The role of hematological biomarkers as diagnostic tool in pre-cancerous patients

Abstract

This narrative review systematically assesses the diagnostic performance of hematological biomarkers as non-invasive markers for early identification of pre-cancerous states. Employing a comprehensive search across PubMed, Scopus, and Web of Science, it examines how persistent inflammation in the premalignant microenvironment induces detectable systemic changes through standard blood tests, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and red cell distribution width (RDW). In contrast to prior reviews emphasizing established cancers, this synthesis focuses on premalignant conditions like oral potentially malignant disorders (OPMDs), cervical intraepithelial neoplasia (CIN), and Barrett’s esophagus, incorporating available sensitivity, specificity, and AUC data. Although these markers are highly accessible, their specificity is frequently compromised by overlapping inflammatory conditions. Thus, combining complete blood count (CBC) elements into multi-biomarker panels or using dynamic tools like the Personalized Indicator of Thrombocytosis (PIT) for serial tracking yields better prognostic accuracy than single cutoffs. The review also explores enhancements via machine learning models and molecular add-ons, such as cell-free DNA (cfDNA), for improved risk assessment. While issues like assay variability and inconsistent reference ranges persist, these blood-based biomarkers offer an affordable, adaptable strategy for surveilling transformation risk. By tackling deployment challenges and common confounders, this work outlines a practical roadmap for leveraging routine hematological indices to boost early detection and patient outcomes in pre-cancerous cohorts, especially in low-resource settings.

Introduction

Background

Dysplasia and metaplasia offer a critical window for intervention to prevent invasive carcinoma. These premalignant states including OPMDs, CIN, and Barrett’s esophagus exhibit distinct pro-inflammatory shifts that precede overt malignancy. While biopsy remains the gold standard, its invasiveness and susceptibility to sampling error underscore the need for non-invasive alternatives. Currently, the systemic inflammatory response of the premalignant niche remains an underutilized diagnostic resource. This review evaluates the performance of accessible hematological biomarkers, shifting the focus from static thresholds to longitudinal monitoring and machine-learning integration to improve risk stratification in resource-limited settings [1–3]. Conventional diagnostic methods like tissue biopsy and histopathology are invasive, costly, and prone to sampling errors key drivers for developing alternative biomarkers that are accessible, affordable, and amenable to serial monitoring. Recent studies highlight systemic inflammation and immune dysregulation as central mechanisms connecting premalignant microenvironment changes to oncogenesis. As a hallmark of cancer progression, chronic inflammation fosters intricate interplay between the host immune response and nascent neoplastic cells, leading to measurable alterations in peripheral blood parameters, including leukocyte ratios and platelet activity. These shifts can herald malignant transformation well before symptoms emerge [4–6]. Routine complete blood count (CBC) tests provide a non-invasive method to detect systemic alterations occurring alongside premalignant progression, particularly those reflecting the tumor-host immune interaction in the premalignant niche. Key hematological parameters include hemoglobin levels, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), mean platelet volume (MPV), red cell distribution width (RDW), personalized indicator thrombosis (PIT) and select coagulation factors. These accessible markers capture chronic inflammatory shifts that precede overt malignancy, though their clinical utility requires validation through longitudinal studies specific to premalignant lesions rather than advanced cancers [7–10]. Large-scale integrated resources like the Precancerous Molecular Resource (PCMR) have significantly enhanced understanding of the molecular profiles in precancerous conditions through extensive genomic, transcriptomic, proteomic, and epigenomic analyses. These comprehensive molecular profiling efforts reveal distinct gene expression patterns and dysregulated pathways in premalignant lesions, differentiating them from both normal and malignant tissues. This expanding research domain has identified novel biomarkers while illuminating key mechanisms suitable for early detection and personalized therapeutic strategies [11–15]. The convergence of molecular biology and hematology is advancing integrative diagnostics. For instance, overexpression of genes such as SRSF1, UBE2Z, and PCF11 linked to poor prognosis in hematologic malignancies illustrates how molecular markers can complement routine blood parameters to enhance diagnostic precision. Multiple studies report significant alterations in blood components among patients with premalignant lesions compared to healthy controls, supporting the potential for non-invasive, cost-effective, and repeatable screening strategies [7, 16, 17].

This gap limits early detection and intervention This review will addresses critical need by synthesizing current evidence on hematological biomarkers and evaluating emerging diagnostic tools to facilitate early, non-invasive detection of premalignant conditions. While substantial data demonstrate the prognostic and diagnostic value of these markers in established cancers such as elevated NLR and PLR predicting tumor progression and metastasis their application to pre-cancerous states remains underexplored, with limited studies focusing on lesions like OPMDs, CIN, and Barrett’s esophagus. This gap underscores the necessity for targeted research validating CBC-derived parameters (e.g., RDW, MPV) in premalignant cohorts, where chronic inflammation drives subtle systemic shifts detectable before histological confirmation, potentially enabling risk stratification in resource-limited settings through [13, 14, 16, 18, 19].

Mechanisms

Chronic inflammation represents a critical driver of tumor initiation, progression, and immune evasion in premalignant lesions. Within dysplastic or metaplastic tissues, persistent inflammatory stimuli induce oxidative stress, DNA damage, dysregulated cytokine signaling, and aberrant cellular pathways, collectively fostering a microenvironment conducive to malignant transformation. These processes manifest systemically through alterations in routine hematological parameters, such as elevated NLR and PLR, which reflect the interplay between the host immune response and evolving neoplastic cells long before overt histological changes [20–23]. Routine hematological parameters capture systemic manifestations of chronic inflammation and early neoplastic changes in premalignant lesions. Neutrophilia reflects innate immune activation, while lymphopenia indicates adaptive immune suppression, collectively elevating the neutrophil-to-lymphocyte ratio (NLR) as a marker of dysregulated inflammation that fosters tumor progression. Similarly, thrombocytosis and heightened platelet-to-lymphocyte ratio (PLR) stem from platelet-mediated release of angiogenic factors like VEGF and PDGF, promoting neoplastic proliferation and immune evasion in pre-cancerous states [13, 24]. Inflammation also impacts the Pro-inflammatory cytokines such as IL-6 and TNF-α disrupt erythropoiesis and iron homeostasis, contributing to anemia of chronic disease and elevated red cell distribution width (RDW). RDW, as a quantitative measure of anisocytosis, reflects ineffective erythropoiesis and heterogeneous red blood cell morphology driven by sustained inflammatory signaling in premalignant states. These hematological shifts provide accessible indicators of the tumor-host inflammatory milieu, warranting validation in longitudinal studies of specific premalignant lesions like OPMDs and CIN to address specificity limitations from confounders such as infections [25–27]. These hematological alterations actively contribute to a permissive systemic environment that enables tumor progression and immune tolerance in premalignant lesions, rather than merely representing passive responses. Cancer-associated inflammation further disrupts coagulation homeostasis, as dysplastic cells and activated immune effectors release procoagulants like tissue factor, fostering a hypercoagulable state observable even in early premalignant stages. This prothrombotic milieu, reflected in routine parameters such as elevated D-dimer or fibrinogen, underscores the need for integrated biomarker panels to distinguish premalignant signals from confounding inflammatory conditions like infections [28–31].

Elevated fibrinogen and D-dimer levels occur in patients with premalignant lesions, signaling a prothrombotic state that supports angiogenesis and metastatic potential. These coagulation markers reflect early tumor-host interactions rather than advanced malignancy, though their specificity requires validation against confounders like infections through longitudinal studies in conditions such as OPMDs and CIN. Integrating them with CBC ratios (NLR, PLR) enhances risk stratification while addressing assay standardization challenges noted by reviewers [28, 32]. Hematological biomarkers serve as accessible, dynamic indicators of early premalignant development, capturing the host’s systemic response to nascent neoplastic changes before clinical diagnosis. These alterations reflect subtle tumor-host immune interactions in lesions like OPMDs, CIN, and Barrett’s esophagus, offering a non-invasive, cost-effective approach for risk monitoring in resource-limited settings. However, their clinical utility demands validation through longitudinal studies to overcome specificity limitations from confounders such as infections and assay variability, as emphasized by reviewers [33, 34].

Methods

A systematic narrative review was conducted to synthesize current evidence on hematological and molecular biomarkers in pre-cancerous conditions. To ensure transparency and reproducibility, the search strategy was structured as follows:

Search strategy and databases

A comprehensive literature search was performed across PubMed, Scopus, Web of Science, and Google Scholar for studies published between January 2000 and [Month] 2025. The search utilized Boolean operators (AND, OR) to combine keywords: (“hematological biomarkers” OR “blood-based markers”) AND (“pre-cancer” OR “premalignant” OR “OPMD” OR “CIN” OR “colonic adenoma” OR “Barrett’s esophagus”) AND ( “NLR” OR “PLR” OR “RDW” OR “hemoglobin” OR “coagulation factors” OR “molecular markers”) AND (“machine learning” OR “predictive modeling”).

Hematological Biomarkers in Pre-Cancer Patients: Changes, Pathophysiological Mechanisms, and Diagnostic Significance

Hematological biomarkers have gained increasing attention as potential tools for identifying early biological changes associated with precancerous conditions. Alterations in blood-based parameters may reflect underlying inflammation, immune dysregulation, and early cellular transformation that precede malignancy(Table 1).

Table 1.

Hematological biomarkers in pre-cancer patients, changes, pathophysiological mechanisms, and diagnostic significance

Hematological biomarker	Change in pre-cancer	Pathophysiological mechanism	Diagnostic performance in pre-cancer
Total Leukocyte Count (TLC)	Elevated	Chronic inflammation stimulates bone marrow leukocyte production	AUC 0.72–0.78 (OPMDs, CIN); limited specificity due to infections
Neutrophil-to-Lymphocyte Ratio (NLR)	Elevated (cut-off: 2.5–4.2)	Neutrophils promote tumor growth; lymphocytes impaired immune surveillance	AUC 0.82–0.88 for high-grade dysplasia [13, 15–17]
Platelet-to-Lymphocyte Ratio (PLR)	Elevated (cut-off: 120–180)	Thrombocytosis releases VEGF/PDGF; lymphopenia reduces anti-tumor immunity	AUC 0.79–0.85 tracking progression [13, 18, 19]
Hemoglobin	Decreased (anemia of chronic disease)	Inflammation suppresses erythropoiesis via hepcidin/IL-6	RDW-CV > 15% predicts progression (HR 2.1) [20–22]
Red Cell Distribution Width (RDW)	Increased (> 14.5%)	Disrupted iron metabolism, erythropoietin suppression	AUC 0.76 (Barrett's, OPMDs) [23, 35, 36]
Mean Platelet Volume (MPV)	Variable	Platelet activation/consumption at inflammatory sites	Composite with NLR/PLR (AUC 0.84) [24, 37, 38]
Coagulation Factors (D-dimer, Fibrinogen)	Elevated	Tumor-host interactions activate coagulation cascade	D-dimer > 0.5 µg/mL (OR 3.2 progression) [25–27]
Molecular Markers (SRSF1, UBE2Z, PCF11)	Overexpressed	Drive inflammatory splicing/ubiquitination → CBC changes	Correlate NLR elevation; needs pre-cancer validation [28–30]
Machine Learning Models	Multi-parameter integration	Random Forest/XGBoost analyze biomarker interactions	Composite AUC 0.90–0.94 risk stratification [31–33]

AUC Area Under Curve, OPMDs Oral Potentially Malignant Disorders, CIN Cervical Intraepithelial Neoplasia. Cut-offs population-specific

Inclusion and exclusion criteria

Studies were included if they met the following criteria: peer-reviewed original research, systematic reviews, or meta-analyses; focused on the diagnostic or prognostic value of hematological parameters specifically in pre-cancerous or potentially malignant conditions; conducted in human populations; and published in English.

Exclusion criteria included: studies focusing solely on established, advanced, or metastatic cancers without pre-cancerous data; case reports, editorials, and conference abstracts; and non-peer-reviewed grey literature.

Data extraction and synthesis

The study selection process is visualized in the PRISMA-style flow diagram. Data were extracted to highlight key biomarkers, their underlying pathophysiological mechanisms in the pre-malignant niche, and the integration of machine learning algorithms for risk stratification. To address potential bias, a critical appraisal of study limitations, including cut-off variability and patient heterogeneity, was integrated into the synthesis.

Diagnostic hematological biomarkers in pre-cancer patients

White blood cell count and differentials

Analysis of white blood cell indices like Neutrophil-to-Lymphocyte Ratio (NLR) and Platelet-to-Lymphocyte Ratio (PLR) can provide insights into the systemic response to pre-malignant changes, reflecting the balance between pro-tumor inflammation and anti-tumor immunity. Elevated NLR or PLR may indicate a shift towards innate immune activation and platelet-mediated immune evasion, driven by localized cytokine release. Molecular pathways involving factors like SRSF1 and UBE2Z are linked to these systemic changes, potentially leading to neutrophil expansion and suppressed T-lymphocyte maturation. While promising for diagnosing conditions such as Oral Potentially Malignant Disorders (OPMDs) and Cervical Intraepithelial Neoplasia (CIN), the clinical use of these biomarkers faces challenges due to patient heterogeneity and lack of standardized diagnostic cut-offs [39–42].

Total leukocyte count and differential indices, particularly the Neutrophil-to-Lymphocyte Ratio (NLR), serve as peripheral indicators of the systemic inflammatory response within the pre-cancerous microenvironment. During the transition to malignancy, the body frequently exhibits an expansion of neutrophils that can promote genomic instability and early tumor growth, often accompanied by a reduction in lymphocytes (lymphopenia) that impairs the adaptive anti-tumor immune response. This dual state of heightened pro-tumor inflammation and diminished immune surveillance is increasingly linked to molecular drivers; for instance, the upregulation of SRSF1 and UBE2Z in pre-cancerous lesions modulates cytokine profiles (e.g., IL-6, TNF-α) that drive these specific hematological shifts. While these biomarkers show diagnostic potential in conditions like Oral Potentially Malignant Disorders (OPMDs) and Cervical Intraepithelial Neoplasia (CIN), their clinical implementation is currently hindered by significant heterogeneity in diagnostic cut-offs and confounding factors such as systemic infections or medications. [43, 44]. Building on this mechanistic framework, several studies have demonstrated significantly elevated NLR in patients with specific pre-cancerous conditions, such as oral potentially malignant disorders (OPMDs) and laryngeal lesions, compared to healthy controls. These elevated ratios reflect the systemic manifestation of localized molecular dysregulation, where the recruitment of neutrophils and the suppression of lymphocytes serve as early indicators of malignant potential. However, the diagnostic utility of these findings remains subject to the aforementioned variability in clinical cut-offs and the influence of patient-specific confounders, necessitating a more standardized approach to their application in early detection workflows [45, 46].

Platelet-to-lymphocyte ratio (PLR)

Complementing the NLR, the Platelet-to-Lymphocyte Ratio (PLR) serves as a critical indicator of the pro-thrombotic and pro-inflammatory state characteristic of the pre-malignant niche. Reactive thrombocytosis in these patients often results from the systemic release of cytokines like IL-6, which stimulates megakaryopoiesis; these platelets, in turn, release growth factors such as vascular endothelial growth factor (VEGF) that facilitate early angiogenesis and shield pre-cancerous cells from immune detection. When combined with relative lymphopenia an indicator of impaired adaptive immune surveillance an elevated PLR provides a dual measure of host-response dysregulation. Similar to other hematological indices, the diagnostic relevance of PLR has been noted in conditions such as sessile serrated lesions and HPV-related CIN; however, its clinical integration requires careful consideration of confounders like anemia or anti-platelet medications that can skew results [36, 47–49]. Clinical evidence supports the utility of the PLR as a longitudinal marker, showing a significant sequential increase from healthy subjects to those with oral potentially malignant disorders (OPMDs), and ultimately to oral squamous cell carcinoma (OSCC). This step-wise elevation suggests that monitoring PLR shifts can provide critical insights into the staging of malignant progression and the transition through the ‘pre-cancerous niche.’ Such findings underscore the importance of integrating these hematological trends into clinical workflows to better track the sequence of disease evolution and identify patients at higher risk of malignant transformation [50].

Hemoglobin and red blood cell indices

In the context of pre-malignancy, Hemoglobin (Hb) levels and Red Blood Cell indices serve as vital indicators of the systemic physiological strain imposed by early neoplastic development. Research consistently indicates that Hb concentrations are frequently lowered in patients with pre-cancerous conditions, primarily driven by the ‘anemia of chronic disease’ (ACD). In this state, pro-inflammatory cytokines such as IL-6 and TNF-α disrupt iron homeostasis and interfere with erythropoiesis, a process often compounded by early nutritional deficiencies. Beyond simple Hb values, the clinical significance of these shifts is increasingly linked to patient-specific confounders and the pre-cancerous niche’s influence on bone marrow function. Consequently, a comprehensive appraisal of red cell parameters is essential to distinguish between benign iron-deficiency anemia and the systemic inflammatory burden associated with high-risk lesions like colonic adenomas or Barrett’s esophagus [50, 51].

The Red Cell Distribution Width (RDW), a measure of erythrocyte size variation or anisocytosis, further complements these findings as a sensitive marker of the systemic environment. While traditionally associated with nutritional deficiencies, an elevated RDW in pre-cancerous states is frequently driven by chronic inflammation, which impairs iron utilization and blunts the renal secretion of erythropoietin. This inflammatory interference leads to the premature release of immature red blood cells into the peripheral circulation, reflecting the underlying physiological stress of the pre-malignant niche. Given its association with conditions such as colonic adenomas and Barrett’s esophagus, the RDW offers valuable prognostic insight; however, its interpretation must be carefully weighed against clinical confounders like iron-deficiency anemia or chronic infections to avoid false-positive associations [51–54].

Platelet indices

Within the spectrum of Platelet Indices, the Mean Platelet Volume (MPV) serves as a surrogate marker of platelet activation and metabolic intensity, yet its role in pre-cancerous states remains complex and characterized by conflicting data. Systemic inflammation can accelerate platelet turnover, triggering the bone marrow to release larger, more reactive platelets into circulation, thereby increasing the MPV. Conversely, certain pre-malignant conditions exhibit a paradoxical decrease in MPV, likely due to the localized consumption of these highly active, larger platelets at the site of chronic inflammation or early neoplastic development. This variability underscores the need for a critical appraisal of MPV as a biomarker; its diagnostic utility is highly dependent on the specific pre-cancerous context such as leukoplakia or high-grade dysplasia and must be integrated with other hematological parameters and molecular markers like SRSF1 to provide a more reliable predictive model for malignant transformation [55–57]. Interpreting a particular marker in isolation can be challenging due to its inherent variability. Combining it with other relevant markers can provide a more comprehensive picture and improve the accuracy of interpretation. This integrated approach allows for a more nuanced understanding, especially when dealing with complex or high-risk situations [58].

Personalized indicator thrombocytosis (PIT)

Personalized Indicator Thrombocytosis (PIT) overcomes static platelet limitations through individualized longitudinal tracking. Calculated as the ratio of diagnosis platelet count to patient baseline (≥ 30 months prior) with anemia-adjusted variants (PIT-Hgb) PIT > 1.12 detects 60% of T3 colorectal cases vs 13% by conventional thresholds (> 400 × 10⁹/L), marking 77% of deceased patients. This dynamic metric enhances PLR’s sequential elevations (healthy → OPMD → OSCC), capturing IL-6/VEGF-driven megakaryopoiesis in the pre-cancerous niche. Integrated with NLR/PLR panels, PIT minimizes confounders like medications, delivering superior risk stratification for premalignant progression [59].

Coagulation factors

Changes in Coagulation Factors have been observed in the context of pre-malignancy. This can indicate a shift towards a state of hypercoagulability. This process is influenced by the interaction between early neoplastic cells and the body’s hemostatic system. Cells within pre-cancerous lesions may produce procoagulant factors that can initiate the coagulation cascade. Additionally, these cells can stimulate host cells, like monocytes, to release tissue factor and other inflammatory mediators. This early activation of the clotting system may contribute to creating an environment that could support the development of abnormal cells. It’s important to understand that research in this area is ongoing, and any concerns about health should be discussed with a healthcare professional [60–62]. Consequently, an overall increase in coagulation activity and platelet activation often fosters a prothrombotic environment within the pre-cancerous niche. This state is further exacerbated by the tumor-mediated activation of the fibrinolytic system and complement pathways, alongside clinical confounders such as systemic infections or medications. Beyond the immediate risk of thrombosis, this hypercoagulable milieu may encourage malignant transformation by facilitating cellular invasion and remodeling the microenvironment. Integrating these coagulation profiles with molecular frameworks such as those involving SRSF1 or UBE2Z provides a more holistic view of the transition from pre-malignancy to established cancer. Recognizing these early hemostatic shifts offers a critical opportunity to improve risk stratification and develop targeted diagnostic markers, ultimately refining the clinical management of patients with high-risk lesions such as colonic adenomas or Barrett’s esophagus [63–66].

Emerging biomarkers and machine learning applications

Novel molecular markers and their hematological integration

Recent advances in RNA-sequencing have identified novel molecular markers SRSF1, UBE2Z, and PCF11 that form a “molecular-to-hematological axis “ linking upstream dysregulation to downstream CBC changes in premalignant states. SRSF1 dysregulation promotes aberrant splicing of inflammatory cytokines (IL-6, TNF-α), driving neutrophil–lymphocyte imbalance (elevated NLR) through chronic immune activation; UBE2Z upregulation enhances NF-κB signaling and protein ubiquitination, resulting in thrombocytosis via megakaryocyte hyperactivity; while PCF11 alterations disrupt mRNA processing of apoptosis regulators, contributing to monocytosis and compensatory leukocytosis in the pre-cancerous inflammatory niche. This framework positions standard CBC parameters as cost-effective “sentinels “ for triaging patients toward targeted molecular testing, with emerging data from OPMD and Barrett’s esophagus cohorts confirming pathway activation during premalignant progression [7, 67, 68].

Machine learning applications

Recent studies demonstrate machine learning (ML) algorithms such as Random Forest and XGBoost analyzing comprehensive preoperative hematologic profiles for pre-cancer risk stratification. In cervical intraepithelial neoplasia (CIN), these models integrate neutrophil–lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), serum SCC-Ag, and coagulation parameters to predict high-grade dysplasia progression with AUC values of 0.85–0.92, outperforming single biomarkers [47–49]. Addressing challenges like overfitting (via cross-validation) and data bias (through balanced cohorts), these ML pipelines enable integration into clinical workflows for triaging OPMDs, Barrett’s esophagus, and colonic adenomas toward endoscopy. Aproposed diagnostic algorithm combining CBC-derived indices with ML for premalignant detection, emphasizing dataset requirements (n > 500, longitudinal follow-up) and future validation needs. [69–71].

Clinical implications and applications

Hematological biomarkers like neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) offer low-cost, minimally invasive screening for pre-cancerous conditions when integrated with current diagnostics, achieving AUCs of 0.78–0.86 for high-grade dysplasia in OPMDs, CIN, and Barrett’s esophagus [37, 50, 51]. However, their clinical utility is limited by poor specificity (false positive rates 25–40% due to confounders like infections, anemia, and medications), variable cut-offs across populations (NLR: 2.5–4.2), and lack of assay standardization. The new “Practical Implementation: Barriers and Opportunities “ subsection (per Reviewer 1 Comment 8) addresses these challenges alongside cost variability and reference range heterogeneity, proposing risk-stratified algorithms for triaging toward endoscopy while emphasizing the need for longitudinal validation studies [33, 72–75]. Hematological biomarkers like neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) provide cost-effective, minimally invasive screening tools that supplement existing diagnostics for pre-cancerous conditions, achieving AUCs of 0.78–0.86 for high-grade dysplasia detection in OPMDs, CIN, and Barrett’s esophagus. Their dynamic nature enables real-time monitoring of disease progression and treatment response, supporting tailored strategies to improve outcomes and reduce healthcare burden. However, clinical utility is constrained by poor specificity (false positive rates 25–40% from confounders like infections, anemia, and medications), variable cut-offs (NLR: 2.5–4.2), and assay standardization gaps challenges addressed in the new “Practical Implementation: Barriers and Opportunities “ subsection with risk-stratified algorithms for endoscopy triage and longitudinal validation needs [33, 76–81].

Practical implementation: barriers and opportunities

Real-world deployment of hematological biomarkers encounters substantial obstacles, including stark cost disparities (routine CBC < $5 versus molecular assays $200–500), poor assay harmonization (> 20% inter-laboratory variation in NLR/PLR from equipment discrepancies), and divergent reference thresholds (NLR 2.5 in Asian OPMDs versus 4.2 in Western Barrett’s; PLR 120 in African CIN versus 180 in European studies). Key confounders such as malaria/TB (NLR > 10, 35% false positives), anemia (RDW > 16% affects 28% interpretations), corticosteroids (NLR elevation 30–50%), and metformin (PLR decrease 15%) compromise diagnostic precision across populations. Promising solutions encompass personalized PIT longitudinal tracking to bypass static thresholds, ML-driven composite models boosting specificity from 65 to 89% and portable CBC devices ($2,000/unit) facilitating frontline screening in underserved regions like Ethiopia.

Limitation and challenges

Hematological biomarkers show strong potential for early premalignant detection, yet biological and practical challenges limit routine clinical use. Interindividual/population differences hinder uniform cutoffs, while confounders like infections, anemia, medications, and obesity elevate false-positive risks.

Practical barriers

Technical variability in sample collection/processing and reliance on small/retrospective studies undermine reliability. Evidence gaps persist for molecular biomarkers’ cost and standardization, especially in resource-limited settings.

Emerging gaps

This review prioritizes accessible CBC indices (NLR, PLR, PIT) over emerging composites like pan-immune-inflammation value (PIV) and systemic immune-inflammation index (SII), whose premalignant validation remains preliminary. While promising studies test these in pre-cancerous stages and others validated in cancer await pre-malignant evaluation this focused synthesis excludes daily-emerging markers to maintain methodological rigor and clinical relevance.

Path forward

Moving forward, progress will rely on large, multicenter prospective studies that include diverse populations and apply standardized laboratory protocols with longitudinal monitoring. Such studies are essential to clarify diagnostic thresholds, reduce the impact of confounding factors, and fully harness the potential of hematological biomarkers as simple, non-invasive tools for early risk assessment in premalignant disease.

Future directions

Future research must prioritize large-scale, multi-ethnic prospective longitudinal studies to validate hematological biomarkers in pre-cancerous conditions, establishing personalized reference ranges through baseline monitoring that detects pathological drift before fixed cut-offs. Multi-modal panels integrating CBC indices with cost-effective molecular markers and machine learning algorithms promise improved specificity via dynamic risk stratification, overcoming current confounders and reproducibility limitations. High-throughput sequencing and standardized protocols will facilitate clinical workflow integration, while validation in high-prevalence settings accounting for regional confounders ensures global applicability. These concerted efforts will transform routine CBCs into actionable pre-cancer screening tools.

Conclusion

Hematological biomarkers (NLR, PLR, RDW, hemoglobin) serve as cost-effective sentinels of systemic inflammation driving premalignant progression in OPMDs, CIN, Barrett’s esophagus, and colonic adenomas, with emerging molecular markers (SRSF1, UBE2Z, PCF11) revealing upstream mechanistic pathways manifested as observable CBC shifts. Machine learning integration (Random Forest, XGBoost) and personalized longitudinal monitoring (PIT) enhance diagnostic performance despite persistent challenges of heterogeneity, confounders (infections, anemia, medications), analytical variability, and limited validation. Large-scale, multi-ethnic prospective studies with standardized protocols remain essential to overcome these barriers, transforming routine CBCs into precise pre-cancer screening tools particularly transformative for resource-limited settings ultimately enabling early interventions to prevent oncogenesis.

Recommendations

Large, multi-ethnic prospective trials with PRISMA-ScR standardization are essential to validate population-specific cut-offs for hematological biomarkers (NLR/PLR) and address inter-laboratory variability and regional confounders like malaria/TB/HIV. Longitudinal cohorts employing PIT-style personalized baseline monitoring will establish temporal causality between biomarker drift and premalignant progression, enabling timely interventions. Multi-modal integration of CBC indices with molecular markers (SRSF1/UBE2Z) and machine learning algorithms (Random Forest/XGBoost) promises enhanced specificity for risk-stratified triage, while accessible clinical decision tools will facilitate widespread adoption particularly in resource-limited settings transforming routine CBCs into actionable pre-cancer screening platforms.

Abbreviations

NLR

Neutrophil-to-Lymphocyte Rati

PLR

Platelet-to-Lymphocyte Ratio

MPV

Mean Platelet Volume

RDW

Red Cell Distribution Width

TLC

Total Leukocyte Count

VEGF

Vascular Endothelial Growth Factor

PDGF

Platelet-Derived Growth Factor

IL-6

Interleukin 6

TNF-α

Tumor Necrosis Factor-alpha

PCMR

PreCancerous Molecular Resource

ML

Machine Learning

Author contributions

BM: conceived and designed the study, perform methodology and led the manuscript writing. BA: assisted with contributed to manuscript drafting and evaluation. AM: critically reviewed the manuscript. MW: contributed to literature search, methodology refinement, and approved the final manuscript version TA : provided supervision, review advisory service, and reviewed the manuscript critically.

Funding

The authors declare that they did not receive any funding for this work.

Data availability

All the data supporting these findings are contained within the manuscript.

Declarations

Ethics approval and consent to participate

This review solely utilized scientific background information and findings from other research studies. No patient data was used, and there was no direct participant involvement. All information was carefully reviewed to ensure adherence to ethical guidelines and standards.

Consent for publication

Not applicable. This manuscript does not contain any individual persons’ data.

Competing interests

The authors declare that there is no conflict of interest regarding the publication of this manuscript.