Evidence map›Paper›PMID 36814212›Full record

ArticleBMC oral health2023

An autophagy-related long non-coding RNA signature in tongue squamous cell carcinoma.

Yinting Ren, Junlong Da, Junyu Ren, Ye Song, Jingying Han

Abstract read
In one paragraph

Article in BMC oral health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

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4 · The record

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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

5 authors.

Yinting RenDepartment of Orthodontics, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.
Junlong DaInstitute of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Junyu RenOral Implant Center, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Ye SongDepartment of Orthodontics, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.
Jingying HanDepartment of Orthodontics, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China. jingying5787@163.com.

Funding

Natural Science Foundation of Heilongjiang Province LH2019H015
6 · The paper itself

Abstract

backgroundTongue squamous cell carcinoma (TSCC) is the most common oral cancer with a poor prognosis. At present, there is not any systematic study on autophagy-related long non-coding RNA (lncRNA) to predict the survival of patients with TSCC. MATERIAL AND

methodsIn this research, the cohort of TSCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analysis showed that ten lncRNAs related to autophagy AC010326.3, AL160006.1, AL122010.1, AC139530.1, AC092747.4, AL139287.1, MIR503HG, AC009318.2, LINC01711, and LINC02560 are significantly correlated with prognosis. Based on these lncRNAs, a prognostic signature was established. This signature has an AUC value of 0.782, which accurately distinguishes patients of TSCC into high-risk and low-risk groups in different clinical hierarchical information (such as gender, age, etc.).

resultsThe clinical nomogram with autophagy-related lncRNA prognostic characteristics has a concordance index of 0.81, and accurately predicts the survival time at 1-year and 3-year of TSCC patients. Related functional enrichment results indicate that the pathways of the high-risk group are enriched on cancer and autophagy.

conclusionsThe autophagy-related lncRNA prognostic signature established in this study could accurately predict the prognosis of TSCC patients and may be a molecular biomarker and therapeutic target.

Indexed as

Carcinoma, Squamous CellRNA, Long NoncodingTongue NeoplasmsAutophagyHumansPrognosisTongueRNA, Long NoncodingAutophagyLong non-coding RNAPrognostic signatureTCGATongue squamous cell carcinoma

Identifiers

PMID36814212
PMCPMC9945660

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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.