Evidence map›Paper›PMID 30613154›Full record

ArticleOncoTargets and therapy2019

Analysis of long non-coding RNAs in glioblastoma for prognosis prediction using weighted gene co-expression network analysis, Cox regression, and L1-LASSO penalization.

Ruqing Liang, Yaqin Zhi, Guizhi Zheng, Bin Zhang, Hua Zhu, Meng Wang

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

Article in OncoTargets and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
4.5field-weighted citation impact, top 4% 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, 78 citations in OpenAlex.

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  6. The role of LncRNA LBX2-AS1 in cancers: functions, mechanisms and potential clinical utility.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2023
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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

6 authors at 3 institutions in 1 country.

Ruqing LiangDepartment of Neurology, Affiliated Hospital of Jining Medical University, Jining, Shandong Province 272000, China.
Yaqin ZhiDepartment of Oncology, Jining No 1 People's Hospital, Jining, Shandong Province 272000, China, liangruqingsd@sina.com.
Guizhi ZhengCollege of Integrated Chinese and Western Medicine, Jining Medical College, Jining, Shangdong 272067, China.
Bin ZhangDepartment of Oncology, Jining No 1 People's Hospital, Jining, Shandong Province 272000, China, liangruqingsd@sina.com.
Hua ZhuDepartment of Oncology, Jining No 1 People's Hospital, Jining, Shandong Province 272000, China, liangruqingsd@sina.com.
Meng WangDepartment of Oncology, Jining No 1 People's Hospital, Jining, Shandong Province 272000, China, liangruqingsd@sina.com.
Jining First People's Hospital · CNAffiliated Hospital of Jining Medical University · CNJining Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study focused on identification of long non-coding RNAs (lncRNAs) for prognosis prediction of glioblastoma (GBM) through weighted gene co-expression network analysis (WGCNA) and L1-penalized least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) model. MATERIALS AND

methodsWGCNA was performed based on RNA expression profiles of GBM from Chinese Glioma Genome Atlas (CGGA), National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), and the European Bioinformatics Institute ArrayExpress for the identification of GBM-related modules. Subsequently, prognostic lncRNAs were determined using LASSO Cox PH model, followed by constructing a risk scoring model based on these lncRNAs. The risk score was used to divide patients into high- and low-risk groups. Difference in survival between groups was analyzed using Kaplan-Meier survival analysis. IncRNA-mRNA networks were built for the prognostic lncRNAs, followed by pathway enrichment analysis for these networks.

resultsThis study identified eight preserved GBM-related modules, including 188 lncRNAs. Consequently, C20orf166-AS1, LINC00645, LBX2-AS1, LINC00565, LINC00641, and PRRT3-AS1 were identified by LASSO Cox PH model. A risk scoring model based on the lncRNAs was constructed that could divide patients into different risk groups with significantly different survival rates. Prognostic value of this six-lncRNA signature was validated in two independent sets. C20orf166-AS1 was associated with antigen processing and presentation and cell adhesion molecule pathways, involving nine common genes. LBX2-AS1, LINC00641, PRRT3-AS1, and LINC00565 were related to focal adhesion, extracellular matrix receptor interaction, and mitogen-activated protein kinase signaling pathways, which shared 12 common genes.

conclusionThis prognostic six-lncRNA signature may improve prognosis prediction of GBM. This study reveals many pathways and genes involved in the mechanisms behind these lncRNAs.

Indexed as

lncRNAnetworkpathwayrisk scoreWGCNA

Identifiers

PMID30613154
PMCPMC6306053
OpenAlexW2903945923

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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 Socratic graph.