Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma
Abstract Background Patients with lung adenocarcinoma (LUAD) receiving drug treatment often have an unpredictive response and there is a lack of effective methods to predict treatment outcome for patients. Dendritic cells (DCs) play a significant role in the tumor microenvironment and the DCs-relate...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | Cancer Cell International |
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| Online Access: | https://doi.org/10.1186/s12935-025-03642-z |
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| author | Zihao Sun Mengfei Hu Xiaoning Huang Minghan Song Xiujing Chen Jiaxin Bei Yiguang Lin Size Chen |
| author_facet | Zihao Sun Mengfei Hu Xiaoning Huang Minghan Song Xiujing Chen Jiaxin Bei Yiguang Lin Size Chen |
| author_sort | Zihao Sun |
| collection | DOAJ |
| description | Abstract Background Patients with lung adenocarcinoma (LUAD) receiving drug treatment often have an unpredictive response and there is a lack of effective methods to predict treatment outcome for patients. Dendritic cells (DCs) play a significant role in the tumor microenvironment and the DCs-related gene signature may be used to predict treatment outcome. Here, we screened for DC-related genes to construct a prognostic signature to predict prognosis and response to immunotherapy in LUAD patients. Methods DC-related biological functions and genes were identified using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing. DCs-related gene signature (DCRGS) was constructed using integrated machine learning algorithms. Expression of key genes in clinical samples was examined by real-time q-PCR. Performance of the prognostic model, DCRGS, for the prognostic evaluation, was assessed using a multiple time-dependent receiver operating characteristic (ROC) curve, the R package, “timeROC”, and validated using GEO datasets. Results Analysis of scRNA-seq data showed that there is a significant upregulation of LGALS9 expression in DCs isolated from malignant pleural effusion samples. Leveraging the Coxboost and random survival forest combination algorithm, we filtered out six DC-related genes on which a prognostic prediction model, DCRGS, was established. A high predictive capability nomogram was constructed by combining DCRGS with clinical features. We found that patients with a high-DCRGS score had immunosuppression, activated tumor-associated pathways, and elevated somatic mutational load and copy number variant load. In contrast, patients in the low-DCRGS subgroup were resistant to chemotherapy but sensitive to the CTLA-4 immune checkpoint inhibitor and targeted therapy. Conclusion We have innovatively established a deep learning-based prediction model, DCRGS, for the prediction of the prognosis of patients with LUAD. The model possesses a strong prognostic prediction performance with high accuracy and sensitivity and could be clinically useful to guide the management of LUAD. Furthermore, the findings of this study could provide an important reference for individualized clinical treatment and prognostic prediction of patients with LUAD. |
| format | Article |
| id | doaj-art-4a490dbb20184c089f32cf83d4b59678 |
| institution | Kabale University |
| issn | 1475-2867 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Cell International |
| spelling | doaj-art-4a490dbb20184c089f32cf83d4b596782025-01-19T12:39:31ZengBMCCancer Cell International1475-28672025-01-0125112010.1186/s12935-025-03642-zPredictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinomaZihao Sun0Mengfei Hu1Xiaoning Huang2Minghan Song3Xiujing Chen4Jiaxin Bei5Yiguang Lin6Size Chen7Department of Immuno-Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical UniversityDepartment of Internal Medicine, The First Affiliated Hospital of Anhui University of Chinese MedicineDepartment of Immuno-Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical UniversityDepartment of Immuno-Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical UniversityDepartment of Immuno-Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical UniversityDepartment of Immuno-Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical UniversityDepartment of Immuno-Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical UniversityDepartment of Immuno-Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical UniversityAbstract Background Patients with lung adenocarcinoma (LUAD) receiving drug treatment often have an unpredictive response and there is a lack of effective methods to predict treatment outcome for patients. Dendritic cells (DCs) play a significant role in the tumor microenvironment and the DCs-related gene signature may be used to predict treatment outcome. Here, we screened for DC-related genes to construct a prognostic signature to predict prognosis and response to immunotherapy in LUAD patients. Methods DC-related biological functions and genes were identified using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing. DCs-related gene signature (DCRGS) was constructed using integrated machine learning algorithms. Expression of key genes in clinical samples was examined by real-time q-PCR. Performance of the prognostic model, DCRGS, for the prognostic evaluation, was assessed using a multiple time-dependent receiver operating characteristic (ROC) curve, the R package, “timeROC”, and validated using GEO datasets. Results Analysis of scRNA-seq data showed that there is a significant upregulation of LGALS9 expression in DCs isolated from malignant pleural effusion samples. Leveraging the Coxboost and random survival forest combination algorithm, we filtered out six DC-related genes on which a prognostic prediction model, DCRGS, was established. A high predictive capability nomogram was constructed by combining DCRGS with clinical features. We found that patients with a high-DCRGS score had immunosuppression, activated tumor-associated pathways, and elevated somatic mutational load and copy number variant load. In contrast, patients in the low-DCRGS subgroup were resistant to chemotherapy but sensitive to the CTLA-4 immune checkpoint inhibitor and targeted therapy. Conclusion We have innovatively established a deep learning-based prediction model, DCRGS, for the prediction of the prognosis of patients with LUAD. The model possesses a strong prognostic prediction performance with high accuracy and sensitivity and could be clinically useful to guide the management of LUAD. Furthermore, the findings of this study could provide an important reference for individualized clinical treatment and prognostic prediction of patients with LUAD.https://doi.org/10.1186/s12935-025-03642-zMachine learningPrognosis predictionLung adenocarcinomaDendritic cellsImmunotherapy |
| spellingShingle | Zihao Sun Mengfei Hu Xiaoning Huang Minghan Song Xiujing Chen Jiaxin Bei Yiguang Lin Size Chen Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma Cancer Cell International Machine learning Prognosis prediction Lung adenocarcinoma Dendritic cells Immunotherapy |
| title | Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma |
| title_full | Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma |
| title_fullStr | Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma |
| title_full_unstemmed | Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma |
| title_short | Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma |
| title_sort | predictive value of dendritic cell related genes for prognosis and immunotherapy response in lung adenocarcinoma |
| topic | Machine learning Prognosis prediction Lung adenocarcinoma Dendritic cells Immunotherapy |
| url | https://doi.org/10.1186/s12935-025-03642-z |
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