Lianbo Yu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Lianbo Yu has a total of 16 co-authors. Here are the top co-authors by number of shared publications:
- Casey M. Cosgrove
Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, The Ohio State University College of Medicine, Columbus, OH, USA
1 shared publication - Colin Hisey
Department of Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA
1 shared publication - David E. Cohn
Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
1 shared publication - David M. O'Malley
Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
1 shared publication - Derek Hansford
Department of Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA
1 shared publication - Floor Jenniskens Backes
The Ohio State University 5 Division of Gynecologic Oncology, Department of Obstetrics & Gynecology, College of Medicine Columbus, Ohio
1 shared publication
Latest Publications
- Open Access
Serum Extracellular Vesicle Protein Signatures Associated with Early-Stage High-Grade Serous Ovarian Carcinoma
Background: High-grade serous ovarian carcinoma (HGSOC) is the most common and lethal subtype of epithelial ovarian cancer and is frequently diagnosed at advanced stages. Because currently available blood-based biomarkers have limited performance in early-stage disease, there is a need to identify circulating biomarker candidates associated with early-stage HGSOC. In this retrospective multi-institutional case–control study, we evaluated whether serum extracellular vesicle (EV)-associated protein signatures distinguish early-stage HGSOC from healthy controls. Methods: Serum samples (n = 252) were obtained retrospectively from multiple institutions and included healthy controls and patients with early- and advanced-stage HGSOC. EV-associated proteins were profiled using liquid chromatography–tandem mass spectrometry (LC–MS/MS) and proximity extension assay (PEA) to identify candidate proteins enriched in early-stage HGSOC. Selected candidates were evaluated by enzyme-linked immunosorbent assay (ELISA), and tissue-level expression was examined in early-stage HGSOC specimens. A multimarker combination model was generated using a smoothed empirical estimate of hyper-volume under the manifold (SHUM) approach and internally assessed by leave-one-out cross-validation. Results: Ten EV-associated serum proteins were prioritized on the basis of differential expression and fold change and were confirmed to be expressed in early-stage HGSOC tissues. In ELISA-based analyses, the combined 10-protein EV panel distinguished early-stage HGSOC from healthy controls with an area under the curve (AUC) of 0.99 in the study dataset, whereas MUC16 (CA-125) showed substantially lower performance in this comparison. The SHUM-based model yielded a true-positive rate of 0.971, a false-positive rate of 0.057, and a Matthews correlation coefficient of 0.915 in the analyzed cohort. Several candidate proteins were differentially enriched in EV fractions but not in matched whole serum. Conclusions: Serum EV-associated proteins are altered in early-stage HGSOC and define a multi-protein signature associated with this disease state in a retrospective case–control setting. These findings support further evaluation of EV-based biomarker candidates in clinically representative and prospectively collected cohorts that include benign gynecologic conditions, symptomatic patients, and pre-diagnostic samples.
Open AccessSerum Extracellular Vesicle Protein Signatures Associated with Early-Stage High-Grade Serous Ovarian Carcinoma
Background: High-grade serous ovarian carcinoma (HGSOC) is the most common and lethal subtype of epithelial ovarian cancer and is frequently diagnosed at advanced stages. Because currently available blood-based biomarkers have limited performance in early-stage disease, there is a need to identify circulating biomarker candidates associated with early-stage HGSOC. In this retrospective multi-institutional case–control study, we evaluated whether serum extracellular vesicle (EV)-associated protein signatures distinguish early-stage HGSOC from healthy controls. Methods: Serum samples (n = 252) were obtained retrospectively from multiple institutions and included healthy controls and patients with early- and advanced-stage HGSOC. EV-associated proteins were profiled using liquid chromatography–tandem mass spectrometry (LC–MS/MS) and proximity extension assay (PEA) to identify candidate proteins enriched in early-stage HGSOC. Selected candidates were evaluated by enzyme-linked immunosorbent assay (ELISA), and tissue-level expression was examined in early-stage HGSOC specimens. A multimarker combination model was generated using a smoothed empirical estimate of hyper-volume under the manifold (SHUM) approach and internally assessed by leave-one-out cross-validation. Results: Ten EV-associated serum proteins were prioritized on the basis of differential expression and fold change and were confirmed to be expressed in early-stage HGSOC tissues. In ELISA-based analyses, the combined 10-protein EV panel distinguished early-stage HGSOC from healthy controls with an area under the curve (AUC) of 0.99 in the study dataset, whereas MUC16 (CA-125) showed substantially lower performance in this comparison. The SHUM-based model yielded a true-positive rate of 0.971, a false-positive rate of 0.057, and a Matthews correlation coefficient of 0.915 in the analyzed cohort. Several candidate proteins were differentially enriched in EV fractions but not in matched whole serum. Conclusions: Serum EV-associated proteins are altered in early-stage HGSOC and define a multi-protein signature associated with this disease state in a retrospective case–control setting. These findings support further evaluation of EV-based biomarker candidates in clinically representative and prospectively collected cohorts that include benign gynecologic conditions, symptomatic patients, and pre-diagnostic samples.