Single-Cell Transcriptomics of Liver Cancer: Hype or Insights?

Potential applications of dual haptoglobin expression in the reclassification and treatment of hepatocellular carcinoma

HCC is a malignancy characterized by high incidence and mortality rates. Traditional classifications of HCC primarily rely on tumor morphology, phenotype, and multicellular molecular levels, which may not accurately capture the cellular heterogeneity within the tumor. This study integrates scRNA-seq and bulk RNA-seq to spotlight HP as a critical gene within a subgroup of HCC malignant cells. HP is highly expressed in HCC malignant cells and lowly expressed in T cells. Within malignant cells, elevated HP expression interacts with C3, promoting Th1-type responses via the C3/C3AR1 axis. In T cells, down-regulating HP expression favors the expression of Th1 cell-associated marker genes, potentially enhancing Th1-type responses. Consequently, we developed a "HP-promoted Th1 response reclassification" gene set, correlating higher activity scores with improved survival rates in HCC patients. Additionally, four predictive models for neoadjuvant treatment based on HP and C3 expression were established: 1) Low HP and C3 expression with high Th2 cell infiltration; 2) High HP and low C3 expression with high Th2 cell infiltration; 3) High HP and C3 expression with high Th1 cell infiltration; 4) Low HP and high C3 expression with high Th1 cell infiltration. In conclusion, the HP gene selected from the HCC malignant cell subgroup (Malignant_Sub 6) might serve as a potential ally against the tumor by promoting Th1-type immune responses. The establishment of the "HP-promoted Th1 response reclassification" gene set offers predictive insights for HCC patient survival prognosis and neoadjuvant treatment efficacy, providing directions for clinical treatments.

Combining a glycolysis‑related prognostic model based on scRNA‑Seq with experimental verification identifies ZFP41 as a potential prognostic biomarker for HCC

Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis, and its heterogeneity affects the response to clinical treatments. Glycolysis is highly associated with HCC therapy and prognosis. The present study aimed to identify a novel biomarker for HCC by exploring the heterogeneity of glycolysis in HCC. The intersection of both marker genes of glycolysis‑related cell clusters from single‑cell RNA sequencing analysis and mRNA data of liver HCC from The Cancer Genome Atlas were used to construct a prognostic model through Cox proportional hazard regression and the least absolute shrinkage and selection operator Cox regression. Data from the International Cancer Genome Consortium were used to validate the results of the analysis. Immune status analysis was then conducted. A significant gene in the prognostic model was identified as a potential biomarker and was verified through in vitro experiments. The results revealed that the glycolysis‑related prognostic model divided patients with HCC into high‑ and low‑risk groups. A nomogram combining the model and clinical features exhibited accurate predictive ability, with an area under the curve of 0.763 at 3 years. The high‑risk group exhibited a higher expression of checkpoint genes and lower tumor immune dysfunction and exclusion scores, suggesting that this group may be more likely to benefit from immunotherapy. The tumor tissues had a higher zinc finger protein (ZFP)41 mRNA and protein expression compared with the adjacent tissues. In vitro analyses revealed that ZFP41 played a crucial role in cell viability, proliferation, migration, invasion and glycolysis. On the whole, the present study demonstrates that the glycolysis‑related prognostic gene, ZFP41, is a potential prognostic biomarker and therapeutic target, and may play a crucial role in glycolysis and malignancy in HCC.

Reciprocal interactions between malignant cells and macrophages enhance cancer stemness and M2 polarization in HBV-associated hepatocellular carcinoma

Background: The tumor microenvironment of cancers has emerged as a crucial component in regulating cancer stemness and plays a pivotal role in cell-cell communication. However, the specific mechanisms underlying these phenomena remain poorly understood. Methods: We performed the single-cell RNA sequencing (scRNA-seq) on nine HBV-associated hepatocellular carcinoma (HCC) patients. The heterogeneity of the malignant cells in pathway functions, transcription factors (TFs) regulation, overall survival, stemness, as well as ligand-receptor-based intercellular communication with macrophages were characterized. The aggressive and stemness feature for the target tumor subclone was validated by the conduction of in vitro assays including sphere formation, proliferation, Annexin V apoptosis, flow cytometry, siRNA library screening assays, and multiple in vivo preclinical mouse models including mouse hepatoma cell and human HCC cell xenograft models with subcutaneous or orthotopic injection. Results: Our analysis yielded a comprehensive atlas of 31,664 cells, revealing a diverse array of malignant cell subpopulations. Notably, we identified a stemness-related subclone of HCC cells with concurrent upregulation of CD24, CD47, and ICAM1 expression that correlated with poorer overall survival. Functional characterization both in vitro and in vivo validated S100A11 as one of the top downstream mediators for tumor initiation and stemness maintenance of this subclone. Further investigation of cell-cell communication within the tumor microenvironment revealed a propensity for bi-directional crosstalk between this stemness-related subclone and tumor-associated macrophages (TAMs). Co-culture study showed that this interaction resulted in the maintenance of the expression of cancer stem cell markers and driving M2-like TAM polarization towards a pro-tumorigenic niche. We also consolidated an inverse relationship between the proportions of TAMs and tumor-infiltrating T cells. Conclusions: Our study highlighted the critical role of stemness-related cancer cell populations in driving an immunosuppressive tumor microenvironment and identified the S100A11 gene as a key mediator for stemness maintenance in HCC. Moreover, our study provides support that the maintenance of cancer stemness is more attributed to M2 polarization than the recruitment of the TAMs.

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq data reveals dynamic changes in tumor-associated neutrophils in the tumor microenvironment of hepatocellular carcinoma and leads to the establishment of a neutrophil-related prognostic model

BACKGROUND

Analysis of hepatocellular carcinoma (HCC) single-cell sequencing data was conducted to explore the role of tumor-associated neutrophils in the tumor microenvironment.

METHODS

Analysis of single-cell sequencing data from 12 HCC tumor cores and five HCC paracancerous tissues identified cellular subpopulations and cellular marker genes. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to establish and validate prognostic models. xCELL, TIMER, QUANTISEQ, CIBERSORT, and CIBERSORT-abs analyses were performed to explore immune cell infiltration. Finally, the pattern of tumor-associated neutrophil roles in tumor microenvironmental components was explored.

RESULTS

A total of 271 marker genes for tumor-associated neutrophils were identified based on single-cell sequencing data. Prognostic models incorporating eight genes were established based on TCGA data. Immune cell infiltration differed between the high- and low-risk groups. The low-risk group benefited more from immunotherapy. Single-cell analysis indicated that tumor-associated neutrophils were able to influence macrophage, NK cell, and T-cell functions through the IL16, IFN-II, and SPP1 signaling pathways.

CONCLUSION

Tumor-associated neutrophils regulate immune functions by influencing macrophages and NK cells. Models incorporating tumor-associated neutrophil-related genes can be used to predict patient prognosis and immunotherapy responses.

Single-cell RNA sequencing technology in human spermatogenesis: Progresses and perspectives

Spermatogenesis, a key part of the spermiation process, is regulated by a combination of key cells, such as primordial germ cells, spermatogonial stem cells, and somatic cells, such as Sertoli cells. Abnormal spermatogenesis can lead to azoospermia, testicular tumors, and other diseases related to male infertility. The application of single-cell RNA sequencing (scRNA-seq) technology in male reproduction is gradually increasing with its unique insight into deep mining and analysis. The data cover different periods of neonatal, prepubertal, pubertal, and adult stages. Different types of male infertility diseases including obstructive and non-obstructive azoospermia (NOA), Klinefelter Syndrome (KS), Sertoli Cell Only Syndrome (SCOS), and testicular tumors are also covered. We briefly review the principles and application of scRNA-seq and summarize the research results and application directions in spermatogenesis in different periods and pathological states. Moreover, we discuss the challenges of applying this technology in male reproduction and the prospects of combining it with other technologies.

The core role of macrophages in hepatocellular carcinoma: the definition of molecular subtypes and the prognostic risk system

Background: In patients with hepatocellular carcinoma (HCC), the tumor microenvironment (TME) is resistant to immunotherapy because of its specificity. It is meaningful to explore the role of macrophage, which is one of the most abundant immune cells in the TME, in cellular communication and its effect on the prognosis and immunotherapy of HCC. Methods: Dimensionality reduction and clustering of the single-cell RNA-seq data from the GSE149614 dataset were carried out to identify the cellular composition of HCC. CellChat was used to analyze the communication between different cells. The specifically highly expressed genes of macrophages were extracted for univariate Cox regression analysis to obtain prognostic genes for HCC cluster analysis, and the risk system of macrophage-specifically highly expressed genes was developed by random forest analysis and multivariate Cox regression analysis. Prognosis, TME infiltration, potential responses to immunotherapy, and antineoplastic drugs were compared among molecular subtypes and between risk groups. Results: We found that HCC included nine identifiable cell types, of which macrophages had the highest communication intensity with each of the other eight cell types. Of the 179 specifically highly expressed genes of macrophage, 56 were significantly correlated with the prognosis of HCC, which classified HCC into three subtypes, which were reproducible and produced different survival outcomes, TME infiltration, and immunotherapy responses among the subtypes. In the integration of four macrophage-specifically highly expressed genes for the development of a risk system, the risk score was significantly involved in higher immune cell infiltration, poor prognosis, immunotherapy response rate, and sensitivity of six drugs. Conclusion: In this study, through single-cell RNA-seq data, we identified nine cell types, among which macrophage had the highest communication intensity with the rest of the cell types. Based on specifically highly expressed genes of macrophage, we successfully divided HCC patients into three clusters with distinct prognosis, TME, and therapeutic response. Additionally, a risk system was constructed, which provided a potential reference index for the prognostic target and preclinical individualized treatment of HCC.

Single-Cell Analysis of Primary Liver Cancer in Mouse Models

Primary liver cancer (PLC), consisting mainly of hepatocellular carcinoma and intrahepatic cholangiocarcinoma, is one of the major causes of cancer-related mortality worldwide. The curative therapy for PLC is surgical resection and liver transplantation, but most PLCs are inoperable at diagnosis. Even after surgery, there is a high rate of tumor recurrence. There is an unmet clinical need to discover more effective treatment options for advanced PLCs. Pre-clinical mouse models in PLC research have played a critical role in identifying key oncogenic drivers and signaling pathways in hepatocarcinogenesis. Furthermore, recent advances in single-cell RNA sequencing (scRNA-seq) have provided an unprecedented degree of resolution in such characterization. In this review, we will summarize the recent studies that utilized pre-clinical mouse models with the combination of scRNA-seq to provide an understanding of different aspects of PLC. We will focus particularly on the potentially actionable targets regarding the cellular and molecular components. We anticipate that the findings in mouse models could complement those in patients. With more defined etiological background, mouse models may provide valuable insights.

scAnalyzeR: A Comprehensive Software Package With Graphical User Interface for Single-Cell RNA Sequencing Analysis and its Application on Liver Cancer

Introduction: The application of single-cell RNA sequencing to delineate tissue heterogeneity and complexity has become increasingly popular. Given its tremendous resolution and high-dimensional capacity for in-depth investigation, single-cell RNA sequencing offers an unprecedented research power. Although some popular software packages are available for single-cell RNA sequencing data analysis and visualization, it is still a big challenge for their usage, as they provide only a command-line interface and require significant level of bioinformatics skills. Methods: We have developed scAnalyzeR, which is a single-cell RNA sequencing analysis pipeline with an interactive and user-friendly graphical interface for analyzing and visualizing single-cell RNA sequencing data. It accepts single-cell RNA sequencing data from various technology platforms and different model organisms (human and mouse) and allows flexibility in input file format. It provides functionalities for data preprocessing, quality control, basic summary statistics, dimension reduction, unsupervised clustering, differential gene expression, gene set enrichment analysis, correlation analysis, pseudotime cell trajectory inference, and various visualization plots. It also provides default parameters for easy usage and allows a wide range of flexibility and optimization by accepting user-defined options. It has been developed as a docker image that can be run in any docker-supported environment including Linux, Mac, and Windows, without installing any dependencies. Results: We compared the performance of scAnalyzeR with 2 other graphical tools that are popular for analyzing single-cell RNA sequencing data. The comparison was based on the comprehensiveness of functionalities, ease of usage and flexibility, and execution time. In general, scAnalyzeR outperformed the other tested counterparts in various aspects, demonstrating its superior overall performance. To illustrate the usefulness of scAnalyzeR in cancer research, we have analyzed the in-house liver cancer single-cell RNA sequencing dataset. Liver cancer tumor cells were revealed to have multiple subpopulations with distinctive gene expression signatures. Conclusion: scAnalyzeR has comprehensive functionalities and demonstrated usability. We anticipate more functionalities to be adopted in the future development.