Objective To test the hypothesis that p53 gene therapy combined with endostatin can enhance tumor response to radiation therapy of RM-1 mouse xenograft prostate cancer and to investigate its mechanism. Methods A mouse prostate cancer model was established. Then mice with xenograft tumor were randomly divided into group A (control), B (radiation), C (radiation and rAdp53), D (radiation and rh-endostatin) and E (radiation and rAdp53 and rh-endostatin). On day 1, rAdp53 was injected intra-tumorously with 1 × 1010 vp per animal to group C and E. From day 1 to 14, rh-endostatin was given 15 mg/kg intraperitoneally daily to group D and E. On day 4 single fraction of 15 Gy was given to tumors in groups B, C, D and E. Normal saline was injected intra-tumorously or intraperitoneaUy accordingly as control. No treatment was done to group A. Tumor volume was measured daily. Samples were collected on Days 5, 10 and 15. Ki67, CD31, p53 and VEGF were detected by means of immunohistochemistry. Results (1) Radiation alone, radiation combined with intra-tumorous injection of Adp53 and/or intraperitoneal injection of rh-endostatin resulted in tumor growth arrest of RM-1 cells in vivo (P = 0.000). Radiation combined with both rAdp53 and rh-endostatin was the most effective treatment (P < 0.05). (2) All the four treatment groups had a decreased expression of mutant type P53 (P = 0.000). The expression of Ki67 in groups B and C were equal (P 0.05) and increasing (P = 0.000), respectively. Group D had a up-down-up curve (P < 0.05), but group E had a up-down one. On day 5 the expresion of VEGF in group E was the lowest (P < 0.05). An increased expression of MVD compared with the control was shown, and MVD in groups C, D and E were always higher than that in the control (P < 0.05). Conclusions The limitation of radiotherapy could be overcome by combination with beth p53 gene therapy and endostatin on the growth of mouse prostate cancer cell. Radiation, rAdp53 and endostatin have their own role but they can be interacted with each other.

Objective To explore the predictive markers of senescence-associated secretory phenotype(SASP)in osteoarthritis(OA).Methods OA datasets were screened by the Gene Expression Omnibus(GEO)database,while SASP-related genes were collected by PubMed.Three machine learning algorithms,including least absolute shrinkage and selection operator(LASSO),support vector machines recursive feature elimination(SVM-RFE),and random forest(RF),were used to screen the candidate predictive markers of SASP genes in OA,and the OA prediction model was constructed using the overlapping genes identified by the machine learning algo-rithms.CIBERSORT was used to explore the degree of peripheral blood immune cell infiltration in OA versus normal samples.The miRNA-transcription factor-mRNA regulatory network of the model genes was predicted using Cytoscape.The most valuable genes of the predic-tion model were experimentally verified by real-time quantitative polymerase chain reaction(RT-qPCR)in OA rats and normal control rats(n= 6 per group).Results One OA dataset was screened by the GEO database,and 125 OA-related SASP genes were isolated.A total of seven intersection genes were obtained by the three machine learning algorithms.The area under the curve of the prediction model was 0.891.The CIBERSORT immune infiltration results showed a significant difference in plasma cell infiltration level between OA and normal samples(P= 0.001 3).The RT-qPCR results showed that the expression level of TNFRSF1Awas significantly higher in the OA versus normal group(P<0.0001).Conclusion TNFRSF1Ais highly expressed in OA and may be a potential predictive marker for it.