Evidence map›Paper›PMID 39160643›Full record

ArticleJournal of cellular and molecular medicine2024

Machine learning and experimental validation of novel biomarkers for hypertrophic cardiomyopathy and cancers.

Hualei Dai, Ying Liu, Meng Zhu, Siming Tao, Chengcheng Hu, Peng Luo, Aimin Jiang, Guimin Zhang

Abstract read
In one paragraph

Article in Journal of cellular and molecular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. 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

8 authors.

Hualei DaiCardiovascular Center, The Affiliated Hospital of Yunnan University, Yunnan University, Kunming, Yunnan, China.
Ying LiuDepartment of Gynecology, Yunnan Cancer Hospital and The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Meng ZhuDepartment of Geriatrics, The Affiliated Huaian Hospital of Xuzhou Medical University, Huaian Second People's Hospital, Huaian, Jiangsu, China.
Siming TaoCardiovascular Center, The Affiliated Hospital of Yunnan University, Yunnan University, Kunming, Yunnan, China.
Chengcheng HuCardiovascular Center, The Affiliated Hospital of Yunnan University, Yunnan University, Kunming, Yunnan, China.
Peng LuoDepartment of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Aimin JiangDepartment of Urology, Changzheng Hospital, Naval Medical University, Shanghai, China.ORCID 0000-0002-9563-983X
Guimin ZhangCardiovascular Center, The Affiliated Hospital of Yunnan University, Yunnan University, Kunming, Yunnan, China.

Funding

Association Foundation Program of Yunnan Provincial Science and Technology Department and Kunming Medical University 202001AY070001-087Association Foundation Program of Yunnan Provincial Science and Technology Department and Kunming Medical University 202001AY070001-243Cardiovascular Ultrasound Innovation Team of Yunnan Province 202305AS350021High-Level Talent Training Support Plan of Yunnan Province YNWR-MY-2018-001High-Level Talent Training Support Plan of Yunnan Province YNWR-MY-2020-024
6 · The paper itself

Abstract

Hypertrophic cardiomyopathy (HCM) is a hereditary cardiac disorder marked by anomalous thickening of the myocardium, representing a significant contributor to mortality. While the involvement of immune inflammation in the development of cardiac ailments is well-documented, its specific impact on HCM pathogenesis remains uncertain. Five distinct machine learning algorithms, namely LASSO, SVM, RF, Boruta and XGBoost, were utilized to discover new biomarkers associated with HCM. A unique nomogram was developed using two newly identified biomarkers and subsequently validated. Furthermore, samples of HCM and normal heart tissues were gathered from our institution to confirm the variance in expression levels and prognostic significance of GATM and MGST1. Five novel biomarkers (DARS2, GATM, MGST1, SDSL and ARG2) associated with HCM were identified. Subsequent validation revealed that GATM and MGST1 exhibited significant diagnostic utility for HCM in both the training and test cohorts, with all AUC values exceeding 0.8. Furthermore, a novel risk assessment model for HCM patients based on the expression levels of GATM and MGST1 demonstrated favourable performance in both the training (AUC = 0.88) and test cohorts (AUC = 0.9). Furthermore, our study revealed that GATM and MGST1 exhibited elevated expression levels in HCM tissues, demonstrating strong discriminatory ability between HCM and normal cardiac tissues (AUC of GATM = 0.79; MGST1 = 0.86). Our findings suggest that two specific cell types, monocytes and multipotent progenitors (MPP), may play crucial roles in the pathogenesis of HCM. Notably, GATM and MGST1 were found to be highly expressed in various tumours and showed significant prognostic implications. Functionally, GATM and MGST1 are likely involved in xenobiotic metabolism and epithelial mesenchymal transition in a wide range of cancer types. GATM and MGST1 have been identified as novel biomarkers implicated in the progression of both HCM and cancer. Additionally, monocytes and MPP may also play a role in facilitating the progression of HCM.

Indexed as

BiomarkersCardiomyopathy, HypertrophicMachine LearningNeoplasmsBiomarkers, TumorFemaleHumansMaleMiddle AgedNomogramsPrognosisBiomarkersBiomarkers, Tumorbiomarkershypertrophic cardiomyopathyimmune infiltrationmachine learningpancancer

Identifiers

PMID39160643
PMCPMC11333198

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.