Objective To investigate the expression of protein gene product 9.5 (PGP9.5) in human gastric cancer, and to find out the relations between its expression and carcinogenesis, invasion and metastasis of gastric cancer. Methods The expression of PGP9.5 was detected by immunohistochemistry (SP) and Western blot in 80 samples of gastric cancer and 8 samples of normal gastric tissues. Results ①Of 80 gastric cancer specimens examined, 56 cases (70.0％) showed staining with PGP9.5 in most tumor cells, whereas no PGP9.5 expression was detected in normal epithelium, which was consistent with the results of Western blot. ②The results of immunohistochemistry revealed that there were significantly correlations between the expression of PGP9.5 and the depth of invasion, the degree of differentiation, and the occurrence of lymph node metastasis (Plt;0.05), respectively; yet, there were no relation between the expression of PGP9.5 and age, gender, histopathologic type and TNM stage (Pgt;0.05). Conclusion PGP9.5 may play an important role in the invasion and metastasis of gastric cancer, and the invasion of gastric cancer could be detected by PGP9.5, which may be a useful molecular marker.
In recent years, deep learning has provided a new method for cancer prognosis analysis. The literatures related to the application of deep learning in the prognosis of cancer are summarized and their advantages and disadvantages are analyzed, which can be provided for in-depth research. Based on this, this paper systematically reviewed the latest research progress of deep learning in the construction of cancer prognosis model, and made an analysis on the strengths and weaknesses of relevant methods. Firstly, the construction idea and performance evaluation index of deep learning cancer prognosis model were clarified. Secondly, the basic network structure was introduced, and the data type, data amount, and specific network structures and their merits and demerits were discussed. Then, the mainstream method of establishing deep learning cancer prognosis model was verified and the experimental results were analyzed. Finally, the challenges and future research directions in this field were summarized and expected. Compared with the previous models, the deep learning cancer prognosis model can better improve the prognosis prediction ability of cancer patients. In the future, we should continue to explore the research of deep learning in cancer recurrence rate, cancer treatment program and drug efficacy evaluation, and fully explore the application value and potential of deep learning in cancer prognosis model, so as to establish an efficient and accurate cancer prognosis model and realize the goal of precision medicine.
Objective To evaluate the safety and efficacy of neoadjuvant therapy followed by minimally invasive esophagectomy (MIE) for locally advanced esophageal cancer. Methods We retrospectively analyzed clinical data of 56 consecutive patients with locally advanced esophageal cancer treated by neoadjuvant therapy followed by surgery in our hospital between January 2015 and December 2016. There were 51 males and 5 females. The patients were divided into 2 groups. Neoadjuvant therapy followed by open surgery esophagectomy group was as an OE group with 25 patients aged 61 (50-73) years. And neoadjuvant therapy followed by MIE was as a MIE group with 31 patients aged 60 (55-79) years. Results The pathologic complete response (pCR) rate of 28 patients with neoadjuvant concurrent chemoradiotherapy was significantly higher than that of 28 patients with neoadjuvant chemotherapy (21.4% vs. 10.7%, P<0.05). The operation time, intraoperative blood loss, R2 rate and the number of lymph nodes dissection in the MIE group were obviously better than those of the OE group with statistical differences (P<0.05). However, there was no significant difference in the number of resected lymph nodes along the bilateral recurrent laryngeal nerves and lymph node metastasis rate (P>0.05) between the two groups. The incidence of postoperative respiratory complications in the MIE group was lower than that of the OE group (P=0.041). There was no significant difference between the two groups in the incidence of other complications, re-operation, re-entry to ICU, median length of stay or perioperative deaths (P>0.05). There was only one patient with neoadjuvant concurrent chemoradiotherapy in the OE group died due to gastric fluid asphyxia caused by trachea-esophageal fistula. Conclusion Neoadjuvant therapy followed by MIE for locally advanced esophageal cancer is safe and feasible. The oncological outcomes seem comparable regardless of OE.