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Leveraging Single-Cell Multi-Omics to Decode Tumor Microenvironment Diversity and Therapeutic Resistance.
Pharmaceuticals (Basel)
January 2025
Department of Biomedicine, Texas A&M University, College Station, TX 77843, USA.
Recent developments in single-cell multi-omics technologies have provided the ability to identify diverse cell types and decipher key components of the tumor microenvironment (TME), leading to important advancements toward a much deeper understanding of how tumor microenvironment heterogeneity contributes to cancer progression and therapeutic resistance. These technologies are able to integrate data from molecular genomic, transcriptomic, proteomics, and metabolomics studies of cells at a single-cell resolution scale that give rise to the full cellular and molecular complexity in the TME. Understanding the complex and sometimes reciprocal relationships among cancer cells, CAFs, immune cells, and ECs has led to novel insights into their immense heterogeneity in functions, which can have important consequences on tumor behavior.
View Article and Find Full Text PDFIntegrative Single-Cell and Bulk RNA Sequencing Identifies a Macrophage-Related Prognostic Signature for Predicting Prognosis and Therapy Responses in Colorectal Cancer.
Int J Mol Sci
January 2025
School of Chemistry and Life Science, Suzhou University of Science and Technology, Suzhou 215011, China.
Colorectal cancer (CRC) is one of the most common malignant tumors, characterized by a high incidence and mortality rate. Macrophages, as a key immune cell type within the tumor microenvironment (TME), play a key role in tumor immune evasion and the progression of CRC. Therefore, identifying macrophage biomarkers is of great significance for predicting the prognosis of CRC patients.
View Article and Find Full Text PDFCellular Senescence in Hepatocellular Carcinoma: Immune Microenvironment Insights via Machine Learning and In Vitro Experiments.
Int J Mol Sci
January 2025
School of Environmental Science and Engineering, Hainan University, Haikou 570228, China.
Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell cycle arrest, plays a crucial role in cancer biology, but its markers and roles in the HCC immune microenvironment remain unclear. Three machine learning methods, namely k nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), are utilized to identify eight key HCC cell senescence markers (HCC-CSMs).
View Article and Find Full Text PDFInvestigation of Exome-Wide Tumor Heterogeneity on Colorectal Tissue-Based Single Cells.
Int J Mol Sci
January 2025
Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, 1085 Budapest, Hungary.
The progression of colorectal cancer is strongly influenced by environmental and genetic conditions. One of the key factors is tumor heterogeneity which is extensively studied by cfDNA and bulk sequencing methods; however, we lack knowledge regarding its effects at the single-cell level. Motivated by this, we aimed to employ an end-to-end single-cell sequencing workflow from tissue-derived sample isolation to exome sequencing.
View Article and Find Full Text PDFTranscriptomic Evidence Reveals the Dysfunctional Mechanism of Synaptic Plasticity Control in ASD.
Genes (Basel)
December 2024
Institute for Complex Systems and Mathematical Biology, King's College, University of Aberdeen, Old Aberdeen AB24 3UE, UK.
Background/objectives: A prominent endophenotype in Autism Spectrum Disorder (ASD) is the synaptic plasticity dysfunction, yet the molecular mechanism remains elusive. As a prototype, we investigate the postsynaptic signal transduction network in glutamatergic neurons and integrate single-cell nucleus transcriptomics data from the Prefrontal Cortex (PFC) to unveil the malfunction of translation control.
Methods: We devise an innovative and highly dependable pipeline to transform our acquired signal transduction network into an mRNA Signaling-Regulatory Network (mSiReN) and analyze it at the RNA level.
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