The application of bacterial metagenomic analysis as a biomarker for cancer detection is emerging. Our aim was to discover gut microbiota signatures with potential utility in the diagnosis of colorectal cancer (CRC) and non-small cell lung cancer (NSCLC). A prospective study was performed on a total of 77 fecal samples from CRC and NSCLC patients and controls. DNA from stool was analyzed for bacterial genomic sequencing using the Ion Torrent™ technology. Bioinformatic analysis was performed using the QIIME2 pipeline. We applied logistic regression to adjust for differences attributable to sex, age, and body mass index, and the diagnostic accuracy of our gut signatures was compared with other previously published results. The feces of patients affected by different tumor types, such as CRC and NSCLC, showed a differential intestinal microbiota profile. After adjusting for confounders, Parvimonas (OR = 53.3), Gemella (OR = 6.01), Eisenbergiella (OR = 5.35), Peptostreptococcus (OR = 9.42), Lactobacillus (OR = 6.72), Salmonella (OR = 5.44), and Fusobacterium (OR = 78.9) remained significantly associated with the risk of CRC. Two genera from the Ruminococcaceae family, DTU089 (OR = 20.1) and an uncharacterized genus (OR = 160.1), were associated with the risk of NSCLC. Our two panels had better diagnostic capacity for CRC (AUC = 0.840) and NSLC (AUC = 0.747) compared to the application of two other published panels to our population. Thus, we propose a gut bacteria panel for each cancer type and show its potential application in cancer diagnosis.

Colorectal cancer consensus molecular subtypes (CMSs) are widely accepted and constitutes the basis for patient stratification to improve clinical practice. We aimed to find whether miRNAs could reproduce molecular subtypes, and to identify miRNA targets associated to the High-stroma/CMS4 subtype. The expression of 939 miRNAs was analyzed in tumors classified in CMS. TALASSO was used to find gene-miRNA interactions. A miR-mRNA regulatory network was constructed using Cytoscape. Candidate gene-miR interactions were validated in 293T cells. Hierarchical-Clustering identified three miRNA tumor subtypes (miR-LS; miR-MI; and miR-HS) which were significantly associated (p < 0.001) to the reported mRNA subtypes. miR-LS correlated with the low-stroma/CMS2; miR-MI with the mucinous-MSI/CMS1 and miR-HS with high-stroma/CMS4. MicroRNA tumor subtypes and association to CMSs were validated with TCGA datasets. TALASSO identified 1462 interactions (p < 0.05) out of 21,615 found between 176 miRs and 788 genes. Based on the regulatory network, 88 miR-mRNA interactions were selected as candidates. This network was functionally validated for the pair miR-30b/SLC6A6. We found that miR-30b overexpression silenced 3′-UTR-SLC6A6-driven luciferase expression in 293T-cells; mutation of the target sequence in the 3′-UTR-SLC6A6 prevented the miR-30b inhibitory effect. In conclusion CRC subtype classification using a miR-signature might facilitate a real-time analysis of the disease course and treatment response.

Background/Objectives: Heart failure (HF) is a highly prevalent clinical syndrome with serious morbidity and mortality. Furthermore, acute heart failure (AHF) is the main cause of hospital admission in people aged 65 years or more. Sodium-glucose cotransporter type 2 inhibitors (SGLT2is) have been shown to improve the survival and quality of life in patients with HF regardless of left ventricular ejection fraction (LVEF). Our aims were to describe the characteristics of adults with multiple pathologies admitted with acute heart failure as the main diagnosis and of the population treated with SGLT2is, as well as to evaluate if their use was associated with lower readmission and mortality rates. Methods: A prospective study of patients from the PROFUND-IC registry who were admitted with AHF as the main diagnosis was conducted. Clinical and analytical characteristics were analyzed, as well as readmissions and mortality. Descriptive and bivariate analyses of the sample between those taking SGLT2is and those who were not were performed, using the chi-square test for qualitative variables and Welch’s test for quantitative measures, as well as the Fisher and Wilcoxon tests as indicated for nonparametric tests. Kaplan–Meier curves were constructed to analyze the readmission and mortality of patients at 12 months based on SGLT2i treatment. Finally, a propensity score matching was performed, guaranteeing that the observed effect of the drug was not influenced by the differences in the characteristics between the groups. Results: There were 750 patients included: 58% were women, and the mean age was 84 years. Functional class II according to the NYHA scale predominated (54%), and the mean LVEF was 51%. SGLT2 inhibitors were prescribed to only 28% of patients. Most of the patients were men (48.6% vs. 39.8%, p = 0.029), they were younger (82 vs. 84 years, p = 0.002), and their LVEF was lower (48% vs. 52%, p < 0.001). Lower mortality was observed in the group treated with SGLT2is, both during baseline admission (2.4% vs. 6.9%, p = 0.017) and at the 12-month follow-up (6.2% vs. 13%, p = 0.023); as well as a lower readmission rate (23.8% vs. 38.9%, p < 0.001). After the propensity score matching, a decrease in the 12-month readmission rate continued to be observed in the group treated with SGLT2is (p = 0.03). Conclusions: SGLT2is use was associated with lower readmission rates at the 12-month follow-up in older adults with multiple pathologies admitted with acute heart failure.

The pathophysiology of gestational diabetes mellitus (GDM) comprises clinical and genetic factors. In fact, GDM is associated with several single nucleotide polymorphisms (SNPs). This study aimed to build a prediction model of GDM combining clinical and genetic risk factors. A total of 1588 pregnant women from the San Carlos Cohort participated in the present study, including 1069 (67.3%) Caucasian (CAU) and 519 (32.7%) Latin American (LAT) individuals, and 255 (16.1%) had GDM. The incidence of GDM was similar in both groups (16.1% CAU and 16.0% LAT). Genotyping was performed via IPLEX Mass ARRAY PCR, selecting 110 SNPs based on literature references. SNPs showing the strongest likelihood of developing GDM were rs10830963, rs7651090, and rs1371614 in CAU and rs1387153 and rs9368222 in LAT. Clinical variables, including age, pre-pregnancy body mass index, and fasting plasma glucose (FPG) at 12 gestational weeks, predicted the risk of GDM (AUC 0.648, 95% CI 0.601–0.695 in CAU; AUC 0.688, 95% CI 0.628–9.748 in LAT), and adding SNPs modestly improved prediction (AUC 0.722, 95%CI 0.680–0.764 in CAU; AUC 0.769, 95% CI 0.711–0.826 in LAT). In conclusion, adding genetic variants enhanced the prediction model of GDM risk in CAU and LAT pregnant women.

Microbiota could be of interest in the diagnosis of colorectal and non-small cell lung cancer (CRC and NSCLC). However, how the microbial components of tissues and feces reflect each other remains unknown. In this work, our main objective is to discover the degree of correlation between the composition of the tissue microbiota and that of the feces of patients affected by CRC and NSCLC. Specifically, we investigated tumor and non-tumor tissues from 38 recruited patients with CRC and 19 with NSCLC. DNA from samples was submitted for 16S rDNA metagenomic sequencing, followed by data analysis through the QIIME2 pipeline and further statistical processing with STATA IC16. Tumor and non-tumor tissue selected genera were highly correlated in both CRC and NSCLC (100% and 81.25%). Following this, we established tissue–feces correlations, using selected genera from a LEfSe analysis previously published. In CRC, we found a strong correlation between the taxa detected in feces and those from colorectal tissues. However, our data do not demonstrate this correlation in NSCLC. In conclusion, our findings strongly reinforce the utility of fecal microbiota as a non-invasive biomarker for CRC diagnosis, while highlighting critical distinctions for NSCLC. Furthermore, our data demonstrate that the microbiota components of tumor and non-tumor tissues are similar, with only minor differences being detected.

Background: Sarcopenia is a comorbidity associated with heart failure, which aggravates its prognosis. Objectives: To analyze the differential characteristics of polypathological patients with acute heart failure (AHF) based on the presence of low muscle strength, as well as to study whether this condition is associated with a worse prognosis. Methods: An observational study of 377 patients with a diagnosis of acute heart failure from the prospective multicentric PROFUND-IC registry was carried out. The main variable is low muscle strength, which is assessed with dynamometry or prehensile strength. Epidemiological and anthropometric characteristics, as well as associated comorbidities, were analyzed. Likewise, the etiology of the AHF episode, the number of admissions in the previous year, and the NYHA scale were also included. Finally, scores on functionality, treatment established, and mortality and readmission rates were studied. Quantitative variables are described as mean, and standard deviation, and qualitative variables are expressed as absolute numbers and percentages. A descriptive and bivariate analysis was performed according to the presence of low muscle strength (handgrip <27 kg in men and <16 kg in women), using the Welch test for quantitative measures and Chi-square for qualitative variables. In addition, Kaplan-Meier curves of readmission and mortality and a logistic regression analysis were also performed. Results: 377 patients were included (56% female, mean age 83 years). 310 (82.23%) had low muscle strength. Those with low muscle strength were older (84 vs. 78 years, p < 0.001), with more cognitive impairment (11.9% vs. 0%, p = 0.021), worse functional class (p = 0.016), lower scores in the Barthel index and Rockwood scale (p < 0.001), and higher in the PROFUND index (p < 0.001). They had higher rates of readmission and mortality without statistically significant differences. The PROFUND index is significantly associated with low muscle strength (OR 1.19, CI (1.09–1.31), p < 0.001). Conclusions: Elderly polypathological patients with acute heart failure and low muscle strength have a higher PROFUND index and a lower probability of survival per year.