PurposeThis study used the Surveillance, Epidemiology, and End Results (SEER) data to investigate the changes in incidence, treatment, and survival of lung cancer from 1973 to 2015.Patients and methodsThe clinical and epidemiological data of patients with lung cancer were obtained from the SEER database. Joinpoint regression models were used to estimate the rate changes in lung cancer related to incidence, treatment, and survival.ResultsFrom 1973 to 2015, the average incidence of lung cancer was 59.0/100,000 person-years. The incidence increased initially, reached a peak in 1992, and then gradually decreased. A higher incidence rate was observed in males than in females and in black patients than in other racial groups. Since 1985, adenocarcinoma became the most prevalent histopathological type. The surgical rate for lung cancer was about 25%, and treatment with chemotherapy showed an increasing trend, while the radiotherapy rate was in downward trend. The surgical rate for non-small-cell lung cancer (NSCLC) was higher than that for small cell lung cancer (SCLC), while chemotherapy for SCLC far exceeded that for NSCLC. Treatment with chemotherapy and radiotherapy for advanced stage had higher rate than early stage. The 5-year relative survival rate of lung cancer increased with time, but <21%.ConclusionIn the past four decades, the lung cancer incidence increased initially and then gradually decreased. Surgical rate experienced a fluctuant reduction, while the chemotherapy rate was in upward trend. The 5-year relative survival rate increased with years, but was still low.

TMEM16A (known as anoctamin 1) Ca2+-activated chloride channel is overexpressed in many tumors. TMEM16A overexpression can be caused by gene amplification in many tumors harboring 11q13 amplification. TMEM16A expression is also controlled in many cancer cells via transcriptional regulation, epigenetic regulation and microRNAs. In addition, TMEM16A activates different signaling pathways in different cancers, e.g. the EGFR and CAMKII signaling in breast cancer, the p38 and ERK1/2 signaling in hepatoma, the Ras-Raf-MEK-ERK1/2 signaling in head and neck squamous cell carcinoma and bladder cancer, and the NFκB signaling in glioma. Furthermore, TMEM16A overexpression has been reported to promote, inhibit, or produce no effects on cell proliferation and migration in different cancer cells. Since TMEM16A exerts different roles in different cancer cells via activation of distinct signaling pathways, we try to develop the idea that TMEM16A regulates cancer cell proliferation and migration in a cell-dependent mechanism. The cell-specific role of TMEM16A may depend on the cellular environment that is predetermined by TMEM16A overexpression mechanisms specific for a particular cancer type. TMEM16A may exert its cell-specific role via its associated protein networks, phosphorylation by different kinases, and involvement of different signaling pathways. In addition, we discuss the role of TMEM16A channel activity in cancer, and its clinical use as a prognostic and predictive marker in different cancers. This review highlights the cell-type specific mechanisms of TMEM16A in cancer, and envisions the promising use of TMEM16A inhibitors as a potential treatment for TMEM16A-overexpressing cancers.

Myeloid-derived suppressor cells (MDSCs) accumulate in tumor-bearing hosts and play a major role in tumor-induced immunosuppression, which hampers effective immunotherapeutic approaches. β-Glucans have been reported to function as potent immunomodulators to stimulate innate and adaptive immune responses, which contributes to their antitumor property. Here, we investigated the effect of particulate β-glucans on MDSCs and found that β-glucan treatment could promote the differentiation of M-MDSCs (monocytic MDSCs) into a more mature CD11c + F4/80 + Ly6C low population via dectin-1 pathway in vitro, which is NF-κB dependent, and the suppressive function of M-MDSCs was significantly decreased. Treatment of orally administered yeast-derived particulate β-glucan drastically downregulated MDSCs but increased the infiltrated DCs and macrophages in tumor-bearing mice, thus eliciting CTL and Th1 responses, inhibiting the suppressive activity of regulatory T cells, thereby leading to the delayed tumor progression. We show here for the first time that β-glucans induce the differentiation of MDSCs and inhibit the regulatory function of MDSCs, therefore revealing a novel mechanism for β-glucans in immunotherapy and suggesting their potential clinical benefit.Keywords: Dectin-1 r Dendritic cells r β-Glucan r Myeloid-derived suppressor cells r Tumor immunotherapy Additional supporting information may be found in the online version of this article at the publisher's web-site IntroductionTumor-elicited immunosuppression is one of the crucial mechanisms of tumor escape. It is probably a pivotal element contributed Correspondence: Prof. Shengjun Wang e-mail: sjwjs@ujs.edu.cn to the failure in cancer immunotherapy. Indeed, accumulating evidence has shown that a population of cells with suppressive activity called myeloid-derived suppressor cells (MDSCs) contributes to the negative regulation of immune responses and plays an essential role in tumor-induced immunosuppression [1][2][3]. MDSCs are a phenotypically heterogeneous cell population that includes myeloid progenitor cells and immature myeloid cells. In healthy individuals, MDSCs are generated in bone marrow β-Glucans are main components of the cell wall of various yeast, fungi, or certain bacteria, which are recognized as pathogenassociated molecular patterns (PAMPs). They are glucose polymers with a backbone of linear β-1,3-linked D-glucose molecules (β-1,3-D-glucan) and β-1,6-linked side chains of varying sizes with distribution frequency [12,13]. The immunostimulatory properties of β-glucans have been identified for centuries [13]. They are regarded as biological response modifiers (BRMs) that enhance the innate immune system and stimulate tumor rejection [14]. Dectin-1, a non-Toll-like pattern recognition receptor for β-glucan that is mainly expressed on myeloid cells, including dendritic cells, monocytes/macrophages, neutrophils, and a subset of T cells [15,16]. Recognition of dectin-1 by β-glucan can activate Raf-1 and Syk kinase signaling pathway, leading to the phosphor...

Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular atlas of chromatin accessibility across 81 immune cell types, we asked if a convolutional neural network (CNN) could learn to infer cell type-specific chromatin accessibility solely from regulatory DNA sequences. With a tailored architecture and an ensemble approach to CNN parameter interpretation, we show that our trained network (“AI-TAC”) does so by rediscovering ab initio the binding motifs for known regulators and some unknown ones. Motifs whose importance is learned virtually as functionally important overlap strikingly well with positions determined by chromatin immunoprecipitation for several TFs. AI-TAC establishes a hierarchy of TFs and their interactions that drives lineage specification and also identifies stage-specific interactions, like Pax5/Ebf1 vs. Pax5/Prdm1, or the role of different NF-κB dimers in different cell types. AI-TAC assigns Spi1/Cebp and Pax5/Ebf1 as the drivers necessary for myeloid and B lineage fates, respectively, but no factors seemed as dominantly required for T cell differentiation, which may represent a fall-back pathway. Mouse-trained AI-TAC can parse human DNA, revealing a strikingly similar ranking of influential TFs and providing additional support that AI-TAC is a generalizable regulatory sequence decoder. Thus, deep learning can reveal the regulatory syntax predictive of the full differentiative complexity of the immune system.