Identification of autophagy-related biomarkers in patients with pulmonary arterial hypertension based on bioinformatics analysis

Identification of TFRC as a biomarker for pulmonary arterial hypertension based on bioinformatics and experimental verification

BACKGROUND

Pulmonary arterial hypertension (PAH) is a life-threatening chronic cardiopulmonary disease. However, there is a paucity of studies that reflect the available biomarkers from separate gene expression profiles in PAH.

METHODS

The GSE131793 and GSE113439 datasets were combined for subsequent analyses, and batch effects were removed. Bioinformatic analysis was then performed to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) and a protein-protein interaction (PPI) network analysis were then used to further filter the hub genes. Functional enrichment analysis of the intersection genes was performed using Gene Ontology (GO), Disease Ontology (DO), Kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA). The expression level and diagnostic value of hub gene expression in pulmonary arterial hypertension (PAH) patients were also analyzed in the validation datasets GSE53408 and GSE22356. In addition, target gene expression was validated in the lungs of a monocrotaline (MCT)-induced pulmonary hypertension (PH) rat model and in the serum of PAH patients.

RESULTS

A total of 914 differentially expressed genes (DEGs) were identified, with 722 upregulated and 192 downregulated genes. The key module relevant to PAH was selected using WGCNA. By combining the DEGs and the key module of WGCNA, 807 genes were selected. Furthermore, protein-protein interaction (PPI) network analysis identified HSP90AA1, CD8A, HIF1A, CXCL8, EPRS1, POLR2B, TFRC, and PTGS2 as hub genes. The GSE53408 and GSE22356 datasets were used to evaluate the expression of TFRC, which also showed robust diagnostic value. According to GSEA enrichment analysis, PAH-relevant biological functions and pathways were enriched in patients with high TFRC levels. Furthermore, TFRC expression was found to be upregulated in the lung tissues of our experimental PH rat model compared to those of the controls, and the same conclusion was reached in the serum of the PAH patients.

CONCLUSIONS

According to our bioinformatics analysis, the observed increase of TFRC in the lung tissue of human PAH patients, as indicated by transcriptomic data, is consistent with the alterations observed in PAH patients and rodent models. These data suggest that TFRC may serve as a potential biomarker for PAH.

Analyzing the molecular mechanism of xuefuzhuyu decoction in the treatment of pulmonary hypertension with network pharmacology and bioinformatics and verifying molecular docking

BACKGROUND

XueFuZhuYu (XFZY), a typical Chinese herbal formula, has remarkable clinical effects for treating Pulmonary Hypertension (PH) with unclear mechanisms. Our research involved the utilization of network pharmacology to explore the traditional Chinese herbal monomers and their related targets within XFZY for PH treatment. Furthermore, molecular docking verification was performed.

METHODS

The XFZY's primary active compounds, along with their corresponding targets, were both obtained from the TCMSP, ChEMBL, and UniProt databases. The target proteins relevant to PH were sifted through OMIM, GeneCards and TTD databases. The common "XFZY-PH" targets were evaluated with Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses with the assistance of R software. The Protein-Protein Interaction (PPI) network and compound-target-pathway network were constructed and a systematic analysis of network parameters was performed by the powerful software Cytoscape. Molecular docking was employed for assessing and verifying the interactions between the core targets and the top Chinese herbal monomer.

RESULTS

The screening included 297 targets of active compounds in XFZY and 8400 PH-related targets. DO analysis of the above common 268 targets indicated that the treatment of the diseases by XFZY is mediated by genes related to Chronic Obstructive Pulmonary Disease (COPD), Obstructive Lung Disease (OLD), ischemia, and myocardial infarction. The findings from molecular docking indicated that the binding energies of 57 ligand-receptor pairs in PH and 20 ligand-receptor pairs in COPD-PH were lower than -7kJ•mol-1.

CONCLUSIONS

This study indicates that XFZY is a promising option within traditional Chinese medicine compound preparation for combating PH, particularly in cases associated with COPD. Our demonstration of the specific molecular mechanism of XFZY anti-PH and its effective active ingredients provides a theoretical basis for better clinical application of the compound.

Upregulation of the key biomarker kinesin family member 20A (KIF20A) is associated with pulmonary artery hypertension

OBJECTIVE

Pulmonary artery hypertension (PAH) is a complex, fatal disease with limited treatments. This study aimed to investigate possible key targets in PAH through bioinformatics.

METHODS

GSE144274 were obtained from Gene Expression Omnibus (GEO) database. Then, differentially expressed genes (DEGs) between idiopathic pulmonary hypertension (IPAH) and healthy subjects were identified and analyzed. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed, and a protein-protein interaction (PPI) network was constructed using STRING. The hub genes were identified by MCODE method. The expression levels of hub genes were validated in vitro and in vivo models. Finally, the ROC analysis was performed based on the level of hub genes in clinical plasma samples.

RESULTS

A total of 363 DEGs were identified. GO analysis on these DEGs were mainly enriched in cell division, inflammatory response, among others. In the KEGG pathways analysis, DEGs mainly involved in cytokine-cytokine receptor interaction, rheumatoid arthritis, and IL-17 signaling pathways were enriched. The DEGs were analyzed with the STRING for PPI network analysis, and 62 hub genes were identified by MCODE. Finally, 6 central genes, KIF18B, SPC25, DLGAP5, KIF20A, CEP55 and ANLN, were screened out due to their novelty role in PAH. The expression of KIF20A was validated to be significantly upregulated both in the lung tissue of hypoxia-induced pulmonary hypertension (HPH) mice and proliferative PASMCs. Additionally, KIF20A levels is evelated in PAH plasma and the area under the curve (AUC) to identify PAH was 0.8591 for KIF20A.

CONCLUSION

The level of KIF20A elevates during the progression of PAH, which suggestes it could be a potential diagnostic and therapeutic target for the PAH.

Diagnostic and prognostic value of autophagy-related key genes in sepsis and potential correlation with immune cell signatures

Background: Autophagy is involved in the pathophysiological process of sepsis. This study was designed to identify autophagy-related key genes in sepsis, analyze their correlation with immune cell signatures, and search for new diagnostic and prognostic biomarkers. Methods: Whole blood RNA datasets GSE65682, GSE134347, and GSE134358 were downloaded and processed. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify autophagy-related key genes in sepsis. Then, key genes were analyzed by functional enrichment, protein-protein interaction (PPI), transcription factor (TF)-gene and competing endogenous RNA (ceRNA) network analysis. Subsequently, key genes with diagnostic efficiency and prognostic value were identified by receiver operating characteristic (ROC) curves and survival analysis respectively. The signatures of immune cells were estimated using CIBERSORT algorithm. The correlation between significantly different immune cell signatures and key genes was assessed by correlation analysis. Finally, key genes with both diagnostic and prognostic value were verified by RT-qPCR. Results: 14 autophagy-related key genes were identified and their TF-gene and ceRNA regulatory networks were constructed. Among the key genes, 11 genes (ATIC, BCL2, EEF2, EIF2AK3, HSPA8, IKBKB, NLRC4, PARP1, PRKCQ, SH3GLB1, and WIPI1) had diagnostic efficiency (AUC > 0.90) and 5 genes (CAPN2, IKBKB, PRKCQ, SH3GLB1 and WIPI1) were associated with survival prognosis (p-value < 0.05). IKBKB, PRKCQ, SH3GLB1 and WIPI1 had both diagnostic and prognostic value, and their expression were verified by RT-qPCR. Analysis of immune cell signatures showed that the abundance of neutrophil, monocyte, M0 macrophage, gamma delta T cell, activated mast cell and M1 macrophage subtypes increased in the sepsis group, while the abundance of resting NK cell, resting memory CD4+ T cell, CD8+ T cell, naive B cell and resting dendritic cell subtypes decreased. Most of the key genes correlated with the predicted frequencies of CD8+ T cells, resting memory CD4+ T cells, M1 macrophages and naive B cells. Conclusion: We identified autophagy-related key genes with diagnostic and prognostic value in sepsis and discovered associations between key genes and immune cell signatures. This work may provide new directions for the discovery of promising biomarkers for sepsis.