Inflammatory, Immune-Metabolic, and Vascular Biomarker Trajectories Across Treatment Modalities and Associations with Treatment-Related Complications in Oral Squamous Cell Carcinoma: A Prospective Study in Taiwan

Introduction

Oral squamous cell carcinoma (OSCC) represents a significant global health burden, with particularly high incidence and mortality rates in Asian populations.¹ Taiwan exhibits the highest age-standardized incidence among South and Southeast Asian countries.² While surgical resection remains the cornerstone of treatment, adjuvant cisplatin-based concurrent chemoradiotherapy (CCRT) is indicated for patients with high-risk pathological features.^3,4 In Taiwan, approximately 65% of patients present with advanced-stage disease requiring multimodality treatment.⁵ Recent evidence suggests that one-year maintenance therapy with tegafur–uracil (UFUR; 100 mg tegafur and 224 mg uracil per capsule) following adjuvant chemoradiotherapy may improve survival outcomes in Asian patients with advanced OSCC.⁶ Because UFUR prolongs systemic 5-fluorouracil exposure, extended treatment may also raise concerns regarding cumulative toxicity.

Treatment-related morbidity is substantial. Surgical complications occur in 20%–50% of cases,⁷ and clinically significant acute and late toxicities are observed after CCRT.^8,9 The clinical challenge is further compounded in populations with high betel quid consumption, such as in Taiwan, where approximately 85% of OSCC cases are associated with betel quid.¹⁰ Chronic betel quid exposure induces systemic inflammation, oxidative stress, and endothelial dysfunction,¹¹ potentially amplifying treatment-related inflammatory and vascular injury.¹²

Peripheral blood-based biomarkers offer accessible means for risk stratification and treatment monitoring. Among blood-derived indices, the neutrophil-to-lymphocyte ratio (NLR), systemic immune–inflammation index (SII), and prognostic nutritional index (PNI) reflect inflammatory, immune, nutritional, and metabolic status, supporting their use as immune-metabolic biomarkers in OSCC. These indices have been associated with treatment tolerance, postoperative complications, and survival outcomes in OSCC.^8,13 Hemoglobin further reflects nutritional reserve and oxygen-carrying capacity, and low pretreatment levels have been consistently associated with poor prognosis in OSCC.^14,15 Serum uric acid (UA), the terminal product of purine catabolism, may serve as a metabolic marker linking systemic inflammation with adverse outcomes, including distant metastasis in head and neck cancers¹⁶ and mortality among patients with cancer who develop chemotherapy-induced febrile neutropenia.¹⁷

Vascular function assessment complements these blood-based inflammatory and immune-metabolic markers and directly evaluates cardiovascular risk. Pulse wave velocity (PWV), a validated measure of arterial stiffness, has demonstrated value in cancer populations. Meta-analytic evidence indicates that anticancer therapies significantly increase arterial stiffness,¹⁸ and PWV measurements are consistently elevated in patients with cancer relative to age-matched controls.¹⁹

Despite the prognostic relevance of individual biomarkers, systematic longitudinal data on inflammatory, immune-metabolic, and vascular responses across multimodality OSCC therapy remain limited. How treatment intensity, from surgery alone to CCRT followed by UFUR maintenance, modulates these biomarkers during acute and extended phases is unclear. Additionally, while anticancer therapies increase arterial stiffness across malignancies, OSCC-specific evidence is limited. This exploratory study aimed to delineate the temporal patterns of these biomarkers across distinct treatment modalities, examine their associations with treatment-related complications, and provide an initial framework for risk-adapted toxicity monitoring in OSCC.

Materials and Methods

Study Design and Participants

Patients with newly diagnosed, pathologically confirmed OSCC at an academic medical center in eastern Taiwan between March 2024 and November 2025 were enrolled in this prospective longitudinal cohort study. Eligible participants were aged ≥ 40 years, had no history of coronary artery bypass surgery, and were able to provide informed consent. Exclusion criteria comprised distant metastasis, previous or concurrent malignancy, second primary tumor, uncontrolled comorbidities, or active infection. The study protocol was approved by the Institutional Review Board of Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation (IRB Number 112-261-B), and written informed consent was obtained from all participants.

Patients were stratified into three treatment groups based on the intended treatment intensity determined by the multidisciplinary tumor board prior to surgery, considering pathological risk factors and performance status. This classification reflected clinical judgment regarding disease aggressiveness and anticipated treatment needs. The surgery group underwent surgery alone. The CCRT group received surgery followed by CCRT comprising radiotherapy of 60–66 Gy to the primary tumor bed and 54–60 Gy to at-risk nodal regions, with weekly cisplatin at 25–40 mg/m² based on renal function, performance status, and treatment tolerance as determined by the treating physician. The CCRT with UFUR group received surgery and CCRT as described, followed by initiation of maintenance therapy with oral UFUR at 300–400 mg daily based on body surface area, with planned duration of one year. Although the CCRT and CCRT with UFUR groups received identical treatment during weeks 0–12, the three-group framework was retained to examine whether baseline characteristics associated with planned maintenance therapy influenced biomarker trajectories. All adjuvant treatments commenced within six weeks after surgery. Perioperative antibiotic prophylaxis was administered according to institutional protocol; however, prophylactic antibiotics were not routinely prescribed for treatment-related neutropenia during adjuvant therapy.

Study Procedure

Patients were evaluated at four time points: baseline (T0, before surgery), and 2 (T1), 12 (T2), and 24 (T3) weeks after surgery. The T3 visit corresponded to about 3 months after the start of UFUR maintenance therapy in the CCRT with UFUR group. At each visit, inflammatory and immune-metabolic biomarkers were obtained from routine blood tests, and arterial stiffness parameters, including arterial stiffness index (ASI) and brachial–ankle pulse wave velocity (baPWV), were measured concurrently. Although the CCRT and CCRT with UFUR groups received identical treatment during weeks 0–12, they were pre-specified as separate groups by the multidisciplinary tumor board based on distinct baseline risk profiles, and the T3 assessment was designed to capture the initial three months of UFUR exposure, enabling exploratory detection of early maintenance-related biomarker trajectories.

The primary endpoints were longitudinal trajectories of inflammatory, immune-metabolic, and vascular biomarkers. Secondary analyses explored baseline predictors of postoperative complications, infections, and CCRT-related toxicities with grade ≥ 3. All patients were followed for at least six months.

Sample size was estimated using the lmmPower function of the longpower package (R version 4.3.0), assuming a medium effect size (Cohen’s d = 0.5), four repeated measures, and 80% statistical power. The minimum required number of participants was 96. Considering an anticipated attrition rate of about 32% based on prior study,⁸ 142 participants were targeted. This sample size was considered adequate to capture temporal trends in biomarker changes and to maintain sufficient statistical precision for exploratory subgroup analyses. The allocation ratio of 1.5:1:1 reflected the higher prevalence of early-stage OSCC cases undergoing surgery alone in routine clinical practice.

Variables and Instruments

Baseline Demographics and Clinical Characteristics

Baseline data, including age at diagnosis, gender, lifestyle factors, tumor characteristics, and pathological features, were extracted from electronic medical records. The Charlson Comorbidity Index (CCI) was used to quantify comorbidity burden.²⁰ Nutritional and metabolic parameters assessed within 1–3 days before surgery included body mass index and lipid profile.

Inflammatory and Immune-Metabolic Biomarkers

Serial laboratory data were collected from complete blood count with differential and serum biochemistry panels at all four assessment time points. Albumin, hemoglobin, white blood cell, platelet, and differential leukocyte counts were recorded at each visit. Derived biomarkers were calculated using standard formulas as previously described:¹³ the neutrophil-to-lymphocyte ratio (NLR) was calculated as the ratio of the absolute neutrophil to lymphocyte counts; the prognostic nutritional index (PNI) was computed as the serum albumin concentration (g/L) plus five times the lymphocyte count (×10⁹/L); and the systemic immune-inflammation index (SII) was defined as the product of the platelet count and the NLR. Serum uric acid was measured from fasting blood samples and analyzed concurrently with other biochemical parameters.

Arterial Stiffness Assessment

Arterial stiffness was measured noninvasively using an oscillometric device (Cardio Vision MS-2000; Osachi, Nagano, Japan). All measurements were performed with patients in the supine position after a 10-minute rest. Systolic, diastolic, and pulse pressures were recorded from the right brachial artery, and the device automatically calculated the ASI from oscillometric waveform analysis. baPWV was measured simultaneously by applying cuffs to both upper arms and ankles. Two consecutive 3-minute recordings were obtained, and the mean value was used for analysis. This oscillometric method correlates strongly with carotid–femoral PWV, the gold standard for central arterial stiffness assessment, and the ASI has been validated as a reliable surrogate marker for atherosclerotic vascular alterations.²¹

Treatment-Related Adverse Events

Postoperative complications were systematically evaluated using standardized criteria. Systemic complications were graded according to the Clavien–Dindo classification,²² and grade ≥ III was considered major. Postoperative infections were included only when microbiologically confirmed by positive bacterial cultures. Recurrent infections at the same anatomical site were regarded as independent events if separated by at least 14 days and accompanied by documented negative interim cultures. Arterial and venous thromboembolic events were prospectively monitored for 6 months postoperatively.

Acute toxicities during CCRT were assessed weekly using the Common Terminology Criteria for Adverse Events (version 5.0),²³ and grade ≥ 3 was defined as severe. Nutritional status was monitored by hospital dietitians who provided individualized dietary counseling. Nasogastric tube feeding was initiated in patients with oral intake < 50% of baseline or with grade ≥ 3 dysphagia refractory to supportive measures for > 48 hours.

Data Analysis

All statistical analyses were performed using SPSS version 28.0 (IBM Corp., Armonk, NY, USA). Continuous variables were expressed as means ± standard deviations and categorical variables as frequencies and percentages. Baseline group differences were assessed using one-way ANOVA or the Kruskal–Wallis test for continuous variables and the chi-square or Fisher’s exact test for categorical variables, as appropriate.

Longitudinal changes in hemoglobin, NLR, SII, PNI, uric acid, ASI, and baPWV were evaluated at baseline and at 2, 12, and 24 weeks after surgery using generalized estimating equation (GEE) models with an exchangeable correlation structure. Interaction terms between treatment group and time were included to assess differential biomarker trajectories. To account for baseline disease severity, clinical stage (III–IV vs. I–II) was included as a covariate in all GEE models.

Binary logistic regression was used to examine factors associated with major postoperative complications, postoperative infections, and grade ≥3 CCRT-related toxicities. Because this was an exploratory study, ROC-derived cutoffs were applied to enhance clinical interpretability rather than to develop a definitive prediction model; thus, all cutoffs should be considered hypothesis-generating and require external validation. Variables significant in univariate analyses (p < 0.05) were further evaluated by ROC analysis using the Youden index, except for hemoglobin, for which a clinically established threshold (11 g/dL) was applied. Significant predictors were subsequently entered into multivariable models. All tests were two-tailed, with p < 0.05 considered statistically significant.

Results

Baseline Characteristics

A total of 198 potentially eligible patients with oral cavity cancer were assessed for eligibility, of whom 160 provided informed consent and were enrolled in this study. Overall, 140 patients completed all follow-up assessments, yielding a retention rate of 87.5%. The attrition rates were 2.5% (T0 to T1), 5.1% (T1 to T2), and 5.4% (T2 to T3). Details of participant recruitment and study flow are presented in Figure 1. Baseline demographic and clinical characteristics are summarized in Table 1.

Table 1 Patient Characteristics

Figure 1 Participant Enrollment, Follow-Up, and Analysis Flow. Abbreviations: CCRT, Concurrent chemoradiotherapy; OP, Operation; OSCC, Oral squamous cell carcinoma; T1, 2 weeks post-surgery; T2, 12 weeks post-surgery; T3, 24 weeks post-surgery.

The mean age of the cohort was 59.5 ± 9.5 years, and males accounted for 84.3% (118/140). The most common primary tumor sites were the buccal mucosa (37.9%), lower gum (25.7%), and tongue (11.4%). Clinical stage distribution differed significantly among treatment groups (p < 0.001), and stage IV disease was observed in 10.0% of the surgery group, 47.5% of the CCRT group, and 70.0% of the CCRT with UFUR group. Pathologic features also varied significantly among groups. Lymphovascular invasion (p = 0.015) and extracapsular spread (p = 0.031) were more frequent in the CCRT with UFUR group. Age, gender, comorbidity burden, and baseline nutritional and metabolic parameters, including body mass index, serum albumin, hemoglobin, lipid profile, and UA, were comparable across the three treatment groups.

Longitudinal Changes in Inflammatory, Immune-Metabolic, and Vascular Biomarkers

In the GEE model (Table 2 and Supplementary Table S1), hemoglobin levels decreased at week 2 across all treatment arms (β = −0.60, 95% CI = −0.95 to −0.25, p < 0.001). At week 12, the surgery group maintained stable hemoglobin levels, whereas both CCRT groups exhibited significant treatment-by-time interactions (CCRT: β = −1.95, 95% CI = −2.64 to −1.26, p < 0.001; CCRT with UFUR: β = −1.85, 95% CI = −2.57 to −1.13, p < 0.001). By week 24, hemoglobin concentrations in all groups approximated baseline values. NLR elevations were observed in both CCRT groups at week 2 (CCRT: β = 1.20, 95% CI = 0.12 to 2.28, p = 0.029; CCRT with UFUR: β = 1.35, 95% CI = 0.21 to 2.49, p = 0.020), with peak values at week 12 (CCRT: β = 4.90, 95% CI = 2.31 to 7.49, p < 0.001; CCRT with UFUR: β = 4.85, 95% CI = 2.14 to 7.56, p < 0.001). SII followed a similar temporal pattern and demonstrated significant treatment-by-time interactions at week 12 (CCRT: β = 890.0, 95% CI = 149.4 to 1630.6, p = 0.019; CCRT with UFUR: β = 980.0, 95% CI = 213.6 to 1746.4, p = 0.012). Both inflammatory markers returned toward baseline by week 24, while the surgery group showed only transient early postoperative changes.

Table 2 Generalized Estimating Equation Analysis of Temporal Trends in Inflammatory, Immune-Metabolic, and Vascular Markers

PNI declined at weeks 2 and 12 (β = −2.15, 95% CI = −3.03 to −1.27, p < 0.001; β = −4.30, 95% CI = −5.47 to −3.13, p < 0.001, respectively) and recovered by week 24 (β = −0.80, 95% CI = −1.90 to 0.30, p = 0.154), and no significant treatment-by-time interactions were detected. Vascular indices showed similar CCRT-related temporal patterns.

ASI showed significant treatment-by-time interactions at week 12 in both CCRT groups (CCRT: β = 10.45, 95% CI = 2.06 to 18.84, p = 0.015; CCRT with UFUR: β = 11.80, 95% CI = 3.13 to 20.47, p = 0.008), as did baPWV (CCRT: β = 2.00, 95% CI = 0.28 to 3.72, p = 0.015; CCRT with UFUR: β = 2.20, 95% CI = 0.40 to 4.00, p = 0.008). The surgery group maintained stable vascular parameters throughout the study period. Because both CCRT groups received identical CCRT during weeks 0–12, their broadly parallel biomarker trajectories during this period were not interpreted as UFUR-specific effects.

Treatment-Related Major Adverse Events and Infections

As summarized in Supplementary Table S2, major postoperative complications were observed across treatment groups, with increasing incidence according to treatment intensity. Postoperative infections, reoperations, and Clavien–Dindo grade ≥ III complications occurred in 20.0%–30.0%, 11.7%–27.5%, and 18.3%–32.5% of patients, respectively.

Among the 80 patients who received CCRT with or without UFUR, the most common grade ≥ 3 acute toxicities were mucositis (62.5%–70.0%), followed by dysphagia (25.0%–30.0%), leukopenia (17.5%–20.0%), anemia (15.0%), and febrile neutropenia (12.5%–15.0%). No thromboembolic events were recorded during the six-month follow-up period.

Predictors of Severe and Infection-Related Treatment Toxicities

Univariate and multivariable analyses of postoperative complications are presented in Table 3. Continuous variables showing significant univariate associations were dichotomized using ROC-derived optimal cutoffs based on the Youden index (Supplementary Table S3 and Supplementary Figure S1). After adjustment for significant univariate predictors, multivariable analysis identified advanced stage (OR = 4.27, 95% CI = 1.37 to 13.37, p = 0.012) and NLR ≥ 3.5 (OR = 1.52, 95% CI = 1.08 to 2.20, p = 0.026) as independent predictors of postoperative infections. For major systemic complications (Clavien–Dindo ≥ III), independent predictors included hemoglobin < 11 g/dL (OR = 3.29, 95% CI = 1.03 to 10.47, p = 0.044) and PNI < 40.5 (OR = 2.85, 95% CI = 1.12 to 7.25, p = 0.028), whereas PNI < 41.4 was also the sole independent predictor of reoperation (OR = 3.20, 95% CI = 1.25 to 8.18, p = 0.015).

Table 3 Logistic Regression Analysis of Clinical Variables Associated with Surgery-Related Complications

Among patients receiving CCRT (Table 4), multivariable analysis identified that febrile neutropenia was independently associated with hemoglobin < 11 g/dL (OR = 7.10, 95% CI = 1.90 to 26.50, p = 0.003), NLR ≥ 3.1 (OR = 1.56, 95% CI = 1.04 to 2.26, p = 0.024), and elevated UA ≥ 5.5 mg/dL (OR = 1.67, 95% CI = 1.06 to 2.65, p = 0.028). Severe mucositis was associated with advanced stage (OR = 8.50, 95% CI = 2.45 to 29.50, p = 0.001), high SII ≥ 1100 (OR = 3.45, 95% CI = 1.17 to 10.21, p = 0.025), and elevated UA ≥ 5.3 mg/dL (OR = 1.90, 95% CI = 1.14 to 3.15, p = 0.014). Severe dysphagia was associated with advanced stage (OR = 7.20, 95% CI = 2.50 to 20.80, p < 0.001) and hemoglobin < 11 g/dL (OR = 5.85, 95% CI = 1.90 to 18.00, p = 0.002).

Table 4 Logistic Regression Analysis of Clinical Variables Associated with Chemoradiotherapy-Related Toxicities

Discussion

This prospective longitudinal study evaluated temporal changes in inflammatory, immune-metabolic, and vascular biomarkers across escalating treatment intensities in OSCC: surgery alone, surgery with CCRT, or surgery with CCRT and UFUR maintenance. Serial assessments at baseline and weeks 2, 12, and 24 revealed intensity-dependent response patterns. Because the two CCRT groups received identical treatment during weeks 0–12, early trajectory differences should be interpreted as reflecting baseline risk stratification and planned treatment intensity rather than a direct UFUR effect. CCRT induced sustained elevations in NLR and SII, transient declines in PNI and hemoglobin at weeks 2–12, and temporary increases in ASI and baPWV at week 12 that returned toward baseline by week 24, whereas surgery alone caused only transient perturbations. Advanced stage, elevated NLR, and low PNI were independent predictors of postoperative complications, whereas elevated NLR, low hemoglobin, elevated SII and UA were associated with an increased risk of CCRT-related toxicities. Collectively, these findings provide an integrated framework for risk stratification across treatment intensities in OSCC.

In the present study, patients receiving postoperative CCRT exhibited sustained elevations of NLR and SII during the 24-week period that peaked at week 12, whereas the surgery-only group showed minimal fluctuations resolving by week 12. These inflammatory dynamics are consistent with cross-malignancy findings, including reports of NLR elevations during chemoradiotherapy in nasopharyngeal cancer by Muangwong et al²⁴ and in cervical cancer by Hyder et al.²⁵ Notably, chronic betel quid exposure, prevalent in eastern Taiwan, may contribute to baseline inflammatory burden in OSCC patients. Arecoline, the principal alkaloid, activates NF-κB and NLRP3 inflammasome pathways while promoting oxidative stress,²⁶ potentially amplifying treatment-induced systemic inflammation.²⁷

While NLR reflects neutrophil-driven inflammation and lymphocyte-mediated immunity, SII additionally incorporates platelets and captures the thromboinflammatory component of treatment response. In our cohort, SII demonstrated parallel elevation patterns, consistent with findings in esophageal²⁸ and lung cancers,²⁹ including the PROCLAIM trial,³⁰ which indicates that SII elevation represents a general systemic reaction to multimodality treatment. These differing trajectories between surgery-alone and CCRT cohorts reflect distinct immune activation kinetics. Surgical inflammation is transient and resolves within 2–4 weeks as wound healing progresses.³¹ By contrast, platinum-based chemotherapy induces sustained oxidative stress and DNA damage and drives persistent IL-6 and TNF-α release.³² Concurrent radiotherapy amplifies these effects through immunogenic cell death and cytokine release (IL-6, IL-8, and TNF-α) leading to neutrophilia, lymphopenia, and platelet activation^33,34 and is collectively reflected in the concurrent NLR and SII elevations observed during CCRT.

Systemic inflammatory and metabolic biomarkers provide prognostic insights, yet longitudinal characterization remains limited, which hinders cross-study comparability.³⁵ Most studies rely on pretreatment values to predict treatment-related toxicities.^36–39 Therefore, the present study used pretreatment values to explore ROC-derived cutoffs that may help characterize patients at higher risk of treatment-related complications. These thresholds should be interpreted as hypothesis-generating rather than definitive prediction tools. The NLR and PNI thresholds align with previous reports,^36,38 while the higher SII cutoff likely reflects OSCC-specific heterogeneity, which emphasizes the importance of population-tailored thresholds.⁴⁰

Elevated pretreatment NLR was associated with postoperative infections and febrile neutropenia during chemoradiotherapy, suggesting that an impaired neutrophil-lymphocyte balance may reflect compromised immune defense across treatment modalities. Beyond OSCC, NLR has emerged as a practical tool for infection risk assessment in immunocompromised settings, including transplantation and major surgery, where dynamic monitoring alongside other inflammatory indices may refine risk stratification. This prognostic role is further supported by the McGill cohort of 456 patients with HNC, in which low NLR was associated with better treatment tolerance.⁸ By contrast, elevated SII predicted severe oral mucositis and likely reflected synergistic neutrophil–platelet activation that amplified mucosal inflammation. This outcome is supported by evidence linking elevated IL-1β, IL-6, TNF-α, and IFN-γ to mucositis severity.^40,41

PNI declined across groups without significant treatment-by-time interactions, whereas hemoglobin showed additional week-12 declines in both CCRT cohorts. The clinical relevance of PNI has been reported in head and neck and lung cancers,^39,42 and the present trajectory suggests that postoperative immune-metabolic deterioration may partly reflect surgical stress, cancer-related catabolism, and nutritional vulnerability rather than CCRT-specific toxicity alone.^43,44 Low pretreatment hemoglobin independently predicted febrile neutropenia, major complications, and dysphagia, consistent with prior evidence linking anemia to poor outcomes in OSCC.¹⁵ Anemia may aggravate tissue hypoxia and impair healing, while its coexistence with malnutrition further increases feeding-tube dependence and delays recovery.^45,46 These findings underscore the importance of perioperative nutritional and hematologic optimization to enhance treatment tolerance in OSCC.

Pretreatment UA was associated with CCRT-related toxicities, including febrile neutropenia and severe mucositis, but not with surgical complications. This treatment-specific pattern is broadly consistent with the dual biological role of UA described in the literature. At physiological concentrations, UA acts as an antioxidant, whereas elevated levels have been shown to activate NADPH oxidase and the NLRP3 inflammasome, promoting reactive oxygen species generation and IL-1β release.^47,48 Elevated UA has also been linked to poorer cancer survival in a meta-analysis of 43 cohorts,⁴⁹ to chemotherapy-induced febrile neutropenia,¹⁷ and to adverse oncologic outcomes in head and neck cancers.¹⁶ Although the underlying mechanisms were not directly examined in this study, it is plausible that chemoradiotherapy amplifies pre-existing oxidative and inflammatory stress, whereas surgery elicits a more localized and transient response.^50,51 Elevated baseline UA may therefore represent a metabolic risk signal more relevant to CCRT-related toxicity than to surgical complications. These findings should be regarded as hypothesis-generating and warrant further mechanistic and interventional investigation.

ASI reflects oscillometric waveform-derived peripheral stiffness, whereas baPWV captures central-to-peripheral pulse propagation; their parallel elevation supports generalized endothelial stress during CCRT. ASI and baPWV increased significantly at week 12, and baPWV rose by 2.0–2.2 m/s, consistent with a meta-analysis reporting an average increase of 2.05 m/s within 2–4 months of antitumor therapy.¹⁸ Both indices returned to baseline by week 24, indicating transient vascular alterations likely reflecting radiation- and platinum-related endothelial stress. Although UFUR maintenance therapy has been associated with improved survival after chemoradiotherapy in locally advanced head and neck squamous cell carcinoma,⁵² the limited 3-month UFUR exposure in the present cohort was likely insufficient to determine maintenance-specific vascular or inflammatory effects. Endothelial dysfunction is a key pathway linking vascular injury with atherosclerotic risk,⁵³ and therapeutic radiation has been associated with cardiovascular manifestations, including vascular injury.⁵⁴

Baseline arterial stiffness markers showed no association with postoperative or CCRT-related complications, consistent with their role as indicators of long-term cardiovascular risk. Head and neck radiotherapy has been associated with an increased risk of cardiovascular disease,⁵⁵ while arterial stiffness has been recognized as a subclinical marker of cardiovascular risk in cancer populations.^18,19 Vallée et al further demonstrated the added predictive value of ASI for cardiovascular risk stratification.⁵⁶ Fluoropyrimidine-related cardiovascular toxicity is commonly discussed in relation to coronary vasospasm and endothelial injury rather than as a well-established cause of persistent arterial stiffening,^57,58 and the observation window likely captured neither sustained vascular remodeling nor maintenance-specific immune shifts. These findings support ongoing vascular monitoring in patients receiving high-intensity CCRT and highlight the need for studies evaluating prolonged UFUR exposure and cardioprotective strategies.

This study has several limitations. First, the single-center design may limit generalizability, and betel quid exposure was not quantified, precluding dose-response analyses. Second, although the sample size was adequate for longitudinal biomarker assessment, statistical power for multivariable complication modeling was limited. In addition, all patients received standard-dose radiotherapy according to institutional protocols, but detailed patient-level dose-volume parameters were not analyzed; therefore, the potential contribution of radiation dose distribution to systemic inflammation, mucosal toxicity, or arterial stiffening could not be evaluated. Third, the CCRT and CCRT with UFUR groups received identical treatment during weeks 0–12, and the limited UFUR exposure may have been insufficient to detect maintenance-specific biomarker changes; however, the three-group framework was retained to reflect real-world treatment stratification and explore whether patients selected for maintenance therapy exhibited distinct trajectory characteristics. Fourth, pretreatment DPYD genotyping was not performed because the DPYD variants routinely screened in European populations occur at very low frequencies in East Asian populations, and no validated genotyping strategy has yet been established for this population.⁵⁹ Accordingly, clinical toxicity monitoring and timely dose adjustment served as the principal safeguards against severe fluoropyrimidine toxicity. Finally, ROC-derived cutoffs showed relatively low specificity and require validation in independent cohorts. Despite these limitations, this prospective study with high retention (87.5%) provides novel longitudinal data on inflammatory, immune-metabolic, and vascular dynamics across treatment intensities in OSCC, offering a framework for biomarker-based risk stratification and supportive care optimization. These findings highlight the feasibility of integrating longitudinal biomarker monitoring into postoperative follow-up to anticipate complications and guide supportive interventions.

Conclusion

This prospective study demonstrated that patients with OSCC exhibited distinct inflammatory, immune-metabolic, and vascular responses across treatment intensities. CCRT induced sustained systemic alterations, whereas surgery alone caused transient changes. Pretreatment UA was associated with chemoradiotherapy-related toxicities, and transient arterial stiffening during CCRT at week 12, captured non-invasively by ASI and baPWV, supports incorporating vascular assessment into on-treatment surveillance.

Readily available blood-based biomarkers may help identify patients at higher risk of postoperative infection, major complications, febrile neutropenia, and severe mucositis, supporting more tailored surveillance and proactive toxicity management.