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DOI 10.1177/11769351251331508

Медицинская библиотека · Cancer informatics · 2025

Single-Cell Transcriptome Analyses of Four Pain Related Genes in Osteosarcoma.

Feleke Mesalie, Lin Haiyingjie, Liu Yun, Mo Liang, Rothzerg Emel, Song Dezhi, Zhao Jinmin, Feng Wenyu, Xu Jiake

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Аннотация

OBJECTIVE

Osteosarcoma (OS) is a rare and complex form of cancer that mostly affects children and adolescents. Pain is a common symptom for patients in OS which causes significant unhappiness and persistent aches. To date, there is minimal knowledge on the mechanisms underlying OS induced pain and few treatment options for patients. Previous genetic studies have demonstrated that the panel of four genes, artemin (ARTN), persephin (PSPN), glial cell line-derived neurotropic factor (GDNF), and neurturin (NRTN) are associated with the regulation of pain processing in OS and analgesic responses.

METHODS

In the present study, by utilising a scRNA-seq OS dataset, we aimed to measure the gene expression levels of four pain related genes, and compare them between the different cell types in human OS tissues and cell lines.

RESULTS

Within a complex and diverse range of cell types in OS tissues, including osteoblastic OS cells, carcinoma associated fibroblasts (CAFs), B cells, myeloid cells 1, myeloid cells 2, NK/T cells, plasmocytes, ARTN and NRTN genes had the highest expression in Osteoblastic OS cells, GDNF gene had a peak expression in carcinoma associated fibroblasts, and PSPN gene in endothelial cells. In addition, all four genes showed deferential pattern of expression in 16 OS cell lines.

CONCLUSION

Future studies should investigate the potential to target deferentially expressed pain-related genes in specific cell types of OS for therapeutic benefit to improve the quality of life for patients living with pain caused by OS.

Для цитирования: Feleke Mesalie, Lin Haiyingjie, Liu Yun, Mo Liang, Rothzerg Emel, Song Dezhi, Zhao Jinmin, Feng Wenyu, Xu Jiake. Single-Cell Transcriptome Analyses of Four Pain Related Genes in Osteosarcoma.. Cancer informatics. 2025. DOI: 10.1177/11769351251331508.
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