RESUMEN
Gastric cancer (GC) is a leading cause of death, and this pathology often receives a diagnosis in an advanced stage. The development of a less invasive and cost-effective test for detection is essential for decreasing the mortality rate and increasing the life expectancy of GC patients. We evaluated the potential targeting of CD54/ICAM1, a marker of gastric cancer stem cells, with miRNAs to detect GC in blood samples. The analyses included 79 blood samples, 38 from GC patients and 41 from healthy donors, who attended INCan, México City. The total RNA was obtained from the blood plasma, and RT-PCR and qPCR were performed to obtain the relative expression of each miRNA. Hsa-miR-335-5p was detected in the plasma of GC patients and healthy donors at the same levels. The ROC curve analyses indicated that this miRNA was not a candidate for the molecular diagnosis of GC. We did not observe a correlation between the expression of hsa-miR-335-5p and clinical variables; however, the Kaplan-Meier analyses indicated that, in patients who survived more than 12 months, a lower expression of hsa-miR-335-5p was correlated with a better prognosis. It would be convenient to evaluate a larger panel of miRNAs, including miRNAs expressed in a limited number of cell types or with a low number targets, to obtain more specific candidates for developing a robust test for the diagnosis/prognosis of GC.
RESUMEN
Background: Molecular diagnosis of cystic fibrosis (CF) is challenging in Mexico due to the population's high genetic heterogeneity. To date, 46 pathogenic variants (PVs) have been reported, yielding a detection rate of 77%. We updated the spectrum and frequency of PVs responsible for this disease in mexican patients. Methods: We extracted genomic DNA from peripheral blood lymphocytes obtained from 297 CF patients and their parents. First, we analyzed the five most frequent PVs in the Mexican population using PCR-mediated site-directed mutagenesis. In patients with at least one identified allele, CFTR sequencing was performed using next-generation sequencing tools and multiplex ligation-dependent probe amplification. For variants not previously classified as pathogenic, we used a combination of in silico prediction, CFTR modeling, and clinical characteristics to determine a genotype-phenotype correlation. Results: We identified 95 PVs, increasing the detection rate to 87.04%. The most frequent variants were p.(PheF508del) (42.7%), followed by p.(Gly542*) (5.6%), p.(Ser945Leu) (2.9%), p.(Trp1204*) and p.(Ser549Asn) (2.5%), and CFTRdel25-26 and p.(Asn386Ilefs*3) (2.3%). The remaining variants had frequencies of <2.0%, and some were exclusive to one family. We identified 10 novel PVs localized in different exons (frequency range: 0.1-0.8%), all of which produced structural changes, deletions, or duplications in different domains of the protein, resulting in dysfunctional ion flow. The use of different in silico software and American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) criteria allowed us to assume that all of these PVs were pathogenic, causing a severe phenotype. Conclusions: In a highly heterogeneous population, combinations of different tools are needed to identify the variants responsible for CF and enable the establishment of appropriate strategies for CF diagnosis, prevention, and treatment.
RESUMEN
Background: Metformin has antineoplastic and cancer protective effects in vitro, sensitizing leukemia cells to chemotherapeutic agents, inducing apoptosis and cell cycle arrest. Aim: To assess the effect of metformin on the induction stage in patients with ALL and its impact on overall survival and relapse. Material and Methods. We included 123 patients treated with metformin and without metformin. The dose used was 850 mg PO at 8 h intervals. The survival analysis was used by Kaplan-Meier method, the difference between the distinct groups was performed using the log Rank test. Results. The overall survival at a median follow up of 700 days of follow-up was 43%, with a disease-free survival of 47%. Regarding the treatment groups, patients with metformin had a lower rate of relapse compared to the group receiving only chemotherapy (6.5% vs 17.1%, p = 0.006). Conclusions. The addition of metformin to the conventional treatment of ALL was associated with an improvement in survival, this association being independent of the type of biological risk at diagnosis.