A Cell Differentiation Trajectory-Related Signature for Predicting the Prognosis of Lung Adenocarcinoma
Objective. To screen the cell differentiation trajectory-related genes and build a cell differentiation trajectory-related signature for predicting the prognosis of lung adenocarcinoma (LUAD). Methods. LUAD single cell mRNA expression profile, TCGA-LUAD transcriptome data were obtained from GEO and...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Genetics Research |
| Online Access: | http://dx.doi.org/10.1155/2022/3483498 |
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| _version_ | 1832547901192011776 |
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| author | Fan Yang Yan Zhao Xiaohan Huang Jin Zhang Ting Zhang |
| author_facet | Fan Yang Yan Zhao Xiaohan Huang Jin Zhang Ting Zhang |
| author_sort | Fan Yang |
| collection | DOAJ |
| description | Objective. To screen the cell differentiation trajectory-related genes and build a cell differentiation trajectory-related signature for predicting the prognosis of lung adenocarcinoma (LUAD). Methods. LUAD single cell mRNA expression profile, TCGA-LUAD transcriptome data were obtained from GEO and TCGA databases. Single-cell RNA-seq data were used for cell clustering and pseudotime analysis after dimensionality reduction analysis, and the cell differentiation trajectory-related genes were acquired after differential expression analysis conducted between the main branches. Then, the consensus clustering analysis was carried out on TCGA-LUAD samples, and the GSEA analysis was performed, then the differences on the expression levels of immune checkpoint genes and immunotherapy response were compared among clusters. The prognostic model was constructed, and the GSE42127 dataset was used to validate. A nomogram evaluation model was used to predict prognosis. Results. Two subsets with distinct differentiation states were found after cell differentiation trajectory analysis. TCGA-LUAD samples were divided into two cell differentiation trajectory-related gene-based clusters, GSEA found that cluster 1 was significantly related to 20 pathways, cluster 2 was significantly enriched in three pathways, and it was also shown that clusters could better predict immune checkpoint gene expression and immunotherapy response. A six cell differentiation-related genes-based prognostic signature was constructed, and the patients in the high-risk group had poorer prognosis than those in the low-risk group. Moreover, a nomogram was constructed based on the prognostic signature and clinicopathological features, and this nomogram had strong predictive performance and high accuracy. Conclusion. The cell differentiation-related signature and the prognostic nomogram could accurately predict survival. |
| format | Article |
| id | doaj-art-1ecbb8c9c8474ec1bb62ad73a46a870a |
| institution | Kabale University |
| issn | 1469-5073 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Genetics Research |
| spelling | doaj-art-1ecbb8c9c8474ec1bb62ad73a46a870a2025-02-03T06:42:55ZengWileyGenetics Research1469-50732022-01-01202210.1155/2022/3483498A Cell Differentiation Trajectory-Related Signature for Predicting the Prognosis of Lung AdenocarcinomaFan Yang0Yan Zhao1Xiaohan Huang2Jin Zhang3Ting Zhang4Department of Thoracic SurgeryDepartment of Respiratory MedicineDepartment of Respiratory and Critical Care MedicineDepartment of Thoracic SurgeryDepartment of Respiratory MedicineObjective. To screen the cell differentiation trajectory-related genes and build a cell differentiation trajectory-related signature for predicting the prognosis of lung adenocarcinoma (LUAD). Methods. LUAD single cell mRNA expression profile, TCGA-LUAD transcriptome data were obtained from GEO and TCGA databases. Single-cell RNA-seq data were used for cell clustering and pseudotime analysis after dimensionality reduction analysis, and the cell differentiation trajectory-related genes were acquired after differential expression analysis conducted between the main branches. Then, the consensus clustering analysis was carried out on TCGA-LUAD samples, and the GSEA analysis was performed, then the differences on the expression levels of immune checkpoint genes and immunotherapy response were compared among clusters. The prognostic model was constructed, and the GSE42127 dataset was used to validate. A nomogram evaluation model was used to predict prognosis. Results. Two subsets with distinct differentiation states were found after cell differentiation trajectory analysis. TCGA-LUAD samples were divided into two cell differentiation trajectory-related gene-based clusters, GSEA found that cluster 1 was significantly related to 20 pathways, cluster 2 was significantly enriched in three pathways, and it was also shown that clusters could better predict immune checkpoint gene expression and immunotherapy response. A six cell differentiation-related genes-based prognostic signature was constructed, and the patients in the high-risk group had poorer prognosis than those in the low-risk group. Moreover, a nomogram was constructed based on the prognostic signature and clinicopathological features, and this nomogram had strong predictive performance and high accuracy. Conclusion. The cell differentiation-related signature and the prognostic nomogram could accurately predict survival.http://dx.doi.org/10.1155/2022/3483498 |
| spellingShingle | Fan Yang Yan Zhao Xiaohan Huang Jin Zhang Ting Zhang A Cell Differentiation Trajectory-Related Signature for Predicting the Prognosis of Lung Adenocarcinoma Genetics Research |
| title | A Cell Differentiation Trajectory-Related Signature for Predicting the Prognosis of Lung Adenocarcinoma |
| title_full | A Cell Differentiation Trajectory-Related Signature for Predicting the Prognosis of Lung Adenocarcinoma |
| title_fullStr | A Cell Differentiation Trajectory-Related Signature for Predicting the Prognosis of Lung Adenocarcinoma |
| title_full_unstemmed | A Cell Differentiation Trajectory-Related Signature for Predicting the Prognosis of Lung Adenocarcinoma |
| title_short | A Cell Differentiation Trajectory-Related Signature for Predicting the Prognosis of Lung Adenocarcinoma |
| title_sort | cell differentiation trajectory related signature for predicting the prognosis of lung adenocarcinoma |
| url | http://dx.doi.org/10.1155/2022/3483498 |
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