BackgroundEarly growth response proteins (EGRs), as a transcriptional regulatory family, are involved in the process of cell growth, differentiation, apoptosis, and even carcinogenesis. However, the role of EGRs in tumors, their expression levels, and their prognostic value remain unclear.MethodsUsing the Oncomine database, Kaplan–Meier Plotter, bcGenExMiner v4.2, cBioPortal, and other tools, the association between the survival data of breast carcinoma (BC) patients and transcriptional levels of four EGRs was investigated.ResultsAccording to the Oncomine database, in comparison to normal tissues, the expression level of EGR2/3 mRNA in BC tissues was decreased, but there was no difference in the expression level of EGR4 mRNA. On the basis of the Scarff-Bloom-Richardson (SBR) grading system, the downregulated expression level of EGR1/2/3 and upregulated expression level of EGR4 were correlated with an increased histological differentiation level, with significant differences (p < 0.05). Kaplan–Meier curves suggest that a reduction in EGR2/3 mRNA expression is related to recurrence-free survival (RFS) in BC patients. In addition, the mRNA expression level of EGR1/2/3 was related to metastatic relapse-free survival (MRFS) in BC patients with metastatic recurrence (p < 0.05).ConclusionEGR1/2/3 can be utilized as an important factor for evaluating prognosis and may be relevant to diagnosis. EGR4 may play a role in the occurrence and development of BC. The specific function and mechanism of EGRs in BC deserve further study.

Gastric cancer (GC) is a common cancerous tumor, and is the third leading cause of cancer mortality worldwide. Although comprehensive therapies of GC have been widely used in clinical set ups, advanced gastric cancer carries is characterized by poor prognosis, probably due to lack of effective prognostic biomarkers. Mammalian histone deacetylase family, histone deacetylases (HDACs), play significant roles in initiation and progression of tumors. Aberrant expression of HDACs is reported in many cancer types including gastric cancer, and may serve as candidate biomarkers or therapeutic targets for GC patients. Gene Expression Profiling Interactive Analysis was used to explore mRNA levels of HDACs in GC. Kaplan–Meier plotter was used to determine the prognostic value of HDACs mRNA expression in GC. Genomic profiles including mutations of HDACs were retrieved from cBioPortal webserver. A protein–protein interaction network was constructed using STRING database. GeneMANIA was used to retrieve additional genes or proteins related to HDACs. R software was used for functional enrichment analyses. Analysis of mRNA levels of HDAC1/2/4/8/9 showed that they were upregulated in GC tissues, whereas HDAC6/10 was downregulated in GC tissues. Aberrant expression of HDAC1/3/4/5/6/7/8/10/11 was all correlated with prognosis in GC. In addition, expression levels of HDACs were correlated with different Lauren classifications, and clinical stages, lymph node status, treatment, and human epidermal growth factor receptor 2 status in GC. The findings of this study showed that HDAC members are potential biomarkers for diagnosis or prognosis of gastric cancer. However, further studies should be conducted to validate these findings.

There is considerable heterogeneity in the genomic drivers of lung adenocarcinoma, which has a dismal prognosis. Bioinformatics analysis was performed on lung adenocarcinoma (LUAD) datasets to establish a multi-autophagy gene model to predict patient prognosis. LUAD data were downloaded from The Cancer Genome Atlas (TCGA) database as a training set to construct a LUAD prognostic model. According to the risk score, a Kaplan-Meier cumulative curve was plotted to evaluate the prognostic value. Furthermore, a nomogram was established to predict the three-year and five-year survival of patients with LUAD based on their prognostic characteristics. Two genes (ITGB1 and EIF2AK3) were identified in the autophagy-related prognostic model, and the multivariate Cox proportional risk model showed that risk score was an independent predictor of prognosis in LUAD patients (HR=3.3, 95%CI= 2.3 to 4.6, P< 0.0001). The Kaplan-Meier cumulative curve showed that low-risk patients had significantly better overall (P<0.0001). The validation dataset GSE68465 further confirmed the nomogram's robust ability to assess the prognosis of LUAD patients. A prognosis model of autophagy-related genes based on a LUAD dataset was constructed and exhibited diagnostic value in the prognosis of LUAD patients. Moreover, real-time qPCR confirmed the expression patterns of EIF2AK3 and ITGB1 in LUAD cell lines. Two key autophagy-related genes have been suggested as prognostic markers for lung adenocarcinoma.

Background: Coagulation factor XIIIa(FXIIIa) plays a critical role in the final stage of blood coagulation. It is extremely important in wound healing, tissue repairing and promoting cell adhesion. The deficiency of the coagulation factor can cause hemorrhage and slow wound healing. Objective: In this study, recombinant pPICZαC-FXIIIa was expressed in Pichia pastoris, purified as well as its biological activity was determined. Methods: The FXIIIa fragment obtained from the human placenta was inserted into pPICZαC to obtain pPICZαC-FXIIIa, which was transformed into X33 after linearization, and FXIIIa inserted into Pichia pastoris X33 was screened for methanol induction. The expressed product was identified by western blotting, then the supernatant was purified by affinity chromatography, and the purified product was determined by plasma coagulation experiment. Results: Polymerase Chain Reaction(PCR) showed that the FXIIIa fragment of 2250 bp was inserted successfully into pPICZαC. The expression and purification products of the same molecular weight as target protein(about 83 kDa) were obtained, which solidified significantly when reacted with plasma. Conclusion: The expression and purification products were successful, with sufficient biological activity, which can be used as a candidate FXIIIa hemostatic agent in genetic engineering.

Gastric cancer has become a prominent research focus due to its role of cancer-related deaths in the worldwide in recent years, especially for advanced gastric cancer for which surgical resection is the only truly effective treatment. However, patients with gastric cancer have a high rate of postoperative recurrence and metastasis. Therefore, studying the mechanisms of gastric cancer development and metastasis, finding diagnostic markers and therapeutic targets has become a hot research topic nowadays. Lactate has long been considered as a metabolic byproduct of aerobic glycolysis in cancer, and an increasing number of studies have shown that lactate can regulate tumor growth through various mechanisms, such as cell cycle regulation, immunosuppression, and energy metabolism. The recent discovery of lactic acidification has attracted much attention and has become one of the hot topics in the field of cancer. Yet, the roles of lactate metabolism-related genes (LMRs) in gastric cancer remains unknown. Accordingly, we aimed to develop a unique lactate-related signal to predict the prognosis of gastric cancer patients, and such LMRs could guide clinicians to make more precise and personalized treatments for gastric cancer patients.