Evidence map›Paper›PMID 42135859›Full record

ArticleHereditas2026

Identification and validation of lactylation-related genes signature and immune infiltration landscape of rheumatoid arthritis based on machine learning.

Xiaoli Hu, Qian Xiao, Lizhou Wang, Yuan Xu, Qiaoqiao Gou, Jing Wen, Shi Zhou

Abstract read
In one paragraph

Article in Hereditas, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

7 authors.

Xiaoli HuUltrasound Center, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Street, Guiyang, 550001, People's Republic of China.
Qian XiaoUltrasound Center, Guiyang Public Health Clinical Center, Guiyang, People's Republic of China.
Lizhou WangDepartment of Interventional Radiology, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Street, Guiyang, 550001, People's Republic of China.
Yuan XuUltrasound Center, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Street, Guiyang, 550001, People's Republic of China.
Qiaoqiao GouUltrasound Center, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Street, Guiyang, 550001, People's Republic of China.
Jing WenUltrasound Center, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Street, Guiyang, 550001, People's Republic of China. supervisor_gmc_edu@vip.163.com.
Shi ZhouDepartment of Interventional Radiology, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Street, Guiyang, 550001, People's Republic of China. zhoushi@gmc.edu.cn.

Funding

Guizhou Provincial Health Commission Science and Technology Fund gzwkj2023-068
6 · The paper itself

Abstract

backgroundThe pathogenic mechanisms underlying rheumatoid arthritis (RA) remain elusive. Lactylation, a novel post-translational modification, may regulate immune and metabolic reprogramming, underscoring the imperative to delineate lactylation-related genes (LRGs) driving RA progression.

methodsLRG expression profiles from RA patients and healthy controls were analyzed from GEO datasets. Immune infiltration and LRG-immune correlations were assessed. A machine-learning framework identified a hub LRG signature, whose metabolic and therapeutic relevance was evaluated via functional enrichment and druggability analyses. qRT-PCR validated hub gene expression in RA MH7A cells model.

resultsTranscriptomic profiling identified 36 differentially expressed LRGs regulating cytokine networks and immune signaling in RA. Disease stratification revealed two molecular subtypes, with Subtype B demonstrating p53 signaling and innate immunity pathway activation via gene set variation analysis (GSVA). Weighted correlation network analysis (WGCNA) identified subtype B-associated modules (504 genes). A machine learning-derived 7-LRG signature (Sdc1, Pfkfb1, Fut8, Adh1b, Kif23, Adh1c, Pkc1) differentiated RA from controls (AUC 0.92). Single-cell resolution analysis localized Sdc1 to plasma cell clusters, correlating with memory B cell expansion and macrophage polarization. Hub LRGs were enriched in glucose metabolism pathways in RA, and Sdc1, Adh1b, and Adh1c were druggable, suggesting potential therapeutic targets. qRT-PCR validation confirmed significant LRG upregulation in RA cellular models.

conclusionOur findings establish lactylation as a key modulator of immune dysregulation in RA pathogenesis. Seven LRG biomarkers were identified and validated, exhibiting dual potential as prognostic indicators and therapeutic targets through lactylation-driven pathway modulation for RA.

Indexed as

Arthritis, RheumatoidMachine LearningTranscriptomeGene Expression ProfilingHumansDiagnostic biomarkerImmune infiltration landscapeLactylation-related genesMachine learningsRheumatoid arthritis

Identifiers

PMID42135859
PMCPMC13343758

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.