Objective: To establish the protein prognostic model for lung adenocarcinoma based on bioinformatics. Methods: The protein data and related clinical data of lung adenocarcinoma were obtained from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA) database respectively, and the proteins associated with the prognosis of lung adenocarcinoma were screened using univariate Cox regression analysis and stepwise regression to establish the prognostic risk model. The model was analyzed by prognostic risk score using multivariate Cox regression, and the area under the curve (AUC) was calculated to evaluate the robustness and accuracy of the model. Results: Three proteins significantly correlated with survival were screened for the construction of the prognostic model. The risk score of the prognostic model was significantly correlated with the prognosis (P＜0.001), which could be used as an independent risk factor to evaluate the prognosis of patients. The AUC of the prognostic model was 0.710, indicating that the model had stable specificity and sensitivity. Conclusion: The prognosis model could accurately predict the overall survival rate of patients with lung adenocarcinoma, which is helpful for early identification of patients with poor prognosis of lung adenocarcinoma and early intervention treatment, and is of great significance to improve the survival rate of patients with lung adenocarcinoma. In addition, three risk proteins screened in this study that promote the progression of lung adenocarcinoma are expected to become new targets for the treatment of lung adenocarcinoma.
ZHONG Wenjie
,
CHEN Changnan
,
LU Honghong
. Protein prognostic model for lung adenocarcinoma based on bioinformatics[J]. Journal of Baotou Medical College, 2022
, 38(11)
: 68
-73
.
DOI: 10.16833/j.cnki.jbmc.2022.11.014
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