Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy

Hepatocellular carcinoma (LIHC) poses a significant health challenge worldwide, primarily due to late-stage diagnosis and the limited effectiveness of current therapies. Cancer stem cells are known to play a role in tumor development, metastasis, and resistance to treatment. A thorough understanding...

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Vydáno v:Frontiers in immunology Ročník 15; s. 1534723
Hlavní autoři: Huang, Shenjun, Tu, Tingting
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland Frontiers Media S.A 07.01.2025
Témata:
Biomarkers, Tumor - genetics
cancer stem cell
Carcinoma, Hepatocellular - diagnosis
Carcinoma, Hepatocellular - genetics
Carcinoma, Hepatocellular - therapy
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
hepatocellular carcinoma
Humans
Immunology
Immunotherapy - methods
Liver Neoplasms - diagnosis
Liver Neoplasms - genetics
Liver Neoplasms - therapy
Machine Learning
Neoplastic Stem Cells - metabolism
Prognosis
S100 Proteins - genetics
S100A10
single cell analysis
Single-Cell Analysis - methods
cancer stem cell
machine learning
single cell analysis
hepatocellular carcinoma
S100A10
ISSN:1664-3224, 1664-3224
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Shrnutí:Hepatocellular carcinoma (LIHC) poses a significant health challenge worldwide, primarily due to late-stage diagnosis and the limited effectiveness of current therapies. Cancer stem cells are known to play a role in tumor development, metastasis, and resistance to treatment. A thorough understanding of genes associated with stem cells is crucial for improving the diagnostic precision of LIHC and for the advancement of effective immunotherapy approaches. This research combines single-cell RNA sequencing with machine learning techniques to identify vital stem cell-associated genes that could act as prognostic biomarkers and therapeutic targets for LIHC. We analyzed various datasets, applying negative matrix factorization alongside machine learning algorithms to reveal gene expression patterns and construct diagnostic models. The XGBoost algorithm was specifically utilized to identify key regulatory genes related to stem cells in LIHC, and the expression levels and prognostic significance of these genes were validated experimentally. Our single-cell analysis identified 16 differential prognostic genes associated with liver cancer stem cells. Cluster analysis and diagnostic models constructed using various machine learning techniques confirmed the significance of these 16 genes in the diagnosis and immunotherapy of LIHC. Notably, the XGBoost algorithm identified S100A10 as the stem cell-related gene most relevant to the prognosis of LIHC patients. Experimental validation further supports S100A10 as a potential prognostic marker for this cancer type. Additionally, S100A10 shows a positive correlation with the stem cell marker POU5F1. The results of this study highlight S100A10 as an essential predictor for liver cancer diagnosis and treatment response, particularly regarding immunotherapy. This research offers valuable insights into the molecular mechanisms underlying LIHC and suggests S100A10 as a promising target for enhancing treatment outcomes in liver cancer patients.
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Edited by: Minghua Ren, First Affiliated Hospital of Harbin Medical University, China
Reviewed by: Jingwei Zhao, Shanghai Jiao Tong University, China
Li Tang, Second Military Medical University, China
ISSN:1664-3224
1664-3224
DOI:10.3389/fimmu.2024.1534723