Evidence map›Paper›PMID 40260909›Full record

ArticleIET systems biology

Identification of Eight Histone Methylation Modification Regulators Associated With Breast Cancer Prognosis.

Yan-Ni Cao, Xiao-Hui Li, Xing-Jie Chen, Kang-Cheng Xu, Jun-Yuan Zhang, Hao Lin, Yu-Xian Liu

Abstract read
In one paragraph

Article in IET systems biology. 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
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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.

Yan-Ni CaoSchool of Artificial Intelligence, Anhui University of Science and Technology, Huainan, China.
Xiao-Hui LiSchool of Artificial Intelligence, Anhui University of Science and Technology, Huainan, China.
Xing-Jie ChenSchool of Artificial Intelligence, Anhui University of Science and Technology, Huainan, China.
Kang-Cheng XuSchool of Artificial Intelligence, Anhui University of Science and Technology, Huainan, China.
Jun-Yuan ZhangSchool of Artificial Intelligence, Anhui University of Science and Technology, Huainan, China.
Hao LinSchool of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.ORCID 0000-0001-6265-2862
Yu-Xian LiuSchool of Artificial Intelligence, Anhui University of Science and Technology, Huainan, China.

Funding

Natural Science Research Project of Anhui Educational Committee 2023AH051200Natural Science Research Project of Anhui Educational Committee 2023AH051201Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology 2022yjrc41Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology 2022yjrc65
6 · The paper itself

Abstract

Histone methylation is an important epigenetic modification process coordinated by histone methyltransferases, histone demethylases and histone methylation reader proteins and plays a key role in the occurrence and development of cancer. This study constructed a risk scoring model around histone methylation modification regulators and conducted a multidimensional comprehensive analysis to reveal its potential role in breast cancer prognosis and drug sensitivity. First, 144 histone methylation modification regulators (HMMRs) were subjected to differential analysis and univariate Cox regression analysis, and nine differentially expressed HMMRs associated with survival were screened out. Next, a risk scoring model consisting of eight HMMRs was constructed using the LASSO regression algorithm, exhibiting independent predictive values in training and validation cohorts. Then, immune analysis shows that patients in the high-risk group divided by the risk scoring model has weakened the immune response. In addition, through functional analysis of differentially expressed genes (DEGs) between high-risk and low-risk groups, we confirmed that the DEGs mainly affected the nucleoplasm and tumour microenvironment. Finally, drug sensitivity analysis demonstrated that our model could be useful for drug screening and identify potential drugs for treating BRCA patients. In conclusion, these eight HMMRs may be key factors in the prognosis and drug sensitivity of BRCA patients.

Indexed as

Breast NeoplasmsHistonesComputational BiologyFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMethylationPrognosisHistonescancerdata analysisdrugsstatistical analysis

Identifiers

PMID40260909
PMCPMC12012758

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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.