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Abstract. Background:. Although increasing abnormal expression of circular RNAs (circRNAs) has been revealed in various cancers, there were a small number of studies about circRNAs in gastric cancer (GC). Here, we explored the expression and function of a novel circRNA, circ_0049447, in GC. Methods:. A total of 80 GC tissues and non-tumorous tissues were collected from the First Affiliated Hospital of China Medical University. And all cells were cultured with 10% fetal bovine serum and incubated at 37°C and 5% CO2. The expression of circ_0049447 was quantified by real-time polymerase chain reaction. The biological function of circ_0049447 on proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) was evaluated by cell counting kit-8 (CCK-8), colony formation assay, transwell migration and invasion assay, and Western blotting. Luciferase report assay was used to verify the direct binding between circ_0049447 and predicted microRNA (miRNA). Furthermore, a xenograft mouse model was used to validate the function of circ_0049447 in vivo. Results:. We demonstrated that circ_0049447 was downregulated in GC (P

BackgroundHuman heparanase plays an important role in cancer development and single nucleotide polymorphisms (SNPs) in the heparanase gene (HPSE) have been shown to be correlated with gastric cancer. The present study examined the associations between individual SNPs or haplotypes in HPSE and susceptibility, clinicopathological parameters and prognosis of gastric cancer in a large sample of the Han population in northern China.Methodology/principal findingsGenomic DNA was extracted from formalin-fixed, paraffin-embedded normal gastric tissue samples from 404 patients and from blood from 404 healthy controls. Six SNPs were genotyped by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. A chi-square (χ2) test and unconditional logistic regression were used to analyze the risk of gastric cancer; a Log-rank test and Cox proportional hazards model were used to produce survival analysis and a Kaplan-Meier method was used to map survival curves. The mean genotyping success rates were more than 99% in both groups. Haplotype CA in the block composed of rs11099592 and rs4693608 had a greater distribution in the group of Borrmann types 3 and 4 (P = 0.037), the group of a greater number of lymph node metastases (N3 vs N0 group, P = 0.046), and moreover was correlated to poor survival (CG vs CA: HR = 0.645, 95%CI: 0.421-0.989, P = 0.044). In addition, genotypes rs4693608 AA and rs4364254 TT were associated with poor survival (P = 0.030, HR = 1.527, 95%CI: 1.042-2.238 for rs4693608 AA; P = 0.013, HR = 1.546, 95%CI: 1.096-2.181 for rs4364254 TT). There were no correlations between individual SNPs or haplotypes and gastric cancer risk.Conclusions/significanceA functional haplotype in HPSE was found, which included the important SNP rs4693608. SNPs in HPSE play an important role in gastric cancer progression and survival, and perhaps may be a molecular marker for prognosis and treatment values.

OBJECTIVE: At present, only the number of metastatic lymph nodes (LNs+) is used for the pN category of AJCC TNM system for colon cancer. Recently, the ratio of metastatic to examined lymph nodes (LNR) has been reported to represent powerful independent predictive capacity in colon cancer. We sought to propose a novel category (nLN) which intergrades LNR and LNs+ into the AJCC staging system for colon cancer. DESIGN: 34476 patients from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) dataset with stage III colon cancer were reviewed. Harrell's C statistic was used to evaluate the predictive capacity. The Cox proportional hazards model was used to construct a novel category. RESULTS: The LNR category had more predictive capacity than the pN category in whole groups of patients (Harrell's C index: 0.6194 vs 0.6113, p = 0.003). Subgroup analysis showed that the LNR category was not better than pN category in predictive capacity if the number of lymph nodes examined was more than 13. We also found that there was significant survival heterogeneity among different pN categories at the same LNR category (P

OBJECTIVE: In addition to pathological TNM (pTNM) staging, the macroscopic staging (surgical TNM, sTNM) is another method used to stage and assess tumors, and it also potentially influences patient treatment guidelines. However, for the same patient, surgeons and pathologists might assess tumor depth differently. We aimed to evaluate the prognosis of patients who exhibit unconformity of intraoperative and postoperative results and propose a revised pT category (r-pT category) to predict survival in colorectal cancer. METHODS AND RESULTS: In our study, 948 colorectal cancer patients were reviewed. We proposed a novel r-pT category in which surgical macroscopic T4b (sT4b) is incorporated into the pT category, namely, patients in the pT3 category with sT4b cancers are reclassified as being in the r-pT4a category; patients in the pT4a category with sT4b cancers are reclassified as being in the r-pT4b category. Cancer-specific survival according to the r-pT category was analyzed using Kaplan-Meier survival curves. A two-step multivariate analysis was used to determine correlations between the r-pT category and the prognosis. Harrell's C statistic was utilized to test the predictive capacity. There were significant prognostic differences among the r-pT subcategories. We substituted the r-pT category for the pT category in current TNM staging in a 2-step multivariate analysis. The Harrell's C statistical analysis results demonstrated that the r-pT category had superior predictive capacity compared to the pT category (Harrell' C: 0.668 vs. 0.636; P = 0.002). CONCLUSIONS: Patients in the pT3 category with sT4b cancers, and patients in the pT4a category with sT4b cancers, are potentially under-staged, reclassification into higher categories could potentially benefit these patients. The results indicate that the r-pT category we proposed is potentially superior to the pT category in the assessment of prognosis for colorectal cancer.

BACKGROUND: The single nucleotide polymorphisms (SNPs) in matrix metalloproteinase 1(MMP-1) play important roles in some cancers. This study examined the associations between individual SNPs or haplotypes in MMP-1 and susceptibility, clinicopathological parameters and prognosis of gastric cancer in a large sample of the Han population in northern China. METHODS: In this case-controlled study, there were 404 patients with gastric cancer and 404 healthy controls. Seven SNPs were genotyped using the MALDI-TOF MS system. Then, SPSS software, Haploview 4.2 software, Haplo.states software and THEsias software were used to estimate the association between individual SNPs or haplotypes of MMP-1 and gastric cancer susceptibility, progression and prognosis. RESULTS: Among seven SNPs, there were no individual SNPs correlated to gastric cancer risk. Moreover, only the rs470206 genotype had a correlation with histologic grades, and the patients with GA/AA had well cell differentiation compared to the patients with genotype GG (OR=0.573; 95%CI: 0.353-0.929; P=0.023). Then, we constructed a four-marker haplotype block that contained 4 common haplotypes: TCCG, GCCG, TTCG and TTTA. However, all four common haplotypes had no correlation with gastric cancer risk and we did not find any relationship between these haplotypes and clinicopathological parameters in gastric cancer. Furthermore, neither individual SNPs nor haplotypes had an association with the survival of patients with gastric cancer. CONCLUSIONS: This study evaluated polymorphisms of the MMP-1 gene in gastric cancer with a MALDI-TOF MS method in a large northern Chinese case-controlled cohort. Our results indicated that these seven SNPs of MMP-1 might not be useful as significant markers to predict gastric cancer susceptibility, progression or prognosis, at least in the Han population in northern China.

OBJECTIVE: The 7th edition of AJCC staging manual implicitly states that only T1 and T2 lesions that lack regional lymph node metastasis but have tumor deposit(s) will be classified in addition as N1c, though it is not consistent in that pN1c is also an option for pT3/T4a tumors in the staging table. Nevertheless, in this TNM classification, how to classify tumor deposits (TDs) in colorectal cancer patients with lymph node metastasis (LNM) and TDs simultaneously is still not clear. The aim of this study is to investigate the possibility of counting TDs as metastatic lymph nodes in TNM classification and to identify its prognostic value for colorectal cancer patients. METHODS AND RESULTS: In this retrospective study, 513 cases of colorectal cancer with LNM were reviewed. We proposed a novel pN (npN) category in which TDs were counted as metastatic lymph nodes in the TNM classification. Cancer-specific survival according to the npN or pN category was analyzed using Kaplan-Meier survival curves. Univariate and multivariate analyses were performed to identify significant prognostic factors. Harrell's C statistic was used to test the predictive capacity of the prognostic models. The results revealed that the TD was a significant prognostic factor in colorectal cancer. Univariate and multivariate analyses uniformly indicated that the npN category was significantly correlated with prognosis. The results of Harrell's C statistical analysis demonstrated that the npN category exhibited a superior predictive capacity compared to the pN category of the 7th edition TNM classification. Moreover, we also found no significant prognostic differences in patients with or without TD in the same npN categories. CONCLUSIONS: The counting of TDs as metastatic lymph nodes in the TNM classification system is potentially superior to the classification in the 7th edition of the TNM staging system to assess prognosis and survival for colorectal cancer patients.

OBJECTIVE: Over the past decades, many studies have used data mining technology to predict the 5-year survival rate of colorectal cancer, but there have been few reports that compared multiple data mining algorithms to the TNM classification of malignant tumors (TNM) staging system using a dataset in which the training and testing data were from different sources. Here we compared nine data mining algorithms to the TNM staging system for colorectal survival analysis. METHODS: Two different datasets were used: 1) the National Cancer Institute's Surveillance, Epidemiology, and End Results dataset; and 2) the dataset from a single Chinese institution. An optimization and prediction system based on nine data mining algorithms as well as two variable selection methods was implemented. The TNM staging system was based on the 7(th) edition of the American Joint Committee on Cancer TNM staging system. RESULTS: When the training and testing data were from the same sources, all algorithms had slight advantages over the TNM staging system in predictive accuracy. When the data were from different sources, only four algorithms (logistic regression, general regression neural network, bayesian networks, and Naïve Bayes) had slight advantages over the TNM staging system. Also, there was no significant differences among all the algorithms (p>0.05). CONCLUSIONS: The TNM staging system is simple and practical at present, and data mining methods are not accurate enough to replace the TNM staging system for colorectal cancer survival prediction. Furthermore, there were no significant differences in the predictive accuracy of all the algorithms when the data were from different sources. Building a larger dataset that includes more variables may be important for furthering predictive accuracy.

OBJECTIVE: The aim of the current study was to investigate which is the most suitable classification for colorectal cancer, log odds of positive lymph nodes (LODDS) classification or the classifications based on the number of positive lymph nodes (pN) and positive lymph node ratio(LNR) in a Chinese single institutional population. DESIGN: Clinicopathologic and prognostic data of 1297 patients with colorectal cancer were retrospectively studied. The log-rank statistics, Cox's proportional hazards model, the Nagelkerke R(2) index and a Harrell's C statistic were used. RESULTS: Univariate and three-step multivariate analyses identified that LNR was a significant prognostic factor and LNR classification was superior to both the pN and LODDS classifications. Moreover, the results of the Nagelkerke R(2) index (0.130) and a Harrell's C statistic (0.707) of LNR showed that LNR and LODDS classifications were similar and LNR was a little better than the other two classifications. Furthermore, for patients in each LNR classification, prognosis was homologous between those in different pN or LODDS classifications. However, for patients in pN1a, pN1b, LODDS2 and LODDS3 classifications, significant differences in survival were observed among patients in different LNR classifications. CONCLUSIONS: For patients with colorectal cancer, the LNR classification is more suitable than pN and LODDS classifications for prognostic assessment in a Chinese single institutional population.