Evidence map›Paper›PMID 38774079›Full record

ArticleHeliyon2024

Multi-omics characterization of macrophage polarization-related features in osteoarthritis based on a machine learning computational framework.

Ping Hu, Beining Li, Zhenyu Yin, Peng Peng, Jiangang Cao, Wanyu Xie, Liang Liu, Fujiang Cao, Bin Zhang

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Review
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ping HuDepartment of Othopaedics, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin, 300052, China.
Beining LiDepartment of Othopaedics, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin, 300052, China.
Zhenyu YinDepartment of Othopaedics, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin, 300052, China.
Peng PengDepartment of Othopaedics, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin, 300052, China.
Jiangang CaoDepartment of Sports Injury and Arthroscopy, Tianjin Hospital of Tianjin University, China.
Wanyu XieDepartment of Othopaedics, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin, 300052, China.
Liang LiuOrthopaedic Center of Beijing Luhe Hospital, Capital Medical University, China.
Fujiang CaoDepartment of Othopaedics, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin, 300052, China.
Bin ZhangDepartment of Othopaedics, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin, 300052, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: OA imposes a heavy burden on patients and society in that its mechanism is still unclear, and there is a lack of effective targeted therapy other than surgery. Methods: The osteoarthritis dataset GSE55235 was downloaded from the GEO database and analyzed for differential genes by limma package, followed by analysis of immune-related modules by xcell immune infiltration combined with the WGCNA method, and macrophage polarization-related genes were downloaded according to the Genecard database, and VennDiagram was used to determine their intersection. These genes were also subjected to gene ontology (GO), disease ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Using machine learning, the key osteoarthritis genes were finally screened. Using single gene GSEA and GSVA, we examined the significance of these key gene functions in immune cell and macrophage pathways. Next, we confirmed the correctness of the hub gene expression profile using the GSE55457 dataset and the ROC curve. Finally, we projected TF, miRNA, and possible therapeutic drugs using the miRNet, TargetScanHuman, ENCOR, and NetworkAnalyst databases, as well as Enrichr. Results: VennDiagram obtained 71 crossover genes for DEGs, WGCNA-immune modules, and Genecards; functional enrichment demonstrated NF-κB, IL-17 signaling pathway play an important role in osteoarthritis-macrophage polarization genes; machine learning finally identified CSF1R, CX3CR1, CEBPB, and TLR7 as hub genes; GSVA analysis showed that CSF1R, CEBPB play essential roles in immune infiltration and macrophage pathway; validation dataset GSE55457 analyzed hub genes were statistically different between osteoarthritis and healthy controls, and the AUC values of ROC for CSF1R, CX3CR1, CEBPB and TLR7 were more outstanding than 0.65. Conclusions: CSF1R, CEBPB, CX3CR1, and TLR7 are potential diagnostic biomarkers for osteoarthritis, and CSF1R and CEBPB play an important role in regulating macrophage polarization in osteoarthritis progression and are expected to be new drug targets.

Indexed as

BioinformaticsMachine learningMacrophage polarizationOsteoarthritisWGCNA

Identifiers

PMID38774079
PMCPMC11106839

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.