Tumor Immune Microenvironment and Immunotherapy in Brain Metastasis From Non-Small Cell Lung Cancer

Wang, Yuchang; Chen, Rui; Wa, Yue; Ding, Shikuan; Yang, Youliang; Liao, Junbo; Tong, Lei; Xiao, Gelei

doi:10.3389/fimmu.2022.829451

Cited by 26 publications

(26 citation statements)

References 101 publications

(121 reference statements)

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“…According to studies, reactive astrocytes help create a fibrotic tumor brain microenvironment that is unfavorable to the effects of immunotherapy ( 115 ). Additionally, one of the significant elements influencing the effectiveness of immunotherapy is the quantity and activity of lymphocytes in brain metastases, and efficient T cells contribute to this improvement ( 116 ).…”

Section: Discussion and Prospectsmentioning

confidence: 99%

Microenvironment and the progress of immunotherapy in clinical practice of NSCLC brain metastasis

Xie

2023

Front. Oncol.

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One of the most frequent distant metastases of lung cancer occurs in the brain. The average natural survival duration for patients with lung cancer who have brain metastases is about 1 to 2 months. Knowledge about brain metastases is currently restricted since they are more difficult to acquire than other metastases. This review begins with an analysis of the immune microenvironment of brain metastases; focuses primarily on the functions of microglia, astrocytes, neurons, and tumor-infiltrating lymphocytes in the microenvironment of brain metastases; and offers an atlas of the immune microenvironment of brain metastases involving significant cells. In an effort to give researchers new research ideas, the study also briefly covers how immunotherapy for non-small cell lung cancer with brain metastases is currently faring.

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Section: Discussion and Prospectsmentioning

confidence: 99%

Microenvironment and the progress of immunotherapy in clinical practice of NSCLC brain metastasis

Xie

2023

Front. Oncol.

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“…Tumor metastasis is triggered by interactions between cancer cells and numerous stromal cell components of the tumor microenvironment, as well as by the accumulation of intrinsic changes in malignant cells [ 31 , 32 ]. Inflammation and infiltration of tumor tissue by immune cells from the host, such as tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells, have been demonstrated to promote tumor development as well as invasion and metastasis [ 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer

Ding

Chen³

et al. 2022

BMC Med Genomics

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Background Lung cancer is a high-incidence cancer, and it is also the most common cause of cancer death worldwide. 80–85% of lung cancer cases can be classified as non-small cell lung cancer (NSCLC). Methods NSCLC transcriptome data and clinical information were downloaded from the TCGA database and GEO database. Firstly, we analyzed and identified the differentially expressed genes (DEGs) between non-metastasis group and metastasis group of NSCLC in the TCGA database, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were consulted to explore the functions of the DEGs. Thereafter, univariate Cox regression and LASSO Cox regression algorithms were applied to identify prognostic metastasis-related signature, followed by the construction of the risk score model and nomogram for predicting the survival of NSCLC patients. GSEA analyzed that differentially expressed gene-related signaling pathways in the high-risk group and the low-risk group. The survival of NSCLC patients was analyzed by the Kaplan–Meier method. ROC curve was plotted to evaluate the accuracy of the model. Finally, the GEO database was further applied to verify the metastasis‑related prognostic signature. Results In total, 2058 DEGs were identified. GO functions and KEGG pathways analysis results showed that the DEGs mainly concentrated in epidermis development, skin development, and the pathway of Neuro active ligand -receptor interaction in cancer. A six-gene metastasis-related risk signature including C1QL2, FLNC, LUZP2, PRSS3, SPIC, and GRAMD1B was constructed to predict the overall survival of NSCLC patients. The reliability of the gene signature was verified in GSE13213. The NSCLC patients were grouped into low-risk and high-risk groups based on the median value of risk scores. And low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for NSCLC. Conclusion Our study identified 6 metastasis biomarkers in the NSCLC. The biomarkers may contribute to individual risk estimation, survival prognosis.

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“…Brain metastases (BMs), especially non-small cell lung cancer (NSCLC) [1], frequently occur in the advanced stages of solid tumors, with a natural average survival time of 1 to 2 months [2]. Principal treatment modalities include surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy [3].…”

Section: Introductionmentioning

confidence: 99%

Prognostic factors in postoperative brain metastases derive from non-small cell lung cancer：a retrospective analysis

Chen,

Sun,

Yang

et al. 2024

Preprint

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Background: Brain metastases are crucial in cancer progression, requiring focused efforts on the screening, early detection, and treatment. However, accurately forecasting the postoperative prognosis of patients with non-small cell lung cancer brain metastasis remains a challenge. This retrospective study aims to discern the factors that influence the prognosis of such patients. Patients and materials: A total of 151 cases from Zhejiang Cancer Hospital were collected. Univariate analysis was conducted using Kaplan-Meier and Log-rank test, while multivariate analysis was performed using Cox proportional hazards regression model. Student’s t-test and chi-square test were employed to examine the differences between the long-term survival and the short-term survival groups. Ultimately, a predictive model was constructed by using R 4.2.1. Results: Univariate analysis identified 12 factors as prognostic factors, showing statistical significance. In multivariate analysis, the primary contributing factors to survival were identified as age, chemotherapy of brain metastases, pathology, surgery of non-small cell lung cancer, targeted drugs, and GPA score. Compared long-term and short-term groups, age, pathology, surgery of lung, targeted therapy, and radiotherapy of brain metastases were statistically differentiating factors. Based on multivariate analysis, we established a clinical predictive model predicting 2-year, 3-year, and 5-year survival rates. Conclusion: Younger age, receiving chemotherapy for brain metastases, adenocarcinoma pathology, lung cancer surgery, targeted therapy, and a high GPA score are associated with longer survival. This model predicts the survival period for patients with non-small cell lung cancer brain metastasis after surgery and helps in selecting more effective treatment plans.

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Tumor Immune Microenvironment and Immunotherapy in Brain Metastasis From Non-Small Cell Lung Cancer

Cited by 26 publications

References 101 publications

Microenvironment and the progress of immunotherapy in clinical practice of NSCLC brain metastasis

Microenvironment and the progress of immunotherapy in clinical practice of NSCLC brain metastasis

Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer

Prognostic factors in postoperative brain metastases derive from non-small cell lung cancer：a retrospective analysis

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