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      Single-Cell Sequencing of Hepatocellular Carcinoma Reveals Cell Interactions and Cell Heterogeneity in the Microenvironment

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          Abstract

          Background

          Hepatocellular carcinoma (HCC) is the main histological subtype of liver cancer, which has the characteristics of poor prognosis and high fatality rate. Single-cell sequencing can provide quantitative and unbiased characterization of cell heterogeneity by analyzing the molecular profile of the whole genome of thousands of single cells. Thus, the purpose of this study was to identify novel prognostic markers for HCC based on single-cell sequencing data.

          Methods

          Single-cell sequencing of 21 HCC samples and 256 normal liver tissue samples in the GSE124395 dataset was collected from the Gene Expression Omnibus (GEO) database. The quality-controlled cells were grouped by unsupervised cluster analysis and identified the marker genes of each cell cluster. Hereafter, these cell clusters were annotated by singleR and CellMarker according to the expression patterns of the marker genes. Pseudotime analysis was performed to construct the trajectory of cell evolution and to define hub genes in the evolution process. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore the potential regulatory mechanism of hub genes in HCC. Next, the differential expression of hub genes and the correlation of the expression of these genes with patients’ survival and diagnosis were investigated in The Cancer Genome Atlas (TCGA) database.

          Results

          A total of 9 clusters corresponding to 9 cell types, including NKT cells, hepatocytes, endothelial cells, Kupffer cells, EPCAM + cells, cancer cells, plasma cells (B cells), immature B cells, and myofibroblasts were identified. We screened 63 key genes related to cell differentiation through trajectory analysis, which were enriched in the process of coagulation. Ultimately, we identified 10 survival-related hub genes in the TCGA database, namely ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN.

          Conclusion

          In conclusion, ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN may serve as hub genes in the diagnosis and prognosis for HCC.

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          Most cited references31

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)
          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)
            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              Comprehensive Integration of Single-Cell Data

              Tim Stuart, Andrew Butler, Paul Hoffman … (2019)
              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Int J Gen Med
                Journal ID (iso-abbrev): Int J Gen Med
                Journal ID (publisher-id): ijgm
                Title: International Journal of General Medicine
                Publisher: Dove
                ISSN (Electronic): 1178-7074
                Publication date (Electronic): 22 December 2021
                Publication date Collection: 2021
                Volume: 14
                Pages: 10141-10153
                Affiliations
                [1 ]Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Zunyi Medical University , Zunyi, People’s Republic of China
                [2 ]Department of Biochemistry and Molecular Biology, Zunyi Medical University , Zunyi, People’s Republic of China
                Author notes
                Correspondence: Cijun Peng Department of Hepatopancreatobiliary Surgery, Affiliated Hospital of Zunyi Medical University , Zunyi, 563000, Guizhou Province, People’s Republic of China Email doctorpengcijun@163.com
                Author information
                http://orcid.org/0000-0002-9462-525X
                Article
                Publisher ID: 338090
                DOI: 10.2147/IJGM.S338090
                PMC ID: 8711111
                SO-VID: a6d88b73-b69f-4c19-98d3-d30521dd5c89
                Copyright © © 2021 Li et al.
                License:

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                Date received : 16 September 2021
                Date accepted : 01 December 2021
                Page count
                Figures: 10, References: 31, Pages: 13
                Categories
                Subject: Original Research

                ScienceOpen disciplines: Medicine
                Keywords: hepatocellular carcinoma,hcc,single-cell sequencing,hub genes,prognostic,diagnostic
                Data availability:
                ScienceOpen disciplines: Medicine
                Keywords: hepatocellular carcinoma, hcc, single-cell sequencing, hub genes, prognostic, diagnostic

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