Cancer immunotherapy is a kind of therapy that can control and eliminate tumors by restarting and maintaining the tumor-immune cycle and restoring the body’s normal anti-tumor immune response. Although immunotherapy has great potential, it is currently only applicable to patients with certain types of tumors, such as melanoma, lung cancer, and cancer with high mutation load and microsatellite instability, and even in these types of tumors, immunotherapy is not effective for all patients. In order to enhance the effectiveness of tumor immunotherapy, this article reviews the research progress of tumor microenvironment immunotherapy, and studies the mechanism of stimulating and mobilizing immune system to enhance anti-tumor immunity. In this review, we focused on immunotherapy against tumor microenvironment (TME) and discussed the important research progress. TME is the environment for the survival and development of tumor cells, which is composed of cell components and non-cell components; immunotherapy for TME by stimulating or mobilizing the immune system of the body, enhancing the anti-tumor immunity. The checkpoint inhibitors can effectively block the inhibitory immunoregulation, indirectly strengthen the anti-tumor immune response and improve the effect of immunotherapy. We also found the checkpoint inhibitors have brought great changes to the treatment model of advanced tumors, but the clinical treatment results show great individual differences. Based on the close attention to the future development trend of immunotherapy, this study summarized the latest progress of immunotherapy and pointed out a new direction. To study the mechanism of stimulating and mobilizing the immune system to enhance anti-tumor immunity can provide new opportunities for cancer treatment, expand the clinical application scope and effective population of cancer immunotherapy, and improve the survival rate of cancer patients.
Gefitinib has shown promising efficacy in the treatment of patients with locally advanced or metastatic EGFR-mutated non-small cell lung cancer (NSCLC). Molecular biomarkers for gefitinib metabolism-related lncRNAs have not yet been elucidated. Here, we downloaded relevant genes and matched them to relevant lncRNAs. We then used univariate, LASSO, and multivariate regression to screen for significant genes to construct prognostic models. We investigated TME and drug sensitivity by risk score data. All lncRNAs with differential expression were selected for GO/KEGG analysis. Imvigor210 cohort was used to validate the value of the prognostic model. Finally, we performed a stemness indices difference analysis. lncRNA-constructed prognostic models were significant in the high-risk and low-risk subgroups. Immune pathways were identified in both groups at low risk. The higher the risk score the greater the value of exclusion, MDSC, and CAF. PRRophetic algorithm screened a total of 58 compounds. In conclusion, the prognostic model we constructed can accurately predict OS in NSCLC patients. Two groups of low-risk immune pathways are beneficial to patients. Gefitinib metabolism was again validated to be related to cytochrome P450 and lipid metabolism. Finally, drugs that might be used to treat NSCLC patients were screened.
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