Evidence map›Paper›PMID 34594376›Full record

ArticleJournal of oncology2021

A Radioresponse-Related lncRNA Biomarker Signature for Risk Classification and Prognosis Prediction in Non-Small-Cell Lung Cancer.

Jiahang Song, Shuming Zhang, Yuanyuan Sun, Junjie Gu, Ziqi Ye, Xinchen Sun, Qiyun Tang

Abstract read
In one paragraph

Article in Journal of oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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.

Jiahang SongDepartment of Gerontology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China.ORCID https://orcid.org/0000-0001-7858-2096
Shuming ZhangDepartment of Gerontology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China.ORCID https://orcid.org/0000-0002-3380-4633
Yuanyuan SunDepartment of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China.ORCID https://orcid.org/0000-0002-3479-1889
Junjie GuDepartment of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China.ORCID https://orcid.org/0000-0002-3393-9220
Ziqi YeDepartment of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China.ORCID https://orcid.org/0000-0003-0064-9005
Xinchen SunDepartment of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China.ORCID https://orcid.org/0000-0001-7183-1862
Qiyun TangDepartment of Gerontology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China.ORCID https://orcid.org/0000-0003-2408-7908

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeRadiotherapy resistance is now recognized as the major obstacle to the effective therapeutic management of non-small-cell lung cancer (NSCLC). As a single biomarker has limited effect in stratifying NSCLC patients, this research aimed to identify long non-coding RNAs (lncRNAs) correlated with radiotherapy response to ameliorate forecast of NSCLC prognosis.

methodsIn a cohort of NSCLC patients with radiotherapy history (

resultsFour radioresponse-related lncRNAs (CASC19, LINC01977, LINC02471, and MAGI2-AS3) were screened to create a prognostic signature. Then, we described a lncRNA signature-based regulatory network and explored the correlation of the immune microenvironment and the signature. Additionally,

conclusionWe provided a novel radioresponse-related lncRNAs signature with excellent clinical potency for an effective prognostic forecast of patients.

Identifiers

PMID34594376
PMCPMC8478572

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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.