Screening the Cancer Genome Atlas Database for Genes of Prognostic Value in Acute Myeloid Leukemia

Ni, Jie; Wu, Yang; Qi, Feng; Li, Xiao; Yu, Shengwen; Liu, Siwen; Feng, Jifeng; Zheng, Yuxiao

doi:10.3389/fonc.2019.01509

Cited by 22 publications

(23 citation statements)

References 63 publications

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“…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%

“…To further explore the interplay among the mRNAs in ceRNA, we constructed a PPI network based on the STRING (The Retrieval of Interacting Genes) online database ( Figure 6B). In the network, TLR8 (Toll Like Receptor 8), ICAM1 (Intercellular Adhesion Molecule 1), TLR6 (Toll Like Receptor 8), and IL10RA (Interleukin 10 Receptor Subunit Alpha) had higher degrees (16,13,10, and 10, respectively) (Supplementary Table 1). The genes encoding these proteins have been confirmed to be associated with immune microenvironment and leukemia progression [24][25][26][27] .…”

Section: Protein-protein Interaction (Ppi) Network Analysismentioning

confidence: 99%

“…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%

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

Wang

Liu

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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Section: Discussionsupporting

confidence: 93%

Section: Protein-protein Interaction (Ppi) Network Analysismentioning

confidence: 99%

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

Wang

Liu

et al. 2020

Preprint

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“…Tumor microenvironment (TME) refers to the surrounding microenvironment of tumor cells, including immune cells, stromal cells, endothelial cells, in ammatory cells, and broblasts [7]. Among them, tumor in ltrating immune cells (Tiics) and stromal cells are two major non-tumor cell components, which have been considered important for the diagnosis and prognostic evaluation of cancer patients [8]. Therefore, understanding the cell composition and function of TME has great potential in effectively preventing cancer recurrence and immune response.…”

Section: Introductionmentioning

confidence: 99%

A Risk Score Model Associated with Tumor Microenvironment Predicts Disease-Free Survival After Radical Prostatectomy

Zhao¹,

Zhang²,

Shi³

et al. 2020

Preprint

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Background Tumor microenvironment (TME) and immune checkpoint inhibitors has been shown to promote active immune responses through different mechanisms. We aimed to identify the important prognostic genes and prognostic characteristics related to TME in prostate cancer (PCa).Methods The gene transcriptome profiles and clinical information of PCa patients were obtained from the TCGA database, and the immune, stromal and estimate scores were calculated by the ESTIMATE algorithm. We evaluated the prognostic value of risk score (RS) model based on univariate Cox and LASSO Cox regression models analysis, and established a nomogram to predict disease-free survival (DFS) in PCa patients. The GSE70768 data set was used for external validation. Finally, 22 subsets of tumor-infiltrating immune cells (Tiics) were analyzed using the Cibersort algorithm.Results In this study, the patients with higher immune, stromal, and estimate scores were associated with poorer DFS, higher Gleason score, and higher AJCC T stage. Based on the immune and stromal scores, the Venny diagram screened out 515 cross DEGs. The univariate COX and Lasso Cox regression models were used to select 18 DEGs from 515 DEGs, and constructed a RS model. The DFS of the high-RS group was significantly lower than that of the low-RS group (P<0.001). The AUC of 1-year, 3-year and 5-year DFS rates in RS model were 0.778, 0.754 and 0.750, respectively. In addition, the RS model constructed from 18 genes was found to be more sensitive than Gleason score (1, 3, 5 year AUC= 0.704, 0.677 and 0.682). The nomograms of DFS were established based on RS and Gleason scores. The AUC of the nomograms in the first, third, and fifth years were 0.802, 0.808, and 0.796, respectively. These results have been further validated in GEO. In addition, the proportion of Tregs was higher in high-RS patients (P<0.05), and the expression of five immune checkpoints (CTLA-4, PD-1, LAG-3, TIM-3 and TIGIT) was higher in high-RS patients (P<0.05).Conclusion We identified 18 TME-related genes from the TCGA database, which were significantly related to DFS in PCa patients.

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“…Algorithms (15,16), such as ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data), have been developed to predict tumor purity and the in ltration of non-tumor cells by calculating immune and stromal scores by utilizing gene expression data from the Cancer Genome Atlas (TCGA) database (17). In this ESTIMATE algorithm, we analyzed immune and stromal cells of their speci c genes expression characteristics to obtain immune and stromal scores and predict invasion of non-tumor cells (18). Subsequent researches have shown the effectiveness of applying the ESTIMATE algorithm in various tumors (17,(19)(20)(21), although its utility on pancreatic cancer have not been fully revealed.…”

Section: Introductionmentioning

confidence: 99%

Identification of Prognosis-Related Genes and Construction of Multi-regulatory Networks in Pancreatic Cancer Microenvironment by Bioinformatics Analysis

Liu

Liao

et al. 2020

Preprint

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Abstract Background: As one of the most lethal cancers, pancreatic cancer has been characterized by abundant supportive tumor-stromal cell microenvironment. Although the advent of tumor-targeted immune checkpoint blockers has brought light to patients with other cancers, its clinical efficacy in pancreatic cancer has been greatly limited due to the protective stroma . Thus, it is urgent to find potential new targets and establish multi-regulatory networks to predict patient prognosis andimprove treatment. Methods: We followed a strategy based on mining the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm to obtain the immune scores and stromal scores. Differentially expressed genes (DEGs) associated with poor overall survival of pancreatic cancer were screened from a TCGA cohort. By comparing global gene expression with high vs. low immune scores and subsequent Kaplan-Meier analysis, DEGs that significantly correlate with poor overall survival of pancreatic cancer in TCGA cohort were extracted. After constructing the protein-protein interaction network using STRING and limiting the genes within the above DEGs, we utilized RAID 2.0, TRRUST v2 database and degree and betweenness analysis to obtain non-coding RNA (ncRNA)-pivotal nodes and TF-pivotal nodes. Finally, multi-regulatory networks have been constructed and pivotal drugs with potential benefit for pancreatic cancer patients were obtained by screening in the DrugBank. Results: In this study, we obtained 246 DEGs that significantly correlate with poor overall survival of pancreatic cancer in the TCGA cohort. With the advent of 38 ncRNA-pivotal nodes and 7 TF-pivotal nodes, the multi-factor regulatory networks were constructed based on the above pivotal nodes. Prognosis-related genes and factors such as HCAR3, PPY, RFWD2, WSPAR and Amcinonide were screened and investigated. Conclusion: The multi-regulatory networks constructed in this study are not only beneficial to improve treatment and evaluate patient prognosis with pancreatic cancer, but also favorable for implementing early diagnosis and personalized treatment. It is suggested that these factors may play an essential role in the progression of pancreatic cancer.

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Screening the Cancer Genome Atlas Database for Genes of Prognostic Value in Acute Myeloid Leukemia

Abstract: Object: To identify genes of prognostic value which associated with tumor microenvironment (TME) in acute myeloid leukemia (AML). Conclusion: We identified 18 TME-related genes which significantly associated with overall survival in AML patients from TCGA database.

Cited by 22 publications

References 63 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

A Risk Score Model Associated with Tumor Microenvironment Predicts Disease-Free Survival After Radical Prostatectomy

Identification of Prognosis-Related Genes and Construction of Multi-regulatory Networks in Pancreatic Cancer Microenvironment by Bioinformatics Analysis

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