Therapy-associated remodeling of pancreatic cancer revealed by single-cell spatial transcriptomics and optimal transport analysis.
bioRxiv : the preprint server for biology
In combination with cell intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged high-plex single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with specific malignant subtypes and neoadjuvant chemotherapy/radiotherapy. We developed Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid co-culture system. Overall, this study demonstrates that characterization of the tumor microenvironment using high-plex single-cell spatial transcriptomics allows for identification of molecular interactions that may play a role in the emergence of chemoresistance and establishes a translational spatial biology paradigm that can be broadly applied to other malignancies, diseases, and treatments.
View details for DOI 10.1101/2023.06.28.546848
View details for PubMedID 37425692
Protein-protein interaction networks as miners of biological discovery.
2022; 22 (15-16): e2100190
Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid (Y2H), mass spectrometry (MS), co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks.
View details for DOI 10.1002/pmic.202100190
View details for PubMedID 35567424
Competitive Endogenous RNA Landscape in Epstein-Barr Virus Associated Nasopharyngeal Carcinoma.
Frontiers in cell and developmental biology
2021; 9: 782473
Non-coding RNAs have been shown to play important regulatory roles, notably in cancer development. In this study, we investigated the role of microRNAs and circular RNAs in Nasopharyngeal Carcinoma (NPC) by constructing a circRNA-miRNA-mRNA co-expression network and performing differential expression analysis on mRNAs, miRNAs, and circRNAs. Specifically, the Epstein-Barr virus (EBV) infection has been found to be an important risk factor for NPC, and potential pathological differences may exist for EBV+ and EBV- subtypes of NPC. By comparing the expression profile of non-cancerous immortalized nasopharyngeal epithelial cell line and NPC cell lines, we identified differentially expressed coding and non-coding RNAs across three groups of comparison: cancer vs. non-cancer, EBV+ vs. EBV- NPC, and metastatic vs. non-metastatic NPC. We constructed a ceRNA network composed of mRNAs, miRNAs, and circRNAs, leveraging co-expression and miRNA target prediction tools. Within the network, we identified the regulatory ceRNAs of CDKN1B, ZNF302, ZNF268, and RPGR. These differentially expressed axis, along with other miRNA-circRNA pairs we identified through our analysis, helps elucidate the genetic and epigenetic changes central to NPC progression, and the differences between EBV+ and EBV- NPC.
View details for DOI 10.3389/fcell.2021.782473
View details for PubMedID 34805186
View details for PubMedCentralID PMC8600047
Integrative genomic characterization of therapeutic targets for pancreatic cancer.
AMER ASSOC CANCER RESEARCH. 2021
View details for Web of Science ID 000720117400014
Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis.
Current gene therapy
2021; 21 (4): 299-303
This study aims to identify the biomarkers for chronic hypersensitivity pneumonitis (CHP) and facilitate the precise gene therapy of CHP.Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the task of differentiating CHP and other interstitial lung diseases, especially idiopathic pulmonary fibrosis (IPF), was challenging.In this study, we analyzed the publically available gene expression profile of 82 CHP patients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers.The CHP biomarkers were selected with advanced feature selection methods: Monte Carlo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrichment analysis and differential co-expression analysis.There were 674 identified CHP biomarkers. The co-expression network of these biomarkers in CHP included more negative regulations and the network structure of CHP was quite different from the network of IPF and control.The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential coexpression network showed great promise in revealing the underlying mechanisms of CHP.
View details for DOI 10.2174/1566523220666201208093325
View details for PubMedID 33292121
Decipher the connections between proteins and phenotypes.
Biochimica et biophysica acta. Proteins and proteomics
2020; 1868 (11): 140503
As the outward-most representation of life, phenotype is the fundamental basis with which humans understand life and disease. But with the advent of molecular and sequencing technique and research, a growing portion of science research focuses primarily on the molecular level of life. Our understanding in molecular variations and mechanisms can only be fully utilized when they are translated into the phenotypic level. In this study, we constructed similarity network for phenotype ontology, and then applied network analysis methods to discover phenotype/disease clusters. Then, we used machine learning models to predict protein-phenotype associations. Each protein was characterized by the functional profiles of its interaction neighbors on the protein-protein interaction network. Our methods can not only predict protein-phenotype associations, but also reveal the underlying mechanisms from protein to phenotype.
View details for DOI 10.1016/j.bbapap.2020.140503
View details for PubMedID 32707349
Applications of Network Analysis in Biomedicine.
Methods in molecular biology (Clifton, N.J.)
2020; 2204: 39-50
The abundance of high-throughput data and technical refinements in graph theories have allowed network analysis to become an effective approach for various medical fields. This chapter introduces co-expression, Bayesian, and regression-based network construction methods, which are the basis of network analysis. Various methods in network topology analysis are explained, along with their unique features and applications in biomedicine. Furthermore, we explain the role of network embedding in reducing the dimensionality of networks and outline several popular algorithms used by researchers today. Current literature has implemented different combinations of topology analysis and network embedding techniques, and we outline several studies in the fields of genetic-based disease prediction, drug-target identification, and multi-level omics integration.
View details for DOI 10.1007/978-1-0716-0904-0_4
View details for PubMedID 32710313