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Research
Identification of a Cell Cycle Gene Signature Predicting Survival in Patients with Lung Squamous Cell Carcinoma
Zhenglun Yu
China Medical University Hospital
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Purpose: Lung cancer (LC) is one of the most important and common malignant tumours, and its incidence and mortality are increasing annually. Lung squamous cell carcinoma (LUSC) is the most common pathological type of LC. A small number of biomarkers have been certified to be consistent with its survival and prognosis by data excavation. However, the moderate forecast effect of a single gene biomarker is not accurate. Thus, we planned to find new gene signatures to preferably predict LUSC.
Methods: Using the mRNA mining method, we enforced mRNA expression analyzing in big LUSC cohorts (n=504) from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were enforced, and relations between genes and the cell cycle were got with the Cox proportional hazards regression model.
Results: We confirmed a set of four genes (CDKN1A, CHEK2, E2F4 and RAD21) that was importantly related to overall survival (OS) in the test succession. Based on the four-gene signature, the patients were separated into high-risk and low-risk teams. Moreover ,multivariate Cox regression analysis showed that the prognostic value of the four-gene signature and clinical factors were mutual independent.
Conclusion: Our research demonstrated connections between the four-gene signature and LUSC. Novel insights into mechanisms of the cell cycle were also revealed after determining that the biomarkers were related to a poor prognosis in LUSC patients.
Keywords
lung squamous cell carcinoma, cell cycle, prognosis, mRNAs, meta-analysis survival
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This is a preprint. It has not completed peer review.
Research
Identification of a Cell Cycle Gene Signature Predicting Survival in Patients with Lung Squamous Cell Carcinoma
Zhenglun Yu
China Medical University Hospital
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, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
General Cell Biology & Physiology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Purpose: Lung cancer (LC) is one of the most important and common malignant tumours, and its incidence and mortality are increasing annually. Lung squamous cell carcinoma (LUSC) is the most common pathological type of LC. A small number of biomarkers have been certified to be consistent with its survival and prognosis by data excavation. However, the moderate forecast effect of a single gene biomarker is not accurate. Thus, we planned to find new gene signatures to preferably predict LUSC.
Methods: Using the mRNA mining method, we enforced mRNA expression analyzing in big LUSC cohorts (n=504) from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were enforced, and relations between genes and the cell cycle were got with the Cox proportional hazards regression model.
Results: We confirmed a set of four genes (CDKN1A, CHEK2, E2F4 and RAD21) that was importantly related to overall survival (OS) in the test succession. Based on the four-gene signature, the patients were separated into high-risk and low-risk teams. Moreover ,multivariate Cox regression analysis showed that the prognostic value of the four-gene signature and clinical factors were mutual independent.
Conclusion: Our research demonstrated connections between the four-gene signature and LUSC. Novel insights into mechanisms of the cell cycle were also revealed after determining that the biomarkers were related to a poor prognosis in LUSC patients.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8