Evidence map›Paper›PMID 30675311›Full record

ArticleOncology letters2019

Long non-coding RNA LOC541471: A novel prognostic biomarker for head and neck squamous cell carcinoma.

Hua Wu, Da Hai Yu, Mian Hua Wu, Teng Huang

Abstract read
In one paragraph

Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 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

4 authors.

Hua WuFirst Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210000, P.R. China.
Da Hai YuDepartment of Radiation Oncology, Affiliated Hospital of Nanjing University of Traditional Chinese Medicine, Nanjing, Jiangsu 210000, P.R. China.
Mian Hua WuFirst Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210000, P.R. China.
Teng HuangDepartment of Radiation Oncology, Affiliated Hospital of Nanjing University of Traditional Chinese Medicine, Nanjing, Jiangsu 210000, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Head and neck squamous cell carcinoma (HNSCC) is an aggressive cancer. Early detection and management of HNSCC may prevent progression of the disease. Long non-coding RNAs (lncRNAs) may serve as prognostic biomarkers for various cancer types. The current study downloaded an RNA-Seq dataset containing 43 tumor-normal pairs. An independent t-test identified that the expression level of lncRNA LOC541471 was significantly increased in tumor tissues compared with healthy tissues. Additionally, the current study demonstrated that high lncRNA LOC541471 expression was significantly associated with increasing lymph node metastasis classification and perineural invasion. A multivariate Cox regression analysis revealed that high lncRNA LOC541471 expression levels were an independent predictor for reduced overall survival (n=487) and relapse-free survival (n=355). According to the anatomic neoplasm subdivision, HNSCC samples were classified as oropharyngeal carcinoma (n=297), oral carcinoma (n=80), laryngeal carcinoma and hypopharyngeal carcinoma (n=123). A negative association was revealed between lncRNA LOC541471 expression and overall survival in all subtypes of HNSCC. Therefore, lncRNA LOC541471 is significantly negatively associated with overall survival and relapse-free survival of patients with HNSCC and may be considered a potential prognostic factor for HNSCC.

Indexed as

head and neck squamous cell carcinomainvasionlong non-coding RNAoverall survivalprognostic biomarker

Identifiers

PMID30675311
PMCPMC6341588

What OpenQuestion holds

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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 OpenQuestion graph.