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Research into the characteristic molecules significantly affecting liver cancer immunotherapy

BACKGROUND: The past decade has witnessed unprecedented scientific breakthroughs, including immunotherapy, which has great potential in clinical applications for liver cancer. METHODS: Public data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) dat...

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Autores principales: Chen, Junhong, Jin, Hengwei, Zhou, Hao, Hei, Xufei, Liu, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9968832/
https://www.ncbi.nlm.nih.gov/pubmed/36860864
http://dx.doi.org/10.3389/fimmu.2023.1029427
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author Chen, Junhong
Jin, Hengwei
Zhou, Hao
Hei, Xufei
Liu, Kai
author_facet Chen, Junhong
Jin, Hengwei
Zhou, Hao
Hei, Xufei
Liu, Kai
author_sort Chen, Junhong
collection PubMed
description BACKGROUND: The past decade has witnessed unprecedented scientific breakthroughs, including immunotherapy, which has great potential in clinical applications for liver cancer. METHODS: Public data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases and analyzed with R software. RESULTS: The LASSO and SVM-RFE machine learning algorithms identified 16 differentially expressed genes (DEGs) related to immunotherapy, namely, GNG8, MYH1, CHRNA3, DPEP1, PRSS35, CKMT1B, CNKSR1, C14orf180, POU3F1, SAG, POU2AF1, IGFBPL1, CDCA7, ZNF492, ZDHHC22, and SFRP2. Moreover, a logistic model (CombinedScore) was established based on these DEGs, showing an excellent prediction performance for liver cancer immunotherapy. Patients with a low CombinedScore might respond better to immunotherapy. Gene Set Enrichment Analysis showed that many metabolism pathways were activated in patients with a high CombinedScore, including butanoate metabolism, bile acid metabolism, fatty acid metabolism, glycine serine and threonine metabolism, and propanoate metabolism. Our comprehensive analysis showed that the CombinedScore was negatively correlated with the levels of most tumor-infiltrating immune cells and the activities of key steps of cancer immunity cycles. Continually, the CombinedScore was negatively associated with the expression of most immune checkpoints and immunotherapy response-related pathways. Moreover, patients with a high and a low CombinedScore exhibited diverse genomic features. Furthermore, we found that CDCA7 was significantly correlated with patient survival. Further analysis showed that CDCA7 was positively associated with M0 macrophages and negatively associated with M2 macrophages, suggesting that CDCA7 could influence the progression of liver cancer cells by affecting macrophage polarization. Next, single-cell analysis showed that CDCA7 was mainly expressed in prolif T cells. Immunohistochemical results confirmed that the staining intensity of CDCA7 was prominently increased in the nucleus in primary liver cancer tissues compared to adjacent non-tumor tissues. CONCLUSIONS: Our results provide novel insights into the DEGs and factors affecting liver cancer immunotherapy. Meanwhile, CDCA7 was identified as a potential therapeutic target in this patient population.
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spelling pubmed-99688322023-02-28 Research into the characteristic molecules significantly affecting liver cancer immunotherapy Chen, Junhong Jin, Hengwei Zhou, Hao Hei, Xufei Liu, Kai Front Immunol Immunology BACKGROUND: The past decade has witnessed unprecedented scientific breakthroughs, including immunotherapy, which has great potential in clinical applications for liver cancer. METHODS: Public data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases and analyzed with R software. RESULTS: The LASSO and SVM-RFE machine learning algorithms identified 16 differentially expressed genes (DEGs) related to immunotherapy, namely, GNG8, MYH1, CHRNA3, DPEP1, PRSS35, CKMT1B, CNKSR1, C14orf180, POU3F1, SAG, POU2AF1, IGFBPL1, CDCA7, ZNF492, ZDHHC22, and SFRP2. Moreover, a logistic model (CombinedScore) was established based on these DEGs, showing an excellent prediction performance for liver cancer immunotherapy. Patients with a low CombinedScore might respond better to immunotherapy. Gene Set Enrichment Analysis showed that many metabolism pathways were activated in patients with a high CombinedScore, including butanoate metabolism, bile acid metabolism, fatty acid metabolism, glycine serine and threonine metabolism, and propanoate metabolism. Our comprehensive analysis showed that the CombinedScore was negatively correlated with the levels of most tumor-infiltrating immune cells and the activities of key steps of cancer immunity cycles. Continually, the CombinedScore was negatively associated with the expression of most immune checkpoints and immunotherapy response-related pathways. Moreover, patients with a high and a low CombinedScore exhibited diverse genomic features. Furthermore, we found that CDCA7 was significantly correlated with patient survival. Further analysis showed that CDCA7 was positively associated with M0 macrophages and negatively associated with M2 macrophages, suggesting that CDCA7 could influence the progression of liver cancer cells by affecting macrophage polarization. Next, single-cell analysis showed that CDCA7 was mainly expressed in prolif T cells. Immunohistochemical results confirmed that the staining intensity of CDCA7 was prominently increased in the nucleus in primary liver cancer tissues compared to adjacent non-tumor tissues. CONCLUSIONS: Our results provide novel insights into the DEGs and factors affecting liver cancer immunotherapy. Meanwhile, CDCA7 was identified as a potential therapeutic target in this patient population. Frontiers Media S.A. 2023-02-13 /pmc/articles/PMC9968832/ /pubmed/36860864 http://dx.doi.org/10.3389/fimmu.2023.1029427 Text en Copyright © 2023 Chen, Jin, Zhou, Hei and Liu 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
Chen, Junhong
Jin, Hengwei
Zhou, Hao
Hei, Xufei
Liu, Kai
Research into the characteristic molecules significantly affecting liver cancer immunotherapy
title Research into the characteristic molecules significantly affecting liver cancer immunotherapy
title_full Research into the characteristic molecules significantly affecting liver cancer immunotherapy
title_fullStr Research into the characteristic molecules significantly affecting liver cancer immunotherapy
title_full_unstemmed Research into the characteristic molecules significantly affecting liver cancer immunotherapy
title_short Research into the characteristic molecules significantly affecting liver cancer immunotherapy
title_sort research into the characteristic molecules significantly affecting liver cancer immunotherapy
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9968832/
https://www.ncbi.nlm.nih.gov/pubmed/36860864
http://dx.doi.org/10.3389/fimmu.2023.1029427
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