Integrative analysis identifies CXCL11 as an immune-related prognostic biomarker correlated with cell proliferation and immune infiltration in multiple myeloma microenvironment

Abstract

Purpose The interaction between tumor cells and tumor microenvironment (TME) has an important impact on progression and prognosis of multiple myeloma (MM), and has been proven to be promising therapeutic targets. This study intended to explore the relationship between TME and prognosis and identify valuable biomarkers of MM. Methods The transcriptomic and clinical information of MM retrieved from the Gene Expression Omnibus (GEO) were used to establish the model. The curve of Kaplan–Meier survival and the time-dependent receiver operating characteristic (ROC) were used to appraise the predictive ability. A nomogram was established for clinical application. Furthermore, the CIBERSORT algorithm was used to investigate the relation between IRGPI with the infiltration of immune cells. We also used histology, as well as in vitro and in vivo experiments to validate these findings. Results The results demonstrated an immune-related gene-based prognostic index (IRGPI) combined with clinical information. Patients were separated into high- and low-risk groups based on risk score, which had significantly difference in survival status and immune infiltrations. Furthermore, we identified CXCL11 as a key factor, which positively promotes the progression of MM and correlate with macrophage M2-like polarization and tumor immune cells infiltration. Conclusion Our findings suggest the IRGPI significantly demonstrate the differential prognosis and prediction of immune cells infiltration. It provides some insights into the complex interaction between myeloma tumor cells and the TME, as well as in the development of a novel biomarker target for anti-MM therapy.

Tumor immune cell infiltration score based model predicts prognosis in multiple myeloma

“…The method has also been used to evaluate infiltration in non-small cell lung cancer. Here, we used ssGSEA to calculate the TIICs of the three data sets, as well as determining the pairwise correlations between immune cells 8 . Cells showing significant correlations (P < 0.0001) were selected, unsupervised clustering of all immune cells was performed and the immune cell pairs displayed as a network diagram.…”