Breath Fingerprint of Colorectal Cancer Patients Based on the Gas Chromatography-Mass Spectrometry Analysis.

The human body emits a multitude of volatile organic compounds (VOCs) via tissues and various bodily fluids or exhaled breath. These compounds collectively create a distinctive chemical profile, which can potentially be employed to identify changes in human metabolism associated with colorectal cancer (CRC) and, consequently, facilitate the diagnosis of this disease. The main goal of this study was to investigate and characterize the VOCs' chemical patterns associated with the breath of CRC patients and controls and identify potential expiratory markers of this disease. For this purpose, gas chromatography-mass spectrometry was applied. Collectively, 1656 distinct compounds were identified in the breath samples provided by 152 subjects. Twenty-two statistically significant VOCs (p-xylene; hexanal; 2-methyl-1,3-dioxolane; 2,2,4-trimethyl-1,3-pentanediol diisobutyrate; hexadecane; nonane; ethylbenzene; cyclohexanone; diethyl phthalate; 6-methyl-5-hepten-2-one; tetrahydro-2H-pyran-2-one; 2-butanone; benzaldehyde; dodecanal; benzothiazole; tetradecane; 1-dodecanol; 1-benzene; 3-methylcyclopentyl acetate; 1-nonene; toluene) were observed at higher concentrations in the exhaled breath of the CRC group. The elevated levels of these VOCs in CRC patients' breath suggest the potential for these compounds to serve as biomarkers for CRC.

Autoimmune gastritis serological biomarkers in gastric cancer patients.

The role of autoimmunity in the pathogenesis of gastric cancer remains controversial. We studied antiparietal cell antibody (anti-PCA) and anti-intrinsic factor antibody (anti-IFA) levels and their associations with pepsinogen I/pepsinogen II levels in patients with gastric adenocarcinoma compared to a control group with mild or no atrophy of the stomach mucosa. Plasma levels of anti-PCA and anti-IFA were measured by ELISA (Inova Diagnostics Inc, San Diego, California, USA). The cutoff value for anti-PCA and anti-IFA positivity was ≥25 units. Altogether 214 patients (126 men, 88 women, median age 64.46, range: 35-86) with confirmed gastric adenocarcinoma and 214 control cases paired for age and sex were included in the study. Positive anti-PCA was present in 22 (10.3%) gastric cancer patients and controls (P ≥ 0.999); positive anti-IFA in 6 (2.8%) and 4 (1.9.%), P < 0.232, respectively. We did not find significant differences in anti-PCA and anti-IFA positivity between gastric cancer patients and the control group; further investigation is required to better understand the potential involvement of autoimmune gastritis in the development of gastric cancer.

The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis.

Colorectal cancer (CRC) is the third most common malignancy and the second most common cause of cancer-related deaths worldwide. While CRC screening is already part of organized programs in many countries, there remains a need for improved screening tools. In recent years, a potential approach for cancer diagnosis has emerged via the analysis of volatile organic compounds (VOCs) using sensor technologies. The main goal of this study was to demonstrate and evaluate the diagnostic potential of a table-top breath analyzer for detecting CRC. Breath sampling was conducted and CRC vs. non-cancer groups (105 patients with CRC, 186 non-cancer subjects) were included in analysis. The obtained data were analyzed using supervised machine learning methods (i.e., Random Forest, C4.5, Artificial Neural Network, and Naïve Bayes). Superior accuracy was achieved using Random Forest and Evolutionary Search for Features (79.3%, sensitivity 53.3%, specificity 93.0%, AUC ROC 0.734), and Artificial Neural Networks and Greedy Search for Features (78.2%, sensitivity 43.3%, specificity 96.5%, AUC ROC 0.735). Our results confirm the potential of the developed breath analyzer as a promising tool for identifying and categorizing CRC within a point-of-care clinical context. The combination of MOX sensors provided promising results in distinguishing healthy vs. diseased breath samples. Its capacity for rapid, non-invasive, and targeted CRC detection suggests encouraging prospects for future clinical screening applications.

Age-Based Comparative Analysis of Colorectal Cancer Colonoscopy Screening Findings.

Background and Objectives: Colorectal cancer (CRC) incidence is rapidly emerging among individuals <50 years, termed as early-onset colorectal cancer (EOCRC). This study aimed to probe variations in tumorigenic pathology and relevant manifestations (polyp and adenoma incidence) between suspected cases of EOCRC and late-onset CRC (LOCRC; ≥50 years of age). Materials and Methods: Between September 2022 and February 2023, colonoscopy-based screening data from 1653 patients were included in this study. All eligible participants were divided into two groups, depending upon patient age, where Group 1 consisted of 1021 patients aged <50 years while Group 2 consisted of 632 patients aged ≥ 50 years. Polyp samples were collected when identified peri-procedurally and characterized according to World Health Organization criteria. Results: Polyp detection rate was 42% for the <50-year age group, while this was 76% for the ≥50-year age group. Additionally, the <50-year age group predominated in hyperplastic polyp manifestation, particularly within the rectum and sigmoid colon. In addition, the ≥50-year age group had increased prevalence of serrated polyps and differing adenoma manifestations. Conclusions: This investigation served to highlight the importance of age stratification for CRC colonoscopy-based screening effectiveness, with particular reference to evaluations that are based on polyp localization within differing colon regions.

Role of pre-existing incomplete intestinal metaplasia in gastric adenocarcinoma: A retrospective case series analysis.

BACKGROUND: Risk stratification for patients with gastric precancerous lesions for endoscopic surveillance remains controversial. AIM: To analysis of patients having developed gastric adenocarcinoma during the period of follow-up. METHODS: We conducted a retrospective study on patients having undergone upper endoscopy prior to the development of gastric adenocarcinoma. The presence and stage of precancerous lesions as well as subtype of intestinal metaplasia at the baseline endoscopy got evaluated. Literature mini-review was performed. RESULTS: Out of 1681 subjects in the Biobank, gastric adenocarcinoma was detected in five cases in whom previous endoscopy data with biopsies either from the corpus or antral part were available. All of the patients had incomplete intestinal metaplasia during the baseline endoscopy; all three subjects in whom intestinal metaplasia subtyping was performed according to Filipe et al, had Type III intestinal metaplasia. Two of the five cases had low Operative Link on Gastritis Assessment (OLGA) and Operative Link on Gastritis Intestinal Metaplasia Assessment (OLGIM) stages (I-II) at the baseline. CONCLUSION: The presence of incomplete intestinal metaplasia, in particular, that of Type III is a better predictor for gastric adenocarcinoma development than OLGA/OLGIM staging system. Subtyping of intestinal metaplasia have an important role in the risk stratification for surveillance decisions.

The Association of Circulating L-Carnitine, γ-Butyrobetaine and Trimethylamine N-Oxide Levels with Gastric Cancer.

Our study aimed to evaluate the association between gastric cancer (GC) and higher concentrations of the metabolites L-carnitine, γ-butyrobetaine (GBB) and gut microbiota-mediated trimethylamine N-oxide (TMAO) in the circulation. There is evidence suggesting that higher levels of TMAO and its precursors in blood can be indicative of either a higher risk of malignancy or indeed its presence; however, GC has not been studied in this regard until now. Our study included 83 controls without high-risk stomach lesions and 105 GC cases. Blood serum L-carnitine, GBB and TMAO levels were measured by ultra-high-performance liquid chromatography-mass spectrometry (UPLC/MS/MS). Although there were no significant differences between female control and GC groups, we found a significant difference in circulating levels of metabolites between the male control group and the male GC group, with median levels of L-carnitine reaching 30.22 (25.78-37.57) nmol/mL vs. 37.38 (32.73-42.61) nmol/mL (p < 0.001), GBB-0.79 (0.73-0.97) nmol/mL vs. 0.97 (0.78-1.16) nmol/mL (p < 0.05) and TMAO-2.49 (2.00-2.97) nmol/mL vs. 3.12 (2.08-5.83) nmol/mL (p < 0.05). Thus, our study demonstrated the association between higher blood levels of L-carnitine, GBB, TMAO and GC in males, but not in females. Furthermore, correlations of any two investigated metabolites were stronger in the GC groups of both genders in comparison to the control groups. Our findings reveal the potential role of L-carnitine, GBB and TMAO in GC and suggest metabolic differences between genders. In addition, the logistic regression analysis revealed that the only significant factor in terms of predicting whether the patient belonged to the control or to the GC group was the blood level of L-carnitine in males only. Hence, carnitine might be important as a biomarker or a risk factor for GC, especially in males.

The Role of an Artificial Intelligence Method of Improving the Diagnosis of Neoplasms by Colonoscopy.

BACKGROUND: Colorectal cancer (CRC) is the third most common cancer worldwide. Colonoscopy is the gold standard examination that reduces the morbidity and mortality of CRC. Artificial intelligence (AI) could be useful in reducing the errors of the specialist and in drawing attention to the suspicious area. METHODS: A prospective single-center randomized controlled study was conducted in an outpatient endoscopy unit with the aim of evaluating the usefulness of AI-assisted colonoscopy in PDR and ADR during the day time. It is important to understand how already available CADe systems improve the detection of polyps and adenomas in order to make a decision about their routine use in practice. In the period from October 2021 to February 2022, 400 examinations (patients) were included in the study. One hundred and ninety-four patients were examined using the ENDO-AID CADe artificial intelligence device (study group), and 206 patients were examined without the artificial intelligence (control group). RESULTS: None of the analyzed indicators (PDR and ADR during morning and afternoon colonoscopies) showed differences between the study and control groups. There was an increase in PDR during afternoon colonoscopies, as well as ADR during morning and afternoon colonoscopies. CONCLUSIONS: Based on our results, the use of AI systems in colonoscopies is recommended, especially in circumstances of an increase of examinations. Additional studies with larger groups of patients at night are needed to confirm the already available data.

The Diagnostic Value of Anti-Parietal Cell and Intrinsic Factor Antibodies, Pepsinogens, and Gastrin-17 in Corpus-Restricted Atrophic Gastritis.

We aimed to determine the diagnostic value of anti-parietal cell antibodies (anti-PCA), anti-intrinsic factor antibodies (anti-IFA), pepsinogen ratio (PGI/II), and gastrin-17 (G-17) in corpus-restricted atrophic gastritis (CRAG) detected by ELISA (Inova, Biohit). Our study compared 29 CRAG cases against 58 age- and sex-matched controls with mild or no atrophy. Anti-PCA and anti-IFA positive cutoff values were ≥25 units for both. PGI/II value <3 was considered characteristic for atrophy; positive cutoff values for G-17 and anti-H. pylori IgG were >5 pg/L and >30 EIU. Anti-PCA was positive in 65.5% For CRAG cases and 13.8% of the controls (p < 0.0001), anti-IFA was positive in 13.8% and 0% (p = 0.01), respectively. Decreased pepsinogen levels were present in 79.3% of CRAG cases and 10.3% of the controls (p < 0.0001). PGI/II ratio was the best single biomarker, with sensitivity = 79%, specificity = 90%, and AUC 0.90. The combined use of PGI/II and anti-PCA resulted in AUC 0.93 for detecting CRAG. Our study suggests that the best combination of non-invasive biomarkers for detecting CRAG is PGI/II with anti-PCA. The addition of G-17 and anti-IFA is of little utility in clinical application.

Assessment of Serum Pepsinogens with and without Co-Testing with Gastrin-17 in Gastric Cancer Risk Assessment-Results from the GISTAR Pilot Study.

Introduction−−Serum pepsinogen tests for gastric cancer screening have been debated for decades. We assessed the performance of two pepsinogen assays with or without gastrin-17 for the detection of different precancerous lesions alone or as a composite endpoint in a Latvian cohort. Methods−−Within the intervention arm of the GISTAR population-based study, participants with abnormal pepsinogen values by ELISA or latex-agglutination tests, or abnormal gastrin-17 by ELISA and a subset of subjects with all normal biomarker values were referred for upper endoscopy with biopsies. Performance of biomarkers, corrected by verification bias, to detect five composite outcomes based on atrophy, intestinal metaplasia, dysplasia or cancer was explored. Results−−Data from 1045 subjects were analysed, of those 273 with normal biomarker results. Both pepsinogen assays showed high specificity (>93%) but poor sensitivity (range: 18.4−31.1%) that slightly improved when lesions were restricted to corpus location (40.5%) but decreased when dysplasia and prevalent cancer cases were included (23.8%). Adding gastrin-17 detection, sensitivity reached 33−45% while specificity decreased (range: 61.1−62%) and referral rate for upper endoscopy increased to 38.6%. Conclusions−−Low sensitivity of pepsinogen assays is a limiting factor for their use in population-based primary gastric cancer screening, however their high specificity could be useful for triage.

Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection.

BACKGROUND: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). METHODS: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. RESULTS: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. CONCLUSIONS: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.