Research progress and the prospect of using single-cell sequencing technology to explore the characteristics of the tumor microenvironment DOI Creative Commons

Wenyige Zhang,

Xue Zhang, Feifei Teng

et al.

Genes & Diseases, Journal Year: 2024, Volume and Issue: 12(1), P. 101239 - 101239

Published: Feb. 3, 2024

In precision cancer therapy, addressing intra-tumor heterogeneity poses a significant obstacle. Due to the of each cell subtype and between cells within tumor, sensitivity resistance different patients targeted drugs, chemotherapy, etc., are inconsistent. Concerning specific tumor type, many feasible treatments or combinations can be used by specifically targeting microenvironment. To solve this problem, it is necessary further study Single-cell sequencing techniques dissect distinct populations isolating using statistical computational methods. This technology may assist in selection combination obtained subset information crucial for rational application therapy. review, we summarized research advances single-cell microenvironment, including most commonly genomic transcriptomic sequencing, their future development direction was proposed. The has been expanded include epigenomics, proteomics, metabolomics, microbiome analysis. integration these omics approaches significantly advanced multiomics technology. innovative approach holds immense potential various fields, such as biological medical investigations. Finally, discussed advantages disadvantages explore

Language: Английский

Emerging therapies in cancer metabolism DOI Creative Commons
Yi Xiao, Tian‐Jian Yu, Ying Xu

et al.

Cell Metabolism, Journal Year: 2023, Volume and Issue: 35(8), P. 1283 - 1303

Published: Aug. 1, 2023

Language: Английский

Citations

96
Applications of machine learning in metabolomics: Disease modeling and classification DOI Creative Commons
Aya Galal,

Marwa Talal,

Ahmed A. Moustafa

et al.

Frontiers in Genetics, Journal Year: 2022, Volume and Issue: 13

Published: Nov. 24, 2022

Metabolomics research has recently gained popularity because it enables the study of biological traits at biochemical level and, as a result, can directly reveal what occurs in cell or tissue based on health disease status, complementing other omics such genomics and transcriptomics. Like high-throughput experiments, metabolomics produces vast volumes complex data. The application machine learning (ML) to analyze data, recognize patterns, build models is expanding across multiple fields. In same way, ML methods are utilized for classification, regression, clustering highly metabolomic This review discusses how modeling diagnosis be enhanced via deep comprehensive profiling using ML. We discuss general layout metabolic workflow fundamental techniques used including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), (DL). Finally, we present advantages disadvantages various provide suggestions different data analysis scenarios.

Language: Английский

Citations

91
Overview and countermeasures of cancer burden in China DOI Open Access

Yian Wang,

Qijia Yan,

Chunmei Fan

et al.

Science China Life Sciences, Journal Year: 2023, Volume and Issue: 66(11), P. 2515 - 2526

Published: April 13, 2023

Language: Английский

Citations

83
Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment DOI Creative Commons
Chaoyi Zhang, Jin Xu,

Rong Tang

et al.

Journal of Hematology & Oncology, Journal Year: 2023, Volume and Issue: 16(1)

Published: Nov. 27, 2023

Research into the potential benefits of artificial intelligence for comprehending intricate biology cancer has grown as a result widespread use deep learning and machine in healthcare sector availability highly specialized datasets. Here, we review new approaches how they are being used oncology. We describe might be detection, prognosis, administration treatments introduce latest large language models such ChatGPT oncology clinics. highlight applications omics data types, offer perspectives on various types combined to create decision-support tools. also evaluate present constraints challenges applying precision Finally, discuss current may surmounted make useful clinical settings future.

Language: Английский

Citations

53
Plasma protein biomarkers for early prediction of lung cancer DOI Creative Commons
Michael Davies, Takahiro Sato, Haitham Ashoor

et al.

EBioMedicine, Journal Year: 2023, Volume and Issue: 93, P. 104686 - 104686

Published: June 26, 2023

Individual plasma proteins have been identified as minimally invasive biomarkers for lung cancer diagnosis with potential utility in early detection. Plasma proteomes provide insight into contributing biological factors; we investigated their future prediction.The Olink® Explore-3072 platform quantitated 2941 496 Liverpool Lung Project samples, including 131 cases taken 1-10 years prior to diagnosis, 237 controls, and 90 subjects at multiple times. 1112 significantly associated haemolysis were excluded. Feature selection bootstrapping differentially expressed proteins, subsequently modelled prediction validated UK Biobank data.For samples 1-3 pre-diagnosis, 240 different cases; 1-5 year 117 of these 150 further identified, mapping pathways. Four machine learning algorithms gave median AUCs 0.76-0.90 0.73-0.83 the respectively. External validation 0.75 (1-3 year) 0.69 (1-5 year), AUC 0.7 up 12 diagnosis. The models independent age, smoking duration, histology presence COPD.The proteome provides which may be used identify those greatest risk cancer. pathways are when is more imminent, indicating that both inherent identified.Janssen Pharmaceuticals Research Collaboration Award; Roy Castle Cancer Foundation.

Language: Английский

Citations

47
Cancer metabolites: promising biomarkers for cancer liquid biopsy DOI Creative Commons
Wenxiang Wang, Zhiwei Rong, Guangxi Wang

et al.

Biomarker Research, Journal Year: 2023, Volume and Issue: 11(1)

Published: June 30, 2023

Abstract Cancer exerts a multitude of effects on metabolism, including the reprogramming cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation cancer cells adaptation to tumor microenvironment. There is growing body evidence suggesting aberrant play pivotal roles tumorigenesis metastasis, have potential serve as biomarkers for personalized therapy. Importantly, high-throughput metabolomics detection techniques machine learning approaches offer tremendous clinical oncology by enabling identification cancer-specific metabolites. Emerging research indicates circulating great promise noninvasive detection. Therefore, this review summarizes reported abnormal cancer-related last decade highlights application liquid biopsy, specimens, technologies, methods, challenges. The provides insights into promising tool applications.

Language: Английский

Citations

46
Rhodiola crenulata alleviates hypobaric hypoxia-induced brain injury by maintaining BBB integrity and balancing energy metabolism dysfunction DOI

Ya Hou,

Fuhan Fan,

Na Xie

et al.

Phytomedicine, Journal Year: 2024, Volume and Issue: 128, P. 155529 - 155529

Published: March 11, 2024

Language: Английский

Citations

32
Tumor metabolic regulators: key drivers of metabolic reprogramming and the promising targets in cancer therapy DOI Creative Commons
Kun Huang, Ying Han, Yihong Chen

et al.

Molecular Cancer, Journal Year: 2025, Volume and Issue: 24(1)

Published: Jan. 9, 2025

Metabolic reprogramming within the tumor microenvironment (TME) is a hallmark of cancer and crucial determinant progression. Research indicates that various metabolic regulators form network in TME interact with immune cells, coordinating response. dysregulation creates an immunosuppressive TME, impairing antitumor In this review, we discuss how affect cell crosstalk TME. We also summarize recent clinical trials involving challenges metabolism-based therapies translation. word, our review distills key regulatory factors their mechanisms action from complex metabolism, identified as regulators. These provide theoretical basis research direction for development new strategies targets therapy based on reprogramming. Refining Depicting between stromal cells during Emphasizing unresolved translation advantages personalized treatment. Providing support therapies.

Language: Английский

Citations

2
Impacts of polypropylene microplastics on lipid profiles of mouse liver uncovered by lipidomics analysis and Raman spectroscopy DOI Open Access
Mingying Liu,

Ju Mu,

Miao Wang

et al.

Journal of Hazardous Materials, Journal Year: 2023, Volume and Issue: 458, P. 131918 - 131918

Published: June 22, 2023

Language: Английский

Citations

25
Multi-omics analysis reveals NNMT as a master metabolic regulator of metastasis in esophageal squamous cell carcinoma DOI Creative Commons
Qi Huang,

Haiming Chen,

Dandan Yin

et al.

npj Precision Oncology, Journal Year: 2024, Volume and Issue: 8(1)

Published: Jan. 30, 2024

Abstract Metabolic reprogramming has been observed in cancer metastasis, whereas metabolic changes required for malignant cells during lymph node metastasis of esophageal squamous cell carcinoma (ESCC) are still poorly understood. Here, we performed single-cell RNA sequencing (scRNA-seq) paired ESCC tumor tissues and nodes to uncover the microenvironment (TME) pathways. By integrating analyses scRNA-seq data with metabolomics plasma samples, found nicotinate nicotinamide metabolism pathway was dysregulated patients (LN + ), exhibiting as significantly increased 1-methylnicotinamide (MNA) both tumors plasma. Further indicated high expression N-methyltransferase (NNMT), which converts active methyl groups from universal donor, S-adenosylmethionine (SAM), stable MNA, contributed MNA LN ESCC. NNMT promotes epithelial–mesenchymal transition (EMT) vitro vivo by inhibiting E-cadherin expression. Mechanically, consumed too much group decreased H3K4me3 modification at promoter inhibited m6A mRNA, therefore transcriptional post-transcriptional level. Finally, a detection method build based on metabolites, showed good performance among patients. For ESCC, this work supports is master regulator cross-talk between cellular epigenetic modifications, may be therapeutic target.

Language: Английский

Citations

14