Infiltration of macrophages in and around tumor nest represents one of the most crucial hallmarks during tumor progression. The mutual interactions with tumor cells and stromal microenvironment contribute to phenotypically polarization of tumor associated macrophages. Macrophages consist of at least two subgroups, M1 and M2. M1 phenotype macrophages are tumor-resistant due to intrinsic phagocytosis and enhanced antitumor inflammatory reactions. Contrastingly, M2 are endowed with a repertoire of tumor-promoting capabilities involving immuno-suppression, angiogenesis and neovascularization, as well as stromal activation and remodeling. The functional signature of M2 incorporates location-related, mutually connected, and cascade-like reactions, thereby accelerating paces of tumor aggressiveness and metastasis. In this review, mechanisms underlying the distinct functional characterization of M1 and M2 macrophages are demonstrated to make sense of M1 and M2 as key regulators during cancer progression.

Determining the RNA binding preferences remains challenging because of the bottleneck of the binding interactions accompanied by subtle RNA flexibility. Typically, designing RNA inhibitors involves screening thousands of potential candidates for binding. Accurate binding site information can increase the number of successful hits even with few candidates. There are two main issues regarding RNA binding preference: binding site prediction and binding dynamical behavior prediction. Here, we propose one interpretable network-based approach, RNet, to acquire precise binding site and binding dynamical behavior information. RNetsite employs a machine learning-based network decomposition algorithm to predict RNA binding sites by analyzing the local and global network properties. Our research focuses on large RNAs with 3D structures without considering smaller regulatory RNAs, which are too small and dynamic. Our study shows that RNetsite outperforms existing methods, achieving precision values as high as 0.701 on TE18 and 0.788 on RB9 tests. In addition, RNetsite demonstrates remarkable robustness regarding perturbations in RNA structures. We also developed RNetdyn, a distance-based dynamical graph algorithm, to characterize the interface dynamical behavior consequences upon inhibitor binding. The simulation testing of competitive inhibitors indicates that RNetdyn outperforms the traditional method by 30%. The benchmark testing results demonstrate that RNet is highly accurate and robust. Our interpretable network algorithms can assist in predicting RNA binding preferences and accelerating RNA inhibitor design, providing valuable insights to the RNA research community.

Clinicopathological and prognostic significance of circulating tumor cells in patients with esophageal cancer: a meta-analysis Jinxuan Hou,1,2,* Kun Zou,2,* Chaogang Yang,2 Xiaohua Leng,1 Yu Xu1,3 1Hubei Key Laboratory of Tumor Biological Behaviors and Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, China; 2Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China; 3Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China *These authors contributed equally to this work Background: The aim of this meta-analysis was to assess the clinicopathological and prognostic significance of circulating tumor cells (CTCs) in patients with esophageal cancer (EC).Methods: We searched PubMed, EMBASE, Science Citation Index Expanded, Cochrane library (from inception to July 2018) with the keywords “esophageal cancer”, “circulating tumor cells”, “prognosis”, and “peripheral blood”. HR, risk ratio (RR), OR, and their 95% CIs were set as effect measures. All analyses were performed by STATA 12.0.Results: Eighteen studies were retrieved; CTC-positive patients were significantly associated with poor progression-free survival (PFS) (HR=2.61; 95% CI=2.08–3.28) and overall survival (OS) (HR=2.50; 95% CI=2.12–2.94). CTC-positive patients were also associated with high recurrence (OR=2.84; 95% CI=1.81–4.44) and poor response of chemoradiotherapy (RR=0.64; 95% CI=0.43–0.96). For clinicopathological characteristics, CTC-positive patients were significantly associated with TNM staging, depth of infiltration, regional lymph nodes metastasis, distant metastasis, lymphatic invasion, and venous invasion.Conclusion: The meta-analysis has confirmed the significant clinicopathological and prognostic value of CTC-positive patients for both PFS and OS in patients with EC. Keywords: esophageal cancer, CTCs, prognosis, meta-analysis

Cell migration-inducing hyaluronidase 1 (CEMIP), a Wnt-related protein and also known as KIAA1199, is implicated in the process of metastatic colonization in a variety of malignant tumors, including breast cancer (BC), which is one of the most frequently diagnosed tumors in women worldwide. In this study, multiple public databases, online analytical tools, and bioinformatics approaches were applied to explore the expression levels, regulatory mechanisms, and biological functions of CEMIP in BC. We illustrated that CEMIP was highly expressed in various kinds of carcinomas, including BC, especially advanced subtypes, and predicted less favorable prognosis (negatively associated with overall survival) in BC patients, which might be an independent prognostic factor. Then, we revealed that the mutation and high expression of CEMIP might lead to it as an oncogene. We also demonstrated that TP53 mutation, DNA hypo-methylation, and the expression changes of three potential upstream transcription factors (EZH2, EGR1, and JUN) of CEMIP were likely to cause the hyperexpression of CEMIP in BC. Moreover, our findings suggested that CEMIP might exert its carcinogenic roles in the tumor microenvironment via participation in the extracellular matrix formation, increasing cancer-associated fibroblast (CAF), M2 macrophage, and neutrophil infiltration and decreasing CD8⁺ T cell infiltration. In summary, our study provided more solid evidence for CEMIP as a prognostic and metastatic biomarker and a potential therapeutic target in BC. Of course, these findings also need more confirmations of basic experiments and further clinical trials in the future.

Esophageal squamous cell carcinoma (ESCC) is a common malignancy with poor prognosis and survival rate. To identify meaningful long non-coding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA) modules related to the ESCC prognosis, The Cancer Genome Atlas-ESCC was downloaded and processed, and then, a weighted gene co-expression network analysis was applied to construct lncRNA co-expression networks, miRNA co-expression networks, and mRNA co-expression networks. Twenty-one hub lncRNAs, seven hub miRNAs, and eight hub mRNAs were clarified. Additionally, a competitive endogenous RNAs network was constructed, and the emerging role of the network involved in head and neck squamous cell carcinoma (HNSCC) was also analyzed using several webtools. The expression levels of eight hub genes (TBC1D2, ATP6V0E1, SPI1, RNASE6, C1QB, C1QC, CSF1R, and C1QA) were different between normal esophageal tissues and HNSCC tissues. The expression levels of TBC1D2 and ATP6V0E1 were related to the survival time of HNSCC. The competitive endogenous RNAs network might provide common mechanisms involving in ESCC and HNSCC. More importantly, useful clues were provided for clinical treatments of both diseases based on novel molecular advances.