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.

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.

BSA-seq has been widely used for identifying the genomic regions affecting a certain trait. In this study, we developed a modi ed BSA/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 ower-branch pattern (FBP) are two important traits associated with the peanut plant architecture because they affect the planting density and light use e ciency. We generated an F 6 population (with two segregating traits) derived from a cross between the inbred lines Pingdu9616 (erect and sequential; ES-type) and Florunner (spreading and alternating; SAtype). The selection of bulks with extreme phenotypes was a key step in this study. Speci cally, 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 LBArelated locus was mapped to a 6.82 Mb region (101,743,223-108,564,267 bp) on chromosome B05, whereas the FBP-related locus was mapped to a 2.16 Mb region (117,682,846,824 bp) on chromosome B02. Furthermore, the marker-based classical QTL mapping method was used to analyze the PF-F 6 population, which con rmed our PR-BSA/BSR results. Therefore, the PR-BSA/BSR method produces accurate and reliable data.

The high-quality genomes and large-scale full-length cDNA sequences of allotetraploid peanuts have been sequenced and released, which has accelerated the functional genomics and molecular breeding research of peanut. In order to understand the difference in the transcriptional levels of wild and cultivated peanuts. In this study, we integrated of second-and third-generation sequencing technologies to sequence full-length transcriptomes in peanut cv. Pingdu9616 and its putative ancestor Arachis monticola. The RNA extracted from six different tissues (i.e., roots, stems, leaves, flowers, needles and pods) were sampled at 20 days after flowering. A total of 31,764 and 33,981 high-quality transcripts were obtained from Monticola and Pingdu9616, respectively. The number of alternative splicing, the unit point mutation of variable adenylation, the number of open reading frames and the two-site mutation were identified in Pingdu9616 more than in Monticola, but the three-site mutation in Pingdu9616 was lower than in Monticola. 1,691 LncRNAs, and 4,000 bp of maximum length of LncRNA was identified in Monticola and Pingdu9616. Furthermore, comparative analysis between transcript data shown that 56 transcription factor families were involved in Monticola, and Pingdu9616 and the number of transcription factors in Pingdu9616 was higher than that in Monticola, the number of expressed genes estimated in flower, root, young pod and leaf organs was higher in Monticola than Pingdu9616. Over all, our study provided a valuable resource of large-scale full-length transcripts for further research of the molecular breeding and functional analysis of genes.

Background: Corona Virus Disease 2019 (COVID-19) is highly infectious and spreads rapidly across the world. Some patients occurred liver damage for unexplained reasons. Our study aims to explore whether Traditional Chinese Medicine (TCM) causes severe abnormal liver function compared to that of Western Medicine during the treatment process of COVID-19.Methods: We selected eligible studies with strict inclusion and exclusion criteria after systematically searching 7 databases up to May 30, 2020. Then, the study quality was assessed by the Cochrane risk of bias tool. Finally, we conducted a meta and subgroup analysis in the random-effects model to calculate risk ratio (RR) and 95% confidence intervals (95% CI) to evaluate the risk changes after adding TCM and the influence factor to RR. Results: A total of 2648 articles were searched, and 3 randomized controlled studies were included. Our results indicated that the occurrence of abnormal liver function had no statistical difference in the group added with TCM from Western Medicine groups. (RR=0.55[0.16,1,85], I2=58%, P=0.09>0.05). We also found that the high heterogeneity existed in two subgroups of common cases and all typecases, and it was statistically significant (I2=76.3%, P=0.04 <0.05）, which means the clinical type of COVID-19 may be the source of heterogeneity.Conclusion: Our research showed that adding TCM wouldn’ t damage the COVID-19 patients’ liver function. On the contrary, it may reduce the risk.

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