Background and Objectives: Iron is an essential micronutrient for many biological functions and has been found to be intimately linked to cancer biology. Although the effects of increased dietary iron consumption in the development of CRC have been previously investigated in several cohort studies, the available evidence on the involvement of iron deficiency in this process is relatively scarce. Previously published papers did not analyze specific outcomes, such as the presence of biologically aggressive histopathological characteristics, that are associated with the subtypes of iron deficiency. The purpose of this study was to investigate the connection between the development of colorectal cancer and the presence of functional iron deficiency (FID), which is defined as insufficient biological availability of iron in the presence of adequate storage reserves, or absolute iron deficiency (AID), which is defined as severely depleted iron storage levels. Materials and Methods: Our paper represents a single center registry-based cohort study. Iron levels were routinely evaluated upon diagnosis of CRC and the collected data were coupled with patient- and tumor-specific data (2018–2022). Spearman’s correlation coefficient and the chi-squared test were used to analyze the association. Results: Out of 129 patients, 75 (58.13%) were anemic. AID was identified in 26.35% of cases and FID was encountered in 51.16% of cases. A statistically significant association between FID and lymphatic invasion was encountered. An analysis of the correlation demonstrated a significant association between anemia and right-sided tumor location. Conclusions: Functional iron deficiency seems to be independently associated with lymphatic invasion. Although a statistically significant correlation with the T or N stage was not demonstrated, the analysis suggested a potential positive relationship between the presence of FID and more aggressive tumor characteristics.

Background/Objectives: Colorectal cancer (CRC) represents one of the most prevalent forms of cancer, with high mortality rates. The aim of this study was to observe and understand the metabolic changes in CRC through targeted metabolomics. Methods: Samples collected from 58 CRC patients and 35 healthy individuals have been analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), targeting two classes of metabolites: amino acids and acylcarnitines. Results: Statistical analysis revealed 26 significantly modified (p-value < 0.01; |FC| > 1.2) metabolites in CRC patients compared to the control group and 22 between colon cancer and control, whereas 8 metabolites differed only significantly between rectal cancer and healthy patients. Some of these significantly modified metabolites characterize cancer-specific adaptations, such as increased energy demand, increased tumor invasiveness, capabilities to promote amino acid synthesis, and tumor resistance against acute immune response. Moreover, receiver operator characteristic (ROC) analysis revealed that a set of two acylcarnitines (C6DC and C4-OH) can differentiate between CRC patients and healthy individuals with a high degree of confidence (AUC 0.837). Conclusions: By implementing a metabolomics approach targeting amino acids and acylcarnitines, several metabolic alterations induced by CRC have been highlighted. Even though these modifications are not specific enough to act as disease markers, they might prove useful for evaluating patient status.

Inflammatory acute phase proteins have been reported to play a crucial role in cancer progression. Various hematologic and inflammatory markers and scores, such as the lymphocyte-to-monocyte ratio, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, systemic inflammation score (SIS), prognostic nutritional index (PNI), Glasgow prognostic score, and, more recently, the Naples prognostic score, have been reported as significant prognostic markers. The aim of this prospective study was to evaluate the prognostic significance of the C reactive protein-to-albumin ratio (CAR) in patients with colon cancer. Materials and Methods: We conducted a prospective observational study on a series of patients who underwent curative surgery for colon cancer. The C reactive protein-to-albumin ratio was determined preoperatively, and we evaluated the correlations between the CAR and various clinical and pathological parameters, as well as the correlation with Overall and Relapse-free survival. Furthermore, we compared the accuracy of the CAR with that of the Naples score. Results: One hundred and ten patients were included in the study. We set 0.4927 as the cut-off value for the CAR according to a receiver operating characteristic curve analysis. Based on the cut-off value, patients were divided into a low CAR group and a high CAR group. The preoperative CAR exhibited statistically significant correlation with tumor volume, T and N stage, number of positive lymph nodes, and grade of tumor differentiation. We also demonstrated a positive correlation between high CAR values and a higher Naples score (p = 0.0005), even when a subgroup analysis was performed for each group individually. Conclusions: The preoperative CAR is a useful prognostic marker in patients with colon cancer. These results may help to design strategies to personalize targeted management approaches among colon cancer patients.

Background: Surface-enhanced Raman spectroscopy (SERS) analysis of urine is a promising liquid biopsy technique for cancer detection. However, its clinical translation is hindered by two major challenges that impact classification efficacy. First, the SERS signal of urine is confounded by fluctuations induced by physiological differences in urine composition such as pH and dilution. Second, the molecular origin of the SERS signal of urine is incompletely understood, limiting the interpretability of machine learning classifiers in terms of specific biochemical markers. Methods: In this pilot study, we analyzed urine samples from breast cancer patients (n = 18) and control subjects (n = 10) at three pH levels (5, 7, and 9). Additionally, we analyzed simulated urine mixtures consisting of uric acid, hypoxanthine, xanthine, and creatinine in physiological concentrations to explain the variation in the SERS spectra at different pH values. Results: Urine at pH 9 yielded the most detailed spectral features. The SERS spectral pattern under alkaline pH reflected greater contributions from hypoxanthine, uric acid, and creatinine, while xanthine contributions diminished due to competitive interactions at the SERS substrate surface. Normalizing SERS signals to the creatinine band at 1420 cm⁻¹ effectively mitigated the confounding effects of urine dilution. Conclusions: Optimizing the pH to 9 and normalizing to creatinine significantly enhances the interpretability and accuracy of SERS-based urine analysis for cancer detection. These findings offer important theoretical and practical advancements for the development of SERS-based liquid biopsy tools for cancer detection.

Colorectal cancer is a widespread digestive disease. In this study, Fourier transform infrared spectroscopy (FT-IR) and ¹H nuclear magnetic resonance (NMR) relaxometry and diffusometry were used for the identification of specific features associated with colorectal cancer from native and deproteinized blood plasma and blood plasma proteins. The blood samples were collected from patients with confirmed colorectal cancer and from healthy volunteers for comparison. The FT-IR spectra were divided into specific regions. A numeric deconvolution procedure was applied in order to quantify the integrated areas of the peaks of interest which were associated with specific responses due to colorectal cancer. The ¹H NMR T₂- and D-distributions were obtained and quantified. Finally, principal component analysis (PCA) was applied to identify the most important NMR and FT-IR parameters for the differentiation between blood of colorectal cancer patients and blood of healthy volunteers.

Background/Objectives: Breast cancer is the most common malignancy among women globally, with an increasing incidence, particularly in younger populations. Achieving complete surgical excision is essential to reduce recurrence. Artificial intelligence (AI), including large language models like ChatGPT, has potential for supporting diagnostic tasks, though its role in surgical oncology remains limited. Methods: This retrospective study evaluated ChatGPT’s performance (ChatGPT-4, OpenAI, March 2025) in predicting surgical margin status (R0 or R1) based on intraoperative mammograms of lumpectomy specimens. AI-generated responses were compared with histopathological findings. Performance was evaluated using sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), F1 score, and Cohen’s kappa coefficient. Results: Out of a total of 100 patients, ChatGPT achieved an accuracy of 84.0% in predicting surgical margin status. Sensitivity for identifying R1 cases (incomplete excision) was 60.0%, while specificity for R0 (complete excision) was 86.7%. The positive predictive value (PPV) was 33.3%, and the negative predictive value (NPV) was 95.1%. The F1 score for R1 classification was 0.43, and Cohen’s kappa coefficient was 0.34, indicating moderate agreement with histopathological findings. Conclusions: ChatGPT demonstrated moderate accuracy in confirming complete excision but showed limited reliability in identifying incomplete margins. While promising, these findings emphasize the need for domain-specific training and further validation before such models can be implemented in clinical breast cancer workflows.