Jennifer Ose

Background: Colorectal cancer (CRC) is a leading cause of cancer-related mortality. Prognosis is primarily guided by tumor stage despite substantial molecular heterogeneity. Urinary metabolomics may capture systemic and tumor-related biology beyond staging and could improve prognostic assessment. We hypothesized that incorporating urinary metabolomic profiles would improve overall survival (OS) prediction performance compared with a stage- and age-based reference model. Method: A total of n = 76 stage I-IV CRC patients recruited as part of the ColoCare Study in Heidelberg Germany with pre-surgery urinary metabolomics were included (23 deaths; median follow-up 3.03 years). Four metabolomics-based penalized Cox models adjusted for tumor stage and age at diagnosis were developed using LASSO, adaptive LASSO, spike-and-slab LASSO, and iterative sure independence screening (iSIS)-LASSO. Model discrimination was assessed using Harrell’s C-index and time-dependent AUC based on the nested cross-validation. Results: Compared with the reference model (Cox model including only tumor stage and age at diagnosis), all metabolomics-based models provided better discrimination. The spike-and-slab LASSO Cox model demonstrated the best performance, achieving a C-index of 0.75 ( vs. 0.68) and consistently higher time-dependent AUCs at 1–5 years of follow-up, with a peak AUC of 0.76 at year 3 ( vs. 0.68). Three urinary metabolites were consistently selected across all metabolomics-based models: indolelactate, 2-hydroxyisobutyrate and a uridine-like metabolite. Conclusions: Urinary metabolomics may improve CRC OS prediction beyond tumor stage and age at diagnosis, especially with the spike-and-slab LASSO Cox model. These results support urinary metabolomics as a promising noninvasive prognostic tool that merits external validation.

Purpose: Fatigue affects up to 90% of patients with cancer during chemotherapy and persists in approximately 30% after treatment completion. This study examined the longitudinal associations between fatigue and 16 inflammatory markers in patients with colorectal cancer (CRC) up to 3 years after surgery. Methods: Patients with Stage I–IV CRC who participated in the prospective ColoCare cohort study were included. Fatigue was measured using the 3‐item fatigue subscale of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC‐QLQ‐C30) at baseline and each subsequent time point (score range: 0–100). Inflammatory markers were measured using blood specimens from each time point (T0: baseline/surgery; T1: 3–9 months; T2: 10–19 months; T3: 20–36 months post‐baseline). Linear mixed‐effects models were used to examine associations between inflammatory markers and fatigue scores over time. Linear regression was used to analyze the relationships between T0/T1 markers and fatigue at T3. Results: There were 271 patients with Stage I–III CRC and 30 patients with Stage IV CRC. Among patients with Stage I–III CRC, mean fatigue peaked at T1 (36.9), with higher fatigue among women than men (44.8 vs. 30.9). In longitudinal mixed‐effects models, twofold higher mean levels of IL‐6 and IL‐17A were associated with higher fatigue over time (3.51 ( p = 0.01) and 3.91 ( p = 0.01), respectively). IL‐4, IL‐17A, and TNF‐α at T1 were nominally associated with higher levels of fatigue at T3. Conclusion: Higher levels of selected inflammatory biomarkers were associated with fatigue up to 3 years after CRC diagnosis. These markers may help identify patients at risk for persistent fatigue and warrant further study as potential therapeutic targets.

Previous research has revealed associations between diet and health‐related quality of life (HRQoL), between diet and kynurenine pathway (KP) metabolites (kynurenines), and between kynurenines and HRQoL in colorectal cancer (CRC) survivors. We examined if kynurenines mediate longitudinal associations between diet and HRQoL in CRC survivors up to 12‐months posttreatment. Repeated measurements were performed in 209 stage I–III CRC survivors. Diet was assessed by seven‐day dietary records. Plasma kynurenines were analyzed using LC–MS/MS. HRQoL outcomes were assessed through the validated EORTC QLQ‐C30. We used confounder‐adjusted multilevel parallel‐multiple mediator models with all kynurenines simultaneously and single mediator models with established KP ratios to estimate total ( c :diet‐HRQoL), direct ( c′ :diet‐HRQoL), metabolite‐specific indirect ( ab : diet‐metabolite‐HRQoL), and total indirect ( ab :diet‐metabolites‐HRQoL) effects. Higher carbohydrate intake was associated with worse role functioning, while higher fiber, alcohol, and zinc intake, and better adherence to the Dutch Healthy Diet (DHD) recommendations were associated with better physical and role functioning ( c ‐path). Associations of fiber and DHD with HRQoL remained statistically significant after controlling for all KP metabolites ( c′ ‐path). All kynurenines simultaneously only mediated associations of higher carbohydrate intake with worse role functioning, and of higher alcohol intake with better physical functioning ( ab ). The kynurenic acid‐to‐quinolinic acid (KA/QA) ratio and hydroxykynurenine ratio (HKr) significantly mediated associations of carbohydrate, protein, fat, alcohol, magnesium, and zinc intake, and adherence to DHD recommendations, with physical and role functioning ( ab ). In conclusion, while all kynurenines simultaneously did not mediate diet‐HRQoL associations, the KA/QA ratio and HKr did mediate several associations within the first year after CRC treatment.

Supplementary Data from Presurgery Adhesion Molecules and Angiogenesis Biomarkers Are Differently Associated with Outcomes in Colon and Rectal Cancer: Results from the ColoCare Study

Supplemental Table 2: Summary patient characteristics for Comparison Set 1

Supplementary Table S3 shows the Cancer and Treatment Distress scores 12 months after surgery by study center.

This figure depicts the flowchart of the two-step interaction test.

This file includes the association parameters (OR [95% CI]) for the identified SNPs with suggestive interaction term for smoking status by genotypes.

Supplemental Figure 3: EVL methylation levels of normal colon for patients with no history of adenoma or cancer after initial colonoscopy (left group) and for levels patients with an adenoma or colorectal cancer detected after initial (middle group). Patients with no follow-up colonoscopies (right group) were not included in the analyses for Comparison Set 2. %mEVL=percentage of DNA that is methylated at the EVL locus assayed.

Supplementary Figure S2 shows pre-surgery and 12-month biomarker concentrations correlated with the CTXD 12 months after surgery at Heidelberg.