Investigating the platelet autophagy effect in maintenance hemodialysis patients by whole genome transcriptome from platelets

Thrombosis and hemorrhage are common complications in maintenance hemodialysis (MHD) patients, potentially attributed to platelet dysfunction. This study aimed to investigate platelet autophagy in MHD patients through whole transcriptome sequencing of platelets. A total of 24 MHD patients and 24 healthy individuals (normal controls) were enrolled. RNA sequencing was performed on platelets, and differentially expressed genes (DEGs) were identified using the DESeq2 package. Functional enrichment analyses, including gene ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis, were conducted to explore the biological functions and signaling pathways of DEGs. Protein–protein interaction analysis, hub gene identification, and machine learning algorithms were applied to predict key autophagy-related genes (ATGs). In addition, competing endogenous RNA networks (circular RNA [circRNA]– microRNA–messenger RNA [mRNA]) and circRNA–mRNA co-expression networks were constructed to reveal regulatory mechanisms of autophagy in platelets. A total of 8798 mRNAs were identified as differentially expressed. Gene set enrichment analysis revealed that these DEGs were significantly enriched in pathways such as focal adhesion and platelet activation, with positive enrichment scores. Protein–protein interaction and hub gene analysis identified Beclin 1, Sequestosome 1, ATG16L1, ATG12, and ATG7 as central ATGs with high interaction degrees. Machine learning approaches, including least absolute shrinkage and selection operator regression, support vector machine, and random forest, further identified Caspase 8, C-X-C motif chemokine receptor 4, vascular endothelial growth factor A, and forkhead box O3 as key autophagy-associated genes in MHD patients. A total of 8842 circRNAs were identified as differentially expressed, and 2739 target mRNAs were predicted from these differentially expressed circRNAs. Among them, 168 circRNAs were associated with ATGs. In the circRNA–mRNA co-expression network, 42 circRNAs and 22 mRNAs were implicated in autophagy regulation. In the competing endogenous RNA network, 17 circRNAs, 40 microRNAs, and 20 mRNAs were identified as potential components involved in platelet autophagy regulation. Key ATGs and regulatory networks were identified in MHD platelets, revealing potential mechanisms of dysfunction. These findings offer insights into thrombosis and bleeding risks and may aid in developing targeted therapies.