Evidence map›Paper›PMID 39959243›Full record

ArticleAmerican journal of translational research2025

Identifcation of candidate biomarkers for polyarteritis nodosa using data-independent acquisition mass spectrometry.

Huimin Ma, Xintian Cai, Delian Zhang, Qing Zhu, Ting Wu, Xiayire Aierken, Ayguzaili Ahmat, Shasha Liu, Nanfang Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of translational research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Huimin MaHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, NHC Key Laboratory of Hypertension Clinical Research, Key Laboratory of Xinjiang Uygur Autonomous Region, Hypertension Research Laboratory, Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases Urumqi, Xinjiang Uygur Autonomous Region, China.
Xintian CaiHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, NHC Key Laboratory of Hypertension Clinical Research, Key Laboratory of Xinjiang Uygur Autonomous Region, Hypertension Research Laboratory, Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases Urumqi, Xinjiang Uygur Autonomous Region, China.
Delian ZhangHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, NHC Key Laboratory of Hypertension Clinical Research, Key Laboratory of Xinjiang Uygur Autonomous Region, Hypertension Research Laboratory, Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases Urumqi, Xinjiang Uygur Autonomous Region, China.
Qing ZhuHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, NHC Key Laboratory of Hypertension Clinical Research, Key Laboratory of Xinjiang Uygur Autonomous Region, Hypertension Research Laboratory, Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases Urumqi, Xinjiang Uygur Autonomous Region, China.
Ting WuHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, NHC Key Laboratory of Hypertension Clinical Research, Key Laboratory of Xinjiang Uygur Autonomous Region, Hypertension Research Laboratory, Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases Urumqi, Xinjiang Uygur Autonomous Region, China.
Xiayire AierkenHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, NHC Key Laboratory of Hypertension Clinical Research, Key Laboratory of Xinjiang Uygur Autonomous Region, Hypertension Research Laboratory, Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases Urumqi, Xinjiang Uygur Autonomous Region, China.
Ayguzaili AhmatHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, NHC Key Laboratory of Hypertension Clinical Research, Key Laboratory of Xinjiang Uygur Autonomous Region, Hypertension Research Laboratory, Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases Urumqi, Xinjiang Uygur Autonomous Region, China.
Shasha LiuHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, NHC Key Laboratory of Hypertension Clinical Research, Key Laboratory of Xinjiang Uygur Autonomous Region, Hypertension Research Laboratory, Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases Urumqi, Xinjiang Uygur Autonomous Region, China.
Nanfang LiHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, NHC Key Laboratory of Hypertension Clinical Research, Key Laboratory of Xinjiang Uygur Autonomous Region, Hypertension Research Laboratory, Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases Urumqi, Xinjiang Uygur Autonomous Region, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesPolyarteritis nodosa (PAN) is a rare autoimmune disease that can cause severe functional impairment. Early diagnosis and timely intervention are essential to reduce disease severity and improve outcomes.

methodsSerum proteins from PAN patients and healthy controls were analyzed using data-independent acquisition mass spectrometry (DIA-MS), identifying 55 differentially expressed proteins. Validation was conducted on an independent set of 35 serum samples (10 healthy controls, 15 disease controls, and 10 PAN patients) to evaluate the diagnostic potential of selected biomarkers.

resultsEighteen proteins showed significantly altered expression in PAN patients compared to controls. A diagnostic panel of seven proteins - AZGP1, F13B, LBP, RBP4, SERPINF1, PGLYRP2, and PPBP - was identified using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model. This panel achieved an area under the receiver operating characteristic (ROC) curve of 0.994, effectively distinguishing PAN patients from controls.

conclusionBy combining DIA-MS technology with the LASSO regression model, this study developed a 7-protein diagnostic panel, providing a highly accurate and efficient tool for PAN diagnosis.

Indexed as

Data independent acquisitionmass spectrometrypolyarteritis nodosaprotein biomarkersserum

Identifiers

PMID39959243
PMCPMC11826199

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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.