Identification of immune-associated gene signature and immune cell infiltration related to overall survival in progressive multiple sclerosis.

Background: Delayed diagnosis and noneffective treatment contribute to the shorter life expectancy in patients with progressive multiple sclerosis (PMS). Studies demonstrate the key role of autoimmunity in PMS, but the prognostic value of immune-associated factors remains unknown. Thus, this study aimed to develop an immune-associated gene (IAG) signature related to overall survival (OS) and conduct an immune cell infiltration analysis using PMS data.

Methods: The differentially expressed IAGs were identified based on gene expression profiles (from the Gene Expression Omnibus database) and IAGs (from the ImmPort database). Univariate and least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analyses were used to develop the IAG signature related to OS. Kaplan-Meier analyses were conducted, and receiver operating characteristic (ROC) curves were generated to assess the performance. Additionally, the differential distribution of immune cells was identified by Wilcoxon rank-sum tests and correlations with IAGs were analyzed using Spearman correlation analyses. Moreover, univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors to develop a prognostic nomogram.

Results: The training group, consisting of 57 PMS lesions and 52 control tissues, was obtained through batch normalization to remove the inter-batch difference. A total of 206 differentially expressed IAGs were identified, and 38 of them were associated with OS. Thereafter, a 4-IAG signature was constructed to calculate the risk score and thus classify PMS patients into high- and low-risk groups according to mean risk score. Patients in the high-risk group had a lower survival time than those in the low-risk group. The Kaplan-Meier plots and ROC curves demonstrated a good performance in both the training and internal validation groups. Additionally, five differentially abundant immune cell types were identified and their relationships with IAGs were analyzed. Finally, risk score, cortical region, and naive B cells were identified as independent prognostic factors, and a nomogram incorporating these factors was developed to predict the OS in PMS.

Conclusion: The novel IAG signature may be a reliable tool for assisting neurologists in predicting the OS for PMS patients in clinical settings. These findings may facilitate personalized treatment and provide insights into the complex mechanism of PMS.