A glycolysis-related signature to improve the current treatment and prognostic evaluation for breast cancer.

Background: As a heterogeneous malignancy, breast cancer (BRCA) shows high incidence and mortality. Discovering novel molecular markers and developing reliable prognostic models may improve the survival of BCRA. Methods: The RNA-seq data of BRCA patients were collected from the training set The Cancer Genome Atlas (TCGA)-BRCA and validation set GSE20685 in the Gene Expression Omnibus (GEO) databases. The "GSVA" R package was used to calculate the glycolysis score for each patient, based on which all the patients were divided into different glycolysis groups. The "limma" package was employed to perform differentially expression genes (DEGs) analysis. Key signature genes were selected by performing un/multivariate and least absolute shrinkage and selection operator (LASSO) C regression and used to develop a RiskScore model. The ESTIMATE and MCP-Counter algorithms were used for quantifying immune infiltration level. The functions of the genes were validated using Western blot, colony formation, transwell and wound-healing assay. Results: The glycolysis score and prognostic analysis showed that high glycolysis score was related to tumorigenesis pathway and a poor prognosis in BRCA as overactive glycolysis inhibited the normal functions of immune cells. Subsequently, we screened five key prognostic genes using the LASSO Cox regression analysis and used them to establish a RiskScore with a high classification efficiency. Based on the results of the RiskScore, it was found that patients in the high-risk group had significantly unfavorable immune infiltration and prognostic outcomes. A nomogram integrating the RiskScore could well predict the prognosis for BRCA patients. Knockdown of PSCA suppressed cell proliferation, invasion and migration of BRCA cells. Conclusion: This study developed a glycolysis-related signature with five genes to distinguish between high-risk and low-risk BRCA patients. A nomogram developed on the basis of the RiskScore was reliable to predict BRCA survival. Our model provided clinical guidance for the treatment of BRCA patients.

In Vitro Nanobody Library Construction by Using Gene Designated-Region Pan-Editing Technology.

Camelid single-domain antibody fragments (nanobodies) are an emerging force in therapeutic biopharmaceuticals and clinical diagnostic reagents in recent years. Nearly all nanobodies available to date have been obtained by animal immunization, a bottleneck restricting the large-scale application of nanobodies. In this study, we developed three kinds of gene designated-region pan-editing (GDP) technologies to introduce multiple mutations in complementarity-determining regions (CDRs) of nanobodies in vitro. Including the integration of G-quadruplex fragments in CDRs, which induces the spontaneous multiple mutations in CDRs; however, these mutant sequences are highly similar, resulting in a lack of sequences diversity in the CDRs. We also used CDR-targeting traditional gRNA-guided base-editors, which effectively diversify the CDRs. And most importantly, we developed the self-assembling gRNAs, which are generated by reprogrammed tracrRNA hijacking of endogenous mRNAs as crRNAs. Using base-editors guided by self-assembling gRNAs, we can realize the iteratively diversify the CDRs. And we believe the last GDP technology is highly promising in immunization-free nanobody library construction, and the full development of this novel nanobody discovery platform can realize the synthetic evolution of nanobodies in vitro.