Chen Liu, Yiying Xiong. Bioinformatics Analysis to Screen the Key Genes and Pathways in the Formation of Early T-cell Precursor Acute Lymphoblastic Leukemia[J]. Diseases & Research, 2022, 2(2): 40-47. DOI: 10.54457/DR.202202004
Citation:
Chen Liu, Yiying Xiong. Bioinformatics Analysis to Screen the Key Genes and Pathways in the Formation of Early T-cell Precursor Acute Lymphoblastic Leukemia[J]. Diseases & Research, 2022, 2(2): 40-47. DOI: 10.54457/DR.202202004
Chen Liu, Yiying Xiong. Bioinformatics Analysis to Screen the Key Genes and Pathways in the Formation of Early T-cell Precursor Acute Lymphoblastic Leukemia[J]. Diseases & Research, 2022, 2(2): 40-47. DOI: 10.54457/DR.202202004
Citation:
Chen Liu, Yiying Xiong. Bioinformatics Analysis to Screen the Key Genes and Pathways in the Formation of Early T-cell Precursor Acute Lymphoblastic Leukemia[J]. Diseases & Research, 2022, 2(2): 40-47. DOI: 10.54457/DR.202202004
Yiying Xiong. E-mail: xiongyiying@foxmail.com. Address: Department of Hematology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
Early T-cell precursor acute lymphoblastic leukemia (EPT-ALL) is a rare type of ALL that shows genetic characteristics of both ALL and acute myeloid leukemia (AML) but has a poorer prognosis and a higher recurrence rate. The aims of this study were to explore the underlying molecular mechanisms and specific biomarkers of EPT-ALL by bioinformatics analysis.
Methods
Two expression profile datasets (GSE8879 and GSE28703) were integrated to identify candidate genes of EPT-ALL. Differentially expressed genes (DEGs) between EPT-ALL and classic T-ALL were identified using edge R package. Protein–protein interaction (PPI) network clustering modules were analyzed with STRING and Cytoscape. In addition, the plugins of Cytoscape was used for hub gene functional enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and PPI network analysis. Survival analysis was performed by GEPIA and CCLE database.
Results
The GSE profiles shared 132 DEGs, including 63 upregulated genes and 69 downregulated genes. KEGG enrichment analysis of DEGs showed that the top 3 pathways were hematopoietic cell lineage, NF-kappaB signaling pathway and T cell receptor signaling pathway. Twenty-seven hub genes were identified from PPI by Cytoscape. High expression of ITGAM (integrin subunit alpha M) and LYN (LYN proto-oncogene) were associated with statistical significantly poorer overall survival rates by survival analysis using GEPIA database. Besides, LYN was differently expressed in EPT-ALL compared with classic T-ALL by searching in CCLE database.
Conclusion
We screened two hub genes which may consider as candidate novel biomarker in EPT. In summary, we elucidated LYN as a biomarker for the differentiation of EPT-ALL from classic T-ALL.
T-cell acute lymphoblastic leukemia (T-ALL) is a malignant clonal expansion of immature T cells, which accounts for 10% to 15% of childhood ALL cases and 25% of adult ALL cases[1,2]. The treatment of ALL is still chemotherapy, combined with radiotherapy if necessary. With the progress of treatment in recent years, the event-free survival (EFS) of T-ALL has been gradually improved, and the 5-year EFS has exceeded 85% in most clinical trials[3-5]. Early T-cell precursor acute lymphoblastic leukemia (EPT-ALL) is a very high-risk subtype of T-ALL that accounts for about 15% of children and 10% to 30% of adults with T-ALL[1,4,6-11]. It is characterized by early arrest of differentiation and has overlapping genetic and transcriptional characteristics with both T-ALL and acute myeloid leukemia (AML)[6]. While the current studies indicate that contemporary response-based T-ALL regimens are successful for the majority of EPT-ALL patients, they still have a higher failure rate of remission induction, a higher recurrence rate and a worse overall survival (OS) compared to patients with classic T-ALL[1,7,12]. Due to the unique biology characteristics and poor prognosis of EPT-ALL, there is an urgent need to develop new therapeutic strategies for this disease. Bioinformatics provides a new approach for the in-depth analysis of tumor biology. In this study, differentially expressed genes (DEGs) from 2 microarrays were analyzed to search for new tumor markers of EPT-ALL that are different from markers of classic T-ALL.
Materials and methods
Data source
The microarray data of GSE28703 (based on GPL13158, Affymetrix HT HG-U133 + PM Array Plate, St Jude Children's Research Hospital, Memphis, TN, USA) and GSE8879 (based on GPL96, Affymetrix Human Genome U133A Array) used to compare the gene expression profiles between EPT-ALL and classic T-ALL were retrieved from the Gene Expression Omnibus (GEO, at the National Center for Biotechnology Information) with the following criteria: (a) clinical specimens from patients; (b) sample size of at least 20; and (c) original datasets.
DEGs
These original GEO datasets were converted into expression measures using the affy R package. The limma R package was subsequently employed to identify DEGs. Both P and adjusted P values were required to be less than 0.05, and ∣log2FC∣ ≥ 1 were chosen as the cutoff criteria based on the Benjamini–Hochberg (BH) procedure. The common DEGs between GSE8879 and GSE28703 were identified by the intersect function in R package. In addition, the Venn diagrams were also generated by R package.
Functions and pathways enrichment
Gene Ontology (GO) and pathway enrichment analyses of DEGs were performed by using DAVID (The Database for Annotation, Visualization and Integrated Discovery, https://david.ncifcrf.gov/). Enriched biological processes (BPs), cellular components (CCs) and molecular functions (MFs) were obtained to analyze the common DEGs at the functional level. P < 0.05 was considered statistically significant. Each module of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was analyzed by DAVID and the Cytoscape plugins (ClueGO version 2.5.7 and CluePedia version 1.5.7).
Protein-protein interaction (PPI) network construction
The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) online database (http://string-db.org) and Cytoscape software (version 3.6.0) were used to construct the PPI network of common DEGs (interaction score>0.4). The Molecular Complex Detection (MCODE) plugin of Cytoscape was used to search clustered subnetworks. The default parameters were set as follows: degree cutoff≥2, node score cutoff≥0.2, K-core≥2, and max depth = 100.
In addition, considering that there are few reports of this rare disease, we selected all 27 hub genes as the target hub genes with the highest degree of connectivity displayed by the Cytoscape plugin cytoHubba. Analysis of the hub genes for the PPI network and coexpression network was performed by STRING.
Hub gene selection and analysis
Hub genes with degrees≥10 were selected. A network of the genes and their coexpressed genes was analyzed using the STRING online database. ClueGO is a plugin of Cytoscape that can visualize the nonredundant biological terms for large clusters of genes in a functionally grouped network. BP GO analysis and KEGG pathway analysis of the hub genes were performed, and the results were visualized through ClueGO (version 2.5.7) and CluePedia (version 1.5.7).
Survival analysis
Kaplan-Meier survival analysis with the log-rank test was used to identify the DEGs related to poor prognosis in EPT-ALL by using gene expression profiling interactive analysis 2 (GEPIA2, http://gepia2.cancer-pku.cn/#index). Because there were no data about EPT-ALL, considering its overlapping characteristics with AML, we analyzed the survival status of patients with AML according to the expression of each hub gene. Additionally, the expression levels of the hub genes between different types of leukemia were verified by the Cancer Cell Line Encyclopedia (CCLE) (https://portals.broadinstitute.org/ccle/about).
Results
The common DEGs
Volcano plots were generated to visualize the distribution of expressed genes between EPT-ALL and classic T-ALL from different studies (Table 1, Fig. 1A and Fig. 1B). After removing the genes without gene symbols and duplicates, 151 genes (73 upregulated and 78 downregulated) and 1401 genes (734 upregulated and 667 downregulated) were identified to be significantly changed in GSE8879 and GSE28703, respectively. A total of 132 genes (63 upregulated and 69 downregulated) were screened as DEGs after taking the intersection by drawing Venn diagrams (Fig. 1C and Fig. 1D). The common upregulated and downregulated genes are listed in Table 1. Heat maps were generated by R based on the FC expression levels of DEGs between GSE8879 and GSE28703 (Fig. 1E and Fig. 1F).
Table
1.
Overlapping DEGs screened from the datasets.
Figure
1.
Volcano plots of genes that were significantly different between EPT-ALL tissues and non-EPT-ALL controls.
A. The DEGs from GSE8879. B. The DEGs from GSE28703. C. Venn diagram for overlapping the common upregulated DEGs between GSE8879 and GSE28703. D. Venn diagram for overlapping the common downregulated DEGs between GSE8879 and GSE28703. E. Heat maps of the DEGs between EPT-ALL tissues and non-EPT-ALL controls in GSE8879. F. Heat maps of the DEGs between EPT-ALL tissues and non-EPT-ALL controls in GSE28703.The X-axis indicates the fold change (log-scaled), whereas the Y-axis shows the P values (log-scaled). Each symbol represents a different gene, and the red/blue color of the symbols categorizes the upregulated/downregulated genes falling under different criteria (P value and fold change threshold). P < 0.05 was considered statistically significant, whereas fold change = 2 was set as the threshold.
For BPs, the B cell receptor signaling pathway, leukocyte migration and hemopoiesis were the commonly enriched terms. With regard to MF category, the common DEGs mainly showed enrichment in protein homodimerization activity, glycoprotein binding and protein binding. For CC ontology, the enriched terms of the common DEGs included cytosol, membrane raft and mast cell granule. Moreover, the KEGG analysis (adjusted P < 0.05) suggested that the top canonical pathways associated with the common DEGs were hematopoietic cell lineage, NF-kappaB signaling pathway and T cell receptor signaling pathway (Fig. 2).
Figure
2.
GO and KEGG pathway analyses of DEGs. P-value < 0.05 was considered statistically significant.
A. KEGG pathways. B. Molecular functions. C. Biological processes. D. Cellular components.
Key candidate gene identification with the DEG PPI network
The PPI network of DEGs in EPT-ALL was acquired by using the online STRING database. A total of 132 genes were submitted to the STRING database, and 3 unidentified genes (LINC01260, TPSB2 and TRBC1) were excluded. Finally, the PPI network included 129 nodes and 234 edges (Fig. S1). Subsequently, five modules of the PPI network were screened by MCODE with default parameters Fig. S2). Module 1 consisted of nine genes, including four upregulated genes (LYN, LAT2, BTK, and LCK) and five downregulated genes (PIK3R3, ZAP70, INPP5D, RASGRP1, and PLCG1). The biological functions and pathways of these genes are listed in Supplementary Table S1.
Then, the central node genes (connections/interactions ≥ 10) were identified as hub genes for EPT-ALL with the highest degree scores by applying the cytoHubba plugin. Twenty-seven genes were analyzed, and the PPI network was constructed based on the hub genes (Fig. 3A and Table S2). The BP and KEGG enrichment analyses of all hub genes are shown in Fig. 3B and 3C. PD-L1 expression and the PD-1 checkpoint pathway in cancer, hematopoietic cell lineage, the Fc epsilon R3 signaling pathway and AML were enriched according to KEGG pathway analysis.
Figure
3.
Interaction network and analysis of the hub genes.
A. Twenty-seven hub genes were screened using the Cytoscape software plugin cytoHubba. B. The biological process functional annotation analysis of hub genes was performed by ClueGO and CluePedia. C. The KEGG functional annotation analysis of hub genes was performed by ClueGO and CluePedia. Different colors of nodes refer to the functional annotation of ontologies. A corrected P-value < 0.05 was considered statistically significant.
The mRNA expressions of ITGAM and LYN were compared between AML and normal whole blood based on RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) database. The transcriptional level of ITGAM was significantly highly expressed in 173 AML tissues compared with 70 normal tissues (P < 0.05). while the high expression of LYN in the same comparison was not statistically significant (Fig. 4A and Fig. 4B).
Figure
4.
The transcriptional expression and K-M of ITGAM and LYN in hematological malignant tissues and normal tissues from patients.
A. The expression level of ITGAM was significantly higher in 173 AML tissues than 70 normal tissues. B. The expression level of LYN was higher in 173 AML tissues than 70 normal tissues. C. ITGAM showed significantly better OS in AML samples by GEPIA2(log-rank P < 0.05). D. LYN showed significantly better OS in AML samples by GEPIA2. E. Transcriptional expression of ITGAM was significantly correlated with hematology malignance and healthy control by CCLE website. F. LYN showed lowest transcriptional expression in AML, T-ALL, B-ALL and healthy control by CCLE website.
Considering the limited data on EPT-ALL and its overlapping genetic characteristics with AML, we submitted all genes to GEPIA2 to determine the clinical relevance. We noted that whether ITGAM or LYN, its high expression was associated with poor prognosis in AML patientss compared with normal, suggesting that they may also be used as prognostic indicators (Fig. 4C, Fig. 4D and Fig. S3).
In order to further elucidate the expression in different kind of leukemia, we compared the expression levels of ITGAM and LYN in AML, B cell ALL (B -ALL), T-ALL and normal tissues in the CCLE database. The transcriptional levels of both ITGAM and LYN were the highest in AML. The expression of ITGAM was second highest in T-ALL, and significantly higher than normal tissues (P = 0.006). However, LYN mRNA expression was the lowest in T-ALL, and significantly lower than normal tissues (P < 0.001) (Fig. 4E and Fig. 4F).
Discussion
Due to both stem cell-like feature and distinct gene mutation spectrum of EPT, it is urgently needed to further explore the potential novel prognostic biomarkers and molecular mechanisms. With the advent of the era of precision medicine, combination therapy with new molecular targeted drugs is a new direction to further improve patient survival in the future. As explored by GO enrichment analysis, B cell receptor signaling may play roles in regulating the progression and prognosis of EPT-ALL which consistent with the results of another article[13]. Besides, enriched KEGG pathways indicated the hematopoietic cell lineage and NF-kappa B signaling pathway which consistent with the results of former article[14]. Meanwhile we constructed PPI modules, analyzed the hub genes and explored the survival status in different expression. Through comparing the expression levels of ITGAM and LYN in different types of leukemias and normal tissues in the CCLE database. The transcriptional level of ITGAM was the highest in AML, followed by T-ALL, and the lowest in B-ALL, which was in accordance with previous reports. However, interestingly, we observed that LYN had the lowest expression in T-ALL, which was completely different from the expression level in the DEG analysis. Thus, we believe that LYN might be a biomarker for differentiating EPT-ALL from T-ALL.
ITGAM, which plays role as development and prognosis of leukemia and many solid tumor, is mainly expressed on bone marrow-derived immune cells that bind to ligands and acts as a major surface antigen family on human leukocyte family member[15-17]. Previous studies have established ITGAM as a novel target for cancer immune therapy[18-20]. Elevated ITGAM may participate in the regulation of the biology of malignant neoplasms, including AML and chronic myeloid leukemia (CML). Its positivity was associated with poor prognosis in AML patients[21,22]. To our knowledge, the expression of ITGAM and its potential prognostic impact on EPT-ALL have seldom been reported.
Lyn kinase has been found to involved in phosphorylation of various signaling molecules and transcription factors and expressed in hematopoietic cell of myeloid and B-lymphoid origin[23,24] which were consistent with the enriched KEGG pathways. It has been reported that B-lineage markers on the leukemia blasts was detected in relapse/remission ETP-ALL patients[13]. LYN play a significant role in promoting the phosphorylation of BTK and SYK and have important roles in activating the B cell receptor pathway[25]. Evelyn et al.[26] has reported the Tregs in Lyn-deficient mice were expanding which may used in suppressing inflammation and autoimmune therapy. At last, these two genes have been reported to have synergic effect in autoimmune diseases such as accelerated lupus-like disease, driving early-onset immune cell dysregulation, autoantibody production and glomerulonephritis, impacted survival[27].
In our study, we at first compare these two datasets. Soumyadeep[28] et al. has reported the potential developmental propensity with GSE28703 with more bioinformatic methods such as linear dimensionality reduction analysis and lineage score calculation algorithm.
In conclusion, ITGAM and LYN may predict poor prognosis and may have important roles in adaptive immune response and hematopoietic cell lineage in EPT-ALL. In this study, we found that LYN may be used in new tailored therapeutic strategies for EPT and enriched the related research. Further studies should evaluate the relationships of ITGAM and LYN expression with clinical features and prognosis. This results may facilitate the identification of a new biomarker to effectively evaluate the risk stratification and type of leukemia and improve treatment. We first report that LYN might be an early diagnostic biomarker that is significantly expressed in EPT-ALL and other types of leukemias.
Abbreviations
ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; BH, Benjamini–Hochberg; BPs, biological processes; CCLE, Cancer Cell Line Encyclopedia; CCs, cellular components; CML, chronic myeloid leukemia; DAVID, The Database for Annotation, Visualization and Integrated Discovery; DEGs, differentially expressed genes; EFS, event-free survival; EPT-ALL, Early T-cell precursor acute lymphoblastic leukemia; GEO, Gene Expression Omnibus; GO, Gene Ontology; ITGAM, integrin subunit alpha M; KEGG, Kyoto Encyclopedia of Genes and Genomes; K-M, Kaplan-Meier; LYN, LYN proto-oncogene; MCODE, Molecular Complex Detection; MFs, molecular functions; OS, overall survival; PPI, protein–protein interaction; STRING, the Search Tool for the Retrieval of Interacting Genes.
Conflicts of interest
The authors have declared that no conflicts of interest exist.
Authors’ contributions
CL performed the statistical analysis and wrote the manuscript. YYX designed this study, wrote the first draft and participated in the revision and proofreading of the manuscript. All authors read and approved the final manuscript.
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