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Screening gene signatures for clinical response subtypes of lung transplantation

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Abstract

Lung is the most important organ in the human respiratory system, whose normal functions are quite essential for human beings. Under certain pathological conditions, the normal lung functions could no longer be maintained in patients, and lung transplantation is generally applied to ease patients’ breathing and prolong their lives. However, several risk factors exist during and after lung transplantation, including bleeding, infection, and transplant rejections. In particular, transplant rejections are difficult to predict or prevent, leading to the most dangerous complications and severe status in patients undergoing lung transplantation. Given that most common monitoring and validation methods for lung transplantation rejections may take quite a long time and have low reproducibility, new technologies and methods are required to improve the efficacy and accuracy of rejection monitoring after lung transplantation. Recently, one previous study set up the gene expression profiles of patients who underwent lung transplantation. However, it did not provide a tool to predict lung transplantation responses. Here, a further deep investigation was conducted on such profiling data. A computational framework, incorporating several machine learning algorithms, such as feature selection methods and classification algorithms, was built to establish an effective prediction model distinguishing patient into different clinical subgroups, corresponding to different rejection responses after lung transplantation. Furthermore, the framework also screened essential genes with functional enrichments and create quantitative rules for the distinction of patients with different rejection responses to lung transplantation. The outcome of this contribution could provide guidelines for clinical treatment of each rejection subtype and contribute to the revealing of complicated rejection mechanisms of lung transplantation.

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Availability of data and material

The data used in this study were retrieved from Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE125004).

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Funding

This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences [XDA26040304, XDB38050200], National Key R&D Program of China [2018YFC0910403], the Fund of the Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences [202002].

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Author notes

  1. Yu-Hang Zhang, Zhan Dong Li and Tao Zeng contributed equally to this work.

Authors and Affiliations

  1. School of Life Sciences, Shanghai University, Shanghai, 200444, China

    Yu-Hang Zhang & Yu-Dong Cai

  2. Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Yu-Hang Zhang

  3. College of Food Engineering, Jilin Engineering Normal University, Changchun, 130052, China

    Zhan Dong Li

  4. Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China

    Tao Zeng & Tao Huang

  5. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China

    Lei Chen

  6. CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Tao Huang

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  1. Yu-Hang Zhang
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Contributions

TH and YDC: designed the study, supervised the project and finalized the manuscript. YHZ, TZ and LC: did the experiments. YHZ and TZ: analyzed the results. YHZ and TZ: drafted the manuscript.

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Correspondence to Tao Huang or Yu-Dong Cai.

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Communicated by Shuhua Xu.

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Supplementary Information

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438_2022_1918_MOESM1_ESM.xlsx

Supplementary file1 Supplementary Material I. Genes filtered by Boruta and their ranks produced by mRMR method. (XLSX 20 KB)

438_2022_1918_MOESM2_ESM.xlsx

Supplementary file2 Supplementary Material II. Performance of IFS with different classification models under different numbers of features. (XLSX 84 KB)

Supplementary file3 Supplementary Material III. List of enriched GO terms. (XLSX 17 KB)

Supplementary file4 Supplementary Material IV. List of enriched KEGG pathways. (XLSX 14 KB)

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Zhang, YH., Li, Z.D., Zeng, T. et al. Screening gene signatures for clinical response subtypes of lung transplantation. Mol Genet Genomics 297, 1301–1313 (2022). https://doi.org/10.1007/s00438-022-01918-x

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