RESUMO
BACKGROUND: There are few biomarkers with an excellent predictive value for postacute myocardial infarction (MI) patients who developed heart failure (HF). This study aimed to screen candidate biomarkers to predict post-MI HF. METHODS: This is a secondary analysis of a single-center cohort study including nine post-MI HF patients and eight post-MI patients who remained HF-free over a 6-month follow-up. Transcriptional profiling was analyzed using the whole blood samples collected at admission, discharge, and 1-month follow-up. We screened differentially expressed genes and identified key modules using weighted gene coexpression network analysis. We confirmed the candidate biomarkers using the developed external datasets on post-MI HF. The receiver operating characteristic curves were created to evaluate the predictive value of these candidate biomarkers. RESULTS: A total of 6,778, 1,136, and 1,974 genes (dataset 1) were differently expressed at admission, discharge, and 1-month follow-up, respectively. The white and royal blue modules were most significantly correlated with post-MI HF (dataset 2). After overlapping dataset 1, dataset 2, and external datasets (dataset 3), we identified five candidate biomarkers, including FCGR2A, GSDMB, MIR330, MED1, and SQSTM1. When GSDMB and SQSTM1 were combined, the area under the curve achieved 1.00, 0.85, and 0.89 in admission, discharge, and 1-month follow-up, respectively. CONCLUSIONS: This study demonstrates that FCGR2A, GSDMB, MIR330, MED1, and SQSTM1 are the candidate predictive biomarker genes for post-MI HF, and the combination of GSDMB and SQSTM1 has a high predictive value.
RESUMO
BACKGROUND: This study aimed to investigate the trend of cardiovascular disease (CVD)-specific mortality in patients with non-small cell lung cancer (NSCLC) and identify prognostic factors for CVD-specific death in stage NSCLC patients. METHODS: In this study, 270,618 NSCLC patients were collected from the Surveillance, Epidemiology, and End Results database. CVD- and NSCLC-specific cumulative mortality and proportion of death were calculated and graphically displayed to describe the probability of specific endpoints. Prognostic factors for CVD-specific mortality were evaluated by cause-specific hazard ratios (HR) with 95% confidence intervals (CI) using the competing risk model with non-cardiovascular death as competing risks. RESULTS: Among all competing causes of death, lung cancer resulted in the highest cumulative mortality, followed by CVDs and other causes. In the proportion of cause-specific death, heart diseases accounted for approximately 5.3% of the total death, only secondary to primary cancer. In all three stages, higher age, squamous cell carcinoma, and no-or-unknown chemotherapy and/or radiotherapy were associated with a higher risk of CVD-specific death, while surgery treatment seemed to be a protective factor. Female gender was statistically related to CVD-specific death in stage I and III patients with HRs of 0.84 (0.78-0.91) and 0.84 (0.77-0.93), respectively. Interestingly, right-sided laterality was correlated with lower CVD-specific mortality with HR of 0.82 (0.74-0.90) in stage III. CONCLUSIONS: This study illustrated the historical trend of CVD-specific death in NSCLC patients and assesses potential prognostic risk factors, highlighting the involvement of cardio-oncology teams in cancer treatment to provide optimal comprehensive care and long-term surveillance for cancer patients.