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Research
Development and Verification of an Immune-Related Gene Pairs Prognostic Signature in Ovarian Cancer
Shengke Wang
Shengjing Hospital of China Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5863-5012
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Ovarian cancer (OV) is the most common gynecological cancer and is a major cause of cancer-related death among women worldwide. Immunotherapy has recently been of great interest, and it has been proven to be an effective treatment strategy. For this reason, the work here attempts to produce a prognostic immune-related gene pair (IRGP) signature to estimate OV patient survival.
Methods: The Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients, whose samples were divided into training, validation, and testing sets. The InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, were used to build an IRGP signature. The functional enrichment analysis was performed to investigate the biological activities of IRGPs. Then, the correlation between risk scores and clinicopathological parameters was further analyzed. Finally, we compared our signature and four existing signatures for OV.
Results: We first identified a 17-IRGP signature significantly associated with survival based on LASSO regression model. The average area under the curve (AUC) values of the training, validation, and all TCGA sets were 0.869, 0.712, and 0.778, respectively. In addition, the average AUC of the independent testing set was 0.73. The 17-IRGP signature noticeably split patients into high- and low-risk groups with different prognostic outcomes. As suggested by a functional study, some biological pathways, including the Toll-like receptor and chemokine signaling pathways, were significantly negatively correlated with risk scores; however, pathways such as the p53 and apoptosis signaling pathways had a positive correlation. Moreover, tumor stage III, IV, grade G1/G2, and G3/G4 samples had significant differences in risk scores. High- and low-risk groups of the four TCGA OV subtypes also had significant prognostic differences. When compared with four other signatures for OV, our 17-IRGP signature had better prognostic prediction performance.
Conclusions: An effective 17-IRGP signature was produced to predict prognostic outcomes in OV, providing new insights into immunological biomarkers.
Keywords
Ovarian cancer, Immune-related gene pairs, TCGA, Prognostic signature, Bioinformatics
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Research
Development and Verification of an Immune-Related Gene Pairs Prognostic Signature in Ovarian Cancer
Shengke Wang
Shengjing Hospital of China Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5863-5012
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 15 Apr, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Translational Medicine
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Ovarian cancer (OV) is the most common gynecological cancer and is a major cause of cancer-related death among women worldwide. Immunotherapy has recently been of great interest, and it has been proven to be an effective treatment strategy. For this reason, the work here attempts to produce a prognostic immune-related gene pair (IRGP) signature to estimate OV patient survival.
Methods: The Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients, whose samples were divided into training, validation, and testing sets. The InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, were used to build an IRGP signature. The functional enrichment analysis was performed to investigate the biological activities of IRGPs. Then, the correlation between risk scores and clinicopathological parameters was further analyzed. Finally, we compared our signature and four existing signatures for OV.
Results: We first identified a 17-IRGP signature significantly associated with survival based on LASSO regression model. The average area under the curve (AUC) values of the training, validation, and all TCGA sets were 0.869, 0.712, and 0.778, respectively. In addition, the average AUC of the independent testing set was 0.73. The 17-IRGP signature noticeably split patients into high- and low-risk groups with different prognostic outcomes. As suggested by a functional study, some biological pathways, including the Toll-like receptor and chemokine signaling pathways, were significantly negatively correlated with risk scores; however, pathways such as the p53 and apoptosis signaling pathways had a positive correlation. Moreover, tumor stage III, IV, grade G1/G2, and G3/G4 samples had significant differences in risk scores. High- and low-risk groups of the four TCGA OV subtypes also had significant prognostic differences. When compared with four other signatures for OV, our 17-IRGP signature had better prognostic prediction performance.
Conclusions: An effective 17-IRGP signature was produced to predict prognostic outcomes in OV, providing new insights into immunological biomarkers.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8