Background The protein meflin encoded by ISLR contains a C2-type immunoglobulin (Ig)-like domain and five leucine-rich repeat (LRR) domains. ISLR is known to play a role in a small number of tumors, but its role in most tumors is unknown. The purpose of this study was to analyze the expression and prognosis of ISLR in pan-cancer, as well as its correlation with tumor immunity. Methods We used multiple databases and R software to conduct bioinformatics analysis to explore the predictive role of ISLR in pan-cancer, mainly involving expression patterns, prognosis, and immune infiltration. Results Compared with normal tissues, the expression of ISLR was significantly increased or decreased in most tumors. Moreover, the high expression of ISLR may cause the prognosis of some tumors to become better or worse. ISLR also affects immune infiltration in a variety of tumors, which affects the clinical prognosis. ISLR is also significantly related to TMB and MSI in pan-cancer and is related to genes encoding immune regulatory genes. ISLR also affects various cancer-and immune-related pathways. Conclusions ISLR is differentially expressed in tumors, may regulate TME, affect tumor prognosis, and is expected to become a prognostic biomarker.

Hepatocellular carcinoma (HCC) ranks among the most lethal malignancies around the world. However, the current management strategies for predicting prognosis in HCC patients remain unreliable. Our study developed a robust prognostic model based on glutamine metabolism associated-genes (GMAGs), utilizing data from The Cancer Genome Atlas database. The prognostic values of model were validated through the databases of the Gene Expression Omnibus and International Cancer Genome Consortium via Kaplan‒Meier curves and receiver operating characteristic (ROC). The potential biological pathways associated with prognostic risk were investigated through different enrichment analysis, and Gene variation analysis. The correlation between prognostic model and therapeutic responses were analyzed. Quantitative real-time PCR (qRT-PCR) and cellular experiments were measured to analyze the GMAGs. Consequently, a prognostic model was constructed of 4 GMAGs (RRM1, RRM2, G6PD, and GPX7) through least absolute shrinkage and selection operator (LASSO) regression analysis. The Kaplan‒Meier curves and ROC curves showed a reliable predictive capacity of prognosis for HCC patients (p < 0.05). The enrichment analyses revealed a multitude of biological pathways that are significantly associated with cancer. Patients with high prognostic risk might be sensitive to immunotherapy (p < 0.05). The results of qRT-PCR revealed that all 4 GMAGs exhibited significantly higher expression levels in HCC samples compared to normal samples (p < 0.05). Moreover, the knockdown of RRM1 suppresses the progression of HCC cells. In this study, we developed a robust prognostic model for predicting the prognosis of HCC patients based on GMAGs, and identified RRM1 as a potential therapeutic target for HCC.