Identification immunophenotyping of lung adenocarcinomas based on the tumor microenvironment

Tan, Qilong; Huang, Yue; Deng, Kui; Lu, Ming-Liang; Wang, Liuying; Rong, Zhiwei; Zhao, Weiwei; Li, Shuang; Xu, Zhenyi; Fan, Lijun; Li, Kang; Li, Zhenzi

doi:10.1002/jcb.29675

Cited by 12 publications

(13 citation statements)

References 33 publications

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“…[26]. These results accorded with previous reports on the functions of immune and stromal cells in the TME of LUAD patients [27][28][29].…”

Section: Kinase Targets Of Hub Genessupporting

confidence: 92%

Identification of genes with therapeutic and prognostic values in lung adenocarcinoma microenvironment

Jiang¹,

Gao²,

Zhang³

et al. 2020

Preprint

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Background As the most diagnosed malignancy, lung cancer is also the primary cause of cancer death in the entire world. The therapy of lung adenocarcinoma (LUAD), which is the most prevalent subtype of lung cancer, draw researchers’ increasing attentions. This research aimed to investigate the tumor microenvironment (TME)-related hub genes which might be novel targets for treatment. Materials and methods LUAD-associated data packages, including RNA-Seq information and clinical data of 522 patients, were obtained from The Cancer Genome Atlas (TCGA). For better evaluation of stromal and immune cell components, immune scores, stromal scores and estimate scores were obtained with ESTIMATE algorithm based on gene expression levels in tumors. The R package heatmap and clustering analysis were used to explore interested genes. Differentially expressed genes (DEGs) were identified by Venn diagram. Protein-protein interaction (PPI) network was applied to explore intrinsic connections of DEGs. Kaplan-Meier (K-M) survival curves, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied to investigate the prognostic values and intricate biological functions of DEGs. The relationships between 4 survival-related hub genes and 6 types of immune cells were examined using TIMER database. The LinkedOmics database was applied to look for kinase targets of hub genes. Results The immune/stromal/estimate scores were significantly correlated with clinical features, including the grades and sizes of LUAD, distant metastasis and outcomes. A total of 702 DEGs, 589 up-regulated and 113 down-regulated, were identified. GO and KEGG analysis showed that the DEGs had significant correlations with tumor immunology. PPI network suggested that the top 8 nodes were FPR2, C3AR1, MCHR1, CCR5, FPR1, CCL19, CCR2 and CXCL10. K-M survival curves indicated that FPR2, C3AR1, MCHR1 and CCR5, as hub genes, were significantly correlated with the overall survival (OS) of LUAD patients. The expression levels of C3AR1 and CCR5 were positively correlated with immune cell infiltration. LYN, LCK and SYK were the targeted kinases of these hub genes. Conclusion FPR2, C3AR1, MCHR1 and CCR5 were TME-related genes and potential biomarkers for the therapy and prognosis of LUAD.

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“…[26]. These results accorded with previous reports on the functions of immune and stromal cells in the TME of LUAD patients [27][28][29].…”

Section: Kinase Targets Of Hub Genessupporting

confidence: 92%

Identification of genes with therapeutic and prognostic values in lung adenocarcinoma microenvironment

Jiang¹,

Gao²,

Zhang³

et al. 2020

Preprint

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“…To improve the prognostic, and most possible a predictive tool for NSCLC, Roy M. recommended that the immunescore should be added to the TNM classification (TNM-I) (26). A study showed that the former displayed T-cells, Interferon, and M1 macrophage signatures and was associated with a better prognosis, while the latter presented with M2 macrophage signatures and immunosuppressive factors such as WNT/ transforming growth factor-β (27).…”

Section: Discussionmentioning

confidence: 99%

A signature of estimate-stromal-immune score-based genes associated with the prognosis of lung adenocarcinoma

Ma¹,

Chen²,

Xiao³

et al. 2021

Transl Lung Cancer Res

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Background: Immune and stromal component evaluation is necessary to establish accurate prognostic markers for the prediction of clinical outcomes in lung adenocarcinoma (LUAD). We aimed to develop a gene signature based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE)-stromal-immune score in LUAD. Methods:The transcriptomic profiles of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA), and the immune and stromal scores were derived using the ESTIMATE algorithm. The prognostic signature genes were selected from the differentially expressed genes (DEGs) using the robust partial likelihood-based cox proportional hazards regression method. The negative log-likelihood and the Akaike Information Criterion (AIC) were used to identify the optimal gene signature. The validation was carried out in 2 independent datasets from the Gene Expression Omnibus (GSE68571 and GSE72094).Results: Patients with high ESTIMATE, stromal, and immune scores had better overall survivals (P=0.0035, P=0.066, and P=0.0077). The expression of thirty-seven genes was related to ESTIMATE-stromal-immune score. A risk stratification model was developed based on a gene signature containing CD74, JCHAIN, and PTGDS. The ESTIMATE-stromal-immune risk score was revealed to be a prognostic factor (P=0.009) after multivariate analysis. Four groups were classified based on this risk stratification model, yielding increasing survival outcomes (log-rank test, P=0.0051). This risk stratification model and other clinicopathological factors were combined to generate a nomogram. The calibration curves showed perfect agreement between the nomogram-predicted outcomes and those actually observed. Similar observations were made in 2 independent cohorts. Conclusions:The gene signature based on the ESTIMATE-stromal-immune score could predict the prognosis of patients with LUAD.

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“…Meanwhile, the data of somatic variations in LUADs were unavailable in their study. Hitherto, several bioinformatics studies (34,47,49,50) have identified the immune-related signatures as a prognostic biomarker in LUADs. However, the roles of immunophenotypederived signatures in predicting response to chemotherapy have not been fully clarified.…”

Section: Discussionmentioning

confidence: 99%

“…Similar to our data, Bi et al ( 46 ) who performed a study based on TCGA mining also found that BTK was an immune-related gene and a promising prognostic factor for LUAD. Recently, Tan et al ( 47 ) performed a bioinformatics study to characterize the immune landscape of LUADs, in which they divided the patients with LUAD into two immunophenotypes based on the tumor microenvironment. The two immunophenotypes were denoted as the Active Immune Class and Exhausted Immune Class.…”

Section: Discussionmentioning

confidence: 99%

“…The former showed significant IFN, T‐cells, M1 macrophage signatures, and better prognosis, while the latter presented an exhausted immune response with activated stromal enrichment, M2 macrophage signatures, and immunosuppressive factors such as WNT/transforming growth factor‐β. In addition, Tan and his colleagues ( 47 ) identified their developed Immune Class as a useful tool to predict the response to ICB. However, merely 32 patients in a metastatic melanoma cohort were included in their prediction of PD-L1 inhibitor response.…”

Section: Discussionmentioning

confidence: 99%

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Characterization of Tumor Microenvironment in Lung Adenocarcinoma Identifies Immune Signatures to Predict Clinical Outcomes and Therapeutic Responses

Chen¹,

Wang²,

Zhang³

et al. 2021

Front. Oncol.

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Background and ObjectiveIncreasing evidence has elucidated the clinicopathological significance of individual TME component in predicting outcomes and immunotherapeutic efficacy in lung adenocarcinoma (LUAD). Therefore, we aimed to investigate whether comprehensive TME-based signatures could predict patient survival and therapeutic responses in LUAD, and to assess the associations among TME signatures, single nucleotide variations and clinicopathological characteristics.MethodsIn this study, we comprehensively estimated the TME infiltration patterns of 493 LUAD patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathological features of LUADs using two proposed computational algorithms. A TMEscore was then developed based on the TME signature genes, and its prognostic value was validated in different datasets. Bioinformatics analysis was used to evaluate the efficacy of the TMEscore in predicting responses to immunotherapy and chemotherapy.ResultsThree TME subtypes were identified with no prognostic significance exhibited. Among them, naïve B cells accounted for the majority in TMEcluster1, while M2 TAMs and M0 TAMs took the largest proportion in TMEcluster2 and TMEcluster3, respectively. A total of 3395 DEGs among the three TME clusters were determined, among which 217 TME signature genes were identified. Interestingly, these signature genes were mainly involved in T cell activation, lymphocyte proliferation and mononuclear cell proliferation. With somatic variations and tumor mutation burden (TMB) of the LUAD samples characterized, a genomic landscape of the LUADs was thereby established to visualize the relationships among the TMEscore, mutation spectra and clinicopathological profiles. In addition, the TMEscore was identified as not only a prognosticator for long-term survival in different datasets, but also a predictive biomarker for the responses to immune checkpoint blockade (ICB) and chemotherapeutic agents. Furthermore, the TMEscore exhibited greater accuracy than other conventional biomarkers including TMB and microsatellite instability in predicting immunotherapeutic response (p < 0.001).ConclusionIn conclusion, our present study depicted a comprehensive landscape of the TME signatures in LUADs. Meanwhile, the TMEscore was proved to be a promising predictor of patient survival and therapeutic responses in LUADs, which might be helpful to the future administration of personalized adjuvant therapy.

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Identification immunophenotyping of lung adenocarcinomas based on the tumor microenvironment

Cited by 12 publications

References 33 publications

Identification of genes with therapeutic and prognostic values in lung adenocarcinoma microenvironment

Identification of genes with therapeutic and prognostic values in lung adenocarcinoma microenvironment

A signature of estimate-stromal-immune score-based genes associated with the prognosis of lung adenocarcinoma

Characterization of Tumor Microenvironment in Lung Adenocarcinoma Identifies Immune Signatures to Predict Clinical Outcomes and Therapeutic Responses

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