Dataset Information

Single-cell analysis reveals the intra-tumor heterogeneity and identifies MLXIPL as a biomarker in the cellular trajectory of hepatocellular carcinoma.

ABSTRACT: Hepatocellular carcinoma (HCC) is a globally prevailing cancer with a low 5-year survival rate. Little is known about its intricate gene expression profile. Single-cell RNA sequencing is an indispensable tool to explore the genetic characteristics of HCC at a more detailed level. In this study, we profiled the gene expression of single cells from human HCC tumor and para-tumor tissues using the Smart-seq 2 sequencing method. Based on differentially expressed genes, we identified heterogeneous subclones in HCC tissues, including five HCC and two hepatocyte subclones. We then carried out hub-gene co-network and functional annotations analysis followed pseudo-time analysis with regulated transcriptional factor co-networks to determine HCC cellular trajectory. We found that MLX interacting protein like (MLXIPL) was commonly upregulated in the single cells and tissues and associated with a poor survival rate in HCC. Mechanistically, MLXIPL activation is crucial for promoting cell proliferation and inhibits cell apoptosis by accelerating cell glycolysis. Taken together, our work identifies the heterogeneity of HCC subclones, and suggests MLXIPL might be a promising therapeutic target for HCC.

SUBMITTER: Dong X

PROVIDER: S-EPMC7814056 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Single-cell analysis reveals the intra-tumor heterogeneity and identifies MLXIPL as a biomarker in the cellular trajectory of hepatocellular carcinoma.

Dong Xiao X Wang Fan F Liu Chuan C Ling Jing J Jia Xuebing X Shen Feifei F Yang Ning N Zhu Sibo S Zhong Lin L Li Qi Q

Cell death discovery 20210118 1

Hepatocellular carcinoma (HCC) is a globally prevailing cancer with a low 5-year survival rate. Little is known about its intricate gene expression profile. Single-cell RNA sequencing is an indispensable tool to explore the genetic characteristics of HCC at a more detailed level. In this study, we profiled the gene expression of single cells from human HCC tumor and para-tumor tissues using the Smart-seq 2 sequencing method. Based on differentially expressed genes, we identified heterogeneous su ...[more]

PMID: 33462196

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Project description:IntroductionMounting evidence has revealed that the interactions and dynamic alterations among immune cells are critical in shaping the tumor microenvironment and ultimately map onto heterogeneous clinical outcomes. Currently, the underlying clinical significance of immune cell evolutions remains largely unexplored in hepatocellular carcinoma (HCC).MethodsA total of 3,817 immune cells and 1,750 HCC patients of 15 independent public datasets were retrieved. The Seurat and Monocle algorithms were used to depict T cell evolution, and nonnegative matrix factorization (NMF) was further applied to identify the molecular classification. Subsequently, the prognosis, biological characteristics, genomic variations, and immune landscape among distinct clusters were decoded. The clinical efficacy of multiple treatment approaches was further investigated.ResultsAccording to trajectory gene expression, three heterogeneous clusters with different clinical outcomes were identified. C2, with a more advanced pathological stage, presented the most dismal prognosis relative to C1 and C3. Eight independent external cohorts validated the robustness and reproducibility of the three clusters. Further explorations elucidated C1 to be characterized as lipid metabolic HCC, and C2 was referred to as cell-proliferative HCC, whereas C3 was defined as immune inflammatory HCC. Moreover, C2 also displayed the most conspicuous genomic instability, and C3 was deemed as "immune-hot", having abundant immune cells and an elevated expression of immune checkpoints. The assessments of therapeutic intervention suggested that patients in C1 were suitable for transcatheter arterial chemoembolization treatment, and patients in C2 were sensitive to tyrosine kinase inhibitors, while patients in C3 were more responsive to immunotherapy. We also identified numerous underlying therapeutic agents, which might be conducive to clinical transformation in the future.ConclusionsOur study developed three clusters with distinct characteristics based on immune cell evolutions. For specifically stratified patients, we proposed individualized treatment strategies to improve the clinical outcomes and facilitate the clinical management.

Dataset Information

Single-cell analysis reveals the intra-tumor heterogeneity and identifies MLXIPL as a biomarker in the cellular trajectory of hepatocellular carcinoma.

Publications

Single-cell analysis reveals the intra-tumor heterogeneity and identifies MLXIPL as a biomarker in the cellular trajectory of hepatocellular carcinoma.

Similar Datasets

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