See the published version of this article at BMC Cancer.
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Research article
Xiaohu Wang
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0701-7353
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Cancer.
Background: Osteosarcoma is a rare malignant bone tumor in adolescents and children. Poor prognosis has always been a difficult problem for patients with osteosarcoma. Recent studies have shown that Tumor infiltrating immune cells (TIICs) are associated with the clinical outcome of osteosarcoma patients. The aim of our research was to construct a risk score model based on TIICs to predict the prognosis of patients with osteosarcoma.
Methods: CIBERSORT algorithm was used to calculate the proportion of 22 TIICs in osteosarcoma samples. Kaplan-Meier curves were drawn to investigate the prognostic value of 22 TIICs. Forward stepwise approach was used to screen a minimal set of immune cells. Multivariate Cox PHR analysis was performed to construct an immune risk score model.
Results: Osteosarcoma samples with CIBERSORT output p value less than 0.05 were selected for research. Kaplan-Meier curves indicated that naive B cells (p=0.047) and Monocytes (p=0.03) in osteosarcoma are associated with poor prognosis. An immune risk score model was constructed base on eight immune cells, and the ROC curve showed that the immune risk score model is reliable in predicting the prognosis of patients with osteosarcoma (AUC=0.724). Besides, a nomogram model base on eight immune cells was constructed to predict the survival rate of patients with osteosarcoma.
Conclusions: TIICs are closely related to the prognosis of osteosarcoma. The immune risk score model based on TIICs is reliable in predicting the prognosis of osteosarcoma.
Keywords
Osteosarcoma, TIICs, Prognosis, Immune risk score model, Nomogram
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CURRENT STATUS: Published
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Posted 10 Jun, 2020
Published
On 21 Oct, 2020
No community comments so far
Editorial decision: Minor revision
On 10 Sep, 2020
Reviewer #4 agreed
On 19 Aug, 2020
Reviewer #3 agreed
On 16 Aug, 2020
Review #2 received
Received 27 Jul, 2020
Review #1 received
Received 27 Jul, 2020
Reviewer #2 agreed
On 06 Jul, 2020
Reviewer #1 agreed
On 28 Jun, 2020
Reviewers invited
Invitations sent on 26 Jun, 2020
Editor assigned
On 08 Jun, 2020
Submission checks complete
On 07 Jun, 2020
Editor invited
On 07 Jun, 2020
First submitted
On 04 Jun, 2020
Version (no preprint)
Posted 10 Jun, 2020
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On 23 Sep, 2020
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This preprint is under consideration at BMC Cancer. A preprint is 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.
Learn more about In Review
Research article
Xiaohu Wang
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0701-7353
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 1
Posted 10 Jun, 2020
Published
On 21 Oct, 2020
No community comments so far
Editorial decision: Minor revision
On 10 Sep, 2020
Reviewer #4 agreed
On 19 Aug, 2020
Reviewer #3 agreed
On 16 Aug, 2020
Review #2 received
Received 27 Jul, 2020
Review #1 received
Received 27 Jul, 2020
Reviewer #2 agreed
On 06 Jul, 2020
Reviewer #1 agreed
On 28 Jun, 2020
Reviewers invited
Invitations sent on 26 Jun, 2020
Editor assigned
On 08 Jun, 2020
Submission checks complete
On 07 Jun, 2020
Editor invited
On 07 Jun, 2020
First submitted
On 04 Jun, 2020
Version (no preprint)
Posted 10 Jun, 2020
Editor assigned
On 24 Sep, 2020
Submission checks complete
On 23 Sep, 2020
Editor invited
On 23 Sep, 2020
This version was not preprinted
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Cancer.
Background: Osteosarcoma is a rare malignant bone tumor in adolescents and children. Poor prognosis has always been a difficult problem for patients with osteosarcoma. Recent studies have shown that Tumor infiltrating immune cells (TIICs) are associated with the clinical outcome of osteosarcoma patients. The aim of our research was to construct a risk score model based on TIICs to predict the prognosis of patients with osteosarcoma.
Methods: CIBERSORT algorithm was used to calculate the proportion of 22 TIICs in osteosarcoma samples. Kaplan-Meier curves were drawn to investigate the prognostic value of 22 TIICs. Forward stepwise approach was used to screen a minimal set of immune cells. Multivariate Cox PHR analysis was performed to construct an immune risk score model.
Results: Osteosarcoma samples with CIBERSORT output p value less than 0.05 were selected for research. Kaplan-Meier curves indicated that naive B cells (p=0.047) and Monocytes (p=0.03) in osteosarcoma are associated with poor prognosis. An immune risk score model was constructed base on eight immune cells, and the ROC curve showed that the immune risk score model is reliable in predicting the prognosis of patients with osteosarcoma (AUC=0.724). Besides, a nomogram model base on eight immune cells was constructed to predict the survival rate of patients with osteosarcoma.
Conclusions: TIICs are closely related to the prognosis of osteosarcoma. The immune risk score model based on TIICs is reliable in predicting the prognosis of osteosarcoma.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
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