Preeclampsia (PE) is a common hypertensive disease in women with pregnancy. With the development of bioinformatics, WGCNA was used to explore specific biomarkers to provide therapy targets efficiently. All samples were obtained from gene expression omnibus (GEO), then we used a package named "WGCNA" to construct a scale-free co-expression network and modules related to PE. Next, the search tool for the retrieval of interacting genes database (STRING) was adopted to structure the protein-protein interaction (PPI) of genes in the hub module. Furthermore, the MCODE plug-in was applied to discern hub clusters of the PPI network. We also utilized clusterprofiler to execute the functional analysis. Finally, hub genes were selected via Venn Plot and confirmed by quantitative real-time polymerase chain reaction. Through the co-expression networks and modules, we ensured the turquoise module was the most significant one related to PE. Functional analysis implied these genes were mainly enriched in the organic hydroxy compound metabolic process and Phosphatidylinositol signal system. Due to connectivity, the PPI network showed that GAPDH and VEGFA were the most conspicuous. Lastly, the Venn Plot screened eight hub genes (LDHA, ENG, OCRL, PIK3CB, FLT1, HK2, PKM, and LEP). LDHA was confirmed to be downregulated in PE tissues (P<0.001). This study revealed vital module and hub genes associated with preeclampsia and indicated that LDHA might be a therapeutic target in the future.