Lactate-related gene signatures as prognostic predictors and comprehensive analysis of immune profiles in nasopharyngeal carcinoma
Abstract Objectives Nasopharyngeal carcinoma (NPC) is an aggressive malignancy with high rates of morbidity and mortality, largely because of its late diagnosis and metastatic potential. Lactate metabolism and protein lactylation are thought to play roles in NPC pathogenesis by modulating the tumor...
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| Format: | Article |
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BMC
2024-12-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-024-05935-9 |
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| author | Changlin Liu Chuping Ni Chao Li Hu Tian Weiquan Jian Yuping Zhong Yanqing Zhou Xiaoming Lyu Yuanbin Zhang Xiao-Jun Xiang Chao Cheng Xin Li |
| author_facet | Changlin Liu Chuping Ni Chao Li Hu Tian Weiquan Jian Yuping Zhong Yanqing Zhou Xiaoming Lyu Yuanbin Zhang Xiao-Jun Xiang Chao Cheng Xin Li |
| author_sort | Changlin Liu |
| collection | DOAJ |
| description | Abstract Objectives Nasopharyngeal carcinoma (NPC) is an aggressive malignancy with high rates of morbidity and mortality, largely because of its late diagnosis and metastatic potential. Lactate metabolism and protein lactylation are thought to play roles in NPC pathogenesis by modulating the tumor microenvironment and immune evasion. However, research specifically linking lactate-related mechanisms to NPC remains limited. This study aimed to identify lactate-associated biomarkers in NPC and explore their underlying mechanisms, with a particular focus on immune modulation and tumor progression. Methods To achieve these objectives, we utilized a bioinformatics approach in which publicly available gene expression datasets related to NPC were analysed. Differential expression analysis revealed differentially expressed genes (DEGs) between NPC and normal tissues. We performed weighted gene coexpression network analysis (WGCNA) to identify module genes significantly associated with NPC. Overlaps among DEGs, key module genes and lactate-related genes (LRGs) were analysed to derive lactate-related differentially expressed genes (LR-DEGs). Machine learning algorithms can be used to predict potential biomarkers, and immune infiltration analysis can be used to examine the relationships between identified biomarkers and immune cell types, particularly M0 macrophages and B cells. Results A total of 1,058 DEGs were identified between the NPC and normal tissue groups. From this set, 372 key module genes associated with NPC were isolated. By intersecting the DEGs, key module genes and lactate-related genes (LRGs), 17 lactate-related DEGs (LR-DEGs) were identified. Using three machine learning algorithms, this list was further refined, resulting in three primary lactate-related biomarkers: TPPP3, MUC4 and CLIC6. These biomarkers were significantly enriched in pathways related to “immune cell activation” and the “extracellular matrix environment”. Additionally, M0 and B macrophages were found to be closely associated with these biomarkers, suggesting their involvement in shaping the NPC immune microenvironment. Conclusion In summary, this study identified TPPP3, MUC4 and CLIC6 as lactate-associated clinical modelling indicators linked to NPC, providing a foundation for advancing diagnostic and therapeutic strategies for this malignancy. |
| format | Article |
| id | doaj-art-fd6e9bbc35d643faa6b035c1b6a9ef2f |
| institution | Kabale University |
| issn | 1479-5876 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-fd6e9bbc35d643faa6b035c1b6a9ef2f2024-12-22T12:44:31ZengBMCJournal of Translational Medicine1479-58762024-12-0122111510.1186/s12967-024-05935-9Lactate-related gene signatures as prognostic predictors and comprehensive analysis of immune profiles in nasopharyngeal carcinomaChanglin Liu0Chuping Ni1Chao Li2Hu Tian3Weiquan Jian4Yuping Zhong5Yanqing Zhou6Xiaoming Lyu7Yuanbin Zhang8Xiao-Jun Xiang9Chao Cheng10Xin Li11Shenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical UniversityShenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical UniversityDepartment of Oncology, The Fifth Affiliated Hospital, Southern Medical UniversityDepartment of Urology Surgery, Third Affiliated Hospital, Southern Medical UniversityShenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical UniversityDepartment of Otolaryngology, Shenzhen Longgang Otolaryngology HospitalShenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical UniversityDepartment of Laboratory Medicine, The Third Affiliated Hospital of Southern Medical UniversityShenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical UniversityDepartment of Healthcare-associated Infection Management, Nanfang Hospital, Southern Medical UniversityDepartment of Otolaryngology, Shenzhen Longgang Otolaryngology HospitalShenzhen Key Laboratory of Viral Oncology, The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical UniversityAbstract Objectives Nasopharyngeal carcinoma (NPC) is an aggressive malignancy with high rates of morbidity and mortality, largely because of its late diagnosis and metastatic potential. Lactate metabolism and protein lactylation are thought to play roles in NPC pathogenesis by modulating the tumor microenvironment and immune evasion. However, research specifically linking lactate-related mechanisms to NPC remains limited. This study aimed to identify lactate-associated biomarkers in NPC and explore their underlying mechanisms, with a particular focus on immune modulation and tumor progression. Methods To achieve these objectives, we utilized a bioinformatics approach in which publicly available gene expression datasets related to NPC were analysed. Differential expression analysis revealed differentially expressed genes (DEGs) between NPC and normal tissues. We performed weighted gene coexpression network analysis (WGCNA) to identify module genes significantly associated with NPC. Overlaps among DEGs, key module genes and lactate-related genes (LRGs) were analysed to derive lactate-related differentially expressed genes (LR-DEGs). Machine learning algorithms can be used to predict potential biomarkers, and immune infiltration analysis can be used to examine the relationships between identified biomarkers and immune cell types, particularly M0 macrophages and B cells. Results A total of 1,058 DEGs were identified between the NPC and normal tissue groups. From this set, 372 key module genes associated with NPC were isolated. By intersecting the DEGs, key module genes and lactate-related genes (LRGs), 17 lactate-related DEGs (LR-DEGs) were identified. Using three machine learning algorithms, this list was further refined, resulting in three primary lactate-related biomarkers: TPPP3, MUC4 and CLIC6. These biomarkers were significantly enriched in pathways related to “immune cell activation” and the “extracellular matrix environment”. Additionally, M0 and B macrophages were found to be closely associated with these biomarkers, suggesting their involvement in shaping the NPC immune microenvironment. Conclusion In summary, this study identified TPPP3, MUC4 and CLIC6 as lactate-associated clinical modelling indicators linked to NPC, providing a foundation for advancing diagnostic and therapeutic strategies for this malignancy.https://doi.org/10.1186/s12967-024-05935-9Nasopharyngeal carcinomaLactylation-related genesInfiltrating immunocytesMachine learning algorithmsGEO |
| spellingShingle | Changlin Liu Chuping Ni Chao Li Hu Tian Weiquan Jian Yuping Zhong Yanqing Zhou Xiaoming Lyu Yuanbin Zhang Xiao-Jun Xiang Chao Cheng Xin Li Lactate-related gene signatures as prognostic predictors and comprehensive analysis of immune profiles in nasopharyngeal carcinoma Journal of Translational Medicine Nasopharyngeal carcinoma Lactylation-related genes Infiltrating immunocytes Machine learning algorithms GEO |
| title | Lactate-related gene signatures as prognostic predictors and comprehensive analysis of immune profiles in nasopharyngeal carcinoma |
| title_full | Lactate-related gene signatures as prognostic predictors and comprehensive analysis of immune profiles in nasopharyngeal carcinoma |
| title_fullStr | Lactate-related gene signatures as prognostic predictors and comprehensive analysis of immune profiles in nasopharyngeal carcinoma |
| title_full_unstemmed | Lactate-related gene signatures as prognostic predictors and comprehensive analysis of immune profiles in nasopharyngeal carcinoma |
| title_short | Lactate-related gene signatures as prognostic predictors and comprehensive analysis of immune profiles in nasopharyngeal carcinoma |
| title_sort | lactate related gene signatures as prognostic predictors and comprehensive analysis of immune profiles in nasopharyngeal carcinoma |
| topic | Nasopharyngeal carcinoma Lactylation-related genes Infiltrating immunocytes Machine learning algorithms GEO |
| url | https://doi.org/10.1186/s12967-024-05935-9 |
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