Identification the prognostic value of immune gene signature and infiltrating immune cells of esophageal cancer patients

Wang, Lin; Qian, Wei; Zhang, Ming; Chen, Lianze; Li, Zinan; Zhou, Chuanjian; He, Miao; Zhao, Lin

doi:10.21203/rs.3.rs-20941/v1

Cited by 5 publications

(5 citation statements)

References 40 publications

(40 reference statements)

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“…In this study, the complex value was positively correlated with macrophage M0, and myeloid dendritic cell activated while negatively correlated with T cell CD4+ memory resting, T cell regulatory (Tregs), and mast cell activated. A recent study reported a prognostic model established by immune genes associated with memory CD4+ T cells, follicular helper cells, and monocytes for patients with ESCA [50], which was inconsistent with our results.…”

Section: Discussioncontrasting

confidence: 99%

A Novel Ferroptosis-Related Gene Signature to Predict Prognosis of Esophageal Carcinoma

Wang

Guo

Sun

et al. 2022

Journal of Oncology

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Objective. This study aimed to develop a novel ferroptosis-related gene-based prognostic signature for esophageal carcinoma (ESCA). Methods. The TCGA-ESCA gene expression profiles and corresponding clinical data were downloaded from the TCGA database. Ferroptosis-related genes were identified from the literature and public databases, which were intersected with the differentially expressed genes between ESCA and normal samples. After univariate Cox regression and random forest analyses, several ferroptosis-related feature genes were identified and used to construct a prognostic signature. Then, the prognostic value of the complex value and the correlation of the complex value with immune cell infiltration were analyzed. Moreover, function analysis, mutation analysis, and molecular docking on the ferroptosis-related feature genes were performed. Results. Based on the TCGA dataset and ferroptosis pathway genes, 1929 ferroptosis-related genes were preliminarily selected. Following univariate Cox regression analysis and survival analysis, 14 genes were obtained. Then, random forest analysis identified 10 ferroptosis key genes. These 10 genes were used to construct a prognostic complex value. It was found that low complex value indicated better prognosis compared with high complex value. In different ESCA datasets, there were similar differences in the proportion of immune cell distribution between the high and low complex value groups. Furthermore, TNKS1BP1, AC019100.7, KRI1, BCAP31, and RP11-408E5.5 were significantly correlated with ESCA tumor location, lymph node metastasis, and age of patients. KRI1 had the highest mutation frequency. BCAP31 had the strongest binding ability with small molecules DB12830, DB05812, and DB07307. Conclusion. We constructed a novel ferroptosis-related gene signature, which has the potential to predict patient survival and tumor-infiltrating immune cells of ESCA.

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Section: Discussioncontrasting

confidence: 99%

A Novel Ferroptosis-Related Gene Signature to Predict Prognosis of Esophageal Carcinoma

Wang

Guo

Sun

et al. 2022

Journal of Oncology

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“…Consistent with our results was the prognostic value found in a prognostic model established by immune genes associated with memory CD4 + T cells, Tfh cells for patients with EC. 55 Furthermore, plasma cells, endothelial cells, Tfh cells, and Tregs were upregulated, while CAFs and NK cells were downregulated in the high-risk group compared with the low-risk group in our model. M2 Macrophages induced enhanced the process of epithelial-mesenchymal transition and immune escape through the PD-1 signaling pathway, promoting the ESCC exacerbation which let ESCC patients have a poor prognosis.…”

Section: Discussionmentioning

confidence: 61%

A Promising Esophageal Cancer Prognostic Signature of Ferroptosis-Related LncRNA to Predict Immune Scenery and Immunotherapy Response

Liu¹,

Shi²,

Wei³

et al. 2021

IJGM

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Purpose Ferroptosis and long non-coding RNA (lncRNA) expression signatures have been associated with the clinical progression and immune-contexture of different solid tumors. The study aimed to identify a prognostic signature of ferroptosis-related lncRNAs (falncRNAs) to forecast the immune scenery and immunotherapy response in esophageal cancer (EC). Patients and Methods Gene expression profiles of EC were extracted from The Cancer Genome Atlas (TCGA) database, and ferroptosis-related genes were downloaded from the FerrDb database, which identified differentially expressed falncRNAs (DEfalncRNAs) via differential analysis. DEfalncRNA pairs associated with prognosis were identified by constructing a matrix, univariate and least absolute shrinkage and selection operator (LASSO) analysis. The prognostic signature was constructed by multivariate analysis. We appraised the forecasting capability of prognostic signature in survival, clinicopathological features, immune landscape, efficacy of immunotherapy, and drug sensitivity. The potential molecular mechanism of signature was investigated by gene set enrichment analysis (GSEA). Results We obtained 18 DEfalncRNA pairs to define a novel prognostic signature that was determined on a discovery cohort of 158 tumor samples and 11 adjacent normal tissues from TCGA and internally validated, with the definition of high- vs low-risk groups based on 3 years overall survival. We demonstrated that the high- vs low-risk groups differed for clinical parameters and computationally predicted drug sensitivity and tumor immune contexture, with the high-risk group having worse survival, more aggressive disease (node involvement, metastasis), reduced drug sensitivity, higher tumor mutation load, and gene signatures of infiltration of pro-tumoral immune cell subsets. The GSEA results revealed that ferroptosis and immunoregulatory pathways were significantly enriched in the high-risk group. Conclusion The prognostic signature based on falncRNAs has the potential to forecast the survival, immune scenery, efficacy of immunotherapy, and drug sensitivity of EC, which is helpful for clinical prediction and individualized treatment.

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“…With the advancement of bioinformatics, e cient algorithms have accelerated the transition of various omics big data to new therapeutic targets. As one of the most robust algorithms for analyzing immune cell in ltration-CIBERSORT-has been used in multiple tissues [13][14][15]. In this study, we used it to explore the landscape of immune in ltration in the glomerulus of DN.…”

Section: Discussionmentioning

confidence: 99%

The Landscape of Immune Cell Infiltration in the Glomerulus of Diabetic Nephropathy: Evidence Based on Bioinformatics

Zhou

Liu

et al. 2021

Preprint

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BackgroundIncreasing evidence suggests that immune cell infiltration contributes to the pathogenesis and progression of diabetic nephropathy (DN). We aim to unveil the immune infiltration pattern in the glomerulus of DN and provide potential targets for immunotherapy. MethodsInfiltrating percentage of 22 types of immune cell in the glomerulus tissues were estimated by the CIBERSORT algorithm based on three transcriptome datasets mined from the GEO database. Differentially expressed genes (DEGs) were identified by the “limma” package. Then immune-related DEGs were identified by intersecting DEGs with immune-related genes (downloaded from Immport database). The protein-protein interactions of Immune-related DEGs were explored using the STRING database and visualized by Cytoscape. The enrichment analyses for KEGG pathways and GO terms were carried out by the gene set enrichment analysis (GSEA) method. Results9 types of immune cell were revealed to be significantly altered in the glomerulus tissues of DN (Up: B cells memory, T cells CD4 naive, Macrophages M2, Dendritic cells resting, Mast cells resting, Mast cells activated; Down: NK cells resting, Monocytes, Neutrophils). Correlation analysis revealed that immune infiltration act as a complicated and tightly regulated network, among which T cells gamma delta and T cells CD4 naive show the most synergistic effect (r = 0.58, p ＜ 0.001); meanwhile, T cells CD8 and T cells CD4 memory resting show the most competitive effect (r = - 0.67, p ＜ 0.001). Several pathways related to immune were significantly activated. Moreover, 6 hub genes with a medium to strong correlation with renal function (eGFR) were identified (ALB, EGF, FOS, CXCR1, CXCR2, CCL2). ConclusionIn the glomerulus of DN, the immune infiltration pattern changed significantly. A complicated and tightly regulated network of immune cells exists in the pathological of DN. The hub genes identified here will facilitate the development of immunotherapy.

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Identification the prognostic value of immune gene signature and infiltrating immune cells of esophageal cancer patients

Cited by 5 publications

References 40 publications

A Novel Ferroptosis-Related Gene Signature to Predict Prognosis of Esophageal Carcinoma

A Novel Ferroptosis-Related Gene Signature to Predict Prognosis of Esophageal Carcinoma

A Promising Esophageal Cancer Prognostic Signature of Ferroptosis-Related LncRNA to Predict Immune Scenery and Immunotherapy Response

The Landscape of Immune Cell Infiltration in the Glomerulus of Diabetic Nephropathy: Evidence Based on Bioinformatics

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