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

Ma, Qianli; Chen, Yang; Xiao, Fei; Yang, Hao; Song, Zhiyi; Zhang, Jin; Okuda, Kunio; Um, Sang‐Won; Silva, Mario; Shimada, Yoshihisa; Si, Chaozeng; Liang, Chaoyang

doi:10.21037/tlcr-21-223

Cited by 28 publications

(20 citation statements)

References 51 publications

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“…To apply it in clinical practice, we sought to construct a prognostic signature for gastric cancer based on ESTIMATE scores. Similar approaches have been applied in studies on different types of cancer ( Ding Q. et al, 2020 ; Meng et al, 2020 ; Ma et al, 2021 ). After constructing the PPI network using the genes in the intersection between high stromal and immune score related DEGs, we identified 9 hub genes in the CXCL12 module, including COL3A1, DDR2, ECM2, EDNRA, FOXF1, ISLR, PDGRB, SPON1, and TNFSF4.…”

Section: Discussionmentioning

confidence: 99%

Establishment of a Prognostic Signature of Stromal/Immune-Related Genes for Gastric Adenocarcinoma Based on ESTIMATE Algorithm

Wang

Peng

et al. 2021

Front. Cell Dev. Biol.

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Different subtypes of gastric cancer differentially respond to immune checkpoint inhibitors (ICI). This study aimed to investigate whether the Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm is related to the classification and prognosis of gastric cancer and to establish an ESTIMATE-based gene signature to predict the prognosis for patients. The immune/stromal scores of 388 gastric cancer patients from TCGA were used in this analysis. The upregulated differentially expressed genes (DEGs) in patients with high stromal/immune scores were identified. The immune-related hub DEGs were selected based on protein-protein interaction (PPI) analysis. The prognostic values of the hub DEGs were evaluated in the TCGA dataset and validated in the GSE15460 dataset using the Kaplan-Meier curves. A prognostic signature was built using the hub DEGs by Cox proportional hazards model, and the accuracy was assessed using receiver operating characteristic (ROC) analysis. Different subtypes of gastric cancer had significantly different immune/stromal scores. High stromal scores but not immune scores were significantly associated with short overall survivals of TCGA patients. Nine hub DEGs were identified in PPI analysisThe expression of these hub DEG negatively correlated with the overall survival in the TCGA cohort, which was validated in the GSE15460 cohort. A 9-gene prognostic signature was constructed. The risk factor of patients was calculated by this signature. High-risk patients had significantly shorter overall survival than low-risk patients. ROC analysis showed that the prognostic model accurately identified high-risk individuals within different time frames. We established an effective 9-gene-based risk signature to predict the prognosis of gastric cancer patients, providing guidance for prognostic stratification.

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Section: Discussionmentioning

confidence: 99%

Establishment of a Prognostic Signature of Stromal/Immune-Related Genes for Gastric Adenocarcinoma Based on ESTIMATE Algorithm

Wang

Peng

et al. 2021

Front. Cell Dev. Biol.

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“…To further reveal the possible mechanism of the relationship between AFF3 expression and clinical characteristics of GC patients, we analyzed its relationship with immune cell infiltration in GC. Accumulated evidence has illustrated that stromal and immune scores are related to prognosis and the infiltration of the immune cell in various cancers 19,25–27 . Accordingly, to evaluate the effect of AFF3 on the prognosis and TME, we assessed the association between ESTIMATE score and AFF3 expression, which showed a positive relation between AFF3 and stromal/immune/estimate score (Figure 6A–C).…”

Section: Resultsmentioning

confidence: 99%

“…Accumulated evidence has illustrated that stromal and immune scores are related to prognosis and the infiltration of the immune cell in various cancers. 19 , 25 , 26 , 27 Accordingly, to evaluate the effect of AFF3 on the prognosis and TME, we assessed the association between ESTIMATE score and AFF3 expression, which showed a positive relation between AFF3 and stromal/immune/estimate score (Figure 6A–C ). These findings demonstrated that AFF3 expression might affect the prognosis of GC and potentially regulate the infiltration of immune cells.…”

Section: Resultsmentioning

confidence: 99%

“… 19 In addition, the density of infiltrated immune cells also has a crucial effect on patients’ prognosis in a variety of cancers; the presence of stromal cells and other immune cells have a strong correlation with the prognosis of multiple cancer. 37 , 38 , 39 Additionally, growing studies have shown that the high immune/stromal/estimate score is an independent prognostic factor(s) for poor overall survival of GC. 3 , 25 , 40 , 41 In our study, the overexpression of AFF3 was positively correlated with the high immune/stromal/estimate.…”

Section: Discussionmentioning

confidence: 99%

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AFF3 is a novel prognostic biomarker and a potential target for immunotherapy in gastric cancer

Zeng

Zhang

et al. 2022

Clinical Laboratory Analysis

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Background Gastric cancer (GC) is one of the most common cancers worldwide with a poor prognosis. The tumor microenvironment (TME) serves a pivotal role in affecting the prognosis and efficacy of immunotherapy. Given the poor prognosis of GC patients and the limitation of immunotherapy, we urged to identify new prognostic and immunotherapeutic biomarkers. Methods The transcriptome data were downloaded from the TCGA, GEO, and GEPIA databases, and performed differential analysis of AFF3 in tumor samples and normal samples. The UALCAN, Kaplan–Meier plotter and GEPIA databases were employed to assess the correlation of AFF3 with clinicopathological characteristics and prognosis. The potential mechanism of AFF3 was explored by the GO and KEGG enrichment. The potential role of AFF3 on tumor‐infiltrating immune cells (TIICs) was explored by TIMER2.0 and TISIDB. TIMER2.0 and SangerBox3.0 databases were, respectively, used to determine the correlation of AFF3 with immune checkpoint (ICs), tumor mutational burden (TMB), and microsatellite instability (MSI) in GC. Results We found significant downregulation of AFF3 in GC tissues as compared with normal tissues. However, GC patients having a higher expression of AFF3 were found to have worse clinicopathological characteristics and prognosis. Moreover, the GO enrichment analysis illustrated that AFF3 might regulate the immune cells in the TME. In addition, the AFF3 was positively correlated with TIICs, ICs, TMB, and MSI. Conclusion Here, we conclude that AFF3 may be a promising potential marker for the diagnosis and prognosis of GC patients, and may influence response to ICIs by affecting TIICs and ICs expression in the TME.

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“…We evaluated ESTIMATEScore, ImmuneScore, StromalScore, and TumorPurity using the ESTIMATE algorithm ( Ma et al, 2021 ). The TIMER algorithm was used to evaluate tumour infiltration of 6 immune cell types ( Li et al, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

Immune Landscape and Classification in Lung Adenocarcinoma Based on a Novel Cell Cycle Checkpoints Related Signature for Predicting Prognosis and Therapeutic Response

et al. 2022

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Lung adenocarcinoma (LUAD) is one of the most common malignancies with the highest mortality globally, and it has a poor prognosis. Cell cycle checkpoints play a central role in the entire system of monitoring cell cycle processes, by regulating the signalling pathway of the cell cycle. Cell cycle checkpoints related genes (CCCRGs) have potential utility in predicting survival, and response to immunotherapies and chemotherapies. To examine this, based on CCCRGs, we identified two lung adenocarcinoma subtypes, called cluster1 and cluster2, by consensus clustering. Enrichment analysis revealed significant discrepancies between the two subtypes in gene sets associated with cell cycle activation and tumor progression. In addition, based on Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we have developed and validated a cell cycle checkpoints-related risk signature to predict prognosis, tumour immune microenvironment: (TIME), immunotherapy and chemotherapy responses for lung adenocarcinoma patients. Results from calibration plot, decision curve analysis (DCA), and time-dependent receiver operating characteristic curve (ROC) revealed that combining age, gender, pathological stages, and risk score in lung adenocarcinoma patients allowed for a more accurate and predictive nomogram. The area under curve for lung adenocarcinoma patients with 1-, 3-, 5-, and 10-year overall survival was: 0.74, 0.73, 0.75, and 0.81, respectively. Taken together, our proposed 4-CCCRG signature can serve as a clinically useful indicator to help predict patients outcomes, and could provide important guidance for immunotherapies and chemotherapies decision for lung adenocarcinoma patients.

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

Cited by 28 publications

References 51 publications

Establishment of a Prognostic Signature of Stromal/Immune-Related Genes for Gastric Adenocarcinoma Based on ESTIMATE Algorithm

Establishment of a Prognostic Signature of Stromal/Immune-Related Genes for Gastric Adenocarcinoma Based on ESTIMATE Algorithm

AFF3 is a novel prognostic biomarker and a potential target for immunotherapy in gastric cancer

Immune Landscape and Classification in Lung Adenocarcinoma Based on a Novel Cell Cycle Checkpoints Related Signature for Predicting Prognosis and Therapeutic Response

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