Evidence map›Paper›PMID 39502334›Full record

ArticleFrontiers in genetics2024

Elucidating macrophage scavenger receptor 1's mechanistic contribution as a shared molecular mediator in obesity and thyroid cancer pathogenesis via bioinformatics analysis.

Fangjian Shang, Zhe Xu, Haobo Wang, Bin Xu, Ning Li, Jiakai Zhang, Xuan Li, Zhen Zhao, Xi Zhang, Bo Liu and 1 more

Abstract read
In one paragraph

Article in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Fangjian ShangDepartment of General Surgery, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Zhe XuDepartment of Urology, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Haobo WangDepartment of General Surgery, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Bin XuDepartment of General Surgery, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Ning LiDepartment of General Surgery, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Jiakai ZhangDepartment of Radiology, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Xuan LiDepartment of Pharmacology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Zhen ZhaoDepartment of General Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Xi ZhangDepartment of General Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Bo LiuDepartment of General Surgery, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Zengren ZhaoDepartment of General Surgery, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Obesity is a disease characterized by the excessive accumulation of fat. Concurrently, thyroid carcinoma (THCA) stands as the foremost endocrine malignancy. Despite the observed escalation in concurrent prevalence of both conditions, the underlying interconnections remain elusive. This indicates the need to identify potential biomarkers to predict the pathways through which obesity and THCA coexist. Methods: The study employed a variety of methods, including differential gene expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and gene enrichment analysis. It was also supplemented with immunohistochemical data from the Human Protein Atlas (HPA), advanced machine learning techniques, and related experiments such as qPCR, to identify important pathways and key genes shared between obesity and THCA. Results: Through differential gene expression analysis, WGCNA, and machine learning methods, we identified three biomarkers (IL6R, GZMB, and MSR1) associated with obesity. After validation analysis using THCA-related datasets and biological experiments, we selected Macrophage Scavenger Receptor 1 (MSR1) as a key gene for THCA analysis. The final analysis revealed that MSR1 is closely related to the degree of immune cell infiltration in patients with obesity and THCA, suggesting that this gene may be a potential intervention target for both obesity and THCA. Discussion: Our research indicates that MSR1 may influence the occurrence and development of obesity and THCA by regulating the infiltration level of immune cells. This lays the foundation for future research on targeted therapies based on their shared mechanisms.

Indexed as

bioinformatics analysismachine learningmacrophage scavenger receptor 1obesitythyroid carcinoma

Identifiers

PMID39502334
PMCPMC11534819

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.