Background: Colorectal cancer (CRC) is a common global malignancy associated with high invasiveness, high metastasis, and poor prognosis. CRC commonly metastasizes to the liver, where the treatment of metastasis is both difficult and an important topic in current CRC management.Methods: Microarrays data of human CRC with liver metastasis (CRCLM) were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database to identify potential key genes. Differentially expressed (DE) genes (DEGs) and DEmiRNAs of primary CRC tumor tissues and metastatic liver tissues were identified. Microenvironment Cell Populations (MCP)-counter was used to estimate the abundance of immune cells in the tumor micro-environment (TME), and weighted gene correlation network analysis (WGCNA) was used to construct the co-expression network analysis.Gene Ontology and Kyoto Encyclopaedia of Gene and Genome (KEGG) pathway enrichment analyses were conducted, and the protein-protein interaction (PPI) network for the DEGs were constructed and gene modules were screened.Results: Thirty-five pairs of matched colorectal primary cancer and liver metastatic gene expression profiles were screened, and 610 DEGs (265 up-regulated and 345 down-regulated) and 284 DEmiRNAs were identified. The DEGs were mainly enriched in the complement and coagulation cascade pathways and renin secretion. Immune infiltrating cells including neutrophils, monocytic lineage, and cancer-associated fibroblasts (CAFs) differed significantly between primary tumor tissues and metastatic liver tissues. WGCN analysis obtained 12 modules and identified 62 genes with significant interactions which were mainly related to complement and coagulation cascade and the focal adhesion pathway. The best subset regression analysis and backward stepwise regression analysis were performed, and eight genes were determined, including F10, FGG, KNG1, MBL2, PROC, SERPINA1, CAV1, and SPP1. Further analysis showed four genes, including FGG, KNG1, CAV1, and SPP1 were significantly associated with CRCLM.Conclusions: Our study implies complement and coagulation cascade and the focal adhesion pathway play a significant role in the development and progression of CRCLM, and FGG, KNG1, CAV1, and SPP1 may be metastatic markers for its early diagnosis.

Background Selective immunoglobulin A deficiency (SIgAD) is the most common primary antibody deficiency disease and frequently reported in the Western countries. However, large‐scale epidemiologic studies on SIgAD in China are still lacking. Methods The clinical information of 555 180 subjects (age >4 years) including the outpatient, inpatient, and healthy subjects who had ordered serum immunoglobulin A, G, M in 9 hospitals of Zhejiang Province in China was collected. The SIgAD individuals were defined as IgA level <0.07 g/L with normal levels of serum IgG and IgM, whose age should be over 4 years, and any other secondary diseases causing SIgAD were also excluded. Then, the geographical and prevalence distribution of SIgAD individuals in Zhejiang Province and patients' clinical characteristics at the time of diagnosis were also reviewed. Result Among these 555 180 subjects who had ordered the immunoglobulin evaluation, the prevalence of SIgAD was 109/555180 (0.02%). The ratio of male to female of these SIgAD individuals was 1:1.37, which also included 87 adults (≥18 years) and 22 children (18 > age >4 years). For adults, the common clinical features were infections (43/87, 49.43%), autoimmune disorders (31/87, 35.63%), allergic cases (5/87, 5.75%), and tumor cases (4/87, 4.60%). Additionally, infectious diseases (20/22, 90.91%), autoimmune disorders (4/22, 18.18%), and allergic cases (1/22, 4.55%) were found in 22 children. Conclusion We first describe a large cohort of SIgAD individuals of Zhejiang Province in China. The incidence was 0.020%. The common clinical features were infection, autoimmune disorders, tumor, and allergy, and the infection rate was higher in children than the adults.

In this work, magnetic tetraethylenepentamine (TEPA)-modified carboxyl–carbon nanotubes were synthesized, characterized, and used as adsorbents to conduct magnetic solid-phase extraction (MSPE) for the preconcentration of seven local anesthetic drugs (procaine, lidocaine, mepivacaine, oxybuprocaine, bupivacaine, tetracaine, and cinchocaine) from human plasma. The separation and determination of analytes were performed on high-performance liquid chromatography with UV detection. Several factors affected the extraction efficiency, such as the amount of adsorbents used, extraction time, sample pH, and optimization of elution conditions. Under optimal conditions, satisfactory linear relationships were obtained in the range of 0.02–5.00 mg/L, with the limits of detection (LOD) ranging from 0.003 mg/L to 0.008 mg/L. The recoveries of analytes for spiked human plasma were in the range of 82.0–108%. Moreover, the precision with intra-day and inter-day RSD values were obtained in the range of 1.5–7.7% and 1.5–8.3%. The results indicated that this method could determine the concentration of seven local anesthetic drugs in human plasma with high precision and repeatability and provide support for the clinical monitoring of the concentration of local anesthetic drugs in human plasma.

Background: Globally, the incidence and mortality of colorectal cancer (CRC) rank amongst the highest of all malignancies. Thus, research aimed at developing new screening strategies and biomarkers for the early detection of CRC is needed. At present, conventional screening methods have limitations; therefore, new testing strategies have been considered. Using metabolomics to explore the molecular changes in CRC tissue is a mainstream method for identifying potential biomarkers and key cancer factors.Methods: In the present study, 27 samples from nine CRC patients were used to analyze the metabolite differences between the tumor, paracancerous, and normal tissues. The metabolite differences in the various stages of CRC (stages IIA, IIB, and IIIC) were analyzed as well. Subsequently, principal component analysis (PCA), permutation, and trend analyses were performed. Weighted gene co-expression and metabolitemetabolite interaction networks were also constructed.Results: A total of 5,834 metabolites were identified among the included samples. Permutation analysis showed a clear separation between the different tissues and different stages. Compared with normal tissues, tumor tissues exhibited 11, 233, and 25 up-regulated metabolites as well as one, 77, and zero down-regulated metabolites in stages IIA, IIB, and IIIC, respectively. Moreover, tumor tissues in stage IIB exhibited more differential metabolites (233 up-regulated and 77 down-regulated). Weighted Gene Correlation Network Analysis (WGCNA) clustered the 5,834 metabolites into 15 different modules, of which four modules were significantly correlated with tissue specificity. Notably, glycerophospholipid metabolism, fatty acid metabolism, and other pathways were enriched in these modules.Conclusions: Fatty acids and glycerophospholipids were significantly related to the development of CRC. This result is of great significance for future targeted screening of CRC biomarkers and further clarifying the nutrient metabolism of cancer cells.