Clinical significance and inflammatory landscapes of a novel recurrence-associated immune signature in early-stage lung adenocarcinoma

Zhang, Chaoqi; Zhang, Zhen; Zhang, Guochao; Zhang, Zhihui; Luo, Yuejun; Wang, Feng; Wang, Sihui; Cao, Yun; Zeng, Qingpeng; Sun, Nan; He, Jie

doi:10.1016/j.canlet.2020.03.016

Cited by 66 publications

(55 citation statements)

References 46 publications

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“…Zhang et al used qRT-PCR to detect the expression of signature genes in frozen tissue samples of patients and further calculated the risk scores of these patients. Consistent with the results of the public database, there were signi cant differences in RFS between the two groups [31]. Yao et al evaluated the protein expression of signature genes through immunohistochemistry (IHC) stained tissue microarrays to verify the application of signatures at the protein level.…”

Section: Discussionmentioning

confidence: 64%

Signature based on immune-related LncRNA can predict overall survival of osteosarcoma patients

Zhang

Khader

et al. 2020

Preprint

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Background: Osteosarcoma is a malignant bone tumor common in children and adolescents. Metastatic status remains the most important guideline for classifying patients and making clinical decisions. Despite many efforts, newly diagnosed patients receive the same therapy that patients have received over the last 4 decades. With the development of high-throughput sequencing technology and the rise of immunotherapy, it is necessary to deeply explore the immune molecular mechanism of osteosarcoma. Methods: We obtained RNA-seq data and clinical information of osteosarcoma patients from TCGA database and TARGET database. With the help of co-expression analysis we identified immune-related lncRNA and then by means of univariate Cox regression analysis prognostic-related lncRNA was screened out. And also by using least absolute shrinkage and selection operator regression method a model based on immune-related lncRNA was constructed. The differences in overall survival, immune infiltration, immune checkpoint gene expression, and tumor microenvironmental immunity type between the two groups were evaluated. Results: We constructed a signature consisting of 13 lncRNA. Our results show that signatures can reliably predict the overall survival of patients with osteosarcoma and can bring net clinical benefits. Further more, the signatures can be used for further risk stratification of the metastasis patients. Patients in the low-risk group had higher immune cell infiltration and immune checkpoint gene expression. The results from gene set variation analysis show that patients in low-risk group are closely related to immune-related pathways when compared with patients in high-risk group. Finally, patients in the low-risk group are more likely to be classified as TMIT I and hence more likely to benefit from immunotherapy. Conclusion: Our signature may be a reliable marker for predicting the overall survival of patients with osteosarcoma. Keywords: Osteosarcoma, TCGA, LncRNA, Tumor immunology, Prognosis.

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Section: Discussionmentioning

confidence: 64%

Signature based on immune-related LncRNA can predict overall survival of osteosarcoma patients

Zhang

Khader

et al. 2020

Preprint

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“…related genes between high-risk and low-risk groups. We found remarkable differences in CD3E, CD4, CD27, CD40LG, NCR3, TNFSF14, and VSIR (Figures 6A-G; Zhang et al, 2020). All of these genes are recognized as marker genes closely related to the efficacy of immunotherapy.…”

Section: Discussionmentioning

confidence: 89%

Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma

Shen

Wang

et al. 2021

Front. Cell Dev. Biol.

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BackgroundLung adenocarcinoma (LUAD) originates mainly from the mucous epithelium and glandular epithelium of the bronchi. It is the most common pathologic subtype of non-small cell lung cancer (NSCLC). At present, there is still a lack of clear criteria to predict the efficacy of immunotherapy. The 5-year survival rate for LUAD patients remains low.MethodsAll data were downloaded from The Cancer Genome Atlas (TCGA) database. We used Gene Set Enrichment Analysis (GSEA) database to obtain immune-related mRNAs. Immune-related lncRNAs were acquired by using the correlation test of the immune-related genes with R version 3.6.3 (Pearson correlation coefficient cor = 0.5, P < 0.05). The TCGA-LUAD dataset was divided into the testing set and the training set randomly. Based on the training set to perform univariate and multivariate Cox regression analyses, we screened prognostic immune-related lncRNAs and given a risk score to each sample. Samples were divided into the high-risk group and the low-risk group according to the median risk score. By the combination of Kaplan–Meier (KM) survival curve, the receiver operating characteristic (ROC) (AUC) curve, the independent risk factor analysis, and the clinical data of the samples, we assessed the accuracy of the risk model. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differentially expressed mRNAs between the high-risk group and the low-risk group. The differentially expressed genes related to immune response between two risk groups were analyzed to evaluate the role of the model in predicting the efficacy and effects of immunotherapy. In order to explain the internal mechanism of the risk model in predicting the efficacy of immunotherapy, we analyzed the differentially expressed genes related to epithelial-mesenchymal transition (EMT) between two risk groups. We extracted RNA from normal bronchial epithelial cell and LUAD cells and verified the expression level of lncRNAs in the risk model by a quantitative real-time polymerase chain reaction (qRT-PCR) test. We compared our risk model with other published prognostic signatures with data from an independent cohort. We transfected LUAD cell with siRNA-LINC0253. Western blot analysis was performed to observed change of EMT-related marker in protein level.ResultsThrough univariate Cox regression analysis, 24 immune-related lncRNAs were found to be strongly associated with the survival of the TCGA-LUAD dataset. Utilizing multivariate Cox regression analysis, 10 lncRNAs were selected to establish the risk model. The K-M survival curves and the ROC (AUC) curves proved that the risk model has a fine predictive effect. The GO enrichment analysis indicated that the effect of the differentially expressed genes between high-risk and low-risk groups is mainly involved in immune response and intercellular interaction. The KEGG enrichment analysis indicated that the differentially expressed genes between high-risk and low-risk groups are mainly involved in endocytosis and the MAPK signaling pathway. The expression of genes related to the efficacy of immunotherapy was significantly different between the two groups. A qRT-PCR test verified the expression level of lncRNAs in LUAD cells in the risk model. The AUC of ROC of 5 years in the independent validation dataset showed that this model had superior accuracy. Western blot analysis verified the change of EMT-related marker in protein level.ConclusionThe immune lncRNA risk model established by us could better predict the prognosis of patients with LUAD.

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“…Last, the analyses were extended to incorporate 30 immune checkpoint molecules, including the B7-CD28 family, the TNF superfamily, and several other immune checkpoint members. 7 In both TCGA and GEO cohorts, high-risk score calculated by the eight-gene signature presented a consistent relationship with immune depletion, immune suppression, and resistance to immune checkpoint blockade, leading to a poor prognosis and a high possibility of recurrence (Figures 3 and 4A-K). Furthermore, the enrichment analyses verified the involvement of risk score in modulating oral cancer immunity in both TCGA and GEO cohorts ( Figure S9).…”

mentioning

confidence: 73%

Clinical significance and immune landscapes of stemness‐related and immune gene set‐based signature in oral cancer

Lin

Zheng²,

Yang

et al. 2021

Clinical & Translational Med

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Clinical significance and inflammatory landscapes of a novel recurrence-associated immune signature in early-stage lung adenocarcinoma

Cited by 66 publications

References 46 publications

Signature based on immune-related LncRNA can predict overall survival of osteosarcoma patients

Signature based on immune-related LncRNA can predict overall survival of osteosarcoma patients

Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma

Clinical significance and immune landscapes of stemness‐related and immune gene set‐based signature in oral cancer

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