Associations between alcohol consumption and anxiety, depression, and health-related quality of life in colorectal cancer survivors

Purpose  Alcohol consumption is a major risk factor for colorectal cancer (CRC). It is currently poorly understood, however, how alcohol and different alcoholic beverage types are related to psychosocial outcomes in CRC survivors.  Methods  We used data of N = 910 CRC survivors from the pooled EnCoRe and PROCORE cohorts and harmonized them into five time points: at diagnosis and 3, 6, 12, and 24 months post-diagnosis. Generalized estimated equation models were used to examine longitudinal associations of alcohol consumption, including consumption of beer, wine, and liquor, with anxiety, depression, and health-related quality of life (HRQoL), while correcting for sociodemographic, lifestyle, and clinical factors.  Results  Survivors were on average 67 years and 37% was female. In the first 2 years post-diagnosis, survivors who consumed more alcoholic drinks/week reported lower anxiety and depressive symptoms and better HRQoL on all domains and symptom scales. This was the case for moderate and heavy amounts of alcohol and mostly for consuming beer and wine, but not for liquor. Associations were more often significant for men and for younger persons (< 67 years at baseline).  Conclusions  Generally, alcohol consumption was observed to be longitudinally related to less anxiety and depression and better HRQoL in CRC survivors. Implications for Cancer Survivors Although alcohol consumption is generally unfavorable due to increased risk of carcinogenesis and worse prognosis after CRC, it seems to be associated with better psychosocial outcomes in the first 2 years after diagnosis and treatment. More research is needed to gain knowledge about reasons for drinking and causality. Netherlands Trial Registry (www.trialregister.nl, NL6904

The Prospective Dutch Colorectal Cancer (PLCRC) cohort: real-world data facilitating research and clinical care

Real-world data (RWD) sources are important to advance clinical oncology research and evaluate treatments in daily practice. Since 2013, the Prospective Dutch Colorectal Cancer (PLCRC) cohort, linked to the Netherlands Cancer Registry, serves as an infrastructure for scientific research collecting additional patient-reported outcomes (PRO) and biospecimens. Here we report on cohort developments and investigate to what extent PLCRC reflects the “real-world”. Clinical and demographic characteristics of PLCRC participants were compared with the general Dutch CRC population (n = 74,692, Dutch-ref). To study representativeness, standardized differences between PLCRC and Dutch-ref were calculated, and logistic regression models were evaluated on their ability to distinguish cohort participants from the Dutch-ref (AU-ROC 0.5 = preferred, implying participation independent of patient characteristics). Stratified analyses by stage and time-period (2013–2016 and 2017–Aug 2019) were performed to study the evolution towards RWD. In August 2019, 5744 patients were enrolled. Enrollment increased steeply, from 129 participants (1 hospital) in 2013 to 2136 (50 of 75 Dutch hospitals) in 2018. Low AU-ROC (0.65, 95% CI: 0.64–0.65) indicates limited ability to distinguish cohort participants from the Dutch-ref. Characteristics that remained imbalanced in the period 2017–Aug’19 compared with the Dutch-ref were age (65.0 years in PLCRC, 69.3 in the Dutch-ref) and tumor stage (40% stage-III in PLCRC, 30% in the Dutch-ref). PLCRC approaches to represent the Dutch CRC population and will ultimately meet the current demand for high-quality RWD. Efforts are ongoing to improve multidisciplinary recruitment which will further enhance PLCRC’s representativeness and its contribution to a learning healthcare system

Negative pressure wound therapy for patients with hard-to-heal wounds: A systematic review

Objective: Despite the lack of evidence, negative pressure wound therapy (NPWT) is commonly used in patients with hard-to-heal wounds. In our medical centre, one third of patients with abdominal wounds infected postoperatively end this therapy prematurely due to negative experiences and prefer standard wound care. This study was designed to explore the effects of NPWT on quality of life (QoL). Method: A search from 2000 to 2019 in eight databases was performed to identify qualitative studies of patients treated with NPWT. Studies were selected by two independent reviewers, who appraised the methodological quality, extracted and structured the data and performed content analysis. Results: A total of fve qualitative studies with good methodological quality, incorporating 51 individual patients, were included. After content analysis, four major themes emerged: reduced freedom of movement caused by an electric device; decreased self-esteem; increased social and professional dependency; and gaining self-control. Conclusion: NPWT has major effects on the physical, psychological and social domains of QoL. Knowledge of these effects may lead to improved treatment decisions for patients with hard-to-heal wounds regarding use of NPWT or standard wound care

eCST:The endoscopic-assisted component separation technique for (complex) abdominal wall reconstruction

Introduction: In 1990, Ramirez introduced his component separation technique (CST) based on enlargement of the abdominal wall for reconstruction of large abdominal wall defects. CST is prone to postoperative wound complications which lead to modification of the technique to an endoscopic assisted CST. The details of the technique are described in detail with illustrations and report the results of a 36 patient cohort. Materials and Methods: Between 2014 and 2018, patients with midline hernias without previous subcutaneous dissection underwent endoscopic-assisted anterior components separation technique (eCST) with retro-rectus mesh enforcement in an expert center for abdominal wall reconstructions. Prospective data were gathered during inpatient care and at least 2 years of follow-up. Results: A total of 36 eCST procedures were performed. Eight patients (22%) had postoperative seroma in the dissection plan between external and internal rectus muscle, 3 (8%) had a hematoma, 1 (3%) had wound dehiscence. Clinical relevant SSEs were present in 4 patients (11%) and consisted of 3 (8%) puncture in seroma, 1 (3%) patient needed a blood transfusion due to large hematoma. One patient was re-operated within 90 days; however, this was the placement of a surgical tracheostomy. Three patients had a recurrence in a mean follow-up length of 24 months. Conclusion: eCST can be useful in selected patients

Label-Free LC-MS^e in Tissue and Serum Reveals Protein Networks Underlying Differences between Benign and Malignant Serous Ovarian Tumors

<div><p>Purpose</p><p>To identify proteins and (molecular/biological) pathways associated with differences between benign and malignant epithelial ovarian tumors.</p><p>Experimental Procedures</p><p>Serum of six patients with a serous adenocarcinoma of the ovary was collected before treatment, with a control group consisting of six matched patients with a serous cystadenoma. In addition to the serum, homogeneous regions of cells exhibiting uniform histology were isolated from benign and cancerous tissue by laser microdissection. We subsequently employed label-free liquid chromatography tandem mass spectrometry (LC-MS^e) to identify proteins in these serum and tissues samples. Analyses of differential expression between samples were performed using Bioconductor packages and in-house scripts in the statistical software package R. Hierarchical clustering and pathway enrichment analyses were performed, as well as network enrichment and interactome analysis using MetaCore.</p><p>Results</p><p>In total, we identified 20 and 71 proteins that were significantly differentially expressed between benign and malignant serum and tissue samples, respectively. The differentially expressed protein sets in serum and tissue largely differed with only 2 proteins in common. MetaCore network analysis, however inferred GCR-alpha and Sp1 as common transcriptional regulators. Interactome analysis highlighted 14-3-3 zeta/delta, 14-3-3 beta/alpha, Alpha-actinin 4, HSP60, and PCBP1 as critical proteins in the tumor proteome signature based on their relative overconnectivity. The data have been deposited to the ProteomeXchange with identifier PXD001084.</p><p>Discussion</p><p>Our analysis identified proteins with both novel and previously known associations to ovarian cancer biology. Despite the small overlap between differentially expressed protein sets in serum and tissue, APOA1 and Serotransferrin were significantly lower expressed in both serum and cancer tissue samples, suggesting a tissue-derived effect in serum. Pathway and subsequent interactome analysis also highlighted common regulators in serum and tissue samples, suggesting a yet unknown role for PCBP1 in ovarian cancer pathophysiology.</p></div

PCBP1 expression in serous ovarian cancer.

<p>Expression of PCBP1 (probeset 208620_at) in serous tumors of low malignant potential (LMP) versus malignant serous ovarian tumors <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108046#pone.0108046-Anglesio1" target="_blank">[28]</a>. PCBP1 was significantly up-regulated (p = 0.003, Welch's t-test) in malignant tumors. Squares represent the individual samples used in the microarray experiment. Boxplots are overlaid with the lower and upper ends of a box indicating the 25th and 75th percentiles, respectively. The solid black line inside a box indicates the median.</p

Proteins in the serum and tumor datasets that are potentially associated with GCR-alpha (A) and SP1 (B) pathways.

<p>The proteins marked in blue were found in the serum dataset, those marked in green in both tumor and serum. Unmarked proteins are specific for the tumor signature (except GCR-alpha and SP1). Proteins are ordered according to their position within the cell (extracellular, membrane bound, cytoplasmic or nucleic). Individual proteins are represented as nodes, the different shapes of the nodes represent the functional class of the proteins. The arrowheads indicate the direction of the interaction, the color of the lines between nodes describes activation (green), inhibition (red), and unspecified (black) interactions. The small circles on top of the protein symbols indicate up-regulation (red) or down-regulation (blue).</p