Background: Although low-grade glioma (LGG) has a good prognosis, it is prone to malignant transformation into high-grade glioma. It has been confirmed that the characteristics of inflammatory factors and immune microenvironment are closely related to the occurrence and development of tumors. It is necessary to clarify the role of inflammatory genes and immune infiltration in LGG.Methods: We downloaded the transcriptome gene expression data and corresponding clinical data of LGG patients from the TCGA and GTEX databases to screen prognosis-related differentially expressed inflammatory genes with the difference analysis and single-factor Cox regression analysis. The prognostic risk model was constructed by LASSO Cox regression analysis, which enables us to compare the overall survival rate of high- and low-risk groups in the model by Kaplan–Meier analysis and subsequently draw the risk curve and survival status diagram. We analyzed the accuracy of the prediction model via ROC curves and performed GSEA enrichment analysis. The ssGSEA algorithm was used to calculate the score of immune cell infiltration and the activity of immune-related pathways. The CellMiner database was used to study drug sensitivity.Results: In this study, 3 genes (CALCRL, MMP14, and SELL) were selected from 9 prognosis-related differential inflammation genes through LASSO Cox regression analysis to construct a prognostic risk model. Further analysis showed that the risk score was negatively correlated with the prognosis, and the ROC curve showed that the accuracy of the model was better. The age, grade, and risk score can be used as independent prognostic factors (p < 0.001). GSEA analysis confirmed that 6 immune-related pathways were enriched in the high-risk group. We found that the degree of infiltration of 12 immune cell subpopulations and the scores of 13 immune functions and pathways in the high-risk group were significantly increased by applying the ssGSEA method (p < 0.05). Finally, we explored the relationship between the genes in the model and the susceptibility of drugs.Conclusion: This study analyzed the correlation between the inflammation-related risk model and the immune microenvironment. It is expected to provide a reference for the screening of LGG prognostic markers and the evaluation of immune response.

Background: Hepatocellular carcinoma (HCC) is one of the malignant tumors with the highest morbidity and mortality worldwide, and its prognosis remains a challenge. Actinidia chinensis Planch (ACP) root has good efficacy against HCC. This study aimed to explore the link between ACP and potential targets of HCC, and to develop a novel immune-based gene signature to predict HCC patient survival.Methods: Transcriptome data and clinical information on HCC were obtained from The Cancer Genome Atlas (TCGA; HCC: 374, normal: 50) and International Cancer Genome Consortium (ICGC) database (HCC: 243, normal: 202). Combined with the 2,483 immune-related genes from the Immport database, we used the least absolute shrinkage and selection operator (LASSO) to construct a prognostic model. Patients were divided into high-risk and low-risk groups by the median of the risk scores of the TCGA cohort. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves were used to estimate the predictability of the model in HCC prognosis, and carried out external validation based on ICGC cohort. We analyzed the correlation of this model with immune cells and immune checkpoint genes. Finally, molecular docking of these genes and the corresponding ACP components. Results:We constructed a prognostic model composed of 3 immune-related genes [epidermal growth factor (EGF), baculoviral inhibitor of apoptosis repeat-containing protein 5 (BIRC5), and secreted phosphoprotein 1 (SPP1)]. And the high-risk group had a lower overall survival (OS) rate compared to the low-risk group (TCGA cohort: P=1.761e-05, ICGC cohort: P=8.716e-04). The outcomes of the AUC of ROC of prognostic risk model to predict for 1-, 2-, and 3-year OS: TCGA cohort: 0.749, 0.710, and 0.653 and ICGC cohort: 0.698, 0.736, and 0.753. Molecular docking results showed that quercetin had good binding activities with SPP1, BIRC5, and EGF, and ursolic acid (UA) and BIRC5 also had this feature.Conclusions: Our study speculates that ACP root anti-HCC may be involved in the immune regulation of the body by targeting EGF, BIRC5 and SPP1, which possess great potential and value as early warning molecules for HCC. This model may provide a reference for individualized diagnosis and treatment for HCC patients.