Factor V (His 1299 Arg) in young Turkish patients with cerebral infarct

Machine learning identifies novel coagulation genes as diagnostic and immunological biomarkers in ischemic stroke

BACKGROUND

Coagulation system is currently known associated with the development of ischemic stroke (IS). Thus, the current study is designed to identify diagnostic value of coagulation genes (CGs) in IS and to explore their role in the immune microenvironment of IS.

METHODS

Aberrant expressed CGs in IS were input into unsupervised consensus clustering to classify IS subtypes. Meanwhile, key CGs involved in IS were further selected by weighted gene co-expression network analysis (WGCNA) and machine learning methods, including random forest (RF), support vector machine (SVM), generalized linear model (GLM) and extreme-gradient boosting (XGB). The diagnostic performance of key CGs were evaluated by receiver operating characteristic (ROC) curves. At last, quantitative PCR (qPCR) was performed to validate the expressions of key CGs in IS.

RESULTS

IS patients were classified into two subtypes with different immune microenvironments by aberrant expressed CGs. Further WGCNA, machine learning methods and ROC curves identified ACTN1, F5, TLN1, JMJD1C and WAS as potential diagnostic biomarkers of IS. In addition, their expressions were significantly correlated with macrophages, neutrophils and/or T cells. GSEA also revealed that those biomarkers may regulate IS via immune and inflammation. Moreover, qPCR verified the expressions of ACTN1, F5 and JMJD1C in IS.

CONCLUSIONS

The current study identified ACTN1, F5 and JMJD1C as novel coagulation-related biomarkers associated with IS immune microenvironment, which enriches our knowledge of coagulation-mediated pathogenesis of IS and sheds light on next-step in vivo and in vitro experiments to elucidate the relevant molecular mechanisms.

A novel protein C inhibitor gene mutation in pediatric stroke patients after bone marrow transplantation

Protein C inhibitor is a heparin dependent serine protease inhibitor found in human plasma, urine and other body fluids. It was originally identified as an inhibitor of activated protein C. Stroke is an important cause of morbidity and mortality in the pediatric age group. In this study we analyzed the protein C inhibitor gene mutations in Turkish pediatric stroke patients. We found a missense mutation of G to A at nucleotide 6760 in exon 2, resulting in a transition serine to asparagine (p.Ser188Asp) and in a child and his father and also we found same alteration in exon 2 in an another pediatric stroke case following bone marrow transplantation.

Combined Thrombophilic Mutations in Women with Unexplained Recurrent Miscarriage

PROBLEM

To compare the prevalence of five common thrombophilic polymorphisms and their combination in women with recurrent miscarriage and a control group.

METHOD OF STUDY

Genomic analysis using polymerase chain reaction (PCR) was carried out in patients with two or more miscarriages and controls for Factor V Leiden, Factor V A1299H (HR2), Factor II G20210A, MTHFR C677T and MTHFR A1298C. Secondary analyses were made for number of miscarriages and gestational age at miscarriage.

RESULTS

None the mutations was associated with significantly increased risk for recurrent miscarriage. The prevalence of combined thrombophilias (4/88 versus 2/88) did not increase the risk for miscarriage (OR 2.04, 95% CI 0.36-11.47). Although virtually all patients with thrombophilia had miscarriages<or=10 weeks, statistical significance was not reached due to the small size of the >10 weeks' subgroup. There was no difference in the distribution of Factor V Leiden (P=1.000), FII G20210A (P=0.652), and MTHFR C677T (P=0.869) between patients with two and three or more miscarriages, whereas MTHFR A1298C was more common among patients with two miscarriages (P=0.017).

CONCLUSIONS

Combinations of the five thrombophilic mutations studied are an uncommon event with heterogeneous pattern and they do not significantly increase the risk for miscarriage.