Liang Qiao

Machine Learning-Driven Extracellular Vesicles Peptidomics Powers Precision Classification of Endometrial Cancer

Endometrial cancer (EC) molecular subtyping is critical for prognosis and treatment but remains hindered by reliance on invasive tissue biopsies and time-consuming genomic sequencing. Here, we present a minimally invasive approach integrating MALDI-TOF mass spectrometry and LC-MS/MS-based peptidomic profiling of plasma extracellular vesicles (EVs) with machine learning for rapid EC screening and subtyping. EVs were isolated from EC patients and controls, and their peptidome fingerprints were analyzed. A machine learning model utilizing 12 discriminative MALDI-TOF MS features, the levels of CA125 and HE4, and clinical features related to cancer risk achieved an AUC of 0.867 in distinguishing EC from the controls. For molecular subtyping (POLE mutant, NSMP, MMRd, P53-abnormal), a multiclassification model demonstrated micro/macro-averaged AUCs of 0.91/0.90. LC-MS/MS identified 7,479 peptides, with fibrinogen α chain (FGA), protease serine 3 (PRSS3), and apolipoprotein A-I (APOA1) emerging as key biomarkers linked to specific subtypes. This study establishes a high-throughput, cost-effective platform for EC management, bridging translational gaps in precision oncology.

Diagnosis and Biomarker Screening of Endometrial Cancer Enabled by a Versatile Exosome Metabolic Fingerprint Platform

Exosomes have emerged as a revolutionary tool for liquid biopsy (LB), as they carry specific cargo from cells. Profiling the metabolites of exosomes is crucial for cancer diagnosis and biomarker discovery. Herein, we propose a versatile platform for exosomal metabolite assay of endometrial cancer (EC). The platform is based on a nanostructured composite material comprising gold nanoparticle-coated magnetic COF with aptamer modification (Fe

Metabolomics and proteomics reveal the inhibitory effect of Lactobacillus crispatus on cervical cancer

Cervical cancer remains a significant global health issue due to its high morbidity and mortality rates. Recently, Lactobacillus crispatus has been recognized for its crucial role in maintaining cervical health. While some studies have explored the use of L. crispatus to mitigate cervical cancer, the underlying mechanisms remain largely unknown. In this study, we employed non-targeted proteomics and metabolomics to investigate how L. crispatus affects the growth of cervical cancer cells (SiHa) and normal cervical cells (Ect1/E6E7). Our findings indicated that the inhibitory effect of L. crispatus on SiHa cells was associated with various biological processes, notably the ferroptosis pathway. Specifically, L. crispatus was found to regulate the expression of proteins such as HMOX1, SLC39A14, VDAC2, ACSL4, and LPCAT3 by SiHa cells, which are closely related to ferroptosis. Additionally, it activated the tricarboxylic acid (TCA) cycle in SiHa cells, leading to increased levels of reactive oxygen species (ROS) and lipid peroxides (LPO). These results revealed the therapeutic potential of L. crispatus in targeting the ferroptosis pathway for cervical cancer treatment, opening new avenues for research and therapy in cervical cancer.