There is increasing evidence that inflammation-based biomarkers are associated with tumor microenvironment which plays important roles in cancer progression. A high lymphocyte-to-monocyte ratio (LMR), has been suggested to indicate favorable prognoses in various epithelial cancers. We performed a meta-analysis to quantify the prognostic value of LMR in advanced-stage epithelial cancers undergoing various treatment. We searched PubMed, EMBASE, Web of science and Cochrane Library up to July 2018 for relevant studies. We included studies assessing the prognostic impact of pretreatment LMR on clinical outcomes in patients with advanced-stage epithelial cancers. The primary outcome was overall survival (OS) and the secondary outcome was progression free survival (PFS). The summary hazard ratio (HR) and 95% confidence interval (CI) were calculated. A total of 8984 patients from 35 studies were included. A high pretreatment LMR was associated with favorable OS (HR = 0.578, 95% CI 0.522–0.641, P < 0.001) and PFS (HR = 0.598, 95% CI 0.465–0.768, P < 0.001). The effect of LMR on OS was observed among various tumor types. A higher pretreatment LMR was associated with improved OS in chemotherapy (n = 10, HR = 0.592, 95% CI 0.518–0.676, P < 0.001), surgery (n = 10, HR = 0.683, 95% CI 0.579–0.807, P < 0.001) and combined therapy (n = 11, HR = 0.507, 95% CI 0.442–0.582, P < 0.001) in the subgroup analysis by different therapeutic strategies. The cut-off value for LMR was 3.0 (range = 2.35–5.46). Subgroup analysis according to the cut-off value showed a significant prognostic value of LMR on OS and PFS in both subgroups. A high pretreatment LMR is associated with favorable clinical outcomes in advanced-stage epithelial cancers undergoing different therapeutic strategies. LMR could be used to improve clinical decision-making regarding treatment in advanced epithelial cancers.

Glioblastoma (GBM) is an extremely aggressive and diverse type of brain tumor that is associated with a poor prognosis. Immunogenic cell death (ICD) is a particular form of cellular demise that is closely linked to antineoplastic immune responses. However, it is important to note that the function of ICD in GBM is not yet fully understood. Utilizing single-cell analysis, AddModuleScore, and weighted gene co-expression network analyses (WGCNA), ICD-related genes were identified at both the bulk transcriptome and single-cell levels. Based on these newly identified ICD-related genes, a novel signature for GBM was developed employing three machine learning methods: LASSO, XGBoost, and RandomForest. This prognostic model comprises four characteristic genes: G0S2, NEUROD1, SERPINE2, and LZTS1. The overall survival was significantly lower for the high-risk group compared to the low-risk group in the TCGA-GBM cohort (P < 0.001), CGGA693 cohort (P < 0.001), CGGA325 cohort (P < 0.001), and CGGA301 cohort (P < 0.001). This dichotomy indicates that the riskscore encapsulates a spectrum of tumor microenvironment phenotypes, ranging from immune-permissive (high-risk group) to immune-excluded (low-risk group), with direct implications for therapeutic response. Tumor microenvironment profiling has revealed that the high-risk patients display elevated stromal and immune scores, alongside reduced tumor purity, indicative of a densely infiltrated, macrophage-rich microenvironment. By elucidating the interaction between ICD and macrophage biology, this study establishes a robust prognostic model that surpasses traditional clinical parameters, offering insights into tumor microenvironment immunity and therapeutic vulnerabilities in GBM.

Lung adenocarcinoma (LUAD), a prevalent type of non-small cell lung cancer (NSCLC), was known for its diversity and intricate tumor microenvironment (TME). Comprehending the interaction among human immune-related genes (IRGs) and the TME is vital in the creation of accurate predictive models and specific treatments. We created a risk score based on IRGs and designed a nomogram to predict the prognosis of LUAD accurately. This involved a thorough examination of TME and the infiltration of immune cells in both high-risk and low-risk LUAD groups. Furthermore, the examination of the association between characteristic genes (BIRC5 and BMP5) and immune cells, along with immune checkpoints in the TME, was also conducted. The findings of our research unveiled unique immune profiles and interactions among individuals in the high- and low-risk categories, which contribute to variations in prognosis. LUAD demonstrated significant associations between BIRC5, BMP5, immune cells, and checkpoints, suggesting their involvement in disease advancement and resistance to medication. Furthermore, by correlating our findings with a multidrug database, we identified specific LUAD patient subsets that might benefit from tailored treatments. Our study establishes a groundbreaking prognostic model for LUAD, which not only underscores the importance of the immune context in LUAD but also paves the way for advancing precision medicine strategies in this complex malignancy.

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