A Comprehensive Bioinformatic Analysis for Identification of Myeloid-Associated Differentiation Marker as a Potential Negative Prognostic Biomarker in Non-Small-Cell Lung Cancer
Objectives: This study aimed to identify a molecular marker associated with the prognosis of non-small-cell lung cancer (NSCLC).Materials and Methods: The RNA sequencing data and clinical information of NSCLC patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Pathology and Oncology Research |
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| Online Access: | https://www.por-journal.com/articles/10.3389/pore.2022.1610504/full |
| _version_ | 1827292171917590528 |
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| author | Min Zhou Yan Chen Xuyu Gu Cailian Wang |
| author_facet | Min Zhou Yan Chen Xuyu Gu Cailian Wang |
| author_sort | Min Zhou |
| collection | DOAJ |
| description | Objectives: This study aimed to identify a molecular marker associated with the prognosis of non-small-cell lung cancer (NSCLC).Materials and Methods: The RNA sequencing data and clinical information of NSCLC patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression gene modules and differentially expressed genes (DEGs) by comparing gene expression between NSCLC tumor tissues and normal tissues. Subsequently, the functional enrichment analysis of the DEGs was performed. Kaplan-Meier survival analysis and the GEPIA2 online tool were performed to investigate the relationship between the expression of these genes of interest and the survival of NSCLC patients, and to validate one most survival-relevent hub gene, as well as validated the hub gene using independent datasets from the GEO database. Further analysis was carried out to characterize the relationship between the hub gene and tumor immune cell infiltration, tumor mutation burden (TMB), microsatellite instability (MSI), and other known biomarkers of lung cancer. The related genes were screened by analyzing the protein-protein interaction (PPI) network and the survival model was constructed. GEPIA2 was applied in the potential analysis of pan-cancer biomarker of hub gene.Results: 57 hub genes were found to be involved in intercellular connectivity from the 779 identified differentially co-expressed genes. Myeloid-associated differentiation marker (MYADM) was strongly associated with overall survival (OS) and disease-free survival (DFS) of NSCLC patients, and high MYADM expression was associated with poor prognosis. Thus, MYADM was identified as a risk factor. Additionally, MYADM was validated as a survival risk factor in NSCLC patients in two independent datasets. Further analysis showed that MYADM was nagetively associated with TMB, and was positively correlated with macrophages, neutrophils, and dendritic cells, suggesting its role in regulating tumor immunity. The MYADM expression differed across many types of cancer and had the potential to serve as a pan-cancer marker.Conclusion:MYADM is an independent prognostic factor for NSCLC patients, which can predict the progression of cancer and play a role in the tumor immune cell infiltration in NSCLC. |
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| id | doaj.art-4476420e15844d7c8f828c01bce083a0 |
| institution | Directory Open Access Journal |
| issn | 1532-2807 |
| language | English |
| last_indexed | 2024-04-24T12:58:31Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Pathology and Oncology Research |
| spelling | doaj.art-4476420e15844d7c8f828c01bce083a02024-04-05T16:29:17ZengFrontiers Media S.A.Pathology and Oncology Research1532-28072022-08-012810.3389/pore.2022.16105041610504A Comprehensive Bioinformatic Analysis for Identification of Myeloid-Associated Differentiation Marker as a Potential Negative Prognostic Biomarker in Non-Small-Cell Lung CancerMin ZhouYan ChenXuyu GuCailian WangObjectives: This study aimed to identify a molecular marker associated with the prognosis of non-small-cell lung cancer (NSCLC).Materials and Methods: The RNA sequencing data and clinical information of NSCLC patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression gene modules and differentially expressed genes (DEGs) by comparing gene expression between NSCLC tumor tissues and normal tissues. Subsequently, the functional enrichment analysis of the DEGs was performed. Kaplan-Meier survival analysis and the GEPIA2 online tool were performed to investigate the relationship between the expression of these genes of interest and the survival of NSCLC patients, and to validate one most survival-relevent hub gene, as well as validated the hub gene using independent datasets from the GEO database. Further analysis was carried out to characterize the relationship between the hub gene and tumor immune cell infiltration, tumor mutation burden (TMB), microsatellite instability (MSI), and other known biomarkers of lung cancer. The related genes were screened by analyzing the protein-protein interaction (PPI) network and the survival model was constructed. GEPIA2 was applied in the potential analysis of pan-cancer biomarker of hub gene.Results: 57 hub genes were found to be involved in intercellular connectivity from the 779 identified differentially co-expressed genes. Myeloid-associated differentiation marker (MYADM) was strongly associated with overall survival (OS) and disease-free survival (DFS) of NSCLC patients, and high MYADM expression was associated with poor prognosis. Thus, MYADM was identified as a risk factor. Additionally, MYADM was validated as a survival risk factor in NSCLC patients in two independent datasets. Further analysis showed that MYADM was nagetively associated with TMB, and was positively correlated with macrophages, neutrophils, and dendritic cells, suggesting its role in regulating tumor immunity. The MYADM expression differed across many types of cancer and had the potential to serve as a pan-cancer marker.Conclusion:MYADM is an independent prognostic factor for NSCLC patients, which can predict the progression of cancer and play a role in the tumor immune cell infiltration in NSCLC.https://www.por-journal.com/articles/10.3389/pore.2022.1610504/fullbioinformaticsbiomarkerNSCLCsurvival-related geneMYADM |
| spellingShingle | Min Zhou Yan Chen Xuyu Gu Cailian Wang A Comprehensive Bioinformatic Analysis for Identification of Myeloid-Associated Differentiation Marker as a Potential Negative Prognostic Biomarker in Non-Small-Cell Lung Cancer Pathology and Oncology Research bioinformatics biomarker NSCLC survival-related gene MYADM |
| title | A Comprehensive Bioinformatic Analysis for Identification of Myeloid-Associated Differentiation Marker as a Potential Negative Prognostic Biomarker in Non-Small-Cell Lung Cancer |
| title_full | A Comprehensive Bioinformatic Analysis for Identification of Myeloid-Associated Differentiation Marker as a Potential Negative Prognostic Biomarker in Non-Small-Cell Lung Cancer |
| title_fullStr | A Comprehensive Bioinformatic Analysis for Identification of Myeloid-Associated Differentiation Marker as a Potential Negative Prognostic Biomarker in Non-Small-Cell Lung Cancer |
| title_full_unstemmed | A Comprehensive Bioinformatic Analysis for Identification of Myeloid-Associated Differentiation Marker as a Potential Negative Prognostic Biomarker in Non-Small-Cell Lung Cancer |
| title_short | A Comprehensive Bioinformatic Analysis for Identification of Myeloid-Associated Differentiation Marker as a Potential Negative Prognostic Biomarker in Non-Small-Cell Lung Cancer |
| title_sort | comprehensive bioinformatic analysis for identification of myeloid associated differentiation marker as a potential negative prognostic biomarker in non small cell lung cancer |
| topic | bioinformatics biomarker NSCLC survival-related gene MYADM |
| url | https://www.por-journal.com/articles/10.3389/pore.2022.1610504/full |
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