Abstract
Background
Young-onset oral cancer is increasingly recognized as a distinct clinical entity, yet prognostic determinants in this population remain poorly defined. This systematic review and meta-analysis aimed to identify and synthesize prognostic factors associated with overall survival among young patients with oral and tongue cancers.
Methods
A comprehensive search of PubMed, Scopus, and Web of Science was conducted on September 23, 2024. Eligible studies included observational cohorts reporting regression-derived prognostic estimates in young patients with oral cancer. Adjusted hazard ratios (aHRs) were pooled using random-effects models with restricted maximum likelihood, whereas unadjusted estimates were narratively summarized. Risk of bias was assessed using the NIH Quality Assessment Tool. Subgroup analyses were not feasible due to limited stratified reporting, and publication bias was not evaluated because all pooled analyses contained fewer than ten studies.
Results
Twenty-four studies encompassing 6,965 young patients were included. Several demographic factors showed no significant association with survival, including age and sex, while Black race was associated with worse outcomes (aHR = 2.79, 95% CI 1.40–5.56). Tumor characteristics linked to poorer prognosis included larger tumor size (aHR = 1.01 per cm, 95% CI 1.00–1.03) and greater depth of invasion (aHR = 1.03, 95% CI 1.01–1.05). High-grade tumors (grade 3–4) (aHR = 2.15, 95% CI 1.52–2.77) and poorly differentiated histology (aHR = 7.75, 95% CI 2.61–23.01) demonstrated strong adverse prognostic associations. Nodal disease significantly increased risk, including higher N stage (aHR = 1.24, 95% CI 1.11–1.37) and N+ status (aHR = 2.05, 95% CI 1.24–2.85). Single-study findings—such as TERTp mutation (aHR = 3.00), PRKCA mutation (aHR = 3.57), Stage IVB, disease recurrence, and several treatment-related variables—suggest possible associations but remain inconclusive.
Conclusions
Among young patients with oral and tongue cancer, nodal involvement, high-grade or poorly differentiated tumors, increased depth of invasion, and larger tumor size were the most consistently associated with poorer survival. Evidence for molecular and treatment-related factors is limited and requires further validation. These findings highlight the need for standardized reporting and prospective studies tailored to young-onset disease.
Keywords: hazard ratio, meta-analysis, prognostic factors, survival, systematic review, tongue cancer, young-onset oral cancer
1. Introduction
Oral squamous cell carcinoma (OSCC) remains a significant global health burden, accounting for more than 350,000 new cases and 177,000 deaths annually (1). Although OSCC traditionally affects individuals in their sixth and seventh decades of life, an increasing proportion of cases now occur in younger adults, often defined as ≤40 or ≤45 years (2, 3). This epidemiologic shift has been documented across multiple geographic regions and has raised substantial clinical interest because young patients frequently present without classical risk factors, especially tobacco and alcohol exposure (4, 5). The biological underpinnings of OSCC in younger adults remain incompletely understood, prompting growing concern that this subgroup may represent a clinically and molecularly distinct disease entity.
Importantly, accumulating evidence suggests that OSCC arising in younger patients may be associated with unfavorable clinical behavior and poorer oncologic outcomes compared with disease in older adults. Several retrospective cohorts have reported higher local recurrence rates, increased locoregional failure, and inferior disease-specific survival in younger patients despite comparable or even earlier-stage presentation (6–11). These observations challenge the historical assumption that younger age confers a prognostic advantage and raise concern that early-onset OSCC may represent a more aggressive disease phenotype rather than simply an age-shifted version of conventional OSCC.
Nonetheless, the prognostic implications of young age remain controversial. While some studies suggest survival outcomes in younger patients are similar to or better than those in older cohorts (12, 13), others consistently demonstrate worse outcomes, particularly with respect to recurrence and cancer-specific mortality (14, 15). Systematic reviews and meta-analyses mirror this inconsistency: some conclude that younger age is not an independent adverse factor (2), whereas others identify younger patients as having higher local recurrence risk despite similar overall survival (3). Collectively, these inconsistencies underscore that young-onset OSCC cannot be assumed to behave indolently and, in fact, may carry a prognostic disadvantage that remains insufficiently characterized.
A particularly important challenge in the literature is the limited availability of adjusted prognostic estimates. Many prior analyses rely on crude survival comparisons that do not account for confounding by tumor stage, nodal burden, margin status, or treatment modality—factors strongly associated with outcome in OSCC regardless of age (16). Moreover, reports focusing specifically on young-onset OSCC often pool diverse oral subsites, despite well-established evidence that tongue cancer, especially in younger populations, may behave differently from other oral cavity subsites (14). Another major gap is the limited and inconsistent investigation of molecular characteristics such as TERT promoter mutations or PRKCA alterations, which have been implicated in the biology of early-onset OSCC but remain underexplored and rarely evaluated in multivariable frameworks (14, 15).
These uncertainties underscore the importance of establishing a robust, evidence-based understanding of prognostic factors in young-onset OSCC, grounded in adjusted regression models rather than univariate descriptions. Given that younger patients often experience significant long-term functional morbidity from surgical and adjuvant treatments, accurate prognostic assessment is essential for risk-adapted management. Determining whether established clinicopathologic and molecular predictors carry similar weight in younger patients—and whether young age itself influences survival—has direct implications for treatment decision-making, survivorship expectations, and future development of age-specific prognostic tools.
Therefore, this systematic review and meta-analysis aimed to synthesize the highest-quality regression-derived prognostic evidence for young-onset oral cancer, focusing strictly on studies reporting adjusted hazard ratios. By dissecting demographic, clinicopathologic, molecular, and treatment-related predictors, this study seeks to clarify the prognostic landscape of young-onset OSCC and address the persistent gaps in the literature regarding whether this rising patient subgroup represents a biologically distinct entity with unique prognostic determinants.
2. Methods
2.1. Study design and reporting framework
This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (17). The protocol was developed a priori and followed established recommendations for prognostic factor research synthesis.
2.2. Search strategy
A comprehensive literature search was performed on September 23, 2024, across PubMed, Scopus, and Web of Science. The search strategy combined controlled vocabulary and free-text keywords related to young age, oral and tongue cancers, and prognostic or regression-based outcomes. The exact search queries used for each database are provided in Supplementary Table S1. No restrictions were applied on publication year. Only English-language publications were considered due to feasibility constraints. All retrieved records were imported into EndNote for reference management, and duplicates were removed prior to screening.
2.3. Eligibility criteria
Studies were eligible for inclusion if they met the following criteria:
involved patients with young-onset squamous cell carcinoma of the oral cavity, defined according to each study’s prespecified age cutoff;
reported prognostic associations (with overall survival) using regression-based measures (e.g., adjusted or unadjusted hazard ratios);
included at least 20 patients to ensure minimum analytic robustness; and
used an observational cohort or registry-based design.
Studies were excluded if they:
focused on salivary gland cancers or non-squamous oral malignancies
did not provide prognostic analyses specific to the young patient subgroup;
lacked prognostic factor reporting;
included fewer than 20 participants;
did not report regression-derived effect estimates (e.g., hazard ratios, risk ratios, or odds ratios); or
constituted reviews, editorials, letters, or case reports.
2.4. Study selection
Two reviewers independently screened titles and abstracts and subsequently reviewed the full texts of potentially eligible articles. Disagreements were resolved through discussion until consensus was achieved. The study selection process is summarized using a PRISMA flow diagram (Figure 1).
Figure 1.
A PRISMA flow diagram showing the results of the database search.
2.5. Data extraction
Data were extracted independently and in duplicate using a standardized form. Extracted variables included study characteristics (country, year, design), sample size, demographic and clinical characteristics, tumor site and stage, treatment modality, and all reported prognostic estimates. Overall survival (OS) was predefined as the primary outcome for quantitative synthesis. Adjusted estimates for disease-specific survival (DSS), disease-free survival (DFS), and other oncologic outcomes were extracted when available but were summarized narratively due to limited and heterogeneous reporting. For each prognostic factor, adjusted hazard ratios (aHRs) were extracted preferentially; unadjusted estimates were collected when adjusted values were unavailable and are presented narratively in Supplementary Table S2. When studies presented multiple adjusted models, the model with the most complete adjustment for confounders was selected.
2.6. Risk of bias assessment
Methodological quality of included studies was evaluated using the National Institutes of Health (NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Each study was rated as good, fair, or poor quality based on criteria such as adequacy of confounding control, outcome assessment, loss to follow-up, and analytical transparency.
2.7. Data synthesis and statistical analysis
Quantitative synthesis focused on adjusted hazard ratios for overall survival (OS). Meta-analyses were performed using a random-effects model with restricted maximum likelihood (REML) estimation to account for between-study heterogeneity. For each prognostic factor reported in at least two studies, log-transformed hazard ratios and their standard errors were pooled and back-transformed for interpretation. Heterogeneity was quantified using the I² statistic. Adjusted prognostic estimates reported for DSS, DFS, or locoregional outcomes were not pooled and are summarized narratively in Supplementary Table S3.
Unadjusted effect estimates, or those reported only once in the literature, were not pooled and instead summarized narratively. Subgroup analyses based on country, geographic region, tumor location, or clinical stage could not be performed due to insufficient stratified reporting across included studies. Publication bias was not assessed because no meta-analysis contained ten or more studies, consistent with recommendations that funnel plot asymmetry tests are unreliable under this threshold.
All statistical analyses were conducted using STATA V18.
3. Results
3.1. Literature search results
The database search yielded a total of 3,307 records, including 793 from PubMed, 2,053 from Scopus, and 461 from Web of Science (Figure 1). After removal of 717 duplicate entries through EndNote, 2,590 unique records proceeded to title and abstract screening. Of these, 2491 were excluded, leaving 99 reports for full-text retrieval. One report could not be retrieved, resulting in 98 articles being assessed for eligibility. Following full-text evaluation, 74 reports were excluded for the following reasons: prognostic analyses not specific to the young patient group (n = 61), absence of prognostic factors (n = 5), inclusion of fewer than 20 cases (n = 1), limited to salivary gland cancer (n = 1), or lack of regression-based effect estimates (n = 7). A total of 23 studies met the inclusion criteria and were incorporated into the qualitative/quantitative synthesis (18–40).
3.2. Baseline characteristics of included studies
The summary of included studies’ characteristics can be found in Table 1. Most evidence came from the United States (9 studies, 37.50%) followed by India (5 studies, 20.83%), and South Korea (2 studies, 8.33%), respectively. All of included studies were retrospective cohort in design. Overall, 6965 young oral cancer patients were included, with male predominance (1925 patients, 56.97%) in studies reporting gender data. Among studies reporting cancer stage-related data, patients with stage I were the most predominant (931 patients, 33.1%), followed by stage II (786 patients, 27.9%), stage III (612 patients, 21.7%), and stage IV (486 patients, 17.3%), respectively. As for location-based cancer distribution, tongue cancer was the most frequently reported site (Table 2).
Table 1.
Baseline characteristics of studies investigating determinants of survival in patients with young oral cavity cancer.
| Author (YOP) | Country | YOI | Design | Sample | Age | Gender | Stage | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Male | Female | I | II | III | IV | ||||||
| Adduri (2014) (18) | India | 2008 | Retrospective Study | 121 | ≤ 45 yrs | 81 | 40 | – | – | – | – |
| Bommakanti (2023) (19) | USA | 2005 - 2014 | Retrospective Study | 3262 | < 45 yrs | – | – | – | – | – | – |
| Decker (2021) (20) | Brazil | 2011 - 2016 | Retrospective Study | 76 | ≤ 60 yrs | 50 | 26 | 16 | 16 | 60 | 60 |
| Deneuve (2022) (21) | France | Jan 2005 - Dec 2015 | Retrospective Study | 185 | ≤ 40 yrs | 105 | 80 | 70 | 75 | 21 | 19 |
| Fan (2014) (22) | China | Jan 2001 - Dec 2010 | Retrospective Study | 100 | < 45 yrs | 66 | 34 | 21 | 28 | 26 | 25 |
| Farhat (2022) (23) | USA | Jan 1992 - Dec 2017 | Retrospective Study | 80 | ≤ 45 yrs | 44 | 36 | 32 | 15 | 16 | 17 |
| Gamez (2018) (24) | USA | 1980 - 2014 | Retrospective Study | 124 | ≤ 40 yrs | 74 | 50 | 65 | 29 | 9 | 12 |
| Iype (2001) (26) | India | 1982 - 1996 | Retrospective Study | 264 | ≤ 35 yrs | 184 | 80 | – | – | – | – |
| Iype (2004) (25) | India | 1982 - 1996 | Retrospective Study | 46 | < 35 yrs | 40 | 6 | 16 | 16 | 30 | 30 |
| Kies (2012) (27) | USA | Sep 2001 - Oct 2004 | Retrospective Study | 23 | 18–49 yrs | 10 | 13 | – | 14 | 14 | 9 |
| Kim (2023) (28) | South Korea | – | Retrospective Study | 44 | ≤ 45 yrs | 29 | 15 | – | – | – | – |
| Lee (2020) (29) | South Korea | – | Retrospective Study | 49 | ≤ 45 yrs | 30 | 19 | 13 | 5 | 14 | 17 |
| Liao (2018) (30) | Taiwan | 2007 - 2014 | Retrospective Study | 457 | ≤ 65 yrs | 427 | 30 | 270 | 270 | 187 | 187 |
| Manuel (2003) (31) | India | 1990 - 1994 | Retrospective Study | 76 | ≤ 45 yrs | 48 | 28 | 17 | 17 | 27 | 15 |
| Mascitti (2020) (32) | Italy | 1991 - 2018 | Retrospective Study | 66 | < 40 yrs | 51 | 15 | – | – | – | – |
| Miller (2019) (33) | USA | 2000 - 2016 | Retrospective Study | 23 | 22–40 yrs | 17 | 6 | 12 | 0 | 9 | 2 |
| Mneimneh (2021) (34) | USA | – | Retrospective Study | 150 | ≤ 45 yrs | 89 | 61 | 78 | 46 | 13 | 9 |
| Myers (2000) (35) | USA | 1973 - 1995 | Retrospective Study | 64 | ≤ 40 yrs | 37 | 27 | – | – | – | – |
| Okuyama (2021) (36) | Japan | April 2008 - March 2017 | Retrospective Study | 101 | AYA | 57 | 50 | 61 | 32 | 8 | 6 |
| Parzefall (2021) (37) | Austria | – | Retrospective Study | 29 | ≤ 45 yrs | 17 | 12 | – | – | – | – |
| Subramaniam (2018) (38) | India | 2004 - 2014 | Retrospective Study | 82 | ≤ 45 yrs | 55 | 27 | 17 | 20 | 31 | 14 |
| Thomas (2012) (39) | USA | 1980 - 2004 | Retrospective Study | 62 | 18–40 yrs | 37 | 25 | – | – | – | – |
| Warnakulasuriya (2007) (40) | UK | 1986 - 2002 | Retrospective Study | 483 | < 45 yrs | – | – | – | – | – | – |
Table 2.
Tumor location distributions of oral cavity cancer among included studies.
| Author (YOP) | Tongue | FOM | Buccal mucosa | Gingiva | Hard Palate | Lip | Retromolar trigone | Alveolus | Palate | Other |
|---|---|---|---|---|---|---|---|---|---|---|
| Adduri (2014) (18) | – | – | – | – | – | – | – | – | – | – |
| Bommakanti (2023) (19) | – | – | – | – | – | – | – | – | – | – |
| Decker (2021) (20) | 26% | 35% | – | – | – | – | – | – | – | 15% |
| Deneuve (2022) (21) | – | – | – | – | – | – | – | – | – | – |
| Fan (2014) (22) | 63% | 11% | 7% | 16% | 3% | – | – | – | – | – |
| Farhat (2022) (23) | 76.25% | – | 3.75% | 10% | – | – | 3.75% | – | – | – |
| Gamez (2018) (24) | 86.30% | – | – | – | – | – | – | – | – | 13.70% |
| Iype (2001) (26) | 52.00% | 1.90% | 26% | – | – | 2.30% | – | 10% | 4.50% | 3.80% |
| Iype (2004) (25) | 36% | – | 37% | – | – | – | – | – | – | – |
| Kies (2012) (27) | – | – | – | – | – | – | – | – | – | – |
| Kim (2023) (28) | – | – | – | – | – | – | – | – | – | – |
| Lee (2020) (29) | 84% | – | – | – | – | – | – | – | – | 16% |
| Liao (2018) (30) | 36.76% | 3.28% | 43.33% | 10.07% | 2.41% | 4.16% | – | – | – | – |
| Manuel (2003) (31) | – | – | – | – | – | – | – | – | – | – |
| Mascitti (2020) (32) | 78.80% | 4.50% | 6.10% | 9.10% | – | – | 1.50% | – | – | – |
| Miller (2019) (33) | – | – | – | – | – | – | – | – | – | – |
| Mneimneh (2021) (34) | 87% | – | 5.33% | 6% | – | 1.33% | – | – | – | – |
| Myers (2000) (35) | – | – | – | – | – | – | – | – | – | – |
| Okuyama (2021) (36) | 94.40% | 3.74% | 0.93% | – | 0% | – | – | – | – | 0% |
| Parzefall (2021) (37) | – | – | – | – | – | – | – | – | – | – |
| Subramaniam (2018) (38) | 80.49% | – | 19.51% | – | – | – | – | – | – | – |
| Thomas (2012) (39) | – | – | – | – | – | – | – | – | – | – |
| Warnakulasuriya (2007) (40) | – | – | – | – | – | – | – | – | – | – |
FOM, floor of mouth; YOP, year of publication.
3.3. Methodological quality
A detailed description of each study’s methodological quality is provided in Table 3. Overall, nine studies had good quality while the remaining 15 studies had fair quality. The main drawback was the lack of confounding control either in the design or analysis phase.
Table 3.
Methodological quality of included studies using the Newcastle Ottawa Scale for cohort studies.
| Author (YOP) | Selection | Comparability | Outcome | Overall Quality | |||||
|---|---|---|---|---|---|---|---|---|---|
| Representativeness of the exposed cohort | Selection of non-exposed cohort | Ascertainment of exposure | Demonstration that outcome of interest was not present at start of study | Based on design and/or analysis | Assessment of outcome | Was follow-up long enough? | Adequacy of follow-up of cohorts | ||
| Adduri (2014) (18) | * | * | * | * | * | * | * | * | Fair |
| Bommakanti (2023) (19) | * | * | * | * | ** | * | * | * | Good |
| Decker (2021) (20) | * | * | * | * | ** | * | * | * | Good |
| Deneuve (2022) (21) | * | * | * | * | * | * | * | * | Fair |
| Fan (2014) (22) | * | * | * | * | * | * | * | * | Fair |
| Farhat (2022) (23) | * | * | * | * | * | * | * | * | Fair |
| Gamez (2018) (24) | * | * | * | * | ** | * | * | * | Good |
| Iype (2001) (26) | * | * | * | * | * | * | * | * | Fair |
| Iype (2004) (25) | * | * | * | * | * | * | * | * | Fair |
| Kies (2012) (27) | * | * | * | * | * | * | * | * | Fair |
| Kim (2023) (28) | * | * | * | * | ** | * | * | * | Good |
| Lee (2020) (29) | * | * | * | * | ** | * | * | * | Good |
| Liao (2018) (30) | * | * | * | * | * | * | * | * | Fair |
| Manuel (2003) (31) | * | * | * | * | * | * | * | * | Fair |
| Mascitti (2020) (32) | * | * | * | * | ** | * | * | * | Good |
| Miller (2019) (33) | * | * | * | * | * | * | * | * | Fair |
| Mneimneh (2021) (34) | * | * | * | * | ** | * | * | * | Good |
| Myers (2000) (35) | * | * | * | * | * | * | * | * | Fair |
| Okuyama (2021) (36) | * | * | * | * | * | * | * | * | Fair |
| Parzefall (2021) (37) | – | * | * | * | ** | * | * | * | Fair |
| Subramaniam (2018) (38) | * | * | * | * | * | * | * | * | Fair |
| Thomas (2012) (39) | * | * | * | * | * | * | * | * | Fair |
| Warnakulasuriya (2007) (40) | * | * | * | * | ** | * | * | * | Good |
Quality ratings were as follows: Good quality: 3 or 4 stars in selection domain AND 1 or 2 stars in comparability domain AND 2 or 3 stars in outcome/exposure domain; Fair quality: 2 stars in selection domain AND 1 or 2 stars in comparability domain AND 2 or 3 stars in outcome/exposure domain; Poor quality: 0 or 1 star in selection domain OR 0 stars in comparability domain OR 0 or 1 stars in outcome/exposure domain. Each “*” equals to 1 point in the methodological quality assessment.
3.4. Demographic factors
Across demographic variables (Figure 2), age was not significantly associated with survival (three studies; aHR = 0.80, 95% CI 0.34–1.25; I² = 99.79%). Female sex similarly showed no significant effect (two studies; aHR = 1.08, 95% CI 0.97–1.20; I² = 0%). Black race demonstrated a significantly higher risk (one study; aHR = 2.79, 95% CI 1.40–5.56). Lack of insurance was evaluated in one study only (aHR = 1.54, 95% CI 0.88–2.63), and thus this observation remains inconclusive. Smoking was not significantly associated with prognosis (two studies; aHR = 0.66, 95% CI –0.16 to 1.47; I² = 92.01%).
Figure 2.
Forest plot showing the meta-analytic estimates of demographic and tumor-related prognosticators of overall survival.
3.5. Tumor characteristics
Among primary tumor measures (Figure 2), tumor greatest dimension did not show a clear prognostic association (one study; aHR = 1.02, 95% CI 0.27–3.80). Tumor size per centimeter was significantly associated with worse outcomes (three studies; aHR = 1.01, 95% CI 1.00–1.03; I² = 96.13%). Tumor thickness was examined in one study (aHR = 1.50, 95% CI 0.19–11.61), which does not allow firm conclusions. Depth of invasion was significantly associated with poorer survival (two studies; aHR = 1.03, 95% CI 1.01–1.05; I² = 0%).
3.6. Pathologic characteristics
Histologic grade (Figure 2) showed a non-significant association in the single study reporting it (aHR = 4.19, 95% CI 0.97–18.07). High-grade tumors (grade 3–4) demonstrated a significantly increased risk (two studies; aHR = 2.15, 95% CI 1.52–2.77; I² = 0%). Moderately differentiated tumors were assessed in one study (aHR = 2.08, 95% CI 0.79–5.52), and moderate-to-poor differentiation likewise relied on a single, non-conclusive estimate (aHR = 2.80, 95% CI 0.75–10.42). Poorly differentiated tumors, evaluated through one study, showed a strong significant association with worse outcomes (aHR = 7.75, 95% CI 2.61–23.01).
Patterns of invasion were examined in one study only (aHR = 2.115, 95% CI 0.79–5.61), providing insufficient evidence for definitive interpretation. Lymphovascular invasion was borderline and non-significant (two studies; aHR = 2.15, 95% CI –0.12 to 4.42; I² = 0%). Perineural invasion (three studies) did not reach statistical significance (aHR = 1.52, 95% CI –1.46 to 4.51; I² = 0%). Extension beyond the organ of origin was evaluated in one study (aHR = 1.86, 95% CI 1.00–3.44), suggesting a possible association but requiring replication. Positive surgical margins were assessed across three studies and showed no statistically significant association (aHR = 1.58, 95% CI 0.99–2.16; I² = 0%).
3.7. TNM status
Advancing T stage (Figure 3) was significantly associated with poorer survival (two studies; aHR = 1.77, 95% CI 1.23–2.31; I² = 0%). Similarly, pT3–4 tumors demonstrated an elevated but non-significant risk (three studies; aHR = 1.06, 95% CI -0.49 – 2.60; I² = 0%). Higher N stage was significantly associated with worse outcomes (two studies; aHR = 1.24, 95% CI 1.11–1.37; I² = 0%), and the presence of nodal metastasis (N+) conferred a markedly increased risk (three studies; aHR = 2.05, 95% CI 1.24–2.85; I² = 0%).
Figure 3.
Forest plot showing the meta-analytic estimates of tumor stage, genetic markers, and treatment-related prognosticators of overall survival.
Stage IVB was assessed in one study only and showed a substantially elevated hazard (aHR = 8.37, 95% CI 2.92–23.98), although this finding remains non-conclusive due to reliance on a single dataset. Disease recurrence, likewise reported in one study, demonstrated a strong association with worse prognosis (aHR = 11.05, 95% CI 3.8–31.52). Distant metastasis was analyzed across two studies and was not significantly associated with survival (aHR = 1.91, 95% CI 0.41–3.41; I² = 0%).
3.8. Genetic and molecular markers
TERTp mutation status (Figure 3) was evaluated in one study and showed a significant association with poorer outcomes (aHR = 3.00, 95% CI 1.03–8.752); however, conclusions remain limited given the single-study evidence base. PRKCA mutation also demonstrated a significant association (one study; aHR = 3.57, 95% CI 1.53–8.56), but similarly should be interpreted cautiously due to non-replication across studies.
3.9. Treatment-related factors
The choice of primary treatment modality showed notable associations with outcomes (Figure 3). Surgery alone was associated with increased risk (one study; aHR = 1.97, 95% CI 1.07–3.64), although this estimate is based on a single study. Conversely, omission of surgery showed a comparable magnitude of increased risk (one study; aHR = 1.98, 95% CI 1.01–3.88), also non-conclusive due to single-study origin. Receipt of adjuvant therapy was strongly associated with poorer survival across three studies (aHR = 8.16, 95% CI 2.85–13.48; I² = 0%), a finding likely reflecting treatment selection for patients with more advanced disease. Lack of radiotherapy was evaluated in one study and showed no significant association (aHR = 1.43, 95% CI 0.97–2.09).
4. Discussion
This systematic review and meta-analysis synthesized adjusted prognostic estimates from 24 studies on young-onset oral cancer and identified a pattern in which tumor biology and stage-related variables — rather than simple chronological age — appear to drive outcomes. Our principal findings were: 1) demographic variables such as younger age and female sex did not show consistent associations with overall survival after adjustment, while Black race was associated with worse prognosis; though not conclusive as this observation was based on a single study; 2) tumour burden and biologic aggressiveness (tumour size per cm, depth of invasion, higher T and N stage, nodal metastasis, and poor histologic grade) were the most robust and reproducible predictors of worse survival; 3) several molecular alterations (e.g. TERTp, PRKCA in single studies) and treatment-selection signals (receipt of adjuvant therapy) showed associations with poorer outcomes but were limited to single-study evidence and likely reflect confounding by indication. These results align with, and extend, the conclusions drawn in recent focused reviews and large pooled analyses of young-onset oral/tongue cancer (2, 3, 14, 15).
4.1. Interpretation in the context of existing evidence
Our finding that conventional tumour factors (size, depth of invasion, T/N stage, nodal positivity, high histologic grade) are associated with poorer survival in younger cohorts is concordant with multiple prior systematic reviews and population studies that emphasize stage and pathological features as primary determinants of outcome across age groups (2, 3, 41). Where prior pooled work has yielded apparently conflicting conclusions about whether younger patients do better or worse overall, those differences are often explained by differences in outcome definition, the inclusion of oropharyngeal subsites, and whether analyses used adjusted estimates. For example, Tagliabue et al. (3) specifically demonstrated that unadjusted comparisons frequently mask the adverse prognostic weight of comorbidity and stage, and that after adjustment older age was associated with worse mortality in tongue cancer, while younger patients had higher local recurrence risk. Similarly, Panda et al. (15) observed better crude overall survival for younger patients but worse disease-free survival and higher recurrence and distant metastasis rates in unmatched analyses, highlighting the importance of confounder control and subsite composition.
The consistent prognostic effect of nodal disease and higher T stage in our pooled analyses mirrors well-established biologic reality and staging data (e.g., AJCC/TNM) and is reinforced by contemporary prognostic-marker reviews for tongue cancer that show depth of invasion and nodal status to be among the strongest clinicopathologic predictors of outcome. Importantly, our pooled effect sizes for nodal metastasis (aHR ≈2.05) and advancing T stage (aHR ≈1.77) are clinically meaningful and concordant with those primary studies and meta-analyses that focus on oral tongue tumors (3, 16).
Molecular and biomarker data in young cohorts remain preliminary. Single-study associations identified in our review (e.g., TERTp and PRKCA mutations) indicate potential biological differences in some tumors arising at younger ages; however, these findings are not yet replicated across independent cohorts and therefore cannot be considered established prognostic markers. This cautious conclusion aligns with recent narrative syntheses and biomarker-focused meta-analyses that call for multicenter validation before adopting such markers clinically (14, 16).
4.2. Strengths of this review
This study pooled adjusted hazard ratios wherever available and prioritized regression-based, confounder-controlled estimates; that approach reduces bias from simple crude comparisons and from treatment selection effects. By focusing on adjusted estimates and using REML random-effects pooling, the synthesis emphasizes effect sizes that are more likely to reflect independent prognostic contributions. Our findings are therefore complementary to prior syntheses that pooled mainly unadjusted or mixed estimates (2, 15).
4.3. Limitations and risk of bias in the evidence base
Despite methodological strengths, the available evidence has important limitations that must temper interpretation. Although the present review focused on OSCC, which represents the dominant histology in young-onset oral cancer, heterogeneity in molecular drivers within OSCC could not be systematically explored due to limited reporting. Most included studies were retrospective, observational, and hospital-based; only a minority reported comprehensive confounder adjustment, and residual confounding (for example by comorbidity, socioeconomic status, access to care, or detailed treatment factors) remains likely. Several prognostic associations were based on single-study estimates (e.g., specific mutations, some histologic subcategories), precluding confident generalization. The NIH quality assessment applied across studies highlighted frequent deficiencies in controlling for confounders and in analytic transparency; these concerns mirror the assessments reported in prior reviews.
Heterogeneity across studies was another important limitation. Studies used different age cutoffs to define “young,” varied in subsite inclusion (oral cavity vs. oropharynx; tongue-only analyses versus mixed oral sites), and differed in stage distribution and treatment approaches, all of which contribute to statistical heterogeneity and limit pooled inference. Several earlier systematic reviews have underlined the same source of heterogeneity and recommendation for standardized definitions (3, 14, 15).
In addition, although several studies reported adjusted associations with disease-specific or disease-free survival, the limited number of studies per outcome and inconsistent reporting precluded quantitative synthesis; these findings are therefore presented narratively in Supplementary Table S3.
Finally, several potentially important prognostic domains (e.g., HPV status in subsites, comprehensive genomic signatures, immunologic markers, and detailed margin and nodal-yield metrics) were either inconsistently reported or absent in most studies; this limited our ability to perform subgroup or meta-regression analyses that might identify effect modification by these factors. Recent narrative syntheses and methodological reviews reach similar conclusions about evidence gaps in molecular and prognostic modelling for younger patients.
4.4. Clinical and research implications
For clinicians, the principal implication is that established tumour factors (size, depth, stage, nodal metastasis, poor differentiation) should remain central to risk stratification and treatment planning in younger patients rather than using chronological age alone as the justification for de-escalation or escalation of therapy. Our pooled results suggest that young patients with adverse pathologic features carry similar or higher risks as older counterparts and therefore warrant guideline-concordant staging, neck management, and adjuvant decision-making based on established pathologic indicators (2, 3).
From a research standpoint, several priorities emerge. First, prospective, multi-institutional cohorts with pre-specified age thresholds and harmonized reporting of clinicopathologic, treatment, and molecular variables are needed to validate single-study molecular signals (e.g., TERTp, PRKCA) and to allow robust multivariable modelling. Second, prognostic model development for young-onset oral cancer should follow best-practice guidance (transparent reporting, adequate events per variable, internal/external validation) because current prognostic models for head and neck subsites frequently suffer high risk of bias and limited external validation. Finally, studies that separate oral cavity subsites (tongue vs. non-tongue) and that stratify by HPV status where relevant will reduce heterogeneity and produce clinically actionable stratification tools (14).
5. Conclusion
In this prognostic synthesis of young-onset oral cancer, established pathological markers of tumour burden and aggressiveness—rather than chronological youth—emerged as the most consistent predictors of adverse survival. Although some molecular findings are intriguing, they are not yet sufficiently replicated to inform practice. Future prospective, multicenter studies with harmonized definitions and comprehensive confounder control are required to validate putative molecular prognosticators and to develop trustworthy age-specific prognostic models. These steps will be necessary before age can be used as an independent criterion to alter standard-of-care treatment pathways.
Funding Statement
The author(s) declared that financial support was not received for this work and/or its publication.
Footnotes
Edited by: Alberto Rodriguez-Archilla, University of Granada, Spain
Reviewed by: Elham Saberian, University of Pavol Jozef Šafárik, Slovakia
Tapanut Ariyanon, Chiang Mai University, Thailand
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.
Author contributions
RS: Conceptualization, Project administration, Supervision, Writing – original draft. RK: Data curation, Methodology, Writing – original draft. JH: Data curation, Formal Analysis, Methodology, Writing – review & editing. GA: Investigation, Methodology, Resources, Writing – review & editing. HG: Validation, Visualization, Writing – original draft. CJ: Data curation, Methodology, Writing – review & editing. AS: Data curation, Methodology, Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
YOP, year of publication; YOI, year of investigation; USA, United States of America; UK, United Kingdom; yr: year.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2026.1780074/full#supplementary-material
References
- 1. Kumar V. A comprehensive review of risk factors, diagnosis, and treatment options for oral squamous cell carcinoma. Curr Cancer Ther Rev. (2025). doi: 10.2174/0115733947379301250703053245 [DOI] [Google Scholar]
- 2. Kaminagakura E, Tango RN, Cruz-Perez D, Bonan R, Yamamoto de Almeida L, de Almeida Lança ML, et al. Oral squamous cell carcinoma outcome in adolescent/young adult: systematic review and M eta-analysis. Head Neck. (2022) 44:548–61. doi: 10.1002/hed.26940, PMID: [DOI] [PubMed] [Google Scholar]
- 3. Tagliabue M, Belloni P, De Berardinis R, Gandini S, Chu F, Zorzi S, et al. A systematic review and meta-analysis of the prognostic role of age in oral tongue cancer. Cancer Med. (2021) 10:2566–78. doi: 10.1002/cam4.3795, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Llewellyn CD, Linklater K, Bell J, Johnson NW, Warnakulasuriya S. An analysis of risk factors for oral cancer in young people: A case-control study. Oral Oncol. (2004) 40:304–13. doi: 10.1016/j.oraloncology.2003.08.015, PMID: [DOI] [PubMed] [Google Scholar]
- 5. Llewellyn C, Johnson N, Warnakulasuriya K. Risk factors for squamous cell carcinoma of the oral cavity in young people—a comprehensive literature review. Oral Oncol. (2001) 37:401–18. doi: 10.1016/S1368-8375(00)00135-4, PMID: [DOI] [PubMed] [Google Scholar]
- 6. Davidson BJ, Root WA, Trock BJ. Age and survival from squamous cell carcinoma of the oral tongue. Head Neck. (2001) 23:273–9. doi: 10.1002/hed.1030, PMID: [DOI] [PubMed] [Google Scholar]
- 7. Garavello W, Spreafico R, Gaini RM. Oral tongue cancer in young patients: A matched analysis. Oral Oncol. (2007) 43:894–7. doi: 10.1016/j.oraloncology.2006.10.013, PMID: [DOI] [PubMed] [Google Scholar]
- 8. Hilly O, Shkedy Y, Hod R, Soudry E, Mizrachi A, Hamzany Y, et al. Carcinoma of the oral tongue in patients younger than 30 years: comparison with patients older than 60 years. Oral Oncol. (2013) 49:987–90. doi: 10.1016/j.oraloncology.2013.07.005, PMID: [DOI] [PubMed] [Google Scholar]
- 9. Kourelis K, Tsue T, Girod D, Tawfik O, Sykes K, Shnayder Y. Negative prognostic factors for head and neck cancer in the young. J BUON. (2013) 18:459–64., PMID: [PubMed] [Google Scholar]
- 10. Park JO, Sun DI, Cho KJ, Joo YH, Yoo HJ, Kim MS. Clinical outcome of squamous cell carcinoma of the tongue in young patients: A stage-matched comparative analysis. Clin Exp otorhinolaryngol. (2010) 3:161–5. doi: 10.3342/ceo.2010.3.3.161, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Zhang YY, Wang DC, Su JZ, Jia LF, Peng X, Yu GY. Clinicopathological characteristics and outcomes of squamous cell carcinoma of the tongue in different age groups. Head Neck. (2017) 39:2276–82. doi: 10.1002/hed.24898, PMID: [DOI] [PubMed] [Google Scholar]
- 12. Bello IO, Almangush A, Heikkinen I, Haglund C, Coletta RD, Kowalski LP, et al. Histological characteristics of early-stage oral tongue cancer in young versus older patients: A multicenter matched-pair analysis. Oral Dis. (2020) 26:1081–5. doi: 10.1111/odi.13288, PMID: [DOI] [PubMed] [Google Scholar]
- 13. Sharma S, Satyanarayana L, Asthana S, Shivalingesh K, Goutham BS, Ramachandra S. Oral cancer statistics in India on the basis of first report of 29 population-based cancer registries. J Oral Maxillofac Pathol. (2018) 22:18–26. doi: 10.4103/jomfp.JOMFP_113_17, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Lenoci D, Moresco E, Cavalieri S, Bergamini C, Torchia E, Botta L, et al. Oral cancer in young adults: incidence, risk factors, prognosis, and molecular biomarkers. Front Oncol. (2024) 14:1452909. doi: 10.3389/fonc.2024.1452909, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Panda S, Mohanty N, Panda S, Mishra L, Gopinath D, Sahoo A, et al. Are survival outcomes different for young and old patients with oral and oropharyngeal squamous cell carcinoma? A systematic review and meta-analysis. Cancers. (2022) 14:1886. doi: 10.3390/cancers14081886, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Almangush A, Heikkinen I, Mäkitie AA, Coletta RD, Läärä E, Leivo I, et al. Prognostic biomarkers for oral tongue squamous cell carcinoma: A systematic review and meta-analysis. Br J Cancer. (2017) 117:856–66. doi: 10.1038/bjc.2017.244, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The prisma 2020 statement: an updated guideline for reporting systematic reviews. bmj. (2021) 372. doi: 10.1136/bmj.n71, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Adduri R, Sr., Kotapalli V, Gupta NA, Gowrishankar S, Srinivasulu M, Ali MM, et al. P53 nuclear stabilization is associated with fhit loss and younger age of onset in squamous cell carcinoma of oral tongue. BMC Clin Pathol. (2014) 14:37. doi: 10.1186/1472-6890-14-37, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Bommakanti KK, Abiri A, Han AY, Goshtasbi K, Kuan EC, St John MA. Stage-specific survival in young patients with oral tongue squamous cell carcinoma. Otolaryngology--head Neck Surg. (2023) 168:398–406. doi: 10.1177/01945998221101191, PMID: [DOI] [PubMed] [Google Scholar]
- 20. Decker JM, Filho OV, Freitas MO, Silva-Fernandes IJ, Dantas TS, Campêlo CS, et al. Pms2: A potential prognostic protein marker in oral squamous cell carcinoma. Med oral patologia Oral y cirugia bucal. (2021) 26:e451–e8. doi: 10.4317/medoral.24303, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Deneuve S, Guerlain J, Dupret-Bories A, Majoufre C, Philouze P, Ceruse P, et al. Oral tongue squamous cell carcinomas in young patients according to their smoking status: A gettec study. Eur Arch oto-rhino-laryngol. (2022) 279:415–24. doi: 10.1007/s00405-021-06793-7, PMID: [DOI] [PubMed] [Google Scholar]
- 22. Fan Y, Zheng L, Mao MH, Huang MW, Liu SM, Zhang J, et al. Survival analysis of oral squamous cell carcinoma in a subgroup of young patients. Asian Pacific J Cancer prevention: APJCP. (2014) 15:8887–91. doi: 10.7314/apjcp.2014.15.20.8887, PMID: [DOI] [PubMed] [Google Scholar]
- 23. Farhat MC, Dyalram D, Ord RA, Lubek JE. Oral squamous cell carcinoma in patients aged 45 and younger: prognosis, survival, and quality of life. Oral surgery Oral medicine Oral Pathol Oral Radiol. (2022) 133:518–25. doi: 10.1016/j.oooo.2021.08.023, PMID: [DOI] [PubMed] [Google Scholar]
- 24. Gamez ME, Kraus R, Hinni ML, Moore EJ, Ma DJ, Ko SJ, et al. Treatment outcomes of squamous cell carcinoma of the oral cavity in young adults. Oral Oncol. (2018) 87:43–8. doi: 10.1016/j.oraloncology.2018.10.014, PMID: [DOI] [PubMed] [Google Scholar]
- 25. Iype EM, Pandey M, Mathew A, Thomas G, Nair MK. Squamous cell cancer of the buccal mucosa in young adults. Br J Oral Maxillofac Surg. (2004) 42:185–9. doi: 10.1016/j.bjoms.2004.02.008, PMID: [DOI] [PubMed] [Google Scholar]
- 26. Iype EM, Pandey M, Mathew A, Thomas G, Sebastian P, Nair MK. Oral Cancer among Patients under the Age of 35 Years. J postgraduate Med. (2001) 47:171–6. [PubMed] [Google Scholar]
- 27. Kies MS, Boatright DH, Li G, Blumenschein G, El-Naggar AK, Brandon Gunn G, et al. Phase ii trial of induction chemotherapy followed by surgery for squamous cell carcinoma of the oral tongue in young adults. Head Neck. (2012) 34:1255–62. doi: 10.1002/hed.21906, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Kim S, Lee C, Kim H, Yoon SO. Genetic characteristics of advanced oral tongue squamous cell carcinoma in young patients. Oral Oncol. (2023) 144:106466. doi: 10.1016/j.oraloncology.2023.106466, PMID: [DOI] [PubMed] [Google Scholar]
- 29. Lee SU, Moon SH, Choi SW, Cho KH, Park JY, Jung YS, et al. Prognostic significance of smoking and alcohol history in young age oral cavity cancer. Oral Dis. (2020) 26:1440–8. doi: 10.1111/odi.13432, PMID: [DOI] [PubMed] [Google Scholar]
- 30. Liao PH, Lee CC. The influence of marital status on survival for patients aged 65 years and younger with oral cavity cancer. Auris nasus larynx. (2018) 45:1227–32. doi: 10.1016/j.anl.2018.03.007, PMID: [DOI] [PubMed] [Google Scholar]
- 31. Manuel S, Raghavan SK, Pandey M, Sebastian P. Survival in patients under 45 years with squamous cell carcinoma of the oral tongue. Int J Oral Maxillofac Surg. (2003) 32:167–73. doi: 10.1054/ijom.2002.0271, PMID: [DOI] [PubMed] [Google Scholar]
- 32. Mascitti M, Tempesta A, Togni L, Capodiferro S, Troiano G, Rubini C, et al. Histological features and survival in young patients with hpv-negative oral squamous cell carcinoma. Oral Dis. (2020) 26:1640–8. doi: 10.1111/odi.13479, PMID: [DOI] [PubMed] [Google Scholar]
- 33. Miller C, Shay A, Tajudeen B, Sen N, Fidler M, Stenson K, et al. Clinical features and outcomes in young adults with oral tongue cancer. Am J Otolaryngol. (2019) 40:93–6. doi: 10.1016/j.amjoto.2018.09.022, PMID: [DOI] [PubMed] [Google Scholar]
- 34. Mneimneh WS, Xu B, Ghossein C, Alzumaili B, Sethi S, Ganly I, et al. Clinicopathologic characteristics of young patients with oral squamous cell carcinoma. Head Neck Pathol. (2021) 15:1099–108. doi: 10.1007/s12105-021-01320-w, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Myers JN, Elkins T, Roberts D, Byers RM. Squamous cell carcinoma of the tongue in young adults: increasing incidence and factors that predict treatment outcomes. Otolaryngology--head Neck Surg. (2000) 122:44–51. doi: 10.1016/s0194-5998(00)70142-2, PMID: [DOI] [PubMed] [Google Scholar]
- 36. Okuyama K, Yanamoto S, Michi Y, Shibata E, Tsuchiya M, Yokokawa M, et al. Multicenter retrospective analysis of clinicopathological features and prognosis of oral tongue squamous cell carcinoma in adolescent and young adult patients. Medicine. (2021) 100:e27560. doi: 10.1097/md.0000000000027560, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Parzefall T, Schnoell J, Monschein L, Foki E, Liu DT, Frohne A, et al. Prkca overexpression is frequent in young oral tongue squamous cell carcinoma patients and is associated with poor prognosis. Cancers (Basel). (2021) 13:2082. doi: 10.3390/cancers13092082, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Subramaniam N, Balasubramanian D, Murthy S, Vidhyadharan S, Thankappan K, Iyer S. Oral cancer in the young with no tobacco exposure: A distinct epidemiological subset? J Head Neck Physicians Surgeons. (2018) 6:86–92. doi: 10.4103/2347-8128.208524 [DOI] [Google Scholar]
- 39. Thomas L, Moore EJ, McGree ME, Olsen KD, Kasperbauer JL, Erickson LA, et al. Prognostic features, human papillomavirus status, and epidermal growth factor receptor expression in oral squamous cell carcinoma in young adults. Am J Otolaryngol. (2012) 33:650–6. doi: 10.1016/j.amjoto.2012.01.009, PMID: [DOI] [PubMed] [Google Scholar]
- 40. Warnakulasuriya S, Mak V, Möller H. Oral cancer survival in young people in south east England. Oral Oncol. (2007) 43:982–6. doi: 10.1016/j.oraloncology.2006.11.021, PMID: [DOI] [PubMed] [Google Scholar]
- 41. de Morais EF, Mafra RP, Gonzaga AKG, de Souza DLB, Pinto LP, da Silveira ÉJD. Prognostic factors of oral squamous cell carcinoma in young patients: A systematic review. J Oral Maxillofac Surg. (2017) 75:1555–66. doi: 10.1016/j.joms.2016.12.017, PMID: [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.