Performance of ATT and UDFF in the diagnosis of non-alcoholic fatty liver: An animal experiment.

Objective: To establish a Bama minipigs model with Non-Alcoholic Fatty Liver (NAFL) induced by a high-fat diet and investigate the application of attenuation coefficient (ATT) and ultrasound-derived fat fraction (UDFF) in the diagnosis of NAFL. Methods: Six-month-old male Bama minipigs were randomly divided into normal control and high-fat groups (n = 3 pigs per group), and fed with a control diet and high-fat diet for 32 weeks. Weight and body length were measured every four weeks, followed by quantitative ultrasound imaging (ATT and UDFF), blood biochemical markers, and liver biopsies on the same day. Using the Non-Alcoholic Fatty Liver Disease (NAFLD) Activity Score (NAS) as a reference, we analyzed the correlation between ATT, UDFF, and their score results. Results: Compared with the normal control group, the body weight, body mass index (BMI), and serum levels of triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) in the High-fat group were significantly different at Week 12 (P < 0.05). Spearman correlation analysis showed that the ATT value was significantly correlated with NAS score (r = 0.76, P < 0.001), and the UDFF value was significantly correlated with NAS score (r = 0.80, P < 0.001). The optimal cut-off value of ATT and UDFF were 0.59 dB/cm/MHz and 5.5%, respectively. These values are optimal for diagnosis of NAFL in Bama minipig model. Conclusion: ATT and UDFF have a high correlation with steatosis, and can be used as a non-invasive method for early screening of hepatic steatosis, which can dynamically monitor the change of disease course.

Simultaneously mapping loci related to two plant architecture traits by phenotypic recombination BSA/BSR in peanut (Arachis hypogaea L.).

KEY MESSAGE: We developed a new method phenotypic recombination BSA/BSR (PR-BSA/BSR), which could simultaneously identify the candidate genomic regions associated with two traits in a segregating population. Bulked segregant analysis sequencing (BSA-seq) has been widely used for identifying the genomic regions affecting a certain trait. In this study, we developed a modified BSA/bulked segregant RNA-sequencing (BSR-seq) method, which we named phenotypic recombination BSA/BSR (PR-BSA/BSR), to simultaneously identify candidate genomic regions associated with two traits in a segregating population. Lateral branch angle (LBA) and flower-branch pattern (FBP) are two important traits associated with the peanut plant architecture because they affect the planting density and light use efficiency. We generated an F6 population (with two segregating traits) derived from a cross between the inbred lines Pingdu9616 (erect and sequential; ES-type) and Florunner (spreading and alternating; SA-type). The selection of bulks with extreme phenotypes was a key step in this study. Specifically, 30 individuals with recombinant phenotypes [i.e., spreading and sequential (SS-type) and erect and alternating (EA-type)] were selected to generate two bulks. The transcriptomes of individuals were sequenced and then the loci related to LBA and FBP were simultaneously detected via a ΔSNP-index strategy, which involved the direction of positive and negative peaks in the ∆SNP-index plot. The LBA-related locus was mapped to a 6.82 Mb region (101,743,223-108,564,267 bp) on chromosome 15, whereas the FBP-related locus was mapped to a 2.16 Mb region (117,682,534-119,846,824 bp) on chromosome 12. Furthermore, the marker-based classical QTL mapping method was used to analyze the PF-F6 population, which confirmed our PR-BSA/BSR results. Therefore, the PR-BSA/BSR method produces accurate and reliable data.

Identification of immunophenotypes in esophageal squamous cell carcinoma based on immune gene sets

PurposeEsophageal squamous cell carcinoma (ESCC) is a malignant tumor with high heterogeneity. Research on molecular mechanisms involved in the process of tumor origination and progression is extremely limited to investigating mechanisms of molecular typing for ESCC.MethodsAfter comprehensively analyzing the gene expression profiles in The Cancer Genome Atlas and Gene Expression Omnibus databases, we identified four immunotypes of ESCC (referred to as C1–C4) based on the gene sets of 28 immune cell subpopulations. The discrepancies in prognostic value, clinical features, drug sensitivity, and tumor components between the immunotypes were individually analyzed.ResultsThe ranking of immune infiltration is C1 > C4 > C3 > C2. These subtypes are characterized by high and low expression of immune checkpoint proteins, enrichment and insufficiency of immune-related pathways, and differential distribution of immune cell subgroups. Poorer survival was observed in the C1 subtype, which we hypothesized could be caused by an immunosuppressive cell population. Fortunately, C1’s susceptibility to anti-PD-1 therapy offers hope for patients with poor prognosis in advanced stages. On the other hand, C4 is sensitive to docetaxel, which may offer novel treatment strategies for ESCC in the future. It is worth noting that immunophenotyping is tightly bound to the abundance of stromal components and stem cells, which could explain the tumor immune escape to some extent. Ultimately, determination of hub genes based on the C1 subtypes provides a reference for the discovery of immunotarget drugs against ESCC.ConclusionThe identification of immunophenotypes in our study provides new therapeutic strategies for patients with ESCC.

Identification of immunophenotypes in esophageal squamous cell carcinoma based on immune gene sets.

PURPOSE: Esophageal squamous cell carcinoma (ESCC) is a malignant tumor with high heterogeneity. Research on molecular mechanisms involved in the process of tumor origination and progression is extremely limited to investigating mechanisms of molecular typing for ESCC. METHODS: After comprehensively analyzing the gene expression profiles in The Cancer Genome Atlas and Gene Expression Omnibus databases, we identified four immunotypes of ESCC (referred to as C1-C4) based on the gene sets of 28 immune cell subpopulations. The discrepancies in prognostic value, clinical features, drug sensitivity, and tumor components between the immunotypes were individually analyzed. RESULTS: The ranking of immune infiltration is C1 > C4 > C3 > C2. These subtypes are characterized by high and low expression of immune checkpoint proteins, enrichment and insufficiency of immune-related pathways, and differential distribution of immune cell subgroups. Poorer survival was observed in the C1 subtype, which we hypothesized could be caused by an immunosuppressive cell population. Fortunately, C1's susceptibility to anti-PD-1 therapy offers hope for patients with poor prognosis in advanced stages. On the other hand, C4 is sensitive to docetaxel, which may offer novel treatment strategies for ESCC in the future. It is worth noting that immunophenotyping is tightly bound to the abundance of stromal components and stem cells, which could explain the tumor immune escape to some extent. Ultimately, determination of hub genes based on the C1 subtypes provides a reference for the discovery of immunotarget drugs against ESCC. CONCLUSION: The identification of immunophenotypes in our study provides new therapeutic strategies for patients with ESCC.

Identification of prognostic biomarkers associated with tumor microenvironment in ceRNA network for esophageal squamous cell carcinoma: a bioinformatics study based on TCGA database.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is the most common histological type of esophageal cancer in the world with high incidence rate and poor prognosis. Infiltrated immune and stromal cells are vital components of tumor microenvironment (TME) and have a significant impact on the progression of ESCC. The competitive endogenous RNA (ceRNA) hypothesis has been proved important in the molecular biological mechanisms of tumor development. However, there are few studies on the relationship between ceRNA and ESCC TME. METHODS: The proportion of tumor-infiltrating immune cells and the amount of stromal and immune cells in ESCC cases were calculated from The Cancer Genome Atlas database using the CIBERSORT and ESTIMATE calculation methods. After stratified identification of differentially expressed genes, WGCNA and miRNA prediction system were applied to construct ceRNA network. Finally, PPI network and survival analysis were selected to discriminate prognostic signature. And the results were verified in two independent groups from Gene Expression Omnibus and Lanzhou, China. RESULTS: We found that high Stromal and ESTIMATE scores were significantly associated with poor overall survival. Three TME-related key prognostic genes were screened, namely, LCP2, CD86, SLA. And the expression of them was significantly correlated with infiltrated immunocytes. It is also found that ESTIMATE Score and the expression of CD86 were both related to TNM system of ESCC. CONCLUSIONS: We identified three novel TME-related prognostic markers and their lncRNA-miRNA-mRNA pathway in ESCC patients, which may provide new strategies for the targeted therapy.

Identification of biomarkers associated with diagnosis and prognosis of gastroesophageal junction adenocarcinoma-a study based on integrated bioinformatics analysis in GEO and TCGA database.

ABSTRACT: Gastroesophageal junction adenocarcinoma (GEJAC) is a malignant tumor with high mortality. Its incidence has increased sharply all over the world in recent years. The study aims to search for potential biomarkers for the diagnosis and prognosis of GEJAC based on the Gene Expression Omnibus database (GEO) database and The Cancer Genome Atlas (TCGA) database.Microarray dataset (GSE96668 and GSE74553) of GEJAC was downloaded from the GEO. After screening overlapping differentially expressed genes (DEGs) by GEO2R and Wayne map, functional enrichment analysis of the DEGs was performed by the DAVID database. Then, a protein-protein interaction (PPI) network was constructed, and the hub gene was identified by using STRING and Cytoscape, as well as the diagnostic value of hub genes was evaluated by the receiver operating characteristic (ROC) curves. Finally, the gene transcriptome profiles of gastric cancer named TCGA-STAD were downloaded from TCGA database to screen the potential prognostic genes and construct the prognostic risk model using Cox proportional hazards regression. Meanwhile, the Kaplan-Meier curve and time-dependent ROC curve were adopted to test the prognostic value of the prognostic gene signature.In this study, we identified 10 hub genes that might have high diagnostic value for GEJAC, and inferred that they might be involved in the occurrence and development of GEJAC. Moreover, we conducted a survival prediction model consisting of 6 genes and proved that they have value to some extent in predicting prognosis for GEJAC patients.