Expression of human prostate-specific antigen (PSA) in a mouse tumor cell line reduces tumorigenicity and elicits PSA-specific cytotoxic T lymphocytes

 Human prostate-specific antigen (PSA) has a highly restricted tissue distribution. Its expression is essentially limited to the epithelial cells of the prostate gland. Moreover, it continues to be synthesized by prostate carcinoma cells. This makes PSA an attractive candidate for use as a target antigen in the immunotherapy of prostate cancer. As a first step in characterizing the specific immune response to PSA and its potential use as a tumor-rejection antigen, we have incorporated PSA into a well-established mouse tumor model. Line 1, a mouse lung carcinoma, and P815, a mouse mastocytoma, have been transfected with the cDNA for human PSA. Immunization with a PSA-expressing tumor cell line demonstrated a memory response to PSA which protected against subsequent challenge with PSA-expressing, but not wild-type, tumors. Tumor-infiltrating lymphocytes could be isolated from PSA-expressing tumors grown in naive hosts and were specifically cytotoxic against a syngeneic cell line that expressed PSA. Immunization with tumor cells resulted in the generation of primary and memory cytotoxic T lymphocytes (CTL) specific for PSA. The isolation of PSA-specific CTL clones from immunized animals further demonstrated that PSA can serve as a target antigen for antitumor CTL. The immunogenicity studies carried out in this mouse tumor model provide a rationale for the design of methods to elicit PSA-specific cell-mediated immunity in humans. Received: 4 April 1996 / Accepted: 31 May 1996     

Capturing pair-wise epistatic effects associated with three agronomic traits in barley

Genetic association mapping has been widely applied to determine genetic markers favorably associated with a trait of interest and provide information for marker-assisted selection. Many association mapping studies commonly focus on main effects due to intolerable computing intensity. This study aims to select several sets of DNA markers with potential epistasis to maximize genetic variations of some key agronomic traits in barley. By doing so, we integrated a MDR (multifactor dimensionality reduction) method with a forward variable selection approach. This integrated approach was used to determine single nucleotide polymorphism pairs with epistasis effects associated with three agronomic traits: heading date, plant height, and grain yield in barley from the barley Coordinated Agricultural Project. Our results showed that four, seven, and five SNP pairs accounted for 51.06, 45.66 and 40.42% for heading date, plant height, and grain yield, respectively with epistasis being considered, while corresponding contributions to these three traits were 45.32, 31.39, 31.31%, respectively without epistasis being included. The results suggested that epistasis model was more effective than non-epistasis model in this study and can be more preferred for other applications.     

Corrigendum of Zhao et al. (2020): a revised infrageneric classification of Lepisorus (Polypodiaceae)

Here, we publish Lepisorus sect. Paragramma (Blume) C.F. Zhao, R. Wei & X.C. Zhang as a combinatio nova to replace the section name in Zhao et al. (2020), which was published as a status nova and turned out to be an invalid name, because we cited an incorrect basionym. A revised infrageneric classification of Lepisorus also is proposed.     

Screening of significant biomarkers related with prognosis of liver cancer by lncRNA-associated ceRNAs analysis

This study aimed to identify significant biomarkers related to the prognosis of liver cancer using long noncoding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) analysis. Differentially expressed mRNA and lncRNAs between liver cancer and paracancerous tissues were screened, and the functions of these mRNAs were predicted by gene ontology and pathway enrichment analyses. A ceRNA network consisting of differentially expressed mRNAs and lncRNAs was constructed. LncRNA FENDRR and lncRNA HAND2-AS1 were hub nodes in the ceRNA network. A risk score assessment model consisting of eight genes (PDE2A, ESR1, FBLN5, ALDH8A1, AKR1D1, EHHADH, ADRA1A, and GNE) associated with prognosis were developed. Multivariate Cox regression suggested that both pathologic_T and risk group could be regarded as independent prognostic factors. Furthermore, a nomogram model consisting of pathologic_T and risk group showed a good prediction ability for predicting the survival rate of liver cancer patients. The nomogram model consisting of pathologic_T and a risk score assessment model could be regarded as an independent factor for predicting prognosis of liver cancer.