Evidence map›Paper›PMID 36203461›Full record

ArticleFrontiers in oncology2022

Plasma tRF-1:29-Pro-AGG-1-M6 and tRF-55:76-Tyr-GTA-1-M2 as novel diagnostic biomarkers for lung adenocarcinoma.

Jianbin You, Guoliu Yang, Yi Wu, Xuan Lu, Shuyu Huang, Qianshun Chen, Chen Huang, Falin Chen, Xunyu Xu, Liangyuan Chen

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
1.0field-weighted citation impact, top 25% 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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.

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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

10 authors at 3 institutions in 1 country.

Jianbin YouDepartment of Clinical Laboratory, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Guoliu YangDepartment of Clinical Laboratory, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Yi WuDepartment of Basic Medical Science, Xiamen Medical College, Xiamen, China.
Xuan LuDepartment of Clinical Laboratory, Zhangzhou Skin Disease Prevention and Treatment Hospital of Fujian Province, Zhangzhou, China.
Shuyu HuangDepartment of Blood Transfusion, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Qianshun ChenDepartment of Thoracic Surgery, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Chen HuangDepartment of Thoracic Surgery, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Falin ChenDepartment of Clinical Laboratory, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Xunyu XuDepartment of Thoracic Surgery, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Liangyuan ChenDepartment of Clinical Laboratory, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Fujian Medical University · CNXiamen Medical CollegeZhangzhou Municipal Hospital of Fujian Province · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: TRNA-derived fragments (tRFs) and tRNA-derived stress-induced RNAs (tiRNAs) are recognized as novel and potential types of non-coding RNAs (ncRNAs), and several tRF/tiRNA signatures are closely associated with tumor diagnosis. This study aimed to analyze the expression profiles of plasma tRFs/tiRNAs and to clarify their diagnostic value in lung adenocarcinoma (LUAD). Methods: The differential expression profiles of plasma tRFs/tiRNAs in patients with four patients with early LUAD, four patients with advanced LUAD, and four healthy controls were analyzed using high-throughput sequencing technology. Then, plasma tRFs/tiRNAs were validated by quantitative real-time polymerase chain reaction (qRT-PCR), and their diagnostic efficiency was appraised by receiver operating characteristic curve analysis. The correlation of candidate plasma tRFs/tiRNAs with clinicopathological features was also analyzed. Finally, bioinformatics analysis was performed to explore and identify the potential biological pathways induced by tRFs/tiRNAs. Results: The sequencing results revealed that tRFs/tiRNAs from plasma samples in patients with LUAD were differently expressed, supporting the necessity of exploring their potential as biomarkers. The validation results of qRT-PCR demonstrated that the expression level of tRF-1:29-Pro-AGG-1-M6 was downregulated in LUAD, while that of tRF-55:76-Tyr-GTA-1-M2 was upregulated, which was consistent with the sequencing data. The areas under the receiver operating characteristic curve of tRF-1:29-Pro-AGG-1-M6 and tRF-55:76-Tyr-GTA-1-M2 were 0.882 and 0.896, respectively, which have significant values in the diagnosis of LUAD. The expressions of tRF-1:29-Pro-AGG-1-M6 and tRF-55:76-Tyr-GTA-1-M2 in LUAD were obviously correlated with various clinicopathological features such as tumor-node-metastasis stage, node stage, and the expression levels of carcinoembryonic antigen. In addition, their expression was significantly altered from before to after tumor resection in LUAD patients. The results of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses further indicated that tRF-1:29-Pro-AGG-1-M6 and tRF-55:76-Tyr-GTA-1-M2 are widely distributed and apparently enriched in several tumor-related signaling pathways. Conclusions: Plasma tRF-1:29-Pro-AGG-1-M6 and tRF-55:76-Tyr-GTA-1-M2 may be promising components in the development of highly sensitive and non-invasive biomarkers for LUAD diagnosis.

Indexed as

biomarkershigh-throughput sequencinglung adenocarcinomaplasmatRFs/tiRNAs

Identifiers

PMID36203461
PMCPMC9530285
OpenAlexW4297905715

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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.