Correlation Analysis of IL-17, IL-21, IL-23 with Non-Alcoholic Liver Fibrosis and Cirrhosis.

Objective: This research aimed to explore the involvement of interleukins (IL) - IL-6, IL-17, IL-21, and IL-23 - in the evolution and diagnosis of non-alcoholic liver fibrosis and cirrhosis. Methods: The study subjects were selected from the patients who visited the Department of Hepatology of X Hospital in Y City from August 2021 to April 2023. Peripheral blood samples were collected. All participants were divided into liver fibrosis, cirrhosis, hepatitis, and healthy subjects four groups. IL-21, IL-17, IL23, IL-6 were detected by double antibody sandwich. Results: The results showed that there was a significant difference in the levels of IL-17, IL-21, and IL-23 among the 4 groups (P<0.0001). ROC curve analysis showed that the AUC values of IL-17, IL-21 and liver fiber 4 items were >0.70, suggesting that the diagnostic efficacy of IL-17, IL-21 was similar to that of liver fiber 4 items. Spearman correlation analysis showed that IL-17 had a positive correlation with collagen type III N-peptide, type IV collagen, and Laminin (P < 0.05), and no correlation with Hyaluronic acid (P > 0.05). Conclusion: IL-17, IL-21, and IL-23 play a pivotal role in the inflammatory pathways associated with liver injuries, establishing themselves as potent auxiliary diagnostic markers in identifying liver fibrosis and cirrhosis.

Progress in research on tumor microenvironment-based spatial omics technologies.

Spatial omics technology integrates the concept of space into omics research and retains the spatial information of tissues or organs while obtaining molecular information. It is characterized by the ability to visualize changes in molecular information and yields intuitive and vivid visual results. Spatial omics technologies include spatial transcriptomics, spatial proteomics, spatial metabolomics, and other technologies, the most widely used of which are spatial transcriptomics and spatial proteomics. The tumor microenvironment refers to the surrounding microenvironment in which tumor cells exist, including the surrounding blood vessels, immune cells, fibroblasts, bone marrow-derived inflammatory cells, various signaling molecules, and extracellular matrix. A key issue in modern tumor biology is the application of spatial omics to the study of the tumor microenvironment, which can reveal problems that conventional research techniques cannot, potentially leading to the development of novel therapeutic agents for cancer. This paper summarizes the progress of research on spatial transcriptomics and spatial proteomics technologies for characterizing the tumor immune microenvironment.

circSPECC1 Promotes Proliferation and Migration of LNCaP Prostate Cancer Cells by Affecting Their Epithelial-Mesenchymal Transition.

Objective: To determine the effects of circSPECC1 (hsa_circ_0000745) on the proliferation and migration of LNCaP prostate cancer cells and to explore the potential molecular mechanism. Methods: Stable circSPECC1 shRNA-expressing and circSPECC1-overexpressing LNCaP cell lines were constructed, and relative gene expression levels were determined by RT-PCR. MTT and clonogenic assays were used to assess proliferative ability while a scratch test was used to analyze cell migration. Western blotting was used to determine protein expression levels. The effects of circSPECC1 on the proliferation of LNCaP prostate cancer cells were observed in vivo. Results: circSPECC1 was found to be derived from the SPECC1 (sperm antigen with calponin homology and coiled-coil domains 1) parent gene and to form a loop. Overexpression of circSPECC1 promoted the proliferation and migration of the LNCaP cells, whereas decreased expression of circSPECC1 inhibited these properties. Overexpression of circSPECC1 promoted the expression of MMP-2, MMP-9, VEGF, vimentin, and N-cad but downregulated the expression of E-cad. Decreased expression of circSPECC1 inhibited the expression of MMP-2, MMP-9, VEGF, vimentin, and N-cad but increased the expression of E-cad. Conclusion: circSPECC1 promotes cell proliferation and migration by affecting the epithelial-mesenchymal transition of LNCaP prostate cancer cells.

Research progress on application of single-cell TCR/BCR sequencing technology to the tumor immune microenvironment, autoimmune diseases, and infectious diseases.

Single-cell omics is the profiling of individual cells through sequencing and other technologies including high-throughput analysis for single-cell resolution, cell classification, and identification as well as time series analyses. Unlike multicellular studies, single-cell omics overcomes the problem of cellular heterogeneity. It provides new methods and perspectives for in-depth analyses of the behavior and mechanism of individual cells in the cell population and their relationship with the body, and plays an important role in basic research and precision medicine. Single-cell sequencing technologies mainly include single-cell transcriptome sequencing, single-cell assay for transposase accessible chromatin with high-throughput sequencing, single-cell immune profiling (single-cell T-cell receptor [TCR]/B-cell receptor [BCR] sequencing), and single-cell transcriptomics. Single-cell TCR/BCR sequencing can be used to obtain a large amount of single-cell gene expression and immunomics data at one time, and combined with transcriptome sequencing and TCR/BCR diversity data, can resolve immune cell heterogeneity. This paper summarizes the progress in applying single-cell TCR/BCR sequencing technology to the tumor immune microenvironment, autoimmune diseases, infectious diseases, immunotherapy, and chronic inflammatory diseases, and discusses its shortcomings and prospects for future application.

Serum organic acid metabolites can be used as potential biomarkers to identify prostatitis, benign prostatic hyperplasia, and prostate cancer.

Background: Noninvasive methods for the early identify diagnosis of prostatitis, benign prostatic hyperplasia (BPH), and prostate cancer (PCa) are current clinical challenges. Methods: The serum metabolites of 20 healthy individuals and patients with prostatitis, BPH, or PCa were identified using untargeted liquid chromatography-mass spectrometry (LC-MS). In addition, targeted LC-MS was used to verify the organic acid metabolites in the serum of a validation cohort. Results: Organic acid metabolites had good sensitivity and specificity in differentiating prostatitis, BPH, and PCa. Three diagnostic models identified patients with PROSTATITIS: phenyllactic acid (area under the curve [AUC]=0.773), pyroglutamic acid (AUC=0.725), and pantothenic acid (AUC=0.721). Three diagnostic models identified BPH: citric acid (AUC=0.859), malic acid (AUC=0.820), and D-glucuronic acid (AUC=0.810). Four diagnostic models identified PCa: 3-hydroxy-3-methylglutaric acid (AUC=0.804), citric acid (AUC=0.918), malic acid (AUC=0.862), and phenyllactic acid (AUC=0.713). Two diagnostic models distinguished BPH from PCa: phenyllactic acid (AUC=0.769) and pyroglutamic acid (AUC=0.761). Three diagnostic models distinguished benign BPH from PROSTATITIS: citric acid (AUC=0.842), ethylmalonic acid (AUC=0.814), and hippuric acid (AUC=0.733). Six diagnostic models distinguished BPH from prostatitis: citric acid (AUC=0.926), pyroglutamic acid (AUC=0.864), phenyllactic acid (AUC=0.850), ethylmalonic acid (AUC=0.843), 3-hydroxy-3-methylglutaric acid (AUC=0.817), and hippuric acid (AUC=0.791). Three diagnostic models distinguished PCa patients with PROSTATITISA < 4.0 ng/mL from those with PSA > 4.0 ng/mL: 5-hydromethyl-2-furoic acid (AUC=0.749), ethylmalonic acid (AUC=0.750), and pyroglutamic acid (AUC=0.929). Conclusions: These results suggest that serum organic acid metabolites can be used as biomarkers to differentiate prostatitis, BPH, and PCa.