BackgroundHepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the two aggressive subtypes of liver cancer (LC). Immense cellular heterogeneity and crosstalk between cancer and healthy cells make it challenging to treat these cancer subtypes. To address these challenges, the study aims to systematically characterize the tumour heterogeneity of LC subtypes using single-cell RNA sequencing (scRNA-seq) datasets. MethodThe study combined 51927 single cells from HCC, ICC, and healthy scRNA-seq datasets. After integrating the datasets, cell groups with similar gene expression patterns are clustered and cluster annotation has been performed based on gene markers. Cell-cell communication analysis (CCA) was implemented to understand the crosstalk between various cell types. Further, differential gene expression analysis and enrichment analysis were carried out to identify unique molecular drivers associated with HCC and ICC. ResultsOur analysis identi ed T-cells, hepatocytes, epithelial cells, and monocyte are the major cell types present in the tumour microenvironment. Among them, abundance of natural killer (NK) cells in HCC, epithelial cells and hepatocytes in ICC were detected. CCA revealed key interaction between T-cells to NK cells in HCC and smooth muscle cells to epithelial cells in the ICC. Additionally, SOX4 and DTHD1 are the top differentially expressed genes (DEGs) in HCC, while keratin and CCL4 are in ICC. Enrichment analysis of DEGs reveals major up-regulated genes in HCC affect protein folding mechanism and in ICC alter pathways involved in cell adhesion. ConclusionThe ndings suggest potential targets for the development of novel therapeutic strategies for the treatment of these two aggressive subtypes of LC.

Dimensionality reduction (DR) methods are applied to extract relevant features from inherently high dimensional and noisy single-cell RNA sequencing (scRNA-seq) data. Choice of DR method could influence the performance of clustering algorithm and subsequent analysis outcomes. We performed a benchmarking study of seven popular DR methods and four clustering algorithms widely used for scRNA-seq datasets. For this purpose, we used three publicly available real scRNA-seq datasets. The performance was evaluated using two clustering metrics viz. adjusted random index (ARI) and normalized mutual index (NMI). We also compared our results with a similar study published by Xiang and colleagues. Overall, we observed higher ARI and NMI scores for DR methods when compared with Xiangs study. We also noticed several differences between our and Xiangs study. Noteworthy, three methods, namely, Independent Component Analysis (ICA), t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) performed consistently well across three datasets. Linear method ICA was best performer on Segerstolpe dataset, while nonlinear methods UMAP and t-SNE best performed on Deng and Chu datasets, respectively. Neural network-based methods Variational Autoencoder (VAE) and Deep Count Autoencoder (DCA) could not perform well probably due to their sensitivity to hyperparameters and overfitting. Among clustering methods, Gaussian Mixture Models (GMMs) performed consistently well across datasets. This might be because GMMs are the universal approximators of posterior probability densities. We conclude that performance of different DR methods is more dataset dependent and for various scRNA-seq datasets different algorithms are more suited and there is no one-fit-all method.

Background Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the two aggressive subtypes of liver cancer (LC). Immense cellular heterogeneity and crosstalk between cancer and healthy cells make it challenging to treat these cancer subtypes. To address these challenges, the study aims to systematically characterize the tumour heterogeneity of LC subtypes using single-cell RNA sequencing (scRNA-seq) datasets. Method The study combined 51927 single cells from HCC, ICC, and healthy scRNA-seq datasets. After integrating the datasets, cell groups with similar gene expression patterns are clustered and cluster annotation has been performed based on gene markers. Cell-cell communication analysis (CCA) was implemented to understand the crosstalk between various cell types. Further, differential gene expression analysis and enrichment analysis were carried out to identify unique molecular drivers associated with HCC and ICC. Results Our analysis identified T-cells, hepatocytes, epithelial cells, and monocyte are the major cell types present in the tumour microenvironment. Among them, abundance of natural killer (NK) cells in HCC, epithelial cells and hepatocytes in ICC were detected. CCA revealed key interaction between T-cells to NK cells in HCC and smooth muscle cells to epithelial cells in the ICC. Additionally, SOX4 and DTHD1 are the top differentially expressed genes (DEGs) in HCC, while keratin and CCL4 are in ICC. Enrichment analysis of DEGs reveals major up-regulated genes in HCC affect protein folding mechanism and in ICC alter pathways involved in cell adhesion. Conclusion The findings suggest potential targets for the development of novel therapeutic strategies for the treatment of these two aggressive subtypes of LC.

Liver cancers including hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are leading cause of death worldwide. Single-cell transcriptomics studies have vast potential in advancing our understanding of cancers by defining the cellular composition of different solid tumor types. We peformed an integrated analysis using single-cell RNA sequencing (scRNA-seq) data from cancerous and healthy liver tissues in order to identify the molecular progression and intercellular heterogeneity across cell types in the liver cancer. Moreover, we performed a subtype specific analyses, separately for HCC and ICC, to identify any molecular drivers uniquely associated with these liver cancers. The scRNA-seq dataset comprising 5 healthy controls and 19 liver cancer patients were collected from Human Cell Atlas and Gene Expression Omnibus (GEO), respectively. Our analyses confirmed upregulation of four previously known malignant cell marker genes, namely, EPCAM, KRT19, KRT7 and S100P in the cancerous liver cells. Of these, KRT7 gene has been reported to be associated with ovarian cancer in the past studies. Noteworthy, four marker genes specific to the G1/S (MCM5 and PCNA) and G2/M phases (HMGB2 and CKS2) of the cell cycle were upregulated in the cancerous liver cells. This indicates that these four marker genes are actively dividing in these two phases in cancerous cells as compared to normal liver cells. Our differential expression analysis identified 2 upregulated genes (ATF3 and S100A11) and 2 downregulated genes (FCN3 and FGB) in the liver cancer. Our subtype based differential expression analysis identified 4 genes (HSPA6, LMNA, ATP1B1 and DCXR) specific to HCC and 3 genes (HSPB1, APOC3 and APOA1) specific to ICC. CD4+ T-cell, Hepatocyte, neutrophil, mesenchymal cells and liver bud hepatic cells are the predominant cell-types in liver cells. Our scRNA-seq study revealed the mesenchymal cells as potential malignant cell types in liver cancers. Our work suggests future research on developing liver cancer subtypes therapies could target these cell types and associated molecular markers.