ArticleInternational journal of medical sciences2020
Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma.
Article in International journal of medical sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Optimizing Surgery Strategies in Stage IB Lung Squamous Cell Carcinoma: Insights from Interpretable Machine Learning.Thoracic cancer · 2026Article
- Advancing prognostic and therapeutic prediction in lung squamous cell carcinoma through integrated multi-omics analysis and 117 machine learning combinations.Journal of thoracic disease · 2025Article
- Prognostic models for large cell neuroendocrine lung carcinoma: a machine learning and regression approach.Translational lung cancer research · 2025Article
- Protein expression profiling identifies a prognostic model for ovarian cancer.BMC women's health · 2022Article
- Identification of a Novel Protein-Based Signature to Improve Prognosis Prediction in Renal Clear Cell Carcinoma.Frontiers in molecular biosciences · 2021Article
- Identification and Validation of a Prognostic 5-Protein Signature for Biochemical Recurrence Following Radical Prostatectomy for Prostate Cancer.Frontiers in surgery · 2021Article
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Authors and funding
9 authors.
Funding
Abstract
Lung squamous cell carcinoma (LUSCC), as the major type of lung cancer, has high morbidity and mortality rates. The prognostic markers for LUSCC are much fewer than lung adenocarcinoma. Besides, protein biomarkers have advantages of economy, accuracy and stability. The aim of this study was to construct a protein prognostic model for LUSCC. The protein expression data of LUSCC were downloaded from The Cancer Protein Atlas (TCPA) database. Clinical data of LUSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 237 proteins were identified from 325 cases of LUSCC patients based on the TCPA and TCGA database. According to Kaplan-Meier survival analysis, univariate and multivariate Cox analysis, a prognostic prediction model was established which was consisted of 6 proteins (CHK1_pS345, CHK2, IRS1, PAXILLIN, BRCA2 and BRAF_pS445). After calculating the risk values of each patient according to the coefficient of each protein in the risk model, the LUSCC patients were divided into high risk group and low risk group. The survival analysis demonstrated that there was significant difference between these two groups (p= 4.877e-05). The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.699, which suggesting that the prognostic risk model could effectively predict the survival of LUSCC patients. Univariate and multivariate analysis indicated that this prognostic model could be used as independent prognosis factors for LUSCC patients. Proteins co-expression analysis showed that there were 21 proteins co-expressed with the proteins in the risk model. In conclusion, our study constructed a protein prognostic model, which could effectively predict the prognosis of LUSCC patients.
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