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Article

Transcriptome Analysis in Mexican Adults with Acute Lymphoblastic Leukemia

by
Gabriela Marisol Cruz-Miranda
1,2,
Irma Olarte-Carrillo
3,
Diego Alberto Bárcenas-López
1,2,
Adolfo Martínez-Tovar
3,
Julian Ramírez-Bello
4,
Christian Omar Ramos-Peñafiel
5,
Anel Irais García-Laguna
3,
Rafael Cerón-Maldonado
1,3,
Didier May-Hau
2 and
Silvia Jiménez-Morales
2,*
1
Programa de Doctorado, Posgrado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
2
Laboratorio de Innovación en Medicina de Precisión Núcleo A, Instituto Nacional de Medicina Genómica, Mexico City 14610, Mexico
3
Laboratorio de Biología Molecular, Servicio de Hematología, Hospital General de México Dr. Eduardo Liceaga, Mexico City 06720, Mexico
4
Subdirección de Investigación Clínica, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City 14080, Mexico
5
Departamento de Hematología, Hospital General de México Dr. Eduardo Liceaga, Mexico City 06720, Mexico
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2024, 25(3), 1750; https://doi.org/10.3390/ijms25031750
Submission received: 23 November 2023 / Revised: 11 January 2024 / Accepted: 16 January 2024 / Published: 1 February 2024
(This article belongs to the Section Molecular Biology)
Browse Figures
Review Reports Versions Notes

Abstract

:
Acute lymphoblastic leukemia (ALL) represents around 25% of adult acute leukemias. Despite the increasing improvement in the survival rate of ALL patients during the last decade, the heterogeneous clinical and molecular features of this malignancy still represent a major challenge for treatment and achieving better outcomes. To identify aberrantly expressed genes in bone marrow (BM) samples from adults with ALL, transcriptomic analysis was performed using Affymetrix Human Transcriptome Array 2.0 (HTA 2.0). Differentially expressed genes (DEGs) (±2-fold change, p-value < 0.05, and FDR < 0.05) were detected using the Transcriptome Analysis Console. Gene Ontology (GO), Database for Annotation, Visualization, and Integrated Discovery (DAVID), and Ingenuity Pathway Analysis (IPA) were employed to identify gene function and define the enriched pathways of DEGs. The protein–protein interactions (PPIs) of DEGs were constructed. A total of 871 genes were differentially expressed, and DNTT, MYB, EBF1, SOX4, and ERG were the top five up-regulated genes. Meanwhile, the top five down-regulated genes were PTGS2, PPBP, ADGRE3, LUCAT1, and VCAN. An association between ERG, CDK6, and SOX4 expression levels and the probability of relapse and death was observed. Regulation of the immune system, immune response, cellular response to stimulus, as well as apoptosis signaling, inflammation mediated by chemokines and cytokines, and T cell activation were among the most altered biological processes and pathways, respectively. Transcriptome analysis of ALL in adults reveals a group of genes consistently associated with hematological malignancies and underscores their relevance in the development of ALL in adults.

1. Introduction

Acute lymphoblastic leukemia (ALL) is a malignant disorder affecting the blood and bone marrow characterized by rapid clinical evolution, biological heterogeneity, and uncontrolled proliferation of lymphoblasts, leading to a progressive loss of differentiation ability [1,2]. This malignancy accounts for about a quarter of acute leukemias in adults and displays high mortality rates, representing an important public health problem [1,2,3,4]. Despite the fact that risk-adapted therapies have improved overall survival (OS) rates in adults with ALL, ranging from 60% to 80%, long-term remission rates in Latin American populations remain low [5,6,7,8]. For instance, studies in Mexico have reported complete remission (CR) rates between 60% and 80% and a 5-year OS of <35% [9,10,11,12,13], which is even worse in the central region. By including adults with ALL from five Mexico City referral Hospitals, The Acute Leukemia Workgroup reported a 3-year OS of 22.1% [12]. Based on the knowledge of childhood ALL, adults receive risk-adapted treatment regimens, drugs directed to specific chimeric genes, and proteins to detect antigens of the tumor cell surface to stimulate antitumor immune response [2,14,15,16]. Nevertheless, adults with ALL are substantially different than children with ALL. Poor prognostic cytogenetic and molecular abnormalities, such as t(9;22) (BCR::ABL) and the Ph-like phenotype, are more frequent in adults than in children (15–25% vs. 2–6%, and >25% vs. 10%, respectively), whereas the good prognosis biomarker t(12;22) (ETV6::RUNX1) is less frequent in adults (<1% vs. 20–25%, respectively) [17]. By using microarray gene expression, thousands of genes can be surveyed at the same time, and the analysis of tumor tissues may contribute to understanding the biological mechanisms underlying lymphoblast transformation and ALL progression. Additionally, microarray analysis may allow us to identify potential biomarkers with clinical significance, new therapeutic targets, and genes involved in drug resistance and relapse [18,19,20]. In this study, we performed the first transcriptomic analysis in Mexican adults with ALL to identify abnormally expressed genes.

2. Results

2.1. Features of Patients

To identify an expression signature associated with ALL in adults, we included forty-three patients with the novo ALL and a control group of five healthy subjects. Of the overall cases, 23 (53.5%) were male and 20 (46.5%) were female, with a median age at diagnosis of 33.8 years (range of 18–57 years). Twenty-seven (62.8%) patients were between 18 and <40 years old at the time of diagnosis and were categorized within the adolescents and young adults group, and the remaining 37.2% were >40 years old. Only five (11.6%) patients were classified as having a standard risk. B-lineage ALL was present in 41 (95.3%) cases, while two cases were T-linage. Sixteen patients relapsed, and six of them died. Death was observed in 16 (37.2%) cases during the time of follow-up. The average survival period for our cohort was 353.2 days (ranging from 18 to 661 days) (Table 1).

2.2. Assessment of Replicability

To ensure the robustness of our findings and the reliability of the control group, we conducted an in-depth analysis of replicability. Through this analysis, we evaluated the consistency and stability of the control group in comparison with subsets of our patient cohort. We performed a correlation analysis on gene expression profiles between the five control subjects and two randomly divided patient subgroups (Group 1: 21 patients; Group 2: 22 patients). Correlation coefficient analysis demonstrated a strong concordance (correlation coefficient = 0.713) between the control group and both subsets of patients. This analysis provides essential validation for the reliability and adequacy of our control group, ensuring robust and consistent results.

2.3. Differential Expressed Genes in Acute Lymphoblastic Leukemia

A supervised hierarchical cluster analysis was performed to compare the gene expression between patients with ALL and the healthy controls. We found 871 DEGs ((FC > 2 or <−2, p value < 0.05, FDR < 0.05), of which 781 (95.1%) were coding genes, and the remaining 90 genes were non-coding RNAs. Within the coding genes, 125 were up-regulated, and 656 were down-regulated in cases compared to healthy subjects (Figure 1). The top ten up-regulated genes included DNTT, MYB, SOX4, EBF1, ERG, FLT3, CD34, STMN1, CDK6, and NAV1, while the most down-regulated genes were PTGS2, PPBP, ADGRE3, LUCAT1, VCAN, TUBB1, PF4, RGS2, SH3BGRL2, and CLEC7A (Table 2).

2.4. Altered Pathways and Biological Processes in Acute Lymphoblastic Leukemia

Gene Ontology analysis revealed the DEGs involved in biological processes relevant to the pathophysiology of ALL, such as apoptosis signaling (p = 4.22 × 10−7), inflammation mediated by chemokine and cytokine signaling (p = 2.23 × 10−5), toll receptor signaling (p = 1.98 × 10−3), interleukin signaling (p = 1.0 × 10−3), and T cell activation (p = 3.88 × 10−2) as the most affected pathways, whereas IPA analysis confirmed the involvement of DEGs in cancer-associated processes, such as the pathogen-induced cytokine storm signaling pathway (p = 1.23 × 10−13), crosstalk between dendritic cells and natural killer cells (p = 6.71 × 10−13), the pyroptosis signaling pathway (p = 4.70 × 10−12), natural killer cell signaling (p = 5.22 × 10−11), and phagosome formation (p = 5.45 × 10−11), which were the most abnormally regulated pathways. Cell-to-cell signaling and interaction, cellular development, cellular growth and proliferation, cell death and survival, and cell signaling were the main molecular and cellular functions (p <1.76 × 10−4) in which DEGs play a role (Supplemental Figure S1). Regarding physiological system development and function (p < 1.75 × 10−4), the main cellular functions were as follows: hematological system development and function, immune cell trafficking, lymphoid tissue structure and development, hematopoiesis, and tissue development. Additionally, coding DEGs were subject to PPI network analysis using the STRING database with confidence over 0.7. We identified 92 nodes with a node degree > 10 (Supplemental Table S2). CD4 ranked first among these genes, followed by PTPRC, FCGR3A, TYROBP, FCGR3B, TLR4, TLR2, and CCR5, based on which the gene–gene interactions network was constructed and visualized by using Cytoscape (Figure 2). After completion of the GO function enrichment analysis, these genes were significantly enriched in the KEEG biological immune-related process (Supplemental Table S3), and the most affected pathways were inflammation mediated by chemokine and cytokine signaling, apoptosis signaling, interleukin signaling, and Toll receptor signaling pathways.

2.5. Validation of DEGs Associated with ALL by Quantitative RT-PCR

RT-PCR was used in a subset of cases and controls to validate the data obtained from microarray analysis. EBF1 and FLT3 genes were selected based on previous reports showing abnormal expression in childhood and adulthood ALL [21]. The gene expression of these two genes displays consistent results obtained in microarray expression analysis, and both genes were up-regulated in patients with ALL in contrast to healthy subjects (Supplemental Figure S2).

2.6. Clinical Association and Survival Analysis

OS analysis was performed for the top ten up-regulated and top ten down-regulated genes, revealing that ERG, SOX4, and CDK6 were associated with a worse prognosis. The associations between the expression (high or low, according to the median of their expression level) and EFS and OS revealed significant differences in hazard ratios (HRs) (Figure 3A–F). For instance, the high expression of ERG (HR = 3.33, 95% CI 1.19–9.34, p < 0.021 and HR = 4.24, 95% CI 1.51–15.63, p < 0.02) and CDK6 (HR = 5.33, 95% CI = 1.55–18.35, p < 0.007 and HR = 5.6, 95% CI = 1.25–25.44, p < 0.024) showed high risk of relapse and death, respectively (Figure 3A–D). Meanwhile, SOX4 expression was associated with EFS (HR = 2.92 95% CI = 1.068–7.99, p < 0.036) but not with OS (HR = 3.37 95% CI = 0.939–12.14, p < 0.62) (Figure 3E,F). Genes such as STMN1 and CD34 were associated either with EFS or OS, respectively. Regarding the hub genes, low expression of CD4 was associated with better outcomes (HR = 0.3 95% CI = 0.115–0.79, p < 0.0147 and HR = 0.27 95% CI = 0.08–0.87, p < 0.089 for EFS and OS, respectively) (Figure 3G,H).

2.7. Gene Expression Correlation Analysis

To identify genes with similar profiles related to ALL, gene expression correlation analysis was performed, including a total of 871 DEGs, and three co-expression modules were detected (Figure 4). The largest module comprised 347 genes, and it included five hub genes, as listed in Supplemental Table S4.

2.8. Potentially Targetable Genes

Based on IPA, we conducted an analysis of genes with potential implications for ALL treatments. This analysis unveiled a comprehensive list of 188 genes with pharmacological relevance. To refine our focus and emphasize the significance of these genes, we implement a stringent selection process. This process led us to identify a subset of nine genes: FLT3, CDK6, CD4, PTPRC, FCGR3A/FCGR3B, TLR4, CCR7, and CD2 (Supplemental Table S5). These genes, apart from being among the top 10 DEGs, were also identified as hub genes according to STRING analysis (Figure 2, Supplemental Table S2).

3. Discussion

The landscape of OS in ALL has evolved during the last decade, partially due to risk-adapted treatment regimens, including interventions like hematopoietic stem cell transplantation and targeted therapies, such as rituximab, imatinib, dasatinib, binatumomab, and inotuzumab [9]. Nevertheless, OS in adults with ALL exhibits distinct contrast across age groups and remains worse in Latin American populations. For instance, ALL adults from high-income countries achieve complete remission (CR) levels of 90%, with long-term OS of around 50% [22]. The current 5-year relative survival rates among Caucasians ages 20 to 49 years old are 47%, decreasing to 28% and 17% for age groups 50–64 and >65, respectively [23]. In Mexico, survival data from adults show improvement from 2.6 years (Disease evolution time: 2 months–15 years) in 2008 to 72.1% (two-year OS) in 2023 [12,24,25,26]. However, the 5-year OS is still low (43.7%), and data concerning adults and elderly adults are even worse (10.58 months). These studies have shown that risk stratification criteria and heterogeneous treatment protocols used across populations could explain low OS in low-income countries. Nevertheless, it has also been reported that there is a high prevalence of poor prognosis biomarkers such as Ph-like in Latin American populations [27,28]; thus, to increase our knowledge of ALL in adults, we performed a transcriptomic analysis using microarray gene expression. To the best of our knowledge, this is the first study reporting results of whole gene expression profiles in adults with ALL from Mexico.

3.1. Potential Biomarkers for Diagnosis and Prognosis

The comparative analysis among healthy subjects and patients revealed the elevated expression of markers of B cell development such as DNTT, SOX4, EBF1, as well as MYB, CDK6, and EBF1, etc. [29,30,31]. The most up-regulated gene was DNTT (TdT). This gene encodes TdT, which is required for the insertion of random nucleotides at VDJ joining regions during B- and T-cell receptor rearrangements; thus, its expression is restricted to normal and malignant pre-B and pre-T lymphocytes during early differentiation [29]. There are no data on the use of DNTT as a potential diagnostic biomarker; most of the studies have been conducted to know its usefulness as a prognostic biomarker. Based on an immunophenotypic test, it was reported that positivity for TdT expression is a prognostic factor in adults with ALL [32], which is in contrast to our results, as no association between DNTT and OS was detected. Conflicting data have also been reported for Acute Myeloid Leukemia (AML), where it has been suggested that DNTT expression is related to FLT3-ITD mutations [33] but not to survival [34]. However, it has also been reported that mutations in DNTT are associated with OS in AML [35], and different approaches could help decipher the clinical relevance of this gene in ALL.
Concerning SOX4 and EBF1, it is known that both genes enable the survival signaling of leukemia cells [36,37]. Higher SOX4 and EBF1 expression levels in patients with ALL, in contrast to healthy subjects, forward their use as promising diagnostic biomarkers. It is notable that in a previous microarray expression analysis in Mexican children with ALL, we also observed a high expression of both genes in relapsed cases [21], and an association between the high expression of SOX4 and worse prognosis was found.
MYB oncogene, which promotes uncontrolled neoplastic cell proliferation and blocked differentiation, has been found to be deregulated in ALL. Moreover, it has been suggested to be a potential prognostic marker and target for tailored therapy [38,39,40]. The levels of MYB and CDK6 have been highly correlated in adults with Ph+. In fact, CDK6 is a relevant target of MYB, having essential roles in the leukemogenesis of leukemic cells of this molecular subtype [41].
In relation to down-regulated genes, PTGS2 and PPBP showed a very low expression in cases against controls. The overexpression of both genes has been associated with several human cancer types [42,43,44,45]. PTGS2 or cyclooxygenase 2 (COX2) encodes prostaglandin–endoperoxide synthase, which is a relevant protein in oncogenic processes and has been shown to have a controversial association with ALL. According to our data, Vicent et al. [46] also observed no expression of COX2 in blood samples taken from acute leukemia patients. However, in contrast to our findings, COX2 was reported as being up-regulated in ALL, and data concerning cancer cell lines, including leukemia cells, have revealed that COX2 inhibition reduces the growth of malignant cells [43,45]. Moreover, COX2 has been suggested to be a potential target for therapeutic intervention to suppress pediatric ALL and improve OS [43,47]. It is well known that COX2 is a pleiotropic protein, and no data from adults with ALL are available; thus, the relationship between COX2 gene and ALL remains undeciphered.
PPBP has been involved in various cellular processes and malignancies [48,49,50,51]; in fact, it has been found to be down-regulated in the plasma of patients with gastric cancer (GC), and has been suggested to be a diagnostic biomarker of that disease [49,52]. Our combination of gene expression profiling with a computational approach using STRING showed PPBP to be a hub gene in ALL, as has been observed in AML, and based on its important role in biological processes, PPBP could be considered a potential drug target in acute leukemias [53,54].
Among the hub genes, CD4 emerges as a key gene in adult ALL. This gene is expressed in peripheral blood monocytes, tissue macrophages, granulocytes, and helper/inducer T cells, and it is fundamental for T cell activation, thymic differentiation, and regulation of T-B cell adhesion [55]. In accordance with other studies relating to leukemia, we observed that the expression levels of CD4 are a prognosis biomarker in ALL and other types of cancer.

3.2. Promising Therapeutic Target Genes

Among DEGs, CDK6 and FLT3 were found to be potentially targetable genes in adults with ALL. In fact, FLT3 is already used as a treatment biomarker in AML [56,57], and data from cell lines showed that FLT3L CAR-T cells specifically kill FLT3+ leukemia [58]. Additionally, it holds potential in terms of monitoring Minimal Residual Disease (MRD) [59]. ERG is an Ets-transcription factor required for normal blood stem cell development [60], and the high expression of this gene has been associated with poor prognosis in AML [61]. It is noteworthy that ERG deletions have been found to occur recurrently in ALL, especially in the DUX4-rearranged subtype, and have a positive impact on the survival of ALL patients.
Regarding the ten top hub genes, CD4, FCGR3A/FCGR3B, and TLR4 with plasma membrane location were identified as potential targeted therapies. Our data revealed that the high expression of CD4 increases the risk of relapse and death (Figure 3). Recently, preclinical studies and a Phase I clinical trial reported that CD4 CAR has cytotoxic effects against T cell malignancies, creating an opportunity to treat cases via the over-expression of this gene [62,63,64].
FCGR3A/FCGR3B emerged as a potentially druggable gene, predominates as a risk prognostic factor in most cancers, and is closely related to tumor immune-related pathways. More relevant, drug sensitivity analysis showed that higher FCGR3A expression predicts better efficacy to treatments based on antileukemic drugs such as Etoposide, Doxorubicin, and Methotrexate [65]. Additionally, IPA analysis suggested that FCGR3A is a target of trastuzumab, a monoclonal antibody proposed to treat ALL cases expressing the epidermal growth factor receptor HER2/neu. This receptor has been found to be overexpressed in around 30% of ALL patients and has been associated with chemoresistance and poor clinical outcomes in adults with this malignancy [66,67].
TLR4 was down-regulated in our patients. The agonist of this gene has been a matter of many studies focused on cancer immunotherapy, and nowadays, FDA approval for clinical application in cancer treatment has been obtained for two TLR4 ligands, Bacillus Calmette-Guérin and monophosphoryl lipid A [68,69].
Preliminary therapeutic target genes exploration represents a potentially relevant step toward improving treatment strategies for ALL patients but also raises the need for further investigation and validation of these genes. Comparing our results through the analysis of transcriptional datasets from adults with ALL across different ethnic groups could help to elucidate whether specific gene expression changes are associated with the disease or circumscribed to our population. However, the availability of microarray expression data of adults with ALL from other populations is deficient. Information such as that could potentially highlight novel population-specific gene expression changes in the Mexican ALL patients, which can be proposed for tailoring therapeutic designs and treatment regimens. Taking into account the valuable insights garnered from this study, it is essential to consider its inherent limitations, which provide a nuanced perspective on the findings and highlight avenues for future research refinement. On the one hand, the blood samples of the healthy subjects were used based on their accessibility and their biological relation with bone marrow, and although the data retrieved from Expression Atlas showed the same expression direction as our data, we cannot discard biases associated with intrinsic aspects of each tissue. On the other hand, the small sample size and the insufficient clinical and biological data of the patients limit conclusive results regarding the use of critical genes as biomarkers for diagnosis and prognosis. While the present findings have provided meaningful correlations and differential expressions, the inclusion of a larger, independent patient cohort would reinforce our results. Replicating the study with a more extensive cohort would offer a more comprehensive representation of the landscape of ALL in adults. Additionally, molecular data such as chromosomal abnormalities, gene mutations, MRD, etc., would contribute to identifying new prognostic biomarkers and potentially targeted molecules with treatment relevance.

4. Materials and Methods

4.1. Biological Samples and Clinical Data Collection

Bone marrow (BM) samples of adults with de novo ALL were collected at the time of diagnosis in the period between July 2018 and September 2021. Cases with Down syndrome were excluded. All patients were recruited in the Hospital General de Mexico (HGM), a health institution located in Mexico City, which attends to cases from different regions of the country. ALL diagnosis was established by a hematologist based on the morphologic and immunophenotype features of leukemia cells. In addition, five blood samples obtained from healthy subjects were included. Clinical and demographic data collected at diagnoses such as gender, age, percentage of leukemic blasts in BM, immunophenotype, as well as initial treatment response, relapse, follow-up duration, and survival status were obtained from the patient’s clinical charts. Written informed consent was obtained from all participants.

4.2. RNA Extraction

For RNA extraction, white blood cells from the BM of patients and peripheral blood of healthy subjects were treated with Trizol reagent (Invitrogen Life Technologies, Carlsbad, CA, USA) and stored at −80 °C until their use. Cryopreserved samples were rapidly thawed, and total RNA was extracted and purified using standard protocols.

4.3. Gene Expression Microarrays Preparation

RNA was extracted from the BM and peripheral blood of patients with ALL and healthy subjects, respectively. The extracted RNA underwent evaluation via capillary electrophoresis using the Agilent bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA) to determine RNA integrity number (RIN), and values > 6.0 were included in the microarray analysis. The GeneChip Human Transcriptome Array 2.0 (HTA 2.0, Affymetrix Inc.; Santa Clara, CA, USA) interrogates both mRNA and lncRNAs. The GeneChip WT Plus Reagent Kit was used for the preparation of the microarray chips. The detailed protocol for sample preparation and microarray processing is available from Affymetrix. In brief, the first-strand cDNA was synthesized from 200 ng of RNA by employing SuperscriptII reverse transcriptase primed with a poly (T) oligomer, which incorporates the T7 promoter. Next, via in vitro transcription, the cRNA was obtained through the cDNA. This cRNA was used as a template for a second cDNA synthesis cycle with the incorporation of dUTPs into the new strand. After, the cDNA was fragmented through uracil-DNAc and purine–pyrimidine endonuclease. The obtained fragments were biotin-labeled (hybridization 45 °C for 16 h), stained, (streptavidin–phycoerythrin conjugate) washed, and scanned following Affymetrix HTA 2.0 chips protocols (Affymetrix Inc, Santa Clara, CA). A visual inspection to detect irregularities and for the data normalization of all Gene Chips was carried out. Quality measures, such as the percentage of present genes and the ratio of endogenous genes, indicated the high overall quality of the samples and assays. Scanning and data extraction of the microarrays was followed by the transformation of fluorescence data into CEL files employing the Affymetrix GeneChip Command Console (AGCC) version 4.0.0.1567 software.

4.4. Gene Expression Profiling Analyses

Microarray gene expression data were processed with the Affymetrix Transcriptome Analysis Console (TAC) v4.0.3 software. Background correction, probe set signal integration, and quantile normalization were performed through the Robust Multichip Analysis (RMA) algorithm, which was implemented through the use of Affymetrix Expression Console (ECS) v1.4 software [70]. To identify DEGs from healthy and tumoral tissues, supervised clustering analysis of gene expression was performed. Genes whose fold-change (FC) between each comparative group was >2.0 or ≤−2.0, with a p value cut-off of 0.05 and a false discovery rate (FDR) of < 0.05 was selected [71]. Probes with unassigned genes were discarded.

4.5. Pathway Enrichment Analyses of Differentially Expressed Genes and Protein–Protein Interaction Network

The Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool was used to evaluate the functional annotation and enrichment analysis of DEGs (https://david.ncifcrf.gov, accessed on 13 November 2023) [72]. The gene enrichment pathway analysis of DEGs and the identification of potential therapeutic target genes were performed through the use of Ingenuity Pathway Analysis (IPA, QIAGEN Redwood City, www.qiagen.com/ingenuity, accessed on 6 November 2023.) Protein–protein interaction (PPI) was evaluated by using the STRING database (https://string-db.org, accessed on 13 November 2023) by using a high order of confidence (>0.07) [73], and a gene–gene interaction network was constructed and visualized by using Cytoscape (v3.10.1) [74], and a p value < 0.05 was considered statically significant.

4.6. Quantitative Real-Time PCR for Microarray Data Validation

By using real-time quantitative reverse transcription PCR (q-RT-PCR), two candidate genes (FC > 2, FDR < 0.05) were selected to validate the microarray expression results as we have described previously [21]. Reactions were carried out using standard protocols. Briefly, cDNA was synthesized from 250 ng of total RNA for each sample using OdT primer and the High-Capacity cDNA Reverse Transcriptions Kit. cDNA reactions were performed in a final volume of 20 μL under the following conditions: at 25 °C for 25 min, at 37 °C for 120 min, and at 85 °C for 5 min in a GeneAmp PCR System 9700 (Applied Biosystems). For quantitative purposes, we used the SYBR Select Master Mix (Applied Biosystems, Carlsbad, CA, USA) method, and PCR was performed in a QuantStudioTM 3 Real-Time PCR system with the following conditions: one cycle at 50 °C for 2 min, at 95 °C for 15 s, at 54.8 °C for 15 s, and 72 °C for 1 min, for a total of 40 cycles. FC was calculated using the 2−DDCt method [75]. Primers were designed using the Primer-BLAST Tool (https://www.ncbi.nlm.nih.gov/tools/primer-blast/, accessed on 13 November 2023) [76] (Supplemental Table S1), and the ACTB gene was used for data normalization.

4.7. Gene Expression Correlation Analysis

Gene–gene expression correlation analysis was performed by using the expression data from microarray experiments that were preprocessed using standard normalization tools (log transformation and quantile normalization) to ensure comparability and reduce technical variations. Subsequently, pairwise correlation coefficients were calculated to assess the strength and direction of the association between the expression profiles of individual genes. To identify significant correlations, statistical tests of Spearman’s rank correlation coefficient were applied, and a p-value threshold of 0.05 was used to determine the statistical significance of the correlations. To account for multiple tests, a correction method, such as the Benjamini–Hochberg procedure, was applied to control the FDR.

4.8. Statistical Analysis

Comparisons of demographic and clinical variables across groups were made using Chi-square, X2, or Fisher’s exact test for categorical data; p-values < 0.05 were considered statistically significant. According to the Shapiro–Wilk test (p < 0.05), gene expression data were not normally distributed; thus, the cut-off value was determined based on the median, and two groups were identified: low and high gene expression. Survival analysis was carried out by using the Kaplan–Meier method. Event-free survival (EFS) and overall survival (OS) were calculated for each of the top 20 DEGs based on the expression levels (high or low). OS was measured from the initial treatment date until the last follow-up date. The log-rank test was used to compare differences between survival curves; a p-value less than 0.05 was considered statistically significant. Adjusted hazard ratios (HRs) and corresponding 95% confidence intervals were calculated to assess the significance of these abnormally expressed genes.

5. Conclusions

The present work represents the first effort to identify DEGs that may be involved in leukemogenesis in Mexican adults and identify potential prognosis biomarkers. Our findings suggest that the expression level of DNTT, MYB, EBF1, PTGS2, and PPBP, among others, in blood samples could discriminate patients with ALL from healthy subjects. As well, the over-expression of ERG, SOX4, and CDK6, and the low expression of the hub gene CD4 are significantly associated with inferior outcomes. Our study presents an important step to understanding the pathophysiology of ALL in adults, but the exact prognostic value of these critical genes requires detailed evaluation using clinical data from a larger cohort of patients with ALL recruited from multiple centers.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms25031750/s1.

Author Contributions

Conceptualization, S.J.-M.; methodology, G.M.C.-M., A.M.-T., J.R.-B., C.O.R.-P., A.I.G.-L. and R.C.-M.; validation, G.M.C.-M., D.A.B.-L. and D.M.-H.; formal analysis, G.M.C.-M., D.A.B.-L., D.M.-H. and S.J.-M.; investigation, G.M.C.-M., I.O.-C., A.M.-T. and S.J.-M.; resources, S.J.-M. and I.O.-C.; writing—original draft preparation, G.M.C.-M. and S.J.-M.; writing—review and editing, G.M.C.-M., S.J.-M. and I.O.-C.; visualization, S.J.-M.; project administration, S.J.-M.; funding acquisition, I.O.-C. and S.J.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Institute of Genomics Medicine, Mexico and Hospital General de México. The funding body had no role in the design of the study, collection, analysis, and interpretation of the data, or preparation of the manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Ethics Committee of the Hospital General de México (number: D1/16/103/03/035).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The expression data are available upon request to the corresponding author. The data are not publicly available due to further analyses are underway.

Acknowledgments

This paper is part of the requirements to obtain a doctoral degree at the Posgrado en Ciencias Biológicas, UNAM to GMCM. Fellowship was provided by Consejo Nacional de Humanidades, Ciencia y Tecnología (CONAHCyT): scholarship 2018-000012-01NACF. We thank the staff of the Microarray Unit at the National Institute of Genomic Medicine, Luis Alfaro-Ruiz, Alejandra I. Valencia-Cruz, and Dan J. Gutierrez-Fuentes, for their technical support during this project.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Faderl, S.; O’Brien, S.; Pui, C.H.; Stock, W.; Wetzler, M.; Hoelzer, D.; Kantarjian, H.M. Adult acute lymphoblastic leukemia: Concepts and strategies. Cancer 2010, 116, 1165–1176. [Google Scholar] [CrossRef] [PubMed]
  2. Juliusson, G.; Hough, R. Leukemia. Prog. Tumor Res. 2016, 43, 87–100. [Google Scholar] [PubMed]
  3. Devine, S.M.; Larson, R.A. Acute leukemia in adults: Recent developments in diagnosis and treatment. CA Cancer J. Clin. 1994, 44, 326–352. [Google Scholar] [CrossRef] [PubMed]
  4. Coccaro, N.; Anelli, L.; Zagaria, A.; Specchia, G.; Albano, F. Next-Generation Sequencing in Acute Lymphoblastic Leukemia. Int. J. Mol. Sci. 2019, 20, 2929. [Google Scholar]
  5. Aldoss, I.; Stein, A.S. Advances in adult acute lymphoblastic leukemia therapy. Leuk. Lymphoma 2018, 59, 1033–1050. [Google Scholar] [PubMed]
  6. Leonard, J.; Stock, W. Progress in adult ALL: Incorporation of new agents to frontline treatment. Hematol. Am. Soc. Hematol. Educ. Program. 2017, 2017, 28–36. [Google Scholar]
  7. Hanbali, A.; Kotb, A.; El Fakih, R.; Alfraih, F.; Shihata, N.; Rasheed, W.; Ahmed, S.O.; Shaheen, M.; Alhayli, S.; Alahmari, A.; et al. Improved survival of adolescents and young adults patients with T-cell acute lymphoblastic leukemia. Int. J. Hematol. Oncol. 2023, 12, IJH42. [Google Scholar] [CrossRef] [PubMed]
  8. Muffly, L.; Yin, J.; Jacobson, S.; Wall, A.; Quiroz, E.; Advani, A.S.; Luger, S.M.; Tallman, M.S.; Litzow, M.R.; Foster, M.C.; et al. Disparities in trial enrollment and outcomes of Hispanic adolescent and young adult acute lymphoblastic leukemia. Blood Adv. 2022, 6, 4085–4092. [Google Scholar]
  9. Cruz-Rodriguez, N.; Combita, A.L.; Enciso, L.J.; Quijano, S.M.; Pinzon, P.L.; Lozano, O.C.; Castillo, J.S.; Li, L.; Bareño, J.; Cardozo, C.; et al. High expression of ID family and IGJ genes signature as predictor of low induction treatment response and worst survival in adult Hispanic patients with B-acute lymphoblastic leukemia. J. Exp. Clin. Cancer Res. 2016, 35, 64. [Google Scholar] [CrossRef]
  10. Gómez-Almaguer, D.; Marcos-Ramírez, E.R.; Montaño-Figueroa, E.H.; Ruiz-Argüelles, G.J.; Best-Aguilera, C.R.; López-Sánchez, M.d.C.; Barrera-Chairez, E.; López-Arrollo, J.L.; Ramos-Peñafiel, C.O.; León-Peña, A.; et al. Acute Leukemia Characteristics are Different around the World: The Mexican Perspective. Clin. Lymphoma Myeloma Leuk. 2017, 17, 46–51. [Google Scholar]
  11. Jaime-Pérez, J.C.; Jiménez-Castillo, R.A.; Herrera-Garza, J.L.; Gutiérrez-Aguirre, H.; Marfil-Rivera, L.J.; Gómez-Almaguer, D. Survival Rates of Adults with Acute Lymphoblastic Leukemia in a Low-Income Population: A Decade of Experience at a Single Institution in Mexico. Clin. Lymphoma Myeloma Leuk. 2017, 17, 60–68. [Google Scholar] [PubMed]
  12. Crespo-Solis, E.; Espinosa-Bautista, K.; Alvarado-Ibarra, M.; Rozen-Fuller, E.; Pérez-Rocha, F.; Nava-Gómez, C.; Ortiz-Zepeda, M.; Álvarez-Vera, J.L.; Ramos-Peñafiel, C.O.; Meillón-García, L.A.; et al. Survival analysis of adult patients with ALL in Mexico City: First report from the Acute Leukemia Workgroup (ALWG) (GTLA). Cancer Med. 2018, 7, 2423–2433. [Google Scholar] [CrossRef] [PubMed]
  13. Quiroz, E.; Aldoss, I.; Pullarkat, V.; Rego, E.; Marcucci, G.; Douer, D. The emerging story of acute lymphoblastic leukemia among the Latin American population-biological and clinical implications. Blood Rev. 2019, 33, 98–105. [Google Scholar] [CrossRef]
  14. Hudecek, M.; Schmitt, T.M.; Baskar, S.; Lupo-Stanghellini, M.T.; Nishida, T.; Yamamoto, T.N.; Bleakley, M.; Turtle, C.J.; Chang, W.C.; Greisman, H.A.; et al. The B-cell tumor-associated antigen ROR1 can be targeted with T cells modified to express a ROR1-specific chimeric antigen receptor. Blood 2010, 116, 4532–4541. [Google Scholar]
  15. Hu, Y.; Zhou, Y.; Zhang, M.; Ge, W.; Li, Y.; Yang, L.; Wei, G.; Han, L.; Wang, H.; Yu, S.; et al. CRISPR/Cas9-Engineered Universal CD19/CD22 Dual-Targeted CAR-T Cell Therapy for Relapsed/Refractory B-cell Acute Lymphoblastic Leukemia. Clin. Cancer Res. 2021, 27, 2764–2772. [Google Scholar] [CrossRef]
  16. Spiegel, J.Y.; Patel, S.; Muffly, L.; Hossain, N.M.; Oak, J.; Baird, J.H.; Frank, M.J.; Shiraz, P.; Sahaf, B.; Craig, J.; et al. CAR T cells with dual targeting of CD19 and CD22 in adult patients with recurrent or refractory B cell malignancies: A phase 1 trial. Nat. Med. 2021, 27, 1419–1431. [Google Scholar] [PubMed]
  17. Jabbour, E.; O’Brien, S.; Konopleva, M.; Kantarjian, H. New insights into the pathophysiology and therapy of adult acute lymphoblastic leukemia. Cancer 2015, 121, 2517–2528. [Google Scholar] [CrossRef]
  18. Papakonstantinou, N.; Ntoufa, S.; Tsagiopoulou, M.; Moysiadis, T.; Bhoi, S.; Malousi, A.; Psomopoulos, F.; Mansouri, L.; Laidou, S.; Papazoglou, D.; et al. Integrated epigenomic and transcriptomic analysis reveals TP63 as a novel player in clinically aggressive chronic lymphocytic leukemia. Int. J. Cancer 2019, 144, 2695–2706. [Google Scholar] [CrossRef]
  19. Arasu, A.; Balakrishnan, P.; Velusamy, T. RNA sequencing analyses reveal differentially expressed genes and pathways as Notch2 targets in B-cell lymphoma. Oncotarget 2020, 11, 4527–4540. [Google Scholar]
  20. Stratmann, S.; Yones, S.A.; Garbulowski, M.; Sun, J.; Skaftason, A.; Mayrhofer, M.; Norgren, N.; Herlin, M.K.; Sundström, C.; Eriksson, A.; et al. Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression. Blood Adv. 2022, 6, 152–164. [Google Scholar] [CrossRef]
  21. Núñez-Enríquez, J.C.; Bárcenas-López, D.A.; Hidalgo-Miranda, A.; Jiménez-Hernández, E.; Bekker-Méndez, V.C.; Flores-Lujano, J.; Solis-Labastida, K.A.; Martínez-Morales, G.B.; Sánchez-Muñoz, F.; Espinoza-Hernández, L.E.; et al. Gene Expression Profiling of Acute Lymphoblastic Leukemia in Children with Very Early Relapse. Arch. Med. Res. 2016, 47, 644–655. [Google Scholar] [CrossRef] [PubMed]
  22. Rytting, M.E.; Jabbour, E.J.; Jorgensen, J.L.; Ravandi, F.; Franklin, A.R.; Kadia, T.M.; Pemmaraju, N.; Daver, N.G.; Ferrajoli, A.; Garcia-Manero, G.; et al. Final results of a single institution experience with a pediatric-based regimen, the augmented Berlin-Frankfurt-Münster, in adolescents and young adults with acute lymphoblastic leukemia, and comparison to the hyper-CVAD regimen. Am. J. Hematol. 2016, 91, 819–823. [Google Scholar]
  23. Bonab, S.F.; Mirakhori, M. Differential expression of apoptosis related genes in the peripheral blood mononuclear cells of acute lymphoblastic/lymphocytic leukemia (ALL) patients. Biomed. Res. Clin. Rev. 2020, 1. [Google Scholar] [CrossRef]
  24. Gallegos-Arreola, M.P.; González-García, J.R.; Figuera, L.E.; Puebla-Pérez, A.M.; Delgado-Lamas, J.L.; Zúñiga-González, G.M. Distribution of CYP1A1*2A polymorphism in adult patients with acute lymphoblastic leukemia in a Mexican population. Blood Cells Mol. Dis. 2008, 41, 91–94. [Google Scholar] [PubMed]
  25. Ramos, C.; Rozen, E.; León, M.; Martínez, T.A.; Olarte, I.; Catellanos, H.; Martínez, C.; Montaño, E.; Kassack, I.J.; Zamora, J.; et al. Results of treatment of acute lymphoblastic leukemia in two cohorts of Mexican patients. Rev. Med. Chil. 2011, 139, 1135–1142. [Google Scholar] [CrossRef] [PubMed]
  26. Rangel-Patiño, J.; Lee-Tsai, Y.L.; Urbalejo-Ceniceros, V.I.; Luna-Pérez, M.E.M.; Espinosa-Bautista, K.A.; Amador-Medina, L.F.; Cabrera-García, Á.; Balderas-Delgado, C.; I Inclán-Alarcón, S.; Neme-Yunes, Y.; et al. A modified CALGB 10403 in adolescents and young adults with acute lymphoblastic leukemia in Central America. Blood Adv. 2023, 7, 5202–5209. [Google Scholar]
  27. Jain, N.; Roberts, K.G.; Jabbour, E.; Patel, K.; Eterovic, A.K.; Chen, K.; Zweidler-McKay, P.; Lu, X.; Fawcett, G.; Wang, S.A.; et al. Ph-like acute lymphoblastic leukemia: A high-risk subtype in adults. Blood 2017, 129, 572–581. [Google Scholar]
  28. Herold, T.; Gökbuget, N. Philadelphia-Like Acute Lymphoblastic Leukemia in Adults. Curr. Oncol. Rep. 2017, 19, 31. [Google Scholar] [CrossRef]
  29. Pilcher, W.; Thomas, B.E.; Bhasin, S.S.; Jayasinghe, R.G.; Yao, L.; Gonzalez-Kozlova, E.; Dasari, S.; Kim-Schulze, S.; Rahman, A.; Patton, J.; et al. Cross center single-cell RNA sequencing study of the immune microenvironment in rapid progressing multiple myeloma. NPJ Genom. Med. 2023, 8, 3. [Google Scholar]
  30. Zhang, J.; McCastlain, K.; Yoshihara, H.; Xu, B.; Chang, Y.; Churchman, M.L.; Wu, G.; Li, Y.; Wei, L.; Iacobucci, I.; et al. Deregulation of DUX4 and ERG in acute lymphoblastic leukemia. Nat. Genet. 2016, 48, 1481–1489. [Google Scholar] [CrossRef]
  31. Graiqevci-Uka, G.; Graiqevci-Uka, V.; Behluli, E.; Spahiu, L.; Liehr, T. Targeted Treatment and Immunotherapy in High-risk and Relapsed/Refractory Pediatric Acute Lymphoblastic Leukemia. Curr. Pediatr. Rev. 2023, 19, 150–156. [Google Scholar]
  32. Kim, D.-Y.; Park, H.-S.; Choi, E.-J.; Lee, J.-H.; Lee, J.-H.; Jeon, M.; Kang, Y.-A.; Lee, Y.-S.; Seol, M.; Cho, Y.-U.; et al. Immunophenotypic markers in adult acute lymphoblastic leukemia: The prognostic significance of CD20 and TdT expression. Blood Res. 2015, 50, 227–234. [Google Scholar] [CrossRef] [PubMed]
  33. De Bellis, E.; Ottone, T.; Mercante, L.; Falconi, G.; Cugini, E.; Consalvo, M.I.; Travaglini, S.; Paterno, G.; Piciocchi, A.; Rossi, E.L.L.; et al. Terminal deoxynucleotidyl transferase (TdT) expression is associated with FLT3-ITD mutations in Acute Myeloid Leukemia. Leuk. Res. 2020, 99, 106462. [Google Scholar] [CrossRef] [PubMed]
  34. Röllig, C.; Kramer, M.; Schliemann, C.; Mikesch, J.H.; Steffen, B.; Krämer, A.; Noppeney, R.; Schäfer-Eckart, K.; Krause, S.W.; Hänel, M.; et al. Does time from diagnosis to treatment affect the prognosis of patients with newly diagnosed acute myeloid leukemia? Blood 2020, 136, 823–830. [Google Scholar] [PubMed]
  35. Zhou, X.; Nie, D.; Zhang, Y.; Liu, Z.; Zhao, Y.; Zhang, J.; Wang, F.; Fang, J.; Cao, P.; Chen, X.; et al. DNTT activation, TdT-aided gene length mutation, and better prognosis in ATG-based regimen allo-HSCT in AML. Mol. Carcinog. 2023, 62, 665–675. [Google Scholar] [PubMed]
  36. Ramezani-Rad, P.; Geng, H.; Hurtz, C.; Chan, L.N.; Chen, Z.; Jumaa, H.; Melnick, A.; Paietta, E.; Carroll, W.L.; Willman, C.L.; et al. SOX4 enables oncogenic survival signals in acute lymphoblastic leukemia. Blood 2013, 121, 148–155. [Google Scholar] [CrossRef]
  37. Fernando, T.R.; Rodriguez-Malave, N.I.; Waters, E.V.; Yan, W.; Casero, D.; Basso, G.; Pigazzi, M.; Rao, D.S. LncRNA Expression Discriminates Karyotype and Predicts Survival in B-Lymphoblastic Leukemia. Mol. Cancer Res. 2015, 13, 839–851. [Google Scholar] [CrossRef]
  38. Fehr, A.; Arvidsson, G.; Nordlund, J.; Lönnerholm, G.; Stenman, G.; Andersson, M.K. Increased MYB alternative promoter usage is associated with relapse in acute lymphoblastic leukemia. Genes Chromosomes Cancer 2023, 62, 597–606. [Google Scholar] [CrossRef]
  39. Bardelli, V.; Arniani, S.; Pierini, V.; Pierini, T.; Di Giacomo, D.; Gorello, P.; Moretti, M.; Pellanera, F.; Elia, L.; Vitale, A.; et al. MYB rearrangements and over-expression in T-cell acute lymphoblastic leukemia. Genes Chromosomes Cancer 2021, 60, 482–488. [Google Scholar] [CrossRef]
  40. Caballero-Palacios, M.C.; Villegas-Ruiz, V.; Ramírez-Chiquito, J.C.; Medina-Vera, I.; Zapata-Tarres, M.; Mojica-Espinosa, R.; Cárdenas-Cardos, R.; Paredes-Aguilera, R.; Rivera-Luna, R.; Juárez-Méndez, S. v-myb avian myeloblastosis viral oncogene homolog expression is a potential molecular diagnostic marker for B-cell acute lymphoblastic leukemia. Asia Pac. J. Clin. Oncol. 2021, 17, 60–67. [Google Scholar] [CrossRef]
  41. De Dominici, M.; Porazzi, P.; Soliera, A.R.; Mariani, S.A.; Addya, S.; Fortina, P.; Peterson, L.F.; Spinelli, O.; Rambaldi, A.; Martinelli, G.; et al. Targeting CDK6 and BCL2 Exploits the “MYB Addiction” of Ph. Cancer Res. 2018, 78, 1097–1109. [Google Scholar] [PubMed]
  42. Chen, D.; Ye, Z.; Lew, Z.; Luo, S.; Yu, Z.; Lin, Y. Expression of NMU, PPBP and GNG4 in colon cancer and their influences on prognosis. Transl. Cancer Res. 2022, 11, 3572–3583. [Google Scholar] [CrossRef] [PubMed]
  43. Salimi, A.; Aghvami, M.; Movahed, M.A.; Zarei, M.H.; Eshghi, P.; Zarghi, A.; Pourahmad, J. Evaluation of Cytotoxic Potentials of Novel Cyclooxygenase-2 Inhibitor against ALL Lymphocytes and Normal Lymphocytes and Its Anticancer Effect through Mitochondrial Pathway. Cancer Investig. 2020, 38, 463–475. [Google Scholar]
  44. Peng, L.; Zhou, Y.; Wang, Y.; Mou, H.; Zhao, Q. Prognostic significance of COX-2 immunohistochemical expression in colorectal cancer: A meta-analysis of the literature. PLoS ONE 2013, 8, e58891. [Google Scholar] [CrossRef] [PubMed]
  45. Hashemi Goradel, N.; Najafi, M.; Salehi, E.; Farhood, B.; Mortezaee, K. Cyclooxygenase-2 in cancer: A review. J. Cell Physiol. 2019, 234, 5683–5699. [Google Scholar] [CrossRef] [PubMed]
  46. Truffinet, V.; Donnard, M.; Vincent, C.; Faucher, J.L.; Bordessoule, D.; Turlure, P.; Trimoreau, F.; Denizot, Y. Cyclooxygenase-1, but not -2, in blast cells of patients with acute leukemia. Int. J. Cancer 2007, 121, 924–927. [Google Scholar] [CrossRef] [PubMed]
  47. Aghvami, M.; Salimi, A.; Eshghi, P.; Zarei, M.H.; Farzaneh, S.; Sattari, F.; Zarghi, A.; Pourahmad, J. Targeting the mitochondrial apoptosis pathway by a newly synthesized COX-2 inhibitor in pediatric ALL lymphocytes. Future Med. Chem. 2018, 10, 2277–2289. [Google Scholar]
  48. Yu, Y.; Tian, X. Analysis of genes associated with prognosis of lung adenocarcinoma based on GEO and TCGA databases. Medicine 2020, 99, e20183. [Google Scholar]
  49. Kim, S.-S.; Shin, H.; Ahn, K.-G.; Park, Y.-M.; Kwon, M.-C.; Lim, J.-M.; Oh, E.-K.; Kim, Y.; Han, S.-M.; Noh, D.-Y. Quantifiable peptide library bridges the gap for proteomics based biomarker discovery and validation on breast cancer. Sci. Rep. 2023, 13, 8991. [Google Scholar] [CrossRef]
  50. Sun, G.; Li, Y.; Peng, Y.; Lu, D.; Zhang, F.; Cui, X.; Zhang, Q.; Li, Z. Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis. J. Cell Physiol. 2019, 234, 15215–15224. [Google Scholar] [CrossRef]
  51. Wang, J.; Hao, J.-P.; Uddin, N.; Wu, Y.; Chen, R.; Li, D.-F.; Xiong, D.-Q.; Ding, N.; Yang, J.-H.; Ding, X.-S. Identification and validation of inferior prognostic genes associated with immune signatures and chemotherapy outcome in acute myeloid leukemia. Aging 2021, 13, 16445–16470. [Google Scholar] [CrossRef] [PubMed]
  52. Su, C.; Li, H.; Peng, Z.; Ke, D.; Fu, H.; Zheng, X. Identification of plasma RGS18 and PPBP mRNAs as potential biomarkers for gastric cancer using transcriptome arrays. Oncol. Lett. 2019, 17, 247–255. [Google Scholar] [CrossRef] [PubMed]
  53. Cai, D.; Liang, J.; Cai, X.-D.; Yang, Y.; Liu, G.; Zhou, F.; He, D. Identification of six hub genes and analysis of their correlation with drug sensitivity in acute myeloid leukemia through bioinformatics. Transl. Cancer Res. 2021, 10, 126–140. [Google Scholar] [PubMed]
  54. Ameri, M.; Alipour, M.; Madihi, M.; Nezafat, N. Identification of intrinsically disordered regions in hub genes of acute myeloid leukemia: A bioinformatics approach. Biotechnol. Appl. Biochem. 2022, 69, 2304–2322. [Google Scholar] [PubMed]
  55. Wu, A.; Wu, B.; Guo, J.; Luo, W.; Wu, D.; Yang, H.; Zhen, Y.; Yu, X.; Wang, H.; Zhou, Y.; et al. Elevated expression of CDK4 in lung cancer. J. Transl. Med. 2011, 9, 38. [Google Scholar]
  56. Niswander, L.M.; Graff, Z.T.; Chien, C.D.; Chukinas, J.A.; Meadows, C.A.; Leach, L.C.; Loftus, J.P.; Kohler, M.E.; Tasian, S.K.; Fry, T.J. Potent preclinical activity of FLT3-directed chimeric antigen receptor T-cell immunotherapy against. Haematologica 2023, 108, 457–471. [Google Scholar] [CrossRef]
  57. Sexauer, A.N.; Tasian, S.K. Targeting FLT3 Signaling in Childhood Acute Myeloid Leukemia. Front. Pediatr. 2017, 5, 248. [Google Scholar] [CrossRef]
  58. Wang, Y.; Xu, Y.; Li, S.; Liu, J.; Xing, Y.; Xing, H.; Tian, Z.; Tang, K.; Rao, Q.; Wang, M.; et al. Targeting FLT3 in acute myeloid leukemia using ligand-based chimeric antigen receptor-engineered T cells. J. Hematol. Oncol. 2018, 11, 60. [Google Scholar] [CrossRef]
  59. Lo Nigro, L.L.; Andriano, N.; Buldini, B.; Silvestri, D.; Villa, T.; Locatelli, F.; Parasole, R.; Barisone, E.; Testi, A.M.; Biondi, A.; et al. FLT3-ITD in Children with Early T-cell Precursor (ETP) Acute Lymphoblastic Leukemia: Incidence and Potential Target for Monitoring Minimal Residual Disease (MRD). Cancers 2022, 14, 2475. [Google Scholar] [CrossRef]
  60. Thoms, J.A.I.; Birger, Y.; Foster, S.; Knezevic, K.; Kirschenbaum, Y.; Chandrakanthan, V.; Jonquieres, G.; Spensberger, D.; Wong, J.W.; Oram, S.H.; et al. ERG promotes T-acute lymphoblastic leukemia and is transcriptionally regulated in leukemic cells by a stem cell enhancer. Blood 2011, 117, 7079–7089. [Google Scholar] [CrossRef]
  61. Bock, J.; Mochmann, L.H.; Schlee, C.; Farhadi-Sartangi, N.; Göllner, S.; Müller-Tidow, C.; Baldus, C.D. ERG transcriptional networks in primary acute leukemia cells implicate a role for ERG in deregulated kinase signaling. PLoS ONE 2013, 8, e52872. [Google Scholar] [CrossRef]
  62. Pinz, K.; Liu, H.; Golightly, M.; Jares, A.; Lan, F.; Zieve, G.W.; Hagag, N.; Schuster, M.; E Firor, A.; Jiang, X.; et al. Preclinical targeting of human T-cell malignancies using CD4-specific chimeric antigen receptor (CAR)-engineered T cells. Leukemia 2016, 30, 701–707. [Google Scholar] [CrossRef] [PubMed]
  63. Ma, G.; Shen, J.; Pinz, K.; Wada, M.; Park, J.; Kim, S.; Togano, T.; Tse, W. Targeting T Cell Malignancies Using CD4CAR T-Cells and Implementing a Natural Safety Switch. Stem Cell Rev. Rep. 2019, 15, 443–447. [Google Scholar]
  64. Feng, J.; Xu, H.; Cinquina, A.; Wu, Z.; Zhang, W.; Sun, L.; Chen, Q.; Tian, L.; Song, L.; Pinz, K.G.; et al. Treatment of aggressive T-cell lymphoma/leukemia with anti-CD4 CAR T cells. Front. Immunol. 2022, 13, 997482. [Google Scholar] [CrossRef]
  65. Li, L.; Huang, Z.; Du, K.; Liu, X.; Li, C.; Wang, D.; Zhang, Y.; Wang, C.; Li, J. Integrative Pan-Cancer Analysis Confirmed that FCGR3A is a Candidate Biomarker Associated With Tumor Immunity. Front. Pharmacol. 2022, 13, 900699. [Google Scholar] [CrossRef] [PubMed]
  66. Haen, S.P.; Schmiedel, B.J.; Rothfelder, K.; Schmied, B.J.; Dang, T.M.; Mirza, N.; Möhle, R.; Kanz, L.; Vogel, W.; Salih, H.R. Prognostic relevance of HER2/neu in acute lymphoblastic leukemia and induction of NK cell reactivity against primary ALL blasts by trastuzumab. Oncotarget 2016, 7, 13013–13030. [Google Scholar] [CrossRef] [PubMed]
  67. Chevallier, P.; Robillard, N.; Wuilleme-Toumi, S.; Méchinaud, F.; Harousseau, J.L.; Avet-Loiseau, H. Overexpression of Her2/neu is observed in one third of adult acute lymphoblastic leukemia patients and is associated with chemoresistance in these patients. Haematologica 2004, 89, 1399–1401. [Google Scholar]
  68. Adams, S. Toll-like receptor agonists in cancer therapy. Immunotherapy 2009, 1, 949–964. [Google Scholar] [CrossRef]
  69. Boushehri, M.A.S.; Lamprecht, A. TLR4-Based Immunotherapeutics in Cancer: A Review of the Achievements and Shortcomings. Mol. Pharm. 2018, 15, 4777–4800. [Google Scholar] [CrossRef]
  70. Purdom, E.; Simpson, K.M.; Robinson, M.D.; Conboy, J.G.; Lapuk, A.V.; Speed, T. FIRMA: A method for detection of alternative splicing from exon array data. Bioinformatics 2008, 24, 1707–1714. [Google Scholar]
  71. Korthauer, K.; Kimes, P.K.; Duvallet, C.; Reyes, A.; Subramanian, A.; Teng, M.; Shukla, C.; Alm, E.J.; Hicks, S.C. A practical guide to methods controlling false discoveries in computational biology. Genome Biol. 2019, 20, 118. [Google Scholar] [CrossRef] [PubMed]
  72. Dennis, G.; Sherman, B.T.; A Hosack, D.; Yang, J.; Gao, W.; Lane, H.C.; A Lempicki, R. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003, 4, P3. [Google Scholar] [CrossRef]
  73. Szklarczyk, D.; Franceschini, A.; Wyder, S.; Forslund, K.; Heller, D.; Huerta-Cepas, J.; Simonovic, M.; Roth, A.; Santos, A.; Tsafou, K.P.; et al. STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015, 43, D447–D452. [Google Scholar] [CrossRef] [PubMed]
  74. Smoot, M.E.; Ono, K.; Ruscheinski, J.; Wang, P.-L.; Ideker, T. Cytoscape 2.8: New features for data integration and network visualization. Bioinformatics 2011, 27, 431–432. [Google Scholar] [CrossRef]
  75. Bárcenas-López, D.A.; Núñez-Enríquez, J.C.; Hidalgo-Miranda, A.; Beltrán-Anaya, F.O.; May-Hau, D.I.; Jiménez-Hernández, E.; Bekker-Méndez, V.C.; Flores-Lujano, J.; Medina-Sansón, A.; Tamez-Gómez, E.L.; et al. Transcriptome Analysis Identifies LINC00152 as a Biomarker of Early Relapse and Mortality in Acute Lymphoblastic Leukemia. Genes 2020, 11, 302. [Google Scholar] [CrossRef]
  76. Ye, J.; Coulouris, G.; Zaretskaya, I.; Cutcutache, I.; Rozen, S.; Madden, T.L. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinform. 2012, 13, 134. [Google Scholar] [CrossRef]
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Figure 1. Analysis of gene expression profiles in acute lymphoblastic leukemia (ALL). Heat map shows the differential gene expression (DGE) patterns between ALL and non-ALL subjects. The heat map is constructed using 871 DGEs. The expression is reported through a color pattern, with red and blue for high and low levels of gene expression, respectively. Color intensity reflects the level of gene expression.
Figure 1. Analysis of gene expression profiles in acute lymphoblastic leukemia (ALL). Heat map shows the differential gene expression (DGE) patterns between ALL and non-ALL subjects. The heat map is constructed using 871 DGEs. The expression is reported through a color pattern, with red and blue for high and low levels of gene expression, respectively. Color intensity reflects the level of gene expression.
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Figure 2. The figure shows the network model visualized using Cytoscape (v3.10.1). Nodes with yellow fill identify genes CD4 primary interaction. The black line represents the PPI relationship between the nodes. Colors coded by biological processes: light blue (immune system process), dark blue (immune response), aqua-green (regulation of immune system process), green (response to stimulus), and pink (response to stress). PPI: protein–protein interaction. Red dot: most ranked nodes.
Figure 2. The figure shows the network model visualized using Cytoscape (v3.10.1). Nodes with yellow fill identify genes CD4 primary interaction. The black line represents the PPI relationship between the nodes. Colors coded by biological processes: light blue (immune system process), dark blue (immune response), aqua-green (regulation of immune system process), green (response to stimulus), and pink (response to stress). PPI: protein–protein interaction. Red dot: most ranked nodes.
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Figure 3. Event-free survival (EFS) and overall survival (OS) analyses of unregulated genes. Expression of ERG (A,B), CDK6 (C,D), SOX4 (E,F) and CD4 (G,H).
Figure 3. Event-free survival (EFS) and overall survival (OS) analyses of unregulated genes. Expression of ERG (A,B), CDK6 (C,D), SOX4 (E,F) and CD4 (G,H).
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Figure 4. Heat map of the gene co-expression network. The heatmap describes the correlation among gene expression. Each row and column of the heatmap corresponds to a single gene. Blue: low correlation; red: high correlation. Color intensity reflects the level of gene correlation.
Figure 4. Heat map of the gene co-expression network. The heatmap describes the correlation among gene expression. Each row and column of the heatmap corresponds to a single gene. Blue: low correlation; red: high correlation. Color intensity reflects the level of gene correlation.
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Table 1. Clinical characteristics of the patients with ALL included in this study.
Table 1. Clinical characteristics of the patients with ALL included in this study.
Characteristicn = 43Percentage
Gender
Male2353.5%
Female2046.5%
Median age at diagnosis in years: 33.8 (18–57)
AYA (18–40) *2762.8%
>40 years1637.2%
Risk classification
Standard511.6%
High 3888.4%
Relapse
Yes1637.2%
No2762.8%
Immunophenotype
B3786%
Pre-B49.3%
T24.7%
Death
Yes1637.2%
No2762.8%
* AYA: adolescents and young adults.
Table 2. The top 10 up-regulated and top 10 down-regulated ranked differentially expressed genes in adult acute lymphoblastic leukemia patients compared to controls.
Table 2. The top 10 up-regulated and top 10 down-regulated ranked differentially expressed genes in adult acute lymphoblastic leukemia patients compared to controls.
Gene SymbolFold Changep ValueFDR 1
DNTT107.254.44 × 10−50.0024
MYB39.053.18 × 10−75.19 × 10−5
SOX419.522.49 × 10−50.0015
EBF119.115 × 10−40.0153
ERG14.72.76 × 10−63 × 10−4
CD349.330.00190.0372
FLT311.552 × 10−40.0069
STMN18.416.29 × 10−50.0032
CDK68.079.75 × 10−68 × 10−4
NAV17.240.00130.0284
SH3BGRL2−20.181.4 × 10−62 × 10−4
CLEC7A−20.911.67 × 10−50.0011
RGS2−21.074.46 × 10−64 × 10−4
PF4−23.091.11 × 10−58 × 10−4
TUBB1−24.78.38 × 10−71 × 10−4
VCAN−25.223.23 × 10−50.0019
LUCAT1−27.026.62 × 10−92.05 × 10−6
ADGRE3−27.112.04 × 10−73.6 × 10−5
PPBP−52.248.79 × 10−50.0041
PTGS2−57.838.5 × 10−81.76 × 10−5
1 FDR: False discovery rate.
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Cruz-Miranda, G.M.; Olarte-Carrillo, I.; Bárcenas-López, D.A.; Martínez-Tovar, A.; Ramírez-Bello, J.; Ramos-Peñafiel, C.O.; García-Laguna, A.I.; Cerón-Maldonado, R.; May-Hau, D.; Jiménez-Morales, S. Transcriptome Analysis in Mexican Adults with Acute Lymphoblastic Leukemia. Int. J. Mol. Sci. 2024, 25, 1750. https://doi.org/10.3390/ijms25031750

AMA Style

Cruz-Miranda GM, Olarte-Carrillo I, Bárcenas-López DA, Martínez-Tovar A, Ramírez-Bello J, Ramos-Peñafiel CO, García-Laguna AI, Cerón-Maldonado R, May-Hau D, Jiménez-Morales S. Transcriptome Analysis in Mexican Adults with Acute Lymphoblastic Leukemia. International Journal of Molecular Sciences. 2024; 25(3):1750. https://doi.org/10.3390/ijms25031750

Chicago/Turabian Style

Cruz-Miranda, Gabriela Marisol, Irma Olarte-Carrillo, Diego Alberto Bárcenas-López, Adolfo Martínez-Tovar, Julian Ramírez-Bello, Christian Omar Ramos-Peñafiel, Anel Irais García-Laguna, Rafael Cerón-Maldonado, Didier May-Hau, and Silvia Jiménez-Morales. 2024. "Transcriptome Analysis in Mexican Adults with Acute Lymphoblastic Leukemia" International Journal of Molecular Sciences 25, no. 3: 1750. https://doi.org/10.3390/ijms25031750

APA Style

Cruz-Miranda, G. M., Olarte-Carrillo, I., Bárcenas-López, D. A., Martínez-Tovar, A., Ramírez-Bello, J., Ramos-Peñafiel, C. O., García-Laguna, A. I., Cerón-Maldonado, R., May-Hau, D., & Jiménez-Morales, S. (2024). Transcriptome Analysis in Mexican Adults with Acute Lymphoblastic Leukemia. International Journal of Molecular Sciences, 25(3), 1750. https://doi.org/10.3390/ijms25031750

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