BackgroundAs crucial regulators and possible biomarkers for cancer development, miRNAs have attracted intensive attention during the last two decades. Among the known miRNAs, miR-135a has been indicated as a tumor suppressor in several cancer types, whereas its roles and mechanisms in gastric cancer (GC) remain largely unclear.Materials and methodsQuantitative PCR (qPCR) was conducted to detect the expression of miR-135a in paired GC tissues as well as cell lines. The prognostic value was evaluated by Kaplan–Meier survival analysis. Wound healing and transwell assays were performed to determine the roles of miR-135a in GC cell migration. Dual-luciferase reporter assay, qPCR, and Western blot analysis were used to validate the targeting of TRAF5 and subsequent NF-κB pathway by miR-135a. Rescue experiments were done to explain the involvement of TRAF5 in mediating the anti-migration effect of miR-135a in GC cells. Finally, the expression of TRAF5 was examined in paired GC tissues.ResultsmiR-135a was confirmed to be decreased in GC tissues and cell lines, and its lower expression predicted worse overall survival. Cellular experiments proved that miR-135a suppressed migration in GC cells. Through directly targeting TRAF5 and subsequently inhibiting NF-κB pathway, miR-135a might efficiently inhibit GC cell metastasis. Furthermore, we found that TRAF5 overexpression was negatively correlated with miR-135a expression in GC tissues.ConclusionOur study indicated that miR-135a serves a suppressing role in GC cell migration by targeting TRAF5 and the downstream NF-κB pathway.

Software Vulnerability Prediction (SVP) is a data-driven technique for software quality assurance that has recently gained considerable attention in the Software Engineering research community. However, the difficulties of preparing Software Vulnerability (SV) related data remains as the main barrier to industrial adoption. Despite this problem, there have been no systematic efforts to analyse the existing SV data preparation techniques and challenges. Without such insights, we are unable to overcome the challenges and advance this research domain. Hence, we are motivated to conduct a Systematic Literature Review (SLR) of SVP research to synthesize and gain an understanding of the data considerations, challenges and solutions that SVP researchers provide. From our set of primary studies, we identify the main practices for each data preparation step. We then present a taxonomy of 16 key data challenges relating to six themes, which we further map to six categories of solutions. However, solutions are far from complete, and there are several ill-considered issues. We also provide recommendations for future areas of SV data research. Our findings help illuminate the key SV data practices and considerations for SVP researchers and practitioners, as well as inform the validity of the current SVP approaches.

Macroautophagy, which will hereafter be referred to as autophagy, is an evolutionarily conserved process, during which cells recycle and remove damaged organelles and proteins in response to cellular stress. However, the mechanisms underlying the regulation of autophagy remain to be fully elucidated. The present study demonstrated that knockdown of zinc finger protein like 1 (ZFPL1) induces autophagy and increases autophagic cell death in NCI‑N87 and BGC‑823 human gastric carcinoma cell lines. To examine the role of ZFPL1 in gastric carcinoma cells, ZFPL1 expression was downregulated by lentiviral infection. Zinc finger domain‑FLAG was used to compete with ZFPL1 for golgin A2/GM130 binding. Autophagy was analyzed by red fluorescent protein‑microtubule‑associated protein 1A/1B‑light chain 3 (LC3) puncta, LC3I to LC3II conversion, and p62 expression. The results demonstrated that knockdown of ZFPL1 was able to significantly increase cell death rate. However, ZFPL1 knockdown exerted almost no effect on the expression of apoptosis‑associated markers, including B cell lymphoma 2 (Bcl‑2), Bcl‑x, Bcl‑2‑associated X protein, BH3 interacting domain death agonist, p53, and the classical caspase family members, caspase‑3, caspase‑8 and caspase‑9. An endogenous ZFPL1‑GM130 association was identified in NCI‑N87 cells and BGC‑823 cells by co‑immunoprecipitation. Furthermore, cell death was restricted following treatment of ZFPL1 knockdown cells with an autophagy inhibitor. Therefore, knockdown of ZFPL1 expression may induce cell death via autophagy, rather than apoptosis. These results suggest that ZFPL1 may serve an important role in regulating autophagy in NCI‑N87 and BGC‑823 cells.

Background: Sentinel lymph node (SLN) pathology result is crucial to predict axillary lymph nodes (ALN) metastasis as well as to determine systemic treatment strategy. Ultrasound has been paid great attention to the evaluation of ALN metastasis. Whether the combination of known clinic-pathological indexes to ultrasound could predict SLN metastasis for ultrasound-ALN-negative breast cancer, and ultimately achieve the goal of avoiding the invasive method of sentinel lymph node biopsy (SLNB), is the current focus. Objective: To discuss the possibility of predicting SLN metastasis using axillary ultrasonography in combination with patients' clinic-pathologic factors by retrospectively analyzing our institution's large cohort of ultrasound-ALN-negative breast cancer patients' data. Method: This study collected consecutive data from the prospective database of Breast Center of Beijing Cancer Hospital from Oct. 2010 to Apr. 2016. Inclusion criteria: Pathologically diagnosed as primary breast cancer by core needle biopsy (CNB); negative ALN by ultrasound (no ALN detected, or the cortex thickness was even and <3mm); no treatment prior to SLNB. The SLN pathological outcomes were correlated with known clinic-pathologic parameters. Univariate analysis was performed by Chi-Square test, with p<0.05 considered as statistically significant difference. Logistic regression analysis was used for the multivariate analysis, the area under curve >0.75 stands for acceptable predicting accuracy. Results: Non-selective consecutive data with a total of 4,936 primary breast cancer cases treated from Oct. 2010 to Apr. 2016 was extracted from the prospective database. Exclusion criteria: Pathologically diagnosed by surgical resection (n=492); carcinoma in situ (n=145); abnormal ALN by ultrasound underwent fine needle aspiration (FNA) or CNB (n= 750); systemic treatment prior to SLNB (n=349); no SLN detected after injection (n=81); male (n=4). A total of 3,115 cases met the inclusion criteria. Among which 2,317 (74.3%) cases were negative SLN pathology and 798 (25.7%) cases were positive SLN pathology. The main findings of this study were that the univariate analysis such as, patients' age, menstruation, tumor size, ER/PR, HER-2 were influencial factors, p<0.05. Multivariate analysis showed that the area under the ROC curve was 0.658(95% CI 0.637-0.679), indicating that the combination of all the clinic-pathologic factors with ultrasound could not stand for acceptable predicting accuracy. Conclusion: Ultrasound together with clinical indexes cannot predict SLN metastasis for ultrasound-ALN-negative breast cancer patients. The result of univariates related to SLNItemsSLN-Negative%(n)SLN-Positive%(n)p valueAge≤4070.5(324)29.5(136)0.036>4075.1(1,993)24.9(662)Premenopausal71.8(1,215)28.2(477)<0.001Postmenopausal77.4(1,102)22.6(321)T size(cm)≤278.4(1,242)21.6(342)<0.001T size(cm)>270.2(1,075)29.8(456)IDC I83.0(455)17.0(93)<0.001IDC II+III71.1(1,603)28.8(649)Other pathology types82.2(259)17.8(56)ER≤10%85.8(652)14.2(108)<0.001ER>10%70.7(1,665)29.3(690)PR≤10%80.3(851)19.7(208)<0.001PR>10%71.3(1,466)28.7(590) HER-2 0,1+,2+&FISH-73.1(1,729)26.9(635)0.008HER-2 3+,2++78.3(588)21.7(163)  Citation Format: Chen X, He Y, Huo L, Li J, Xie Y, Wang T, Fan Z, Ouyang T. Ultrasound together with clinical indexes cannot predict sentinel lymph node metastasis for ultrasound-axillary lymph node-negative breast cancer patients [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD2-01.