Cancer-associated fibroblast-secreted FGF7 as an ovarian cancer progression promoter DOI Creative Commons

Songwei Feng,

Bo Ding,

Zhu Dai

и другие.

Journal of Translational Medicine, Год журнала: 2024, Номер 22(1)

Опубликована: Март 15, 2024

Abstract Background Ovarian cancer (OC) is distinguished by its aggressive nature and the limited efficacy of current treatment strategies. Recent studies have emphasized significant role cancer-associated fibroblasts (CAFs) in OC development progression. Methods Employing sophisticated machine learning techniques on bulk transcriptomic datasets, we identified fibroblast growth factor 7 (FGF7), derived from CAFs, as a potential oncogenic factor. We investigated relationship between FGF7 expression various clinical parameters. A series vitro experiments were undertaken to evaluate effect CAFs-derived cell activities, such proliferation, migration, invasion. Single-cell analysis was also conducted elucidate interaction receptor. Detailed mechanistic investigations sought clarify pathways through which fosters Results Our findings indicate that higher levels correlate with advanced tumor stages, increased vascular invasion, poorer prognosis. significantly enhanced revealed inhibits ubiquitination degradation hypoxia-inducible 1 alpha (HIF-1α) via FGFR2 interaction. Activation FGF7/HIF-1α pathway resulted upregulation mesenchymal markers downregulation epithelial markers. Importantly, vivo neutralizing antibodies targeting substantially reduced growth. Conclusion Neutralizing medium or inhibiting HIF-1α signaling reversed effects FGF7-mediated EMT, emphasizing dependence EMT activation. These suggest FGF7/HIF-1α/EMT axis may offer new therapeutic opportunities intervene

Язык: Английский

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine DOI Open Access
Xiujing He, Xiaowei Liu,

Fengli Zuo

и другие.

Seminars in Cancer Biology, Год журнала: 2022, Номер 88, С. 187 - 200

Опубликована: Дек. 31, 2022

Язык: Английский

Процитировано

164
Clinical application of advanced multi-omics tumor profiling: Shaping precision oncology of the future DOI Creative Commons
Dilara Akhoundova, Mark A. Rubin

Cancer Cell, Год журнала: 2022, Номер 40(9), С. 920 - 938

Опубликована: Сен. 1, 2022

Язык: Английский

Процитировано

97
WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics DOI Creative Commons
John M Elizarraras, Yuxing Liao, Zhiao Shi

и другие.

Nucleic Acids Research, Год журнала: 2024, Номер 52(W1), С. W415 - W421

Опубликована: Май 29, 2024

Enrichment analysis, crucial for interpreting genomic, transcriptomic, and proteomic data, is expanding into metabolomics. Furthermore, there a rising demand integrated enrichment analysis that combines data from different studies omics platforms, as seen in meta-analysis multi-omics research. To address these growing needs, we have updated WebGestalt to include capabilities both metabolites multiple input lists of analytes. We also significantly increased speed, revamped the user interface, introduced new pathway visualizations accommodate updates. Notably, adoption Rust backend reduced gene set time by 95% 270.64 12.41 s network topology-based 89% 159.59 17.31 our evaluation. This performance improvement accessible R package newly Python package. Additionally, database reflect current status each source expanded collection pathways, networks, signatures. The 2024 update represents significant leap forward, offering support metabolomics, streamlined capabilities, remarkable enhancements. Discover updates more at https://www.webgestalt.org.

Язык: Английский

Процитировано

89
Missing data in multi-omics integration: Recent advances through artificial intelligence DOI Creative Commons
Javier E. Flores, Daniel Claborne, Zachary D. Weller

и другие.

Frontiers in Artificial Intelligence, Год журнала: 2023, Номер 6

Опубликована: Фев. 9, 2023

Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these is only possible an integrated, multi-omic perspective. This has presented the need for development integration approaches that are able to capture complex, often non-linear, define biological adapted challenges combining heterogenous data across 'omic views. A principal challenge missing because all biomolecules not measured in samples. Due either cost, instrument sensitivity, or other experimental factors, sample may be one techologies. Recent methodological developments artificial intelligence statistical learning have greatly facilitated analyses multi-omics data, however many techniques assume access completely observed data. subset methods incorporate mechanisms handling partially samples, focus this review. We describe recently developed approaches, noting their primary use cases highlighting each method's approach additionally provide overview traditional workflows limitations; we discuss potential avenues further as well how issue its current solutions generalize beyond context.

Язык: Английский

Процитировано

67
Dealing with dimensionality: the application of machine learning to multi-omics data DOI Creative Commons
Dylan Feldner-Busztin, Panos Firbas Nisantzis, Shelley J. Edmunds

и другие.

Bioinformatics, Год журнала: 2023, Номер 39(2)

Опубликована: Янв. 11, 2023

Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order support human users data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts omics data produced next generation sequencing and other -omics assays. A systematic assessment current literature can help identify key trends potential gaps methodology applications. We surveyed on ML multi-omic integration quantitatively explored goals, techniques involved this field. were particularly interested examining how researchers use deal with volume complexity these datasets.Our main finding that used those address challenges few samples many features. Dimensionality reduction reduce feature count alongside models also appropriately handle relatively samples. Popular include autoencoders, random forests vector machines. found field heavily influenced The Cancer Genome Atlas dataset, which accessible contains diverse experiments.All processing scripts available at GitLab repository: https://gitlab.com/polavieja_lab/ml_multi-omics_review/ or Zenodo: https://doi.org/10.5281/zenodo.7361807.Supplementary Bioinformatics online.

Язык: Английский

Процитировано

51
Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare DOI Creative Commons
Alex E. Mohr, Carmen P. Ortega‐Santos, Corrie M. Whisner

и другие.

Biomedicines, Год журнала: 2024, Номер 12(7), С. 1496 - 1496

Опубликована: Июль 5, 2024

The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in publications within just two years (2022-2023) since its first referenced mention 2002, as indexed the National Library Medicine. emerging demonstrated capability to provide comprehensive insights into complex biological systems, representing transformative force health diagnostics therapeutic strategies. However, several challenges are evident when merging varied omics data sets methodologies, interpreting vast dimensions, streamlining longitudinal sampling analysis, addressing ethical implications managing sensitive information. review evaluates these while spotlighting pivotal milestones: development targeted methods, use artificial intelligence formulating indices, integration sophisticated

Язык: Английский

Процитировано

51
Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives DOI
Nian‐Nian Zhong, Hanqi Wang, Xinyue Huang

и другие.

Seminars in Cancer Biology, Год журнала: 2023, Номер 95, С. 52 - 74

Опубликована: Июль 18, 2023

Язык: Английский

Процитировано

46
Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection DOI Creative Commons
Hongwei Liu, Wei Zhang, Yihao Zhang

и другие.

Computational and Structural Biotechnology Journal, Год журнала: 2024, Номер 23, С. 2798 - 2810

Опубликована: Июнь 29, 2024

The widespread use of high-throughput sequencing technologies has revolutionized the understanding biology and cancer heterogeneity. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients' outcome clinical response. However, an open-source R package covering state-of-the-art algorithms for user-friendly access yet be developed. Thus, we proposed a flexible computational framework construct machine learning-based integration model with elegant performance (Mime). Mime streamlines process developing predictive high accuracy, leveraging complex datasets identify critical genes associated prognosis. An in silico combined de novo PIEZO1-associated signatures constructed by demonstrated accuracy predicting outcomes patients compared other published models. Furthermore, could also precisely infer immunotherapy response applying different Mime. Finally, SDC1 selected from potential as glioma target. Taken together, our provides solution constructing will greatly expanded provide valuable insights into current fields. is available GitHub (https://github.com/l-magnificence/Mime).

Язык: Английский

Процитировано

30
Gut Microbiota and Blood Metabolites Related to Fiber Intake and Type 2 Diabetes DOI Open Access
Zheng Wang, Brandilyn A. Peters, Bing Yu

и другие.

Circulation Research, Год журнала: 2024, Номер 134(7), С. 842 - 854

Опубликована: Март 28, 2024

BACKGROUND: Consistent evidence suggests diabetes-protective effects of dietary fiber intake. However, the underlying mechanisms, particularly role gut microbiota and host circulating metabolites, are not fully understood. We aimed to investigate metabolites associated with intake their relationships type 2 diabetes (T2D). METHODS: This study included up 11 394 participants from HCHS/SOL (Hispanic Community Health Study/Study Latinos). Diet was assessed two 24-hour recalls at baseline. examined associations microbiome measured by shotgun metagenomics (350 species/85 genera 1958 enzymes; n=2992 visit 2), serum metabolome untargeted metabolomics (624 metabolites; n=6198 baseline), between fiber-related bacteria (n=804 2). prospective microbial-associated (n=3579 baseline) incident T2D over 6 years. RESULTS: identified multiple bacterial genera, species, related enzymes Several (eg, Butyrivibrio , Faecalibacterium ) involved in degradation xylanase EC3.2.1.156) were positively intake, inversely prevalent T2D, favorably T2D-related metabolic traits. 159 47 which T2D. 18 these bacteria, including several microbial indolepropionate 3-phenylpropionate) risk Both favorable metabolites. The especially attenuated after further adjustment for CONCLUSIONS: Among United States Hispanics/Latinos, profiles These findings advance our understanding relationship diet

Язык: Английский

Процитировано

29
Artificial intelligence in metabolomics: a current review DOI

Jinhua Chi,

Jingmin Shu,

Ming Li

и другие.

TrAC Trends in Analytical Chemistry, Год журнала: 2024, Номер 178, С. 117852 - 117852

Опубликована: Июль 3, 2024

Язык: Английский

Процитировано

23