About the lab

Mlab, initiated by Dr. Mingwei Liu, focuses on bioinformatics and its application, especially Immuno-oncological & Gut Microbiota​​​​​​​ Bioinformatics, medical knowledge mapping, and clinical application transformation, as well as medical laboratory education and related research.

Featured research (2)

Melanoma is the most malignant form of skin cancer, which seriously threatens human life and health. Anti-PD-1 immunotherapy has shown clinical benefits in improving patients’ overall survival, but some melanoma patients failed to respond. Effective therapeutic biomarkers are vital to evaluate and optimize benefits from anti-PD-1 treatment. Although the establishment of immunotherapy biomarkers is well underway, studies that identify predictors by gene network-based approaches are lacking. Here, we retrieved the existing datasets (GSE91061, GSE78220 and GSE93157, 79 samples in total) on anti-PD-1 therapy to explore potential therapeutic biomarkers in melanoma using weighted correlation network analysis (WGCNA), function validation and clinical corroboration. As a result, 13 hub genes as critical nodes were traced from the key module associated with clinical features. After receiver operating characteristic (ROC) curve validation by an independent dataset (GSE78220), six hub genes with diagnostic significance were further recovered. Moreover, these six genes were revealed to be closely associated not only with the immune system regulation, immune infiltration, and validated immunotherapy biomarkers, but also with excellent prognostic value and significant expression level in melanoma. The random forest prediction model constructed using these six genes presented a great diagnostic ability for anti-PD-1 immunotherapy response. Taken together, IRF1, JAK2, CD8A, IRF8, STAT5B, and SELL may serve as predictive therapeutic biomarkers for melanoma and could facilitate future anti-PD-1 therapy.

Lab head

Mingwei Liu
Department
  • College of Laboratory Medicine
About Mingwei Liu
  • Oncological Bioinformatics and Clinical Translation, including cancer biomarker identification with corroborative semantic knowledge (knowledge graph) and omics data analysis in the context of clinical data, insights into molecular immunotherapy and disease-related pan-gut microbiota or probiotics, as well as the deep glance into the relationship between tumor genes, expression variations, molecular typing, molecular mechanisms, function networks, the tumorigenesis and cancer developments.

Members

Fei Long
Fei Long
  • Not confirmed yet
Zixuan Chai
Zixuan Chai
  • Not confirmed yet
Xuanyi Wang
Xuanyi Wang
  • Not confirmed yet
Guizhi Pan
Guizhi Pan
  • Not confirmed yet