Immune-related genes (IRGs) have been increasingly recognized as critical determinants in the multistage processes of cancer development and progression. However, the functional roles of IRGs in the incidence and progression of LGG remain to be studied. This study is aimed at establishing a robust IRGs signature through systematic bioinformatics analysis, followed by an in-depth investigation of the molecular mechanisms underlying its functional roles. A key objective is to dissect the intricate interplay between IRGs expression patterns and the composition/functional orientation of tumor-infiltrating immune cells inside the tumor microenvironment. Furthermore, our findings are aimed at providing novel evidence to facilitate molecular diagnosis and advance immunotherapeutic strategies for LGG. RNA sequencing datasets, accompanied by detailed and pertinent clinical information pertinent to LGG, were meticulously retrieved from databases including TCGA and CGGA. To measure the levels of immune cell distribution across the specimens, we employed the sophisticated ssGSEA, which incorporated 29 immune infiltration-related information, enabling stratification of cases into immunity-low (immunity_L) and immunity-high (immunity_H) clusters. This classification provided crucial insights for understanding the immune landscape of LGG and its potential clinical implications. To further investigate, we identified differentially expressed IRGs by intersecting the list of DEGs with the IRGs curated from the ImmPort website. Subsequent feature selection employed Cox proportional hazards regression and LASSO regression to derive a prognostic IRGs signature in the TCGA cohort. This model facilitated risk stratification of patients into low-risk and high-risk subgroups. The established signature's predictive efficacy was rigorously validated in the CGGA cohort through comprehensive analytical approaches. This included Kaplan-Meier survival analysis for prognostic stratification, time-dependent receiver operating characteristic (ROC) curve construction for quantifying predictive accuracy, principal component analysis (PCA) for visualizing sample distribution patterns, and subgroup stratification to assess consistency across clinical variables. Additionally, ssGSEA was utilized to profile the TME, and correlation analyses were performed between the IRG-derived risk score and immune checkpoint expression levels. Finally, we selected CXCL10, ICAM1, IL18, ITGAL, SOCS3, and TLR3 to establish a six-gene IRGs signature for LGG. Based on this feature, we divided patients into low- and high-risk subgroups and found that high-risk patients consistently exhibited shorter OS. Notably, the risk score based on this signature emerged as an independent predictor of OS. TME analysis showed more immune infiltration in the high-risk subgroup. Correlation analysis further revealed a strong positive association between the risk score and TIM3 expression in both TCGA and CGGA datasets, with significantly higher TIM3 expression in the high-risk group. Individual analyses of these six genes revealed that elevated expression levels of CXCL10, ICAM1, IL18, ITGAL, SOCS3, and TLR3 were detected in tumor tissues compared to adjacent normal tissues. Notably, overexpression of these immunoregulatory genes demonstrated a significant correlation with unfavorable clinical outcomes in patients' survival analysis. Similar results were obtained in the tissue samples validation conducted at our center. The study developed an innovative signature encompassing six IRGs that accurately predicts prognosis, offers potential for identifying prognostic biomarkers, and may guide individualized immunotherapy for LGG.