Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture

Yukun Mao; Xueying Hou; Su Fu; Jie Luan

doi:10.1016/j.gendis.2023.101087

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Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture

Full-length article | 更新时间：2024-02-04

- Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture
- Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture
- Genes & Diseases 2024年11卷第3期
- 作者机构：
  
  Breast Plastic and Reconstructive Surgery Center, Plastic Surgery Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College,Beijing,China,100144
- 作者简介：
- 基金信息：
- DOI：10.1016/j.gendis.2023.101087
  中图分类号：
- 纸质出版日期：2024，
  
  网络出版日期：2024-5-1，
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Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture[J]. Genes & Diseases, 2024,11(3).

Yukun Mao, Xueying Hou, Su Fu, et al. Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture[J]. Genes & Diseases, 2024,11(3).
Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture[J]. Genes & Diseases, 2024,11(3). DOI： 10.1016/j.gendis.2023.101087.

Yukun Mao, Xueying Hou, Su Fu, et al. Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture[J]. Genes & Diseases, 2024,11(3). DOI： 10.1016/j.gendis.2023.101087.

摘要

Abstract

Capsular contracture is a prevalent and severe complication that affects the postoperative outcomes of patients who receive silicone breast implants. At present

prosthesis replacement is the major treatment for capsular contracture after both breast augmentation procedures and breast reconstruction following breast cancer surgery. However

the mechanism(s) underlying breast capsular contracture remains unclear. This study aimed to identify the biological features of breast capsular contracture and reveal the potential underlying mechanism using RNA sequencing. Sample tissues from 12 female patients (15 breast capsules) were divided into low capsular contracture (LCC) and high capsular contracture (HCC) groups based on the Baker grades. Subsequently

41 lipid metabolism-related genes were identified through enrichment analysis

and three of these genes were identified as candidate genes by SVM-RFE and LASSO algorithms. We then compared the proportions of the 22 types of immune cells between the LCC and HCC groups using a CIBERSORT analysis and explored the correlation between the candidate hub features and immune cells. Notably

PRKAR2B was positively correlated with the differentially clustered immune cells

which were M1 macrophages and follicular helper T cells (area under the ROC = 0.786). In addition

the expression of PRKAR2B at the mRNA or protein level was lower in the HCC group than in the LCC group. Potential molecular mechanisms were identified based on the expression levels in the high and low PRKAR2B groups. Our findings indicate that PRKAR2B is a novel diagnostic biomarker for breast capsular contracture and might also influence the grade and progression of capsular contracture.

关键词

Keywords

BiomarkerBreast capsular contractureImmune infiltrationMachine learning algorithmsPRKAR2B

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