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

Yige Yin; Qianwen Cui; Jiarong Zhao; Qiang Wu; Qiuyan Sun; Hong-Qiang Wang; Wulin Yang

doi:10.1016/j.ajpath.2024.03.013

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

Am J Pathol. 2024 Apr 22:S0002-9440(24)00161-5. doi: 10.1016/j.ajpath.2024.03.013. Online ahead of print.

Authors

Yige Yin¹, Qianwen Cui², Jiarong Zhao³, Qiang Wu⁴, Qiuyan Sun³, Hong-Qiang Wang⁵, Wulin Yang⁶

Affiliations

¹ 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.
² 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.
³ 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.
⁴ School of Basic Medical Sciences, Anhui Medical University, Hefei, China; Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
⁵ Biological Molecular Information System Laboratory, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Hefei, China.
⁶ 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.

PMID: 38657836
DOI: 10.1016/j.ajpath.2024.03.013

Abstract

Mesothelial cells with reactive hyperplasia are difficult to distinguish from malignant mesothelioma cells 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 experiments. This study 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 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 another two 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. 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.