1. Signaling protein signature predicts clinical outcome of non-small-cell lung cancer
- Author
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Sai-Sai Guo, Jun Wang, Shuofeng Hu, Xuan-He Qin, Yida Liao, Tao Zhou, Kunkun Sun, Ailing Li, Yu-Cheng Zhang, Na Wang, Danhua Shen, Fan Yang, Shuai Chen, Yan Huang, Qing Xia, Xiaomin Ying, Yan-Hong Tai, Xiao Li, Wei-Hua Li, Teng Li, Lin Gong, Yuan Cao, Jin Wu, Lu Chen, Qing Zhao, Jing Chen, Kezhong Chen, Bao-Feng Jin, and Xiao-Yan Zhan
- Subjects
0301 basic medicine ,Oncology ,Male ,Cancer Research ,medicine.medical_specialty ,Lung Neoplasms ,Protein signature ,Adenocarcinoma ,lcsh:RC254-282 ,03 medical and health sciences ,0302 clinical medicine ,Surgical oncology ,Internal medicine ,Carcinoma, Non-Small-Cell Lung ,Squamous cell carcinoma ,Antineoplastic Combined Chemotherapy Protocols ,Genetics ,medicine ,Biomarkers, Tumor ,Humans ,Prospective Studies ,Prospective cohort study ,Lung cancer ,Survival rate ,business.industry ,Proportional hazards model ,Gene Expression Profiling ,Middle Aged ,medicine.disease ,lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,Prognosis ,Gene Expression Regulation, Neoplastic ,Survival Rate ,030104 developmental biology ,Tissue Array Analysis ,030220 oncology & carcinogenesis ,Cohort ,Carcinoma, Squamous Cell ,Immunohistochemistry ,Female ,business ,Non-small-cell lung cancer ,Follow-Up Studies ,Signal Transduction ,Research Article - Abstract
Background Non-small-cell lung cancer (NSCLC) is characterized by abnormalities of numerous signaling proteins that play pivotal roles in cancer development and progression. Many of these proteins have been reported to be correlated with clinical outcomes of NSCLC. However, none of them could provide adequate accuracy of prognosis prediction in clinical application. Methods A total of 384 resected NSCLC specimens from two hospitals in Beijing (BJ) and Chongqing (CQ) were collected. Using immunohistochemistry (IHC) staining on stored formalin-fixed paraffin-embedded (FFPE) surgical samples, we examined the expression levels of 75 critical proteins on BJ samples. Random forest algorithm (RFA) and support vector machines (SVM) computation were applied to identify protein signatures on 2/3 randomly assigned BJ samples. The identified signatures were tested on the remaining BJ samples, and were further validated with CQ independent cohort. Results A 6-protein signature for adenocarcinoma (ADC) and a 5-protein signature for squamous cell carcinoma (SCC) were identified from training sets and tested in testing sets. In independent validation with CQ cohort, patients can also be divided into high- and low-risk groups with significantly different median overall survivals by Kaplan-Meier analysis, both in ADC (31 months vs. 87 months, HR 2.81; P
- Published
- 2017