Evidence map›Paper›PMID 36237238›Full record

ArticleTranslational cancer research2022

Screening and verification of prognostic lncRNA markers related to immune infiltration in the metastasis of osteosarcoma.

Chuan He, Xiaolong Wang, Linying Ni, Chunyu Song, Wei Mai

Open access · diamondAbstract read
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Article in Translational cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 54% of its field
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Chuan HeDepartment of Orthopedics, Harbin Medical University Cancer Hospital, Harbin, China.
Xiaolong WangDepartment of Orthopedics, Harbin Medical University Cancer Hospital, Harbin, China.
Linying NiDepartment of Orthopedics, Harbin Medical University Cancer Hospital, Harbin, China.
Chunyu SongDepartment of Orthopedics, Harbin Medical University Cancer Hospital, Harbin, China.
Wei MaiDepartment of Orthopedics, Harbin Medical University Cancer Hospital, Harbin, China.
Third Affiliated Hospital of Harbin Medical University · CNHarbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: We sought to screen and verify the long non-coding ribonucleic acids (lncRNAs) related to immune infiltration in metastatic osteosarcoma (OS). Methods: We downloaded the RNA-sequencing expression data from The Cancer Genome Atlas (TCGA) database as the training data set. We downloaded the GSE39055 data set from the National Center for Biotechnology Information, Gene Expression Omnibus as the validation data set. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to screen the optimized lncRNA combinations. Kaplan-Meier curves were used to evaluate the associations between the lncRNAs and actual prognosis. The independent survival prognosis clinical factors were obtained by univariate and multivariate Cox analyses. A nomogram was established to explore the correlation between survival rate and risk information. The Tumor IMmune Estimation Resource was applied to estimate the composition of 6 subtypes of immune infiltration cells. Results: In total, 1,398 lncRNAs and 14,631 messenger RNAs were screened from TCGA data set, and divided into the low and high immunity groups. The Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data (ESTIMATE) scores differed significantly between the samples in the two groups. Additionally, 5 optimized lncRNA combinations were obtained using the LASSO algorithm. Risk factors including age, metastatic tumor, and risk-score (RS) were related to the prognosis of OS patients. The survival rates predicted by the nomogram model were consistent with the actual survival rates of OS patients. Finally, we found that RS was negatively correlated with the proportion of immune cells. Conclusions: In total, 5 feature lncRNAs were identified as novel biomarkers for OS. Next, a RS nomogram model was constructed based on the 5 identified lncRNAs. This model predicted the survival rates and prognoses of OS patients well.

Indexed as

immune infiltrationlong non-coding ribonucleic acid (lncRNA)metastasisOsteosarcoma (OS)

Identifiers

PMID36237238
PMCPMC9552095
OpenAlexW4298030734

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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.