An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence
Copyright © 2024 Shu, Liu, Luo, Tang, Chen, Li, Miao, Duan, Yan, Sheng, Ouyang, Wang, Jiang, Deng, Wang, Li and Wang..
Background: This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes.
Methods: Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel. The elastic net approach was employed to pinpoint immune-related genes significantly impacting DFS. We developed the IRGPM for rectal cancer using the random forest technique. Based on the IRGPM, we calculated prognostic risk scores to categorize patients into high-risk and low-risk groups. Comparative analysis of immune characteristics between these groups was conducted.
Results: In this study, 407 LARC samples were analyzed. The elastic net identified a signature of 20 immune-related genes, forming the basis of the IRGPM. Kaplan-Meier survival analysis revealed a lower 5-year DFS in the high-risk group compared to the low-risk group. The receiver operating characteristic (ROC) curve affirmed the model's robust predictive capability. Validation of the model was performed in the GSE190826 cohort and our institution's cohort. Gene expression differences between high-risk and low-risk groups predominantly related to cytokine-cytokine receptor interactions. Notably, the low-risk group exhibited higher immune scores. Further analysis indicated a greater presence of activated B cells, activated CD8 T cells, central memory CD8 T cells, macrophages, T follicular helper cells, and type 2 helper cells in the low-risk group. Additionally, immune checkpoint analysis revealed elevated PDCD1 expression in the low-risk group.
Conclusions: The IRGPM, developed through random forest and elastic net methodologies, demonstrates potential in distinguishing DFS among LARC patients receiving standard treatment. Notably, the low-risk group, as defined by the IRGPM, showed enhanced activation of adaptive immune responses within the tumor microenvironment.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
Frontiers in oncology - 14(2024) vom: 30., Seite 1294440 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Shu, Pei [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Revised 27.02.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fonc.2024.1294440 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368967743 |
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| 245 | 1 | 3 | |a An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence |
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| 520 | |a Copyright © 2024 Shu, Liu, Luo, Tang, Chen, Li, Miao, Duan, Yan, Sheng, Ouyang, Wang, Jiang, Deng, Wang, Li and Wang. | ||
| 520 | |a Background: This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes | ||
| 520 | |a Methods: Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel. The elastic net approach was employed to pinpoint immune-related genes significantly impacting DFS. We developed the IRGPM for rectal cancer using the random forest technique. Based on the IRGPM, we calculated prognostic risk scores to categorize patients into high-risk and low-risk groups. Comparative analysis of immune characteristics between these groups was conducted | ||
| 520 | |a Results: In this study, 407 LARC samples were analyzed. The elastic net identified a signature of 20 immune-related genes, forming the basis of the IRGPM. Kaplan-Meier survival analysis revealed a lower 5-year DFS in the high-risk group compared to the low-risk group. The receiver operating characteristic (ROC) curve affirmed the model's robust predictive capability. Validation of the model was performed in the GSE190826 cohort and our institution's cohort. Gene expression differences between high-risk and low-risk groups predominantly related to cytokine-cytokine receptor interactions. Notably, the low-risk group exhibited higher immune scores. Further analysis indicated a greater presence of activated B cells, activated CD8 T cells, central memory CD8 T cells, macrophages, T follicular helper cells, and type 2 helper cells in the low-risk group. Additionally, immune checkpoint analysis revealed elevated PDCD1 expression in the low-risk group | ||
| 520 | |a Conclusions: The IRGPM, developed through random forest and elastic net methodologies, demonstrates potential in distinguishing DFS among LARC patients receiving standard treatment. Notably, the low-risk group, as defined by the IRGPM, showed enhanced activation of adaptive immune responses within the tumor microenvironment | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a immune related gene | |
| 650 | 4 | |a neoadjuvant chemoradiotherapy | |
| 650 | 4 | |a prognostic model | |
| 650 | 4 | |a rectal carcinoma | |
| 700 | 1 | |a Liu, Ning |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Xu |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Yuanling |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Zhebin |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Dandan |e verfasserin |4 aut | |
| 700 | 1 | |a Miao, Dong |e verfasserin |4 aut | |
| 700 | 1 | |a Duan, Jiayu |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Ouying |e verfasserin |4 aut | |
| 700 | 1 | |a Sheng, Leiming |e verfasserin |4 aut | |
| 700 | 1 | |a Ouyang, Ganlu |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Sen |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Dan |e verfasserin |4 aut | |
| 700 | 1 | |a Deng, Xiangbing |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Ziqiang |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Qingyun |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xin |e verfasserin |4 aut | |
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