Investigation of potential protein biomarkers for the screening of placental-mediated fetal growth restriction disorders using targeted proteomics Olink technology

Xinyao Zhou¹Wuqian Wang²Luan Chen³Yingjun Yang⁴Xing Wei¹Jia Zhou¹Kuan Sun¹Jianjun Zhu⁵Xiaofang Sun²Shengying Qin³Luming Sun^1*

• ¹Shanghai First Maternity and Infant Hospital, Shanghai, China
• ²The Third Affiliated Hospital of Guangdong Medical University, Foshan, Guangdong Province, China
• ³Shanghai Jiao Tong University, Shanghai, Shanghai Municipality, China
• ⁴Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, Shanghai Municipality, China
• ⁵Jiaxing Maternity and Child Health Care Hospital, Jiaxing, Zhejiang, China

Background: Effective intrauterine treatments for placental-mediated fetal growth restriction (FGR) remain limited, necessitating reliable protein biomarkers for early diagnosis and management.In this study, we analyzed differential protein expression in peripheral blood plasma samples from 44 placental-mediated FGR patients and 44 normal pregnant women using the Olink-Explore-384-Inflammation panel. The analysis identified significant differences in protein expression levels, followed by enrichment analyses to explore the underlying biological mechanisms. Protein-protein interaction (PPI) network analysis and Least Absolute Shrinkage and Selection Operator (LASSO) modeling were used to identify key proteins as potential biomarkers.We identified 225 proteins with significantly altered expression between FGR patients and normal pregnancies. Proteins such as Placental Growth Factor (PGF) and Hepatocyte Growth Factor (HGF) were previously found to be strongly associated with FGR. In addition, we discovered novel proteins potentially associated with FGR, including ESM1 and TIMP3. Enrichment analyses revealed that several pathways, including placental dysfunction, inflammatory responses, and oxidative stress, may play crucial roles in FGR pathophysiology. PPI network analysis further identified key proteins such as ANGPT2, CD40, and HGF, as potentially linked to FGR. LASSO modeling validated PGF and ESM1 as important biomarkers.Additionally, integrating a multi-protein panel with blood flow disruption analysis significantly improved diagnostic accuracy.Our findings provide valuable insights into the molecular mechanisms of FGR, identifying key proteins as potential biomarkers. The multi-protein panel model offers a promising tool for early screening and diagnosis of FGR.