Evidence map›Paper›PMID 34487018›Full record

ArticleCancer biomarkers : section A of Disease markers2022

Development and validation of stemness associated LncRNA based prognostic model for lung adenocarcinoma patients.

Annesha Chatterjee, Seema Khadirnaikar, Sudhanshu Shukla

Abstract read
In one paragraph

Article in Cancer biomarkers : section A of Disease markers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Annesha Chatterjee
Seema Khadirnaikar
Sudhanshu Shukla

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn increasing number of studies are indicating that the stemness phenotype is a critical determinant of the Lung adenocarcinoma (LUAD) patient's response. Thus, it is crucial to identify novel biomarkers for stemness determination.

objectiveHere, we aim to develop a robust LncRNAs based prognostic signature with a stemness association for the LUAD patients.

methodsRNA-seq and clinical data were downloaded from the existing database. The data were analysed using Cox regression, KM-plot, GSEA, and T-test.

resultsInitially, we used the TCGA dataset to characterize the stemness phenotype in LUAD. The commonly expressed LncRNAs in TCGA and MCTP cohort were then used as input for the Cox-regression analysis. The top three LncRNAs were selected to build a prognostic model, which was the best prognosticator in multivariate analysis with stage and previously published prognosticators. The characterization of poor surviving patients using various analysis showed high stemness properties and low expression of differentiation markers. Furthermore, we validated the prognostic score in an independent MCTP cohort of patients. In the MCTP cohort, prognostic score significantly predicted survival independent of stage and previous prognosticators.

conclusionTaken together, in this study, we have developed and validated a new prognostic score associated with the stemness phenotype.

Indexed as

Adenocarcinoma of LungLung NeoplasmsRNA, Long NoncodingBiomarkers, TumorHumansPrognosisBiomarkers, TumorRNA, Long NoncodingbiomarkerLung cancerprognostic modelstemness

Identifiers

PMID34487018
PMCPMC12364133

What OpenQuestion holds

Textmetadata
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.