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Primary research
Shan Wang
Chongqing Medical University Affiliated Children's Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7476-1799
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Due to the extremely high mortality rate of children with high-risk Neuroblastoma (NB), there is an urgent need for new indicators to further classify children in the high-risk group for more precise treatment. The purpose of our research is to explore the immune-related genes in NB in the high-risk group, and to further identify and develop a prognostic nomogram based on immune IRG signatures.
Methods
Through bioinformatics analysis to explore the abnormal expression of immune-related genes in the high-risk group. Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis were conducted to identify the immune and overall survival (OS) related mRNA. The accuracy of the risk score is evaluated by Kaplan-Meier method and receiver operating characteristics (ROC) analysis, which is used to build a nomogram in combination with other clinical characteristics.. Quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to detect the accuracy of our results.
Results
A total of 127 common differentially expressed immune genes were found between the high-risk group and the non-high-risk group of the two data sets. Four immune-related genes (IRG) related to prognosis were identified and a risk score was established. Kaplan–Meier survival analysis and time-dependent ROC analysis showed that the 4-IRG risk score has satisfactory predictive potential and achieved consistency in the verification of external data sets. Subsequently, the risk score combined with clinical characteristics draws a nomogram. The reliability of the results was verified on 29 cases of NB tissues by qRT-PCR.
Conclusions
Overall, we have developed a powerful multi-gene classifier that can effectively classify NB patients into low- and high-risk groups with poor prognosis, and draw a nomogram for children in the high-risk group. This feature can help select high-risk patients who need more aggressive adjuvant target therapy or immunotherapy.
Keywords
bioinformatics analysis, prognostic model, risk score formula, immunology, high-risk neuroblastoma, qRT-PCR
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Primary research
Shan Wang
Chongqing Medical University Affiliated Children's Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7476-1799
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 14 Oct, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cancer Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Due to the extremely high mortality rate of children with high-risk Neuroblastoma (NB), there is an urgent need for new indicators to further classify children in the high-risk group for more precise treatment. The purpose of our research is to explore the immune-related genes in NB in the high-risk group, and to further identify and develop a prognostic nomogram based on immune IRG signatures.
Methods
Through bioinformatics analysis to explore the abnormal expression of immune-related genes in the high-risk group. Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis were conducted to identify the immune and overall survival (OS) related mRNA. The accuracy of the risk score is evaluated by Kaplan-Meier method and receiver operating characteristics (ROC) analysis, which is used to build a nomogram in combination with other clinical characteristics.. Quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to detect the accuracy of our results.
Results
A total of 127 common differentially expressed immune genes were found between the high-risk group and the non-high-risk group of the two data sets. Four immune-related genes (IRG) related to prognosis were identified and a risk score was established. Kaplan–Meier survival analysis and time-dependent ROC analysis showed that the 4-IRG risk score has satisfactory predictive potential and achieved consistency in the verification of external data sets. Subsequently, the risk score combined with clinical characteristics draws a nomogram. The reliability of the results was verified on 29 cases of NB tissues by qRT-PCR.
Conclusions
Overall, we have developed a powerful multi-gene classifier that can effectively classify NB patients into low- and high-risk groups with poor prognosis, and draw a nomogram for children in the high-risk group. This feature can help select high-risk patients who need more aggressive adjuvant target therapy or immunotherapy.
Figure 1
Figure 1
Figure 2
Figure 2
Figure 3
Figure 3
Figure 4
Figure 4
Figure 5
Figure 5
Figure 6
Figure 6
Figure 7
Figure 7
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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