Understanding tumour cell heterogeneity and its implication for immunotherapy in liver cancer using single-cell analysis

Targeting CDCP1 boost CD8+ T cells-mediated cytotoxicity in cervical cancer via the JAK/STAT signaling pathway

BACKGROUND

Cervical cancer remains a global health challenge. The identification of new immunotherapeutic targets may provide a promising platform for advancing cervical cancer treatment.

OBJECTIVE

This study aims to investigate the role of CUB domain-containing protein 1 (CDCP1) in cervical cancer progression and evaluate its potential as a therapeutic target.

METHODS

We performed comprehensive analyses using patient cohorts and preclinical models to examine the association between CDCP1 expression and cervical cancer prognosis. Then in immunodeficient and immunocompetent mouse models, we further investigated the impact of CDCP1 on the tumor immune microenvironment, focusing on its effects on tumor-infiltrating T cells, including cytotoxic T lymphocytes (CTLs) and regulatory T cells (Tregs). Mechanistic studies were performed to elucidate the pathways involved in CDCP1-mediated immune modulation, in particular its interaction with the T cell receptor CD6 and the activation of the JAK-STAT signaling pathway.

RESULTS

Our results show that CDCP1 overexpression is associated with poor prognosis and T cell infliction in cervical cancer. Specifically, it affects the activity of CTLs and Tregs. Mechanistically, CDCP1 binds to CD6 and inhibits the JAK-STAT pathway of T cells. The study further demonstrates that targeting CDCP1 with the inhibitor 8-prenylnaringenin (8PN) effectively suppresses tumor growth in vivo and enhances antitumor immunity.

CONCLUSIONS

CDCP1 plays a critical role in cervical cancer progression by modulating the tumor immune microenvironment. Targeting CDCP1 offers a promising therapeutic strategy to improve the outcome of patients with cervical cancer.

Characterization of cell states in biliary tract cancers identifies mechanisms of therapeutic resistance in a phase II trial of DKN-01/nivolumab

Biliary tract cancers demonstrate profound therapeutic resistance, and broadly effective therapies for refractory disease are lacking. We conducted a single-arm, second-line phase II trial combining DKN-01, a humanized monoclonal antibody targeting Dickkopf-1 (DKK-1), and nivolumab to treat patients with advanced biliary tract cancer (NCT04057365). No objective responses were seen. To identify mechanisms of treatment failure, we analyzed paired pre-treatment and on-treatment biopsies using scRNA-seq and constructed a detailed molecular classification of malignant and immune cells. We annotated five biliary tract cancer malignant cell states: classical, basal, mesenchymal, neural-like, and endothelial-like. Neural-like and endothelial-like states, which drive therapeutic resistance in other cancers, have not previously been described in BTC. Malignant cell states co-varied with distinct immune cell states, revealing diverse mechanisms of myeloid and T-cell mediated immune suppression, including M2 myeloid and terminally exhausted T cell programs that were induced by DKN-01/nivolumab. Here, we provide the first systematic classification of functionally annotated cell states in biliary tract cancer and provide new insight into resistance mechanisms to an immunotherapy combination that can inform the next generation of trials.

Deciphering the spatial organization of fibrotic microenvironment in silica particles-induced pulmonary fibrosis

Silicosis represents a form of interstitial lung disease induced by the inhalation of silica particles in production environments. A key pathological characteristic of silica-induced pulmonary fibrosis is its localized tissue heterogeneity, which presents significant challenges in analyzing transcriptomic data due to the loss of important spatial context. To address this, we integrate spatial gene expression data with single-cell analyses and achieve a detailed mapping of cell types within and surrounding fibrotic regions, revealing significant shifts in cell populations in normal and diseased states. Additionally, we explore cell interactions within fibrotic zones using ligand-receptor mapping, deepening our understanding of cellular dynamics in these areas. We identify a subset of fibroblasts, termed Inmt fibroblasts, that play a suppressive role in the fibrotic microenvironment. Validating our findings through a comprehensive suite of bioinformatics, histological, and cell culture studies highlights the role of monocyte-derived macrophages in shifting Inmt fibroblast populations into profibrotic Grem1 fibroblast, potentially disrupting lung homeostasis in response to external challenges. Hence, the spatially detailed deconvolution offered by our research markedly advances the comprehension of cell dynamics and environmental interactions pivotal in the development of pulmonary fibrosis.

Utilization of Microfluidic Droplet-Based Methods in Diagnosis and Treatment Methods of Hepatocellular Carcinoma: A Review

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and is associated with high morbidity and mortality. One of the main challenges in the management of HCC is late clinical presentation and thus diagnosis of the disease, which results in poor survival. The pathogenesis of HCC is complex and involves chronic liver injury and genetic alterations. Diagnosis of HCC can be made either by biopsy or imaging; however, conventional tissue-based biopsy methods and serological biomarkers such as AFP have limited clinical applications. While hepatocellular carcinoma is associated with a range of molecular alterations, including the activation of oncogenic signaling pathways, such as Wnt-TGFβ, PI3K-AKT-mTOR, RAS-MAPK, MET, IGF, and Wnt-β-catenin and TP53 and TERT promoter mutations, microfluidic applications have been limited. Early diagnosis is crucial for advancing treatments that would address the heterogeneity of HCC. In this context, microfluidic droplet-based methods are crucial, as they enable comprehensive analysis of the genome and transcriptome of individual cells. Single-cell RNA sequencing (scRNA-seq) allows the examination of individual cell transcriptomes, identifying their heterogeneity and cellular evolutionary relationships. Other microfluidic methods, such as Drop-seq, InDrop, and ATAC-seq, are also employed for single-cell analysis. Here, we examine and compare these microfluidic droplet-based methods, exploring their advantages and limitations in liver cancer research. These technologies provide new opportunities to understand liver cancer biology, diagnosis, treatment, and prognosis, contributing to scientific efforts in combating this challenging disease.

New HCC Subtypes Based on CD8 Tex-Related lncRNA Signature Could Predict Prognosis, Immunological and Drug Sensitivity Characteristics of Hepatocellular Carcinoma

Purpose

Hepatocellular carcinoma has become one of the severe diseases threatening human health. T cell exhaustion is deemed as a reason for immunotherapy resistance. However, little is known about the roles of CD8 Tex-related lncRNAs in HCC.

Materials and Methods

We processed single-cell RNA sequencing to identify CD8 Tex-related genes. CD8 Tex-related lncRNAs were identified based on their correlations with mRNAs. Unsupervised clustering approach was used to identify molecular clusters of CD8 Tex-related lncRNAs. Differences in prognosis and immune infiltration between the clusters were explored. Machine learning algorithms were used to construct a prognostic signature. Samples were classified as low- and high-risk groups based on their risk scores. We identified prognosis-related lncRNAs and constructed a ceRNA network. In vitro experiments were conducted to investigate the impacts of CD8 Tex-related lncRNAs on proliferation and apoptosis of HCC cells.

Results

We clarified cell types within two HCC single-cell datasets. We identified specific markers of CD8 Tex cells and analyzed their potential functions. Twenty-eight lncRNAs were identified as CD8 Tex-related. Based on CD8 Tex-related lncRNAs, samples were categorized into two distinct clusters, which exhibited significant differences in survival rates and immune infiltration. Ninety-six algorithm combinations were employed to establish a prognostic signature. RSF emerged as the one with the highest C-index. Patients in high- and low-risk groups exhibited marked differences in prognosis, enriched pathways, mutations and drug sensitivities. MCM3AP-AS1, MAPKAPK5-AS1 and PART1 were regarded as prognosis-related lncRNAs. A ceRNA network was constructed based on CD8 Tex-related lncRNAs and mRNAs. Experiments on cell lines and organoids indicated that downregulation of MCM3AP-AS1, MAPKAPK5-AS1 and PART1 suppressed cell proliferation and induced apoptosis.

Conclusion

CD8 Tex-related lncRNAs played crucial roles in HCC progression. Our findings provided new insights into the regulatory mechanisms of CD8 Tex-related lncRNAs in HCC.

Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research

Single-cell RNA-seq (scRNA-seq) is a revolutionary technology that allows for the genomic investigation of individual cells in a population, allowing for the discovery of unusual cells associated with cancer and metastasis. ScRNA-seq has been used to discover different types of cancers with poor prognosis and medication resistance such as lung cancer, breast cancer, ovarian cancer, and gastric cancer. Besides, scRNA-seq is a promising method that helps us comprehend the biological features and dynamics of cell development, as well as other disorders. This review gives a concise summary of current scRNA-seq technology. We also explain the main technological steps involved in implementing the technology. We highlight the present applications of scRNA-seq in cancer research, including tumor heterogeneity analysis in lung cancer, breast cancer, and ovarian cancer. In addition, this review elucidates potential applications of scRNA-seq in lineage tracing, personalized medicine, illness prediction, and disease diagnosis, which reveals that scRNA-seq facilitates these events by producing genetic variations on the single-cell level.

Landscape and prognostic values of lymphocytes in patients with hepatocellular carcinoma undergoing transarterial embolization

Transarterial embolization, the first-line treatment for hepatocellular carcinoma, does not always lead to promising outcomes in all patients. A better understanding of how the immune lymphocyte changes after transarterial embolization might be the key to improve the efficacy of transarterial embolization. However, there are few studies evaluating immune lymphocytes in transarterial embolization patients. Therefore, we aimed to evaluate the short- and long-term effects of transarterial embolization on lymphocyte subsets in patients with hepatocellular carcinoma to identify those that predict transarterial embolization prognosis. Peripheral blood samples were collected from 44 patients with hepatocellular carcinoma at the following time points: 1 d before the initial transarterial embolization, 3 d after the initial transarterial embolization, and 1 mo after the initial transarterial embolization and subjected to peripheral blood mononuclear cell isolation and flow cytometry. Dynamic changes in 75 lymphocyte subsets were recorded, and their absolute counts were calculated. Tumor assessments were made every 4 to 6 wk via computed tomography or magnetic resonance imaging. Our results revealed that almost all lymphocyte subsets fluctuated 3 d after transarterial embolization, but only Tfh and B cells decreased 1 mo after transarterial embolization. Univariate and multivariate Cox regression showed that high levels of Th2 and conventional killer Vδ2 cells were associated with longer progressive-free survival after transarterial embolization. Longer overall survival after transarterial embolization was associated with high levels of Th17 and viral infection-specific Vδ1 cells and low levels of immature natural killer cells. In conclusion, transarterial embolization has a dynamic influence on the status of lymphocytes. Accordingly, several lymphocyte subsets can be used as prognostic markers for transarterial embolization.

miRNome targeting NF-κB signaling orchestrates macrophage-triggered cancer metastasis and recurrence

Whether and how tumor intrinsic signature determines macrophage-elicited metastasis remain elusive. Here, we show, in detailed studies of data regarding 7,477 patients of 20 types of human cancers, that only 13.8% ± 2.6%/27.9% ± 3.03% of patients with high macrophage infiltration index exhibit early recurrence/vascular invasion. In parallel, although macrophages enhance the motility of various hepatoma cells, their enhancement intensity is significantly heterogeneous. We identify that the expression of malignant Dicer, a ribonuclease that cleaves miRNA precursors into mature miRNAs, determines macrophage-elicited metastasis. Mechanistically, the downregulation of Dicer in cancer cells leads to defects in miRNome targeting NF-κB signaling, which in turn enhances the ability of cancer cells to respond to macrophage-related inflammatory signals and ultimately promotes metastasis. Importantly, transporting miR-26b-5p, the most potential miRNA targeting NF-κB signaling in hepatocellular carcinoma, can effectively reverse macrophage-elicited metastasis of hepatoma in vivo. Our results provide insights into the crosstalk between Dicer-elicited miRNome and cancer immune microenvironments and suggest that strategies to remodel malignant cell miRNome may overcome pro-tumorigenic activities of inflammatory cells.

Translational research on drug development and biomarker discovery for hepatocellular carcinoma

Translational research plays a key role in drug development and biomarker discovery for hepatocellular carcinoma (HCC). However, unique challenges exist in this field because of the limited availability of human tumor samples from surgery, the lack of homogenous oncogenic driver mutations, and the paucity of adequate experimental models. In this review, we provide insights into these challenges and review recent advancements, with a particular focus on the two main agents currently used as mainstream therapies for HCC: anti-angiogenic agents and immunotherapy. First, we examine the pre-clinical and clinical studies to highlight the challenges of determining the optimal therapeutic combinations with biologically effective dosage for HCC. Second, we discuss biomarker studies focusing on anti-PD1/anti-PD-L1-based combination therapy. Finally, we discuss the progress made in our collective understanding of tumor immunology and in multi-omics analysis technology, which enhance our understanding of the mechanisms underlying immunotherapy, characterize different patient subgroups, and facilitate the development of novel combination approaches to improve treatment efficacy. In summary, this review provides a comprehensive overview of efforts in translational research aiming at advancing our understanding of and improving the treatment of HCC.

The anti-hepatocellular carcinoma effect of Aidi injection was related to the synergistic action of cantharidin, formononetin, and isofraxidin through BIRC5, FEN1, and EGFR

ETHNOPHARMACOLOGICAL RELEVANCE

Aidi injection (ADI) is a popular anti-tumor Chinese patent medicine, widely used in clinics for the treatment of hepatocellular carcinoma (HCC) with remarkable therapeutic effects through multiple targets and pathways. However, the scientific evidence of the synergistic role of the complex chemical component system and the potential mechanism for treating diseases are ignored and remain to be elucidated.

AIM OF THE STUDY

This study aimed to elucidate and verify the cooperative association between the potential active ingredient of ADI, which is of significance to enlarge our understanding of its anti-HCC molecular mechanisms.

MATERIALS AND METHODS

Firstly, the anti-HCC effect of ADI was evaluated in various HCC cells and the zebrafish xenograft model. Subsequently, a variety of bioinformatic technologies, including network pharmacology, weighted gene co-expression network analysis (WGCNA), meta-analysis of gene expression profiles, and pathway enrichment analysis were performed to construct the competitive endogenous RNA (ceRNA) network of ADI intervention in HCC and to establish the relationship between the critical targets/pathways and the key corresponding components, which were involved in ADI against HCC in a synergistic way and were validated by molecular biology experiments.

RESULTS

ADI exerted remarkable anti-HCC in vitro cells and in vivo zebrafish model, especially that the Hep 3B2.1-7 cell showed substantial sensibility to ADI. The ceRNA network revealed that the EGFR/PI3K/AKT signaling pathway was identified as the promising pathway. Furthermore, the meta-analysis also demonstrated the critical role of BIRC5 and FEN1 as key targets. Finally, the synergistic effect of ADI was revealed by discovering the inhibitory effect of cantharidin on BIRC5, formononetin on FEN1 and EGFR, as well as isofraxidin on EGFR.

CONCLUSION

Our study unveiled that the incredible protective effect of ADI on HCC resulted from the synergistic inhibition effect of cantharidin, formononetin, and isofraxidin on multiple targets/pathways, including BIRC5, FEN1, and EGFR/PI3K/AKT, respectively, providing a scientific interpretation of ADI against HCC and a typical example of pharmacodynamic evaluation of other proprietary Chinese patent medicine.

Disulfidptosis-related signature predicts prognosis and characterizes the immune microenvironment in hepatocellular carcinoma

BACKGROUND

Disulfidptosis is a type of programmed cell death caused by excessive cysteine-induced disulfide bond denaturation leading to actin collapse. Liver cancer has a poor prognosis and requires more effective intervention strategies. Currently, the prognostic and therapeutic value of disulfidptosis in liver cancer is not clear.

METHODS

We investigated the features of 16 disulfidptosis-related genes (DRGs) of HCC patients in the TCGA and classified the patients into two disulfidptosis pattern clusters by consensus clustering analysis. Then, we constructed a prognostic model using LASSO Cox regression. Next, the microenvironment and drug sensitivity were evaluated. Finally, we used qPCR and functional analysis to verify the reliability of hub DRGs.

RESULTS

Most of the DRGs showed significantly higher expression in cancer tissues than in adjacent tissues. Our prognostic model, the DRG score, can well predict the survival of HCC patients. There were significant differences in survival, features of the microenvironment, effects of immunotherapy, and drug sensitivity between the high- and low-DRG score groups. Ultimately, we demonstrated that a few hub DRGs have differential mRNA expression between liver cancer cells and normal cells and that the protective gene LCAT can inhibit liver cancer metastasis in vitro.

CONCLUSION

We established a novel risk model based on DRG scores to predict HCC patient prognosis, drug sensitivity and immunotherapy efficacy, which provides new insight into the relationship between disulfidptosis and HCC and provides valuable assistance for the personalized treatment of HCC.

SCAR: Single-cell and Spatially-resolved Cancer Resources

Advances in sequencing and imaging technologies offer a unique opportunity to unravel cell heterogeneity and develop new immunotherapy strategies for cancer research. There is an urgent need for a resource that effectively integrates a vast amount of transcriptomic profiling data to comprehensively explore cancer tissue heterogeneity and the tumor microenvironment. In this context, we developed the Single-cell and Spatially-resolved Cancer Resources (SCAR) database, a combined tumor spatial and single-cell transcriptomic platform, which is freely accessible at http://8.142.154.29/SCAR2023 or http://scaratlas.com. SCAR contains spatial transcriptomic data from 21 tumor tissues and single-cell transcriptomic data from 11 301 352 cells encompassing 395 cancer subtypes and covering a wide variety of tissues, organoids, and cell lines. This resource offers diverse functional modules to address key cancer research questions at multiple levels, including the screening of tumor cell types, metabolic features, cell communication and gene expression patterns within the tumor microenvironment. Moreover, SCAR enables the analysis of biomarker expression patterns and cell developmental trajectories. SCAR also provides a comprehensive analysis of multi-dimensional datasets based on 34 state-of-the-art omics techniques, serving as an essential tool for in-depth mining and understanding of cell heterogeneity and spatial location. The implications of this resource extend to both cancer biology research and cancer immunotherapy development.

RNF20 Reduces Cell Proliferation and Warburg Effect by Promoting NLRP3 Ubiquitination in Liver Cancer

The present study explored that the effects and its possible mechanisms of ring finger protein 20 (RNF20) in Postoperative survival rate of liver cancer in clinical. All the serum samples were collected from our hospital. Quantitative polymerase chain reaction (PCR) and microarray analysis, and RNA pull down assay were used in this study. We found that the serum RNF20 mRNA expression level in patients with liver cancer were down-regulated. Postoperative survival rate of RNF20 high expression was higher than that of RNF20 low expression. Then, over-expression of RNF20 diminished liver cancer cell proliferation and metastasis. RNF20 reduced Warburg effect of liver cancer. RNF20 expression regulated NOD-like receptor protein 3 (NLRP3) expression and increased NLRP3 Ubiquitination. NLRP3 participated in the effects of RNF20 on cell proliferation, and not affected on Warburg effect of liver cancer. Our study demonstrated that the serum RNF20 expression level was down-regulated in liver cancer, and promoted postoperative survival rate. RNF20 can reduce cancer progression of liver cancer by NLRP3 signal pathway, suggesting that it may prove to be a potential therapeutic target for postoperative survival rate of liver cancer.

Multidimensional Analysis of PANoptosis-Related Molecule CASP8: Prognostic Significance, Immune Microenvironment Effect, and Therapeutic Implications in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) presents significant challenges in diagnosis and treatment. Understanding the role of PANoptosis-related molecules in HCC is crucial for advancing therapeutic strategies.

Methods

We conducted a comprehensive analysis using public data from the Cancer Genome Atlas, Human Protein Atlas, Tumor Immune Single Cell Hub, and STRING databases. Techniques included Kaplan-Meier survival curves, Cox regression, LASSO analysis, and various computational methods for understanding the tumor microenvironment. We also employed ClueGO, gene set enrichment analysis, and other algorithms for biological enrichment analysis.

Results

CASP8 emerged as a significant molecule in HCC, correlated with poor survival outcomes. Its expression was predominant in the nucleoplasm and cytosol and varied across different cancer types. Biological enrichment analysis revealed CASP8's association with critical cellular activities and immune responses. In the tumor microenvironment, CASP8 showed correlations with various immune cell types. A nomogram plot was developed for better clinical prognostication. Mutation analysis indicated a higher frequency of TP53 mutations in patients with elevated CASP8 expression. In addition, CASP8 was found to regulate YEATS2 in HCC, highlighting a potential pathway in tumor progression.

Conclusions

Our study underscores the multifaceted role of CASP8 in HCC, emphasizing its prognostic and therapeutic significance. The regulatory relationship between CASP8 and YEATS2 opens new avenues for understanding HCC pathogenesis and treatment strategies.

Intratumor heterogeneity: models of malignancy emergence and evolution

Cancer is a complex and heterogeneous disease characterized by the accumulation of genetic alterations that drive uncontrolled cell growth and proliferation. Evolutionary dynamics plays a crucial role in the emergence and development of tumors, shaping the heterogeneity and adaptability of cancer cells. From the perspective of evolutionary theory, tumors are complex ecosystems that evolve through a process of microevolution influenced by genetic mutations, epigenetic changes, tumor microenvironment factors, and therapy-induced changes. This dynamic nature of tumors poses significant challenges for effective cancer treatment, and understanding it is essential for developing effective and personalized therapies. By uncovering the mechanisms that determine tumor heterogeneity, researchers can identify key genetic and epigenetic changes that contribute to tumor progression and resistance to treatment. This knowledge enables the development of innovative strategies for targeting specific tumor clones, minimizing the risk of recurrence and improving patient outcomes. To investigate the evolutionary dynamics of cancer, researchers employ a wide range of experimental and computational approaches. Traditional experimental methods involve genomic profiling techniques such as next-generation sequencing and fluorescence in situ hybridization. These techniques enable the identification of somatic mutations, copy number alterations, and structural rearrangements within cancer genomes. Furthermore, single-cell sequencing methods have emerged as powerful tools for dissecting intratumoral heterogeneity and tracing clonal evolution. In parallel, computational models and algorithms have been developed to simulate and analyze cancer evolution. These models integrate data from multiple sources to predict tumor growth patterns, identify driver mutations, and infer evolutionary trajectories. In this paper, we set out to describe the current approaches to address this evolutionary complexity and theories of its occurrence.

A modified immune cell infiltration score achieves ideal stratification for CD8+ T cell efficacy and immunotherapy benefit in hepatocellular carcinoma

Immunotherapy, which aims to enhance the function of T cells, has emerged as a novel therapeutic approach for hepatocellular carcinoma (HCC). Nevertheless, the clinical utility of using flow cytometry to assess immune cell infiltration (ICI) is hindered by its cumbersome procedures, prompting the need for more accessible methods. Here, we acquired gene expression profiles and survival data of HCC from TCGA and GSE10186 datasets. The patients were categorized into two clusters of ICI, and a set of 11 characteristic genes responsible for the differentiation performance of these ICI clusters were identified. Subsequently, we successfully developed a modified ICI score (mICIS) by utilizing the expression levels of these genes. The efficacy of our mICIS was confirmed via mass cytometry, flow cytometry, and immunohistochemistry. Our research indicated that the favorable overall survival (OS) rate could be attributed to the improved function of anti-tumor leukocytes rather than their infiltration. Furthermore, we observed that the low score group exhibited lower expression levels of T-cell exhaustion-associated genes, which was confirmed in both HCC tissues from patients and mice, which demonstrated that the benefits of the low scores were due to enhanced active/cytotoxic CD8+ T cells and reduced exhausted CD8+ T cells. Additionally, our mICIS stratified the benefits derived from immunotherapies. Lastly, we observed a misalignment between CD8+ T-cell infiltration and function in HCC. In summary, our mICIS demonstrated proficiency in assessing the OS rate of HCC and offering significant stratified data pertaining to distinct responses to immunotherapy.

Computational single cell oncology: state of the art

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

CCF-GNN: A Unified Model Aggregating Appearance, Microenvironment, and Topology for Pathology Image Classification

Pathology images contain rich information of cell appearance, microenvironment, and topology features for cancer analysis and diagnosis. Among such features, topology becomes increasingly important in analysis for cancer immunotherapy. By analyzing geometric and hierarchically structured cell distribution topology, oncologists can identify densely-packed and cancer-relevant cell communities (CCs) for making decisions. Compared to commonly-used pixel-level Convolution Neural Network (CNN) features and cell-instance-level Graph Neural Network (GNN) features, CC topology features are at a higher level of granularity and geometry. However, topological features have not been well exploited by recent deep learning (DL) methods for pathology image classification due to lack of effective topological descriptors for cell distribution and gathering patterns. In this paper, inspired by clinical practice, we analyze and classify pathology images by comprehensively learning cell appearance, microenvironment, and topology in a fine-to-coarse manner. To describe and exploit topology, we design Cell Community Forest (CCF), a novel graph that represents the hierarchical formulation process of big-sparse CCs from small-dense CCs. Using CCF as a new geometric topological descriptor of tumor cells in pathology images, we propose CCF-GNN, a GNN model that successively aggregates heterogeneous features (e.g., appearance, microenvironment) from cell-instance-level, cell-community-level, into image-level for pathology image classification. Extensive cross-validation experiments show that our method significantly outperforms alternative methods on H&E-stained and immunofluorescence images for disease grading tasks with multiple cancer types. Our proposed CCF-GNN establishes a new topological data analysis (TDA) based method, which facilitates integrating multi-level heterogeneous features of point clouds (e.g., for cells) into a unified DL framework.

Role of TOP2A and CDC6 in liver cancer

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with high mortality worldwide, which is characterized by aggressive growth and metastasis. However, the relationship between TOP2A and CDC6 and HCC remains unclear. GSE121248 and GSE101728 profiles for liver cancer were downloaded from the gene expression omnibus database generated using GPL21047and GPL570. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis was performed. The construction and analysis of protein-protein interaction network, functional enrichment analysis, gene set enrichment analysis. Gene expression heat map was drawn and survival analysis was performed. Comparative toxicogenomics database analysis were performed to find the disease most related to the core gene. TargetScan was used to screen miRNAs regulating central DEGs. 885 DEGs were identified. According to gene ontology analysis, they were mainly enriched in organic acid metabolism process, metabolic pathway, p53 signal pathway and PPAR signal pathway. The enrichment items are similar to the GOKEGG enrichment items of differentially expressed genes, mainly in the process of organic acid metabolism, p53 signal pathway and PPAR signal pathway. In the enrichment project of metascape, gene ontology has PIDPLK1 pathway, mitotic cell cycle, tumor retinoblastoma gene. The construction and analysis of protein-protein interaction network obtained 10 core genes (TOP2A, CDK1, ASPM, RACGAP1, ZWINT, CDC6, AURKA, NCAPG, BUB1B, CCNB1), and found that these core genes were highly expressed in tumor tissues and low in normal tissues. Comparative toxicogenomics database analysis showed that 10 genes (TOP2A, CDK1, ASPM, RACGAP1, ZWINT, CDC6, AURKA, NCAPG, BUB1B, CCNB1) were related to necrosis, inflammation, HCC, liver cirrhosis, and adenoid cystic carcinoma. TOP2A and CDC6 are highly expressed in liver cancer, which may become molecular targets for early diagnosis and precise treatment.

Mesenchymal stem/stromal cells- a principal element for tumour microenvironment heterogeneity

The heterogeneity of the tumor microenvironment (TME) is a major obstacle in cancer treatment, making most therapeutic interventions palliative rather than curative. Previous studies have suggested that the reason for the low efficacy of immunotherapy and the relapse of the original responders over time may be due to the complex network of mesenchymal stem/stromal cells (MSCs), a population of multipotent progenitor cells existing in a variety of tissues. Cancer-associated MSCs (CA-MSCs) have already been isolated from various types of tumors and are characterized by their vigorous pro-tumorigenic functions. Although the roles of CA-MSCs from different sources vary widely, their origins are still poorly understood. Current evidence suggests that when local resident or distally recruited MSCs interact with tumor cells and other components in the TME, "naïve" MSCs undergo genetic and functional changes to form CA-MSCs. In this review, we mainly focus on the multiple roles of CA-MSCs derived from different sources, which may help in elucidating the formation and function of the entire TME, as well as discover innovative targets for anti-cancer therapies.

Protocol to dissociate and isolate wide-diversity single cells by density gradient centrifugation from human hepatoblastoma tissue

Single-cell transcriptome sequencing can characterize various cell types in human liver tissue and facilitate understanding of hepatoblastoma heterogeneity. Here, we present a protocol for isolating hepatocytes and immune cells from human hepatoblastoma samples with high viability. We describe steps for tissue processing, enzymatic digestion, Percoll density gradient separation, cell lysis, cell suspension quality control, and scRNA library construction. We then detail sequencing and data analysis. This protocol is applicable to preparing single-cell suspensions from other human liver tissue samples.

Quantitative phosphoproteomics reveals molecular pathway network alterations in human early-stage primary hepatic carcinomas: potential for 3P medical approach

Objective

Hepatic carcinoma is one of the most common types of malignant tumors in the digestive system, and its biological characteristics determine its high rate of metastasis and recurrence after radical resection, leading to a poor prognosis for patients. Increasing evidence demonstrates that phosphoproteins and phosphorylation-mediated molecular pathways influence the occurrence and development of hepatic carcinoma. It is urgent need to develop early-stage biomarkers for improving diagnosis, therapy, medical service, and prognostic assessment. We hypothesize that phosphoproteome and phosphorylation-mediated signaling pathway networks significantly differ in human early-stage primary hepatic carcinomas relative to control liver tissues, which will identify the key differentially phosphorylated proteins and phosphorylation-mediated signaling pathway network alterations in human early-stage primary hepatic carcinoma to innovate predictive diagnosis, prognostic assessment, and personalized medical services and progress beyond the state of the art in the framework of predictive, preventive, and personalized medicine (PPPM).

Methods

Tandem mass tag (TMT)-based quantitative proteomics coupled with TiO2 enrichment of phosphopeptides was used to identify phosphorylation profiling, and bioinformatics was used to analyze the pathways and biological functions of phosphorylation profiling between early-stage hepatic carcinoma tissues and tumor-adjacent normal control tissues. Furthermore, the integrative analysis with transcriptomic data from TCGA database obtained differently expressed genes (DEGs) corresponding to differentially phosphorylated proteins (DPPs) and overall survival (OS)-related DPPs.

Results

A total of 1326 phosphopeptides derived from 858 DPPs in human early-stage primary hepatic carcinoma were identified. KEGG pathway network analysis of 858 DPPs revealed 33 statistically significant signaling pathways, including spliceosome, glycolysis/gluconeogenesis, B-cell receptor signaling pathway, HIF-1 signaling pathway, and fatty acid degradation. Gene Ontology (GO) analysis of 858 DPPs revealed that protein phosphorylation was involved in 57 biological processes, 40 cellular components, and 37 molecular functions. Protein-protein interaction (PPI) network constructed multiple high-combined scores and co-expressed DPPs. Integrative analysis of transcriptomic data and DPP data identified 105 overlapped molecules (DPPs; DEGs) between hepatic carcinoma tissues and control tissues and 125 OS-related DPPs. Overlapping Venn plots showed 14 common molecules among datasets of DPPs, DEGs, and OS-related DDPs, including FTCD, NDRG2, CCT2, PECR, SLC23A2, PNPLA7, ANLN, HNRNPM, HJURP, MCM2, STMN1, TCOF1, TOP2A, and SSRP1. The drug sensitivities of OS-related DPPs were identified, including LMOD1, CAV2, UBE2E2, RAPH1, ANXA5, HDLBP, CUEDC1, APBB1IP, VCL, SRSF10, SLC23A2, EPB41L2, ESR1, PLEKHA4, SAFB2, SMARCAD1, VCAN, PSD4, RDH16, NOP56, MEF2C, BAIAP2L2, NAGS, SRSF2, FHOD3, and STMN1.

Conclusions

Identification and annotation of phosphoproteomes and phosphorylation-mediated signaling pathways in human early-stage primary hepatic carcinoma tissues provided new directions for tumor prevention and treatment, which (i) helps to enrich phosphorylation functional research and develop new biomarkers; (ii) enriches phosphorylation-mediated signaling pathways to gain a deeper understanding of the underlying mechanisms of early-stage primary hepatic carcinoma; and (iii) develops anti-tumor drugs that facilitate targeted phosphorylated sites. We recommend quantitative phosphoproteomics in early-stage primary hepatic carcinoma, which offers great promise for in-depth insight into the molecular mechanism of early-stage primary hepatic carcinoma, the discovery of effective therapeutic targets/drugs, and the construction of reliable phosphorylation-related biomarkers for patient stratification, predictive diagnosis, prognostic assessment, and personalized medical services in the framework of PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00335-3.

Immunology and immunotherapy of cholangiocarcinoma

Cholangiocarcinoma is the second most common primary liver cancer. Its incidence is low in the Western world but is rising globally. Surgery, chemotherapy and radiation therapy have been the only treatment options for decades. Progress in our molecular understanding of the disease and the identification of druggable targets, such as IDH1 mutations and FGFR2 fusions, has provided new treatment options. Immunotherapy has emerged as a potent strategy for many different types of cancer and has shown efficacy in combination with chemotherapy for cholangiocarcinoma. In this Review, we discuss findings related to key immunological aspects of cholangiocarcinoma, including the heterogeneous landscape of immune cells within the tumour microenvironment, the immunomodulatory effect of the microbiota and IDH1 mutations, and the association of immune-related signatures and patient outcomes. We introduce findings from preclinical immunotherapy studies, discuss future immune-mediated treatment options, and provide a summary of results from clinical trials testing immune-based approaches in patients with cholangiocarcinoma. This Review provides a thorough survey of our knowledge on immune signatures and immunotherapy in cholangiocarcinoma.

Integrative single-cell RNA and ATAC sequencing reveals that the FOXO1-PRDX2-TNF axis regulates tendinopathy

Introduction

Tendinopathy, the most common form of chronic tendon disorder, leads to persistent tendon pain and loss of function. Profiling the heterogeneous cellular composition in the tendon microenvironment helps to elucidate rational molecular mechanisms of tendinopathy.

Methods and results

In this study, through a multi-modal analysis, a single-cell RNA- and ATAC-seq integrated tendinopathy landscape was generated for the first time. We found that a specific cell subpopulation with low PRDX2 expression exhibited a higher level of inflammation, lower proliferation and migration ability, which not only promoted tendon injury but also led to microenvironment deterioration. Mechanistically, a motif enrichment analysis of chromatin accessibility showed that FOXO1 was an upstream regulator of PRDX2 transcription, and we confirmed that functional blockade of FOXO1 activity induced PRDX2 silencing. The TNF signaling pathway was significantly activated in the PRDX2-low group, and TNF inhibition effectively restored diseased cell degradation.

Discussion

We revealed an essential role of diseased cells in tendinopathy and proposed the FOXO1-PRDX2-TNF axis is a potential regulatory mechanism for the treatment of tendinopathy.

Mutant-allele dispersion correlates with prognosis risk in patients with advanced non-small cell lung cancer

BACKGROUND

Intra-tumor heterogeneity (ITH) contributes to lung cancer progression and resistance to therapy. To evaluate ITH and determine whether it may be employed as a predictive biomarker of prognosis in patients with advanced non-small cell lung cancer (NSCLC), we used a novel algorithm called mutant-allele dispersion (MAD).

METHODS

In the study, 103 patients with advanced NSCLC were enrolled. Using a panel of 425 cancer-related genes, next-generation sequencing (NGS) was performed on tumor specimens that had been collected. From NGS data, we derived MAD values, and we next looked into their relationships with clinical variables and different mutation subtypes.

RESULTS

The median MAD among 103 NSCLC patients was 0.73. EGFR mutation, tyrosine kinase inhibitor (TKI) therapy, radiotherapy, and chemotherapy cycles were all substantially correlated with the MAD score. In patients with lung adenocarcinoma (LUAD), correlation analysis revealed that the MAD score was substantially linked with Notch pathway mutation (P = 0.021). A significant relationship between high MAD and shorter progression-free survival (PFS) was found (HR = 2.004, 95%CI 1.269-3.163, P = 0.003). In patients with advanced NSCLC, histological type (P = 0.004), SMARCA4 mutation (P = 0.038), and LRP1B mutation (P = 0.006) were all independently associated with prognosis. The disease control rate was considerably greater in the low MAD group compared to the high MAD group in 19 LUAD patients receiving immunotherapy (92.9% vs. 40%, P = 0.037). TKI-PFS was longer in 37 patients with low MAD who received first-line TKI therapy (P = 0.014).

CONCLUSION

Our findings suggested that MAD is a practical and simple algorithm for assessing ITH, and populations with high MAD values are more likely to have EGFR mutations. MAD can be used as a potential biomarker to predict not only the prognosis of NSCLC but also the efficacy of immunotherapy and TKI therapy in patients with advanced NSCLC.

Peripheral immune characteristics of hepatitis B virus-related hepatocellular carcinoma

Background

Liver cancer is the sixth most common cancer worldwide and the third leading cause of cancer-related death. As a chronic liver disease, many studies have shown that the immune response plays a key role in the progression of liver cancer. Chronic hepatitis B virus (HBV) infection is one of the high-risk factors for HCC, accounting for 50%–80% of HCC cases worldwide, and little is known about the immune status of HBV associated hepatocellular carcinoma (HBV-HCC), therefore, we aimed to explore the changes in peripheral immunity in patients with HBV-HCC.

Methods

In this study, patients with HBV-HCC (n=26), patients with hepatitis B-related cirrhosis (HBV-LC) (n=31) and healthy volunteers (n=49) were included. The lymphocytes and their subpopulation phenotypes in peripheral blood were characterized. In addition, we explored the effect of viral replication on peripheral immunity in patients with HCC and analyzed the circulating immunophenotypic characteristics at different stages of HCC with flow cytometry.

Results

Firstly, our results showed that the percentages of total αβ T cells in the peripheral blood of HBV-HCC patients was significantly decreased compared to healthy subjects. Secondly, we found that naïve CD4+ T cells in HBV-HCC patients were significantly reduced, terminally differentiated CD8+ T cells, homing memory CD8+ T cells and Th2 cells were increased in peripheral circulation in HBV-HCC patients. Moreover, in the peripheral blood of HBV-HCC patients, expression of TIGIT on CD4+ T cells and PD-1 on the surface of Vδ 1 T cells was increased. In addition, we found that sustained viral replication resulted in up-regulation of TIM3 expression on CD4+ T cells, and TIM3+ γδ T cells increased in peripheral circulation in patients with advanced HBV-HCC.

Conclusion

Our study showed that circulating lymphocytes in HBV-HCC patients exhibited features of immune exhaustion, especially in HCC patients with persistent viral replication and in patients with intermediate and advanced HBV-HCC, including decreased frequency of T cells and elevated expression of inhibitory receptors including TIGIT and TIM3 on CD4+ T cells and γδ T cells. Meanwhile, our research suggests that the combination of CD3+ T cell and CD8+HLADR+CD38+ T cell may be a potential diagnostic indicator for HBV-HCC. These findings could help us to better understand the immune characteristics of HBV-HCC and explore the immune mechanisms and immunotherapy strategies for HBV-HCC.

Overexpression of YEATS2 Remodels the Extracellular Matrix to Promote Hepatocellular Carcinoma Progression via the PI3K/AKT Pathway

Hepatocellular carcinoma (HCC) is one of the most common cancers and the fourth leading cause of death in men. YEATS domain containing 2 (YEATS2) gene encodes a scaffolding subunit of the ATAC complex. We found that YEATS2 was upregulated in HCC tissues and was associated with a poor prognosis. However, the role of YEATS2 in HCC remains unclear. The purpose of this study was to investigate the effect of YEATS2 on the progression of HCC and to elucidate its related mechanisms. We found that overexpression of YEATS2 promoted tumor cell proliferation, migration, and invasion through the PI3K/AKT signaling pathway and regulation of extracellular matrix. These findings help to understand the role of YEATS2 in HCC, and YEATS2 may become a new target for HCC therapy.

H4-methylation regulators mediated epitranscriptome patterns and tumor microenvironment infiltration characterization in hepatocellular carcinoma

Epigenetic modifications are involved in the remodeling of the tumor microenvironment (TME) and the regulation of immune response. Nonetheless, the role of histone H4 methylation (H4M) modification in the TME and immune regulation of hepatocellular carcinoma (HCC) is unknown. As a result, the purpose of this research is to discover H4M-mediated modification patterns and their effects on TME and immunologic characteristics in HCC. A total of 2305 samples were enrolled from 13 different cohorts. With the help of consensus clustering analysis, three distinct H4M modification patterns were identified. The cell-infiltrating characteristics of TME under these three patterns were highly consistent with their enriched biological processes and clinical outcome. The H4Mscore was then created using principal component analysis algorithm to quantify the H4M modification pattern of each individual tumor and was systematically correlated with representative tumor characteristics. We found that analyzing H4M modification patterns within individual tumors could predict TME infiltration, homologous recombination deficiency (HRD), intratumor heterogeneity, proliferation activity, mRNA stemness index, and prognosis. The group with a low H4Mscore had an inflamed TME phenotype and a better immunotherapy response, as well as a better survival outcome. The prognostic value of H4Mscore was validated in three internal cohorts and five external cohorts, respectively. In external immunotherapy cohorts, the low H4Mscore was also linked to an enhanced response to anti-PD-1/L1 and anti-CTLA4 immunotherapy and a better prognosis. This study revealed that H4M modification played an important role in forming TME diversity and complexity. Evaluating the H4M modification pattern of individual tumors could help us learn more about TME and develop more effective immunotherapy strategies.

Towards artificial intelligence to multi-omics characterization of tumor heterogeneity in esophageal cancer

Esophageal cancer is a unique and complex heterogeneous malignancy, with substantial tumor heterogeneity: at the cellular levels, tumors are composed of tumor and stromal cellular components; at the genetic levels, they comprise genetically distinct tumor clones; at the phenotypic levels, cells in distinct microenvironmental niches acquire diverse phenotypic features. This heterogeneity affects almost every process of esophageal cancer progression from onset to metastases and recurrence, etc. Intertumoral and intratumoral heterogeneity are major obstacles in the treatment of esophageal cancer, but also offer the potential to manipulate the heterogeneity themselves as a new therapeutic strategy. The high-dimensional, multi-faceted characterization of genomics, epigenomics, transcriptomics, proteomics, metabonomics, etc. of esophageal cancer has opened novel horizons for dissecting tumor heterogeneity. Artificial intelligence especially machine learning and deep learning algorithms, are able to make decisive interpretations of data from multi-omics layers. To date, artificial intelligence has emerged as a promising computational tool for analyzing and dissecting esophageal patient-specific multi-omics data. This review provides a comprehensive review of tumor heterogeneity from a multi-omics perspective. Especially, we discuss the novel techniques single-cell sequencing and spatial transcriptomics, which have revolutionized our understanding of the cell compositions of esophageal cancer and allowed us to determine novel cell types. We focus on the latest advances in artificial intelligence in integrating multi-omics data of esophageal cancer. Artificial intelligence-based multi-omics data integration computational tools exert a key role in tumor heterogeneity assessment, which will potentially boost the development of precision oncology in esophageal cancer.

Single-cell gene regulatory network prediction by explainable AI

The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patterns for tumor cells and normal epithelial cells and identifies subnetworks that are observed only in (subgroups of) tumor cells of certain patients. While current state-of-the-art methods are limited by their ability to only predict average networks for cell populations, our approach facilitates the reconstruction of networks down to the level of single cells which can be utilized to characterize the heterogeneity of gene regulation within and across tumors.

Genetics of Hepatocellular Carcinoma: From Tumor to Circulating DNA

Hepatocellular carcinoma (HCC) accounts for 90% of primary hepatic malignancies and is one of the major causes of cancer-related death. Over the last 15 years, the molecular landscape of HCC has been deciphered, with the identification of the main driver genes of liver carcinogenesis that belong to six major biological pathways, such as telomere maintenance, Wnt/b-catenin, P53/cell cycle regulation, oxidative stress, epigenetic modifiers, AKT/mTOR and MAP kinase. The combination of genetic and transcriptomic data composed various HCC subclasses strongly related to risk factors, pathological features and prognosis. However, translation into clinical practice is not achieved, mainly because the most frequently mutated genes are undruggable. Moreover, the results derived from the analysis of a single tissue sample may not adequately catch the intra- and intertumor heterogeneity. The analysis of circulating tumor DNA (ctDNA) is broadly developed in other types of cancer for early diagnosis, prognosis and monitoring under systemic treatment in order to identify primary and secondary mechanisms of resistance. The aim of this review is to describe recent data about the HCC molecular landscape and to discuss how ctDNA could be used in the future for HCC detection and management.

The Clinical Relevance and Immune Correlation of SLC10 Family Genes in Liver Cancer

Background and Aim

This study was aimed to reveal the clinical relevance and immune correlation of the SLC10 family genes in liver cancer.

Methods

A comprehensive bioinformatics analysis was utilized to determine the gene expression, genetic alterations, DNA methylation, clinical significance, survival association and immune correlation of seven SLC10 family genes in liver cancer. The multiplexed immunohistochemical technique was applied to determine the association between SLC10A3 protein expression and immune cells, and the correlation between SLC10A3 protein and immune checkpoints (PD1 and PD-L1) in a cohort of 32 individuals with liver cancer.

Results

The expression of SLC10 family genes was different between normal liver tissues and malignant liver tissues. SLC10A5 showed the highest alteration rate (8%), followed by SLC10A3 (2.8%). Low expression of SLC10A1 was indicative of poor tumor grade and advanced tumor stage in liver cancer. Scatter plots uncovered that expression of SLC10A3 was inversely associated with SLC10A1 and SLC10A5 expression in liver cancer. The expression of SLC10A1 and SLC10A5 was strongly associated with their DNA methylation. SLC10A1 expression was a reliable genetic biomarker for the prediction of survival outcomes in liver cancer population. Expression of SLC10 family genes was remarkably linked with the abundance of most immune infiltrating cells in liver cancer, and SLC10A3 was the most significant member. The multiplexed immunohistochemical technique confirmed that there existed the significant correlations between SLC10A3 protein expression and CD4 T cells, CD20 B cells and the close association with PD-1 in the stromal area from malignant tissues.

Conclusion

The expressions of SLC10 family genes were different between normal liver tissues and malignant liver tissues, and they were correlated with each other in liver cancer. SLC10A1 possesses the most significant correlation with survival outcomes. SLC10A3 exhibited the most significant relationship with immune cells, as revealed by bioinformatics analysis and multispectral imaging technique.

Multiregional single-cell dissection of tumor and immune cells reveals stable lock-and-key features in liver cancer

Intratumor heterogeneity may result from the evolution of tumor cells and their continuous interactions with the tumor microenvironment which collectively drives tumorigenesis. However, an appearance of cellular and molecular heterogeneity creates a challenge to define molecular features linked to tumor malignancy. Here we perform multiregional single-cell RNA sequencing (scRNA-seq) analysis of seven liver cancer patients (four hepatocellular carcinoma, HCC and three intrahepatic cholangiocarcinoma, iCCA). We identify cellular dynamics of malignant cells and their communication networks with tumor-associated immune cells, which are validated using additional scRNA-seq data of 25 HCC and 12 iCCA patients as a stable fingerprint embedded in a malignant ecosystem representing features of tumor aggressiveness. We further validate the top ligand-receptor interaction pairs (i.e., LGALS9-SLC1A5 and SPP1-PTGER4 between tumor cells and macrophages) associated with unique transcriptome in additional 542 HCC patients. Our study unveils stable molecular networks of malignant ecosystems, which may open a path for therapeutic exploration.

Hypomethylation-mediated upregulation of the WASF2 promoter region correlates with poor clinical outcomes in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common and lethal cancers worldwide. Wiskott–Aldrich syndrome protein family member 2 (WASF2) is an integral member of the actin cytoskeleton pathway, which plays a crucial role in cell motility. In this study, we aimed to explore the role of WASF2 in HCC carcinogenesis and its regulatory mechanism.

Methods

WASF2 expression in HCC was analyzed using six public RNA-seq datasets and 66 paired tissues from patients with HCC. The role of WASF2 in normal hepatocyte cell phenotypes was evaluated using a WASF2 overexpression vector in vitro; it was evaluated in HCC cell phenotypes using small interfering RNA (siRNA) in vitro and in vivo. Epigenetic regulatory mechanism of WASF2 was assessed in the Cancer Genome Atlas liver hepatocellular carcinoma project (TCGA_LIHC) dataset and also validated in 38 paired HCC tissues. Site mutagenesis, bisulfite sequencing polymerase chain reaction (BSP), methylation-specific polymerase chain reaction (MSP), and quantitative MSP (qMSP) were used for evaluating WASF2 methylation status.

Results

WASF2 is overexpressed in HCC and is clinically correlated with its prognosis. WASF2 overexpression promoted normal hepatocyte proliferation. WASF2 inactivation decreased the viability, growth, proliferation, migration, and invasion of Huh-7 and SNU475 HCC cells by inducing G2/M phase arrest. This induced cell death and inhibited epithelial–mesenchymal transition, hindering actin polymerization. In addition, WASF2 knockdown using siWASF2 in a xenograft mouse model and a lung metastasis model exerted tumor suppressive effect. There was a negative correlation between WASF2 methylation status and mRNA expression. The methylation pattern of CpG site 2 (− 726 bp), located in the WASF2 promoter, plays an important role in the regulation of WASF2 expression. Furthermore, the cg242579 CpG island in the WASF2 5′ promoter region was hypomethylated in HCC compared to that in the matched non-tumor samples. Patients with high WASF2 methylation and low WASF2 expression displayed the highest overall survival.

Conclusions

WASF2 is overexpressed and hypomethylated in HCC and correlates with patient prognosis. WASF2 inactivation exerts anti-tumorigenic effects on HCC cells in vitro and in vivo, suggesting that WASF2 could be a potential therapeutic target for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02365-7.

Is There a Place for Somatostatin Analogues for the Systemic Treatment of Hepatocellular Carcinoma in the Immunotherapy Era?

Patients with advanced hepatocellular carcinoma (HCC) have a very limited survival rate even after the recent inclusion of kinase inhibitors or immune checkpoint inhibitors in the therapeutic armamentarium. A significant problem with the current proposed therapies is the considerable cost of treatment that may be a serious obstacle in low- and middle-income countries. Implementation of somatostatin analogues (SSAs) has the potential to overcome this obstacle, but due to some negative studies their extensive evaluation came to a halt. However, experimental evidence, both in vitro and in vivo, has revealed various mechanisms of the anti-tumor effects of these analogues, including inhibition of cancer cell proliferation and angiogenesis and induction of apoptosis. Favorable indirect effects such as inhibition of liver inflammation and fibrosis and influence on macrophage-mediated innate immunity have also been noted and are presented in this review. Furthermore, the clinical application of SSAs is both presented and compared with clinical trials of kinase and immune checkpoint inhibitors (ICIs). No direct trials have been performed to compare survival in the same cohort of patients, but the cost of treatment with SSAs is a fraction compared to the other modalities and with significantly less serious side effects. As in immunotherapy, patients with viral HCC (excluding alcoholics), as well as Barcelona stage B or C and Child A patients, are the best candidates, since they usually have a survival prospect of at least 6 months, necessary for optimum results. Reasons for treatment failures are also discussed and further research is proposed.

Construction of a tumor immune infiltration macrophage signature for predicting prognosis and immunotherapy response in liver cancer

Liver cancer is an extraordinarily heterogeneous malignant disease. The tumor microenvironment (TME) and tumor-associated macrophages (TAMs) are the major drivers of liver cancer initiation and progression. It is critical to have a better understanding of the complicated interactions between liver cancer and the immune system for the development of cancer immunotherapy. Based on the gene expression profiles of tumor immune infiltration cells (TIICs), upregulated genes in TAMs and downregulated genes in other types of immune cells were identified as macrophage-specific genes (MSG). In this study, we combined MSG, immune subtypes, and clinical information on liver cancer to develop a tumor immune infiltration macrophage signature (TIMSig). A four-gene signature (S100A9, SLC22A15, TRIM54, and PPARGC1A) was identified as the TAM-related prognostic genes for liver cancer, independent of multiple clinicopathological parameters. Survival analyses showed that patients with low TIMSig had a superior survival rate than those with high TIMSig. Additionally, clinical immunotherapy response and TIMSig was observed as highly relevant. In addition, TIMSig could predict the response to chemotherapy. Collectively, the TIMSig could be a potential tool for risk-stratification, clinical decision making, treatment planning, and oncology immunotherapeutic drug development.

Protocols to culture and harvest hepatic tumor organoids for metabolic assays

Three-dimensional organoids, which resemble the pathophysiology and structural architecture of the original tissues, are a preferable in vitro model system for assessing metabolic activities in response to various environmental or nutritional changes. Here, we describe step-by-step protocols to establish and culture mouse and human hepatic tumor organoids. We also describe two straightforward and efficient approaches to harvest tumor organoids for investigating the effects of metabolites/drugs on viability and metabolic functions of tumor organoids.

For complete details on the use and execution of this protocol, please refer to Tong et al. (2018), Leung et al. (2020), Tong et al. (2021), and Xu et al. (2021).

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Protocol to culture mouse and human hepatic tumor organoids in metabolic assays

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Two optimized approaches to harvest organoids for downstream functional assays

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Straightforward and efficient testing of candidate metabolites/drugs in organoids

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Vascular Disruptive Hydrogel Platform for Enhanced Chemotherapy and Anti-Angiogenesis through Alleviation of Immune Surveillance

Patients undergoing immunotherapy always exhibit a low-response rate due to tumor heterogeneity and immune surveillance in the tumor. Angiogenesis plays an important role in affecting the status of tumor-infiltrated lymphocytes by inducing hypoxia and acidosis microenvironment, suggesting its synergistic potential in immunotherapy. However, the antitumor efficacy of singular anti-angiogenesis therapy often suffers from failure in the clinic due to the compensatory pro-angiogenesis signaling pathway. In this work, classic injectable thermosensitive PLGA-PEG-PLGA copolymer was used to construct a platform to co-deliver CA4P (vascular disruptive agent) and EPI for inducing immunogenic cell death of cancer cells by targeting the tumor immune microenvironment. Investigation of 4T1 tumor-bearing mouse models suggests that local administration of injectable V+E@Gel could significantly inhibit the proliferation of cancer cells and prolong the survival rate of 4T1 tumor-bearing mouse models. Histological analysis further indicates that V+E@Gel could effectively inhibit tumor angiogenesis and metastasis by down-regulating the expression of CD34, CD31, MTA1 and TGF-β. Moreover, due to the sustained release kinetics of V+E@Gel, its local administration relieves the immune surveillance in tumor tissues and thus induces a robust and long-lasting specific antitumor immune response. Overall, this work provides a new treatment strategy through the mediation of the tumor immune microenvironment by vascular disruption to fulfill enhanced chemotherapy and immunotherapy.

Effect of Transdermal Fentanyl Patch Combined with Enhanced Recovery after Surgery on the Curative Effect and Analgesic Effect of Liver Cancer

Its goal was to see how a transdermal fentanyl patch combined with accelerated recovery after surgery (ERAS) affected the treatment efficacy and analgesic effect of liver cancer, as well as to help patients with liver cancer choose the right analgesic treatment and nursing mode. 150 patients with liver cancer were divided into group A (transdermal fentanyl patch), group B (ERAS), and group C (transdermal fentanyl patch combined with ERAS). Patients in the three groups were compared in terms of pain, survival, psychological status, adverse responses, postoperative recovery, and patient satisfaction. The results showed that under different treatment and nursing methods, the number of patients with mild cancer pain in the three groups was increased, especially the number of patients with mild cancer pain in group C (P < 0.05). Besides, the quality of life score of patients in each group was decreased. Patients who received the combination analgesia had a significantly higher quality of life than those who received simply a transdermal fentanyl patch or ERAS (P < 0.05). The scores of both the Hamilton anxiety scale (HAMA) and Hamilton depression rating scale (HAMD) of patients with the combined analgesia were decreased signally (P < 0.05). There were few patients with combined analgesia who had adverse reactions (P < 0.05). After surgery, the time of the first anal exhaust, first defecation, and first ambulation in group C were shorter than those in the other two groups (P < 0.05). To summarize, combining the two techniques aided in the recovery of gastrointestinal function as well as the physical recovery of patients following surgery. Furthermore, combining the two approaches produced a clear analgesic impact, which could improve patients' quality of life while also having a favorable clinical adoption effect.

Embolization therapy with microspheres for the treatment of liver cancer: State-of-the-art of clinical translation

Embolization with microspheres is a therapeutic strategy based on the selective occlusion of the blood vessels feeding a tumor. This procedure is intraarterially performed in the clinical setting for the treatment of liver cancer. The practice has evolved over the last decade through the incorporation of drug loading ability, biodegradability and imageability with the subsequent added functionality for the physicians and improved clinical outcomes for the patients. This review highlights the evolution of the embolization systems developed through the analysis of the marketed embolic microspheres for the treatment of malignant hepatocellular carcinoma, namely the most predominant form of liver cancer. Embolic microspheres for the distinct modalities of embolization (i.e., bland embolization, chemoembolization and radioembolization) are here comprehensively compiled with emphasis on material characteristics and their impact on microsphere performance. Moreover, the future application of the embolics under clinical investigation is discussed along with the scientific and regulatory challenges ahead in the field. STATEMENT OF SIGNIFICANCE: Embolization therapy with microspheres is currently used in the clinical setting for the treatment of most liver cancer conditions. The progressive development of added functionalities on embolic microspheres (such as biodegradability, imageability or drug and radiopharmaceutical loading capability) provides further benefit to patients and widens the therapeutic armamentarium for physicians towards truly personalized therapies. Therefore, it is important to analyze the possibilities that advanced biomaterials offer in the field from a clinical translational perspective to outline the future trends in therapeutic embolization.

Reshaping the systemic tumor immune environment (STIE) and tumor immune microenvironment (TIME) to enhance immunotherapy efficacy in solid tumors

The development of combination immunotherapy based on the mediation of regulatory mechanisms of the tumor immune microenvironment (TIME) is promising. However, a deep understanding of tumor immunology must involve the systemic tumor immune environment (STIE) which was merely illustrated previously. Here, we aim to review recent advances in single-cell transcriptomics and spatial transcriptomics for the studies of STIE, TIME, and their interactions, which may reveal heterogeneity in immunotherapy responses as well as the dynamic changes essential for the treatment effect. We review the evidence from preclinical and clinical studies related to TIME, STIE, and their significance on overall survival, through different immunomodulatory pathways, such as metabolic and neuro-immunological pathways. We also evaluate the significance of the STIE, TIME, and their interactions as well as changes after local radiotherapy and systemic immunotherapy or combined immunotherapy. We focus our review on the evidence of lung cancer, hepatocellular carcinoma, and nasopharyngeal carcinoma, aiming to reshape STIE and TIME to enhance immunotherapy efficacy.

Dissecting the Single-Cell Transcriptome Network of Immune Environment Underlying Cervical Premalignant Lesion, Cervical Cancer and Metastatic Lymph Nodes

Cervical cancer (CC) is one of the most common malignancy in women worldwide. It is characterized by a natural continuous phenomenon, that is, it is in the initial stage of HPV infection, progresses to intraepithelial neoplasia, and then develops into invasion and metastasis. Determining the complexity of tumor microenvironment (TME) can deepen our understanding of lesion progression and provide novel therapeutic strategies for CC. We performed the single-cell RNA sequencing on the normal cervix, intraepithelial neoplasia, primary tumor and metastatic lymph node tissues to describe the composition, lineage, and functional status of immune cells and mesenchymal cells at different stages of CC progression. A total of 59913 single cells were obtained and divided into 9 cellular clusters, including immune cells (T/NK cells, macrophages, B cells, plasma cells, mast cells and neutrophils) and mesenchymal cells (endothelial cells, smooth muscle cells and fibroblasts). Our results showed that there were distinct cell subpopulations in different stages of CC. High-stage intraepithelial neoplasia (HSIL) tissue exhibited a low, recently activated TME, and it was characterized by high infiltration of tissue-resident CD8 T cell, effector NK cells, Treg, DC1, pDC, and M1-like macrophages. Tumor tissue displayed high enrichment of exhausted CD8 T cells, resident NK cells and M2-like macrophages, suggesting immunosuppressive TME. Metastatic lymph node consisted of naive T cell, central memory T cell, circling NK cells, cytotoxic CD8+ T cells and effector memory CD8 T cells, suggesting an early activated phase of immune response. This study is the first to delineate the transcriptome profile of immune cells during CC progression using single-cell RNA sequencing. Our results indicated that HSIL exhibited a low, recently activated TME, tumor displayed immunosuppressive statue, and metastatic lymph node showed early activated phase of immune response. Our study enhanced the understanding of dynamic change of TME during CC progression and has implications for the development of novel treatments to inhibit the initiation and progression of CC.

High Levels of DEAH-Box Helicases Relate to Poor Prognosis and Reduction of DHX9 Improves Radiosensitivity of Hepatocellular Carcinoma

Background

Liver hepatocellular carcinoma (LIHC), one of the most common primary malignancies, exhibits high levels of molecular and clinical heterogeneity. Increasing evidence has confirmed the important roles of some RNA helicase families in tumor development, but the function of the DEAH-box RNA helicase family in LIHC therapeutic strategies has not yet been clarified.

Methods

The LIHC dataset was downloaded from The Cancer Genome Atlas (TCGA). Consensus clustering was applied to group the patients. Least absolute shrinkage and selection operator Cox regression and univariate and multivariate Cox regression were used to develop and validate a prognostic risk model. The Tumor Immune Estimation Resource and Tumor Immune Single Cell Hub databases were used to explore the role of DEAH-box RNA helicases in LIHC immunotherapy. In vitro experiments were performed to investigate the role of DHX9 in LIHC radiosensitivity.

Results

Twelve survival-related DEAH-box RNA helicases were identified. High helicase expression levels were associated with a poor prognosis and clinical features. A prognostic model comprising six DEAH-box RNA helicases (DHX8, DHX9, DHX34, DHX35, DHX38, and DHX57) was constructed. The risk score of this model was found to be an independent prognostic indicator, and LIHC patients with different prognosis were distinguished by the model in the training and test cohorts. DNA damage repair pathways were also enriched in patients with high-risk scores. The six DEAH-box RNA helicases in the risk model were substantially related to innate immune cell infiltration and immune inhibitors. In vitro experiments showed that DHX9 knockdown improved radiosensitivity by increasing DNA damage.

Conclusion

The DEAH-box RNA helicase signature can be used as a reliable prognostic biomarker for LIHC. In addition, DHX9 may be a definitive indicator and therapeutic target in radiotherapy and immunotherapy for LIHC.

High-throughput single-сell sequencing in cancer research

With advances in sequencing and instrument technology, bioinformatics analysis is being applied to batches of massive cells at single-cell resolution. High-throughput single-cell sequencing can be utilized for multi-omics characterization of tumor cells, stromal cells or infiltrated immune cells to evaluate tumor progression, responses to environmental perturbations, heterogeneous composition of the tumor microenvironment, and complex intercellular interactions between these factors. Particularly, single-cell sequencing of T cell receptors, alone or in combination with single-cell RNA sequencing, is useful in the fields of tumor immunology and immunotherapy. Clinical insights obtained from single-cell analysis are critically important for exploring the biomarkers of disease progression or antitumor treatment, as well as for guiding precise clinical decision-making for patients with malignant tumors. In this review, we summarize the clinical applications of single-cell sequencing in the fields of tumor cell evolution, tumor immunology, and tumor immunotherapy. Additionally, we analyze the tumor cell response to antitumor treatment, heterogeneity of the tumor microenvironment, and response or resistance to immune checkpoint immunotherapy. The limitations of single-cell analysis in cancer research are also discussed.

Molecular pathogenesis and systemic therapies for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains one of the most prevalent and deadliest cancers. The poor outcome associated with HCC is dramatically changing due to the advent of effective systemic therapies. Here we discuss the molecular pathogenesis of HCC, molecular classes and determinants of heterogeneity. In addition, effective single-agent and combination systemic therapies involving immunotherapies as standard of care are analyzed. Finally, we propose a flowchart of sequential therapies, explore mechanisms of resistance and address the need for predictive biomarkers.

Intraoperative fluorescence molecular imaging accelerates the coming of precision surgery in China

Purpose

China has the largest cancer population globally. Surgery is the main choice for most solid cancer patients. Intraoperative fluorescence molecular imaging (FMI) has shown its great potential in assisting surgeons in achieving precise resection. We summarized the typical applications of intraoperative FMI and several new trends to promote the development of precision surgery.

Methods

The academic database and NIH clinical trial platform were systematically evaluated. We focused on the clinical application of intraoperative FMI in China. Special emphasis was placed on a series of typical studies with new technologies or high-level evidence. The emerging strategy of combining FMI with other modalities was also discussed.

Results

The clinical applications of clinically approved indocyanine green (ICG), methylene blue (MB), or fluorescein are on the rise in different surgical departments. Intraoperative FMI has achieved precise lesion detection, sentinel lymph node mapping, and lymphangiography for many cancers. Nerve imaging is also exploring to reduce iatrogenic injuries. Through different administration routes, these fluorescent imaging agents provided encouraging results in surgical navigation. Meanwhile, designing new cancer-specific fluorescent tracers is expected to be a promising trend to further improve the surgical outcome.

Conclusions

Intraoperative FMI is in a rapid development in China. In-depth understanding of cancer-related molecular mechanisms is necessary to achieve precision surgery. Molecular-targeted fluorescent agents and multi-modal imaging techniques might play crucial roles in the era of precision surgery.

Recent insights into the pathogeneses and therapeutic targets of liver diseases: Summary of the 4th Chinese American Liver Society/Society of Chinese Bioscientists in America Hepatology Division Symposium in 2021

The 4th Chinese American Liver Society (CALS)/Society of Chinese Bioscientists in America (SCBA) Hepatology Division Annual Symposium was held virtually on October 29-30, 2021. The goal of the CALS Symposium was to present and discuss the recent research data on the pathogeneses and therapeutic targets of liver diseases among the CALS members, trainees and invited speakers. Here we briefly introduce the history of the CALS/SCBA Hepatology Division and highlight the presentations that focus on the current progresses on basic and translational research in liver metabolism, bile acid biology, alcohol-related liver disease, drug-induced liver injury, cholestatic liver injury, non-alcoholic fatty liver disease/non-alcoholic steatohepatitis and liver cancer.

An Overview on Single-Cell Technology for Hepatocellular Carcinoma Diagnosis

Hepatocellular carcinoma is a primary liver cancer caused by the accumulation of genetic mutation patterns associated with epidemiological conditions. This lethal malignancy exhibits tumor heterogeneity, which is considered as one of the main reasons for drug resistance development and failure of clinical trials. Recently, single-cell technology (SCT), a new advanced sequencing technique that analyzes every single cell in a tumor tissue specimen, aids complete insight into the genetic heterogeneity of cancer. This helps in identifying and assessing rare cell populations by analyzing the difference in gene expression pattern between individual cells of single biopsy tissue which normally cannot be identified from pooled cell gene expression pattern (traditional sequencing technique). Thus, SCT improves the clinical diagnosis, treatment, and prognosis of hepatocellular carcinoma as the limitations of other techniques impede this cancer research progression. Application of SCT at the genomic, transcriptomic, and epigenomic levels to promote individualized hepatocellular carcinoma diagnosis and therapy. The current review has been divided into ten sections. Herein we deliberated on the SCT, hepatocellular carcinoma diagnosis, tumor microenvironment analysis, single-cell genomic sequencing, single-cell transcriptomics, single-cell omics sequencing for biomarker development, identification of hepatocellular carcinoma origination and evolution, limitations, challenges, conclusions, and future perspectives.