Evidence map›Paper›PMID 40819162›Full record

ArticleHereditas2025

Identification of anoikis-related genes in heart failure: bioinformatics and experimental validation.

Lina Zhang, Jianjun Gu, Yan Jiang, Juan Xue, Ye Zhu

Abstract read
In one paragraph

Article in Hereditas, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Lina Zhang *Department of Cardiology, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
Jianjun Gu *Department of Respiratory and Critical Care Medicine, Affiliated Hospital and Medical School of Nantong University, Nantong, China.
Yan JiangDepartment of Cardiology, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
Juan XueDepartment of Obstetrics, Xuzhou Cancer Hospital, Xuzhou, 221005, Jiangsu Province, China.
Ye ZhuDepartment of Cardiology, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, China. 307971331@qq.com.

Funding

Nantong Natural Science Foundation Project JC2024014National Natural Science Foundation of China 81800250
6 · The paper itself

Abstract

backgroundHeart failure (HF) is a common clinical syndrome caused by ventricular dysfunction and one of the leading causes of mortality worldwide. Previous studies have suggested that anoikis is relevant to HF. This study aimed to identify hub genes associated with anoikis that may offer therapeutic targets for HF. MATERIALS AND

methodsGene expression data for GSE36074 were obtained from the Gene Expression Omnibus (GEO) and anoikis-related genes (ARGs) were extracted from GeneCards. GEO2R was used to screen for differentially expressed genes (DEGs), then by overlapping DEGs with ARGs, differentially expressed ARGs (DEARGs) were screened. The biological functions of the DEARGs were determined using DAVID. Subsequently, two machine learning (ML) algorithms were employed to identify hub DEARGs: least absolute shrinkage and selection operator (LASSO) and random forest (RF). In addition, miRNA-hub DEARGs and drug-hub DEARGs networks were constructed. Lastly, the hub DEARGs were validated by quantitative reverse transcription PCR (RT-qPCR) and Immunofluorescence (IF).

resultsA total of 138 DEARGs were identified in GSE36074. Functional analysis of DEARGs revealed that they were primarily enriched in the positive regulation of the apoptotic process, PI3K-Akt, and FoxO signaling pathways. Subsequently, two hub DEARGs (Tln1 and TGFβ2) were screened using LASSO and RF algorithms. According to the miRNA-hub DEARGs networks, Tln1 and TGFβ2 were regulated by 34 and 68 miRNAs, respectively. Moreover, drug-hub DEARGs networks showed that Gemogenovatucel-t, Lerdelimumab, Belagenpumatucel-l, Fresolimumab, Bintrafusp alfa, Trabedersen and Luspatercept-aamt are potential drugs that could target TGFβ2. Finally, RT-qPCR and IF validation of two key DEARGs (Tln1 and TGFβ2) supported our bioinformatics analysis.

conclusionsThese findings suggest that Tln1 and TGFβ2 may play important roles in HF development through the regulation of anoikis and may serve as therapeutic targets for HF. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

AnoikisComputational BiologyHeart FailureGene Expression ProfilingGene Regulatory NetworksHumansMachine LearningMicroRNAsMicroRNAsAnoikisBioinformaticsHeart failureHub genesMachine learning

Identifiers

PMID40819162
PMCPMC12357471

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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.