Evidence map›Paper›PMID 38212768›Full record

ArticleJournal of biomedical science2024

Exploring the relationship between metabolism and immune microenvironment in osteosarcoma based on metabolic pathways.

Changwu Wu, Jun Tan, Hong Shen, Chao Deng, Christian Kleber, Georg Osterhoff, Nikolas Schopow

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 1 pooled it
8.3field-weighted citation impact, top 2% 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

34 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

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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

7 authors at 2 institutions in 2 countries.

Changwu WuDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Jun TanDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China. tanjunsea@csu.edu.cn.
Hong ShenNational Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China. hongshen2000@csu.edu.cn.ORCID http://orcid.org/0000-0002-6456-8231
Chao DengDepartment of Orthopedics, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Christian KleberSarcoma Center, Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany.
Georg OsterhoffSarcoma Center, Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany.
Nikolas SchopowSarcoma Center, Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany.
Central South University · CNUniversity Hospital Leipzig · DE

Funding

China Postdoctoral Science Foundation No. 2022M723560China Postdoctoral Science Foundation No. 2023M733960National Natural Science Foundation of China No. 82203397National Natural Science Foundation of China No. 82303253Natural Science Foundation of Changsha No. kq2202375Natural Science Foundation of Hunan Province No. 2022JJ40814Youth Foundation of Xiangya Hospital NO. 2021Q06
6 · The paper itself

Abstract

backgroundMetabolic remodeling and changes in tumor immune microenvironment (TIME) in osteosarcoma are important factors affecting prognosis and treatment. However, the relationship between metabolism and TIME needs to be further explored.

methodsRNA-Seq data and clinical information of 84 patients with osteosarcoma from the TARGET database and an independent cohort from the GEO database were included in this study. The activity of seven metabolic super-pathways and immune infiltration levels were inferred in osteosarcoma patients. Metabolism-related genes (MRGs) were identified and different metabolic clusters and MRG-related gene clusters were identified using unsupervised clustering. Then the TIME differences between the different clusters were compared. In addition, an MRGs-based risk model was constructed and the role of a key risk gene, ST3GAL4, in osteosarcoma cells was explored using molecular biological experiments.

resultsThis study revealed four key metabolic pathways in osteosarcoma, with vitamin and cofactor metabolism being the most relevant to prognosis and to TIME. Two metabolic pathway-related clusters (C1 and C2) were identified, with some differences in immune activating cell infiltration between the two clusters, and C2 was more likely to respond to two chemotherapeutic agents than C1. Three MRG-related gene clusters (GC1-3) were also identified, with significant differences in prognosis among the three clusters. GC2 and GC3 had higher immune cell infiltration than GC1. GC3 is most likely to respond to immune checkpoint blockade and to three commonly used clinical drugs. A metabolism-related risk model was developed and validated. The risk model has strong prognostic predictive power and the low-risk group has a higher level of immune infiltration than the high-risk group. Knockdown of ST3GAL4 significantly inhibited proliferation, migration, invasion and glycolysis of osteosarcoma cells and inhibited the M2 polarization of macrophages.

conclusionThe metabolism of vitamins and cofactors is an important prognostic regulator of TIME in osteosarcoma, MRG-related gene clusters can well reflect changes in osteosarcoma TIME and predict chemotherapy and immunotherapy response. The metabolism-related risk model may serve as a useful prognostic predictor. ST3GAL4 plays a critical role in the progression, glycolysis, and TIME of osteosarcoma cells.

Indexed as

Bone NeoplasmsOsteosarcomaHumansImmunotherapyMetabolic Networks and PathwaysPrognosisTumor MicroenvironmentVitaminsVitaminsMetabolismOsteosarcomaPrognosisST3GAL4Treatment responseTumor immune microenvironmentVitamin and cofactor

Identifiers

PMID38212768
PMCPMC10785352
OpenAlexW4390793614

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.