Background: Angiogenesis is indispensable for the sustained survival and progression of both embryonic development and tumorigenesis. This intricate process is tightly regulated by a multitude of pro-angiogenic genes. The presence of gene modules facilitating angiogenesis has been substantiated in both embryonic development and the context of tumor proliferation. However, it remains unresolved whether the pro-angiogenic gene modules expressed during embryonic development also exist in tumors.
Methods: This study performed a pan-cancer single-cell RNA sequencing (scRNA-seq) analysis on samples from 332 patients across seven cancer types: thyroid carcinoma, lung cancer, breast cancer, hepatocellular carcinoma, colorectal cancer, ovarian carcinoma, and prostate adenocarcinoma. Data processing was carried out using the Seurat R package, with rigorous quality control to filter high-quality cells and mitigate batch effects across datasets. We used principal component analysis (PCA), shared nearest neighbor graph-based clustering, and Uniform Manifold Approximation and Projection (UMAP) to visualize cell types and identify distinct cell clusters. Myeloid cell subpopulations were further analyzed for the expression of embryonic pro-angiogenic gene modules (EPGM) and tumor pro-angiogenic gene modules (TPGM).
Results: The analysis identified nine major cell types within the tumor microenvironment, with myeloid cells consistently exhibiting elevated expression of both tumor pro-angiogenic gene modules (TPGM) and EPGM across all tumor types. In particular, myeloid cells, including macrophages and monocytes, showed high EPGM expression, indicating an active role of embryonic pro-angiogenesis pathways in tumors. A subset analysis revealed 20 distinct myeloid subtypes with varying EPGM and TPGM expression across different cancers. Treatment and disease stage influenced these gene expressions, with certain subtypes, such as HSPAhi/STAT1+ macrophages in breast cancer, displaying reduced pro-angiogenic gene activity post-treatment.
Conclusion: This study provides evidence that tumors may exploit EPGM to enhance vascularization and support sustained growth, as evidenced by the elevated EPGM expression in tumor-associated myeloid cells. The consistent presence of EPGM in TAMs across multiple cancer types suggests a conserved mechanism wherein tumors harness embryonic angiogenic pathways to facilitate their progression. Distinct EPGM expression patterns in specific myeloid cell subsets indicate potential therapeutic targets, particularly in cases where EPGM activation contributes to resistance against anti-angiogenic therapies. These findings shed new light on the molecular mechanisms underlying tumor angiogenesis and highlight the prognostic relevance of EPGM expression in cancer, underscoring its potential as a biomarker for clinical applications.
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http://dx.doi.org/10.1002/cam4.70373 | DOI Listing |
Sci Rep
December 2024
Department of Thyroid Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
Although CCL17 has been reported to exert a vital role in many cancers, the related studies in the thyroid carcinoma have never reported. As a chemokine, CCL17 plays a positive role by promoting the infiltration of immune cells into the tumor microenviroment (TME) to influence tumor invasion and metastasis. Therefore, this study is aimed to investigate the association of CCL17 level with potential prognostic value on tumor immunity in the thyroid carcinoma (THCA) based on the bioinformatics analysis.
View Article and Find Full Text PDFNat Commun
December 2024
State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Biomedical Pioneering Innovative Center (BIOPIC) and Beijing Advanced Innovation Center for Genomics (ICG), Center for Bioinformatics (CBI), Peking University, 100871, Beijing, China.
Deciphering how noncoding DNA determines gene expression is critical for decoding the functional genome. Understanding the transcription effects of noncoding genetic variants are still major unsolved problems, which is critical for downstream applications in human genetics and precision medicine. Here, we integrate regulatory-specific neural networks and tissue-specific gradient-boosting trees to build SVEN: a hybrid sequence-oriented architecture that can accurately predict tissue-specific gene expression level and quantify the tissue-specific transcriptomic impacts of structural variants across more than 350 tissues and cell lines.
View Article and Find Full Text PDFNat Commun
December 2024
State Key Laboratory of Plant Diversity and Specialty Crops, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
Fruit ripening is a highly-orchestrated process that requires the fine-tuning and precise control of gene expression, which is mainly governed by phytohormones, epigenetic modifiers, and transcription factors. How these intrinsic regulators coordinately modulate the ripening remains elusive. Here we report the identification and characterization of FvALKBH10B as an N-methyladenosine (mA) RNA demethylase necessary for the normal ripening of strawberry (Fragaria vesca) fruit.
View Article and Find Full Text PDFJ Agric Food Chem
December 2024
Agronomy College, Guizhou University, Huaxi, 550025 Guiyang, Guizhou, P. R. China.
Safflower ( L.) is a valuable oil crop due to its bioactive ingredients and high linoleic acid content, which contribute to its antioxidant properties and potential for preventing atherosclerosis. Current research on safflower focuses on understanding the biosynthesis of seed oil through omics strategies, yet there is a lack of comprehensive knowledge of the dynamic changes in lipids and the regulatory mechanisms during seed development.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Microbiology and Immunology, University at Buffalo, The State University of New York, 955 Main Street, Buffalo, New York, NY 14203, United States.
Network-based methods utilize protein-protein interaction information to identify significantly perturbed subnetworks in cancer and to propose key molecular pathways. Numerous methods have been developed, but to date, a rigorous benchmark analysis to compare the performance of existing approaches is lacking. In this paper, we proposed a novel benchmarking framework using synthetic data and conducted a comprehensive analysis to investigate the ability of existing methods to detect target genes and subnetworks and to control false positives, and how they perform in the presence of topological biases at both gene and subnetwork levels.
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