Personalized prognostic signature for lung cancer based on 15 transcription-related gene pairs

Liao Zili; Ren Zhihao; Zhang Boxiang; Zhu Ruiyu

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Personalized prognostic signature for lung cancer based on 15 transcription-related gene pairs

Liao Zili,Ren Zhihao,Zhang Boxiang,Zhu Ruiyu *

School of Pharmaceutical Sciences, Jiangnan University, Wuxi 214122, People's Republic of China;School of Pharmaceutical Sciences, Jiangnan University, Wuxi 214122, People's Republic of China;School of Pharmaceutical Sciences, Jiangnan University, Wuxi 214122, People's Republic of China;School of Pharmaceutical Sciences, Jiangnan University, Wuxi 214122, People's Republic of China

*Correspondence author

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Funding: none

Opened online:21 February 2023

Accepted by: none

Citation: Liao Zili,Ren Zhihao,Zhang Boxiang.Personalized prognostic signature for lung cancer based on 15 transcription-related gene pairs[OL]. [21 February 2023] http://en.paper.edu.cn/en_releasepaper/content/4759077

Lung cancer is the most aggressive malignancy and the leading cause of cancer deaths worldwide. Currently, reliable biomarkers are lacking in the diagnosis, prognosis and treatment of lung cancer. Given the significant role of transcription factors in tumorigenesis and progression, we aimed to establish a signature based on transcription-related gene pairs for the first time to predict the prognosis of lung cancer patients. The gene expression data and clinical information of 1568 lung cancer patients were obtained from The Cancer Genome Atlas data portal (TCGA) and Gene Expression Omnibus (GEO) as a training cohort and validation cohort, respectively. Through univariate Cox analysis and Least Absolute Shrinkage and Selection Operator (LASSO) analysis, we screened 15 transcription-related gene pairs to construct the transcription-related prognostic signature. Based on this signature, the samples were classified into high-risk group and low-risk group. Kaplan-Meier analysis and independent prognostic analysis showed that transcription-related prognostic signature predicted overall survival in lung cancer patients (p < 0.001). Compared with multiple clinical and pathological factors, the results of multivariate Cox regression analysis indicated that the signature was an independent prognostic factor in patients with lung cancer. Further analysis revealed the cellular pathways associated with this signature and the relationship between this signature and immune cell content. In conclusion, we established for the first time the signature of transcription-related genes on prognosis as an indicator to assess the overall survival in lung cancer patients. Our study provides new ideas for developing cancer prognostic signature and discovering new drug targets.

Keywords:Bioinformatics; Lung cancer; transcription-related gene pairs; prognostic signature

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