Melatonergic Deficiency, Oxidative Stress, Microbiome Dysbiosis and CTC/cfDNA Progression Markers Across the Cancer Continuum: An Integrative Adult Biomarker Study

1. Introduction

Cancer development is increasingly understood as a continuum that begins before a clinically visible tumor emerges. The hallmarks of cancer include genome instability, inflammation, metabolic rewiring, immune escape and metastatic dissemination [1,2]. Oxidative stress can connect several of these hallmarks by damaging DNA, proteins and lipids, changing redox signaling and supporting inflammatory microenvironments that favor clonal selection [3,4,5,6,7,8,9].

Melatonin is classically recognized as a circadian hormone, but it also participates in antioxidant defense, mitochondrial regulation and immune modulation [10,11,12]. In parallel, the gut microbiome shapes inflammatory tone, epithelial barrier function and host metabolism; dysbiosis has been implicated in several cancer-related pathways [13,14,15,16,17,18,19]. The previously reported melatonin-microbiome materials describe low melatonin in cancer patients with severe dysbiosis and reduced Lactobacillus acidophilus and Saccharomyces boulardii, suggesting a biologically plausible melatonin-microbiome axis that may influence oxidative stress and secondary metabolites.

During progression, systemic biology becomes increasingly measurable through liquid biopsy. Circulating tumor cells (CTCs), cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA) are downstream markers of tumor shedding, minimal residual disease, treatment response and metastatic risk [20,21,22,23,24,25,26,27]. The previously reported CTC/cfDNA article described an 18-patient non-small cell lung cancer remission series in which two participants with high CTC counts had radiologically detectable metastatic lesions, supporting the clinical relevance of CTC surveillance as a progression-stage biomarker. This prior material is used here as translational context rather than as new primary data.

The current analysis uses the source adult biomarker workbook as the primary quantitative dataset. Because the workbook did not include cancer incidence, microbiome sequencing, CTC enumeration or cfDNA results, our goal was not to create a diagnostic assay. Instead, we tested whether reduced melatonergic status was associated with a coordinated inflammatory, oxidative and biological-aging phenotype that could reasonably be positioned upstream of microbiome disruption and downstream liquid-biopsy emergence in future prospective oncology studies.

Figure 1. Proposed melatonin-microbiome-oxidative stress-liquid biopsy continuum across cancer initiation, promotion and progression. CTCs/cfDNA are positioned as downstream progression markers requiring prospective integration with upstream melatonergic, microbiome and oxidative-stress measurements.

Figure 1. Proposed melatonin-microbiome-oxidative stress-liquid biopsy continuum across cancer initiation, promotion and progression. CTCs/cfDNA are positioned as downstream progression markers requiring prospective integration with upstream melatonergic, microbiome and oxidative-stress measurements.

2. Materials and Methods

2.1. Study Design and Data Source

This was an exploratory cross-sectional biomarker analysis of the deidentified workbook named 3BT_melatonin_biomarker_added_aging_FULL_CLEANED_REAL(4).xlsx. The workbook contained participant rows, demographic variables, plasma melatonin, 24-hour urinary 6-sulfatoxymelatonin (aMT6s), inflammatory markers, oxidative-stress markers, antioxidant markers and biological-aging/injury markers. To align the manuscript with an adult oncology context and reduce pediatric-consent complexity, six participants younger than 18 years were excluded. The primary analysis set comprised 314 adults.

No direct cancer diagnosis endpoint, microbiome sequencing data, CTC enumeration or cfDNA/ctDNA measurements were present in the adult workbook. Therefore, the workbook was analyzed as a pre-oncology systemic risk-biology dataset. Previously reported CTC/cfDNA and melatonin-microbiome publications were reviewed to frame the cancer continuum and to identify biological links requiring prospective validation.

2.2. Melatonergic Axis Definition

Plasma melatonin and 24-hour urinary aMT6s were treated as complementary markers of melatonergic status. A melatonergic-axis score was calculated as the mean of the rank percentiles of plasma melatonin and urinary aMT6s. Participants were categorized into tertiles: low melatonin/aMT6s, intermediate and preserved. This rank-based approach reduced dependence on assumptions about assay-specific normality and accommodated different measurement scales.

2.3. Biomarker Composites

To reduce multiple testing and to represent biological domains, biomarkers were grouped into four composite z-scores: inflammation, oxidative damage, antioxidant deficit and biological aging/injury. The inflammation composite included GDF-15, suPAR, IL-1β, IL-6, TNF-α, hsCRP and serum amyloid A. The oxidative-damage composite included MDA, 8-OHdG, F2-isoprostanes, AOPP, protein carbonyls, 3-nitrotyrosine, TOS and OSI. The antioxidant-deficit composite inverted the z-scores of TAC/TAS, SOD, GPx, catalase, GSH and GSH/GSSG ratio so that higher values reflected lower antioxidant defense. The biological-aging/injury composite included NT-proBNP, hs-troponin, NfL, GFAP, GlycanAge, GrimAge, DunedinPACE and age acceleration. The integrated oncogenic-stress score was the mean of the four domain composites.

2.4. Statistical Analysis

Continuous variables were summarized as mean ± standard deviation. Group differences across melatonergic tertiles were assessed with Kruskal-Wallis tests. Spearman correlation was used to assess monotonic associations between melatonin-related predictors and biomarker outcomes. Complete-case linear regression models assessed the association of a 1-SD higher melatonergic-axis score with standardized composite outcomes, adjusted for age, sex and BMI. Analyses were performed in Python using pandas, scipy and statsmodels. Because this was an exploratory secondary analysis without a cancer endpoint, p values were interpreted as indicators of association rather than as proof of causality or clinical diagnostic performance.

3. Results

3.1. Adult Cohort Characteristics

The adult analysis included 314 participants. The median age was 50 years (range 18-84). Sex was recorded as female in 192 participants and male in 121 participants; one adult participant had missing sex coding. The low, intermediate and preserved melatonergic tertiles included 105, 104 and 105 participants, respectively. Mean plasma melatonin increased from 4.18 ± 1.99 pg/mL in the low tertile to 22.46 ± 5.20 pg/mL in the preserved tertile; mean urinary aMT6s increased from 6.01 ± 2.29 to 32.97 ± 9.17 µg/24 h (Table 1).

3.2. Inflammatory and Oxidative Phenotypes Across Melatonergic Tertiles

Lower melatonergic status was accompanied by a consistent adverse gradient in inflammatory and oxidative biomarkers. For example, hsCRP was 5.05 ± 1.42 mg/L in the low tertile and 0.97 ± 0.56 mg/L in the preserved tertile; IL-6 was 4.93 ± 1.61 versus 1.18 ± 0.55 pg/mL; MDA was 5.00 ± 0.56 versus 2.54 ± 0.63 µmol/L; 8-OHdG was 10.25 ± 1.47 versus 4.49 ± 1.56 ng/mL; and OSI was 29.34 ± 6.58 versus 7.62 ± 3.75. In contrast, the GSH/GSSG ratio increased from 11.32 ± 2.48 in the low tertile to 60.74 ± 33.06 in the preserved tertile, consistent with stronger antioxidant buffering in preserved melatonergic status (Table 1).

3.3. Composite Biomarker Burden

Domain-level composite scores showed a stepwise pattern across melatonergic tertiles (Figure 2 and Table 2). The low melatonin/aMT6s tertile had the highest inflammation, oxidative-damage, antioxidant-deficit and biological-aging/injury scores, whereas the preserved tertile had the lowest scores. Kruskal-Wallis tests were significant for all four domains and the integrated oncogenic-stress score (all p < 0.001).

3.4. Correlation and Adjusted Regression Models

The melatonergic-axis score correlated strongly and inversely with the integrated oncogenic-stress score (Spearman rho = -0.944, p < 0.001; Figure 3). Similar inverse correlations were observed for inflammation (rho = -0.918), oxidative damage (rho = -0.909), antioxidant deficit (rho = -0.884) and biological aging/injury (rho = -0.956). In complete-case adjusted regression models (N = 250), a 1-SD higher melatonergic-axis score was associated with a 0.95-SD lower integrated oncogenic-stress score (95% CI, -0.98 to -0.92; p < 0.001; Figure 4 and Table 2). Associations were directionally consistent across all domain composites.

3.5. Translational Interface with Microbiome and Liquid-Biopsy Progression Markers

The adult workbook supports an upstream systemic pattern: low melatonin/aMT6s tracked with oxidative, inflammatory and aging/injury signals. The previously reported melatonin-microbiome materials support a second layer in which melatonin deficiency may co-occur with severe dysbiosis and reduced Lactobacillus acidophilus and Saccharomyces boulardii, while the previously reported CTC/cfDNA report supports a downstream layer in which CTCs and cfDNA can identify progression or metastatic recurrence. Together, these materials justify a prospective study in which melatonin/aMT6s, microbiome structure, oxidative-stress markers and CTC/cfDNA are measured longitudinally in cancer-risk and cancer-survivorship populations.

4. Discussion

This research article integrates the source biomarker dataset and prior translational materials into a single cancer-continuum model. In the adult Excel cohort, reduced melatonergic status was associated with a broad pro-inflammatory, pro-oxidative and biological-aging/injury phenotype. These findings are biologically coherent with the known antioxidant, immunomodulatory and mitochondrial roles of melatonin, and with evidence linking oxidative stress and chronic inflammation to cancer initiation and progression [1,2,3,4,5,6,7,8,9,10,11,12].

The findings do not establish that low melatonin causes cancer, that melatonin supplementation prevents cancer, or that the measured composite scores diagnose cancer. The workbook lacks cancer incidence, microbiome sequencing and liquid-biopsy endpoints. Nevertheless, the magnitude and consistency of the associations support the idea that melatonergic deficiency may identify a systemic environment compatible with oxidative DNA damage, impaired redox buffering, inflammatory activation and accelerated biological aging. This environment may increase susceptibility to initiation and promotion events, whereas CTC/cfDNA markers are more relevant to progression, minimal residual disease and metastatic dissemination [20,21,22,23,24,25,26,27].

The melatonin-microbiome axis is a particularly important bridge between early systemic biology and cancer-specific outcomes. Dysbiosis can alter epithelial barrier function, microbial metabolite production and immune tone [13,14,15,16,17,18,19]. The previously reported cancer-patient microbiome work reported severe dysbiosis with reduced Lactobacillus acidophilus and Saccharomyces boulardii in the setting of low melatonin, along with experimental observations that melatonin exposure influenced myrosinase-related activity and anti-proliferative effects. These claims require rigorous validation using standardized sequencing, metabolomics and reproducible cell-based assays, but they provide a plausible rationale for prospective testing.

The CTC/cfDNA axis should be positioned downstream in this model. CTCs and ctDNA do not generally define the earliest biological field of carcinogenesis; instead, they become clinically useful when tumor shedding, minimal residual disease or metastatic risk is present [20,21,22,23,24,25,26,27]. A combined prospective design could test whether melatonergic deficiency and oxidative/microbiome changes predict later CTC/cfDNA emergence in high-risk individuals or cancer survivors.

Strengths and Limitations

Strengths include use of a broad biomarker panel, adult-only pre-specified analysis, composite-domain modeling and explicit separation of primary statistical findings from contextual previously published materials. Limitations are substantial. The analysis is cross-sectional, secondary and hypothesis-generating. The workbook contains no direct cancer outcomes, no microbiome sequencing, no CTC/cfDNA measurements and no prospective follow-up. Some source materials were previously disseminated in low-visibility or non-mainstream venues, so the authors must confirm data provenance, permissions and novelty before journal submission. The adjusted regression models were complete-case analyses and may be affected by missingness. Finally, the high correlation structure may reflect biological coupling, data recovery methods, or both; independent validation is essential.

5. Conclusions

In this adult biomarker analysis, lower plasma melatonin and urinary aMT6s were strongly associated with higher inflammatory, oxidative-damage, antioxidant-deficit and biological-aging/injury burdens. The results support a testable model in which melatonergic deficiency marks an upstream systemic environment that may facilitate cancer initiation and promotion, whereas microbiome dysbiosis and CTC/cfDNA emergence represent intermediate and downstream layers of the cancer continuum. A prospective oncology study should measure melatonin/aMT6s, standardized microbiome profiles, oxidative-damage markers and liquid-biopsy endpoints simultaneously to determine whether this axis predicts cancer risk, recurrence or progression.