Immune-Stromal Score Signature: Novel Prognostic Tool of the Tumor Microenvironment in Lung Adenocarcinoma

Qi, Xin; Qi, Chunjian; Qin, Bo; Kang, Xindan; Hu, Yi; Han, Weidong

doi:10.3389/fonc.2020.541330

Cited by 27 publications

(24 citation statements)

References 28 publications

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“…The stromal and immune scores are measures of the proportion of stromal and immune cells in the tumor tissue, respectively, and the ESTIMATE score indicate tumor purity, which is defined as the percentage of tumor cells in the TME and is closely related to the prognosis of cancer. The findings showed a significant correlation between risk scores and all three of these other factors, possibly predicting an effect of the TME on lymph node metastasis (Qi et al, 2020). Tumor-infiltrating B cells are a key component of adaptive immunity, and data on the antitumor effects of B cells in NSCLC are inconsistent (Hu et al, 2019).…”

Section: Discussionmentioning

confidence: 98%

Identification and Validation of Immune-Related Gene Signature for Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma

Jia

Sui

Zhang

et al. 2021

Front. Mol. Biosci.

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Lung cancer is a serious malignancy, and lung adenocarcinoma (LUAD) is the most common pathological subtype. Immune-related factors play an important role in lymph node metastasis. In this study, we obtained gene expression profile data for LUAD and normal tissues from the TCGA database and analyzed their immune-related genes (IRGs), and observed that 459 IRGs were differentially expressed. Further analysis of the correlation between differentially expressed IRGs and lymph node metastasis revealed 18 lymph node metastasis-associated IRGs. In addition, we analyzed the mutations status, function and pathway enrichment of these IRGs, and regulatory networks established through TF genes. We then identified eight IRGs (IKBKB, LTBR, MIF, PPARD, PPIA, PSME3, S100A6, SEMA4B) as the best predictors by LASSO Logistic analysis and used these IRGs to construct a model to predict lymph node metastasis in patients with LUAD (AUC 0.75; 95% CI: 0.7064–0.7978), and survival analysis showed that the risk score independently affected patient survival. We validated the predictive effect of risk scores on lymph node metastasis and survival using the GEO database as a validation cohort and the results showed good agreement. In addition, the risk score was highly correlated with infiltration of immune cells (mast cells activated, macrophages M2, macrophages M0 and B cells naïve), immune and stromal scores, and immune checkpoint genes (LTBR, CD40LG, EDA2R, and TNFRSF19). We identified key IRGs associated with lymph node metastasis in LUAD and constructed a reliable risk score model, which may provide valuable biomarkers for LUAD patients and further reveal the mechanism of its occurrence.

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Section: Discussionmentioning

confidence: 98%

Identification and Validation of Immune-Related Gene Signature for Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma

Jia

Sui

Zhang

et al. 2021

Front. Mol. Biosci.

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“…TME consists of surrounding cells and noncellular components. Tumor-infiltrating stromal cells and immune cells as the major cell components of surrounding cells that play a significant role in cancer prognosis [9][10][11]. For example, Vigano et al found that tumor-infiltrating lymphocytes and macrophages were associated with prognosis in intrahepatic cholangiocellular carcinoma patients after complete surgery [12].…”

Section: Introductionmentioning

confidence: 99%

Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

Chen¹,

Chen²,

Lu³

et al. 2021

Preprint

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Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

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“…Increasing evidence suggested that tumor microenvironment (TME) which is composed of tumor cells, immune cells, stromal cells, inflammatory mediators and extracellular matrix [ 2 ], taking part in the tumor progression and drug resistance [ 3 , 4 ]. Among them, immune cells and inflammatory mediators have been proved to be valuable for the prognostic of LUAD [ 5 ]. Much attention has been paid on the immune microenvironment of LUAD.…”

Section: Introductionmentioning

confidence: 99%

Construction of the optimization prognostic model based on differentially expressed immune genes of lung adenocarcinoma

Zhai

Zhao

Wang³

et al. 2021

BMC Cancer

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Background Lung adenocarcinoma (LUAD) is the most common pathology subtype of lung cancer. In recent years, immunotherapy, targeted therapy and chemotherapeutics conferred a certain curative effects. However, the effect and prognosis of LUAD patients are different, and the efficacy of existing LUAD risk prediction models is unsatisfactory. Methods The Cancer Genome Atlas (TCGA) LUAD dataset was downloaded. The differentially expressed immune genes (DEIGs) were analyzed with edgeR and DESeq2. The prognostic DEIGs were identified by COX regression. Protein-protein interaction (PPI) network was inferred by STRING using prognostic DEIGs with p value< 0.05. The prognostic model based on DEIGs was established using Lasso regression. Immunohistochemistry was used to assess the expression of FERMT2, FKBP3, SMAD9, GATA2, and ITIH4 in 30 cases of LUAD tissues. Results In total,1654 DEIGs were identified, of which 436 genes were prognostic. Gene functional enrichment analysis indicated that the DEIGs were involved in inflammatory pathways. We constructed 4 models using DEIGs. Finally, model 4, which was constructed using the 436 DEIGs performed the best in prognostic predictions, the receiver operating characteristic curve (ROC) was 0.824 for 3 years, 0.838 for 5 years, 0.834 for 10 years. High levels of FERMT2, FKBP3 and low levels of SMAD9, GATA2, ITIH4 expression are related to the poor overall survival in LUAD (p < 0.05). The prognostic model based on DEIGs reflected infiltration by immune cells. Conclusions In our study, we built an optimal prognostic signature for LUAD using DEIGs and verified the expression of selected genes in LUAD. Our result suggests immune signature can be harnessed to obtain prognostic insights.

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Immune-Stromal Score Signature: Novel Prognostic Tool of the Tumor Microenvironment in Lung Adenocarcinoma

Cited by 27 publications

References 28 publications

Identification and Validation of Immune-Related Gene Signature for Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma

Identification and Validation of Immune-Related Gene Signature for Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma

Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

Construction of the optimization prognostic model based on differentially expressed immune genes of lung adenocarcinoma

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