Research Trends and Dynamics in Single-cell RNA Sequencing for Musculoskeletal Diseases: A Scientometric and Visualization Study

International Journal of Medical Sciences, Journal Year: 2025, Volume and Issue: 22(3), P. 528 - 550

Published: Jan. 1, 2025

Background: Worldwide, approximately 1.7 billion people are afflicted with musculoskeletal (MSK) diseases, posing significant health challenges. The introduction of single-cell RNA sequencing (scRNA-seq) technology provides novel insights and approaches to comprehend the onset, progression, treatment MSK diseases. Nevertheless, there is a remarkable lack analytical descriptive studies regarding trajectory, essential research directions, current situation, pivotal focuses, upcoming perspectives. Therefore, aim this present comprehensive overview advancements made in scRNA-seq for disorders over past 15 years. Methods: It utilizes robust dataset derived from Web Science Core Collection, encompassing January 1, 2009, through September 6, 2024. To achieve this, advanced methodologies were applied conduct thorough scientometric visual analyses. Results: findings underscore preeminent role China, which contributes 63.49% total publications, thereby exerting substantial impact within domain. Notable contributions came institutions such as Shanghai Jiao Tong University, Sun Yat-sen Harvard Medical School, Liu Yun being leading contributor. Frontiers Immunology published greatest number papers field. This study identified joint bone neoplasms, fractures, intervertebral disc degeneration main focuses. Conclusion: extensive analysis benefits both experienced novice researchers by facilitating immediate access critical data, fostering innovation

Language: Английский

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Mechanisms and Strategies of Immunosenescence Effects on Non-Small Cell Lung Cancer (NSCLC) Treatment: A Comprehensive Analysis and Future Directions

Seminars in Cancer Biology, Journal Year: 2025, Volume and Issue: 109, P. 44 - 66

Published: Jan. 9, 2025

Language: Английский

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An artificial intelligence network‐guided signature for predicting outcome and immunotherapy response in lung adenocarcinoma patients based on 26 machine learning algorithms

Cell Proliferation, Journal Year: 2023, Volume and Issue: 56(4)

Published: Feb. 23, 2023

Abstract The immune cells play an increasingly vital role in influencing the proliferation, progression, and metastasis of lung adenocarcinoma (LUAD) cells. However, potential cells' specific genes‐based model remains largely unknown. In current study, by analysing single‐cell RNA sequencing (scRNA‐seq) data bulk data, tumour‐infiltrating cell (TIIC) associated signature was developed based on a total 26 machine learning (ML) algorithms. As result, TIIC score could predict survival outcomes LUAD patients across five independent datasets. showed superior performance to 168 previously established signatures LUAD. Moreover, immunofluorescence staining tissue array prognostic value. Our research revealed solid connection between tumour immunity as well metabolism. Additionally, it has been discovered that can forecast genomic change, chemotherapeutic drug susceptibility, and—most significantly—immunotherapeutic response. newly demonstrated biomarker, facilitated selection population who would benefit from future clinical stratification.

Language: Английский

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A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer

Indian Journal of Surgical Oncology, Journal Year: 2024, Volume and Issue: 16(1), P. 257 - 278

Published: Sept. 5, 2024

Language: Английский

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Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 4, 2024

Abstract Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using TCGA-LUAD dataset as discovery cohort, novel joint framework VAEjMLP based on variational autoencoder (VAE) multilayer perceptron (MLP) proposed. And Shapley Additive Explanations (SHAP) method introduced evaluate contribution feature genes classification decision, which helped us develop biologically meaningful biomarker potential scoring algorithm. Nineteen LUAD were identified, involved in regulation immune metabolic functions A prognostic risk model constructed by HLA-DRB1, SCGB1A1, HLA-DRB5 screened Cox regression analysis, dividing patients into high-risk low-risk groups. The validated external datasets. group characterized enrichment pathways higher infiltration compared group. While, accompanied an increase pathway activity. There significant differences between high- groups reprogramming aerobic glycolysis, amino acids, lipids, well angiogenic activity, epithelial-mesenchymal transition, tumorigenic cytokines, inflammatory response. Furthermore, more sensitive Afatinib, Gefitinib, Gemcitabine predicted pRRophetic This provides signatures capable revealing landscapes LUAD, may shed light identification other cancer biomarkers.

Language: Английский

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Unlocking the potential of T‐cell metabolism reprogramming: Advancing single‐cell approaches for precision immunotherapy in tumour immunity

Clinical and Translational Medicine, Journal Year: 2024, Volume and Issue: 14(3)

Published: March 1, 2024

Abstract As single‐cell RNA sequencing enables the detailed clustering of T‐cell subpopulations and facilitates analysis metabolic states metabolite dynamics, it has gained prominence as preferred tool for understanding heterogeneous cellular metabolism. Furthermore, synergistic or inhibitory effects various pathways within T cells in tumour microenvironment are coordinated, increased activity specific generally corresponds to functional activity, leading diverse behaviours related immune cells, which shows potential tumour‐specific induce persistent responses. A holistic how heterogeneity governs function subsets is key obtaining field‐level insights into immunometabolism. Therefore, exploring mechanisms underlying interplay between metabolism functions will pave way precise immunotherapy approaches future, empower us explore new methods combating tumours with enhanced efficacy.

Language: Английский

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Integration analysis of cell division cycle-associated family genes revealed potential mechanisms of gliomagenesis and constructed an artificial intelligence-driven prognostic signature

Cellular Signalling, Journal Year: 2024, Volume and Issue: 119, P. 111168 - 111168

Published: April 9, 2024

Language: Английский

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Application progress of artificial intelligence in tumor diagnosis and treatment

Frontiers in Artificial Intelligence, Journal Year: 2025, Volume and Issue: 7

Published: Jan. 7, 2025

The rapid advancement of artificial intelligence (AI) has introduced transformative opportunities in oncology, enhancing the precision and efficiency tumor diagnosis treatment. This review examines recent advancements AI applications across imaging diagnostics, pathological analysis, treatment optimization, with a particular focus on breast cancer, lung liver cancer. By synthesizing findings from peer-reviewed studies published over past decade, this paper analyzes role diagnostic accuracy, streamlining therapeutic decision-making, personalizing strategies. Additionally, addresses challenges related to integration into clinical workflows regulatory compliance. As continues evolve, its oncology promise further improvements patient outcomes, though additional research is needed address limitations ensure ethical effective deployment.

Language: Английский

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Artificial Intelligence‐Guided Identification of IGFBP7 as a Critical Indicator in Lactic Metabolism Determines Immunotherapy Response in Stomach Adenocarcinoma

Journal of Cellular and Molecular Medicine, Journal Year: 2025, Volume and Issue: 29(1)

Published: Jan. 1, 2025

ABSTRACT Due to considerable tumour heterogeneity, stomach adenocarcinoma (STAD) has a poor prognosis and varies in response treatment, making it one of the main causes cancer‐related mortality globally. Recent data point significant role for metabolic reprogramming, namely dysregulated lactic acid metabolism, evolution STAD treatment resistance. This study used series artificial intelligence‐related approaches identify IGFBP7, Schlafen family member, as critical factor determining immunotherapy metabolism patients. Computational analyses revealed that high (LM) state was associated with survival Further biological network‐based investigations identified key subnetwork closely linked LM. Machine learning techniques, such random forest least absolute shrinkage selection operator, highlighted IGFBP7 crucial indicator STAD. Functional annotations showed expression important immune inflammatory pathways. In vitro experiments demonstrated silencing suppressed cell proliferation migration. Furthermore, heightened susceptibility several chemotherapeutic drugs elevated levels. conclusion, this work sheds light on mechanisms by which lactate metabolism‐related affects milieu The results possible therapeutic target predictive biomarker

Language: Английский

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Elucidating the Mechanism of VVTT Infection Through Machine Learning and Transcriptome Analysis

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(3), P. 1203 - 1203

Published: Jan. 30, 2025

The vaccinia virus (VV) is extensively utilized as a vaccine vector in the treatment of various infectious diseases, cardiovascular immunodeficiencies, and cancers. Tiantan strain (VVTT) has been instrumental an irreplaceable eradication smallpox China; however, it still presents significant adverse toxic effects. After WHO recommended that routine vaccination be discontinued, Chinese government stopped national program 1981. outbreak monkeypox 2022 focused people’s attention on Orthopoxvirus. However, there are limited reports safety side effects VVTT. In this study, we employed combination transcriptomic analysis machine learning-based feature selection to identify key genes implicated VVTT infection process. We four learning algorithms, including random forest (RF), minimum redundancy maximum relevance (MRMR), eXtreme Gradient Boosting (XGB), least absolute shrinkage operator cross-validation (LASSOCV), for selection. Among these, XGB was found most effective used further screening, resulting optimal model with ROC curve 0.98. Our revealed involvement pathways such spinocerebellar ataxia p53 signaling pathway. Additionally, identified three critical targets during infection—ARC, JUNB, EGR2—and validated these using qPCR. research elucidates mechanism by which infects cells, enhancing our understanding vaccine. This knowledge not only facilitates development new more vaccines but also contributes deeper comprehension viral pathogenesis. By advancing molecular mechanisms underlying infection, study lays foundation Such insights crucial strengthening global health security ensuring resilient response future pandemics.

Language: Английский

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