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Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma

BACKGROUND: Hepatocellular carcinoma (HCC) is a complex disease with a poor outlook for patients in advanced stages. Immune cells play an important role in the progression of HCC. The metabolism of sphingolipids functions in both tumor growth and immune infiltration. However, little research has foc...

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Autores principales: Zhang, Xin, Zhuge, Jinke, Liu, Jinhui, Xia, Zhijia, Wang, Huixiong, Gao, Qiang, Jiang, Hao, Qu, Yanyu, Fan, Linlin, Ma, Jiali, Tan, Chunhua, Luo, Wei, Luo, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10063861/
https://www.ncbi.nlm.nih.gov/pubmed/37006285
http://dx.doi.org/10.3389/fimmu.2023.1153423
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author Zhang, Xin
Zhuge, Jinke
Liu, Jinhui
Xia, Zhijia
Wang, Huixiong
Gao, Qiang
Jiang, Hao
Qu, Yanyu
Fan, Linlin
Ma, Jiali
Tan, Chunhua
Luo, Wei
Luo, Yong
author_facet Zhang, Xin
Zhuge, Jinke
Liu, Jinhui
Xia, Zhijia
Wang, Huixiong
Gao, Qiang
Jiang, Hao
Qu, Yanyu
Fan, Linlin
Ma, Jiali
Tan, Chunhua
Luo, Wei
Luo, Yong
author_sort Zhang, Xin
collection PubMed
description BACKGROUND: Hepatocellular carcinoma (HCC) is a complex disease with a poor outlook for patients in advanced stages. Immune cells play an important role in the progression of HCC. The metabolism of sphingolipids functions in both tumor growth and immune infiltration. However, little research has focused on using sphingolipid factors to predict HCC prognosis. This study aimed to identify the key sphingolipids genes (SPGs) in HCC and develop a reliable prognostic model based on these genes. METHODS: The TCGA, GEO, and ICGC datasets were grouped using SPGs obtained from the InnateDB portal. A prognostic gene signature was created by applying LASSO-Cox analysis and evaluating it with Cox regression. The validity of the signature was verified using ICGC and GEO datasets. The tumor microenvironment (TME) was examined using ESTIMATE and CIBERSORT, and potential therapeutic targets were identified through machine learning. Single-cell sequencing was used to examine the distribution of signature genes in cells within the TME. Cell viability and migration were tested to confirm the role of the key SPGs. RESULTS: We identified 28 SPGs that have an impact on survival. Using clinicopathological features and 6 genes, we developed a nomogram for HCC. The high- and low-risk groups were found to have distinct immune characteristics and response to drugs. Unlike CD8 T cells, M0 and M2 macrophages were found to be highly infiltrated in the TME of the high-risk subgroup. High levels of SPGs were found to be a good indicator of response to immunotherapy. In cell function experiments, SMPD2 and CSTA were found to enhance survival and migration of Huh7 cells, while silencing these genes increased the sensitivity of Huh7 cells to lapatinib. CONCLUSION: The study presents a six-gene signature and a nomogram that can aid clinicians in choosing personalized treatments for HCC patients. Furthermore, it uncovers the connection between sphingolipid-related genes and the immune microenvironment, offering a novel approach for immunotherapy. By focusing on crucial sphingolipid genes like SMPD2 and CSTA, the efficacy of anti-tumor therapy can be increased in HCC cells.
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spelling pubmed-100638612023-04-01 Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma Zhang, Xin Zhuge, Jinke Liu, Jinhui Xia, Zhijia Wang, Huixiong Gao, Qiang Jiang, Hao Qu, Yanyu Fan, Linlin Ma, Jiali Tan, Chunhua Luo, Wei Luo, Yong Front Immunol Immunology BACKGROUND: Hepatocellular carcinoma (HCC) is a complex disease with a poor outlook for patients in advanced stages. Immune cells play an important role in the progression of HCC. The metabolism of sphingolipids functions in both tumor growth and immune infiltration. However, little research has focused on using sphingolipid factors to predict HCC prognosis. This study aimed to identify the key sphingolipids genes (SPGs) in HCC and develop a reliable prognostic model based on these genes. METHODS: The TCGA, GEO, and ICGC datasets were grouped using SPGs obtained from the InnateDB portal. A prognostic gene signature was created by applying LASSO-Cox analysis and evaluating it with Cox regression. The validity of the signature was verified using ICGC and GEO datasets. The tumor microenvironment (TME) was examined using ESTIMATE and CIBERSORT, and potential therapeutic targets were identified through machine learning. Single-cell sequencing was used to examine the distribution of signature genes in cells within the TME. Cell viability and migration were tested to confirm the role of the key SPGs. RESULTS: We identified 28 SPGs that have an impact on survival. Using clinicopathological features and 6 genes, we developed a nomogram for HCC. The high- and low-risk groups were found to have distinct immune characteristics and response to drugs. Unlike CD8 T cells, M0 and M2 macrophages were found to be highly infiltrated in the TME of the high-risk subgroup. High levels of SPGs were found to be a good indicator of response to immunotherapy. In cell function experiments, SMPD2 and CSTA were found to enhance survival and migration of Huh7 cells, while silencing these genes increased the sensitivity of Huh7 cells to lapatinib. CONCLUSION: The study presents a six-gene signature and a nomogram that can aid clinicians in choosing personalized treatments for HCC patients. Furthermore, it uncovers the connection between sphingolipid-related genes and the immune microenvironment, offering a novel approach for immunotherapy. By focusing on crucial sphingolipid genes like SMPD2 and CSTA, the efficacy of anti-tumor therapy can be increased in HCC cells. Frontiers Media S.A. 2023-03-17 /pmc/articles/PMC10063861/ /pubmed/37006285 http://dx.doi.org/10.3389/fimmu.2023.1153423 Text en Copyright © 2023 Zhang, Zhuge, Liu, Xia, Wang, Gao, Jiang, Qu, Fan, Ma, Tan, Luo and Luo https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Zhang, Xin
Zhuge, Jinke
Liu, Jinhui
Xia, Zhijia
Wang, Huixiong
Gao, Qiang
Jiang, Hao
Qu, Yanyu
Fan, Linlin
Ma, Jiali
Tan, Chunhua
Luo, Wei
Luo, Yong
Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma
title Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma
title_full Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma
title_fullStr Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma
title_full_unstemmed Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma
title_short Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma
title_sort prognostic signatures of sphingolipids: understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10063861/
https://www.ncbi.nlm.nih.gov/pubmed/37006285
http://dx.doi.org/10.3389/fimmu.2023.1153423
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