Evidence map›Paper›PMID 30598095›Full record

ArticleBMC systems biology2018

Analysis of significant protein abundance from multiple reaction-monitoring data.

Jongsu Jun, Jungsoo Gim, Yongkang Kim, Hyunsoo Kim, Su Jong Yu, Injun Yeo, Jiyoung Park, Jeong-Ju Yoo, Young Youn Cho, Dong Hyeon Lee and 7 more

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC systems biology, 2018. 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, top 87% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Review
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

17 authors at 2 institutions in 1 country.

Jongsu JunDepartment of Statistics, Seoul National University, Seoul, South Korea.
Jungsoo GimGraduate School of Public Health, Seoul National University, Seoul, South Korea.
Yongkang KimDepartment of Statistics, Seoul National University, Seoul, South Korea.
Hyunsoo KimDepartment of Biomedical Engineering, Seoul National University College of Medicine, Seoul, South Korea.
Su Jong YuDepartment of Internal Medicine and Liver Research Institute, Seoul National University, Seoul, South Korea.
Injun YeoDepartment of Biomedical Engineering, Seoul National University College of Medicine, Seoul, South Korea.
Jiyoung ParkDepartment of Biomedical Engineering, Seoul National University College of Medicine, Seoul, South Korea.
Jeong-Ju YooDepartment of Internal Medicine and Liver Research Institute, Seoul National University, Seoul, South Korea.
Young Youn ChoDepartment of Internal Medicine and Liver Research Institute, Seoul National University, Seoul, South Korea.
Dong Hyeon LeeDepartment of Internal Medicine and Liver Research Institute, Seoul National University, Seoul, South Korea.
Eun Ju ChoDepartment of Internal Medicine and Liver Research Institute, Seoul National University, Seoul, South Korea.
Jeong-Hoon LeeDepartment of Internal Medicine and Liver Research Institute, Seoul National University, Seoul, South Korea.
Yoon Jun KimDepartment of Internal Medicine and Liver Research Institute, Seoul National University, Seoul, South Korea.
Seungyeoun LeeDepartment of Mathematics and Statistics, Sejong University, Seoul, South Korea.
Jung-Hwan YoonDepartment of Internal Medicine and Liver Research Institute, Seoul National University, Seoul, South Korea.
Youngsoo KimDepartment of Biomedical Engineering, Seoul National University College of Medicine, Seoul, South Korea.
Taesung ParkDepartment of Statistics, Seoul National University, Seoul, South Korea. tspark@stats.snu.ac.kr.
Seoul National University · KRSejong University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiscovering reliable protein biomarkers is one of the most important issues in biomedical research. The ELISA is a traditional technique for accurate quantitation of well-known proteins. Recently, the multiple reaction-monitoring (MRM) mass spectrometry has been proposed for quantifying newly discovered protein and has become a popular alternative to ELISA. For the MRM data analysis, linear mixed modeling (LMM) has been used to analyze MRM data. MSstats is one of the most widely used tools for MRM data analysis that is based on the LMMs. However, LMMs often provide various significance results, depending on model specification. Sometimes it would be difficult to specify a correct LMM method for the analysis of MRM data. Here, we propose a new logistic regression-based method for Significance Analysis of Multiple Reaction Monitoring (LR-SAM).

resultsThrough simulation studies, we demonstrate that LMM methods may not preserve type I error, thus yielding high false- positive errors, depending on how random effects are specified. Our simulation study also shows that the LR-SAM approach performs similarly well as LMM approaches, in most cases. However, LR-SAM performs better than the LMMs, particularly when the effects sizes of peptides from the same protein are heterogeneous. Our proposed method was applied to MRM data for identification of proteins associated with clinical responses of treatment of 115 hepatocellular carcinoma (HCC) patients with the tyrosine kinase inhibitor sorafenib. Of 124 candidate proteins, LMM approaches provided 6 results varying in significance, while LR-SAM, by contrast, yielded 18 significant results that were quite reproducibly consistent.

conclusionAs exemplified by an application to HCC data set, LR-SAM more effectively identified proteins associated with clinical responses of treatment than LMM did.

Indexed as

BiomarkersBiomarkers, TumorCarcinoma, HepatocellularComputational BiologyHumansLiver NeoplasmsProtein Interaction MappingBiomarkersBiomarkers, TumorHepatocellular carcinoma (HCC)Logistic regression-based method for significance analysis of multiple reaction monitoring (LR-SAM)Multiple reaction-monitoring (MRM)ProteinSorafenib response

Identifiers

PMID30598095
PMCPMC6311902
OpenAlexW2907159995

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.