignant tumors. This study aims to explore the temporal-spatial evolution of the glucose metabolism in HCC using single-cell sequencing and spatial transcriptomics (ST), and validates G6PD as a potential therapeutic target for HCC.

Methods

We collected single-cell sequencing data from 7 HCC and adjacent non-cancerous tissues from the GSE149614 database, and ST data from 4 HCC tissues from the HRA000437 database. Pseudotime analysis was performed on the single-cell data, while ST data was used to analyze spatial metabolic activity. High-throughput sequencing and experiments, including wound healing, CCK-8, and transwell assays, were conducted to validate the role and regulatory mechanisms of G6PD in HCC.

Results

Our study identified a progressive upregulation of PPP-related genes during tumorigenesis. ST analysis revealed elevated PPP metabolic scores in the central and intermediate tumor regions compared to the peripheral zones. High-throughput sequencing and experimental validation further suggested that G6PD-mediated regulation of HCC cell proliferation, migration, and invasion is likely associated with glutathione metabolism and ROS production. Finally, Cox regression analysis cofirmed G6PD as an independent prognostic factor for overall survival in HCC patients.

Conclusion

Our study provides novel insights into the changes in glucose metabolism in HCC from both temporal and spatial perspectives. We experimentally demonstrated that G6PD regulates proliferation, migration, and invasion in HCC and propose G6PD as a prognostic marker and therapeutic metabolic target for the HCC.

Tumor cell villages define the co-dependency of tumor and microenvironment in liver cancer

Spatial cellular context is crucial in shaping intratumor heterogeneity. However, understanding how each tumor establishes its unique spatial landscape and what factors drive the landscape for tumor fitness remains significantly challenging. Here, we analyzed over 2 million cells from 50 tumor biospecimens using spatial single-cell imaging and single-cell RNA sequencing. We developed a deep learning-based strategy to spatially map tumor cell states and the architecture surrounding them, which we referred to as Spatial Dynamics Network (SDN). We found that different tumor cell states may be organized into distinct clusters, or 'villages', each supported by unique SDNs. Notably, tumor cell villages exhibited village-specific molecular co-dependencies between tumor cells and their microenvironment and were associated with patient outcomes. Perturbation of molecular co-dependencies via random spatial shuffling of the microenvironment resulted in destabilization of the corresponding villages. This study provides new insights into understanding tumor spatial landscape and its impact on tumor aggressiveness.

Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments

The tumor microenvironment (TME) plays a crucial role in orchestrating tumor cell behavior and cancer progression. Recent advances in spatial profiling technologies have uncovered novel spatial signatures, including univariate distribution patterns, bivariate spatial relationships, and higher-order structures. These signatures have the potential to revolutionize tumor mechanism and treatment. In this review, we summarize the current state of spatial signature research, highlighting computational methods to uncover spatially relevant biological significance. We discuss the impact of these advances on fundamental cancer biology and translational research, address current challenges and future research directions.

TM4SF1 overexpression in tumor-associated endothelial cells promotes microvascular invasion in hepatocellular carcinoma

Background

Microvascular invasion (MVI) is linked to poor prognosis, early recurrence and post-surgical intrahepatic metastasis of hepatocellular carcinoma (HCC) but roles of tumor-associated endothelial cells (TECs) remain unclear. The aim of the current study was to investigate the role of TECs in microvascular invasion in HCC.

Methods

Single-cell RNA sequencing (scRNA-seq) data from three patients with MVI and two patients with non-MVI HCC were used to identify TECs subpopulations via Seurat R package. Using bioinformatics analysis identified co-expression modules associated with MVI in TECs. Differential gene expression analysis, KME values and Gene Expression Profiling Interactive Analysis (GEPIA) survival were utilized to identify genes with significant involvement. TECs subgroup developmental trajectory was analyzed using monocle2. Five additional spatial transcriptomics (ST) datasets and four HCC postoperative pathological specimens were used to validate the differential expression of subgroups of TECs and hub genes between MVI and non-MVI groups.

Results

Distinct TECs subgroups had significant heterogeneity between datasets from MVI and non-MVI patients. MVI samples had TECs subgroups with increased levels of the epithelial-mesenchymal transition (EMT), endothelial cell migration and angiogenesis. Opposing EMT development was found in MVI TECs relative to non-MVI TECs. TM4SF1 was highly expressed in TECs undergoing the EMT and is thought to be linked to MVI.

Conclusion

TECs with elevated TM4SF1 expression facilitate MVI during HCC via an effect on the EMT, suggesting the potential of TM4SF1 as a therapeutic target.

Deciphering ovarian cancer heterogeneity through spatial transcriptomics, single-cell profiling, and copy number variations

High-grade serous ovarian carcinoma (HGSOC) poses a formidable clinical challenge due to multidrug resistance (MDR) caused by tumor heterogeneity. To elucidate the intricate mechanisms underlying HGSOC heterogeneity, we conducted a comprehensive analysis of five single-cell transcriptomes and eight spatial transcriptomes derived from eight HGSOC patients. This study provides a comprehensive view of tumor heterogeneity across the spectrum of gene expression, copy number variation (CNV), and single-cell profiles. Our CNV analysis revealed intratumor heterogeneity by identifying distinct tumor clones, illuminating their evolutionary trajectories and spatial relationships. We further explored the homogeneity and heterogeneity of CNV across tumors to pinpoint the origin of heterogeneity. At the cellular level, single-cell RNA sequencing (scRNA seq) analysis identified three meta-programs that delineate the functional profile of tumor cells. The communication networks between tumor cell clusters exhibited unique patterns associated with the meta-programs governing these clusters. Notably, the ligand-receptor pair MDK - NCL emerged as a highly enriched interaction in tumor cell communication. To probe the functional significance of this interaction, we induced NCL overexpression in the SOVK3 cell line and observed enhanced tumor cell proliferation. These findings indicate that the MDK - NCL interaction plays a crucial role in promoting HGSOC tumor growth and may represent a promising therapeutic target. In conclusion, this study comprehensively unravels the multifaceted nature of HGSOC heterogeneity, providing potential therapeutic strategies for this challenging malignancy.

Spatial Organization of Macrophages in CTL-Rich Hepatocellular Carcinoma Influences CTL Antitumor Activity

Despite the pivotal role of CTLs in antitumor immunity, a substantial proportion of CTL-rich patients with hepatocellular carcinoma (HCC) experience early relapse or immunotherapy resistance. However, spatial immune variations impacting the heterogeneous clinical outcomes of CTL-rich HCCs remain poorly understood. In this study, we compared the single-cell and spatial landscapes of 20 CTL-rich HCCs with distinct prognoses using multiplexed in situ staining and validated the prognostic value of myeloid spatial patterns in a cohort of 386 patients. Random forest and Cox regression models identified macrophage aggregation as a distinctive spatial pattern characterizing a subset of CTL-rich HCCs with an immunosuppressive microenvironment and poor prognosis. Integrated analysis of single-cell and spatial transcriptomics, combined with in situ staining validation, revealed that spatial aggregation enhanced protumoral macrophage reprogramming in HCCs, marked by lipid metabolism orientation, M2-like polarization, and increased adjacent CTL exhaustion. This spatial effect on macrophage reprogramming was replicated in HCC-conditioned human macrophage cultures, which showed an enhanced capability to suppress CTLs. Notably, increased macrophage aggregation was associated with higher response rates to anti-PD-1 immunotherapy. These findings suggest that the spatial distribution of macrophages is a biomarker of their functional diversities and microenvironment status, which holds prognostic and therapeutic implications.

Spatial transcriptomic analysis of tumor microenvironment in esophageal squamous cell carcinoma with HIV infection

BACKGROUND

Human Immunodeficiency Virus (HIV) is one of the most prevalent viruses, causing significant immune depletion in affected individuals. Current treatments can control HIV and prolong patients' lives, but new challenges have emerged. Increasing incidence of cancers occur in HIV patients. Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers observed in HIV patients. However, the spatial cellular characteristics of HIV-related ESCC have not been explored, and the differences between HIV-ESCC and typical ESCC remain unclear.

METHODS

We performed spatial transcriptome sequencing on HIV-ESCC samples to depict the microenvironment and employed cell communication analysis and multiplex immunofluorescence to investigate the molecular mechanism in HIV-ESCC.

RESULTS

We found that HIV-ESCC exhibited a unique cellular composition, with fibroblasts and epithelial cells intermixed throughout the tumor tissue, lacking obvious spatial separation, while other cell types were sparse. Besides, HIV-ESCC exhibited an immune desert phenotype, characterized by a low degree of immune cell infiltration, with only a few SPP1+ macrophages showing immune resistance functions. Cell communication analysis and multiplex immunofluorescence staining revealed that tumor fibroblasts in HIV-ESCC interact with CD44+ epithelial cells via COL1A2, promoting the expression of PIK3R1 in epithelial cells. This interaction activates the PI3K-AKT signaling pathway, which contributes to the progression of HIV-ESCC.

CONCLUSIONS

Our findings depict the spatial microenvironment of HIV-ESCC and elucidate a molecular mechanism in the progression of HIV-ESCC. This will provide us insights into the molecular basis of HIV-ESCC and potential treatment strategies.

The spatial landscape of cancer hallmarks reveals patterns of tumor ecological dynamics and drug sensitivity

Tumors are complex ecosystems of interacting cell types. The concept of cancer hallmarks distills this complexity into underlying principles that govern tumor growth. Here, we explore the spatial distribution of cancer hallmarks across 63 primary untreated tumors from 10 cancer types using spatial transcriptomics. We show that hallmark activity is spatially organized, with the cancer compartment contributing to the activity of seven out of 13 hallmarks, while the tumor microenvironment (TME) contributes to the activity of the rest. Additionally, we discover that genomic distance between tumor subclones correlates with differences in hallmark activity, even leading to clone-hallmark specialization. Finally, we demonstrate interdependent relationships between hallmarks at the junctions of TME and cancer compartments and how they relate to sensitivity to different neoadjuvant treatments in 33 bladder cancer patients from the DUTRENEO trial. In conclusion, our findings may improve our understanding of tumor ecology and help identify new drug biomarkers.

Systematic inference of super-resolution cell spatial profiles from histology images

Inferring cell spatial profiles from histology images is critical for cancer diagnosis and treatment in clinical settings. In this study, we report a weakly-supervised deep-learning method, HistoCell, to directly infer super-resolution cell spatial profiles consisting of cell types, cell states and their spatial network from histology images at the single-nucleus-level. Benchmark analysis demonstrates that HistoCell robustly achieves state-of-the-art performance in terms of cell type/states prediction solely from histology images across multiple cancer tissues. HistoCell can significantly enhance the deconvolution accuracy for the spatial transcriptomics data and enable accurate annotation of subtle cancer tissue architectures. Moreover, HistoCell is applied to de novo discovery of clinically relevant spatial organization indicators, including prognosis and drug response biomarkers, across diverse cancer types. HistoCell also enable image-based screening of cell populations that drives phenotype of interest, and is applied to discover the cell population and corresponding spatial organization indicators associated with gastric malignant transformation risk. Overall, HistoCell emerges as a powerful and versatile tool for cancer studies in histology image-only cohorts.

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.

STMiner: Gene-centric spatial transcriptomics for deciphering tumor tissues

Analyzing spatial transcriptomics data from tumor tissues poses several challenges beyond those of healthy samples, including unclear boundaries between different regions, uneven cell densities, and relatively higher cellular heterogeneity. Collectively, these bias the background against which spatially variable genes are identified, which can result in misidentification of spatial structures and hinder potential insight into complex pathologies. To overcome this problem, STMiner leverages 2D Gaussian mixture models and optimal transport theory to directly characterize the spatial distribution of genes rather than the capture locations of the cells expressing them (spots). By effectively mitigating the impacts of both background bias and data sparsity, STMiner reveals key gene sets and spatial structures overlooked by spot-based analytic tools, facilitating novel biological discoveries. The core concept of directly analyzing overall gene expression patterns also allows for a broader application beyond spatial transcriptomics, positioning STMiner for continuous expansion as spatial omics technologies evolve.

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.

Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study

BACKGROUND AND OBJECTIVE

Though several clinicopathological features are identified as prognostic indicators, potentially prognostic radiomic models are expected to preoperatively and noninvasively predict survival for HCC. Traditional radiomic models are lacking in a consideration for intratumoral regional heterogeneity. The study aimed to establish and validate the predictive power of multiple habitat radiomic models in predicting prognosis of hepatocellular carcinoma (HCC).

METHODS

A total of 232 HCC patients were retrospectively included, including a training/validation cohort and two external testing cohorts from 4 centers. For habitat radiomics, intratumoral habitat partitioning based on CT images was first performed by using Otsu thresholding method. Second, a total of 350 habitat radiomic models were constructed to select the optimal model. Then, both ROC curve analyses and Kaplan-Meier survival curve analyses were applied to assess the predictive performances. Ultimately, an immune status profiling was conducted based on bioinformatic analyses and multiplex immunohistochemistry (mIHC) assays to reveal the potential mechanisms.

RESULTS

A total of 4 habitats were segmented, and the corresponding habitat radiomic models were constructed based on each habitat and an integration of all the four habitats. Generally, habitat radiomic models outperformed traditional radiomic models in stratifying prognosis for HCC. The habitat radiomic model based on the segmented habitat 4 involving decision tree (DT) screening and random forest (RF) classifier was identified as the optimal model with an AUCmean of 0.806. Distinct resting natural killer (NK) cell infiltrations significantly contributed to the prognosis stratification of HCC by the optimal habitat radiomic model.

CONCLUSIONS

The habitat radiomic model based on CT images was potentially predictive of overall survival for HCC, with a superiority over the traditional radiomic model. The prognostic power of the habitat radiomic model was partly attributed to the distinct immune status captured in the CT images.

RASGRF2 as a potential pathogenic gene mediating the progression of alcoholic hepatitis to alcohol-related cirrhosis and hepatocellular carcinoma

BACKGROUND AND AIMS

Alcoholic hepatitis (AH) and hepatocellular carcinoma (HCC) are common liver diseases. Chronic inflammation caused by AH can progress to alcoholic cirrhosis (AC) and eventually HCC.

METHODS

This study sought to ascertain potential shared genes between AH and HCC through the utilization of multiple transcriptome databases. Employing an immune infiltration analysis, and calculating the correlation between shared genes and immune infiltration results, in conjunction with independent bulk transcriptome validation sets, led to the identification of core shared genes. Subsequently, single-cell transcriptome data, clinical sample immunohistochemistry experiments, and overexpressed core shared genes in HepG2 cells were employed to validate the core shared genes of AH and HCC.

RESULTS

Through the bulk transcriptome discovery sets of AH and HCC, 206 potential shared genes were identified. After screening with two machine learning algorithms, five shared genes remained. Combining the results of the immune infiltration and bulk transcriptome results from an independent validation cohort, the core shared gene was determined to be RASGRF2. Single-cell data further demonstrated that RASGRF2 and its downstream genes were highly expressed in AH, AC, and HCC tissues. Spatial transcriptome data indicated that RASGRF2 was highly expressed in HCC tumor tissues. Compared with the paracancerous tissues, the RASGRF2 gene was significantly overexpressed in HCC tissues. Overexpression of RASGRF2 in HepG2 cells resulted in significantly enhanced migration, invasion, and proliferation abilities.

CONCLUSION

RASGRF2 serve as a pathogenic gene that mediates the progression of AH to AC and potentially to HCC.

Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges

Cancer stem cells (CSCs) often exhibit stem-like attributes that depend on an intricate stemness-promoting cellular ecosystem within their niche. The interplay between CSCs and their niche has been implicated in tumor heterogeneity and therapeutic resistance. Normal stem cells (NSCs) and CSCs share stemness features and common microenvironmental components, displaying significant phenotypic and functional plasticity. Investigating these properties across diverse organs during normal development and tumorigenesis is of paramount research interest and translational potential. Advancements in next-generation sequencing (NGS), single-cell transcriptomics, and spatial transcriptomics have ushered in a new era in cancer research, providing high-resolution and comprehensive molecular maps of diseased tissues. Various spatial technologies, with their unique ability to measure the location and molecular profile of a cell within tissue, have enabled studies on intratumoral architecture and cellular cross-talk within the specific niches. Moreover, delineation of spatial patterns for niche-specific properties such as hypoxia, glucose deprivation, and other microenvironmental remodeling are revealed through multilevel spatial sequencing. This tremendous progress in technology has also been paired with the advent of computational tools to mitigate technology-specific bottlenecks. Here we discuss how different spatial technologies are used to identify NSCs and CSCs, as well as their associated niches. Additionally, by exploring related public data sets, we review the current challenges in characterizing such niches, which are often hindered by technological limitations, and the computational solutions used to address them.

SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

The rapid development of spatial transcriptomics (ST) technologies has enabled transcriptome-wide profiling of gene expression in tissue sections. Despite the emergence of single-cell resolution platforms, most ST sequencing studies still operate at a multicell resolution. Consequently, deconvolution of cell identities within the spatial spots has become imperative for characterizing cell-type-specific spatial organization. To this end, we developed Spatial Deconvolution Explorer (SpatialDeX), a regression model-based method for estimating cell-type proportions in tumor ST spots. SpatialDeX exhibited comparable performance to reference-based methods and outperformed other reference-free methods with simulated ST data. Using experimental ST data, SpatialDeX demonstrated superior performance compared with both reference-based and reference-free approaches. Additionally, a pan-cancer clustering analysis on tumor spots identified by SpatialDeX unveiled distinct tumor progression mechanisms both within and across diverse cancer types. Overall, SpatialDeX is a valuable tool for unraveling the spatial cellular organization of tissues from ST data without requiring single-cell RNA-seq references. Significance: The development of a reference-free method for deconvolving the identity of cells in spatial transcriptomics datasets enables exploration of tumor architecture to gain deeper insights into the dynamics of the tumor microenvironment.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Imaging-based surrogate classification for risk stratification of hepatocellular carcinoma with microvascular invasion to adjuvant hepatic arterial infusion chemotherapy: a multicenter retrospective study

BACKGROUND

Patients with microvascular invasion (MVI)-positive hepatocellular carcinoma (HCC) have shown promising results with adjuvant hepatic arterial infusion chemotherapy (HAIC) with FOLFOX after curative resection. The authors aim to develop an imaging-derived biomarker to depict MVI-positive HCC patients more precisely and promote individualized treatment strategies of adjuvant HAIC.

MATERIALS AND METHODS

Patients with MVI-positive HCC were identified from five academic centers and utilized for model development ( n =470). Validation cohorts were pooled from a previously reported prospective clinical study conducted [control cohort ( n =145), adjuvant HAIC cohort ( n =143)] (NCT03192618). The primary endpoint was recurrence-free survival (RFS). Imaging features were thoroughly reviewed, and multivariable logistic regression analysis was employed for model development. Transcriptomic sequencing was conducted to identify the associated biological processes.

RESULTS

Arterial phase peritumoral enhancement, boundary of the tumor enhancement, tumor necrosis stratification, and boundary of the necrotic area were selected and incorporated into the nomogram for RFS. The imaging-based model successfully stratified patients into two distinct prognostic subgroups in both the training, control, and adjuvant HAIC cohorts (median RFS, 6.00 vs. 66.00 months, 4.86 vs. 24.30 months, 11.46 vs. 39.40 months, all P <0.01). Furthermore, no significant statistical difference was observed between patients at high risk of adjuvant HAIC and those in the control group ( P =0.61). The area under the receiver operating characteristic curve at 2 years was found to be 0.83, 0.84, and 0.73 for the training, control, and adjuvant HAIC cohorts, respectively. Transcriptomic sequencing analyses revealed associations between the radiological features and immune-regulating signal transduction pathways.

CONCLUSION

The utilization of this imaging-based model could help to better characterize MVI-positive HCC patients and facilitate the precise subtyping of patients who genuinely benefit from adjuvant HAIC treatment.

Multi-omics analysis unveils the role of inflammatory cancer-associated fibroblasts in chordoma progression

Cancer-associated fibroblasts (CAFs) constitute the primary cellular component of the stroma in chordomas, characterized by an abundance of mucinous stromal elements, potentially facilitating their initiation and progression; however, this inference has yet to be fully confirmed. In this study, single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), bulk RNA-seq, multiplexed quantitative immunofluorescence (QIF), and in vivo and in vitro experiments were performed to determine the heterogeneity, spatial distribution, and clinical significance of CAFs in chordoma. ScRNA-seq was performed on 87,693 single cells derived from seven tumor samples and four control nucleus pulposus samples. A distinct CAF cluster distinguished by the upregulated expression of inflammatory genes and enriched functionality in activating inflammation-associated cells was identified. Pseudotime trajectory and cell communication analyses suggested that this inflammatory CAF (iCAF) subset originated from normal fibroblasts and interacted extensively with tumors and various other cell types. By integrating the scRNA-seq results with ST, the presence of iCAF in chordoma tissue was further confirmed, indicating their positioning at a distance from the tumor cells. Bulk RNA-seq data analysis from 126 patients revealed a correlation between iCAF signature scores, chordoma invasiveness, and poor prognosis. QIF validation involving an additional 116 patients found that although iCAFs were not in close proximity to tumor cells compared with other CAF subsets, their density correlated with malignant tumor phenotypes and adverse outcomes. In vivo and in vitro experiments further confirmed that iCAFs accelerate the malignant progression of chordomas. These findings could provide insights into the development of novel therapeutic strategies. © 2024 The Pathological Society of Great Britain and Ireland.

The heterogeneity of cellular metabolism in the tumour microenvironment of hepatocellular carcinoma with portal vein tumour thrombus

Given the growing interest in the metabolic heterogeneity of hepatocellular carcinoma (HCC) and portal vein tumour thrombus (PVTT). This study comprehensively analysed the metabolic heterogeneity of HCC, PVTT, and normal liver samples using multi-omics combinations. A single-cell RNA sequencing dataset encompassing six major cell types was obtained for integrated analysis. The optimal subtypes were identified using cluster stratification and validated using spatial transcriptomics and fluorescent multiplex immunohistochemistry. Then, a combined index based meta-cluster was calculated to verify its prognostic significance using multi-omics data from public cohorts. Our study first depicted the metabolic heterogeneity landscape of non-malignant cells in HCC and PVTT at multiomics levels. The optimal subtypes interpret the metabolic characteristics of PVTT formation and development. The combined index provided effective predictions of prognosis and immunotherapy responses. Patients with a higher combined index had a relatively poor prognosis (p <0.001). We also found metabolism of polyamines was a key metabolic pathway involved in conversion of metabolic heterogeneity in HCC and PVTT, and identified ODC1 was significantly higher expressed in PVTT compared to normal tissue (p =0.03). Our findings revealed both consistency and heterogeneity in the metabolism of non-malignant cells in HCC and PVTT. The risk stratification based on cancer-associated fibroblasts and myeloid cells conduce to predict prognosis and guide treatment. This offers new directions for understanding disease development and immunotherapy responses.

Roles of Cellular Neighborhoods in Hepatocellular Carcinoma Pathogenesis

The development of hepatocellular carcinoma (HCC) involves an intricate interplay among various cell types within the liver. Unraveling the orchestration of these cells, particularly in the context of various etiologies, may hold the key to deciphering the underlying mechanisms of this complex disease. The advancement of single-cell and spatial technologies has revolutionized our ability to determine cellular neighborhoods and understand their crucial roles in disease pathogenesis. In this review, we highlight the current research landscape on cellular neighborhoods in chronic liver disease and HCC, as well as the emerging computational approaches applicable to delineate disease-associated cellular neighborhoods, which may offer insights into the molecular mechanisms underlying HCC pathogenesis and pave the way for effective disease interventions.

Application of Spatial Transcriptomics in Digestive System Tumors

In the field of digestive system tumor research, spatial transcriptomics technologies are used to delve into the spatial structure and the spatial heterogeneity of tumors and to analyze the tumor microenvironment (TME) and the inter-cellular interactions within it by revealing gene expression in tumors. These technologies are also instrumental in the diagnosis, prognosis, and treatment of digestive system tumors. This review provides a concise introduction to spatial transcriptomics and summarizes recent advances, application prospects, and technical challenges of these technologies in digestive system tumor research. This review also discusses the importance of combining spatial transcriptomics with single-cell RNA sequencing (scRNA-seq), artificial intelligence, and machine learning in digestive system cancer research.

Spatially-resolved analyses of muscle invasive bladder cancer microenvironment unveil a distinct fibroblast cluster associated with prognosis

Background

Muscle-invasive bladder cancer (MIBC) is a prevalent cancer characterized by molecular and clinical heterogeneity. Assessing the spatial heterogeneity of the MIBC microenvironment is crucial to understand its clinical significance.

Methods

In this study, we used imaging mass cytometry (IMC) to assess the spatial heterogeneity of MIBC microenvironment across 185 regions of interest in 40 tissue samples. We focused on three primary parameters: tumor (T), leading-edge (L), and nontumor (N). Cell gating was performed using the Cytobank platform. We calculated the Euclidean distances between cells to determine cellular interactions and performed single-cell RNA sequencing (scRNA-seq) to explore the molecular characteristics and mechanisms underlying specific fibroblast (FB) clusters. scRNA-seq combined with spatial transcriptomics (ST) facilitated the identification of ligand-receptor (L-R) pairs that mediate interactions between specific FB clusters and endothelial cells. Machine learning algorithms were used to construct a prognostic gene signature.

Results

The microenvironments in the N, L, and T regions of MIBC exhibited spatial heterogeneity and regional diversity in their components. A distinct FB cluster located in the L region-identified as S3-is strongly associated with poor prognosis. IMC analyses demonstrated a close spatial association between S3 and endothelial cells, with S3-positive tumors exhibiting increased blood vessel density and altered vascular morphology. The expression of vascular endothelial growth factor receptor and active vascular sprouting were significant in S3-positive tumors. scRNA-seq and ST analyses indicated that the genes upregulated in S3 were associated with angiogenesis. NOTCH1-JAG2 signaling pathway was identified as a significant L-R pair specific to S3 and endothelial cell interactions. Further analysis indicated that YAP1 was a potential regulator of S3. Machine learning algorithms and Gene Set Variation Analysis were used to establish an S3-related gene signature that was associated with the poor prognosis of tumors including MIBC, mesothelioma, glioblastoma multiforme, lower-grade glioma, stomach adenocarcinoma, uveal melanoma, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, and lung squamous cell carcinoma.

Conclusions

We assessed the spatial landscape of the MIBC microenvironment and revealed a specific FB cluster with prognostic potential. These findings offer novel insights into the spatial heterogeneity of the MIBC microenvironment and highlight its clinical significance.

Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study

BACKGROUND

Tertiary lymphoid structures (TLS) within the tumor microenvironment have been associated with cancer prognosis and therapeutic response. However, the immunological pattern of a high peritumoral TLS (pTLS) density and its clinical potential in hepatocellular carcinoma (HCC) remain poor. This study aimed to elucidate biological differences related to pTLS density and develop a radiomic classifier for predicting pTLS density in HCC, offering new insights for clinical diagnosis and treatment.

METHODS

Spatial transcriptomics (n=4) and RNA sequencing data (n=952) were used to identify critical regulators of pTLS density and evaluate their prognostic significance in HCC. Baseline MRI images from 660 patients with HCC who had undergone surgery treatment between October 2015 and January 2023 were retrospectively recruited for model development and validation. This included training (n=307) and temporal validation (n=76) cohorts from Xiangya Hospital, and external validation cohorts from three independent hospitals (n=277). Radiomic features were extracted from intratumoral and peritumoral regions of interest and analyzed using machine learning algorithms to develop a predictive classifier. The classifier's performance was evaluated using the area under the curve (AUC), with prognostic and predictive value assessed across four independent cohorts and in a dual-center outcome cohort of 41 patients who received immunotherapy.

RESULTS

Patients with HCC and a high pTLS density experienced prolonged median overall survival (p<0.05) and favorable immunotherapy response (p=0.03). Moreover, immune infiltration by mature B cells was observed in the high pTLS density region. Spatial pseudotime analysis and immunohistochemistry staining revealed that expansion of pTLS in HCC was associated with elevated CXCL9 and CXCL10 co-expression. We developed an optimal radiomic-based classifier with excellent discrimination for predicting pTLS density, achieving an AUC of 0.91 (95% CI 0.87, 0.94) in the external validation cohort. This classifier also exhibited promising stratification ability in terms of overall survival (p<0.01), relapse-free survival (p<0.05), and immunotherapy response (p<0.05).

CONCLUSION

We identified key regulators of pTLS density in patients with HCC and proposed a non-invasive radiomic classifier capable of assisting in stratification for prognosis and treatment.

Targeting the immune privilege of tumor-initiating cells to enhance cancer immunotherapy

Tumor-initiating cells (TICs) possess the ability to evade anti-tumor immunity, potentially explaining many failures of cancer immunotherapy. Here, we identify CD49f as a prominent marker for discerning TICs in hepatocellular carcinoma (HCC), outperforming other commonly used TIC markers. CD49f-high TICs specifically recruit tumor-promoting neutrophils via the CXCL2-CXCR2 axis and create an immunosuppressive milieu in the tumor microenvironment (TME). Reciprocally, the neutrophils reprogram nearby tumor cells toward a TIC phenotype via secreting CCL4. These cells can evade CD8+ T cell-mediated killing through CCL4/STAT3-induced and CD49f-stabilized CD155 expression. Notably, while aberrant CD155 expression contributes to immune suppression, it also represents a TIC-specific vulnerability. We demonstrate that either CD155 deletion or antibody blockade significantly enhances sensitivity to anti-PD-1 therapy in preclinical HCC models. Our findings reveal a new mechanism of tumor immune evasion and provide a rationale for combining CD155 blockade with anti-PD-1/PD-L1 therapy in HCC.

Integrated Single-Cell and Spatial Transcriptome Reveal Metabolic Gene SLC16A3 as a Key Regulator of Immune Suppression in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most lethal cancers, usually diagnosed at an advanced stage. Metabolic reprogramming plays a significant role in HCC progression, probably related to immune evasion, yet the key gene is unclear. In this study, six metabolism-related genes with prognostic implications were screened. Correlation analysis between the key genes and immune cell subtypes was conducted, and a prominent gene strongly associated with immunosuppression, SLC16A3, was identified. Overexpression of SLC16A3 is associated with the loss of T-cell function and might lead to the upregulation of several immunosuppressive proteins. Gene function enrichment analysis showed genes correlated with SLC16A3 primarily involved in cell adhesion. Single-cell analysis showed that the SLC16A3 gene was mainly expressed in macrophages, especially some tumour-promoting macrophages. Further analysis of spatial transcriptome data indicated that SLC16A3 was enriched at the tumour invasion front. The mIHC revealed that patients with high SLC16A3 expression exhibited significantly reduced infiltration of GZMB+ cells. And SLC16A3 inhibitors significantly suppressed the proliferation of HCC, while simultaneously enhancing T-cell cytotoxicity and reducing exhaustion. These results reveal the phenomenon of immune escape mediated by metabolic reprogramming and suggest that SLC16A3 may serve as a novel target for intervention.

Development of mutated β-catenin gene signature to identify CTNNB1 mutations from whole and spatial transcriptomic data in patients with HCC

Background & Aims

Patients with β-catenin (encoded by CTNNB1)-mutated hepatocellular carcinoma (HCC) demonstrate heterogenous responses to first-line immune checkpoint inhibitors (ICIs). Precision-medicine based treatments for this subclass are currently in clinical development. Here, we report derivation of the Mutated β-catenin Gene Signature (MBGS) to predict CTNNB1-mutational status in patients with HCC for future application in personalized medicine treatment regimens.

Methods

Co-expression of mutant-Nrf2 and hMet ± mutant-β-catenin in murine livers in mice led to HCC development. The MBGS was derived using bulk RNA-seq and intersectional transcriptomic analysis of β-catenin-mutated and non-mutated HCC models. Integrated RNA/whole-exome-sequencing and spatial transcriptomic data from multiple cohorts of patients with HCC was assessed to address the ability of MBGS to detect CTNNB1 mutation, the tumor immune microenvironment, and/or predict therapeutic responses.

Results

Bulk RNA-seq comparing HCC specimens in mutant β-catenin-Nrf2, β-catenin-Met and β-catenin-Nrf2-Met to Nrf2-Met HCC model yielded 95 common upregulated genes. In The Cancer Genome Atlas (TCGA)-LIHC dataset, differential gene expression analysis with false discovery rate (FDR) = 0.05 and log2(fold change) >1.5 on the 95 common genes comparing CTNNB1-mutated vs. wild-type patients narrowed the gene panel to a 13-gene MBGS. MBGS predicted CTNNB1-mutations in TCGA (n = 374) and French (n = 398) patient cohorts with AUCs of 0.90 and 0.94, respectively. Additionally, a higher MBGS expression score was associated with lack of significant improvement in overall survival or progression-free survival in the atezolizumab-bevacizumab arm vs. the sorafenib arm in the IMbrave150 cohort. MBGS performed comparable or superior to other CTNNB1-mutant classifiers. MBGS overlapped with Hoshida S3, Boyault G5/G6, and Chiang CTNNB1 subclass tumors in TCGA and in HCC spatial transcriptomic datasets visually depicting these tumors to be situated in an immune excluded tumor microenvironment.

Conclusions

MBGS will aid in patient stratification to guide precision medicine therapeutics for CTNNB1-mutated HCC subclass as a companion diagnostic, as anti-β-catenin therapies become available.

Impact and implications

As precision medicine for liver cancer treatment becomes a reality, diagnostic tools are needed to help classify patients into groups for the best treatment choices. We have developed a molecular signature that could serve as a companion diagnostic and uses bulk or spatial transcriptomic data to identify a unique subclass of liver tumors. This subgroup of liver cancer patients derive limited benefit from the current standard of care and are expected to benefit from specialized directed therapies that are on the horizon.

Spatial Transcriptomics Reveals the Transcriptomic Signatures in a Mouse Model of Pediatric Metabolic Dysfunction-Associated Steatohepatitis

Metabolic dysfunction-associated steatohepatitis (MASH) is considered the progressive form of metabolic dysfunction-associated steatotic liver disease, which is the leading cause of chronic liver disease in children. However, the pathogenesis of pediatric MASH remains poorly understood because of the lack of animal models. In this study, a mouse model of pediatric MASH was developed and its hepatic transcriptomic profile was characterized using spatial transcriptomics technology. C57BL/6J mice were fed a Western diet (WD) along with weekly injections of carbon tetrachloride (CCl4) from the age of 3 weeks and lasting up to 8 weeks. After 5 weeks of feeding, WD + CCl4-treated mice showed significant liver injury without the development of insulin resistance. Histologically, WD + CCl4 induced key features of type 2 MASH, the most common type observed in children, characterized by liver steatosis, portal inflammation, and portal fibrosis. Spatial transcriptomics analysis of liver tissues indicated that cluster 0 in the mouse from the WD + CCl4 group was enriched in pathways associated with lipid metabolism. Further investigation revealed that cytochrome p450 2E1 was the top marker gene of cluster 0, and its expression was increased in the periportal area of mice from the WD + CCl4 group. These findings suggest that this mouse model of pediatric MASH mirrors the histologic features of human MASH, and the up-regulation of cytochrome p450 2E1 may be linked to the disease pathogenesis.

Multi-omics-driven discovery of invasive patterns and treatment strategies in CA19-9 positive intrahepatic cholangiocarcinoma

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor with a poor prognosis, predominantly CA19-9 positive. High CA19-9 levels correlate with increased aggressiveness and worse outcomes. This study employs multi-omics analysis to reveal molecular features and identify therapeutic targets of CA19-9 positive ICC, aiming to support individualized treatment.

METHODS

Data from seven clinical cohorts, two whole-exome sequencing cohorts, six RNA sequencing/microarray cohorts, one proteomic cohort, 20 single-cell RNA sequencing samples, and one spatial transcriptome sample were analyzed. Key findings were validated on tissue microarrays from 52 ICC samples.

RESULTS

CA19-9 positive ICC exhibited poorer OS (median 24.1 v.s. 51.5 months) and RFS (median 11.7 v.s. 28.2 months) compared to negative group (all P < 0.05). Genomic analysis revealed a higher KRAS mutation frequency in the positive group and a greater prevalence of IDH1/2 mutations in the negative group (all P < 0.05). Transcriptomic analysis indicated upregulated glycolysis pathways in CA19-9 positive ICC. Single-cell analysis identified specific glycolysis-related cell subclusters associated with poor prognosis, including Epi_SLC2A1, CAF_VEGFA, and Mph_SPP1. Higher hypoxia in the CA19-9 positive group led to metabolic reprogramming and promoted these cells' formation. These cells formed interactive communities promoting epithelial-mesenchymal transition (EMT) and angiogenesis. Drug sensitivity analysis identified six potential therapeutic drugs.

CONCLUSIONS

This study systematically elucidated the clinical, genomic, transcriptomic, and immune features of CA19-9 positive ICC. It reveals glycolysis-associated cellular communities and their cancer-promoting mechanisms, enhancing our understanding of ICC and laying the groundwork for individualized therapeutic strategies.

Single cell-spatial transcriptomics and bulk multi-omics analysis of heterogeneity and ecosystems in hepatocellular carcinoma

This study profiled global single cell-spatial-bulk transcriptome landscapes of hepatocellular carcinoma (HCC) ecosystem from six HCC cases and a non-carcinoma liver control donor. We discovered that intratumoral heterogeneity mainly derived from HCC cells diversity and pervaded the genome-transcriptome-proteome-metabolome network. HCC cells are the core driving force of taming tumor-associated macrophages (TAMs) with pro-tumorigenic phenotypes for favor its dominant growth. Remarkably, M1-types TAMs had been characterized by disturbance of metabolism, poor antigen-presentation and immune-killing abilities. Besides, we found simultaneous cirrhotic and HCC lesions in an individual patient shared common origin and displayed parallel clone evolution via driving disparate immune reprograms for better environmental adaptation. Moreover, endothelial cells exhibited phenotypically conserved but executed differential functions in a space-dependent manner. Further, the spatiotemporal traits of rapid recurrence niche genes were identified and validated by immunohistochemistry. Our data unravels the great significance of HCC cells in shaping vibrant tumor ecosystems corresponding to clinical scenarios.

Integrated analysis of single-cell, spatial and bulk RNA-sequencing identifies a cell-death signature for predicting the outcomes of head and neck cancer

Background

Cell death plays an essential role in carcinogenesis, but its function in the recurrence and postoperative prognosis of head and neck cancer (HNC), which ranks as the 7th most common malignancy globally, remains unclear.

Methods

Data from five main subtypes of HNC related single-cell RNA sequencing (scRNA-seq) were recruited to establish a single-cell atlas, and the distribution of cell death models (CDMs) across different tissues as well as cell subtypes were analyzed. Bulk RNA-seq from the Cancer Genome Atlas Program (TCGA) dataset was subjected to a machine learning-based integrative procedure for constructing a consensus cell death-related signature risk score (CDRscore) model and validated by external data. The biofunctions including different expression analysis, immune cell infiltration, genomic mutations, enrichment analysis as well as cellchat analysis were compared between the high- and low- risk score groups categorized by this CDRscore model. Finally, samples from laryngeal squamous cell cancer (LSCC) were conducted by spatial transcriptomics (ST) to further validate the results of CDRscore model.

Results

T cells from HNC patients manifested the highest levels of cell death while HPV infection attenuates malignant cell death based on single-cell atlas. CDMs are positively correlated with the tumor-cell stemness, immune-related score and T cells are infiltrated. A CDRscore model was established based on the transcription of ten cell death prognostic genes (MRPL10, DDX19A, NDFIP1, PCMT1, HPRT1, SLC2A3, EFNB2, HK1, BTG3 and MAP2K7). It functions as an independent prognostic factor for overall survival in HNC and displays stable and powerful performance validated by GSE41613 and GSE65858 datasets. Patients in high CDRscore manifested worse overall survival, more active of epithelial mesenchymal transition, TGF-β-related pathways and hypoxia, higher transcription of T cell exhausted markers, and stronger TP53 mutation. ST from LSCC showed that spots with high-risk scores were colocalized with TGF-β and the proliferating malignant cells, additionally, the risk scores have a negative correlation with TCR signaling but positive association with LAG3 transcription.

Conclusion

The CDRscore model could be utilized as a powerful prognostic indicator for HNC.

Integration of single-cell and spatial transcriptome sequencing identifies CDKN2A as a senescent biomarker in endothelial cells implicating hepatocellular carcinoma malignancy

PURPOSE

Highly complex tumor microenvironment makes hepatocellular carcinoma (HCC) as one of the most malignant tumors worldwide. The role of cellular senescence in HCC has been gradually recognized. The present study aimed to comprehensively elucidate the senescence-related features of HCC in single-cell and spatial dimension.

METHODS

Single-cell RNA sequencing (scRNA-Seq) data was used to clarify the heterogeneity of senescence-related genes (SRGs) among multiple cell types within HCC. Spatial transcriptome RNA sequencing (stRNA-Seq) data was used for depicting SRGs features in spatial dimension. A prognostic model based on SRGs was constructed by using of bulk sequencing (bulk-Seq) data of HCC. The cell-cell interaction of senescent endothelial cells (ECs) in tumor microenvironment was analyzed. Then, the role of senescent ECs was verified through in vitro and in vivo experiments.

RESULTS

The level of senescence demonstrated substantial heterogeneity among different cell types within tumor microenvironment of HCC, where ECs exhibited the most prominent senescent phenotype. Senescent ECs activated specific regulatory pathways through communicating with other cell types, with a potential impact on tumor progression. Spatial analysis revealed senescent ECs mainly located in the core region of HCC. The interaction of senescent ECs and immune cells implicated their role in tumor progression and immunotherapeutic response. In addition, CDKN2A was identified as an independent risk factor for HCC prognosis by constructing a prognostic model. Patients with high risk displayed an even worse outcome. The experimental verification indicated senescence of ECs determined by CDKN2A exhibited a secretory phenotype. Furthermore, senescent ECs with CDKN2A overexpression promote the proliferation and migration of HCC.

CONCLUSION

The present study recognizes the critical effect of senescent ECs defined by CDKN2A in the promotion of tumor progression, which sheds new light on the investigation of ECs senescence in HCC.

Transcriptome analysis revealed the genes and major pathways involved in prunetrin treated hepatocellular carcinoma cells

Liver cancer represents a complex and severe ailment that poses tough challenges to global healthcare. Transcriptome sequencing plays a crucial role in enhancing our understanding of cancer biology and accelerating the development of more effective methods for cancer diagnosis and treatment. In the course of our current investigation, we identified a total of 1,149 differentially expressed genes (DEGs), encompassing 499 upregulated and 650 downregulated genes, subsequent to prunetrin (PUR) treatment. Our methodology encompassed gene and pathway enrichment analysis, functional annotation, KEGG pathway assessments, and protein-protein interaction (PPI) analysis of the DEGs. The preeminent genes within the DEGs were found to be associated with apoptotic processes, cell cycle regulation, the PI3k/Akt pathway, the MAPK pathway, and the mTOR pathway. Furthermore, key apoptotic-related genes exhibited close interconnections and cluster analysis found three interacting hub genes namely, TP53, TGFB1 and CASP8. Validation of these genes was achieved through GEPIA and western blotting. Collectively, our findings provide insights into the functional landscape of liver cancer-related genes, shedding light on the molecular mechanisms driving disease progression and highlighting potential targets for therapeutic intervention.