From inflammation to precision medicine in colon cancer: Methodological considerations and future directions

Abstract

A recent study by Zhu SS et al evaluated the prognostic value of the systemic immune-inflammation index and serum lactoferrin in older patients with colon cancer. While this work highlights the potential role of inflammation-based biomarkers in predicting survival, several methodological and analytical concerns limitations constrain its clinical applicability. These include a small sample size, a single-center observational design, a short follow-up duration, incomplete adjustment for confounding variables, and reliance on cut-off thresholds derived from receiver operating characteristic analyses, which increases the risk of overfitting. Moreover, the reported predictive accuracy was moderate, yet the findings were presented as clinically decisive, warranting caution in interpretation. Future studies should aim for multicenter, prospective cohorts with larger sample sizes, longer follow-up periods, and the integration of established prognostic indices and molecular biomarkers. Incorporating rigorous statistical validation and exploring biomarker dynamics over time would strengthen external validity. Addressing these issues could advance the development of reliable, inflammation-based prognostic tools and support individualized treatment strategies for elderly colon cancer patients.

Key Words: Colon cancer; Elderly; Precision medicine; Systemic immune-inflammation index; Lactoferrin; Prognostic biomarkers; Survival; Methodological critique

Core Tip: A study by Zhu SS et al reported that the systemic immune-inflammation index and serum lactoferrin levels predict survival in elderly patients with colon cancer. While promising, the study was limited by a small, single-center cohort, a short follow-up period, and an incomplete adjustment for confounders. Predictive accuracy was only moderate, yet conclusions were overstated. To ensure clinical utility, future research should validate systemic immune-inflammation index and lactoferrin in larger, multicenter studies with longer follow-up, benchmark them against established prognostic indices, and incorporate molecular markers. Such efforts are crucial to establishing reliable, externally validated prognostic tools for the individualized management of older colon cancer patients.

TO THE EDITOR

I read with great interest the recent article by Zhu et al[1], which evaluated the prognostic significance of the systemic immune-inflammation index (SII) and serum lactoferrin (LF) in older adults for postoperative survival in colon cancer. This work highlighted that elevated levels of SII and LF were associated with poorer postoperative survival outcomes, suggesting their potential role as accessible inflammation-based biomarkers for disease prognosis in this underserved population. These findings provide a promising foundation for integrating inflammatory markers into precision oncology approaches for elderly patients with colon cancer. However, several methodological concerns merit discussion to strengthen both its clinical impact and scientific robustness.

First, the most immediate limitation was the restricted sample size of only 62 patients from a single center. The study was likely underpowered for the multivariate analyses performed, which raises questions about the robustness of the conclusions. Small numbers pose a substantial risk of type I and II errors, and may also result in overfitting when numerous candidate predictors are analyzed simultaneously. Additionally, excluding patients with autoimmune diseases, those receiving preoperative chemotherapy, or those with incomplete follow-up limits the generalizability of the results. Larger and multicenter cohorts are crucial to adequately power predictive modeling and validation, as supported by recent meta-analyses emphasizing the effects of sample size on biomarker accuracy[2].

Second, the description of adjustment for potential confounders in the regression models was incomplete, particularly those with a substantial bearing on survival, such as adjuvant chemotherapy, comorbidities, and detailed molecular classification (e.g., microsatellite instability (MSI) status, RAS/RAF mutational status), and functional and performance status beyond the Eastern Cooperative Oncology Group performance status scale. Additionally, residual confounding from frailty, perioperative complications, or systemic inflammatory conditions was not acknowledged. In addition, the approach to handling confounding should be clarified by either including these variables in the models, undertaking propensity score adjustment in light of small event counts, or, at a minimum, more explicitly discussing the risk of residual and unmeasured confounding in the limitations section[4].

Third, precise outcome definition was another critical area. Both logistic regression (for short-term survival) and Cox proportional hazards models for disease-free survival and overall survival were applied; however, their concurrent use may confuse readers and inflate the family-wise type I error rate due to multiple testing. Additionally, the outcomes were defined based on clinical records, with recurrence adjudicated according to radiological evidence. Similarly, time-to-event analyses were conducted, and censoring was reportedly used to address competing risks, which is particularly relevant in an elderly cohort. Additionally, there was a lack of clarity regarding the censoring methodology and the adjudication of recurrence (biochemical vs radiological), which is relevant in older adults with multiple comorbidities.

Furthermore, the predictive accuracy, with area-under-curve values in the range of 0.72-0.75, is moderate rather than high. Cut-off points for SII and LF were derived post-hoc using the Youden index from receiver operating characteristic analysis, which, given the small size, risks optimism bias and overfitting. These thresholds, before any clinical application, require both internal and external validation methods, such as bootstrapping or cross-validation, and should be considered in future analyses to ensure reproducibility and reliability. Moreover, multiple testing adjustments were incompletely applied; harmonizing the statistical correction approach would reduce bias. In addition, the statistical method would benefit from explicit information on model calibration, internal cross-validation procedures, and the handling of missing data. Providing clear definitions and describing time-to-event and censoring approaches would be beneficial, as they would add transparency and improve reproducibility[4,5].

Fourth, the results should be interpreted cautiously. While both SII and LF were reported as independent predictors, the hazard ratios had wide confidence intervals, reflecting fragility in the findings. The significant interaction between SII and LF suggests a synergistic effect of systemic inflammation and immune modulation on tumor progression. This interaction may reflect complex pathways that influence both cancer-associated inflammation and the host immune response, warranting further investigation in both biology and clinical levels. The median follow-up of 18 months is short for colon cancer prognosis, where recurrence and mortality curves extend over several years. A longer follow-up, ideally three to five years, would provide stronger validation[6,7].

Fifth, the biological mechanisms underlying SII and LF as prognostic markers were insightfully described; however, the manuscript could be strengthened by more clearly addressing the clinical utility of these biomarkers. For example, while the SII × LF interaction was noted to be significant, its clinical implications were not fully explored. Additionally, the authors did not compare their findings to other well-established prognostic scores, such as the neutrophil-to-lymphocyte ratio, the Glasgow prognostic score, or the prognostic nutritional index, which could have provided context for the incremental value of SII and LF. The limitations section could be more robustly addressed to account for unmeasured confounding, immortal time bias, and possible reverse causation, all of which are relevant in observational cohort studies[8]. Additionally, integration within current risk stratification pathways, cost implications, and the steps necessary for implementation in routine practice should be outlined to maximize translational impact[9]. Comparative analyses involving established prognostic indices, such as neutrophil-to-lymphocyte ratio, Glasgow prognostic score, and prognostic nutritional index, are warranted to contextualize the incremental value of SII and LF.

Finally, generalizability of these findings also deserves attention. It is recommended that the discussion more explicitly acknowledge this limitation and purposefully direct future research toward diverse, multicenter cohorts. In addition, prospective efforts might combine SII and LF with other established or emerging biomarkers, using more sophisticated risk modeling techniques. To improve prognostic precision, future studies should incorporate key molecular biomarkers, such as microsatellite instability and RAS/RAF mutations, which are known to influence colon cancer biology. Additionally, risk modeling approaches, including the development of nomograms and the application of machine learning algorithms, may enhance predictive accuracy and clinical utility.

In conclusion, while Zhu et al[1] provide promising preliminary data on SII and LF as prognostic markers in elderly colon cancer, and contribute to the evolving landscape of precision oncology in older adults. However, methodological limitations, such as a small sample size, lack of comprehensive confounder adjustment, and moderate predictive accuracy, necessitate cautious interpretation. Rigorous multicenter validation, integration of molecular markers, and advanced statistical modeling are the essential next steps toward clinical application. The successful clinical translation of SII and LF biomarkers will require integration into existing risk stratification pathways, consideration of cost implications, and clear implementation strategies for routine practice.