Identification of an Autophagy-Related Signature Based on Whole Bone Marrow Sequencing for the Prognosis and Immune Microenvironment Characterization of Multiple Myeloma
Myeloma (MM) is a malignant plasma cell disorder, which is incurable owing to its drug resistance. Autophagy performs an integral function in homeostasis, survival, and drug resistance in multiple myeloma (MM). Therefore, the purpose of the present research was to identify potential autophagy-relate...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Immunology Research |
| Online Access: | http://dx.doi.org/10.1155/2022/3922739 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832553621453012992 |
|---|---|
| author | Licheng Li Ting Chen Jishi Wang Mengxing Li Qinshan Li |
| author_facet | Licheng Li Ting Chen Jishi Wang Mengxing Li Qinshan Li |
| author_sort | Licheng Li |
| collection | DOAJ |
| description | Myeloma (MM) is a malignant plasma cell disorder, which is incurable owing to its drug resistance. Autophagy performs an integral function in homeostasis, survival, and drug resistance in multiple myeloma (MM). Therefore, the purpose of the present research was to identify potential autophagy-related genes (ARGs) in patients with MM. We downloaded the transcriptomic data (GSE136400) of patients with MM, as well as the corresponding clinical data from the Gene Expression Omnibus (GEO); the patients were classified at random into two groups in a ratio of 6: 4, with 212 samples in the training dataset and 142 samples in the test dataset. Both multivariate and univariate Cox regression analyses were performed to identify autophagy-related genes. The univariate Cox regression analysis demonstrated that 26 ARGs had a significant correlation with overall survival (OS). We constructed an autophagy-related risk prognostic model based on six ARGs: EIF2AK2 (ENSG00000055332), KIF5B (ENSG00000170759), MYC (ENSG00000136997), NRG2 (ENSG00000158458), PINK1 (ENSG00000158828), and VEGFA (ENSG00000112715) using LASSO-Cox regression analysis to predict risk outcomes, which revealed substantially shortened OS duration in the high-risk cohort in contrast with that in the low-risk cohort. Therefore, the ARG-based model significantly predicted the MM patients’ prognoses and was verified in an internal test set. Differentially expressed genes were found to be predominantly enriched in pathways associated with inflammation and immune regulation. Immune infiltration of tumor cells resulted in the formation of a strong immunosuppressive microenvironment in high-risk patients. The potential therapeutic targets of ARGs were subsequently analyzed via protein–drug network analysis. Therefore, a prognostic model for MM was established via a comprehensive analysis of ARGs, through using the clinical models; we have further revealed the molecular landscape features of multiple myeloma. |
| format | Article |
| id | doaj-art-9dddadac0a814df2b7d1946f4528fb39 |
| institution | Kabale University |
| issn | 2314-7156 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Immunology Research |
| spelling | doaj-art-9dddadac0a814df2b7d1946f4528fb392025-02-03T05:53:33ZengWileyJournal of Immunology Research2314-71562022-01-01202210.1155/2022/3922739Identification of an Autophagy-Related Signature Based on Whole Bone Marrow Sequencing for the Prognosis and Immune Microenvironment Characterization of Multiple MyelomaLicheng Li0Ting Chen1Jishi Wang2Mengxing Li3Qinshan Li4Clinical Medical CollegeClinical Medical CollegeClinical Medical CollegeClinical Medical CollegeDepartment of Obstetrics and GynecologyMyeloma (MM) is a malignant plasma cell disorder, which is incurable owing to its drug resistance. Autophagy performs an integral function in homeostasis, survival, and drug resistance in multiple myeloma (MM). Therefore, the purpose of the present research was to identify potential autophagy-related genes (ARGs) in patients with MM. We downloaded the transcriptomic data (GSE136400) of patients with MM, as well as the corresponding clinical data from the Gene Expression Omnibus (GEO); the patients were classified at random into two groups in a ratio of 6: 4, with 212 samples in the training dataset and 142 samples in the test dataset. Both multivariate and univariate Cox regression analyses were performed to identify autophagy-related genes. The univariate Cox regression analysis demonstrated that 26 ARGs had a significant correlation with overall survival (OS). We constructed an autophagy-related risk prognostic model based on six ARGs: EIF2AK2 (ENSG00000055332), KIF5B (ENSG00000170759), MYC (ENSG00000136997), NRG2 (ENSG00000158458), PINK1 (ENSG00000158828), and VEGFA (ENSG00000112715) using LASSO-Cox regression analysis to predict risk outcomes, which revealed substantially shortened OS duration in the high-risk cohort in contrast with that in the low-risk cohort. Therefore, the ARG-based model significantly predicted the MM patients’ prognoses and was verified in an internal test set. Differentially expressed genes were found to be predominantly enriched in pathways associated with inflammation and immune regulation. Immune infiltration of tumor cells resulted in the formation of a strong immunosuppressive microenvironment in high-risk patients. The potential therapeutic targets of ARGs were subsequently analyzed via protein–drug network analysis. Therefore, a prognostic model for MM was established via a comprehensive analysis of ARGs, through using the clinical models; we have further revealed the molecular landscape features of multiple myeloma.http://dx.doi.org/10.1155/2022/3922739 |
| spellingShingle | Licheng Li Ting Chen Jishi Wang Mengxing Li Qinshan Li Identification of an Autophagy-Related Signature Based on Whole Bone Marrow Sequencing for the Prognosis and Immune Microenvironment Characterization of Multiple Myeloma Journal of Immunology Research |
| title | Identification of an Autophagy-Related Signature Based on Whole Bone Marrow Sequencing for the Prognosis and Immune Microenvironment Characterization of Multiple Myeloma |
| title_full | Identification of an Autophagy-Related Signature Based on Whole Bone Marrow Sequencing for the Prognosis and Immune Microenvironment Characterization of Multiple Myeloma |
| title_fullStr | Identification of an Autophagy-Related Signature Based on Whole Bone Marrow Sequencing for the Prognosis and Immune Microenvironment Characterization of Multiple Myeloma |
| title_full_unstemmed | Identification of an Autophagy-Related Signature Based on Whole Bone Marrow Sequencing for the Prognosis and Immune Microenvironment Characterization of Multiple Myeloma |
| title_short | Identification of an Autophagy-Related Signature Based on Whole Bone Marrow Sequencing for the Prognosis and Immune Microenvironment Characterization of Multiple Myeloma |
| title_sort | identification of an autophagy related signature based on whole bone marrow sequencing for the prognosis and immune microenvironment characterization of multiple myeloma |
| url | http://dx.doi.org/10.1155/2022/3922739 |
| work_keys_str_mv | AT lichengli identificationofanautophagyrelatedsignaturebasedonwholebonemarrowsequencingfortheprognosisandimmunemicroenvironmentcharacterizationofmultiplemyeloma AT tingchen identificationofanautophagyrelatedsignaturebasedonwholebonemarrowsequencingfortheprognosisandimmunemicroenvironmentcharacterizationofmultiplemyeloma AT jishiwang identificationofanautophagyrelatedsignaturebasedonwholebonemarrowsequencingfortheprognosisandimmunemicroenvironmentcharacterizationofmultiplemyeloma AT mengxingli identificationofanautophagyrelatedsignaturebasedonwholebonemarrowsequencingfortheprognosisandimmunemicroenvironmentcharacterizationofmultiplemyeloma AT qinshanli identificationofanautophagyrelatedsignaturebasedonwholebonemarrowsequencingfortheprognosisandimmunemicroenvironmentcharacterizationofmultiplemyeloma |