Pan-Cancer Analysis of Radiotherapy Benefits and Immune Infiltration in Multiple Human Cancers

Wen, Pengbo; Gao, Yang; Chen, Bin; Qi, Xiaojing; Hu, Guanshuo; Xu, An; Xia, Junfeng; Wu, Lijun; Han, Lu; Zhao, Guoping

doi:10.3390/cancers12040957

Cited by 13 publications

(7 citation statements)

References 44 publications

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“…According to the immune score and whether they had epilepsy, all patients were divided into four groups: C1(high immune score, seizure), C2 (high immune score, non-seizure), C3 (low immune score, seizure), and C4 (low immune score, non-seizure). Here, the immune scores that were >0 were considered high while immune scores that were <0 were considered low (Wen et al, 2020). Through survival analysis, we found that a high immune score suggested a poor prognosis in LGG patients with epilepsy (p = 0.0025, Figure 2A).…”

Section: The Level Of Immune Infiltration Affected the Prognosismentioning

confidence: 98%

Multi-Omics Data Integration Analysis of an Immune-Related Gene Signature in LGG Patients With Epilepsy

Cheng

Duan

et al. 2021

Front. Cell Dev. Biol.

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BackgroundThe tumor immune microenvironment significantly affects tumor occurrence, progression, and prognosis, but its impact on the prognosis of low-grade glioma (LGG) patients with epilepsy has not been reported. Hence, the purpose of this study is to explore its effect on LGG patients with epilepsy.MethodsThe data of LGG patients derived from the TCGA database. The level of immune cell infiltration and the proportion of 22 immune cells were evaluated by ESTIMATE and CIBERSORT algorithms, respectively. The Cox and LASSO regression analysis was adopted to determine the DEGs, and further established the clustering and risk score models. The association between genomic alterations and risk score was investigated using CNV and somatic mutation data. GSVA was adopted to identify the immunological pathways, immune infiltration and inflammatory profiles related to the signature genes. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and GDSC database were used to predict the patient’s response to immunotherapy and chemotherapy, respectively.ResultsThe prognosis of LGG patients with epilepsy was associated with the immune score. Three prognostic DEGs (ABCC3, PDPN, and INA) were screened out. The expression of signature genes was regulated by DNA methylation. The clustering and risk score models could stratify glioma patients into distinct prognosis groups. The risk score was an independent predictor in prognosis, with a high risk-score indicating a poor prognosis, more malignant clinicopathological and genomic aberration features. The nomogram had the better predictive ability. Patients at high risk had a higher level of macrophage infiltration and increased inflammatory activities associated with T cells and macrophages. While the higher percentage of NK CD56bright cell and more active inflammatory activity associated with B cell were present in the low-risk patients. The signature genes participated in the regulation of immune-related pathways, such as IL6-JAK-STAT3 signaling, IFN-α response, IFN-γ response, and TNFA-signaling-via-NFKB pathways. The high-risk patients were more likely to benefit from anti-PD1 and temozolomide (TMZ) treatment.ConclusionAn immune-related gene signature was established based on ABCC3, PDPN, and INA, which can be used to predict the prognosis, immune infiltration status, immunotherapy and chemotherapy response of LGG patients with epilepsy.

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Section: The Level Of Immune Infiltration Affected the Prognosismentioning

confidence: 98%

Multi-Omics Data Integration Analysis of an Immune-Related Gene Signature in LGG Patients With Epilepsy

Cheng

Duan

et al. 2021

Front. Cell Dev. Biol.

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“…Patients with high risk scores had significantly reduced number of plasma cells, activated CD4 memory cells, follicular helper T cells, and resting dendritic cells and increased number of naïve CD4 T cells, monocytes, M2 macrophages, and resting mast cells. Activated CD4 memory cells were associated with favorable outcomes in patients with cervical cancer ( Ju et al, 2020 ) and favorable outcomes after radiotherapy in patients with multiple cancers ( Ju et al, 2020 ; Wen et al, 2020 ). In gastric cancer, patients with low risk scores had increased number of activated CD4 memory cells and had superior prognosis ( Zhao et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Prognostic Classifier Based on Lipid Metabolism–Related Genes in Gastric Cancer

Wei

Luo

et al. 2021

Front. Mol. Biosci.

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Background: Dysregulation of lipid metabolism plays important roles in the tumorigenesis and progression of gastric cancer (GC). The present study aimed to establish a prognostic model based on the lipid metabolism–related genes in GC patients.Materials and Methods: Two GC datasets from the Gene Expression Atlas, GSE62254 (n = 300) and GSE26942 (n = 217), were used as training and validation cohorts to establish a risk predictive scoring model. The efficacy of this model was assessed by ROC analysis. The association of the risk predictive scores with patient characteristics and immune cell subtypes was evaluated. A nomogram was constructed based on the risk predictive score model and other prognostic factors.Results: A risk predictive score model was established based on the expression of 19 lipid metabolism–related genes (LPL, IPMK, PLCB3, CDIPT, PIK3CA, DPM2, PIGZ, GPD2, GPX3, LTC4S, CYP1A2, GALC, SGMS1, SMPD2, SMPD3, FUT6, ST3GAL1, B4GALNT1, and ACADS). The time-dependent ROC analysis revealed that the risk predictive score model was stable and robust. Patients with high risk scores had significantly unfavorable overall survival compared with those with low risk scores in both the training and validation cohorts. A higher risk score was associated with more aggressive features, including a higher tumor grade, a more advanced TNM stage, and diffuse type of Lauren classification of GC. Moreover, distinct immune cell subtypes and signaling pathways were found between the high–risk and low–risk score groups. A nomogram containing patients’ age, tumor stage, adjuvant chemotherapy, and the risk predictive score could accurately predict the survival probability of patients at 1, 3, and 5 years.Conclusion: A novel 19-gene risk predictive score model was developed based on the lipid metabolism–related genes, which could be a potential prognostic indicator and therapeutic target of GC.

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“…Radiotherapy, either as monotherapy or as part of combination therapy, is used for the localized treatment of malignancies. According to a review article on cancers, the infiltration rate of activated memory CD4 + T cells and activated mast cells in the immune TME under radiotherapy are closely related to overall survival in multiple cancers [ 51 ]. In a mouse model of breast cancer, the combination of localized radiotherapy with CTLA-4 blockade for poorly immunogenic metastatic cancers refractory to anti-CTLA-4 monotherapy significantly improved survival [ 52 ].…”

Section: Combination Of Icis and Radiotherapymentioning

confidence: 99%

Targeting the Tumor Microenvironment for Improving Therapeutic Effectiveness in Cancer Immunotherapy: Focusing on Immune Checkpoint Inhibitors and Combination Therapies

Chyuan

Chu

Hsu

2021

Cancers

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Immune checkpoints play critical roles in the regulation of T-cell effector function, and the effectiveness of their inhibitors in cancer therapy has been established. Immune checkpoint inhibitors (ICIs) constitute a paradigm shift in cancer therapy in general and cancer immunotherapy in particular. Immunotherapy has been indicated to reinvigorate antitumor T-cell activity and dynamically modulate anticancer immune responses. However, despite the promising results in the use of immunotherapy in some cancers, numerous patients do not respond to ICIs without the existence of a clear predictive biomarker. Overall, immunotherapy involves a certain degree of uncertainty and complexity. Research on the exploration of cellular and molecular factors within the tumor microenvironment (TME) aims to identify possible mechanisms of immunotherapy resistance, as well as to develop novel combination strategies involving the specific targeting of the TME for cancer immunotherapy. The combination of this approach with other types of treatment, including immune checkpoint blockade therapy involving multiple agents, most of the responses and effects in cancer therapy could be significantly enhanced, but the appropriate combinations have yet to be established. Moreover, the in-depth exploration of complexity within the TME allows for the exploration of pathways of immune dysfunction. It may also aid in the identification of new therapeutic targets. This paper reviews recent advances in the improvement of therapeutic efficacy on the immune context of the TME and highlights its contribution to cancer immunotherapy.

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Pan-Cancer Analysis of Radiotherapy Benefits and Immune Infiltration in Multiple Human Cancers

Cited by 13 publications

References 44 publications

Multi-Omics Data Integration Analysis of an Immune-Related Gene Signature in LGG Patients With Epilepsy

Multi-Omics Data Integration Analysis of an Immune-Related Gene Signature in LGG Patients With Epilepsy

Development and Validation of a Prognostic Classifier Based on Lipid Metabolism–Related Genes in Gastric Cancer

Targeting the Tumor Microenvironment for Improving Therapeutic Effectiveness in Cancer Immunotherapy: Focusing on Immune Checkpoint Inhibitors and Combination Therapies

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