Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results.

Transcriptomics is widely used to assess the state of biological systems. There are many tools for the different steps, such as normalization, differential expression, and enrichment. While numerous studies have examined the impact of method choices on differential expression results, little attention has been paid to their effects on further downstream functional analysis, which typically provides the basis for interpretation and follow-up experiments. To address this, we introduce FLOP, a comprehensive nextflow-based workflow combining methods to perform end-to-end analyses of transcriptomics data. We illustrate FLOP on datasets ranging from end-stage heart failure patients to cancer cell lines. We discovered effects not noticeable at the gene-level, and observed that not filtering the data had the highest impact on the correlation between pipelines in the gene set space. Moreover, we performed three benchmarks to evaluate the 12 pipelines included in FLOP, and confirmed that filtering is essential in scenarios of expected moderate-to-low biological signal. Overall, our results underscore the impact of carefully evaluating the consequences of the choice of preprocessing methods on downstream enrichment analyses. We envision FLOP as a valuable tool to measure the robustness of functional analyses, ultimately leading to more reliable and conclusive biological findings.

Anastomotic leak in colorectal cancer surgery: Contribution of gut microbiota and prediction approaches.

AIM: To monitor prospectively the occurrence of colorectal anastomotic leakage (CAL) in patients with colon cancer undergoing resectional surgery, characterizing the microbiota in both faeces and mucosal biopsies of anastomosis. In a second stage, we investigated the ability to predict CAL using machine learning models based on clinical data and microbiota composition. METHOD: A total of 111 patients were included, from whom a faecal sample was obtained, as well as biopsy samples from proximal and distal sites in the healthy margins of the tumour piece. The microorganisms present in the samples were investigated using microbial culture and 16S rDNA massive sequencing. Collagenase and protease production was determined, as well as the presence of genes responsible for expressing enzymes with these activities. Machine learning analyses were developed using clinical and microbiological data. RESULTS: The incidence of CAL was 9.0%, and CAL was associated with collagenase/protease-producing Enterococcus. Significant differences were found in the microbiota composition of proximal and distal biopsy samples, but not in faecal samples, among patients who developed CAL. Clinical predictors of CAL were 5-day C-reactive protein and heart disease, whereas 3-day C-reactive protein and diabetes were negative predictors. CONCLUSION: Biopsy samples from surgical margins, rather than faecal samples, are the most appropriate samples for exploring the contribution of the intestinal microbiota to CAL. Enterococci are only enriched in the anastomosis after surgery, and their collagenases and proteases are involved in the degradation of the anastomotic scar.