Identification of prognostic risk model based on plasma cell markers in hepatocellular carcinoma through single-cell sequencing analysis

Yuanqi Li; Hao Huang; Qi Wang; Xiao Zheng; Yi Zhou; Xiangyin Kong; Tao Huang; Jinping Zhang; You Zhou

doi:10.3389/fgene.2024.1363197

Identification of prognostic risk model based on plasma cell markers in hepatocellular carcinoma through single-cell sequencing analysis

Front Genet. 2024 May 27:15:1363197. doi: 10.3389/fgene.2024.1363197. eCollection 2024.

Authors

Yuanqi Li^{1

2

3}, Hao Huang^{1

2

3}, Qi Wang^{1

2

3}, Xiao Zheng^{1

2

3}, Yi Zhou^{1

2

3}, Xiangyin Kong⁴, Tao Huang⁵, Jinping Zhang⁶, You Zhou^{1

2

3}

Affiliations

¹ Tumor Biological Diagnosis and Treatment Center, The Third Affiliated Hospital of Soochow University, Changzhou, China.
² Jiangsu Engineering Research Center for Tumor Immunotherapy, Changzhou, China.
³ Institute of Cell Therapy, Soochow University, Changzhou, China.
⁴ CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
⁵ Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
⁶ Institutes of Biology and Medical Sciences, Soochow University, Suzhou, China.

Abstract

Hepatocellular carcinoma (HCC) represents a substantial global health burden. Tumorinfiltrating B lymphocytes (TIL-Bs) contribute to tumor progression and significantly impact the efficacy of tumor therapy. However, the characteristics of TIL-Bs in HCC and their effect on HCC therapy remain elusive. Single-cell RNA sequencing (scRNAseq) was applied to investigate the heterogeneity, cellular differentiation and cell-cell communication of TIL-Bs in HCC. Further, the Cancer Genome Atlas-liver hepatocellular carcinoma (TCGA-LIHC) and liver cancer institutes (LCI) cohorts were applied to construct and validate the plasma cell marker-based prognostic risk model. The relationship between the prognostic risk model and the responsiveness of immunotherapy and chemotherapy in patients with HCC were estimated by OncoPredict and tumor immune dysfunction and exclusion (TIDE) algorithm. Finally, we established nomogram and calibration curves to evaluate the precision of the risk score in predicating survival probability. Our data identified five subtypes of TIL-Bs in HCC, each exhibiting varying levels of infiltration in tumor tissues. The interactions between TIL-Bs and other cell types contributed to shaping distinct tumor microenvironments (TME). Moreover, we found that TIL-Bs subtypes had disparate prognostic values in HCC patients. The prognostic risk model demonstrated exceptional predictive accuracy for overall survival and exhibited varying sensitivities to immunotherapy and chemotherapy among patients with HCC. Our data demonstrated that the risk score stood as an independent prognostic predictor and the nomogram results further affirmed its strong prognostic capability. This study reveals the heterogeneity of TIL-Bs and provides a prognostic risk model based on plasma cell markers in HCC, which could prove valuable in predicting prognosis and guiding the choice of suitable therapies for patients with HCC.

Keywords: RNA; hepatocellular carcinoma; plasma cells; prognostic risk model; single-cell RNA sequencing; therapeutics; tumor-infiltrating B lymphocytes.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was financially supported by National Natural Science Foundation of China (31701111), Natural Science Foundation of Universities in Jiangsu Province (23KJB310024), Top Talent of Changzhou “The 14th Five-Year Plan” High-Level Health Talents Training Project (2022260), Major Science and Technology Project of Changzhou Health Commission (ZD202203), Applied Basic Research Project of Changzhou (CJ20220107), Young Talent Development Plan of Changzhou Health Commission (CZQM2020008).