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Classification prediction of early pulmonary nodes based on weighted gene correlation network analysis and machine learning

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Abstract

Objective

To use weighted gene correlation network analysis (WGCNA) and machine learning algorithm to predict classification of early pulmonary nodes with public databases.

Methods

The expression data and clinical data of lung cancer patients were firstly extracted from public database (GTEx and TCGA) to study the differentially expressed genes (DEGs) of lung adenocarcinoma (LUAD). The intersection of three R packages (Dseq2, Limma, EdgeR) methods were selected as candidate DEGs for further study. WGCNA was used to obtain relevant modules and key genes of lung cancer classification, GO and KEGG enrichment analysis was performed. The model was built using two machine learning methods, Least Absolute Shrinkage and Selection Operator (LASSO) regression and tumor classification was also predicted with extreme Gradient Boosting (XGBoost) algorithm.

Results

DEGs analysis revealed that there were 1306 LUAD genes. WGCNA module analysis showed that a total of 116 genes were significantly related to classification, and module genes were mainly related to 14 KEGG pathways. The machine learning algorithm identified 10 target genes by LASSO regression analysis of differential genes, and 18 genes were identified by XGBoost model. A total of 6 genes were found from the intersection of the above methods as classification signatures of early pulmonary nodules, including “HMGB3” “ARHGAP6” “TCF21” “FCN3” “COL6A6” “GOLM1”.

Conclusion

Using DEGs analysis, WGCNA method and machine learning algorithm, six gene signatures related to early stage of LUAD, which can assist clinicians in disease classification prediction.

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Funding

This work was supported by Chongqing medical scientific research project (Joint project of Chongqing Health Commision and Science and Technology Bureau) under Grant No.2022DBXM005.

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Author notes

  1. Guang Li and Meng Yang contributed to the work equally and should be regarded as co-first authors.

Authors and Affiliations

  1. Department of Radiotherapy, Chongqing University Cancer Hospital, Chongqing, China

    Guang Li & Fu **

  2. Department of Equipment, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Meng Yang

  3. Department of Bioinformatics, Chongqing Medical University, Chongqing, China

    Longke Ran

Authors
  1. Guang Li
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  2. Meng Yang
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  3. Longke Ran
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  4. Fu **
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Contributions

FJ and LR conceived of the presented idea. GL developed the theory and performed the computations. MY verified the analytical methods. All authors discussed the results and contributed to the final manuscript.

Corresponding authors

Correspondence to Longke Ran or Fu **.

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Li, G., Yang, M., Ran, L. et al. Classification prediction of early pulmonary nodes based on weighted gene correlation network analysis and machine learning. J Cancer Res Clin Oncol 149, 3915–3924 (2023). https://doi.org/10.1007/s00432-022-04312-7

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  • DOI: https://doi.org/10.1007/s00432-022-04312-7

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