T-ALL whole blood samples were obtained from the Department of Pathology and Clinical Haematology, registered under the Leukaemia and Lymphoma Society at King George's Medical University, Lucknow, U.P., India. Twenty whole blood samples were collected from each group (T-ALL patients and healthy controls) in an aseptic environment in ethylenediaminetetraacetic acid (EDTA) vials. Only patients who had not undergone any chemotherapy cycles were included in the study to ensure accuracy. Patients' diagnoses were based on age, symptoms, complete blood counts and bone marrow examination. Immunophenotyping markers, including CD34, CD33, CD14, CD20, CD10, CD19, HLA-DR, TdT, CD2, CD3, CD5, CD7, CD13, CD19, CD20, CD23, CD45, CD64, CD79a, CD117, and CD 200, were used. The pharmaceutical contents are listed in Table 1. The samples were sent to the Department of Biochemistry, KGMU, Lucknow, India, for the molecular study. Blood smears were prepared on glass slides and stained with Leishman stain for approximately two minutes until a pinkish tinge transformation occurred. In suspected leukemia cases, the films show turbid coats after Leishman staining.10

Inclusion criteria: All under 18-year-old patients with a first-time diagnosis of T-ALL were included in this study.

Exclusion criteria: Smokers and patients with other acute or chronic diseases were excluded from this study.

A combination of monoclonal antibodies was used to confirm the T-ALL samples. These antibodies came in different types, such as anti-CD45, anti-CD34, anti-CD33, anti-CD79a, anti-CD13, anti-CD19, anti-CD25, anti-CD20, anti-CD7, anti-CD10, anti-CD14, anti-CD64, anti-CD117, HLA-DR, anti-CD3, anti-CD4 and anti-CD8. The antibodies were labeled with FITC, PE, PerCP, and PE-Cy7. All these reagents were obtained from BD Biosciences in San Jose, CA, USA.

The stain lyse method was used to confirm the disease. Each bone marrow or white blood sample (100 μL) was mixed with 20 μL of an antibody cocktail in each tube and incubated for 15 min in a dark environment. Fluorescence-activated cell sorting lysis solution was added to each tube and the samples were centrifuged at 200–300 g for five minutes. The supernatant was discarded and the samples were washed with sheath fluid three times before being resuspended in 0.5 mL of sheath fluid and run on a pre-calibrated flow cytometer. The data were analyzed and procured from FACSCanto (BD Biosciences).

Protein was isolated from whole blood and bone marrow samples using radioimmunoprecipitation assay (RIPA) buffer, and the integrity and purity were analyzed using ultraviolet spectrophotometry (quantitative analysis). The concentration of protein was measured by bicinchoninic acid (BCA) estimation (Abcam) (quantitative analysis) at 562 nm following the manufacturer's protocol and the integrity and purity of the protein were measured by sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE qualitative analysis).

Proteins isolated from T-ALL samples (20 samples: 20 whole blood and two bone marrow samples) and healthy controls (20 whole blood samples) were analyzed by LC-MS/MS. Ten random protein samples were selected from 20 sets of isolated proteins from whole blood and bone marrow tissues from T-ALL and ten from healthy controls. These were pooled and each sample tagged with a code to see the individual data sets. The BCA method normalized the sample concentration and then the samples were pooled using the same volume (100 µL from each protein sample) and 200 µL of each sample was sent in separate vials for the LC-MS/MS analysis.

One hundred micrograms of protein samples were digested and diluted with 50 mM NH4HCO3. The samples were treated with 100 mM dithiothreitol for 1 hour at 95 °C and 250 mM IDA was added in a dark environment for 45 min at room temperature. After digestion, the samples were incubated with trypsin overnight at 37 °C. The samples were mixed with 50 μL of 0.1 % formic acid in water and dried under vacuum. Finally, the samples were centrifuged at 10,000 rpm and the supernatant collected in Eppendorf tubes.

Ten microliters of the cleaned sample was separated into peptides using a BHE C18 ultra-performance liquid chromatography UPLC column. The peptides were then analyzed using a Waters Synapt G2 Q-TOF instrument with an electrospray ionization source to obtain the complete integrated sequence of the sample. The MassLynx 4.1 WATERS peptide editor software was used to analyze the raw data. Using PLGS software, WATERS, individual peptide MS/MS spectra were matched to the database sequence. The decoy database strategy filters peptide and protein identifications at a false discovery rate of 1 %. Proteins that could not be differentiated based on MS/MS spectra alone were grouped into protein groups. UPLC connected with a Waters Synapt G2 (QTOF) was used to acquire mass spectrometry data. During the processing of the sample, cysteine sites were modified to carbamidomethylated cysteine, and the methionine sites were prone to oxidation, which was considered a variable modification to the mass.11–13 Quantitative details are given in Table S1.

In this study, the functional interactions of proteins were predicted using the STRING database. This database integrates both known and predicted protein-protein interactions (PPIs). Only human experimental proteins with an interaction score greater than 0.4 were included to construct the PPI networks. The proteins are represented as network nodes and their interactions are illustrated as edges.

RNA was extracted from whole blood and bone marrow tissues using the TRIzol method from Ambion. The concentration and integrity of the RNA were confirmed using a Nanodrop 2000 UV–Vis spectrophotometer (Thermo Scientific). RNA with an absorbance ratio of 1.9–2.0 at 260/280 nm was utilized. The isolated mRNA was reverse-transcribed using a high-capacity cDNA reverse transcription kit from a specific manufacturer. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was carried out using PowerUp SYBR green master mix from ABI on a 7500 fast real-time polymerase chain reaction system (Applied Biosystem, Thermo Scientific). Quantification was executed using the ΔΔ Ct method with β-actin as a reference gene, and the RT-PCR outcomes were analyzed by DataAssist software (Thermo Scientific). Oligonucleotide primers are listed in Table S2. All primers were analyzed with positive controls by performing melting profiles after qRT-PCR, and product sizes were verified by 2.2 % agarose gel electrophoresis. The PCR conditions were 40 cycles of 15 s at 95 °C, 15 s at the annealing temperature (60 °C for all other genes), and 15 s at 72 °C. Specimens were assayed in duplicate in at least three independent experiments.

Whole blood samples were also collected in a plain vial for serum analysis. Serum samples were analyzed using a commercially available enzyme-linked immunosorbent assay (ELISA) kit (ELL ELISA kit abx387124, Human copine (CPNE4) ELISA kit- MBS7204256, TNFRSF10D ELISA Kit MBS700345, S100A8 ELISA Kit LS-F4982) following the manufacturer's instructions.

The data analysis was conducted using GraphPad Prism-9 and SPSS version 16.0. Controls were used for all comparisons and any differences observed were denoted by *p-value <0.01, **p-value <0.001, ***p-value <0.0001, ****p-value <0.00001. The quantitative data are the mean ± standard deviation (SD) derived from at least three samples per data point.

In this study, blood and bone marrow samples were analyzed from patients with T-ALL. The morphology of blast crisis cells from peripheral blood smears, bone marrow aspirate smears, bone marrow trephine biopsy specimens, immunophenotypic findings and flow cytometry are presented in Figure 1(A). Leishman dye was used to stain the blast cells. Different biomarkers, such as CD34, CD33, CD14, CD20, CD10, CD19, HLA-DR, TdT, CD2, CD3, CD5, CD7, CD13, CD19, CD20, CD23, CD45, CD64, CD79a, CD117, and CD 200, were also identified using flow cytometry. The expressions of T-lymphoid markers (CD3, cytoplasmic CD3, CD2, CD5, CD7) and immaturity markers (CD34, TdT, HLA-DR) were also studied. The analysis of different patients' flow cytometry data is presented in Figure 1 (B-I) and summarized in Table 1. The results showed that 95 % of patients had approximately 14,000 blast cells in 20,000 events, with 90 % of blast cells gated on dim CD45 and extended to the monocytic region on CD45 versus the side scatter plot. The findings of flow cytometry analysis confirmed the presence of blast cells in T-ALL patients.

Figure 1.

The clinical diagnosis of T-ALL patients was confirmed by peripheral blood and bone marrow tissues. (A) Representative images of Leishman staining of the blood of T-ALL patients, in which the majority of cells were in the blast crisis phase (arrows show blast cells). (B-I) Flow cytometry results show the blast cells in T-ALL patients, which were plotted on CD45 with side scatter and plotted on the marker and marker vs. marker for the confirmation of T-ALL. Blast cells were gated on CD45 vs. side scatter. The expressions of T-lymphoid markers (CD3, cytoplasmic CD3, CD2, CD5, CD7) and immaturity markers (CD34, TdT, HLA-DR) were studied.

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This study examined the proteomes of twenty T-ALL patients and twenty healthy controls, with 1467 proteins quantified in the label-free analysis. Protein isolation from blood samples was validated by BCA and SDS-PAGE tests. A differential protein level comparison was then made between blood samples from healthy controls and T-ALL patients. Proteins upregulated by >2-fold or downregulated by <2-fold in blood were deemed significant. The outcomes revealed nine upregulated proteins and 35 downregulated proteins, with a heat map created to illustrate the changes in blood proteins. This heat map indicated 35 differentially expressed proteins and nine signature proteins unique to T-ALL patients all of which were not found in healthy controls. These findings suggest that these proteins have a distinct signature in T-ALL only, as shown in Figure 2 (A-Q). The study also found that several proteins significantly decreased by 1.5-fold, including WD repeat-containing protein 26, interleukin 1, CD99 antigen, caspase recruitment domain-containing protein 9, NADPH oxidase, RING-type E3 ubiquitin-protein ligase, circadian locomotor output cycles protein kaput, a disintegrin and metalloproteinase domain-containing protein 8, neurogenic locus notch homolog protein 4, integrin beta-2, gamma-interferon-inducible protein 16, zinc finger protein Aiolos, mitogen-activated protein kinase, interferon-responsive finger protein 1, interleukin-1 receptor-associated kinase-like 2, and interleukin-18 receptor accessory protein. On the other hand, eleven lysine-rich leukemia proteins, triggering receptor expressed on myeloid cells 1, cisplatin resistance-associated-overexpressed protein, X-ray radiation resistance-associated protein 1 (XRRA1), tumor necrosis factor receptor superfamily member 10D, protein S100-A8, and copine-4, were significantly increased by more than 3-fold in T-ALL patients compared to healthy controls. All other proteins are shown in Supplementary Table S3.

Figure 2.

Label-free screening of differential proteins in T-ALL by using liquid chromatography-tandem mass spectrometry (LC-MS/MS). (A) shows the integrity of proteins isolated from healthy controls and T-ALL patients (Lanes 1–4 show healthy controls, and Lanes 5–9 show T-ALL patients), whereas (B-I) shows protein estimation, which was analyzed by the bicinchoninic acid kit. (J-M) shows the LC-MS/MS spectra of healthy controls and T-ALL patients. (N-P) shows 35 differentially expressed proteins in healthy controls and T-ALL patients, whereas the heatmap also shows the overall cumulative differential expression of proteins expressed in both groups. (Q) Nine signature proteins were found only in T-ALL patients and not in healthy controls.

The molecular factors specific to T-ALL were validated by mRNA and protein level analysis. The genes included in this section were explicitly activated in T-ALL. The quantitative LC-MS/MS data were further validated by RT-PCR, which included XRRA1, TREM1, Copine-4, Eleven-nineteen lysine-rich leukemia protein (ELL), Cisplatin resistance-associated-overexpressed protein, tumor necrosis factor receptor superfamily member, Protein S100-A8, WD repeat-containing protein 26, NADPH oxidase 1, Interferon-responsive finger protein 1, Notch 4, Interleukin-1 receptor-associated kinase-like 2 and the CD99 antigen. The fold changes (control vs. TA-LL) for these genes were 5.6171, 4.7539, 3.5434, 3.5379, 3.2699, 3.0153, 2.7212, −1.8781, −2.4439, −2.9769, −3.1133, −3.2393 and −3.6502, respectively (p-value >0.0001) as shown in Figure 3 (A-M) and Table 2. The most significant factors were further validated by ELISA, which included eleven-nineteen lysine-rich leukemia protein (ELL), S100A8, tumor necrosis factor receptor superfamily member 10D, and copine 4 with fold changes of 1.8248, 1.7931, 1.7932, and 1.7847, respectively, as shown in Figure 4 (A-D) and Table 3.

Figure 3.

Selected gene expression was confirmed at the mRNA level using reverse transcription polymerase chain reaction (RT-PCR). (A-M) Gene expression was quantified using RT-PCR in T-ALL patients (n = 20) and healthy controls (n = 20), where X-ray radiation resistance-associated protein 1, TREM1, Copine-4, Eleven-nineteen lysine-rich leukemia protein (ELL), Cisplatin resistance-associated-overexpressed protein, Tumor necrosis factor receptor superfamily member, Protein S100-A8, WD repeat-containing protein 26, NADPH oxidase 1, Interferon-responsive finger protein 1, Notch 4, Interleukin-1 receptor-associated kinase-like 2, and CD99 antigen had fold changes (Control vs. T-ALL) of 5.6171, 4.7539, 3.5434, 3.5379, 3.2699, 3.0153, 2.7212, −1.8781, −2.4439, −2.9769, −3.1133, −3.2393, and −3.6502, respectively (p-value >0.0001). All quantitative data are the mean ± standard deviation (SD), ****p-value <0.00001 and Student's t-test (paired).

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Figure 4.

The expressions of genes were further validated using enzyme-linked immunosorbent assay (ELISA - quantitative estimation) and western blotting (qualitative analysis). (A-D) Quantitative estimation (ELISA) of proteins that were found to be significantly high in T-ALL samples, such as eleven-nineteen lysine-rich leukemia protein (ELL), S100A8, tumor necrosis factor receptor superfamily member 10D, and Copine 4, with fold changes of 1.8248, 1.7931, 1.7932, and 1.7847 (p-value >0.0001), respectively.

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The LC-MS/MS data were analyzed using STRING tools with humans as the background library, followed by gene ontology (GO) annotations to identify significantly differentially expressed proteins. The analysis revealed notable changes in T-ALL patients, including positive regulation of nuclear factor-kappa B (NF-kB) transcription factor activity, response to stimulus, signal transduction, metabolic process, molecular function, cellular metabolic process, intracellular signal transduction, cellular process, macromolecule metabolic process, nucleic acid-templated transcription, nitrogen compound metabolic process, cell death, neutrophil extravasation, mapk cascade and gene expression. Moreover, reactome pathway analysis showed significant changes in the immune system, innate immune system, cell surface interactions at the vascular wall, cytokine signaling in the immune system, signaling by NOTCH and the NOD1/2 signaling pathway. In contrast, tissue enrichment analysis revealed changes in chronic lymphocytic leukemia cells, lymphoid tissue, the hematopoietic system and leukocytes. GO analysis confirmed significant differences in protein function, particularly those associated with cell proliferation, differentiation and metastasis, in T-ALL patients. Detailed GO results are shown in Supplementary Table S4 (a-n).

Selected genes that showed high expression levels in T-ALL patients were studied to determine their potential diagnostic value. Nonparametric receiver operating characteristic (ROC) curve analysis for 13 significant genes using relative expression (RQ) of T-ALL cases to plot the curve showed that all genes significantly differed from the diagonal line, differentiating healthy controls from T-ALL patients. The RQ value cutoff for the significant genes where the ROC curve was closest to 0.1, corresponding to 100 % sensitivity and specificity, was analyzed to identify potential diagnostic markers that correctly identify healthy individuals and T-ALL patients. Susceptible genes while not compromising specificity at the optimal cutoff were selected. This analysis identified eleven-nineteen lysine-rich leukemia protein (cutoff = 0.977; sensitivity = 0.8; specificity = 1), triggering receptor expressed on myeloid cells 1 (cutoff = 0.98; sensitivity = 1; specificity = 1), cisplatin resistance-associated-overexpressed protein (cutoff = 1.09; sensitivity = 1; specificity = 0.9), XRRA1 (cutoff = 0.635; sensitivity = 1; specificity = 1), tumor necrosis factor receptor superfamily member 10D (cutoff = 1.08; sensitivity = 1; specificity = 0.9), protein S100-A8 (cutoff = 1.09; sensitivity = 1; specificity = 0.8), copine-4 (cutoff = 12.2; sensitivity = 1; specificity = 1) and other significant genes. The findings suggest that these genes hold potential as diagnostic markers for T-ALL, as shown in Figure 6 (A-M) and Table 4.