Bioinformatics and machine learning approaches reveal key genes and underlying molecular mechanisms of atherosclerosis: A review.

Atherosclerosis (AS) causes thickening and hardening of the arterial wall due to accumulation of extracellular matrix, cholesterol, and cells. In this study, we used comprehensive bioinformatics tools and machine learning approaches to explore key genes and molecular network mechanisms underlying AS in multiple data sets. Next, we analyzed the correlation between AS and immune fine cell infiltration, and finally performed drug prediction for the disease. We downloaded GSE20129 and GSE90074 datasets from the Gene expression Omnibus database, then employed the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts algorithm to analyze 22 immune cells. To enrich for functional characteristics, the black module correlated most strongly with T cells was screened with weighted gene co-expression networks analysis. Functional enrichment analysis revealed that the genes were mainly enriched in cell adhesion and T-cell-related pathways, as well as NF-κ B signaling. We employed the Lasso regression and random forest algorithms to screen out 5 intersection genes (CCDC106, RASL11A, RIC3, SPON1, and TMEM144). Pathway analysis in gene set variation analysis and gene set enrichment analysis revealed that the key genes were mainly enriched in inflammation, and immunity, among others. The selected key genes were analyzed by single-cell RNA sequencing technology. We also analyzed differential expression between these 5 key genes and those involved in iron death. We found that ferroptosis genes ACSL4, CBS, FTH1 and TFRC were differentially expressed between AS and the control groups, RIC3 and FTH1 were significantly negatively correlated, whereas SPON1 and VDAC3 were significantly positively correlated. Finally, we used the Connectivity Map database for drug prediction. These results provide new insights into AS genetic regulation.

Expression level and clinical significance of LncRNA PVT1 in the serum of patients with LEASO.

Objective: Our study aims to investigate the long non-coding RNA plasmacytoma variant translocation 1 (lncRNA PVT1) in lower extremity arteriosclerosis obliterans (LEASO) patient serum and its clinical significance in LEASO. Patients and Methods: From July 2021 to April 2022, 133 LEASO patients diagnosed at the Qingdao Municipal Hospital were included. Among them, 44 complicated with coronary artery disease (CAD) were classified as the LEASO with CAD group. The remaining 89 were marked as the LEASO group, which was classified into single (n = 48) and double (n = 41) lower limb groups, with the former being subclassified into the left (n = 28) and right (n = 20) lower limb groups based on the affected sites. Fifty healthy individuals who came to our hospital for physical examination during the same period were randomly included and defined as the Healthy Control group. PVT1 expression was detected in serum samples from each group using a quantitative reverse transcriptase-polymerase chain reaction , and differences in expression levels were calculated. The ankle-brachial index (ABI) of patients in the LEASO group was measured using a sphygmomanometer, and its correlation with PVT1 was analyzed. Clinical data and laboratory test results (including blood routine, liver and renal function, and blood lipids) were collected for all patients upon admission. Logistic regression analyses were performed to determine the influence of PVT1 and laboratory test results on LEASO. The diagnosis and prediction of LEASO were obtained by combing PVT1 with laboratory test indicators. Results: It was found that lncRNA PVT1 expression was the highest in the serum of the LEASO with CAD group, followed by the LEASO and control groups (P < 0.05). Within the LEASO group, no significant difference in PVT1 expression was seen between the left and right limbs (P > 0.05), nor between the single and double lower limb groups. Furthermore, the PVT1 expression increased with the Rutherford grades, indicating a negative correlation between PVT1 and ABI. Logistic regression analysis revealed that triglycerides (OR = 2.972, 95% CI [1.159-7.618]), cholesterol (OR = 6.655, 95% CI [1.490-29.723]), C-reactive protein (OR = 1.686, 95% CI [1.218-2.335]), and PVT1 (OR = 2.885, 95% CI [1.350-6.167]) were independent risk factors for LEASO. Finally, strong sensitivity was observed in the receiver operating characteristic curve when combining PVT1 with meaningful laboratory indicators to diagnose and predict LEASO. Conclusion: lncRNA PVT1 promotes LEASO occurrence and progression and is related to atherosclerosis severity. The expression of PVT1 was negatively correlated with ABI. Logistic regression analysis suggested that blood lipid levels and inflammatory reactions might be related to LEASO occurrence. PVT1 was incorporated into laboratory indicators to predict LEASO. The subject's working curve area was large, and the prediction results were highly sensitive.