Construction of a Cancer Stem Cell related Histone Acetylation Regulatory Genes Prognostic Model for Hepatocellular Carcinoma via Bioinformatics Analysis: Implications for Tumor Chemotherapy and Immunity

Qian Dai; Jie Zhu; Jing Yang; Chun-Yan Zhang; Wen-Jing Yang; Bai-Shen Pan; Xin-Rong Yang; Wei Guo; Bei-Li Wang

doi:10.2174/011574888X305642240327041753

Construction of a Cancer Stem Cell related Histone Acetylation Regulatory Genes Prognostic Model for Hepatocellular Carcinoma via Bioinformatics Analysis: Implications for Tumor Chemotherapy and Immunity

Curr Stem Cell Res Ther. 2024 Mar 28. doi: 10.2174/011574888X305642240327041753. Online ahead of print.

Authors

Qian Dai¹, Jie Zhu¹, Jing Yang¹, Chun-Yan Zhang^{1

2}, Wen-Jing Yang¹, Bai-Shen Pan^{1

3

4}, Xin-Rong Yang^{5

6}, Wei Guo^{1

3

4}, Bei-Li Wang^{1

2

3

4}

Affiliations

¹ Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
² Department of Laboratory Medicine, Shanghai Geriatric Medical Center, Shanghai, China.
³ Department of Laboratory Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, China.
⁴ Department of Laboratory Medicine, Wusong Branch, Zhongshan Hospital, Fudan University, Shanghai, China.
⁵ Department of Liver Surgery & Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China.
⁶ Cancer Center, Shanghai Zhongshan Hospital, Fudan University, Shanghai, China.

PMID: 38561604
DOI: 10.2174/011574888X305642240327041753

Abstract

Background: Cancer stem cells (CSC) play an important role in the development of Liver Hepatocellular Carcinoma (LIHC). However, the regulatory mechanisms between acetylation- associated genes (HAGs) and liver cancer stem cells remain unclear.

Objective: To identify a set of histone acetylation genes (HAGs) with close associations to liver cancer stem cells (LCSCs), and to construct a prognostic model that facilitates more accurate prognosis assessments for LIHC patients.

Methods: LIHC expression data were downloaded from the public databases. Using mRNA expression- based stemness indices (mRNAsi) inferred by One-Class Logistic Regression (OCLR), Differentially Expressed Genes (DEGs) (mRNAsi-High VS. mRNAsi-Low groups) were intersected with DEGs (LIHC VS. normal samples), as well as histone acetylation-associated genes (HAGs), to obtain mRNAsi-HAGs. A risk model was constructed employing the prognostic genes, which were acquired through univariate Cox and Least Shrinkage and Selection Operator (LASSO) regression analyses. Subsequently, independent prognostic factors were identified via univariate and multivariate Cox regression analyses and then a nomogram for prediction of LIHC survival was developed. Additionally, immune infiltration and drug sensitivity analysis were performed to explore the relationships between prognostic genes and immune cells. Finally, the expressions of selected mRNAsi-HAGs were validated in the LIHC tumor sphere by quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) assay and western blot analysis.

Results: Among 13 identified mRNAsi-HAGs, 3 prognostic genes (HDAC1, HDAC11, and HAT1) were selected to construct a risk model (mRNAsi-HAGs risk score = 0.02 * HDAC1 + 0.09 * HAT1 + 0.05 * HDAC11). T-stage, mRNAsi, and mRNAsi-HAGs risk scores were identified as independent prognostic factors to construct the nomogram, which was proved to predict the survival probability of LIHC patients effectively. We subsequently observed strongly positive correlations between mRNAsi-HAGs risk score and tumor-infiltrating T cells, B cells and macrophages/monocytes. Moreover, we found 8 drugs (Mitomycin C, IPA 3, FTI 277, Bleomycin, Tipifarnib, GSK 650394, AICAR and EHT 1864) had significant correlations with mRNAsi-HAGs risk scores. The expression of HDAC1 and HDAC11 was higher in CSC-like cells in the tumor sphere.

Conclusion: This study constructed a mRNAsi and HAGs-related prognostic model, which has implications for potential immunotherapy and drug treatment of LIHC.

Keywords: Liver hepatocellular carcinoma; bioinformatics.; histone acetylation; immune infiltration; prognostic model; stem cell.