Screening of immune related gene and survival prediction of lung adenocarcinoma patients based on LightGBM model_Journal of Biomedical Engineering

Authors：

 MENG Xiangfu , TIAN Youfa , ZHANG Xiaoyan

1. School of Electronics and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125000, P. R. China;

Corresponding author：

MENG Xiangfu, Email: marxi@126.com

Keywords：

Lung adenocarcinoma; Bioinformatics; Ensemble learning; Immune related gene; LightGBM

DOI：

10.7507/1001-5515.202305038

Video：

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Lung cancer is one of the malignant tumors with the greatest threat to human health, and studies have shown that some genes play an important regulatory role in the occurrence and development of lung cancer. In this paper, a LightGBM ensemble learning method is proposed to construct a prognostic model based on immune relate gene (IRG) profile data and clinical data to predict the prognostic survival rate of lung adenocarcinoma patients. First, this method used the Limma package for differential gene expression, used CoxPH regression analysis to screen the IRG to prognosis, and then used XGBoost algorithm to score the importance of the IRG features. Finally, the LASSO regression analysis was used to select IRG that could be used to construct a prognostic model, and a total of 17 IRG features were obtained that could be used to construct model. LightGBM was trained according to the IRG screened. The K-means algorithm was used to divide the patients into three groups, and the area under curve (AUC) of receiver operating characteristic (ROC) of the model output showed that the accuracy of the model in predicting the survival rates of the three groups of patients was 96%, 98% and 96%, respectively. The experimental results show that the model proposed in this paper can divide patients with lung adenocarcinoma into three groups [5-year survival rate higher than 65% (group 1), lower than 65% but higher than 30% (group 2) and lower than 30% (group 3)] and can accurately predict the 5-year survival rate of lung adenocarcinoma patients.

Citation： MENG Xiangfu, TIAN Youfa, ZHANG Xiaoyan. Screening of immune related gene and survival prediction of lung adenocarcinoma patients based on LightGBM model. Journal of Biomedical Engineering, 2024, 41(1): 70-79. doi: 10.7507/1001-5515.202305038 Copy

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36.	刘少博, 黄波. 基于生物信息学方法识别肺腺癌预后相关基因及预后风险模型的构建. 中国免疫学杂志, 2021, 37(23): 2880-2892.
37.	马国玉, 熊庆, 蒋国庆, 等. 基于生物信息学方法识别肺腺癌预后相关基因. 昆明医科大学学报, 2020, 41(7): 30-37.

1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
2. 刘邓, 杨啸林, 孟祥福. RcaNet: 一种预测肿瘤突变负荷的深度学习模型. 中国生物医学工程学报, 2023, 42(1): 51-61.
3. Thai A, Solomon B, Sequist L, et al. Lung cancer. Lancet, 2021, 398(10299): 535-554.
4. Foret P, Kleiner A, Mobahi H, et al. Sharpness-aware minimization for efficiently improving generalization. arXiv, 2021, 79(5): 122-126.
5. Denisenko T V, Budkevich I N, Zhivotovsky B, et al. Cell death-based treatment of lung adenocarcinoma. Cell Death Dis, 2018, 9(2): 117.
6. Li L, Sun Y, Feng M, et al. Clinical significance of blood-based miRNAs as biomarkers of non-small cell lung cancer. Oncol Lett, 2018, 15(6): 8915-8925.
7. 赵丹, 牟海军. 基于TCGA数据库应用生物信息学方法分析和挖掘肺腺癌预后和诊断miRNA研究. 当代医学, 2022, 28(4): 33-36.
8. 张满堂, 吴小永, 杜敏. 非小细胞肺癌组织中TP酶激活蛋白SH3功能结合蛋白2的表达及临床意义. 临床肺科杂志, 2023, 28(2): 189-194.
9. Yang L, Wang S, Zhou Y, et al. Evaluation of the 7th and 8th editions of the AJCC/UICC TNM staging systems for lung cancer in a large North American cohort. Oncotarget, 2017, 8(40): 66784-66795.
10. 黄正品, 黄钢. 肺腺癌免疫相关基因预后模型的构建与应用. 生物技术, 2022, 32(3): 313-320.
11. Miller H A, Berkel V V, Frieboes H B. Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data. Metabolomics, 2022, 18(8): 1-12.
12. 陈丽, 朱裴松, 钱铁云, 等. 基于边采样的网络表示学习模型. 软件学报, 2018, 29(3): 756-771.
13. 陈亦琦, 钱铁云, 李万理, 等. 基于复合关系图卷积的属性网络嵌入方法. 计算机研究与发展, 2020, 57(8): 1674-1682.
14. Anika C, Olivier G. Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics, 2019, 35(14): i446-i454.
15. Zhu X, Yao J, Huang J. Deep convolutional neural network for survival analysis with pathological images// 2016 IEEE International Conference on Bioinformatics (BIBM). Shenzhen: IEEE, 2017: 544-547.
16. Thedinga K, Herwig R. A gradient tree boosting and network propagation derived pan-cancer survival network of the tumor microenvironment. iScience, 2022, 25(1): 103617.
17. Kourou K, Exarchos T P, Exarchos K P, et al. Machine learning applications in cancer prediction and prognosis. Comput Struct Biotechnol J, 2014, 13: 8-17.
18. Liu S, Wang Z, Zhu R. Three differential expression analysis methods for RNA sequencing: limma, EdgeR, DESeq2. J Vis Exp, 2021, 18(175): e62528.
19. Yang C H, Moi S H, Fu O Y, et al. Identifying risk stratification associated with a cancer for overall survival by deep learning-based CoxPH. IEEE Access, 2019, 7(99): 67708-67717.
20. 杜也, 米热阿依·阿布都热孜克, 左冉, 等. 基于LASSO回归筛选影响肺腺癌患者预后的糖酵解相关基因. 中国肿瘤临床, 2023, 50(01): 16-21.
21. Kanungo T, Mount D M, Netanyahu N S, et al. An efficient k-means clustering algorithm: analysis and implementation. IEEE Comput Soc, 2002, 24(7): 881-892.
22. Chen T, Tong H, Benesty M. xgboost: eXtreme Gradient Boosting. BibSonomy, 2016, 1(4): 1-4.
23. Qi M. LightGBM: A highly efficient gradient boosting decision tree // NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc, 2017: 3149-3157.
24. Karsoliya S. Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture. Int J Eng Trends Technol, 2012, 3(6): 714-717.
25. Nichols J A, Herbert C H W, Baker M A B. Machine learning: applications of artificial intelligence to imaging and diagnosis. Biophys Rev, 2018, 11(1): 111-118.
26. Cherif W. Optimization of K-NN algorithm by clustering and reliability coefficients: application to breast-cancer diagnosis. Proc Comput Sci, 2018, 127: 293-299.
27. Altman N, Krzywinski M. Points of significance: Clustering. Nat Methods, 2017, 14(6): 545-546.
28. Dagogo-Jack I, Shaw A T. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Cli Oncol, 2018, 15(2): 81-94.
29. Haque M N, Tazin T, Khan M M, et al. Predicting characteristics associated with breast cancer survival using multiple machine learning approaches. Comput Math Methods Med, 2022, 2022: 1249692.
30. 彭华. 肺癌易感基因的研究进展. 中国药物经济学, 2019, 14(9): 126-128.
31. 卞秀森, 李光, 关欣宇, 等. UHRF1通过调控细胞自噬抑制肺腺癌细胞增殖的分子机制研究. 实用肿瘤学杂志, 2018, 32(6): 498-502.
32. Woo S, Corces, Ryan M, et al. The chromatin accessibility landscape of primary human cancers. iScience, 2018, 362(6413): eaav18989.
33. 刘凤燕, 张元媛, 张琪, 等. 基于TCGA数据库构建肺腺癌相关免疫基因预后模型. 河南大学学报(自然科学版), 2023, 53(2): 186-195.
34. 李昂, 谢俞宁, 仵红娇, 等. 肺腺癌预后关键基因的筛选、验证及其调控通路分析. 山东医药, 2020, 60(23): 1-5.
35. 范兴. 肺腺癌关键预后基因的筛选和分析. 太原: 山西财经大学, 2023.
36. 刘少博, 黄波. 基于生物信息学方法识别肺腺癌预后相关基因及预后风险模型的构建. 中国免疫学杂志, 2021, 37(23): 2880-2892.
37. 马国玉, 熊庆, 蒋国庆, 等. 基于生物信息学方法识别肺腺癌预后相关基因. 昆明医科大学学报, 2020, 41(7): 30-37.

Journal of Biomedical Engineering

Screening of immune related gene and survival prediction of lung adenocarcinoma patients based on LightGBM model

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