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Integrative analysis of the prognostic value and immune microenvironment of mitophagy-related signature for multiple myeloma

BACKGROUND: Multiple myeloma (MM) is a fatal malignant tumor in hematology. Mitophagy plays vital roles in the pathogenesis and drug sensitivity of MM. METHODS: We acquired transcriptomic expression data and clinical index of MM patients from NCI public database, and 36 genes involved in mitophagy f...

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Autores principales: Jia, Yachun, Liu, Rui, Shi, Luyi, Feng, Yuandong, Zhang, Linlin, Guo, Ni, He, Aili, Kong, Guangyao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496355/
https://www.ncbi.nlm.nih.gov/pubmed/37700273
http://dx.doi.org/10.1186/s12885-023-11371-7
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author Jia, Yachun
Liu, Rui
Shi, Luyi
Feng, Yuandong
Zhang, Linlin
Guo, Ni
He, Aili
Kong, Guangyao
author_facet Jia, Yachun
Liu, Rui
Shi, Luyi
Feng, Yuandong
Zhang, Linlin
Guo, Ni
He, Aili
Kong, Guangyao
author_sort Jia, Yachun
collection PubMed
description BACKGROUND: Multiple myeloma (MM) is a fatal malignant tumor in hematology. Mitophagy plays vital roles in the pathogenesis and drug sensitivity of MM. METHODS: We acquired transcriptomic expression data and clinical index of MM patients from NCI public database, and 36 genes involved in mitophagy from the gene set enrichment analysis (GSEA) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted to construct a risk score prognostic model. Kaplan–Meier survival analysis and receiver operation characteristic curves (ROC) were conducted to identify the efficiency of prognosis and diagnosis. ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) was performed to uncover the level of immune infiltration. QRT-PCR was performed to verify gene expression in clinical samples of MM patients. The sensitivity to chemotherapy drugs was evaluated upon the database of the genomics of drug sensitivity in cancer (GDSC). RESULTS: Fifty mitophagy-related genes were differently expressed in two independent cohorts. Ten out of these genes were identified to be related to MM overall survival (OS) rate. A prognostic risk signature model was built upon on these genes: VDAC1, PINK1, VPS13C, ATG13, and HUWE1, which predicted the survival of MM accurately and stably both in training and validation cohorts. MM patients suffered more adverse prognosis showed more higher risk core. In addition, the risk score was considered as an independent prognostic element for OS of MM patients by multivariate cox regression analysis. Functional pathway enrichment analysis of differentially expressed genes (DEGs) based on risk score showed terms of cell cycle, immune response, mTOR pathway, and MYC targets were obviously enriched. Furthermore, MM patients with higher risk score were observed lower immune scores and lower immune infiltration levels. The results of qRT-PCR verified VDAC1, PINK1, and HUWE1 were dysregulated in new diagnosed MM patients. Finally, further analysis indicated MM patients showed more susceptive to bortezomib, lenalidomide and rapamycin in high-risk group. CONCLUSION: Our research provided a neoteric prognostic model of MM based on mitophagy genes. The immune infiltration level based on risk score paved a better understanding of the participation of mitophagy in MM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-023-11371-7.
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spelling pubmed-104963552023-09-13 Integrative analysis of the prognostic value and immune microenvironment of mitophagy-related signature for multiple myeloma Jia, Yachun Liu, Rui Shi, Luyi Feng, Yuandong Zhang, Linlin Guo, Ni He, Aili Kong, Guangyao BMC Cancer Research BACKGROUND: Multiple myeloma (MM) is a fatal malignant tumor in hematology. Mitophagy plays vital roles in the pathogenesis and drug sensitivity of MM. METHODS: We acquired transcriptomic expression data and clinical index of MM patients from NCI public database, and 36 genes involved in mitophagy from the gene set enrichment analysis (GSEA) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted to construct a risk score prognostic model. Kaplan–Meier survival analysis and receiver operation characteristic curves (ROC) were conducted to identify the efficiency of prognosis and diagnosis. ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) was performed to uncover the level of immune infiltration. QRT-PCR was performed to verify gene expression in clinical samples of MM patients. The sensitivity to chemotherapy drugs was evaluated upon the database of the genomics of drug sensitivity in cancer (GDSC). RESULTS: Fifty mitophagy-related genes were differently expressed in two independent cohorts. Ten out of these genes were identified to be related to MM overall survival (OS) rate. A prognostic risk signature model was built upon on these genes: VDAC1, PINK1, VPS13C, ATG13, and HUWE1, which predicted the survival of MM accurately and stably both in training and validation cohorts. MM patients suffered more adverse prognosis showed more higher risk core. In addition, the risk score was considered as an independent prognostic element for OS of MM patients by multivariate cox regression analysis. Functional pathway enrichment analysis of differentially expressed genes (DEGs) based on risk score showed terms of cell cycle, immune response, mTOR pathway, and MYC targets were obviously enriched. Furthermore, MM patients with higher risk score were observed lower immune scores and lower immune infiltration levels. The results of qRT-PCR verified VDAC1, PINK1, and HUWE1 were dysregulated in new diagnosed MM patients. Finally, further analysis indicated MM patients showed more susceptive to bortezomib, lenalidomide and rapamycin in high-risk group. CONCLUSION: Our research provided a neoteric prognostic model of MM based on mitophagy genes. The immune infiltration level based on risk score paved a better understanding of the participation of mitophagy in MM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-023-11371-7. BioMed Central 2023-09-12 /pmc/articles/PMC10496355/ /pubmed/37700273 http://dx.doi.org/10.1186/s12885-023-11371-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Jia, Yachun
Liu, Rui
Shi, Luyi
Feng, Yuandong
Zhang, Linlin
Guo, Ni
He, Aili
Kong, Guangyao
Integrative analysis of the prognostic value and immune microenvironment of mitophagy-related signature for multiple myeloma
title Integrative analysis of the prognostic value and immune microenvironment of mitophagy-related signature for multiple myeloma
title_full Integrative analysis of the prognostic value and immune microenvironment of mitophagy-related signature for multiple myeloma
title_fullStr Integrative analysis of the prognostic value and immune microenvironment of mitophagy-related signature for multiple myeloma
title_full_unstemmed Integrative analysis of the prognostic value and immune microenvironment of mitophagy-related signature for multiple myeloma
title_short Integrative analysis of the prognostic value and immune microenvironment of mitophagy-related signature for multiple myeloma
title_sort integrative analysis of the prognostic value and immune microenvironment of mitophagy-related signature for multiple myeloma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496355/
https://www.ncbi.nlm.nih.gov/pubmed/37700273
http://dx.doi.org/10.1186/s12885-023-11371-7
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