cn.Background: Colon cancer is one of the most common cancers in the world. Targeting biomarkers is helpful for the diagnosis and treatment of colon cancer. This study aimed to identify biomarkers in colon cancer, in addition to those that have already been reported, using microarray datasets and bioinformatics analysis.Methods: We downloaded two mRNA microarray datasets (GSE44076 and GSE47074) for colon cancer from the Gene Expression Omnibus (GEO) database and the most recent colon cancer data (COAD) fromThe Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) between colon cancer and adjacent normal tissues were determined based on these three datasets. Additionally, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and protein-protein interaction (PPI) network analysis. The hub genes in the PPI network were then selected and analysed.Results: We identified 150 DEGs and the GO enrichment analysis revealed that these DEGs were enriched in functions related to accelerating the cell cycle, promoting tumour cell accumulation, promoting cell division, positively regulating cell division, and negatively regulating apoptosis. The KEGG pathway analysis indicated that the DEGs were also involved in the cell cycle pathway. In the PPI network, 34 hub genes were found to be enriched in cell division. Prognostic analysis of the 34 hub genes revealed that eight genes (CCNB1, CHEK1, DEPDC1, ECT2, GINS2, HMMR, KIF14, and KIF18A) were associated with the prognosis of colon cancer. And our qRT-PCR results confirmed that DEPDC1, ECT2, GINS2, HMMR and KIF18A were highly expressed in colon cancer cells.

Background The study was intended to establish predictive nomogram models for predicting total early mortality (the probability of surviving less than or equal to 3 months) and cancer-specific early mortality in patients with stage IV gastric cancer. This was the first study to establish prognostic survival in patients with stage IV gastric cancer. Material/Methods Patients from the SEER database were identified using inclusion and exclusion criteria. Their clinical characteristics were statistically analyzed. The Kaplan-Meier method and the log-rank test were used to compare the influences of different factors on survival time. Logistic regression models were conducted to explore the correlative factors of early mortality. A nomogram was established based on factors significant in the logistic regression model and an internal validation was performed. Results Of the 11,036 eligible patients included in the study, 4932 (44.7%) patients resulted in total early death (42.6% died of the cancer and 2.1% died of other reasons). Larger tumor size, poor differentiation, and liver metastasis were positively related to cancer-specific early mortality. Surgery was negatively related to total early mortality and cancer-specific early mortality, while cardia was only negatively associated with total early death. Predictive nomogram models for total early mortality and cancer-specific early mortality have been validated internally. The areas under the receiver operating characteristics curve were 73.5%, and 68.0%, respectively, and the decision curve analysis also proved the value of the models. Conclusions The nomogram models proved to be a suitable tool for predicting the early mortality in stage IV gastric cancer.

Background: Combination therapy with immune checkpoint inhibitors (ICIs) has been widely used for clinical treatment in recent years, which has a better survival benefit. However, not all patients can derive clinical benefit from combination immunotherapy. Therefore, it is necessary to explore the biomarkers of combination immunotherapy.Methods: We retrieved articles from electronic databases including PubMed, EMBASE and Cochrane. The statistical analysis was performed using RevMan software. Progression free survival (PFS), overall survival (OS) and objective response rate (ORR) were the outcome indicators. In the unselect population, we compared combination therapy with other treatments. In addition, we also conducted subgroup analysis on PFS, OS and ORR according to PD-L1 status.Results: Seven studies were included in the analysis for a total of 3,515 cases. In the unselected population, we found that combination therapy has longer PFS, OS, and better ORR than other treatments for cancer patients. The longer PFS was showed in PD-L1 ≥ 5% cases (HR = 0.64, 95% CI: 0.56–0.76; p < 0.001) than PD-L1 ≥ 1% cases (HR = 0.72, 95% CI: 0.66–0.79; p < 0.001), while ORR and OS have not related to the status of PD-L1.Conclusion: This study supported the efficacy of combination therapy with immune checkpoint inhibitors (ICIs), and also showed that PFS in patients with malignant tumors is positively correlated with PD-L1 expression. Due to the limited number of trials included, more high-quality clinical randomized controlled trials should be conducted to confirm the review findings.

Objective. To evaluate the effectiveness and safety of acupuncture moxibustion therapy (AMT) for the breast cancer-related lymphedema (BCRL). Methods. Four English databases (MEDLINE, PubMed, Embase, and Cochrane CENTRAL) and four Chinese databases were searched from their inception to Feb 1, 2020. Eligible randomized controlled trials (RCTs) investigating AMT against any type of controlled intervention in patients for BCRL and assessing clinically relevant outcomes (total effective rate, circumference difference, and Karnofsky performance score) were included. The methodological quality of all selected trials was estimated in accordance with the guidelines published by the Cochrane Collaboration. Review Manager 5.3 was used to conduct analyses. Results. Twelve eligible RCTs are confirmed. Most of the trials selected are regarded as low methodological quality. Compared with Western medicine, physiotherapy, and functional training, traditional AMT has significantly higher treatment effect (RR 1.03 (95% CI: 1.22, 1.45); p<0.00001). In comparison with physiotherapy, AMT is better in reducing edema symptoms (MD = −0.77; 95% CI (−1.13–0.41); p<0.00001). Moreover, pooled results demonstrate that AMT results in better outcomes than functional training and Western medicine in improving Karnofsky performance score of BCRL patients (SMD = 0.69; 95% CI (0.38–1.00); p<0.00001). Conclusion. This systematic review and meta-analysis provides evidence that AMT is serviceable and safe in treating BCRL. With the limited number of available studies and methodology drawbacks, further high-quality RCTs with reasonable designs are still warranted.

Background. Acupuncture-moxibustion therapy (AMT), as an integral part of complementary and alternative medicine, has been used for centuries in treatment of numerous diseases. Nevertheless, there is no available supportive evidence on the efficacy and safety of acupuncture-moxibustion therapy in patients with chemotherapy-induced leukopenia (CIL). The purpose of this study is to evaluate the efficacy and safety of acupuncture-moxibustion therapy in treating chemotherapy-induced leukopenia. Methods. Relevant studies were searched in nine databases up to September 19, 2020. Two reviewers independently screened the studies for eligibility, extracted data, and assessed the methodological quality of selected studies. Meta-analysis of the pooled mean difference (MD) and risk ratio (RR) with their respective 95% confidence intervals (CI) were calculated. Results. 17 studies (1206 patients) were included, and the overall quality of the included studies was moderate. In comparison with medical therapy, AMT has a better clinical efficacy for CIL (RR, 1.24; 95% CI, 1.17-1.32; P < 0.00001 ) and presents advantages in increasing leukocyte count (MD, 1.10; 95% CI, 0.67–1.53; P < 0.00001 ). Also, the statistical results show that AMT performs better in improving the CIL patients’ Karnofsky performance score (MD, 5.92; 95% CI, 3.03–8.81; P < 0.00001 ). Conclusion. This systematic review and meta-analysis provides updated evidence that AMT is a safe and effective alternative for the patients who suffered from CIL.