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
Haibo Wang
The Affiliated Hospital of Qingdao University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: Cancer metabolism and specifically lipid metabolism play an important role in breast cancer (BC) progression and metastasis. However, the role of lipid metabolism-associated genes (LMGs) in the diagnosis of breast cancer remains unknown.
Methods: The expression profiles and clinical follow-up information of BC were downloaded from The Cancer Genome Atlas (TCGA), and metabolic genes were downloaded from the Gene Set Enrichment Analysis (GSEA) dataset. Univariate cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed on the differentially expressed metabolism-related genes. Then, the formula of the metabolism-related risk model was composed, and the risk score of each patient was calculated. The breast cancer patients were divided into high-risk and low-risk groups with a cutoff of the median expression value of the risk score, and the prognostic analysis was also used to analyze the survival time between these two groups. Finally, we analyzed the expression, interaction and correlation among the lipid metabolism-associated genes risk model.
Results: The results from the prognostic analysis indicated that the survival was significantly poorer in the high-risk group than in the low-risk group in TCGA, single-sample gene set enrichment analysis (ssGSEA) shows it is plausible that lipid metabolism is highly correlated with tumor immunity.
Conclusion: Lipid metabolism-associated genes may become a new prognostic indicator predicting the survival of BC patients. The prognostic genes (n=16) may help provide new strategies for tumor therapy.
Keywords
Bioinformatics, lipid metabolism associated genes, breast cancer, Lipid metabolism, TCGA
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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
Haibo Wang
The Affiliated Hospital of Qingdao 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 28 Sep, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biomedical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Cancer metabolism and specifically lipid metabolism play an important role in breast cancer (BC) progression and metastasis. However, the role of lipid metabolism-associated genes (LMGs) in the diagnosis of breast cancer remains unknown.
Methods: The expression profiles and clinical follow-up information of BC were downloaded from The Cancer Genome Atlas (TCGA), and metabolic genes were downloaded from the Gene Set Enrichment Analysis (GSEA) dataset. Univariate cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed on the differentially expressed metabolism-related genes. Then, the formula of the metabolism-related risk model was composed, and the risk score of each patient was calculated. The breast cancer patients were divided into high-risk and low-risk groups with a cutoff of the median expression value of the risk score, and the prognostic analysis was also used to analyze the survival time between these two groups. Finally, we analyzed the expression, interaction and correlation among the lipid metabolism-associated genes risk model.
Results: The results from the prognostic analysis indicated that the survival was significantly poorer in the high-risk group than in the low-risk group in TCGA, single-sample gene set enrichment analysis (ssGSEA) shows it is plausible that lipid metabolism is highly correlated with tumor immunity.
Conclusion: Lipid metabolism-associated genes may become a new prognostic indicator predicting the survival of BC patients. The prognostic genes (n=16) may help provide new strategies for tumor therapy.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
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