Evidence map›Paper›PMID 42904164›Full record

ArticleiScience2026

Key genes and pathways of pulmonary hypertension identified by integrating bulk and single-cell RNA sequencing.

Peipei Wu, Jingru Huan, Chengliang Liu, Haiyang Jin, Ran Wang, Yong Wang, Cheng Cao, Binbin Zhang

Abstract read
In one paragraph

Article in iScience, 2026. 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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.

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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

8 authors.

Peipei WuDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, China.
Jingru HuanDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, China.
Chengliang LiuDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, China.
Haiyang JinDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, China.
Ran WangDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, China.
Yong WangDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, China.
Cheng CaoDepartment of Thoracic Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, China.
Binbin ZhangDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pulmonary hypertension (PH) is a progressive vascular disease, yet the underlying cell-type-specific molecular networks and diagnostic biomarkers remain incompletely defined. Integrating single-cell and bulk transcriptomics, we identified 14 pulmonary cell types in PH, with smooth muscle cells (SMCs) as a central communication hub interacting with monocytes and macrophages. High-dimensional weighted gene co-expression network analysis (hdWGCNA) revealed 4 SMC modules enriched in extracellular matrix organization, TGF-β, and HIF-1 pathways. A five-gene diagnostic signature distinguished PH from controls with an area under the curve of 0.895. Immune infiltration and enrichment analysis showed that activated CD4 memory T cells, activated dendritic cells, and resting mast cells increased in the PH group, while angiogenesis and epithelial-mesenchymal transition pathway were upregulated. In hypoxic human pulmonary artery SMCs, milk fat globule-epidermal growth factor-8 (MFGE8), secreted frizzled-related protein 2 (SFRP2), S100 calcium-binding protein A11 (S100A11), and procollagen C-endopeptidase enhancer 2 (PCOLCE2) were upregulated, and their knockdown suppressed proliferation, highlighting them as potential biomarkers and drivers of vascular remodeling, revealing a new molecular mechanism for PH and providing insights for potential therapeutic targets.

Indexed as

bioinformatics analysisimmune infiltrationpulmonary hypertensionsingle-cell RNA sequencing

Identifiers

PMID42904164
PMCPMC13682350

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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.