Plasma lipidomic profiling reveals six candidate biomarkers for the prediction of incident stroke in patients with hypertension

INTRODUCTION

The burden of stroke in patients with hypertension is very high, and its prediction is critical.

OBJECTIVES

We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension.

METHODS

This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography-tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension.

RESULTS

Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783-0.957).

CONCLUSIONS

We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.

Cellular Senescence in Non-Small Cell Lung Cancer

Lung cancer has the highest mortality rate amongst all malignancies worldwide, and is the second-highest incidence of cancer in women. Non-small cell lung cancer (NSCLC) is responsible for approximately 80% of lung cancer cases. Recent studies indicate that cellular senescence may be a promising cancer biomarker. However, the regulation of cellular senescence and its underlying mechanisms in NSCLC are not yet fully understood. Here, we present a comprehensive analysis of the genes linked to cellular senescence in NSCLC. We also describe the secretory phenotype associated with NSCLC and examine its immune profile and prognostic potential. Our findings offer novel insights into the development of effective NSCLC treatments.