Leveraging single‐cell RNA‐seq for uncovering naïve B cells associated with better prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a typical highly heterogeneous solid tumor with high morbidity and mortality worldwide, especially in China; however, the immune microenvironment of HCC has not been clarified so far. Here, we employed single-cell RNA sequencing (scRNA-seq) on diethylnitrosamine (DEN)-induced mouse HCC model to dissect the immune cell dynamics during tumorigenesis. Our findings reveal distinct immune profiles in both precancerous and cancerous lesions, indicating early tumor-associated immunological alterations. Notably, specific T and B cell subpopulations are preferentially enriched in the HCC tumor microenvironment (TME). Furthermore, we identified a subpopulation of naïve B cells with high CD83 expression, correlating with improved prognosis in human HCC. These signature genes were validated in The Cancer Genome Atlas HCC RNA-seq dataset. Moreover, cell interaction analysis revealed that subpopulations of B cells in both mouse and human samples are activated and may potentially contribute to oncogenic processes. In summary, our study provides insights into the dynamic immune microenvironment and cellular networks in HCC pathogenesis, with a specific emphasis on naïve B cells. These findings emphasize the significance of targeting TME in HCC patients to prevent HCC pathological progression, which may give a new perspective on the therapeutics for HCC.

Authors

Institutions

Publication Details

Journal
MedComm
Published
2024-09-01
DOI
https://doi.org/10.1002/mco2.563
Citations
3
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.70

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Leveraging single‐cell RNA‐seq for uncovering naïve B cells associated with better prognosis of hepatocellular carcinoma

Jian He, Yingxin Lin, Qingjia Sun, Tao Wang et al.
3 citations
MedComm
Single-cell and spatial transcriptomics
0.70
article

Leveraging single‐cell RNA‐seq for uncovering naïve B cells associated with better prognosis of hepatocellular carcinoma

Jian He, Yingxin Lin, Qingjia Sun, Tao Wang, Rui Gao, Xianchao Zhou
article en
3 citations

Abstract

Hepatocellular carcinoma (HCC) is a typical highly heterogeneous solid tumor with high morbidity and mortality worldwide, especially in China; however, the immune microenvironment of HCC has not been clarified so far. Here, we employed single-cell RNA sequencing (scRNA-seq) on diethylnitrosamine (DEN)-induced mouse HCC model to dissect the immune cell dynamics during tumorigenesis. Our findings reveal distinct immune profiles in both precancerous and cancerous lesions, indicating early tumor-associated immunological alterations. Notably, specific T and B cell subpopulations are preferentially enriched in the HCC tumor microenvironment (TME). Furthermore, we identified a subpopulation of naïve B cells with high CD83 expression, correlating with improved prognosis in human HCC. These signature genes were validated in The Cancer Genome Atlas HCC RNA-seq dataset. Moreover, cell interaction analysis revealed that subpopulations of B cells in both mouse and human samples are activated and may potentially contribute to oncogenic processes. In summary, our study provides insights into the dynamic immune microenvironment and cellular networks in HCC pathogenesis, with a specific emphasis on naïve B cells. These findings emphasize the significance of targeting TME in HCC patients to prevent HCC pathological progression, which may give a new perspective on the therapeutics for HCC.

MedCommVol. 5(9)
Université Claude Bernard Lyon 1 (FR), The University of Sydney (AU), Union Hospital (HK), Shanghai Jiao Tong University (CN), Jilin University (CN), Institut National des Sciences Appliquées de Lyon (FR), Université Jean Monnet (FR)
National Natural Science Foundation of China, Shanghai Jiao Tong University, School of Medicine, Shanghai Jiao Tong University
Good health and well-being
Openalex Percentile: Top 30%
Single-cell and spatial transcriptomics
0.70
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.