Evidence map›Paper›PMID 35515114›Full record

ArticleFrontiers in oncology2022

Identification of Cell Subpopulations and Interactive Signaling Pathways From a Single-Cell RNA Sequencing Dataset in Osteosarcoma: A Comprehensive Bioinformatics Analysis.

Rong Wu, Xiaojie Dou, Haidong Li, Zhenguo Sun, Heng Li, Yuxin Shen, Wei Weng, Jikang Min

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.5field-weighted citation impact, top 17% of its field
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

8 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
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  5. Article
  6. Article
  7. Cellular and Genetic Background of Osteosarcoma.Current issues in molecular biology · 2023
    Review
  8. 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

8 authors at 1 institution in 1 country.

Rong WuDepartment of Orthopaedics, The First People's Hospital of Huzhou, The First Affiliated Hospital of Huzhou University, Huzhou, China.
Xiaojie DouDepartment of Orthopaedics, The First People's Hospital of Huzhou, The First Affiliated Hospital of Huzhou University, Huzhou, China.
Haidong LiDepartment of Orthopaedics, The First People's Hospital of Huzhou, The First Affiliated Hospital of Huzhou University, Huzhou, China.
Zhenguo SunDepartment of Orthopaedics, The First People's Hospital of Huzhou, The First Affiliated Hospital of Huzhou University, Huzhou, China.
Heng LiDepartment of Orthopaedics, The First People's Hospital of Huzhou, The First Affiliated Hospital of Huzhou University, Huzhou, China.
Yuxin ShenDepartment of Orthopaedics, The First People's Hospital of Huzhou, The First Affiliated Hospital of Huzhou University, Huzhou, China.
Wei WengDepartment of Orthopaedics, The First People's Hospital of Huzhou, The First Affiliated Hospital of Huzhou University, Huzhou, China.
Jikang MinDepartment of Orthopaedics, The First People's Hospital of Huzhou, The First Affiliated Hospital of Huzhou University, Huzhou, China.
Huzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Osteosarcoma is a type of highly aggressive bone tumor arising from primitive cells of mesenchymal origin in adults and is associated with a high rate of tumor relapse. However, there is an urgent need to clarify the molecular mechanisms underlying osteosarcoma development. The present study performed integrated bioinformatics analysis in a single-cell RNA sequencing dataset and explored the potential interactive signaling pathways associated with osteosarcoma development. Single-cell transcriptomic analysis of osteosarcoma tissues was performed by using the Seurat R package, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes was performed by using the clusterProfiler R package, and the cell-cell interaction analysis was performed by using the CellPhoneDB package. Our results showed that 11 clustered cell types were identified across 11 osteosarcoma tissues, with cell types including "osteoblastic", "myeloid", "osteoblastic_proli", "osteoclast", and "tumor-infiltrating lymphocytes (TILs)" as the main types. The DEGs between different cell types from primary, metastatic, and recurrent osteosarcomas were mainly enriched in the GO terms including "negative regulation of hydrolase activity", "regulation of peptidase activity", "regulation of binding", "negative regulation of proteolysis", and "negative regulation of peptidase activity" and in the KEGG pathways including "transcriptional misregulation in cancer", "cellular senescence", "apoptosis", "FoxO signaling pathway", "cell cycle", "NF-kappa B signaling pathway", "p53 signaling pathway", "pentose phosphate pathway", and "protein export". For the cell-cell communication network analysis, the different interaction profiles between cell types were detected among primary, metastatic, and recurrent osteosarcomas. Further exploration of the KEGG pathway revealed that these ligand/receptor interactions may be associated with the NF-κB signaling pathway and its interacted mediators. In conclusion, the present study for the first time explored the scRNA-seq dataset in osteosarcoma, and our results revealed the 11 clustered cell types and demonstrated the novel cell-cell interactions among different cell types in primary, metastatic, and recurrent osteosarcomas. The NF-κB signaling pathway may play a key role in regulating the TME of osteosarcoma. The present study may provide new insights into understanding the molecular mechanisms of osteosarcoma pathophysiology.

Indexed as

cell typesinteractionNF-κBosteosarcomascRNA sequencing

Identifiers

PMID35515114
PMCPMC9066489
OpenAlexW4224290103

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Registered trials

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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 OpenQuestion graph.