Evidence map›Paper›PMID 39253245›Full record

ArticleHeliyon2024

A diagnostic signature developed based on the necroptosis-related genes and its association with immune infiltration in osteosarcoma.

Yiying Bian, Jixiang Shi, Ziyun Chen, Ji Fang, Weidong Chen, Yutong Zou, Hao Yao, Jian Tu, Yan Liao, Xianbiao Xie and 1 more

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Review
  2. Article
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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

11 authors.

Yiying BianDepartment of Musculoskeletal Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
Jixiang ShiDepartment of Musculoskeletal Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
Ziyun ChenDepartment of Musculoskeletal Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
Ji FangDepartment of Musculoskeletal Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
Weidong ChenDepartment of Musculoskeletal Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
Yutong ZouDepartment of Musculoskeletal Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
Hao YaoDepartment of Musculoskeletal Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
Jian TuDepartment of Musculoskeletal Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
Yan LiaoDepartment of Musculoskeletal Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
Xianbiao XieDepartment of Musculoskeletal Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
Jingnan ShenDepartment of Musculoskeletal Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Osteosarcoma is a bone-derived malignancy that often leads to lung metastasis and death. Material and methods: The RNA-seq data of TARGET-osteosarcoma were collected from TARGET database. GSE16088 and GSE12865 datasets of osteosarcoma x from Gene Expression Database (GEO) were donwloaded. ConsensusClusterPlus was used for molecular subtype classification. Univariate Cox and Lasso regression was employed to develop a risk model. To analyze the regulatory effects of model feature genes on the malignant phenotype of osteosarcoma cell lines, qRT-PCR, Transwell and wound healing assays were performed. The abundance of immune cell infiltration was assessed using MCP-Counter, Gene Set Enrichment Analysis (GSEA), and ESTIMATE. The Tumor Immune Dysfunction and Exclusion (TIDE) software was employed to evaluate immunotherapy and response to conventional chemotherapy drugs. Results: Three clusters (C1, C2 and C3) were classified using 39 necroptosis score-associated genes. In general, C1 and C2 showed better prognosis outcome and lower death rate than C3. Specifically, C2 could benefit more from immunotherapy, while C3 was more sensitive to traditional medicines, and C1 had higher immune cell infiltration. Next, an 8-gene signature and a risk score model were developed, with a low risk score indicating better survival and immune cell infiltration. ROC analysis showed that 1-, 3-, and 5-year overall survival of osteosarcoma could be correctly predicted by the risk score model. Cellular experiments revealed that the model feature gene IFITM3 promoted the osteosarcoma cell migration and invasion. Furthermore, the overall survival of osteosarcoma patients from TARGET and validation datasets can be accurately evaluated using the nomogram model. Conclusions: Our prognostic model developed using necroptosis genes could facilitate the prognostic prediction for patients suffering from osteosarcoma, offering potential osteosarcoma targets.

Indexed as

NecroptosisOsteosarcomaPrognosisRisk modelTumor microenvironment (TME)

Identifiers

PMID39253245
PMCPMC11381599

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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.