Toward a Liver Cell Atlas: Understanding Liver Biology in Health and Disease at Single-Cell Resolution

Spatial proximity of tumor-immune interactions predicts patient outcome in hepatocellular carcinoma

BACKGROUND AND AIMS

The fitness and viability of a tumor ecosystem are influenced by the spatial organization of its cells. We aimed to study the structure, architecture, and cell-cell dynamics of the heterogeneous liver cancer tumor microenvironment using spatially resolved multiplexed imaging.

APPROACH AND RESULTS

We performed co-detection by indexing multiplexed immunofluorescence imaging on 68 HCC biopsies from Thai patients [(Thailand Initiative in Genomics and Expression Research for Liver Cancer (TIGER-LC)] as a discovery cohort, and then validated the results in an additional 190 HCC biopsies from Chinese patients [Liver Cancer Institute (LCI)]. We segmented and annotated 117,270 and 465,632 cells from the TIGER-LC and LCI cohorts, respectively. We observed 4 patient groups of TIGER-LC (IC1, IC2, IC3, and IC4) with distinct tumor-immune cellular interaction patterns. In addition, patients from IC2 and IC4 had much better overall survival than those from IC1 and IC3. Noticeably, tumor and CD8 + T-cell interactions were strongly enriched in IC2, the group with the best patient outcomes. The close proximity between the tumor and CD8 + T cells was a strong predictor of patient outcome in both the TIGER-LC and the LCI cohorts. Bulk transcriptomic data from 51 of the 68 HCC cases were used to determine tumor-specific gene expression features of our classified subtypes. Moreover, we observed that the presence of immune spatial neighborhoods in HCC as a measure of overall immune infiltration is linked to better patient prognosis.

CONCLUSIONS

Highly multiplexed imaging analysis of liver cancer reveals tumor-immune cellular heterogeneity within spatial contexts, such as tumor and CD8 + T-cell interactions, which may predict patient survival.

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.

Exosome-Related FTCD Facilitates M1 Macrophage Polarization and Impacts the Prognosis of Hepatocellular Carcinoma

BACKGROUND

Exosomes are essential for hepatocellular carcinoma (HCC) progression and have garnered significant interest as novel targets for diagnostic, prognostic, and therapeutic approaches. This study aims to identify potential exosome-related biomarkers for the development of useful strategies for HCC diagnosis and therapy.

METHODS

Three datasets obtained from the Gene Expression Omnibus (GEO) were utilized to identify differentially expressed genes (DEGs) in HCC. Through Gene Ontology (GO) analysis and protein-protein interaction (PPI) network, overall survival (OS) analysis, Cox analyses, and diethylnitrosamine (DEN)-induced HCC mouse model detection, exosome-related hub gene was screened out, followed by a prognostic value assessment and immune-correlates analysis based on the Cancer Genome Atlas (TCGA) dataset. The hub gene-containing exosomes derived from Hepa1-6 cells were isolated and characterized using differential ultracentrifugation, transmission electron microscopy scanning, and Western blot. Ultrasound-guided intrahepatic injection, cell co-culture, CCK-8, and flow cytometry were performed to investigate the effects of the hub gene on macrophage infiltration and polarization in HCC.

RESULTS

A total of 83 DEGs enriched in the extracellular exosome term, among which, FTCD, HRA, and C8B showed the strongest association with the progression of HCC. FTCD was independently associated with a protective effect in HCC and selected as the hub gene. The presence of FTCD in exosomes was confirmed. FTCD-stimulated macrophages were polarized towards the M1 type and suppressed HCC cells proliferation.

CONCLUSIONS

FTCD is a potential exosome-related biomarker for HCC diagnosis, prognosis, and treatment. The crosstalk between FTCD-containing exosomes and macrophages in HCC progression deserves further investigation.

Insights on epithelial cells at the single-cell level in hepatocellular carcinoma prognosis and response to chemotherapy

Background: Hepatocellular carcinoma (HCC) originates from Epithelial cells, and epithelial lineage plasticity has become a promising research direction for advancing HCC treatment. This study aims to focus on Epithelial cells to provide target insights for detecting HCC prognosis and response to drug therapy. Methods: Single-cell RNA sequencing (scRNA-seq) data from GSE149614 were clustered using Seurat, and the differentiation and evolution of epithelial cells were analyzed by Monocle 2. Scissor+ and Scissor- Epithelial cells associated with the prognostic phenotypes of bulk RNA-seq of HCC were screened using the Scissor algorithm for differential analysis to screen candidate genes. Candidate genes were overlapped with prognostic related genes screened by univariate Cox regression, and the Least Absolute Shrinkage and Selection Operator (LASSO) sparse penalty was imposed on the intersection genes to construct a risk assessment system. Results: Eight major cell subpopulations of HCC were identified, among which the proportion of epithelial cells in non-tumor liver tissues and HCC tissues was significantly different, and its proportion increased with advanced clinical stage. During the progression of HCC, the whole direction of epithelial cells differentiation trajectory was towards enhanced cell proliferation. Differential analysis between Scissor+ and Scissor- epithelial cells screened 1,265 upregulated and 191 downregulated prognostic candidate genes. Wherein, the upregulated genes were enriched in Cell processes, Genetic information processing, Metabolism and Human disease with Infection. Nevertheless, immune system related pathways took the main proportions in downregulated genes enriched pathways. There were 17 common genes between upregulated candidate genes and prognostic risk genes, of which CDC20, G6PD and PLOD2 were selected as components for constructing the risk assessment system. Risk score showed a significant correlation with tumor stage, epithelial-mesenchymal transition (EMT) related pathways and 22 therapeutic drugs, and was an independent prognostic factor for HCC. Conclusion: This study revealed the cellular composition of HCC, the differentiation evolution and functional landscape of epithelial cells in the further deterioration of HCC, and established a 3-gene risk model, which was closely related to clinical features, EMT, and drug sensitivity prediction. These findings provided insights in patient prognosis and drug therapy detection for HCC.

Unraveling the Complexity of Liver Disease One Cell at a Time

The human liver is a complex organ made up of multiple specialized cell types that carry out key physiological functions. An incomplete understanding of liver biology limits our ability to develop therapeutics to prevent chronic liver diseases, liver cancers, and death as a result of organ failure. Recently, single-cell modalities have expanded our understanding of the cellular phenotypic heterogeneity and intercellular cross-talk in liver health and disease. This review summarizes these findings and looks forward to highlighting new avenues for the application of single-cell genomics to unravel unknown pathogenic pathways and disease mechanisms for the development of new therapeutics targeting liver pathology. As these technologies mature, their integration into clinical data analysis will aid in patient stratification and in developing treatment plans for patients suffering from liver disease.

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.