This study aimed to demonstrate the function and molecular mechanism of protein regulator of cytokinesis 1 (PRC1) in the carcinogenesis of non-small cell lung cancer (NSCLC).

Lung cancer is the third most frequently diagnosed cancer in the world, with lung adenocarcinoma (LUAD) as the most common pathological type. But studies on the predictive effect of a single gene on LUAD are limited. We aimed to discover new predictive markers for LUAD.

Lung cancer is the most lethal malignancy worldwide. Recently, it has been recognized that metabolic reprogramming is a complex and multifaceted factor, contributing to the process of lung cancer. Tryptophan (Try) is an essential amino acid, and Try and its metabolites can regulate the progression of lung cancer. Here, we review the pleiotropic functions of the Try metabolic pathway, its metabolites, and key enzymes in the pathogenic process of lung cancer, including modulating the tumor environment, promoting immune suppression, and drug resistance. We summarize the recent advance in therapeutic drugs targeting the Try metabolism and kynurenine pathway and their clinical trials.

Bmi1 is an integral component of the Polycomb Repressive Complex 1 (PRC1) and is involved in the pathogenesis of multiple cancers. It also plays a key role in the functioning of endogenous stem cells and cancer stem cells. Previous work implicated a role for cancer stem cells in the pathogenesis of pancreatic cancer. We hypothesized that Bmi1 plays an integral role in enhancing pancreatic tumorigenicity and the function of cancer stem cells in pancreatic ductal adenocarcinoma.

Spherical CoCO3 powder with a small particle size and high density was successfully prepared using a continuous carbonate liquid precipitation method with a raw material of cobalt chloride solution, a precipitant of NH4HCO3, and without a template. The effects of the concentration of ammonium carbonate, process pH, and feeding rate on the tap density and apparent density of cobalt carbonate were investigated. It was found that the apparent and tap density values of 4.4 µm of cobalt carbonate were 1.27 g/cm3 and 1.86 g/cm3, respectively, when the initial concentration of NH4HCO3 solution was 60 g/L, the pH was 7.15-7.20, and the feeding rate of cobalt chloride was 2 L/h. The anisotropic growth process of the crystal lattice plane of CoCO3 under the aforementioned optimal conditions were studied. The results demonstrated that the crystal grew fastest along the (110) facet orientation, which was the dominant growth surface, determining the final morphology of the primary particles. The scanning electron microscopy (SEM) and high-resolution transmission electron microscopy (HR-TEM) results demonstrated that the primary particle morphology of the cobalt carbonate was a nanosheet. The unit cell of cobalt carbonate, of a hexagonal structure in the horizontal direction, grew horizontally along the (110) facet orientation, while 20-35 unit cells of the carbon carbonate were stacked along the c-axis in the thickness direction. Finally, the sheet-shaped particles were agglomerated into dense spherical secondary particles, as presented through the crystal re-crystallization model.

Hepatocellular carcinoma (HCC) is known for high mortality and limited available treatments. Aberrant activation of the Wnt and Notch signaling pathways is critical to liver carcinogenesis and progression. Here, we identified a small molecule, bruceine D (BD), as a Notch inhibitor, using an RBP-Jκ-dependent luciferase-reporter system. BD significantly inhibited liver tumor growth and enhanced the therapeutic effects of sorafenib in various murine HCC models. Mechanistically, BD promotes proteasomal degradation of β-catenin and the depletion of its nuclear accumulation, which in turn disrupts the Wnt/β-catenin-dependent transcription of the Notch ligand Jagged1 in HCC. Our findings provide important information about a novel Wnt/Notch crosstalk inhibitor that is synergistic with sorafenib for treatment of HCC, and therefore have high clinical impact.

Fast, robust and technology-independent computational methods are needed for supervised cell type annotation of single-cell RNA sequencing data. We present SciBet, a supervised cell type identifier that accurately predicts cell identity for newly sequenced cells with order-of-magnitude speed advantage. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. Facing the exponential growth in the size of single cell RNA datasets, this user-friendly and cross-platform tool can be widely useful for single cell type identification.

Although metformin, a first-line drug for treating diabetes, may play an important role in inhibition of epithelial ovarian cancer cell growth and cancer stem cells (CSCs), metformin at low dose showed less effect on the proliferation of ovarian cancer cells. In this study, we evaluated the effect of metformin at low dose on ovarian CSCs in order to understand the molecular mechanisms underlying.

Epithelial-mesenchymal transition (EMT) process, which is regulated by genes of inducible factors and transcription factor family of signaling pathways, transforms epithelial cells into mesenchymal cells and is involved in tumor invasion and progression and increases tumor tolerance to clinical interventions. This study constructed a multigene marker for lung predicting the prognosis of lung adenocarcinoma (LUAD) patients by bioinformatic analysis based on EMT-related genes. Gene sets associated with EMT were downloaded from the EMT-gene database, and RNA-seq of LUAD and clinical information of patients were downloaded from the TCGA database. Differentially expressed genes were screened by difference analysis. Survival analysis was performed to identify genes associated with LUAD prognosis, and overlapping genes were taken for all the three. Prognosis-related genes were further determined by combining LASSO regression analysis for establishing a prediction signature, and the risk score equation for the prognostic model was established using multifactorial COX regression analysis to construct a survival prognostic model. The model accuracy was evaluated using subject working characteristic curves. According to the median value of risk score, samples were divided into a high-risk group and low-risk group to observe the correlation with the clinicopathological characteristics of patients. Combined with the results of one-way COX regression analysis, HGF, PTX3, and S100P were considered as independent predictors of LUAD prognosis. In lung cancer tissues, HGF and PTX3 expression was downregulated and S100P expression was upregulated. Kaplan-Meier, COX regression analysis showed that HGF, PTX3, and S100P were prognostic independent predictors of LUAD, and high expressions of all the three were all significantly associated with immune cell infiltration. The present study provided potential prognostic predictive biological markers for LUAD patients, and confirmed EMT as a key mechanism in LUAD progression.

This study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of 18F-FDG positron emission tomography (PET) images.

To develop a nomogram that discriminates lung cancer from benign lung nodules through metabolic profiling.

In 2017, we released GEPIA (Gene Expression Profiling Interactive Analysis) webserver to facilitate the widely used analyses based on the bulk gene expression datasets in the TCGA and the GTEx projects, providing the biologists and clinicians with a handy tool to perform comprehensive and complex data mining tasks. Recently, the deconvolution tools have led to revolutionary trends to resolve bulk RNA datasets at cell type-level resolution, interrogating the characteristics of different cell types in cancer and controlled cohorts became an important strategy to investigate the biological questions. Thus, we present GEPIA2021, a standalone extension of GEPIA, allowing users to perform multiple interactive analysis based on the deconvolution results, including cell type-level proportion comparison, correlation analysis, differential expression, and survival analysis. With GEPIA2021, experimental biologists could easily explore the large TCGA and GTEx datasets and validate their hypotheses in an enhanced resolution. GEPIA2021 is publicly accessible at http://gepia2021.cancer-pku.cn/.

The objective of this study was to investigate the possible association between the single nucleotide polymorphism (SNP), rs35569394, of the vascular endothelial growth factor gene (VEGF) and the risk of esophageal cancer (EC) in the Han Chinese population. A total of 290 EC subjects and 322 ethnically matched unrelated healthy controls free from the esophageal disease were studied. Genomic DNA was isolated from peripheral blood by salting out. Genotyping of VEGF rs35569394 polymorphism was carried out via polymerase chain reaction followed by agarose gel electrophoresis. The results showed that the distribution of genotypes was significantly different across the gender groups (p=0.032) and clinical stages (p=0.034). VEGF rs35569394 was associated with EC risk (p= 0.012, OR=1.34). A gender analysis break-down showed that rs35569394-D allele frequency was significantly higher in females than in the controls (p=0.0004, OR=1.81). Moreover, significant associations were also found in females under the dominant model (II versus ID+DD: χ2=8.18, p=0.003, OR=2.12) and the recessive model (II+ID versus DD: χ2=8.25, p=0.004, OR=2.39). Additionally, we found that the genotype, rs35569394-DD, was associated with a complete response + partial response to chemotherapy when compared with rs35569394-II (χ2=4.67, p=0.030, OR=0.47). In conclusion, our case-control study showed that the VEGF rs35569394 was significantly associated with the clinical stages and the increased risk of EC in Han Chinese females. In addition, the genotype rs35569394-DD showed a better response to chemotherapy.

Dendritic cells (DCs) are antigen-presenting cells that can activate T cells and initiate a primary immune response. Personalized DC vaccines have demonstrated a modest antitumor potential in some clinical pilot studies. However, those vaccines are difficult to manufacture and have a limited antitumor response. In this study, a lentiviral vector-programmed DC vaccine with high antitumor responses is developed. By transfecting with a lentiviral vector, the DC vaccine is loaded with MG-7 antigen (MG-7Ag). Three representative gastric cancer cell lines, such as KATO-3, MKN45, and SNU16, are used to estimate the in vitro cytotoxic effect of the MG-7Ag DC vaccine. Furthermore, we examine the in vivo antitumor efficacy of specific cytotoxic T lymphocytes (CTLs) induced by the MG-7Ag DC vaccine in patient-derived xenograft (PDX) mice models. The current data demonstrate that the MG-7Ag DC vaccine induced a potent CTL activity. Those CTLs have a significant cytotoxic effect on both KATO-3 and MKN45 with high level of MG-7 expression. In addition, MG-7Ag DC vaccine-mediated CTLs significantly inhibit the growth of tumor xenografts in nude mice. The MG-7Ag DC vaccine activate the cytotoxic effect of lymphocytes and can be employed as a vaccine in gastric cancer immunotherapy.

Lung cancer has significantly higher incidence and mortality rates worldwide. In this study, we analyzed the metabolic profiles of non-small cell lung cancer (NSCLC) patients and constructed prediction models for smokers and nonsmokers with internal validation.

To evaluate the relationship between ambient air pollutants and chronic obstructive pulmonary disease in relatively low-polluted areas in China.

Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.

Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrate that our ROGUE metric is broadly applicable, and enables accurate, sensitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast, B cell and brain data, we identify additional subtypes and demonstrate the application of ROGUE-guided analyses to detect precise signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas.