Plasma Proteomic Profiles in Pediatric Acute Lymphoblastic Leukemia: A Systematic Review and Meta-Analysis

Plasma Proteomic Profiles in Pediatric Acute Lymphoblastic Leukemia: A Systematic Review and Meta-Analysis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 13 November 2025 V1 Latest version Share on Plasma Proteomic Profiles in Pediatric Acute Lymphoblastic Leukemia: A Systematic Review and Meta-Analysis Authors : Najia Tabassum , Talat Mirza , and Shahzad Ali Jiskani 0000-0002-8014-3743 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176301370.06139412/v1 235 views 96 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: Plasma proteomics profiling provides unique insight into the systemic molecular milieu accompanying pediatric acute lymphoblastic leukemia (ALL). Yet, discovery-phase studies have yielded fragmented and inconsistent findings. This systematic review and meta-analysis aimed to integrate plasma proteomic research to delineate reproducible protein alterations and biological pathways associated with pediatric ALL. Methods: Following PRISMA guidelines, comprehensive searches of PubMed, Scopus, Web of Science, Google Scholar and Cochrane Library identified studies comparing plasma pediatric ALL patients and healthy controls. Eligible studies underwent QUADOMICS-based quality appraisal. Quantitative data were pooled using random-effects meta-analysis. Functional enrichment analysis was performed to identify shared biological processes and pathway convergence across studies. Results: Nine eligible studies encompassing 679 unique plasma proteins were analyzed. Despite heterogeneity in proteomic platforms and study designs, consistent patterns of systemic dysregulation emerged. Acute-phase and inflammatory proteins, including ceruloplasmin, haptoglobin, alpha-1-antitrypsin, fibrinogen alpha-chain, and S100A8/A9, were recurrently upregulated, while apolipoprotein A1, transthyretin, and gelsolin were consistently downregulated. Meta-analysis demonstrated a significant pooled overexpression of S100A8 (SMD: 2.72, 95% CI: 1.14-4.29; p < p.001; I 2 = 0%), consistently appears across independent proteomic studies, supporting its candidacy as a biomarker; however, validation is harmonized, prospective cohorts using targeted assays is required before clinical application. Enrichment analysis revealed convergence on complement activation, coagulation cascade, lipid transport, and inflammatory signaling pathways, indicating a systemic immune-metabolic reprogramming associated with leukemic burden. Conclusion: This synthesis defined a reproducible plasma proteomic signatures of pediatric ALL, dominated by acute-phase and metabolic proteins that reflect the interplay between leukemic biology and host response. The consistent elevation of S100A8 underscore its potential utility as a minimally invasive biomarker for disease detection, monitoring, and risk stratification. Harmonized, multicenter validation using standardized targeted proteomics and longitudinal designs is essential to translate these findings into clinical precision tools for pediatric leukemia management. 1. Introduction Acute lymphoblastic leukemia (ALL) is the most common malignancy in children, representing approximately 25% of pediatric cancers worldwide (1). Each year, more than 57,000 new cases of childhood ALL are diagnosed globally, with the highest incidence reported in high-income regions such as North America and Europe, where age-standardized rates approach 3-4 cases per 100,000 children per year (2). In contrast, incidence is lower in many parts of sub-Saharan Africa and South Asia, though underdiagnosis and limited cancer registries contribute to uncertainty in these estimates (3). Survival outcomes mirrors these disparities. While cure rates in high-income countries now exceeds 85-90%, overall survival in many low- and middle- income countries (LMICs) remains 30-60%, reflecting inequalities in access to diagnostics, risk-adapted therapy, and supportive care. Despite remarkable improvements in treatment over the last five decades, important challenges persist. Relapse occurs in 15-20% of children, and once it develops, survival falls below 50%, even with intensive salvage regimens. Treatment-related mortality and long-term toxicities, including cardiotoxicity, endocrine dysfunction, and secondary malignancies, further impact survivors’ quality of life (4,5). Together, these challenges underscore the urgent need for refined approaches to early risks stratification, relapse prediction, and treatment monitoring, which could help tailor therapy more precisely while reducing unnecessary toxicity. Current risk classification strategies in pediatric ALL integrate clinical features such as age and presenting leukocyte count with biological characteristics including immunophenotype, cytogenetics, molecular aberrations, and minimal residual disease (MRD) (6). These parameters have been essential in guiding therapy, but they do not fully explain the heterogeneity of outcome. For instance, children harboring favorable genetic lesions such as ETV6-RUNX1 fusion generally achieve excellent outcomes, yet some relapse unexpectedly (7). Conversely, subsets of patients with high-risk abnormalities, including BCR-ABL1-like ALL, achieve long-term remission under optimized protocols (8). Such inconsistencies highlight the limitations of relying solely on genomic or transcriptomic markers, which may not capture the dynamic functional state of leukemia cells or their interactions with the host microenvironment. Proteomics, the large-scale study of proteins, offers a powerful approach to bridge this gap between genotype and phenotype. Proteins are the effectors of cellular processes and directly reflect both tumor biology and systemic host responses. Plasma and serum proteomics are particularly appealing in pediatric ALL because sampling is minimally invasive and feasible for repeated monitoring (9–11). Circulating proteins participate in pathways central to leukemogenesis and treatment response, including immune regulation, apoptosis, oxidative stress, coagulation, and lipid metabolism. Unlike DNA mutations or RNA expression, protein expression can fluctuate over time, making it an attractive source of biomarkers for diagnosis, prognosis, and real-time assessment of therapeutic response (12,13). Over the last two decades, several exploratory proteomics studies sought to define plasma protein signatures in pediatric ALL. Using diverse platforms, from two-dimensional electrophoresis and MALDI-TOF mass spectrometry to isobaric labeling techniques and modern SWATH-MS, investigators have identified altered levels of acute-phase proteins, coagulation factors, apolipoproteins, and cytoskeletal elements. Braoudaki et al. reported differential expression of ceruloplasmin, clusterin, and apolipoproteins between high- and low-risk ALL, while gelsolin and S100A9 were downregulated in more aggressive cases (14). Other groups, including Calderon-Rodriguez in Colombia and Broto in Brazil, described plasma signatures associated with relapse, risk stratification, and treatment outcomes (15,16). Collectively, these findings suggest that plasma proteomics could complement genomic profiling by identifying functional, non-invasive biomarkers. However, findings remain fragmented and inconsistent. Small sample sizes, variable inclusion criteria, differences in sample processing, and heterogeneity in proteomic platforms and analytical pipelines have produced conflicting results. Many studies remain exploratory, lacking independent validation or clinical translation. Importantly, there has been no systemic effort to synthesize data to identify proteins consistently dysregulated across pediatric ALL populations. Without such synthesis, it remains difficult to distinguish reproducible signals from context-dependent findings, limiting the potential for clinical implementation. Recent advances make such a synthesis timely. Large-scale plasma proteome reference maps, such as the 2025 Cell Atlas profiling over 53,000 individuals, provide valuable baselines against which disease-associated alterations can be interpreted (17). At the same time, mass spectrometry workflows based on data-independent acquisition and advanced bioinformatics now permit deeper, more reproducible proteome coverage. Furthermore, proteomics is increasingly being integrated with genomics, transcriptomics, and metabolomics to generate a holistic picture of leukemogenesis (10). In ALL, emerging evidence suggests that proteomic profiles may stratify subtypes, predict treatment response, and provide prognostic information independent traditional markers. Proteins such as apolipoprotein A1, ceruloplasmin, clusterin, fibrinogen chains, and complement factors have repeatedly been implicated in prognosis, while pathway analyses have implicated metabolic reprogramming and systemic inflammation as hallmarks of disease progression (9). Nevertheless, without pooled analysis, it is difficult to assess the reproducibility and magnitude of these associations. Against this backdrop, we conducted a systematic review and meta-analysis to consolidate the existing evidence on plasma proteomic alterations in pediatric ALL. Our objective is to catalog reported differentially expressed proteins, identify those consistently dysregulated across populations, and evaluate their potential clinical utility for diagnosis, risk stratification, and treatment monitoring. Where possible, we derived pooled effect estimates for proteins reported in multiple independent studies. We also critically assessed methodological quality, examined sources of heterogeneity, and highlighted directions for future research. To our knowledge, this is the first systematic effort to integrate the fragmented literature on plasma proteomic in pediatric ALL. By clarifying the current state of evidence and highlighting robust candidate biomarkers, this work aims to provide a foundation for translating proteomic insights into clinically useful tools. Ultimately, advances in plasma proteomics may enable more precise risk stratification, earlier relapse detection, and improved therapeutic strategies, with the potential to reduce disparities and improve outcomes for children with ALL worldwide. 2. Materials and Methods Protocol, and Registration This systematic review and meta-analysis was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA 2020) (18). The protocol was prospectively registered at PROSPERO (CRD420251148697). Because the included investigations were proteomic discovery and validation studies, methodological quality was appraised using QUADOMICS checklist, developed specifically for evaluating bias in –omics research, and the certainty of evidence for meta-analyzed proteins was assessed though a modified GRADE framework tailored for proteomic data. Eligibility Criteria We included studies that met the following criteria: • Population: Children between 1 to 16 years at the time of ALL diagnosis. Studies including mixed-age populations were eligible only if data for the pediatric cohort can be extracted separately. Infantile leukemia (16 years) cohorts were excluded. • Exposure/Interventions: Plasma proteomic profiling using any discovery-phase (e.g., 2D-PAGE, LC-MS/MS, iTRAQ, SWATH-MS, MALDI-TOF) or validation/targeted method (e.g., ELISA, Western blot, targeted MS). • Comparator: Studies having a comparator group, such as healthy children or children with non-ALL conditions. Studies without a comparative group were also included to provide comprehensive overview of reported proteins due to exploratory nature of the domain. • Outcome: Differential protein expression (e.g., fold-change normalized intensity, spectral counts) among pediatric ALL patients. • Study Design: Observational studies, discovery proteomics, and targeted validation studies were included. • Exclusion Criteria: Case reports, reviews, editorials, conference abstracts without a full manuscript, genome-only or transcriptomic-only studies, and studies involving only animal or in vitro models were excluded. Information Sources and Search Strategy A comprehensive and systematic search was conducted in the following electronic databases from their inception to the present, with no data or language restrictions: • PubMed/MEDLINE • Scopus • Web of Science • Cochrane Library • Google Scholar The search strategy used a combination of MeSH terms and keywords. A draft search strategy for PubMed is provided below: • Domain 1: Disease (leukemia, lymphoid OR acute lymphoblastic leukemia OR lymphoblastic leukemia OR ALL) • Domain 2: Population (child OR children OR childhood OR pediatric OR paediatric OR adolescent) • Domain 3: Method (proteomics OR proteomic OR protein expression OR protein marker OR mass spectrometry OR 2D electrophoresis OR 2D-PAGE OR LC-MS/MS OR iTRAQ OR SWATH OR MALDI-TOF OR label-free proteomics OR shotgun proteomics) The search strategy was adapted for each database’s specific syntax. We also performed a manual search of the reference lists of all included studies and relevant reviews to identify any additional studies. Forward citation tracking was performed for key publications. Study Selection The study selection process was conducted by two independent reviewers. • Title and Abstract Screening: The two reviewers independently screened all titles and abstracts identified by the search strategy against the eligibility criteria. • Full-Text Review: The full texts of all potentially relevant studies were retrieved and independently assessed by both reviewers. • Conflict Resolution: Any disagreement at either stage was resolved through discussion by a third reviewer who arbitrated and made the final decision. The selection process was documented in a PRISMA 2020 flow diagram ( Figure 1 ), which details the number of studies identified, screened, and included/excluded at each stage, with reasons for exclusion. Figure 1: PRISMA Flow Diagram for Selection of Studies Data Extraction A standardized data extraction form was developed. Two independent reviewers extracted the data from the included studies. The extracted data included: • Study Characteristics: First author, publication year, country, study design, and funding source. • Population Characteristics: Number of participants, age range, sex distribution, ALL subtype (B-cell vs. T-cell), and treatment phase (e.g., diagnosis, remission, relapse). • Laboratory Details: Sample type, proteomic platform, and validation methods (if any). • Protein Data: Protein name (gene name and/or protein ID), protein family or pathway, direction of dysregulation (upregulated vs. downregulated), fold-change, and statistical values. • Clinical Associations: Any reported correlations between protein levels and clinical outcomes (e.g., survival, relapse-free survival) or biological features (e.g., cytogenetics). Any discrepancies in data extraction was resolved by a third reviewer. Risk of Bias Assessment The quality and risk of bias of the included studies was assessed independently by two reviewers. Due to discover-phase omic nature of all included studies, an adapted checklist for omics research (QUADOMICS) was used, focusing on key areas such as sample handling and collection, quality control, data normalization, statistical correction for multiple comparisons and use of validation cohorts (19). Disagreements was resolved through discussion by third reviewer. The results of the risk of bias assessment is reported narratively. Data Synthesis and Analysis Meta-Analysis For each protein reported in two or more controlled studies, the standardized mean difference (SMD) and 95% confidence interval (CI) were computed to estimate the direction and magnitude of differential abundance between ALL and control groups. Studies that report only directionality without quantitative estimates were incorporated qualitatively. All analysis were conducted in Review Manager 5.4. Random-effect models based on Restricted Maximum Likelihood (REML) estimation was applied to account for between-study variance. Heterogeneity was evaluated with the I 2 and τ 2 statistics, and prediction intervals were reported where relevant. Narrative Synthesis A narrative synthesis was conducted for studies that cannot be included in meta-analysis, such as those with a single-study protein finding. The results was organized and presented in tables, categorizing proteins by their family or biological pathway, direction of dysregulation, and potential clinical relevance. Confidence in Cumulative Evidence The overall certainty of evidence for each meta-analyzed protein was assessed using a modified Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. We adapted the GRADE criteria to the context of omics research, considering factors such as risk of bias, inconsistency of results, imprecision, and publication bias. 3. Results Study Selection and Characteristics The initial database search yielded 6,447 records. Additional records extracted 29 studies. After removal of duplicates and screening of titles and abstracts, 27 studies underwent full-text review. Of these, nine studies met the inclusion criteria and were included in the final synthesis and meta-analysis ( Figure 1 ). Collectively, the included studies were conducted across various countries including Greece, Brazil, Colombia, China, and India. Publication years ranged from 2009 to 2024, reflecting over 15 years of discovery-phase proteomic research in pediatric acute lymphoblastic leukemia (ALL). All studies employed untargeted proteomic platforms including 2D-PAGE, MALDI-TOF, iTRAQ, and label-free LC-MS/MS, with several studies incorporating targeted validation using ELISA or Western blot. Studies focused on mixture of B-ALL, T-ALL or both. Additionally, the discovery phases covered various stages including diagnosis, post-induction, and relapse. Risk of Bias and Methodological Quality Risk of bias was evaluated using the QUADOMICS framework ( Table 1 ). Overall, two studies demonstrated low risk of bias (≥80%), while the remaining were rates moderate (50-70%). Common limitations include insufficient reporting of blinding during test interpretation, unclear representativeness of the patient spectrum, and limited assessment of overfitting. Conversely, nearly all studies provided adequate descriptions of sample handling, analytical reproducibility, and reference standard accuracy. No study was deemed high risk. Table 1: Risk of Bias Assessment (QUADOMICS) 1 Braoudaki et al. (14) 2013 ● 2 Broto et al. (15) 2020 ● 3 Calderon-Rodriguez et al. (16) 2019 ● 4 Cavalcante et al. (20) 2016 ● 5 Jiang et al. (21) 2025 ● 6 Shi et al. (22) 2009 ● 7 Singh et al. (23) 2024 ● 8 Yu et al. (24) 2022 ● 9 Yu et al. (25) 2019 ● Proteomic Alterations Across Studies A total of 679 unique proteins were reported as differentially expressed between pediatric ALL and controls across the included studies ( Table 2 ). Dysregulated proteins primarily mapped to pathways involved in acute-phase response, lipid metabolism, immune regulation, and coagulation. Notably, the most consistently protein across datasets were ceruloplasmin, haptoglobin, alpha-1-antitrypsin (SERPINA1) fibrinogen alpha chain, and S100A8/A9, while apolipoprotein A1, transthyretin, and gelsolin were consistently downregulated. These findings coverage the biological processes implicated in oxidative stress, inflammation, and metabolic reprogramming, hallmarks of leukemogenesis. To clarify biological relevance beyond simple frequency counts, we separated proteins into consistently replicated across multiple studies and single-study/novel candidates. For each protein/small group, we give a short mechanistic rationale for how it may related to leukemogenesis or host response, supporting targeted follow-up. Consistently Replicated Proteins S100A8/S100A9 was upregulated across studies; their reproducibility was elevated across independent datasets and confirmed by meta-analysis for S100A8 in our pooled analysis. Biologically, S100A8/A9 are damage-associated molecular patterns (DAMPs) that modulate neutrophil or chemoresistance in myeloid activation, NF-κB and MAPK signaling, ROS generation and can promote proliferation or chemoresistance in myeloid/lymphoid malignancies; this supports their candidacy as circulating biomarkers and potential microenvironmental mediators. Ceruloplasmin was also upregulated. It is an acute phase copper-binding oxidase involved in iron metabolism and redox biology; elevation may reflect oxidative stress and altered iron handling in leukemia processes that influence proliferation and genomic stability. Functional follow-up should test whether ceruloplasmin levels correlate with blast ROS, DNA damage, or iron-dependent signaling. Likewise, other proteins including haptoglobulin, α-1-antitrypsin, and fibrinogen subunits were also upregualted. These point to systemic inflammation and a pro-coagulant state; mechanistically, coagulation/inflammation crosstalk can support tumor growth, alter drug pharmacokinetics, and increase thrombosis risk in hematologic malignancies. On the other hand, apoplipoprotein A1, transthyretin, and gelsonin were downregulated, suggesting altered lipid transport, nutritional/metabolic states, and cytoskeletal regulation. Gelsonin downregulation plausibly effects apoptosis and cell motility; APOA1/TTR change may reflect hepatic response or cancer-associated metabolic remodeling. These molecules are attractive for targeted validation panels because they are robustly measurable by immunoassay ( Table 2 ). Single-study/Exploratory Candidates Many proteins were identified in a single study and/or exploratory candidates including PIKfyve, aggrecan core protein, fibulin-1, LRG1, SPARC, CCT3, ENO1, componetns of autophagy/endosomal trafficking (PIKfyve family) etc. Many of these occur in a single reports but are mechanistically plausible e.g., PIKfyve/endosomal regulation may impact receptor trafficking and drug resistance; SPARC/LRG1 relate to ECM and microenvironment interaction; glycolytic enzymes reflect metabolic reprogramming. We recommend a prioritized “screen-to-validate” workflow: (i) short-listing by cross-platform detectability (proteolytic peptides or ELISA availability), (ii) orthogonal replication in an independent cohort, and (iii) targeted functional assays (siRNA/CRISPR, recombinant protein exposure) to test causal effects on blast survival or microenvironment remodeling ( Table 2 ). Table 2: Characteristics of Studies included in Systematic Review and Meta-analysis 1 Broto et al. (2020, Brazil) (15) 17 Nano-UPLC-MS/MS Immune turbidity; flow cytometry 5 Transthyretin, IFN-γ, Vitronectin Transport; stem cell differentiation; innate immune response Mechanism related to disease resolution Inflammatory and coagulatory signaling 2 Calderon-Rodrigez et al. (2019, Colombia) (16) 12 Label-free quantification using Nano-LC-MS/MS ELISA 3 PIKfyve, Aggrecan core protein, Coagulation factor V Lipid kinase; endosomal process; autophagy; ECM remodeling Diagnostic markers; therapeutic targets Coagulation, ECM organization, adhesion, oxidative stress 3 Jiang et al. (2024, China) (21) 93 TMT-based quantitative proteomics Parallel reaction monitoring; ELISA 4 CXCL13, SERP5, Fibulin-1 Chemokine/immune response; WNT signaling Differentiating markers Immune/innate response predictors; hyperactivation of B-cell signaling 4 Singh et al. (2024, India) (23) 40 Label-free LC-MS/MS RT-PCR; ELISA 24 X-ray resistance-associated protein, CCT3, ENO1 DNA-damage response (ATM/CHK1/2); cell cycle regulation Early-stage diagnosis and prognosis Drug/radiation resistance; signaling modulation 5 Yu et al. (2022, China) (24) 40 2-D electrophoresis; MALDI-TOF MS ELISA 6 Acotinase 2, SYNCRIP TCA cycle; inflammatory and immune modulation Early-stage diagnosis Target for immunotherapy 6 Yu et al. (2019, China) (25) 44 iTRAQ-based quantitative proteomics; 2-D electrophoresis ELISA 4 S100A8, LRG1, SPARC Inflammation; NF-κB signaling; immune regulation Chemoresistance; prognostic biomarker Immune regulation; inflammatory signaling; angiogenesis 7 Braoudaki et al. (2013, Greece) (14) 52 2-D electrophoresis; MALDI-TOD MS Western blotting 12 α-1-Antitrypsin, α-2-Macroglobulin, albumin Acute-phase response; protease inhibition Poor survival; prognostic markers Acute-phase reactants; lipid metabolism/ statin response 8 Cavalcante et al. (2016, Brazil) (20) 20 Nano-UPLC Tandem; Nano-ESI-MSE MS None 9 C3, α-2-Macroglobulin; α-1-Antitrypsin Complement activation; protease inhibitor/transport Early diagnosis; treatment response Blood coagulation; complement activation 9 Shi et al. (2009, China) (22) 148 SELD-TOF-MS; LC-MS/MS Protein-chip immunoassays 3 CTAP-III, PF4, C3a complement Coagulation and platelet activation Diagnostic markers Complement and coagulation cascades ATM/CHK1/2, ataxia telangiectasia mutated/checkpoint kinase 1 and 2; C3, complement component 3; C3a, complement component 3a; CCT3, chaperon containing TCP1 subunit 3; CTAP-III, connective tissue-activating peptide III; CXCL13, chemokine (C-X-C motif) ligand 13; ECM, extracellular matrix; ELISA, enzyme-linked immunosorbent assay; ENO1, alpha-enolase-1; ESI-MSE, electrospray ionization mass spectrometry at elevated energy; iTRAQ, isobaric tag for relative and absolute quantitation; LC-MS/MS, liquid chromatography-tandem mass spectrometry; LRG1, leucine-rich alpha-2-glycoprotein-1; MALDI-TOF, matrix-associated laser desorption/ionization-time of flight; Nano-LC-MS/MS, nano-liquid chromatography-tandem mass spectrometry; Nano-UPLC-MS/MS, nano-ultra performance liquid chromatography-tandem mass spectrometry; Nano-ESI-MSE, nano-electrospray ionization-mass spectrometry at elevated energy; NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells; PF4, platelet factor 4; PIKfyve, phosphatidylinositol-3-phosphate 5-kinase; RT-PCR, reverse transcription polymerase chain reaction; S100A8, S100 calcium-binding protein A8; SELDI-TOF-MS, surface-enhanced laser desorption/ionization-time of flight mass spectrometry; SPARC, secreted protein acidic and rich in cysteine; SYNCRIP, synaptotagmin-binding cytoplasmic RNA-interacting protein; TCA cycle, tricarboxylic acid cycle; TMT, tandem mass tag; UPLC, ultra-performance liquid chromatography Meta-Analysis of Recurrently Reported Proteins Meta-analysis was feasible for only one protein, S100A8 that was reported in at least two independent datasets with extractable quantitative data (23,25). Pooled analysis was conducted using a random-effect model to accommodate between-study heterogeneity. S100A8 demonstrated a significant pooled increase in ALL compared to controls (SMD = 2.72; 95% CI: 1.14-4.29; p < 0.001); I 2 = 0%), suggesting robust evaluation of this pro-inflammatory calcium binding protein ( Figure 2 ). Figure 2: Forest Plot of S100A8 Protein for Meta-analysis Pathway and Functional Enrichment Functional enrichment of recurrently dysregulated proteins using Reactome and Gene Ontology annotations revealed predominant involvement in: • Complement and coagulation cascade: FGA, FGB, HP, CP, C3 • Lipid transport and metabolism: APOA1, APOA4, APOC1 • Inflammatory and immune regulation: S100A8, S100A9, SERPINA1 • Cytoskeletal organization: GSN, ACTG1 These pathways collectively underscore a systemic inflammatory milieu and metabolic remodeling in pediatric ALL, consistent with known immunometabolic reprogramming of leukemic blasts and host responses ( Table 3 ). Table 3: Functional Characterization and Biological Interpretation of Proteins Identified across Proeomic Studies Complement and Coagulation Cascade 69 F13A; FGA; FGB; FGG; PLG; PLG_HUMAN; PF4; CTAP-III; C3; C3A; CFB; CFI; CPN1; CPN2; CP; KNG1; HRG; VTN; F13B; PLG_SYM; SERPINF2; SERPINA1; A2M; FGB_HUMAN; FGA_HUMAN; FGG_HUMAN Complement activation, coagulation, platelet function, acute-phase immune defense Indicates systemic inflammation and coagulation activations; potential biomarkers of disease severity and thrombosis risk in pediatric ALL (14–16,20,22) Lipid Transport and Metabolism 64 APO; ALB; TTR; CETP; LCAT; PLTP; LPA; LPL; FABP; NPC1; SCP2; ANGPLT3; LDLR; CD36; PON; ORM; SERPIN; CP; FASN; LRP1; HP; RBP4; AHSG; VTN; FGB; FGA; FGG; GC Lipid transport, HDL assembly/remodeling, cholesterol efflux and lipoprotein-mediated signaling Reflects metabolic remodeling and disturbed lipid handling in leukemia; potential biomarkers of metabolic state and therapy-associated metabolic changes (14–16,25) Inflammatory and Immune Regulation 120 S100A8; S100A9; IFNG; CXCL13; LRG1; SPARC; SERPIN; CLU; C3; PF4; CTAP-III; CCL; CXCL1; CXCL8; IL6; AL10; TNF; HP; CRP; CFB; CF1; FGA; FGB; FGG; ORM; AHSG; HPX; ITIH; C4; CFH; A2M; HRP; LCN2; HSP Innate and adaptive immune signaling, cytokine/chemokine activity, neutrophil activation, and protease-antiprotease regulation Suggests a strong pro-inflammatory systemic signature and altered immune regulation in the host response to leukemic blasts; candidate biomarker set for disease activity/response (14–16,21) ECM Remodeling and Cell-Matrix Interaction 95 SPARC; FBLN1; VTN; ACAN; LUM; COL; FN1; THBS; MMP; TIMP; DCN; BGN; TNC; FBN1; LAM; NID1; HSPG2; ITG; LGALS; PLOD; SEPRIN; ANXA2; AHSH; CLU; HP; APO; ALB; HSP; FGG; FGA; FGB Extracellular matrix organization, adhesion, angiogenesis, and tissue remodeling Reflects bone marrow microenvironment remodeling, altered adhesion, and stromal interaction that can facilitate leukemic invasion/retention and affect therapy responsiveness (15,16,24) Cytoskeletal Organization and Cell Motility 70 ACT; GSN; CCT; TUB; MYH; ACTN; TPM; CFL; APRC; DSTN; PFN; CAPZ; ANXA; VIM; TLN1; FLN; GNA; GNB; MAP; ACTRL Actin-filament dynamics, cytoskeletal remodeling, chaperonin-assisted folding, and motility Indicates cytoskeletal reprogramming enabling leukemic blasts’ motility, niche interaction, and possibly dissemination (14,15,23,25) Energy and Mitochondrial Metabolism (TCA/Glycolysis) 85 ACO; ENO; PKM; LDH; GAPH, ALDOA; PGK1; PGAM1; TPI; IDH; SDH; MDH; FH; CS; OGDH; DLAT; PDH; NDUF; ATP5; UQCR; CYC1; COX; PRDX; GPD; DLD; ACADVL; HADH; HSP Central carbon metabolism (glycolysis, TCA cycle), mitochondrial energy generation, and metabolic enzymes supporting proliferation Consistent with metabolic reprogramming (Warburg-like features and mitochondrial adaptations) in leukemic blasts; possible metabolic vulnerabilities (15,23,25) Oxidative Stress and Redox Regulation 51 HP; PRDX; SOD; GPX; GSR; GST; TXN; CAT; NQO1; HMOX; ACO2; LDH; ALDH; A2M; HRP; HBA; HBB; HBD; HBG; HPX, CP; VTN; SERPINA1; ALB; AHSG; ORM Detoxification of ROS, maintenance of redox homeostasis, oxidative stress response Indicates oxidative stress and redox imbalance in the disease state, which can affect signaling, genomic stability, and therapeutic sensitivity (15,16,23) Endosomal Trafficking and Autophagy 24 PIkfyve; MTMR; RAB; VPS; EEA; LAMP; ATG; SQSTM1; LC3B; HSPA8 Endosomal membrane dynamics, autophagic flux regulation, and intracellular trafficking May related to altered protein turnover, vesicle-mediated signaling, and drug-resistance mechanisms in leukemic cells (14–16) DNA Damage Response and Cell Cycle Regulation 30 ATM; CHK; XRCC; RAD50; MRE11A; NBN; TP53; CDK; CCN; MCM; PCNA; CCT; HSP DNA repair, checkpoint control, replication stress response, and cell-cycle progression Suggests mechanisms underpinning genomic instability and possible therapeutic resistance; relevant for prognosis and targeting checkpoint pathways (14,15,23) Chemokine/ Cytokine Signaling 18 CXCL; CCL; PF4; CTAP-III; IL6; IL8; 1L10; TNF; IFNG Leukocyte chemoattraction, B-cell homing, platelet-chemokine cross-talk Supports altered leukocyte recruitment and microenvironment cross-talk that may shape immune response to leukemia (15,21,22) Protease Inhibition and Acute-Phase Response 40 SERPIN; A2M; HP; CRP; ORM; AHSG; CP; TTR; ALB; APCS; CFB; FGA; FGB; FGG; ITIH; LRG1; CLU; C3; CF; C4; LCN2; VTN; TF; HRP Protease inhibition, anti-proteolytic protection, and hepatic acute-phase reaction Reflects systemic acute-phase response and protease-antiprotease balance; candidate biomarkers for inflammation status (14,15,20) A2M, alpha-2-macroglobulin; A2ML1, alpha-2-macroglobulin-like 1; ACADVL; acyl-CoA dehydrogenase very long chain; ACAN, aggrecan; ACO, acotinase; ACT, actin; ACTN, alpha actinin; AHSG, alpha-2-heremans-schmid glycoprotein (fetuin A); ALB, albumin; ALDH, aldehyde dehydrogenase; ALDOA, aldolase A; ANGPTL3, angiopoietin-like 3; ANXA, annexin; APCS, serum amyloid P component; APO, apolipoproteins; APRC, actin-related protein complex; ATP5, adenosine triphosphatase synthase subunits; ATG, autophagy related protein; BGN, biglycan; C3, complement component 3; C4, complement component 4; C5, complement component 5; CAPZ, capping actin protein; CAT, catalase; CCL; chemokine C-C motif ligand family; CXCL, C-X-C motif ligand family; CCN, cyclin family; CD36; cluster of differentiation 36; CDK, cyclin-dependent kinase; CETP, cholesteryl ester transfer protein; CHK, checkpoint kinase; CLU, clusterin; COL, collagen family; CP, ceruloplasmin; CPT, carboxypeptidase; CRP, C-reactive protein; CS, citrate synthase; CTAP-III, connective tissue-activating peptide III; CYC1, cytochrome C1; DLD, dihydrolipoamide dehydrogenase; DLAT, dihydrolipoamide acetyltransferase; DSTN, destrin; DCN, decorin; ECM, extracellular matrix; EEA, early endosomal antigen; ENO, enolase; FABP, fatty acid binding protein; FASN, fatty acid synthase; FBLN1, fibulin-1; FBN1m fibrillin-1; FGA, fibrinogen alpha chain; FGB, fibrinogen beta chain; FGG, fibrinogen gamma chain; FH, fumarate hydratase; FLN, filamin; FN1, fibronectin 1; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GC, vitamin D-binding protein; GNA, guanine nucleotide-binding protein alpha subunit; GNB, guanine nucleotide-binding protein beta subunit; GPX, glutathione peroxidase; GPD, glycerol-3-phosphate dehydrogenase; GSR, glutathione reductase; GST, glutathione S-transferase; GSN, gelsolin; HAD, hydroxyacyl-CoA dehydrogenase; HMOX, heme oxygenase; HP, haptoglobin; HPX, hemopexin; HRP, haptoglobin-related protein; HSPG, heparin sulfate proteoglycan (perlecan); IDH, isocitrate dehydrogenase; IFNG, interferon gamma; IL6, interleukin 6; IL8, interleukin 8; IL10, interleukin 10; ITG, integrin; ITIH, inter-alpha-trypsin inhibitor family; KNG1, kininogen 1; LAM, laminin; LAMP, lysosomal associated membrane protein; LP, lipoprotein; LC3B, mitotubule-associated protein 1A/1B light chain 3B; LCAT, lecithin-cholesterol acyltransferase; LCN2, lipocalin-2; LDH, lactate dehydrogenase; LDLR, low-density lipoprotein receptor; LGALS, gelactin family; LPA, lipoprotein A; LRG1, leucine-rich alpha-2-glycoprotein 1; LRP1, low-density lipoprotein receptor-related protein 1; LUM, lumican; MCM, minichromosome maintenance complex; MDH, malate dehydrogase; MMP, matrix metalloproteinase; MRE11A; meiotic recombination 11 homolog A; MTMT, myotubularin phosphatase family; MYH, myosin heavy chain; NBN, nibrin; NDUF, NADH dehydrogenase (ubiquinone) subunits; NID1, nidogen-1; NQO1, NAD(P)H quinone dehydrogenase 1; NPC1, Neimann-Pick C1 protein; OGDH, oxoglutarate dehydrogenase; ORM, orosomucoid; PCK, pyruvate kinase; PDH, pyruvate dehydrogenase; PF4, platelet factor 4; PGAM, phosphoglycerate mutase; PGK1, phosphoglycerate kinase; TPI1, phosphoglyerate triosephosphate isomerase; Pikfyve, phosphatidylinositol-3-phosphate 5-kinase; PLOD, procollagen-lysine dioxygenase; PLG, plasminogen; PLTP, phospholipid transfer protein; PON, paraoxosane; PS, protein S; RAB, Ras-associated binding protein; RBP, retinol-binding protein 4; ROS, reactive oxygen species; S100A8, S100 calcium binding protein A8; S100A9, S100 calcium binding protein A9; SCP2, sterol carrier protein 2; SDH, succinate dehydrogenase; SERPIN, serine protease inhibitor family; SQSTM1, sequestosome 1; SPARC, secreted protein acidic and rich in cysteine (osteonectin); SOD, superoxide dismutase; TCA, tricarboxylic acid cycle; TF, transferrin; TFPI, tissue factor pathway inhibitor; THBS, thrombospondin; TIMP, tissue inhibitors of metalloproteinase; TLN1, talin-1; TNF, tumor necrosis factor; TP53, tumor protein 53; TPI, triosephosphate isomerase; TPM, tropomyosin; TTR, transthyretin; TXN, thioredoxin; TXNRD, thioredoxin reductase; UQCR, ubiquinol-cytochrome c reductase comples; VTN, vitronectin; VIM, vimentin; VPS, vacuolar protein sorting-associated protein; XRCC, X-ray repair cross complementing protein family Certainty of Evidence The GRADE assessment rated the overall certainty as “moderate” for S100A8. Downgrading was primarily due to small sample sizes and potential publication bias. Nevertheless, consistency across independent studies, biological plausibility, and pathway convergence support the robustness of main findings (Table 4) . Table 4: GRADE Assessment of Protein S100A8 across Studies S100A8 2 Consistent Discovery proteomics: SMD = 2.72; p < 0.001 Moderate Downgraded Not downgraded Suspected Moderate 4. Discussion This systematic review represents the first comprehensive synthesis of plasma proteomic alterations in pediatric acute lymphoblastic leukemia (ALL), offering an integrated view of disease-associated protein signatures across global cohorts. By consolidation evidence from nine independent studies spanning more than fifteen years of research, we identified 679 unique plasma proteins differentially expressed between children with ALL and controls. Despite considerable methodological diversity, recurrent pattern emerged, highlighting consistent dysregulation of acute-phase, lipid transport, and immune-regulatory proteins. Across studies, ceruloplasmin, haptoglobin, alpha-1-antritrypsin, fibrinogen alpha chain, and S100A8/A9 were consistently upregulated, whereas apolipoprotein A1, transthyretin, and gelsolin were typically downregulated (14–16,20,21,23,25). Meta-analysis confirmed a significant pooled upregulation of S100A8 (SMD = 2.72, 95% CI: 1.14-4.29; p < 0.001; I 2 = 0%), supporting its robustness as a reproducible biomarker of disease activity (23,25). Functional enrichment revealed convergence on complement and coagulation cascades, lipid metabolism, and inflammatory signaling, indicating a systemic reprogramming of immune-metabolic pathways in pediatric ALL. The identified protein signatures align with established pathogenic mechanisms of ALL and reflect both leukemia-intrinsic biology and host systemic responses. Elevated acute-phase proteins such as ceruloplasmin and haptoglobin are hallmarks of sustained inflammation and oxidative stress, pathways that facilitate leukemic proliferation via cytokine-mediated signaling and redox imbalance (26,27). Similarly, increased alpha-1-antitrypsin and fibrinogen subunits suggest heightened protease inhibition and coagulation activity, consistent with prothrombotic milieu observed in leukemia and other hematologic malignancies (28–32). In contrast, decreased apolipoprotein A1 and transthyretin levels imply disrupted lipid transport and altered metabolic homeostasis, potentially reflecting cachexia, impaired hepatic function, or metabolic shifts accompanying leukemogenesis (33–35). Downregulation of gelsolin, a key cytoskeletal and apoptotic regulator, further supports the hypothesis of cytoskeletal remodeling and resistance to programmed cell death, both crucial in leukemic cell persistence and treatment resistance (36,37). Among the recurrently altered proteins, the consistent elevation of S100A8/A9 is particularly noteworthy. These calcium-binding alarmins serve as pivotal mediators of inflammation and myeloid activation, amplifying leukocyte recruitment and reactive oxygen species generation. Within the leukemic microenvironment, they contribute to niche remodeling, promote blast proliferation, and moderate chemotherapy resistance through NF-κB and MAPK signaling (38–40). The reproducibility of S100A8 across independent datasets underscores its potential as both a diagnostic and prognostic biomarker, and possibly a therapeutic target in pediatric ALL. The proteomic alterations identified here are consistent with previous transcriptomic, metabolic, and cytokine studies indicating a strong interplay between inflammation, metabolic reprogramming, and leukemogenesis in pediatric ALL. Multi-omics integration studies have similarly reported enrichment of acute-phase reactant, apolipoproteins, and coagulation factors as discriminative features distinguishing ALL from controls and correlating with disease burden and treatment response (41–43). However, earlier investigation yielded variable findings due to differences in sample type, proteomic platforms, and analytical thresholds. By systemically integrating data across studies, this review clarifies which protein signatures are reproducible, particularly those related to inflammatory and lipid metabolic pathways, thereby refining the collective understanding of ALL-associated proteomic changes. Furthermore, parallels with adult ALL and other hematologic malignancies suggest that systemic inflammation and metabolic dysregulation are shared hallmarks of leukemogenesis (44,45). Nonetheless, the strong S100A8 signal observed in pediatric cohorts may reflect distinct immune-metabolic dynamics of childhood leukemia, potentially influenced by developmental immunity and age-dependent host-tumor interactions. While our synthesis highlights reproducible systemic alterations, most notably the pooled evaluation of S100A8, these findings do not resolve mechanistic causality in leukemogenesis. The proteomic literature is dominated by discover-phase studies with variable platforms, small cohort, inconsistent pre-analytical workflows, and divergent analytical thresholds; together these generate fragmentary overlap across studies and limit direct inference to specific oncogenic pathways. Consequently, although acute-phase/inflammatory and immune-metabolic signatures recur, the available evidence is better suited to nominate candidate biomarkers and hypotheses than to define definitive, leukemia-driving mechanisms. This caveat should temper translational claims and motivate targeted mechanistic and longitudinal validation prior to clinical implementation. A comparison with existing synthesis shows a similar pattern. Pan-cancer meta-analyses and proteomic syntheses report robust associations between S100A8/A9 and adverse biological phenotypes, consistent with our pooled S100A8 signal and supporting its role as a reproducible inflammatory marker across malignancies (40,46). Conversely, systemic synthesis of proteomic data in pediatric neuro/CSF or other small-sample domains highlight the same methodological heterogeneity and emphasize limited cross-sectional concordance, a limitation that our review also encountered (9,47). Taken together, these comparisons indicate that while S100A8/A9 and acute-phase proteins repeatedly surface as disease-associated signals, the field currently lacks the standardized, high-powered proteomic meta-analyses necessary to move from association to mechanism in pediatric ALL. The appropriate next steps are (1) harmonized, multi-center targeted-proteomics studies (e.g., PRM/SRM panels of S100A8/A9, ceruloplasmin, gelsolin, APOA1, TTR) with prospectively collected, well-annotated clinical cohorts; (2) functional studies that interrogate whether these circulating proteins are passive host responses or active modulators of leukemic cell biology (e.g., S100A8/9 effect on blast proliferation, survival and NF-κB); and (3) integrated multi-omics analyses that can test whether plasma signatures reflect tumor-intrinsic processes or systemic host reactions (46,48). 5. Conclusion Our pooled synthesis identifies a reproducible plasma proteomic signature in pediatric ALL, dominated by acute-phase, inflammatory and metabolic proteins with S100A8 as the most robustly replicated signal. However, methodological heterogeneity and the predominance of exploratory studies preclude definitive claims about leukemogenic mechanisms. There remains a substantial gap between discovery-phase associations and mechanistic proof; targeted, harmonized, prospective validation, and experimental interrogating are required to determine whether using circulating proteins are passive markers of disease be considered hypothesis-generating leads for mechanistic and translational research rather than established mechanistic pathways. Ethics Approval and Consent to Participate Not applicable. This study is based entirely on previously published research and publicly available data, and did not involve direct interaction with human participants; therefore ethical approval was not required. Consent to Participate Not applicable. This study did not involve direct human participation or collection of individual data; all data were obtained from previously published studies. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Human Ethics and Consent to Participate Declarations Not applicable. This study involved analysis of data from previously published literature, and no new data were collected from human participants. References 1. Simbaña-Rivera K, Torres-Roman JS, Julca-Marin D, Guerrero J, Quispe-Vicuña C, Guerrero González JA, et al. Mortality by Childhood Acute Lymphoblastic Leukemia: A Regional Analysis in Peru and Ecuador. Asian Pac J Cancer Prev. 2025;26(8):2879–87. 2. Wang L, Yao X, Yang L. 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Keywords all leukemia pediatric hematology/oncology Authors Affiliations Najia Tabassum Ziauddin University View all articles by this author Talat Mirza Ziauddin University View all articles by this author Shahzad Ali Jiskani 0000-0002-8014-3743 [email protected] Ziauddin University View all articles by this author Metrics & Citations Metrics Article Usage 235 views 96 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Najia Tabassum, Talat Mirza, Shahzad Ali Jiskani. Plasma Proteomic Profiles in Pediatric Acute Lymphoblastic Leukemia: A Systematic Review and Meta-Analysis. Authorea . 13 November 2025. DOI: https://doi.org/10.22541/au.176301370.06139412/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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