RESUMEN
BACKGROUND: Hyperglycemia can promote the development of prostate cancer (PCa). Differential expression levels of miRNAs between PCa patients and controls were also reported. Therefore, we examined the relationship between hyperglycemia and miRNA levels in PCa. METHODS: Relative expression of urinary miR-574-3p, miR-375, miR-205-5p, miR-200b-3p, miR-187-3p, miR-182-5p, and miR-100-5p were investigated in 105 PCa patients and 138 noncancer controls by Real-Time quantitative PCR. Fasting plasma glucose measurements were retrieved from clinical records. The differential miRNA expressions among groups were compared using non-parametric tests. Correlations with glucose and prostate-specific antigen (PSA) were tested using Pearson correlation coefficient. RESULTS: When we analyzed miRNA expression according to glycemic state, significant down-regulations were found for miR-200b-3p, miR-187-3p, miR-182-5p, and miR-100-5p in noncancer controls with high glucose. The lowest down-regulations were observed for miR-187-3p, miR-182-5p, and miR-100-5p. Subsequently, when hyperglycemia was considered in PCa, significant dysregulations of selected miRNAs were found in hyperglycemic PCa patients than in controls with high glucose. In particular, miR-375 and miR-182-5p showed a 3-FC in hyperglycemic PCa patients than controls who left hyperglycemia untreated. Conversely, only a down-regulation of miR-574-3p was observed in PCa patients regardless of glycemic status and only modest down-regulation of miR-574-3p, miR-200b-3p, miR-187-3p and miR-182-5p were found in normoglycemic PCa patients. Next, significant correlations between miRNAs and glucose (miR-200b-3p, miR-100-5p) and PSA (miR-205-5p and miR-187-3p) were detected in controls. Similarly, miR-205-5p and miR-187-3p were correlated with glucose in PCa patients, while miR-574-3p and miR-375 showed inverse relationships. CONCLUSIONS: miRNA dysregulations can occur in hyperglycemic PCa patients as compared to noncancer controls who left hyperglycemia untreated. Hyperglycemia can consistently promote the expression of miR-375 and miR-182-5p. Uncontrolled hyperglycemic state could contribute to the creation of a suitable microenvironment for later PCa development by promoting gene expression.
RESUMEN
Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80-0.95 and 0.83, 95% C.I. 0.74-0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set.