Evidence map›Paper›PMID 40029555›Full record

ArticleMolecular and cellular biochemistry2025

Molecular characterization underlying IFN-α2 treatment in polycythemia vera: a transcriptomic overview.

Fang-Fang Liu, Ke Li

Abstract read
PubMed Publisher
In one paragraph

Article in Molecular and cellular biochemistry, 2025. 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

2 authors.

Fang-Fang LiuDepartment of Pathology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, People's Republic of China.
Ke LiDepartment of Blood Transfusion, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Hankou District, Wuhan, 430030, Hubei, People's Republic of China. likelff1@hust.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polycythemia vera (PV) is the most common chronic myeloproliferative neoplasm (MPN) in adults. Pegylated interferon-α2 (IFN-α2) is an effective and safe drug for the treatment of PV. However, the mechanisms of its action in PV are still not fully understood. Using the WGCNA and Limma algorithm, we found a subset of IFN-α2 sensitive genes and four gene co-expression modules. Meanwhile, we also found 820 genes were differentially expressed in PV compared with healthy controls. By integrating the above results, several differentially expressed genes (DEGs) that were up- or down-regulated in PV but showed opposite alterations in the IFN-α2-treated group were found. These genes were mainly related to three types of biological processes (metal ion homeostasis, metabolic/catabolic process, and Jak-STAT signaling pathway), the dysfunctions of which were prevalent in PV. Moreover, we applied another threshold-free analysis method to compare global gene expression between IFN-α2 treated PV, PV, and control groups. Results showed the transcriptome changes of PV versus controls were negatively correlated with that of IFN-α2 treated versus untreated PV, indicating IFN-α2 treatment could partially reverse the dysregulated gene expression profile due to PV pathology. In summary, interferon may alleviate the progression of PV through multiple pathways. The findings may be of assistance in understanding the molecular basis underlying this treatment.

Indexed as

Interferon-alphaPolycythemia VeraTranscriptomeFemaleGene Expression ProfilingHumansMaleInterferon-alphaCo-expression analysisIFN-alpha2ModulesPolycythemia veraTranscriptional profiles

Identifiers

What OpenQuestion holds

Textmetadata
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.