Evidence map›Paper›PMID 41614774›Full record

ArticleCurrent issues in molecular biology2025

Dihydromyricetin Remodels the Tumor Immune Microenvironment in Hepatocellular Carcinoma: Development and Validation of a Prognostic Model.

Yang Xu, Chao Gu, Wei Li, Fei Lan, Jingkun Mao, Xiao Tan, Pengfei Li

Abstract read
In one paragraph

Article in Current issues in molecular biology, 2025. 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
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. Article
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.

Yang XuDepartment of Medicine, Tarim University, Alar 843300, China.ORCID 0009-0009-5974-047X
Chao GuDepartment of Medicine, Tarim University, Alar 843300, China.
Wei LiDepartment of Medicine, Tarim University, Alar 843300, China.
Fei LanDepartment of Medicine, Tarim University, Alar 843300, China.
Jingkun MaoDepartment of Medicine, Tarim University, Alar 843300, China.
Xiao TanDepartment of Medicine, Tarim University, Alar 843300, China.
Pengfei LiDepartment of Medicine, Tarim University, Alar 843300, China.

Funding

Tarim University TDGJZD2501Tarim University TDKCSZ22542
6 · The paper itself

Abstract

backgroundDihydromyricetin (DHM), a natural dihydroflavonol, exhibits diverse pharmacological properties, including anti-inflammatory, antioxidant, and anti-tumor effects. However, its potential mechanism of action in the individualized therapy of hepatocellular carcinoma (HCC) remains unclear.

methodsPotential therapeutic targets of DHM were identified using the Swiss Target Prediction database. The overlap between these targets and differentially expressed genes in HCC was analyzed to determine therapeutic targets. A prognostic model was constructed based on these genes, and patients were stratified into high- and low-risk groups. The associations between risk scores, clinical pathological characteristics, and overall survival were analyzed using Cox regression and Kaplan-Meier survival curves. The relationships between risk score and immune cell infiltration, immunosuppressive factors, and anticancer drug susceptibility were evaluated.

resultsA three-gene prognostic model was established, comprising

conclusionsThis study developed a prognostic model based on the potential target genes of DHM in HCC. This model effectively stratifies HCC patients, identifying a high-risk subgroup characterized by an immunosuppressive microenvironment. These findings provide a theoretical foundation for exploring DHM as a promising natural adjuvant for cancer immunotherapy.

Indexed as

dihydromyricetinhepatocellular carcinomanetwork pharmacologyprognostic modeltumor immune microenvironment

Identifiers

PMID41614774
PMCPMC12732133

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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.