Due to high energy and material metabolism requirements, mitochondria are frequently active in tumor cells. Our study found that the high energy metabolism status is positively correlated with the poor prognosis of patients with lung adenocarcinoma. We constructed a scoring system (mitoRiskscore) based on the gene expression of specific mitochondrial localized proteins through univariate and lasso cox regression. Patients with high mitoRiskscore tend to have a shorter survival time after surgery. Compared with the typical TNM grading system, the mitoRiskscore gene panel had higher prediction accuracy. A large number of external verification results ensured its universality. Additionally, the mitoRiskscore could evaluate the metabolic pattern and chemotherapy sensitivity of the tumor samples. Lung adenocarcinoma with higher mitoRiskscore were more active in glycolysis and oxidative phosphorylation, and the expression of proliferation-related pathway genes were also significantly up-regulated. In contrast, patients with low mitoRiskscore had similar metabolic patterns to normal tissues. In order to improve the accuracy of prediction ability and promote clinical usage, we developed a nomogram that combined mitoRiskscore and clinical prognostic factors to predict the 3-year, 5-year, and 10-year survival rate of patients. We also performed in vitro experiments to verify the function of the key genes in mitoRiskscore panel. In conclusion, the mitoRiskscore scoring system may assist clinicians judge the postoperative survival rate and chemotherapy of patients with lung adenocarcinoma.
Authors: Weifeng Chen1, Jingyao Wang1, Qiumei Zhao, Dandan Liu, Donglin Sun, Ningxia Xie, Haohao Zhang, Deji Ye and Xiaoren Zhang*
This repository shows all the steps of our research (except in vitro experiments).
This repository contains working scripts, raw data, and generated figures.
The work sequence of this repository is consistent with the sequence in our manuscript.
All scripts are implemented in R (version 4.1.0) under Windows system.
Please contact Weifeng Chen (chenweifengaa@163.com) and Jingyao Wang (wangjingyao2002@163.com) for questions and comments.