Platelet Indices in Colorectal Cancer Patients with Synchronous Liver Metastases

Identification of intestinal microbiome associated with lymph-vascular invasion in colorectal cancer patients and predictive label construction

Objective

To identify differences between the composition, abundance, and biological function of the intestinal microbiome of patients with and without lymph-vascular invasion (LVI) colorectal cancer (CRC) and to construct predictive labels to support accurate assessment of LVI in CRC.

Method

134 CRC patients were included, which were divided into two groups according to the presence or absence of LVI, and their intestinal microbiomes were sequenced by 16SrRNA and analyzed for differences. The transcriptome sequencing data of 9 CRC patients were transformed into immune cells abundance matrix by CIBERSORT algorithm, and the correlation among LVI-associated differential intestinal microbiomes, immune cells, immune-related genes and LVI-associated differential GO items and KEGG pathways were analyzed. A random forest (RF) and eXtreme Gradient Boosting (XGB) model were constructed to predict the LVI of CRC patients based on the differential microbiome.

Result

There was no significant difference in α-diversity and β-diversity of intestinal microbiome between CRC patients with and without LVI (P > 0.05). Linear discriminant analysis Effect Size (LEfSe) analysis showed 34 intestinal microbiomes enriched in CRC patients of the LVI group and 5 intestinal microbiomes were significantly enriched in CRC patients of the non-lymph-vascular invasion (NLVI) group. The RF and XGB prediction models constructed with the top 15% of the LVI-associated differential intestinal microbiomes ranked by feature significance had good efficacy.

Conclusions

There are 39 intestinal flora with significantly different species abundance between the LVI and NLVI groups. g:Alistipes.s:Alistipes_indistinctus is closely associated with colorectal cancer vascular invasion. LVI-associated differential intestinal flora may be involved in regulating the infiltration of immune cells in CRC and influencing the expression of immune-related genes. LVI-associated differential intestinal flora may influence the process of vascular invasion in CRC through a number of potential biological functions. RF prediction models and XGB prediction models constructed based on microbial markers of gut flora can be used to predict CRC-LVI conditions.

Preoperative PDW levels predict pulmonary metastasis in patients with hepatocellular carcinoma

Background

In hepatocellular carcinoma (HCC), pulmonary metastasis (PM) after hepatectomy is associated with poor clinical outcomes. The crucial phases of tumour cell proliferation, angiogenesis, and metastasis all entail platelet activation. In HCC, platelet distribution width (PDW) suggests platelet size changes and predicts a worse prognosis. The aim of this study was to assess the association between PDW and PMs in HCC patients receiving hepatectomy.

Material/methods

From January 2013 to December 2015, a cohort of patients who underwent hepatectomy for HCC at the Harbin Medical University Cancer Hospital in China were retrospectively evaluated. The relationship between PDW levels and clinical and demographic parameters was examined. To investigate the relationships between predicted factors and PM, a competing risk model was used. From January 2016 to December 2018, a validation cohort of 109 patients from the First Affiliated Hospital of Harbin Medical University was studied independently.

Results

In the primary cohort, 19 out of 214 patients had postoperative PMs. In HCC patients with PM, PDW levels were lower than in those without PM. There was a significant difference in the cumulative incidence of 2-year PM between the high-PDW and low-PDW groups after controlling for competing risk events (death prior to the development of PM) (p < 0.001). In addition, PDW was also found to be an independent predictor for PM in a multivariable competing risk analysis. The results were externally validated in another cohort.

Conclusions

In HCC, preoperative PDW is significantly associated with PM. PDW could be a biomarker for post-operative PM in HCC patients.

Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data

Background: Current diagnostic methods for colorectal cancer (CRC) are colonoscopy and sigmoidoscopy, which are invasive and complex procedures with possible complications. This study aimed to determine models for CRC identification that involve minimally invasive, affordable, portable, and accurate screening variables. Methods: This was a retrospective study that used data from electronic medical records of patients with CRC and healthy individuals between July 2017 and June 2018. Laboratory data, including liver enzymes, lipid profiles, complete blood counts, and tumor biomarkers, were extracted from the electronic medical records. Five machine learning models (logistic regression, random forest, k-nearest neighbors, support vector machine [SVM], and naïve Bayes) were used to identify CRC. The performances were evaluated using the areas under the curve (AUCs), sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV). Results: A total of 1164 electronic medical records (CRC patients: 582; healthy controls: 582) were included. The logistic regression model achieved the highest performance in identifying CRC (AUC: 0.865, sensitivity: 89.5%, specificity: 83.5%, PPV: 84.4%, NPV: 88.9%). The first four weighted features in the model were carcinoembryonic antigen (CEA), hemoglobin (HGB), lipoprotein (a) (Lp(a)), and high-density lipoprotein (HDL). A diagnostic model for CRC was established based on the four indicators, with an AUC of 0.849 (0.840-0.860) for identifying all CRC patients, and it performed best in discriminating patients with late colon cancer from healthy individuals with an AUC of 0.905 (0.889-0.929). Conclusions: The logistic regression model based on CEA, HGB, Lp(a), and HDL might be a powerful, noninvasive, and cost-effective method to identify CRC.