國家衛生研究院 NHRI:Item 3990099045/15581
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    题名: Identification of tumor microenvironment and prognosis risk prediction through proteomics analysis in stage Ilung adenocarcinoma
    作者: Chen, HY;Lu, YF;Chang, YH;Chen, YJ;Chen, YJ;Yu, SL;Chen, JS
    贡献者: Institute of Population Health Sciences
    摘要: Introduction: In the Taiwan Lung Cancer Screening for Never-Smoker Trial (TALENT), 95.7% of detected invasive lung cancers were stage I. CT screening increases early-stage lung cancer detection and reduces mortality from lung cancer. However, still about 24.5% of stage I nonsmall-cell lung cancer develops recurrence or a second primary cancer after surgery. Methods: The protein mass spectrometry data from Taiwan Lung Cancer Moonshot project was collected to explore the tumor microenvironment. To identify and verify biomarkers that were associated with recurrence-free survival and overall survival of postoperative LUAD, we used the GSE31210 cohort as the training set; lung adenocarcinoma in GSE30219 cohort, MSKCC cohort, GSE8894 cohort, OncoSG cohort, respectively. MaxQuant employs the Andromeda search engine (v.1.6.7.0) was used to analyze the raw data of MS. To identify the differential abundance of proteins between tumor and NAT, a Wilcoxon signed-rank test was applied to test the difference in the protein abundance between tumor and NAT. Protein expression data of participants’ tumors and NAT were imported into Qiagen’s Ingenuity Pathway Analysis system. Gene expression levels of patients with LUAD were quantile normalized and log2 transformed. Cox model with univariate and stepwise selection was conducted to discover final prognostic biomarkers. To define the high risk group and low risk group by the prognostic biomarkers, risk score (RS) was calculated in each patient of the same cohort. RS ¼b1c1+b2c2+...+bncn, where bi is the b coefficients from the Cox model and ci is the expression value of each prognostic biomarker. Patient with risk score higher than the median was defined as a high risk group and a risk score lower than the median was categorized as a low risk group. The Kaplan-Meier method was used to estimate the recurrence-free survival and overall survival and the log-rank test was applied to evaluate the survival difference between two risk groups. Univariate and multivariate Cox proportional hazards regression analysis were used to estimate hazard ratio (HR) and 95% confidence interval (CI). All tests were two-sided and p-values < 0.05 were considered significant. Results: After performing bioinformatics analysis of differentially expressed proteins between tumor and normal adjacent tissues in 74 stage I lung adenocarcinoma (LUAD) (85% non-smokers), results showed that cellular movement, immune cell trafficking and cancer were the top 3 enriched pathways of diseases and biological functions. The increased downstream biological activities were related to cancer, and the most decreased biological functions are cellular movement, immune cell trafficking, inflammatory response, and cell-to-cell signaling and interaction, respectively. Through the proteomic profiling of the tumor microenvironment in LUAD, five genes were identified to be of prognostic significance. The prognostic genes were used to construct risk score model and validated in 7 independent LUAD cohorts. The risk score model was an independent predictor of recurrence-free survival and overall survival in both early and advanced-stage LUAD. Conclusions: This model might help clinicians to identify patients who are at risk of recurrence and guide the development of new treatment strategies. Keywords: Lung adenocarcinoma, proteomics, tumor microenvironment.
    日期: 2023-11
    關聯: Journal of Thoracic Oncology. 2023 Nov;18(11, Suppl.):S553.
    Link to: http://dx.doi.org/10.1016/j.jtho.2023.09.1033
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1556-0864&DestApp=IC2JCR
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