Immune-Related LncRNAs as Prognostic Factors for Pediatric Rhabdoid Tumor of the Kidney
Copyright © 2022 Ye Hong et al..
Background: Immune-related long noncoding RNAs (IrlncRNAs) are recognized as important prognostic factors in a variety of cancers, but thus far, their prognostic value in pediatric rhabdoid tumor of the kidney (pRTK) has not been reported. Here, we clarified the associations between IrlncRNAs and overall survival (OS) of pRTK patients and constructed a model to predict their prognosis.
Methods: We accessed RNA sequencing data and corresponding clinical data of pRTK from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. An expression profile of immune-related genes (Irgenes) and lncRNAs of pRTK was extracted from the RNA sequencing data. IrlncRNAs were defined by co-expression analysis of lncRNAs and Irgenes. The limma R package was used to identify differential expression IrlncRNAs. Univariate and multivariate Cox regression analyses were conducted to build a prognostic IrlncRNAs model. The performance of this prognostic model was validated by multimethods, like ROC curve analysis.
Results: A total of 1097 IrlncRNAs were defined. Univariate Cox regression analysis identified 7 IrlncRNAs (AC004791.2, AP003068.23, RP11-54O7.14, RP11-680F8.1, TBC1D3P1-DHX40P1, TUNAR, and XXbac-BPG308K3.5) and were significantly associated with OS. Multivariate regression analysis constructed the best prognostic model based on the expression of AC004791.2, AP003068.23, RP11-54O7.14, TBC1D3P1-DHX40P1, and TUNAR. According to the prognostic model, a risk score of each patient was calculated, and patients were divided into high-risk and low-risk groups accordingly. The survival time of low-risk patients was significantly better than high-risk patients (p < 0.001). Univariate (hazard ratio 1.098, 95% confidence interval 1.048-1.149, p value <0.001) and multivariate (hazard ratio 1.095, 95% confidence interval 1.043-1.150, p value <0.001) analyses confirmed that the prognostic model was reliable and independent in prediction of OS. Time-dependent ROC analysis showed that 1-year survival AUC of prognostic model, stage, age, and sex was 0.824, 0.673, 0.531, and 0.495, respectively, which suggested that the prognostic model was the best predictor of survival in pRTK patients.
Conclusions: The prognostic model based on 5 IrlncRNAs was robust and could better predict the survival of pRTK than other clinical factors. Additionally, the mechanism of regulation and action of prognosis-associated lncRNAs could provide new avenues for basic research to explore the mechanism of tumor initiation and development in order to prevent and treat pRTK.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2022 |
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| Enthalten in: |
Disease markers - 2022(2022) vom: 30., Seite 4752184 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Hong, Ye [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 28.06.2022 Date Revised 16.07.2022 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1155/2022/4752184 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM342774956 |
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| 245 | 1 | 0 | |a Immune-Related LncRNAs as Prognostic Factors for Pediatric Rhabdoid Tumor of the Kidney |
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| 500 | |a Date Revised 16.07.2022 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2022 Ye Hong et al. | ||
| 520 | |a Background: Immune-related long noncoding RNAs (IrlncRNAs) are recognized as important prognostic factors in a variety of cancers, but thus far, their prognostic value in pediatric rhabdoid tumor of the kidney (pRTK) has not been reported. Here, we clarified the associations between IrlncRNAs and overall survival (OS) of pRTK patients and constructed a model to predict their prognosis | ||
| 520 | |a Methods: We accessed RNA sequencing data and corresponding clinical data of pRTK from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. An expression profile of immune-related genes (Irgenes) and lncRNAs of pRTK was extracted from the RNA sequencing data. IrlncRNAs were defined by co-expression analysis of lncRNAs and Irgenes. The limma R package was used to identify differential expression IrlncRNAs. Univariate and multivariate Cox regression analyses were conducted to build a prognostic IrlncRNAs model. The performance of this prognostic model was validated by multimethods, like ROC curve analysis | ||
| 520 | |a Results: A total of 1097 IrlncRNAs were defined. Univariate Cox regression analysis identified 7 IrlncRNAs (AC004791.2, AP003068.23, RP11-54O7.14, RP11-680F8.1, TBC1D3P1-DHX40P1, TUNAR, and XXbac-BPG308K3.5) and were significantly associated with OS. Multivariate regression analysis constructed the best prognostic model based on the expression of AC004791.2, AP003068.23, RP11-54O7.14, TBC1D3P1-DHX40P1, and TUNAR. According to the prognostic model, a risk score of each patient was calculated, and patients were divided into high-risk and low-risk groups accordingly. The survival time of low-risk patients was significantly better than high-risk patients (p < 0.001). Univariate (hazard ratio 1.098, 95% confidence interval 1.048-1.149, p value <0.001) and multivariate (hazard ratio 1.095, 95% confidence interval 1.043-1.150, p value <0.001) analyses confirmed that the prognostic model was reliable and independent in prediction of OS. Time-dependent ROC analysis showed that 1-year survival AUC of prognostic model, stage, age, and sex was 0.824, 0.673, 0.531, and 0.495, respectively, which suggested that the prognostic model was the best predictor of survival in pRTK patients | ||
| 520 | |a Conclusions: The prognostic model based on 5 IrlncRNAs was robust and could better predict the survival of pRTK than other clinical factors. Additionally, the mechanism of regulation and action of prognosis-associated lncRNAs could provide new avenues for basic research to explore the mechanism of tumor initiation and development in order to prevent and treat pRTK | ||
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| 700 | 1 | |a Zhu, Jia |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Juan |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Jun-Ting |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Fei-Fei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Lian |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Xin-Ke |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Su-Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Yi-Zhuo |e verfasserin |4 aut | |
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