Identification of prognostic genes in the acute myeloid leukemia immune microenvironment based on TCGA data analysis

Yan, Haimeng; Qu, Jianwei; Cao, Wen; Liu, Yang; Zheng, Gaofeng; Zhang, Enfan; Cai, Zhen

doi:10.1007/s00262-019-02408-7

“…In addition, immune scores were significantly correlated with cytogenetic risk and overall survival, indicating that immune microenvironment plays an important role in the development and progression of AML. These results are consistent with recent reports that included all AML patients in TCGA [11][12][13], however our study excluded patients with other tumors.…”

Section: Discussionsupporting

confidence: 93%

“…The ESTIMATE program is a common method to explore the microenvironment of many tumors [10]. Recently, it has also been used to explore the prognostic genes in the microenvironment of AML patients [11][12][13]. Most studies have focused on differentially expressed genes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Immune-related Competing Endogenous RNA Network Predicts Prognosis of Acute Myeloid Leukemia

Wang

¹

,

Lu

²

,

Liu

³

et al. 2020

Preprint

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Background Acute myeloid leukemia is a genetically, biologically and clinically heterogeneous hematopoietic malignancy that is highly dependent on the bone marrow microenvironment. Infiltrated immune cells and stromal cells are an important part of the bone marrow microenvironment and significantly affect the progression of acute myeloid leukemia. Recently, the competing endogenous RNA hypothesis has gained great interests in the study of molecular and biological mechanisms of tumor occurrence and progression. However, research on how competing endogenous RNA relates to leukemia tumor microenvironment remains uninvestigated.Methods In this study, mRNA, miRNA and lncRNA data and clinical information of the acute myeloid leukemia cohort were obtained from The Cancer Genome Atlas database, and the immune and stromal scores were calculated using the ESTIMATE algorithm.Results We found that immune scores were significantly correlated with cytogenetic risk and overall survival, and also identified microenvironment-related mRNAs, miRNAs, and lncRNAs based on the immune and stromal scores. Differentially expressed mRNAs and lncRNAs were applied to weighted correlation network analysis to identify the modules most relevant to the immune microenvironment of acute myeloid leukemia. Using miRNA database to predict miRNA-targeted genes, we established the immune-related competing endogenous RNA network consisting of 33 lncRNAs, 21 miRNAs and 135 mRNAs. Prognostic analysis was performed on the genes in the immune-related competing endogenous RNA network to screen out 15 lncRNAs, 2 miRNAs and 31 mRNAs with prognostic values.Conclusions In summary, we identified a novel immune-related mRNA-miRNA-lncRNA competing endogenous RNA network associated with the prognosis of acute myeloid leukemia, which may contribute to better understanding of the development and progression of acute myeloid leukemia and to serve as novel therapeutic targets.

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“…In this algorithm, the authors analyzed the specific gene expression profile of immune and stromal cells and obtained immune and stromal scores to predict the infiltration of non-malignant cells. There are reports of application of the ESTIMATE algorithm to prostate cancer [8] , breast cancer [9] , colon cancer [10] , glioblastoma [11] , and acute myeloid leukemia [12] . However, no detailed evaluation of the immune and/or stromal scores of LUAD has been conducted.…”

Section: Estimate (Estimation Of Stromal and Immune Cells In Malignanmentioning

confidence: 99%

Identification of key prognosis-associated genes in the lung adenocarcinoma

Ma

¹

,

Wu

²

,

Tao

³

et al. 2020

Preprint

0

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Non-small cell lung cancer (NSCLC) is one of the most common causes of cancer-related death globally, and lung adenocarcinoma (LUAD) accounts for almost 40% of all lung cancer cases. In recent years, despite better understanding of the pathogenesis of the disease and achievements in the multimodal treatment of tumors, there is an urgent need to identify new diagnostic and prognostic biomarkers In this study, we aim to identify the potential key genes related to the pathogenesis and prognosis of LUAD by using comprehensive bioinformatics analysis. The gene expression profile was downloaded from The Cancer Genome Atlas (TCGA) database, and we calculated the LUAD immune scores and stromal scores by using the ESTIMATE algorithm. Based on these scores, we further quantified the immune and stromal components and obtained the differentially expressed genes (DEGs) in the tumor. Overall survival analysis could better reflect the impact of genes related to immune and stromal cells on the prognosis. Moreover, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were conducted, while protein-protein interaction (PPI) network was obtained from STRING. Analysis of the correlation revealed that these genes are mainly involved in the immune/inflammatory response. In conclusion, our study showed that 11 prognostic genes (CD33, IRF8, CD80, CD53, IL16, LY86, CD79B, TYROBP, CD1E, CD1C, and CD1B) might show a potentially good performance in predicting overall survival in patients with LUAD. In summary, we identified the key genes related to the microenvironment, which can further serve as the prognostic biomarkers and therapeutic targets for LUAD.

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“…In this algorithm, the authors analyzed the speci c gene expression pro le of immune and stromal cells and obtained immune and stromal scores to predict the in ltration of non-malignant cells. There are reports of application of the ESTIMATE algorithm to prostate cancer [8] , breast cancer [9] , colon cancer [10] , glioblastoma [11] , and acute myeloid leukemia [12] . However, no detailed evaluation of the immune and/or stromal scores of LUAD has been conducted.…”

Section: Estimate (Estimation Of Stromal and Immune Cells In Malignanmentioning

confidence: 99%

Identification of Key Prognosis-Associated Genes in the Lung Adenocarcinoma

Ma

¹

,

Wu

²

,

Tao

³

et al. 2020

Preprint

0

View full text Add to dashboard Cite

Non-small cell lung cancer (NSCLC) is one of the most common causes of cancer-related death globally, and lung adenocarcinoma (LUAD) accounts for almost 40% of all lung cancer cases. In recent years, despite better understanding of the pathogenesis of the disease and achievements in the multimodal treatment of tumors, there is an urgent need to identify new diagnostic and prognostic biomarkers In this study, we aim to identify the potential key genes related to the pathogenesis and prognosis of LUAD by using comprehensive bioinformatics analysis. The gene expression profile was downloaded from The Cancer Genome Atlas (TCGA) database, and we calculated the LUAD immune scores and stromal scores by using the ESTIMATE algorithm. Based on these scores, we further quantified the immune and stromal components and obtained the differentially expressed genes (DEGs) in the tumor. Overall survival analysis could better reflect the impact of genes related to immune and stromal cells on the prognosis. Moreover, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were conducted, while protein-protein interaction (PPI) network was obtained from STRING. Analysis of the correlation revealed that these genes are mainly involved in the immune/inflammatory response. In conclusion, our study showed that 11 prognostic genes (CD33, IRF8, CD80, CD53, IL16, LY86, CD79B, TYROBP, CD1E, CD1C, and CD1B) might show a potentially good performance in predicting overall survival in patients with LUAD. In summary, we identified the key genes related to the microenvironment, which can further serve as the prognostic biomarkers and therapeutic targets for LUAD.

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Identification of prognostic genes in the acute myeloid leukemia immune microenvironment based on TCGA data analysis

Cited by 97 publications

References 35 publications

A Novel Immune-related Competing Endogenous RNA Network Predicts Prognosis of Acute Myeloid Leukemia

A Novel Immune-related Competing Endogenous RNA Network Predicts Prognosis of Acute Myeloid Leukemia

Identification of key prognosis-associated genes in the lung adenocarcinoma

Identification of Key Prognosis-Associated Genes in the Lung Adenocarcinoma

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