Association of inflammation-related and microRNA gene expression with cancer-specific mortality of colon adenocarcinoma

Purpose: Inflammatory genes and microRNAs have roles in colon carcinogenesis; therefore, they may provide useful biomarkers for colon cancer. This study examines the potential clinical utility of an inflammatory gene expression signature as a prognostic biomarker for colon cancer in addition to previously examined miR-21 expression. Experimental Design: Quantitative reverse transcriptase-PCR. was used to measure the expression of 23 inflammatory genes in colon adenocarcinomas and adjacent noncancerous tissues from 196 patients. These data were used to develop models for cancer-specific mortality on a training cohort (n = 57), and this model was tested in both a test (n = 56) and a validation (n = 83) cohort. Expression data for miR-21 were available for these patients and were compared and combined with inflammatory gene expression. Results: PRG1, IL-10, CD68, IL-23a, and IL-12a expression in noncancerous tissue, and PRG1, ANXA1, IL-23a, IL-17a, FOXP3, and HLA-DRA expression in tumor tissues were associated with poor prognosis based on Cox regression (|Z-score| >1.5) and were used to generate the inflammatory risk score (IRS). IRS was associated with cancer-specific mortality in the training, test (P = 0.01), and validation (P = 0.02) cohorts. This association was strong for stage II cases (P = 0.002). Expression of miR-21 was associated with IL-6, IL-8, IL-10, IL-12a, and NOS2a, providing evidence that the function of this micro-RNA and these inflammatory genes are linked. Both IRS and miR-21 expression were independently associated with cancer-specific mortality, including stage II patients alone. Conclusion: IRS and miR-21 expression are independent predictors of colon cancer prognosis and may provide a clinically useful tool to identify high-risk patients. © 2009 American Association for Cancer Research.link_to_OA_fulltex

A review on metabolomics data analysis for cancer applications

Cancer cells undergo metabolic changes that contribute to tumorigenesis, which can be determined using metabolomics data produced by techniques such as nuclear magnetic resonance and mass spectroscopy, and analyzed through statistical and machine learning methods. Since these data represent well the metabolic phenotype of these cells, they are very relevant in cancer research, to better understand tumour cells metabolism and help in efforts of biomarker and drug target discovery. This mini-review focuses on data analysis methods that are commonly used to extract knowledge from cancer metabolomics data, such as univariate analysis and supervised and unsupervised multivariate data analysis, including clustering and machine learning.This work is co-funded by the North Portugal Regional Operational Programme, under the “Portugal 2020”, through the European Regional Development Fund (ERDF), within project SISBI- RefaNORTE-01-0247-FEDER-003381. This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte.info:eu-repo/semantics/publishedVersio