Integrated Bioinformatics and Machine Learning Analysis Identify ACADL as a Potent Biomarker of Reactive Mesothelial Cells.

Journal: The American journal of pathology
PMID: 38657836

Abstract

Mesothelial cells with reactive hyperplasia are difficult to distinguish from malignant mesothelioma cells based on cell morphology. This study aimed to identify and validate potential biomarkers that distinguish mesothelial cells from mesothelioma cells through machine learning combined with immunohistochemistry. It integrated the gene expression matrix from three Gene Expression Omnibus data sets (GSE2549, GSE12345, and GSE51024) to analyze the differently expressed genes between normal and mesothelioma tissues. Then, three machine learning algorithms, least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were used to screen and obtain four shared candidate markers, including ACADL, EMP2, GPD1L, and HMMR. The receiver operating characteristic curve analysis showed that the area under the curve for distinguishing normal mesothelial cells from mesothelioma was 0.976, 0.943, 0.962, and 0.956, respectively. The expression and diagnostic performance of these candidate genes were validated in two additional independent data sets (GSE42977 and GSE112154), indicating that the performances of ACADL, GPD1L, and HMMR were consistent between the training and validation data sets. Finally, the optimal candidate marker ACADL was verified by immunohistochemistry assay. Acyl-CoA dehydrogenase long chain (ACADL) was stained strongly in mesothelial cells, especially for reactive hyperplasic mesothelial cells, but was negative in malignant mesothelioma cells. Therefore, ACADL has the potential to be used as a specific marker of reactive hyperplasic mesothelial cells in the differential diagnosis of mesothelioma.

Authors

  • Yige Yin
    School of Basic Medical Sciences, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
  • Qianwen Cui
    Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China; Science Island Branch, Graduate School of University of Science and Technology of China, Hefei, China.
  • Jiarong Zhao
    Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China; Medical Pathology Center, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China.
  • Qiang Wu
    Department of Radiotherapy, West China Hospital, Sichuan University, Chengdu, PR China; Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, PR China.
  • Qiuyan Sun
    Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Shenyang, China; Key Laboratory of Cancer Etiology and Prevention, Liaoning Education Department, The First Hospital of China Medical University, Shenyang, China; Key Laboratory of GI Cancer Etiology and Prevention, Liaoning Province, The First Hospital of China Medical University, Shenyang, China.
  • Hong-Qiang Wang
    Biological Molecular Information System Laboratory, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
  • Wulin Yang
    School of Basic Medical Sciences, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China; Science Island Branch, Graduate School of University of Science and Technology of China, Hefei, China. Electronic address: yangw@cmpt.ac.cn.

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