Authors
- Yinyi Chen, From the Department of Clinical Laboratory (Chen, Han, Liu, Wang, Wu, Yu, Tan); from the Department of Blood Transfusion (Chen), The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, and from the Department of Clinical Laboratory (Luo), The Second Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Jiangxi, China.
- Kexin Han, From the Department of Clinical Laboratory (Chen, Han, Liu, Wang, Wu, Yu, Tan); from the Department of Blood Transfusion (Chen), The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, and from the Department of Clinical Laboratory (Luo), The Second Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Jiangxi, China.
- Yanzhao Liu, From the Department of Clinical Laboratory (Chen, Han, Liu, Wang, Wu, Yu, Tan); from the Department of Blood Transfusion (Chen), The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, and from the Department of Clinical Laboratory (Luo), The Second Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Jiangxi, China.
- Qunxia Wang, From the Department of Clinical Laboratory (Chen, Han, Liu, Wang, Wu, Yu, Tan); from the Department of Blood Transfusion (Chen), The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, and from the Department of Clinical Laboratory (Luo), The Second Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Jiangxi, China.
- Yang Wu, From the Department of Clinical Laboratory (Chen, Han, Liu, Wang, Wu, Yu, Tan); from the Department of Blood Transfusion (Chen), The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, and from the Department of Clinical Laboratory (Luo), The Second Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Jiangxi, China.
- Simei Chen, From the Department of Clinical Laboratory (Chen, Han, Liu, Wang, Wu, Yu, Tan); from the Department of Blood Transfusion (Chen), The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, and from the Department of Clinical Laboratory (Luo), The Second Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Jiangxi, China.
- Jianlin Yu, From the Department of Clinical Laboratory (Chen, Han, Liu, Wang, Wu, Yu, Tan); from the Department of Blood Transfusion (Chen), The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, and from the Department of Clinical Laboratory (Luo), The Second Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Jiangxi, China.
- Yi Luo, From the Department of Clinical Laboratory (Chen, Han, Liu, Wang, Wu, Yu, Tan); from the Department of Blood Transfusion (Chen), The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, and from the Department of Clinical Laboratory (Luo), The Second Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Jiangxi, China.
- Liming Tan, From the Department of Clinical Laboratory (Chen, Han, Liu, Wang, Wu, Yu, Tan); from the Department of Blood Transfusion (Chen), The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, and from the Department of Clinical Laboratory (Luo), The Second Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Jiangxi, China.
Abstract
Objectives: To identify potential diagnostic markers for small cell lung cancer (SCLC) and investigate the correlation with immune cell infiltration. Methods: GSE149507 and GSE6044 were used as the training group, while GSE108055 served as validation group A and GSE73160 served as validation group B. Differentially expressed genes (DEGs) were identified and analyzed for functional enrichment. Machine learning (ML) was used to identify candidate diagnostic genes for SCLC. The area under the receiver operating characteristic curves was applied to assess diagnostic efficacy. Immune cell infiltration analyses were carried out. Results: There were 181 DEGs identified. The gene ontology analysis showed that DEGs were enriched in 455 functional annotations, some of which were associated with immunity. The kyoto encyclopedia of genes and genomes analysis revealed that there were 9 signaling pathways enriched. The disease ontology analysis indicated that DEGs were related to 116 diseases. The gene set enrichment analysis results displayed multiple items closely related to immunity. ZWINT and NRCAM were screened using ML and further validated as diagnostic genes. Significant differences were observed in SCLC with normal lung tissue samples among immune cell infiltration characteristics. Strong associations were found between the diagnostic genes and immune cell infiltration. Conclusion: This study identified 2 diagnostic genes, ZWINT and NRCAM , that were related to immune cell infiltration by integrating bioinformatics analysis and ML algorithms. These genes could serve as potential diagnostic biomarkers and provide possible molecular targets for immunotherapy in SCLC.
Article Type
Research Article
Recommended Citation
Chen, Yinyi; Han, Kexin; Liu, Yanzhao; Wang, Qunxia; Wu, Yang; Chen, Simei; Yu, Jianlin; Luo, Yi; and Tan, Liming
(2024)
"Identification of effective diagnostic genes and immune cell infiltration characteristics in small cell lung cancer by integrating bioinformatics analysis and machine learning algorithms,"
Saudi Medical Journal: Vol. 45:
Iss.
8, Article 3.
DOI: https://doi.org/10.15537/smj.2024.45.8.20240170
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