MicroRNA profiling reveals novel biomarkers for cardiovascular and psychological health in plateau psycho CVD

Table of Contents

RNA-sequencing profiling of circulating miRNA signature

After stringent quality control, we selected five samples from each of the four groups for next-generation sequencing. One sample from the PPC group failed quality assessment and was excluded from further analysis. The principal component analysis (PCA) of the samples is presented in Supplementary File 2, Figure S1 A. In the plasma samples, sequences ranging from 18 to 40 nucleotides were identified as small non-coding RNA (sncRNA) transcripts. Exploratory data analysis indicated that 46% of the sequences were enriched for sncRNA. Additionally, 11% of the sequences were mapped to other non-coding RNAs (ncRNA) within the human genome, while 19.36% represented unannotated sequences. Only 1% of reads were unmapped molecules of the same size as sncRNA. Following filtering and correction, bioinformatic analysis identified a total of 2,658 miRNAs (Supplementary Table 1). A heat map showing the top 100 most highly expressed miRNAs is provided in Supplementary File 2, Figure S1 B.

Differential miRNA expression

Through differential analysis, we found that the PPC group had 87 upregulated and 108 downregulated miRNAs compared to the control (C) group (Fig. 2A; Supplementary File 1: Table 2). In comparison to the PP group, there were 12 upregulated and 9 downregulated miRNAs (Fig. 2D), while the PPC group had 20 upregulated and 50 downregulated miRNAs compared to the PC group (Fig. 2E). Notably, the expression of miRNAs in the PPC group was more similar to that in the PP group, which aligns with the findings from PCA and heatmap analyses. Additionally, when comparing the PP group to the C group, we identified 81 upregulated and 147 downregulated miRNAs (Fig. 2B; Supplementary File 1: Table 3). Figure 2F shows the differential expression between the PP group and the PC group. The analysis reveals significant differences in the expression levels of key biomarkers, which highlight the distinct molecular profiles between the two groups. Finally, the comparison between the PC and C groups revealed the fewest differentially expressed miRNAs, with 51 upregulated and 18 downregulated (Fig. 2C; Supplementary File 1: Table 4), and a higher proportion of upregulated miRNAs relative to those in the PPC and PP groups.

We ranked the miRNAs based on fold-change differences and selected the top five upregulated and downregulated miRNAs. In the PPC group, the top upregulated miRNAs were as follows: hsa-miR-7976 (logFC = 5.62, Qvalue = 5.07e−03), hsa-miR-1976 (logFC = 5.27, Qvalue = 8.16e−03), hsa-miR-4685-3p (logFC = 3.80, Qvalue = 6.65e−04), hsa-miR-885-3p (logFC = 3.77, Qvalue = 3.05e−02), and hsa-miR-451a (logFC = 3.72, Qvalue = 1.34e−06) (Supplementary File 1, Table 2).

For downregulated miRNAs in the PPC group, the top candidates were: hsa-miR-5187-5p (logFC = − 8.53, Qvalue = 5.42e−06), hsa-miR-1250-5p (logFC = − 7.53, Qvalue = 4.08e−05), hsa-miR-128-1-5p (logFC = − 6.49, Qvalue = 4.83e−02), hsa-miR-143-3p (logFC = − 6.29, Qvalue = 4.84e−05), and hsa-miR-411-5p (logFC = -6.08, Qvalue = 5.29e−03) (Supplementary File 1, Table 2). Notably, the fold-changes of downregulated miRNAs were generally higher than those of the upregulated ones.

In addition to the PPC group, we also analyzed the differential miRNAs in the PP and PC groups. The upregulated miRNAs in the PP group were hsa-miR-1976, hsa-miR-206, hsa-miR-4685-3p, hsa-miR-96-5p, and hsa-miR-375. Notably, hsa-miR-1976 and hsa-miR-4685-3p were also significantly upregulated in the PPC group. The downregulated miRNAs in the PP group included hsa-miR-409-5p, hsa-miR-30b-3p, hsa-miR-493-3p, hsa-miR-380-5p, and hsa-miR-379-3p (Supplementary File 1, Table 3).

In the PC group, the top upregulated miRNAs were hsa-miR-202-3p, hsa-miR-1976, hsa-miR-550a-3-5p, hsa-miR-3150b-3p, and hsa-miR-548ad-5p. Once again, hsa-miR-1976 was identified as a significantly upregulated miRNA, consistent with its expression in the PPC group (Supplementary File 1, Table 4).

These findings suggest that hsa-miR-1976 is a consistently upregulated miRNA across the PPC, PP, and PC groups in the plateau environment.

Differential miRNA targets

To further investigate the biological functions of the differential miRNAs and their mechanisms of action, we selected the top five miRNAs and utilized the “multiMiR” R package for target gene exploration. As shown in Fig. 3, among the upregulated miRNAs in the PPC group, hsa-miR-1976 had the highest target gene score, followed by hsa-miR-4685-3p (Fig. 3A, Supplementary File 1, Table 5). Conversely, among the downregulated miRNAs, hsa-miR-143-3p exhibited the highest target gene score, with hsa-miR-128-1-5p following closely (Fig. 3B, Supplementary File 1, Table 6). In the PP group, the target gene scores for upregulated miRNAs did not show significant differences, but hsa-miR-30b-3p stood out among the downregulated miRNAs (Fig. 3B, Supplementary File 1, Tables 8 & 9). Lastly, in the PC group, hsa-miR-1976 also exhibited strong performance in regulating target genes (Fig. 3B, Supplementary File 1, Tables 10 & 11).

Notably, hsa-miR-1976 is consistently upregulated across the PPC, PP, and PC groups, and it also has the highest target gene score, indicating its potential significance in the context of plateau-related health issues.

To identify the potential biological functions and cellular processes associated with the differentially expressed miRNAs, we performed KEGG and GO enrichment analysis using the “clusterProfiler” R package. This approach helps assess whether certain pathways are enriched in specific miRNA-regulated gene sets and allows us to explore the biological roles of these target proteins.

miRNA qPCR validation

As shown in Fig. 4, the ROC analysis revealed that all four miRNAs exhibited area under the curve (AUC) values greater than 0.7, indicating strong discriminatory power: hsa-miR-1976 (AUC = 0.778), hsa-miR-4685-3p (AUC = 0.750), hsa-miR-143-3p (AUC = 0.765), and hsa-miR-128-1-5p (AUC = 0.781). These validation results were consistent with the findings from high-throughput sequencing and enrichment analysis. Taken together, the qPCR data support the notion that these four differentially expressed miRNAs play a crucial role in the pathogenesis of Plateau Psycho-CVD.

Enrichment analysis

In the PPC group, we identified significant enrichment in several pathways, including the PI3K-Akt signaling pathway (P.adjust = 6.04e−06), Neurotrophin signaling pathway (P.adjust = 2.22e−05), HIF-1 signaling pathway (P.adjust = 6.32e−06), MAPK signaling pathway (P.adjust = 6.30e−05), and TNF signaling pathway (P.adjust = 5.60e−03) (Fig. 5B, Supplementary File 1, Table 12). These findings suggest that these miRNA-targeted pathways are likely involved in the complex stress response and regulatory mechanisms contributing to both psychological and cardiovascular dysfunction in Plateau. GO enrichment analysis revealed that the downregulated miRNAs in the PPC group primarily target genes involved in key processes of peripheral nervous system development. Specifically, these miRNAs were associated with the activation of peripheral nervous system development (P.adjust = 7.09e−03), myelination in the peripheral nervous system (P.adjust = 1.46e−02), and peripheral nervous system axon ensheathment (P.adjust = 1.81e−02) (Fig. 5D, Supplementary File 1, Table 13). These results indicate that downregulated miRNAs may play a crucial role in regulating nervous system development and function under the dual strain of psychological stress and cardiovascular dysfunction in Plateau environments. The upregulated miRNAs in the PPC group were found to primarily target the inhibition of several critical biological processes, including the glycolytic process, ADP catabolic process, regulation of membrane permeability, and regulation of mitochondrial membrane permeability. These miRNAs also influenced apoptotic mitochondrial changes and the regulation of cell division, implicating them in cell survival and energy metabolism pathways (Fig. 5C, Supplementary File 1, Table 13). Additionally, these miRNAs were associated with tumor-related signaling pathways, suggesting a potential link between these regulatory mechanisms and cancer development or progression (Fig. 5A, Supplementary File 1, Table 12).

Analysis of the PP group revealed that the upregulated miRNAs primarily inhibited key signaling pathways such as Transcriptional misregulation in cancer, Pentose phosphate pathway, Prolactin signaling pathway, Carbon metabolism, FoxO signaling pathway, and Cellular senescence. Additionally, these miRNAs suppressed important biological processes, including the pentose-phosphate shunt, NADPH regeneration, glucose 6-phosphate metabolic process, NADP metabolic process, pyridine nucleotide metabolic process, nicotinamide nucleotide metabolic process, and response to nutrient levels. This suggests that these miRNAs play a regulatory role in metabolism and cellular stress responses. In contrast, the downregulated miRNAs in the PP group primarily activated pathways related to Motor proteins and Endocytosis. They also promoted key biological processes such as dendrite development, synapse organization, axonal transport, regulation of dendrite development, dendrite morphogenesis, and regulation of neuron projection development. These findings indicate that the downregulated miRNAs are involved in enhancing neuronal growth, connectivity, and transport mechanisms (Supplementary File 1: Tables 14 & 15, Supplementary File 2: Figure S2). Supplementary File 2: Fig. 3 outlines the regulatory mechanisms and biological processes associated with the differential miRNAs in the PC group. However, it is important to note that these findings did not reach statistical significance.

In summary, the PPC group showed significant regulation of key pathways, including PI3K-Akt and Neurotrophin, indicating a connection between psychological and cardiovascular stress in Plateau. Downregulated miRNAs affected peripheral nervous system development, while upregulated miRNAs inhibited metabolism and survival pathways. In the PP group, upregulated miRNAs suppressed cancer-related and metabolic pathways, while downregulated miRNAs promoted neuronal growth and connectivity. Although findings from the PC group lacked statistical significance, they still suggest relevant biological processes. Overall, these results highlight the role of miRNAs in managing stress responses at high altitudes and their implications for psychological and cardiovascular health.

Protein–protein interaction (PPI) network analysis

We conducted a PPI analysis for the proteins regulated by the differentially expressed miRNAs. Among the upregulated miRNAs, the protein with the highest degree score was MYC, followed by MAPK1 (Fig. 6A). In contrast, the downregulated miRNAs primarily targeted AKT1 and STAT3. These findings suggest that MYC and MAPK1 play significant roles in the regulatory networks influenced by upregulated miRNAs, while AKT1 and STAT3 are crucial for the pathways associated with downregulated miRNAs (Fig. 6B). This highlights the potential impact of these proteins in the plateau stress responses and related pathologies.