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Aims: To analyze the clinical relevance of O6-methylguanine-DNA methyltransferase in rectal adenocarcinoma treated with chemoradiotherapy followed by surgery. Methods: Tissue samples from 29 rectal adenocarcinoma patients were obtained after chemoradiotherapy. O6-methylguanine-DNA methyltransferase promoter methylation status was established by methylation-specific polymerase chain reaction. O6-methylguanine-DNA methyltransferase protein levels were determined by immunohistochemistry. Clinicopathologic variables, including treatment regression grade, recurrence, lymph node invasion, and stage and differentiation grade of the tumor, were determined. Results: The O6-methylguanine-DNA methyltransferase gene promoter was methylated in 81.5% of samples. Most patients (88.9%) showed low O6-methylguanine-DNA methyltransferase protein expression. O6-methylguanine-DNA methyltransferase methylation status was not correlated with any of the clinicopathological variables determined in rectal adenocarcinomas selected for chemoradiotherapy. Conclusion: O6-methylguanine-DNA methyltransferase methylation status is not correlated with clinicopathologic variables examined in rectal adenocarcinoma selected for chemoradiotherapy, although its role as a biomarker awaits further investigation.

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses ('gained' genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.

Abstract The COVID-19 pandemic remains a significant global health threat, with uncertainties persisting regarding the factors determining whether individuals experience mild symptoms, severe conditions, or succumb to the disease. This study presents an NMR metabolomics-based approach, analysing 80 serum and urine samples from COVID-19 patients (34 intensive care patients and 46 hospitalized patients) and 32 from healthy controls. Our research identifies discriminant metabolites and clinical variables relevant to COVID-19 diagnosis and severity. These discriminant metabolites play a role in specific pathways, mainly “Phenylalanine, tyrosine and tryptophan biosynthesis”, “Phenylalanine metabolism”, “Glycerolipid metabolism” and “Arginine and proline metabolism”. We propose a three-metabolite diagnostic panel—comprising isoleucine, TMAO, and glucose—that effectively discriminates COVID-19 patients from healthy individuals, achieving high efficiency. Furthermore, we found an optimal biomarker panel capable of efficiently classify disease severity considering both clinical characteristics (obesity/overweight, dyslipidemia, and lymphocyte count) together with metabolites content (ethanol, TMAO, tyrosine and betaine).