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Shengchi Chen
Department of Oncology, Nanping First Hospital Affiliated to Fujian Medical University
Corresponding Author
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Background: Current international prognostic index is widely questioned on the risk stratification of peripheral T-cell lymphoma and do not accurately predict the outcome for patients. We postulated that multiple mRNAs could combined into a single model to improve risk stratification and to guide Clinicians implementing personalized therapeutic regimen for these patients. Methods: The gene expression profiles with clinical characteristics were selected and downloaded from the Gene Expression Omnibus (GEO) database. weighted gene co-expression network analysis (WGCNA) was used to screening genes in selected module which most closely related to PTCLs. Then build a gene classifier using a Lasso Cox Regression model and validated the prognostic accuracy of this mRNA signature in an internal validation cohort. Finally, a prognostic nomogram was constructed and performance was assessed by calibration plot and the concordance index (C-index). Results: 799 WGCNA-selected mRNAs in black module were identified and a mRNA signature which based on DOCK2, GSTM1, H2AFY, KCNAB2, LAPTM5 and SYK for PTCLs was developed. Significantly statistical difference can be seen in overall survival of PTCLs between low risk group and high risk group(training set :hazard ratio [HR] 4.3, 95% CI 2.4–7.4, p<0·0001; internal testing set :hazard ratio [HR] 2.4, 95% CI 1.2–4.8, p<0·01).Multivariate regression demonstrated that the signature was an independently prognostic factor contrast to age and gender. Furthermore, receiver operating characteristic analysis indicated that this signature exhibited excellent diagnostic efficiency for overall survival. Moreover, the nomogram which combined the six-genes risk signature and multiple clinical factors suggesting that predicted survival probability agreed well with the actual survival probability. Conclusions: The signature is a reliable prognostic tool for patients with PTCLs and it has the potential for clinicians to implement personalized therapeutic regimen for patients with stage PTCLs.
Keywords
peripheral T-cell lymphomas, Prognostic, predictive, signature
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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.
Research
Shengchi Chen
Department of Oncology, Nanping First Hospital Affiliated to Fujian Medical University
Corresponding Author
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 05 Mar, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Current international prognostic index is widely questioned on the risk stratification of peripheral T-cell lymphoma and do not accurately predict the outcome for patients. We postulated that multiple mRNAs could combined into a single model to improve risk stratification and to guide Clinicians implementing personalized therapeutic regimen for these patients. Methods: The gene expression profiles with clinical characteristics were selected and downloaded from the Gene Expression Omnibus (GEO) database. weighted gene co-expression network analysis (WGCNA) was used to screening genes in selected module which most closely related to PTCLs. Then build a gene classifier using a Lasso Cox Regression model and validated the prognostic accuracy of this mRNA signature in an internal validation cohort. Finally, a prognostic nomogram was constructed and performance was assessed by calibration plot and the concordance index (C-index). Results: 799 WGCNA-selected mRNAs in black module were identified and a mRNA signature which based on DOCK2, GSTM1, H2AFY, KCNAB2, LAPTM5 and SYK for PTCLs was developed. Significantly statistical difference can be seen in overall survival of PTCLs between low risk group and high risk group(training set :hazard ratio [HR] 4.3, 95% CI 2.4–7.4, p<0·0001; internal testing set :hazard ratio [HR] 2.4, 95% CI 1.2–4.8, p<0·01).Multivariate regression demonstrated that the signature was an independently prognostic factor contrast to age and gender. Furthermore, receiver operating characteristic analysis indicated that this signature exhibited excellent diagnostic efficiency for overall survival. Moreover, the nomogram which combined the six-genes risk signature and multiple clinical factors suggesting that predicted survival probability agreed well with the actual survival probability. Conclusions: The signature is a reliable prognostic tool for patients with PTCLs and it has the potential for clinicians to implement personalized therapeutic regimen for patients with stage PTCLs.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
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