042) and OS (P=0.024). read more Interestingly, the high NANCI/high NKX2-1 duplex group had longer DFS and OS than the other three groups (71.25 vs 46.3 months, P=0.009 and 81.3 vs 56.1 months, P=0.004, respectively). In the multivariate analysis, the high NANCI/high NKX2-1 duplex was identified as an independent prognostic factor for longer DFS (HR 0.346, 95% CI, 0.169-0.709; P=0.004) and OS (HR 0.309, 95% CI, 0.121-0.786; P=0.014). CONCLUSIONS NANCI and the NANCI-NKX2-1 duplex impacts prognosis in stage I NSCLC patients. Tumors are characterized by metabolic dysregulation, reprogramming, and the presence of metabolites, which can act both as energy mediators and signaling messengers. Measuring the concentration and composition of metabolites in the tumor microenvironment can help to better understand the tumor pathology and might improve therapeutic treatments. Metabolomics can provide a description of the physiological and pathological status, as well as help to identify biomarkers of the disease. Additionally, mass spectrometry-based tissue imaging techniques can show the spatial distribution of metabolites. In this chapter we present protocols for the extraction and analysis of metabolites and lipids, with emphasis on liquid chromatography-mass spectrometry and mass spectrometry imaging. © 2020 Elsevier Inc. All rights reserved.Variation in cell-type decomposition can be of interest to changes in biology by themselves, or a confounder in comparing admixed samples with different phenotypes. Here, I present and review various algorithms that may be used for purposes of deconvolving epigenomic data from admixed samples to yield estimates that may be factored into downstream analyses. More specifically, I present a protocol based on an adaptation of the CIBERSORT algorithm, previously developed for gene-expression, to methylation data. © 2020 Elsevier Inc. All rights reserved.The importance of the immune system in combating many different types of malignancies has been firmly established. Indeed, the success of immune checkpoint-blocking antibodies in cancer treatment has focused the attention of many researchers on the tumor immune microenvironment. As one consequence, the analysis of the number, type, and activation status of tumor-infiltrating leukocytes (TILs) has become an area of great interest. Detailed knowledge of the immunological profile of a given tumor has the potential to predict patient response to therapy and overall survival. This chapter is intended to summarize the steps necessary for basic comparative analysis of the infiltration and activation status of immune cells in various human solid tumors, using freely available data obtained from The Cancer Genome Atlas (TCGA). © 2020 Elsevier Inc. All rights reserved.Mouse models of human cancer have been used extensively to circumvent the complexity in human patients. However, murine models often inadequately recapitulate the human cancer-immune interface partly due to important differences between mouse and human immune systems. Immunodeficient mice when transplanted with CD34+ hematopoietic progenitor cells (HPCs) develop multilineage human immune cells. While there remain limitations, efforts have been made to improve the function of human immune system. Thus, humanized mice, defined as mice with human immune system, have become an emerging model to study human cancers. Humanized mouse models have been used for various areas of cancer research including adoptive transfer of chimeric antigen receptor (CAR)-modified T cells, neoantigen vaccination to increase T cell repertoire and reprograming tumor microenvironment. Here, we describe the essential techniques to generate humanized mouse models for immuno-oncology studies. © 2020 Elsevier Inc. All rights reserved.By binding RNA in a sequence- and/or structure-dependent manner, RNA-binding proteins (RBPs) and their target RNA form a ribonucleoprotein complex involved in the RNA's fate. In this context, RBPs were shown to act as key players for post-transcriptional gene regulation by controlling RNA editing, splicing, polyadenylation, translocation, and stability. So far, over 1900 RBPs were identified and their deregulation has been associated with the development and progression of various disorders including cancer. Although a number of sophisticated approaches are available, our knowledge about direct RNA-RBP interactions is, however, quite limited. Here we present a protocol with restricted requirements for equipment and devices to identify RBPs. This approach is based on (i) the purification of biotinylated RNA, (ii) chromatographic separation of co-purified proteins, and (iii) their identification by mass spectrometry. © 2020 Elsevier Inc. All rights reserved.The major mechanisms of posttranscriptional gene regulation involve microRNAs (miRs) and RNA-binding proteins (RBPs). Recent studies not only identified functionally and characterized such factors, but rather investigated their use as biomarkers and suitability as biopharmaceuticals. Indeed, some miR-based drugs are currently tested in clinical studies as potential anti-viral and as anti-cancer agents. For the chemical application, a profound knowledge of the binding affinities of miRs and RBPs to their target RNA is essential. The authors recently identified several miRs regulating the non-classical human leukocyte antigen (HLA)-G, and characterized their binding affinity to the 3' untranslated region (UTR) of HLA-G. These miRs identified by miTRAP were classified into high affinity and low affinity miRs, which were either key regulators or fine tuners of HLA-G. While the miTRAP method has been described in detail, a novel modified miTRAP technique has been established, which completely consists of commercially available components and uses a simplified cloning strategy. This technique allows the identification and characterization of miRs and RBPs for any RNA sequence of interest. © 2020 Elsevier Inc. All rights reserved.We have used RNA interference (RNAi) screening technology to reveal unknown components of biological signaling pathways including survival mechanisms of estrogen-independent breast cancer cell growth and cancer cell resistance to immune attack. In this chapter, a detailed protocol describing the use of RNAi screening to identify factors important for the proliferation of estrogen-independent MCF7 breast cancer cells will be described. Resistance to therapies that target the estrogen pathway remains a challenge in the treatment of estrogen receptor-positive breast cancer. To address this challenge, small interfering-RNA (siRNA)-based libraries targeting an estrogen receptor (ER)- and aromatase-centered network, including 631 genes relevant to estrogen signaling, was designed and constructed for RNAi screening. This protocol will include the following parts (1) selection of RNAi transfection reagent for specific cells; (2) optimization of RNAi screening conditions using Z'-factor; (3) procedure of ER-network gene siRNA library screening using automated machines under optimized experimental conditions; and (4) method of analysis for RNAi screening data to identify specific determinants important for cell proliferation.