Single-cell atlas of tumor cell evolution in response to therapy in hepatocellular carcinoma and intrahepatic cholangiocarcinoma

Ma, Lichun; Wang, Limin; Khatib, Subreen A.; Chang, Ching-Wen; Heinrich, S.; Dominguez, Dana A.; Forgues, Marshonna; Candia, Julián; Hernandez, Maria O.; Kelly, Michael C.; Zhao, Yongmei; Tran, Bao; Hernandez, Jonathan M.; Davis, Jeremy L.; Kleiner, David E.; Wood, Bradford J.; Greten, Tim F.; Wang, Xin Wei

doi:10.1016/j.jhep.2021.06.028

Cited by 204 publications

(159 citation statements)

References 40 publications

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“…Human Liver Browser ( http://itzkovitzwebapps.weizmann.ac.il/webapps/home/session.html?app=HumanLiverBrowser ) and Single‐cell Atlas in Liver Cancer (scAtlasLC, https://scatlaslc.ccr.cancer.gov/ ) are public databases of single‐cell transcriptomic profiles for HCC. 27 , 28 The expression of the target genes in malignant and non‐malignant cells in HCC was analysed by Human Liver Browser and scAtlasLC.…”

Section: Methodsmentioning

confidence: 99%

Prognostic evaluation and immune infiltration analysis of five bioinformatic selected genes in hepatocellular carcinoma

Lai

Tai

Jiang

et al. 2021

J Cellular Molecular Medi

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Despite the development in hepatocellular carcinoma (HCC) treatment in recent years, the therapeutic outcome of HCC remains unfavourable. This study examines the prognosis of HCC from a genetic level using clinical databases and single‐cell data to identify genes with a high prognostic value. Three up‐regulated genes (UBE2S, PTTG1, and CDC20) and two down‐regulated genes (SOCS2 and DNASE1L3) in HCC tissues were identified. Various analyses confirmed its correlation with tumour stage (p < 0.01) and patient survival time (log‐rank p < 0.001). Immune analysis, single‐cell analysis, and gene set enrichment analysis (GSEA) were employed to provide insight on how they affect cancer progression, and we observed a close relation between these genes and tumour immune infiltration. Eventually, we constructed a risk score system that risk score = (0.0465) × UBE2S + (0.1851) × CDC20 + (−0.0461) × DNASE1L3 + (−0.2279) × SOCS2 (5‐year area under curve = 0.706). The risk score system may serve as an effective novel prognostic system for HCC patients. This study might provide novel ideas for prognostic or therapeutic biomarkers for HCC.

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Section: Methodsmentioning

confidence: 99%

Prognostic evaluation and immune infiltration analysis of five bioinformatic selected genes in hepatocellular carcinoma

Lai

Tai

Jiang

et al. 2021

J Cellular Molecular Medi

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“…Osteopontin is a phosphorylated glycoprotein linked with many human diseases. Overexpression of osteopontin is well documented with its pivotal role in HCC progression by regulating different immune cell types in TME [42,57]. Losic et al integrated three techniques: RNA, DNA, and TCR-sequencing with SNP array data to quantify transcriptomic ITH.…”

Section: Single-cell Transcriptomicsmentioning

confidence: 99%

An Overview on Single-Cell Technology for Hepatocellular Carcinoma Diagnosis

Aliya

Lee

Alhammadi

et al. 2022

IJMS

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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.

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“…Transcriptome-wide analyses on a single cell level of human CML, CRC, HCC, LC, and PDAC were based on previously published scRNA-seq datasets [49][50][51][52][53]. Within the CML dataset, plasma cells from multiple myeloma (MM) patients were used.…”

Section: Single-cell Rna-seq (Scrna-seq) Analysismentioning

confidence: 99%

“…For the whole genome comparison, only control and malignant epithelial cells were compared ([51] GSE132465). The annotation for the HCC dataset was provided in the original study, where it was referred to as "Type" [52]. The HCC dataset contained malignant hepatocytes as well as tumor-associated macrophages, T cells, B cells, cancer-associated fibroblasts, tumor-associated endothelial cells, and unclassified cells.…”

Section: Single-cell Rna-seq (Scrna-seq) Analysismentioning

confidence: 99%

“…In contrast, scRNA-seq allows the separation and analysis of distinct cell populations [62]. Therefore, we analyzed publicly available scRNA-seq datasets [49][50][51][52][53] and focused on cell types that were described as putative origins of malignant transformation (MM: plasma cells [63]; CRC: epithelial cells [64]; HCC: hepatocytes [65]; LC: epithelial cells [66]; PDAC: ductal cells [67]). We first compared control and malignant samples (Figure S4B, not provided for CML GSE175385), and except for the PDAC dataset (PR-JCA001063), we found no significant age differences, which is why we decided to compare malignant and control cells exclusively from the same sample in the PDAC dataset (ductal cell type 1 cells = control and ductal cell type 2 = malignant).…”

Section: Scrna-seq Analysis Reveals the Presence Of Distinct Cancer C...mentioning

confidence: 99%

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Single-Cell Transcriptomics Reveals the Expression of Aging- and Senescence-Associated Genes in Distinct Cancer Cell Populations

Saul

Kosinsky

2021

Cells

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The human aging process is associated with molecular changes and cellular degeneration, resulting in a significant increase in cancer incidence with age. Despite their potential correlation, the relationship between cancer- and ageing-related transcriptional changes is largely unknown. In this study, we aimed to analyze aging-associated transcriptional patterns in publicly available bulk mRNA-seq and single-cell RNA-seq (scRNA-seq) datasets for chronic myelogenous leukemia (CML), colorectal cancer (CRC), hepatocellular carcinoma (HCC), lung cancer (LC), and pancreatic ductal adenocarcinoma (PDAC). Indeed, we detected that various aging/senescence-induced genes (ASIGs) were upregulated in malignant diseases compared to healthy control samples. To elucidate the importance of ASIGs during cell development, pseudotime analyses were performed, which revealed a late enrichment of distinct cancer-specific ASIG signatures. Notably, we were able to demonstrate that all cancer entities analyzed in this study comprised cell populations expressing ASIGs. While only minor correlations were detected between ASIGs and transcriptome-wide changes in PDAC, a high proportion of ASIGs was induced in CML, CRC, HCC, and LC samples. These unique cellular subpopulations could serve as a basis for future studies on the role of aging and senescence in human malignancies.

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Single-cell atlas of tumor cell evolution in response to therapy in hepatocellular carcinoma and intrahepatic cholangiocarcinoma

Cited by 204 publications

References 40 publications

Prognostic evaluation and immune infiltration analysis of five bioinformatic selected genes in hepatocellular carcinoma

Prognostic evaluation and immune infiltration analysis of five bioinformatic selected genes in hepatocellular carcinoma

An Overview on Single-Cell Technology for Hepatocellular Carcinoma Diagnosis

Single-Cell Transcriptomics Reveals the Expression of Aging- and Senescence-Associated Genes in Distinct Cancer Cell Populations

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