Macrophages played an important role in the progression and treatment of head and neck squamous cell carcinoma (HNSCC). We employed weighted gene co-expression network analysis (WGCNA) to identify macrophage-related genes (MRGs) and classify patients with HNSCC into two distinct subtypes. A macrophage-related risk signature (MRS) model, comprising nine genes: IGF2BP2, PPP1R14C, SLC7A5, KRT9, RAC2, NTN4, CTLA4, APOC1, and CYP27A1, was formulated by integrating 101 machine learning algorithm combinations. We observed lower overall survival (OS) in the high-risk group and the high-risk group showed elevated expression levels in most of the immune checkpoint and human leukocyte antigen (HLA) genes, suggesting a strong immune evasion capacity. Correspondingly, TIDE score positively correlated with risk score, implying that high-risk tumors may resist immunotherapy more effectively. At the single-cell level, we noted macrophages in the tumor microenvironment (TME) predominantly stalled in the G2/M phase, potentially hindering epithelial-mesenchymal transition and playing a crucial role in the inhibition of tumor progression. Finally, the proliferation and migration abilities of HNSCC cells significantly decreased after the expression of IGF2BP2 and SLC7A5 reduced. It also decreased migration ability of macrophages and facilitated their polarization towards the M1 direction. Our study constructed a novel MRS for HNSCC, which could serve as an indicator for predicting the prognosis, immune infiltration and immunotherapy for HNSCC patients.
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http://dx.doi.org/10.1038/s41598-024-70430-6 | DOI Listing |
Chemistry
December 2024
Pandit Deendayal Energy University, Chemistry, Gandhinagar, Gujarat-382077, India, Gandhinagar, INDIA.
The accurate discrimination among various volatile organic compounds, especially ethanol and acetone possess a serious concern for metal oxide based chemiresistive sensors. The work presents a systematic approach to address the issue by utilizing superior sensing potentiality of Zn0.5Ni0.
View Article and Find Full Text PDFJAMA Netw Open
December 2024
Division of Geriatrics, Department of Medicine, University of California, San Francisco.
JAMA Netw Open
December 2024
Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London, United Kingdom.
Importance: Issues related to social connection are increasingly recognized as a global public health priority. However, there is a lack of a holistic understanding of social connection and its health impacts given that most empirical research focuses on a single or few individual concepts of social connection.
Objective: To explore patterns of social connection and their associations with health and well-being outcomes.
Int Urol Nephrol
December 2024
Department of Thoracic Surgery, West China Hospital, Sichuan University, No. 37, Guoxue Lane, Wuhou District, Chengdu, 610041, Sichuan Province, China.
This paper evaluated the bibliometric study by Li et al. (Int Urol Nephrol, 2024) on machine learning in renal medicine. Although the study claims to summarize the forefront trends and hotspots in this field, several key issues require further clarification to effectively guide future research.
View Article and Find Full Text PDFEmerg Radiol
December 2024
Emergency Radiology, Department of Radiology, Massachusetts General Hospial, Boston, USA.
Background: Emergency/trauma radiology artificial intelligence (AI) is maturing along all stages of technology readiness, with research and development (R&D) ranging from data curation and algorithm development to post-market monitoring and retraining.
Purpose: To develop an expert consensus document on best research practices and methodological priorities for emergency/trauma radiology AI.
Methods: A Delphi consensus exercise was conducted by the ASER AI/ML expert panel between 2022-2024.
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