Construction of Lipid Metabolism-Related Gene Prognostic Model and Immunoinfiltration Analysis of Ovarian Cancer

Xingfen WANG; Yue DENG; Lihua YANG

doi:10.12259/j.issn.2095-610X.S20240403

Volume 45 Issue 4

Apr. 2024

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Xingfen WANG, Yue DENG, Lihua YANG. Construction of Lipid Metabolism-Related Gene Prognostic Model and Immunoinfiltration Analysis of Ovarian Cancer[J]. Journal of Kunming Medical University, 2024, 45(4): 17-25. doi: 10.12259/j.issn.2095-610X.S20240403

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Xingfen WANG, Yue DENG, Lihua YANG. Construction of Lipid Metabolism-Related Gene Prognostic Model and Immunoinfiltration Analysis of Ovarian Cancer[J]. Journal of Kunming Medical University, 2024, 45(4): 17-25. doi: 10.12259/j.issn.2095-610X.S20240403

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Construction of Lipid Metabolism-Related Gene Prognostic Model and Immunoinfiltration Analysis of Ovarian Cancer

doi: 10.12259/j.issn.2095-610X.S20240403

Dept. of Gynecology，The 2nd Affiliated Hospital of Kunming Medical University，Kunming Yunnan 650101，China

Received Date: 2024-01-03
Available Online: 2024-04-07
Publish Date: 2024-04-29

Abstract

Abstract

Objective To construct a lipid metabolism-related prognostic model for ovarian cancer （OC）, and to investigate the role of lipid metabolism-related biomarkers and the degree of immune cell infiltration in prognosis prediction of OC. Methods Transcriptional data and clinical data of OC samples were downloaded from the TCGA database, and lipid metabolism-related genes （LMRGs） were obtained from the MSigDB database. The samples were randomly divided into training and validation sets at a 1∶1 ratio using the caret package. Univariate Cox analysis was used to identify LMRGs significantly associated with OC prognosis. LASSO-Cox analysis was performed to select model genes for building a prognostic model. The prognostic model was evaluated using Kaplan-Meier curves and receiver operating characteristic （ROC） curves, followed by internal validation using TCGA data. Finally, a column chart was constructed, and immune infiltration analysis was conducted using the CIBERSORT algorithm. Results An 8-gene prognostic model for ovarian cancer was established. Survival analysis showed significant differences in prognosis between the high-risk and low-risk groups （P < 0.05）. The AUC indicated that the model had moderate predictive efficacy. Multivariate Cox analysis demonstrated that LMrisk was an independent prognostic factor for ovarian cancer patients （P < 0.001）. Immune infiltration analysis revealed the association between LMrisk and immune response in ovarian cancer. Conclusion The prognostic model developed in this study can serve as a new tool for evaluating prognosis in ovarian cancer patients. LMrisk may be a robust prognostic biomarker with potential clinical application.
- Ovarian cancer,
- Lipid metabolism,
- Prognostic model,
- Immune cell infiltration

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References(28)

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