Analysis of the expression patterns of AVP, IGF-1, and TNF-α, APP, CD44, IFN-β IFN A β-6, α-syn, and NFL and CLU genes in generalized and focal seizures

Abstract

Objective: The aim of our study was to investigate the relationship between clinical indicators and gene dysregulation in different types of epilepsy, while also seeking to identify a diagnostic model capable of distinguishing between focal and generalized seizures. This highlights the critical importance of understanding clinical indicators and gene dysregulation for targeted therapeutic interventions to effectively address the specific seizure types effectively.

Materials and methods: In this study, we conducted a comprehensive analysis of the peripheral blood of epilepsy patients (n = 100) and a control group (n = 51) to determine the differential gene expression. Our analysis involved a range of statistical approaches, including correlation analysis to establish the association between clinical indicators and gene dysregulation, and principal component analysis to highlight distinct disease group from control group. Furthermore, we developed diagnostic models using logistic regression to aid in the accurate diagnosis of epilepsy.

Results: Among several selected genes in this study such as AVP (AUC = 0.832, p < 0.0001), IGF-1 (AUC = 0.658, p = 0.0015), TNF-α (AUC = 0.8970, p < 0.0001), APP (AUC = 0.742, p < 0.0001), CD44 (AUC = 0.614, p = 0.021) and NfL (AUC = 0.937, p < 0.0001), and CLU (AUC = 0.923, p < 0.0001) have shown the outstanding discrimination. In addition to this, when all genes were included in the model, the overall diagnostic power increased significantly (AUC = 0.9968). A differential diagnostic model for focal and generalized seizures was established which discloses AUC = 0.7027, (95 % CL, 0.5765 to 0.8289, p = 0.0019).

Conclusion: The conclusions drawn from these findings represented that this is the first study to highlight the distinctive gene patterns of both focal and generalized seizures, implying that peripheral blood can serve as a diagnostic source to distinguish between these seizures types, aiding in the accurate classification of epilepsy. The findings from this study indicate a promising direction for investigating more targeted pharmacological interventions directed to address the distinct needs of both focal and generalized epilepsy, which offers advancements in treatment strategies for distinctive seizure types.

Fig. 1

Establishment of distinct gene expression profiles. Profiles of differentially expressed genes (a) depicts serum levels of AVP and IL-6 in epileptic patients and control group. Different patterns of dysregulated genes are revealed in (b) Boxplots with Log₂fold values. Individual plot is depicted in.

Fig. 2

Principal Component Analysis. Distinct clusters were formed (a), successfully distinguishing illness samples from control samples. Dots represent samples, and each colour represents a group of people who were recruited. Panel (b) depicts a variable plot. Each contributing gene is represented visually by an arrow. The longer the arrow, the greater its influence on the variance. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3

Correlational analysis between differentially expressed genes and clinical variables. A non-significant (a) Negative correlation was found between α-syn and disease duration (r = −0.141, p = 0.1641). Non-significant positive correlation was found between IFN-β and disease duration (r = 0.151, p = 0.1232) (Fig. 3 b). As well, seizure type and disease duration had shown notable negative association (r = −0.2168, p = 0.030) (Fig. 3c). Finally, a significant negative correlation was established between IFN-β and age of onset (r = −0.1964, p = 0.050) (Fig. 3d).

Fig. 4

ROC curve evaluation to discriminate between disease group and control group. Logistic regression of all predictors (genes) was performed to assess the discriminating power between the disease group and the control group.

Fig. 5

ROC curve analysis Selected 11 genes differentially diagnosing between focal and generalized epilepsy.