Matrix metalloproteinase-9 in relation to patients with complications after colorectal surgery: a systematic review

Purpose: Anastomotic leakage (AL) is the most severe complication following colorectal resection and is associated with increased mortality. The main group of enzymes responsible for collagen and protein degradation in the extracellular matrix is matrix metalloproteinases. The literature is conflicting regarding anastomotic leakage and the degradation of extracellular collagen by matrix metalloproteinase-9 (MMP-9). In this systematic review, the

A multicentre cohort study of serum and peritoneal biomarkers to predict anastomotic leakage after rectal cancer resection

Aim: Anastomotic leakage (AL) is one of the most feared complications after rectal resection. This study aimed to assess a combination of biomarkers for early detection of AL after rectal cancer resection. Method: This study was an international multicentre prospective cohort study. All patients received a pelvic drain after rectal cancer resection. On the first three postoperative days drain fluid was collected daily and C-reactive protein (CRP) was measured. Matrix metalloproteinase-2 (MMP2), MMP9, glucose, lactate, interleukin 1-beta (IL1β), IL6, IL10, tumour necrosis factor alpha (TNFα), Escherichia coli, Enterococcus faecalis, lipopolysaccharide-binding protein and amylase were measured in the drain fluid. Prediction models for AL were built for each postoperative day using multivariate penalized logistic regression. Model performance was estimated by the c-index for discrimination. The model with the best performance was visualized with a nomogram and calibration was plotted. Results: A total of 292 patients were analysed; 38 (13.0%) patients suffered from AL, with a median interval to diagnosis of 6.0 (interquartile ratio 4.0–14.8) days. AL occurred less often after partial than after total mesorectal excision (4.9% vs 15.2%, P = 0.035). Of all patients with AL, 26 (68.4%) required reoperation. AL was more often treated by reoperation in patients without a diverting ileostomy (18/20 vs 8/18, P = 0.03). The prediction model for postoperative day 1 included MMP9, TNFα, diverting ileostomy and surgical technique (c-index = 0.71). The prediction model for postoperative day 2 only included CRP (c-index = 0.69). The prediction model for postoperative day 3 included CRP and MMP9 and obtained the best model performance (c-index = 0.78). Conclusion: The combination of serum CRP and peritoneal MMP9 may be useful for earlier prediction of AL after rectal cancer resection. In clinical practice, this combination of biomarkers should be interpreted in the clinical context as with any other diagnostic tool

Characteristics of Early-Onset vs Late-Onset Colorectal Cancer: A Review.

The incidence of early-onset colorectal cancer (younger than 50 years) is rising globally, the reasons for which are unclear. It appears to represent a unique disease process with different clinical, pathological, and molecular characteristics compared with late-onset colorectal cancer. Data on oncological outcomes are limited, and sensitivity to conventional neoadjuvant and adjuvant therapy regimens appear to be unknown. The purpose of this review is to summarize the available literature on early-onset colorectal cancer. Within the next decade, it is estimated that 1 in 10 colon cancers and 1 in 4 rectal cancers will be diagnosed in adults younger than 50 years. Potential risk factors include a Westernized diet, obesity, antibiotic usage, and alterations in the gut microbiome. Although genetic predisposition plays a role, most cases are sporadic. The full spectrum of germline and somatic sequence variations implicated remains unknown. Younger patients typically present with descending colonic or rectal cancer, advanced disease stage, and unfavorable histopathological features. Despite being more likely to receive neoadjuvant and adjuvant therapy, patients with early-onset disease demonstrate comparable oncological outcomes with their older counterparts. The clinicopathological features, underlying molecular profiles, and drivers of early-onset colorectal cancer differ from those of late-onset disease. Standardized, age-specific preventive, screening, diagnostic, and therapeutic strategies are required to optimize outcomes

Familial multiple discoid fibromas is linked to a locus on chromosome 5 including the FNIP1 gene

Previously, we reported a series of families presenting with trichodiscomas, inherited in an autosomal dominant pattern. The phenotype was named familial multiple discoid fibromas (FMDF). The genetic cause of FMDF remained unknown so far. Trichodiscomas are skin lesions previously reported to be part of the same spectrum as the fibrofolliculoma observed in Birt-Hogg-Dubé syndrome (BHD), an inherited disease caused by pathogenic variants in the FLCN gene. Given the clinical and histological differences with BHD and the exclusion of linkage with the FLCN locus, the phenotype was concluded to be distinct from BHD. We performed extensive clinical evaluations and genetic testing in ten families with FMDF. We identified a FNIP1 frameshift variant in nine families and genealogical studies showed common ancestry for eight families. Using whole exome sequencing, we identified six additional rare variants in the haplotype surrounding FNIP1, including a missense variant in the PDGFRB gene that was found to be present in all tested patients with FMDF. Genome-wide linkage analysis showed that the locus on chromosome 5 including FNIP1 was the only region reaching the maximal possible LOD score. We concluded that FMDF is linked to a haplotype on chromosome 5. Additional evaluations in families with FMDF are required to unravel the exact genetic cause underlying the phenotype. When evaluating patients with multiple trichodisomas without a pathogenic variant in the FLCN gene, further genetic testing is warranted and can include analysis of the haplotype on chromosome 5

Predicting outcomes of pelvic exenteration using machine learning

Aim: We aim to compare machine learning with neural network performance in predicting R0 resection (R0), length of stay > 14 days (LOS), major complication rates at 30 days postoperatively (COMP) and survival greater than 1 year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. Method: A deep learning computer was built and the programming environment was established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic regression, a support vector machine and an artificial neural network (ANN) were trained. Twenty per cent of the data were used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC). Results: Machine learning models and ANNs were trained on 1147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting LOS > 14 days with an AUROC of 0.793 using preoperative and operative data. Visualized logistic regression model weights indicate a varying impact of variables on the outcome in question. Conclusion: This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods