Evidence map›Paper›PMID 41391060›Full record

ArticleDiscover oncology2025

Neuronal inflammatory genes-based machine learning model for breast cancer: a novel perspective on clinical prognosis and tumor immunity.

Hongxing Wang, Haihong Hu, Jingdi Zhang, Bo Hao, Hongxia Zhu, Wendi Zhan, Siyu Wang, Zhuo Li, Taolan Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Discover oncology, 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

9 authors.

Hongxing Wang *Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Haihong Hu *Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Jingdi Zhang *Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Bo HaoDepartment of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Hongxia ZhuDepartment of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Wendi ZhanDepartment of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Siyu WangDepartment of Medical Oncology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Zhuo LiDepartment of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China. zhuoli71@126.com.
Taolan ZhangDepartment of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China. TaolZhan@usc.edu.cn.

Funding

Science and technology innovation Program of Hengyang City 202250045223the Clinical Research 4310 Program of the First Affiliated Hospital of the University of South China 20224310NHYCG04the National Natural Science Foundation of China 82473965the Natural Science Foundation of Hunan Province 2023JJ50156the Scientific Research Project of the Hunan Provincial Department of Education 24B0413
6 · The paper itself

Abstract

backgroundBreast cancer heterogeneity complicates personalized treatment and prognosis. Current clinical prognostic indicators remain limited, and neuronal inflammation's crucial role in breast cancer progression is underexplored. This study identified neuronal inflammation-related biomarkers and constructed a prognostic model to improve risk evaluation and treatment.

methodsUnsupervised clustering classified patients into subtypes according to the expression level of neuronal inflammation-related genes (NIRGs). The risk score was calculated to divide patients into different risk groups. The prognostic genes were identified by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses to construct a machine learning prognostic model. Single-cell RNA sequencing (scRNA-seq) analysis screened the key prognostic genes. Cell subtypes were manually annotated and cell communication was analyzed using the CellChat package.

resultsCompared to the high-risk group, patients in the low-risk group showed richer immune infiltration and more favorable prognostic outcomes. Notably, a time-saving and user-friendly web tool ( http://wys.helyly.top/cox-whx/cox.html ) was applied to predict patients' survival and treatment response. The scRNA-seq analysis identified VDAC1 as the most neuronally inflammation-associated gene. Cell communication analysis indicated a strong interaction among VDAC1

conclusionThe NIRGs-based prognostic model demonstrated good predictive performance. VDAC1 plays as a central role in tumor-immune cell interactions and neuronal inflammation. This study provides a novel perspective on neuronal inflammation and the prognosis and immunity of breast cancer, contributing to the identification of new therapeutic targets. The web tool facilitates clinical translation, bridging research and patient care.

Indexed as

Breast cancerClinical prognosisNeuronal inflammationTumor immunityVDAC1

Identifiers

PMID41391060
PMCPMC12819958

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.