Originator: Computational Framework Separating Single-Cell RNA-Seq by Genetic and Contextual Information.

Single-cell RNA sequencing data from complex human tissues are prone to blood contamination in sample preparation, and some comprise cells of different genetic makeups, necessitating rigorous preprocessing and cell filtering prior to the downstream functional analysis. Our proposed new computational framework, Originator, deciphers single cells by genetic origin and separates blood cells from tissue-resident cells. It improves the quality of data analysis, exemplified by pancreatic ductal adenocarcinoma and placenta tissues.

Astaxanthin Synergizes with Ionizing Radiation (IR) in Oral Squamous Cell Carcinoma (OSCC).

Astaxanthin (ATX) is known for its antioxidant and anti-inflammation functions yet its role in cancers requires more research. This study is aimed to reveal the potential synergetic effect of ATX with ionizing radiation (IR) in OSCC. Cell survival was measured after human OSCC cells including CAL27 and SCC9, and normal human oral keratinocytes (NHOKs) were treated with different concentrations of ATX for 24 h. Colony formation assays were performed after OSCC cells were treated with IR, ATX (20 µ M), or combined and survival fraction was analyzed. Malondialdehyde (MDA), glutathione (GSH), and intercellular iron levels were measured. Western blot method was used to measure the ferroptosis-related proteins, GPX4, SLC7A11, and ACSL4. In xenograft mice model, we evaluated the tumor volumes, tumor growth, and examined the GPX4/ACSL4 proteins in tumor tissues using Immunohistochemistry (IHC). ATX inhibited viability of OSCC cells but not NHOK. In OSCC cells, ATX further enhanced the cell death induced by IR. In addition, ATX promoted the MDA content, Iron levels but inhibited the GSH regulated by IR in cells. ATX could synergize with IR, further inhibiting GPX4, SLC7A11 and promoting ACSL4 in OSCC cells. In vivo, ATX and IR treatment inhibited OSCC tumor growth and the group with combined treatment showed the most inhibitory effect. GPX4 was inhibited by IR and further inhibited in the combined group while ACSL4 was promoted by IR and enhanced more significantly in the combined group. ATX might synergize with IR treatment in OSCC partly via ferroptosis.

Deep learning and transfer learning identify breast cancer survival subtypes from single-cell imaging data.

BACKGROUND: Single-cell multiplex imaging data have provided new insights into disease subtypes and prognoses recently. However, quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at a population scale are currently missing. METHODS: We quantified hundreds of single-cell resolution cell-cell interaction features through neighborhood calculation, in addition to cellular phenotypes. We applied these features to a neural-network-based Cox-nnet survival model to identify survival-associated features. We used non-negative matrix factorization (NMF) to identify patient survival subtypes. We identified atypical subpopulations of triple-negative breast cancer (TNBC) patients with moderate prognosis and Luminal A patients with poor prognosis and validated these subpopulations by label transferring using the UNION-COM method. RESULTS: The neural-network-based Cox-nnet survival model using all cellular phenotype and cell-cell interaction features is highly predictive of patient survival in the test data (Concordance Index > 0.8). We identify seven survival subtypes using the top survival features, presenting distinct profiles of epithelial, immune, and fibroblast cells and their interactions. We reveal atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets. CONCLUSIONS: This work provides an approach to bridge single-cell level information toward population-level survival prediction.

It may be possible to separate patients with cancer into different groups or subtypes based on the features of their tumor, such as the interactions between different types of cells in the tumor. In this study, we develop a computer-based model to calculate the interactions between cells in breast cancer images. We use these interactions to identify seven subtypes of patients with breast cancer with differences in their survival. We identify some subpopulations of patients with atypical survival outcomes. This work may ultimately help clinicians and researchers to identify patients with breast cancer at increased risk of poorer outcomes and to tailor their treatments accordingly.

Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study.

OBJECTIVES: To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites. DESIGN: Case-cohort design within a prospective cohort study. SETTING: Cambridge, UK. POPULATION OR SAMPLE: A total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB. METHODS: An untargeted metabolomic analysis of maternal serum samples at 12, 20, 28 and 36 weeks of gestation was performed. We applied six supervised machine learning methods and a weighted Cox model to measurements at 28 weeks of gestation and sPTB, followed by feature selection. We used logistic regression with elastic net penalty, followed by best subset selection, to reduce the number of predictive metabolites further. We applied coefficients from the chosen models to measurements from different gestational ages to predict sPTB and sETB. MAIN OUTCOME MEASURES: sPTB and sETB. RESULTS: We identified 47 metabolites, mostly lipids, as important predictors of sPTB by two or more methods and 22 were identified by three or more methods. The best 4-predictor model had an optimism-corrected area under the receiver operating characteristics curve (AUC) of 0.703 at 28 weeks of gestation. The model also predicted sPTB in 12-week samples (0.606, 95% CI 0.544-0.667) and 20-week samples (0.657, 95% CI 0.597-0.717) and it predicted sETB in 36-week samples (0.727, 95% CI 0.606-0.849). A lysolipid, 1-palmitoleoyl-GPE (16:1)*, was the strongest predictor of sPTB at 12 weeks of gestation (0.609, 95% CI 0.548-0.670), 20 weeks (0.630, 95% CI 0.569-0.690) and 28 weeks (0.660, 95% CI 0.599-0.722), and of sETB at 36 weeks (0.739, 95% CI 0.618-0.860). CONCLUSIONS: We identified and internally validated maternal serum metabolites predictive of sPTB. A lysolipid, 1-palmitoleoyl-GPE (16:1)*, is a novel predictor of sPTB and sETB. Further validation in external populations is required.

Modifying Escherichia coli to mimic Shigella for medical microbiology laboratory teaching: a new strategy to improve biosafety in class.

Introduction: Laboratory teaching of medical microbiology involves highly pathogenic microorganisms, thus posing potential biosafety risks to the students and the teacher. To address these risks, non/low-pathogenic microorganisms were modified to mimic highly pathogenic ones or highly pathogenic microorganisms were attenuated directly using the CRISPR/Cas9 technology. This study describes the modification of Escherichia coli DH5α to mimic Shigella and its evaluation as a safe alternative for medical laboratory teaching. Methods: To generate E. coli DH5α△FliC△tnaA2a, the tnaA and FliC genes in E. coli DH5α were knocked out using CRISPR/Cas9 technology; a plasmid bearing the O-antigen determinant of S. flexneri 2a was then constructed and transformed. Acid tolerance assays and guinea pig eye tests were used to assess the viability and pathogenicity, respectively. Questionnaires were used to analyze teaching effectiveness and the opinions of teachers and students. Results: The survey revealed that most teachers and students were inclined towards real-time laboratory classes than virtual classes or observation of plastic specimens. However, many students did not abide by the safety regulations, and most encountered potential biosafety hazards in the laboratory. E. coli DH5α△FliC△tnaA2a was biochemically and antigenically analogous to S. flexneri 2a and had lower resistance to acid than E. coli. There was no toxicity observed in guinea pigs. Most of teachers and students were unable to distinguish E. coli DH5α△FliC△tnaA2a from pure S. flexneri 2a in class. Students who used E. coli DH5α△FliC△tnaA2a in their practice had similar performance in simulated examinations compared to students who used real S. flexneri 2a, but significantly higher than the virtual experimental group. Discussion: This approach can be applied to other high-risk pathogenic microorganisms to reduce the potential biosafety risks in medical laboratory-based teaching and provide a new strategy for the development of experimental materials.

Deep-learning and transfer learning identify new breast cancer survival subtypes from single-cell imaging data.

Quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at population scale are currently missing. Here, we computationally extracted hundreds of features describing single-cell based cell-cell interactions and cellular phenotypes from a large, published cohort of cyto-images of breast cancer patients. We applied these features to a neural-network based Cox-nnet survival model and obtained high accuracy in predicting patient survival in test data (Concordance Index > 0.8). We identified seven survival subtypes using the top survival features, which present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. We identified atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets. This work provides important guidelines on bridging single-cell level information towards population-level survival prediction. STATEMENT OF TRANSLATIONAL RELEVANCE: Our findings from a breast cancer population cohort demonstrate the clinical utility of using the single-cell level imaging mass cytometry (IMC) data as a new type of patient prognosis prediction marker. Not only did the prognosis prediction achieve high accuracy with a Concordance index score greater than 0.8, it also enabled the discovery of seven survival subtypes that are more distinguishable than the molecular subtypes. These new subtypes present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. Most importantly, this study identified and validated atypical subpopulations of TNBC patients with moderate prognosis (GATA3 over-expression) and Luminal A patients with poor prognosis (KRT6 and ACTA2 over-expression and CDH1 under-expression), using multiple large breast cancer cohorts.

Severe preeclampsia is not associated with significant DNA methylation changes but cell proportion changes in the cord blood - caution on the importance of confounding adjustment.

Epigenome-wide DNA methylation analysis (EWAS) is an important approach to identify biomarkers for early disease detection and prognosis prediction, yet its results could be confounded by other factors such as cell-type heterogeneity and patient characteristics. In this study, we address the importance of confounding adjustment by examining DNA methylation patterns in cord blood exposed to severe preeclampsia (PE), a prevalent and potentially fatal pregnancy complication. Without such adjustment, a misleading global hypomethylation pattern is obtained. However, after adjusting cell type proportions and patient clinical characteristics, most of the so-called significant CpG methylation changes associated with severe PE disappear. Rather, the major effect of PE on cord blood is through the proportion changes in different cell types. These results are validated using a previously published cord blood DNA methylation dataset, where global hypomethylation pattern was also wrongfully obtained without confounding adjustment. Additionally, several cell types significantly change as gestation progress (eg. granulocyte, nRBC, CD4T, and B cells), further confirming the importance of cell type adjustment in EWAS study of cord blood tissues. Our study urges the community to perform confounding adjustments in EWAS studies, based on cell type heterogeneity and other patient characteristics.

Exosome-mediated long noncoding RNA (lncRNA) PART1 suppresses malignant progression of oral squamous cell carcinoma via miR-17-5p/SOCS6 axis.

BACKGROUND: Exosomes derived from oral squamous cell carcinoma (OSCC) could modulate OSCC development. This study aimed to explore effects of exosome-mediated lncRNA PART1 on OSCC cells. METHODS: This study was performed in Tianjin Medical University Cancer Institute from February 2021 to March 2022. Bioinformatic analysis was performed on the public database GEPIA (http://gepia.cancer-pku.cn/). Exosomes isolated from cell lines squamous cell carcinoma 9 (SCC9) and Centre Antoine Lacassagne-27 (CAL27) were identified by transmission electron microscope and western blot. Exosome-mediated lncRNA PART1, microRNA-17-5p(miR-17-5p) and suppressor of cytokine signaling 6(SOCS6) RNA expressions were assessed by quantitative reverse transcription polymerase chain reaction (RT-qPCR). Cell counting kit8(CCK-8), caspase-3 activity, and flow cytometry were applied to evaluate OSCC cell viabilities and apoptosis. Meanwhile, OSCC cell migratory ability and invasiveness were evaluated using transwell assay. Bindings between miR-17-5p and lncRNA PART1 or SOCS6 were validated using the luciferase reporter test. Western blot was used for detecting the protein levels of SOCS6, phosphorylated signal transducer and activator of transcription 3 (STAT3) and STAT3. RESULTS: : According to GEPIA, lncRNA PART1 was downregulated in OSCC tissue samples and cells, and it had a positive correlation with the good prognosis of Head and neck squamous cell cancer (HNSCC) patients. After the exosomes from OSCC cells were isolated and verified, PART1 was then confirmed to be secreted by exosomes. Further, overexpression of exosome-mediated lncRNA PART1 inhibited OSCC cell viabilities, migration, and invasiveness but facilitated OSCC cell apoptosis. PART1 upregulated SOCS6 through sponging miR-17-5p. Moreover, exosome-mediated lncRNA PART1 suppressed the phosphorylation of STAT3. DISCUSSION: Exosome-mediated lncRNA PART1 could mediate the OSCC progression via miR-17-5p/SOCS6 axis in vitro, suggesting that lncRNA PART1 might be a target for treating OSCC.

ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs.

Single-cell RNA sequencing technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD) that defines a drug score to recommend drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. ASGARD shows significantly better average accuracy on single-drug therapy compared to two bulk-cell-based drug repurposing methods. We also demonstrated that it performs considerably better than other cell cluster-level predicting methods. In addition, we validate ASGARD using the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. We find that many top-ranked drugs are either approved by the Food and Drug Administration or in clinical trials treating corresponding diseases. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD .

CD73/NT5E is a Potential Biomarker for Cancer Prognosis and Immunotherapy for Multiple Types of Cancers.

Cluster of Differentiations 73 (CD73)/ecto-5'-nucleotidase (NT5E) is a novel type of immune molecular marker expressed on many tumor cells and involved in regulating the essential immune functions and affecting the prognosis of cancer patients. However, it is not clear how the NT5E is linked to the infiltration levels of the immune cells in pan-cancer patients and their final prognosis. This study explores the role of NT5E in 33 tumor types using GEPIA, TIMER, Oncomine, BioGPS databases, and several bioinformatic tools. The findings reveal that the NT5E is abnormally expressed in a majority of the types of cancers and can be used for determining the prognosis prediction ability of different cancers. Moreover, NT5E is significantly related to the infiltration status of numerous immune cells, immune-activated pathways, and immunoregulator expressions. Last, specific inhibitor molecules, like NORNICOTINE, AS-703026, and FOSTAMATINIB, which inhibit the expression of NT5E in various types of cancers, are screened with the CMap. Thus, it is proposed that NT5E can be utilized as a potential biomarker for predicting the prognosis of cancer patients and determining the infiltration of various immune cells in different types of cancers.

A Dual-Filtration System for Single-Cell Sequencing of Circulating Tumor Cells and Clusters in HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer death worldwide. Identification and sequencing of circulating tumor (CT) cells and clusters may allow for noninvasive molecular characterization of HCC, which is an unmet need, as many patients with HCC do not undergo biopsy. We evaluated CT cells and clusters, collected using a dual-filtration system in patients with HCC. We collected and filtered whole blood from patients with HCC and selected individual CT cells and clusters with a micropipette. Reverse transcription, polymerase chain reaction, and library preparation were performed using a SmartSeq2 protocol, followed by single-cell RNA sequencing (scRNAseq) on an Illumina MiSeq V3 platform. Of the 8 patients recruited, 6 had identifiable CT cells or clusters. Median age was 64 years old; 7 of 8 were male; and 7 of 8 had and Barcelona Clinic Liver Cancer stage C. We performed scRNAseq of 38 CT cells and 33 clusters from these patients. These CT cells and clusters formed two distinct groups. Group 1 had significantly higher expression than group 2 of markers associated with epithelial phenotypes (CDH1 [Cadherin 1], EPCAM [epithelial cell adhesion molecule], ASGR2 [asialoglycoprotein receptor 2], and KRT8 [Keratin 8]), epithelial-mesenchymal transition (VIM [Vimentin]), and stemness (PROM1 [CD133], POU5F1 [POU domain, class 5, transcription factor 1], NOTCH1, STAT3 [signal transducer and activator of transcription 3]) (P < 0.05 for all). Patients with identifiable group 1 cells or clusters had poorer prognosis than those without them (median overall survival 39 vs. 384 days; P = 0.048 by log-rank test). Conclusion: A simple dual-filtration system allows for isolation and sequencing of CT cells and clusters in HCC and may identify cells expressing candidate genes known to be involved in cancer biology. Presence of CT cells/clusters expressing candidate genes is associated with poorer prognosis in advanced-stage HCC.

ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs.

Intercellular heterogeneity is a major obstacle to successful precision medicine. Single-cell RNA sequencing (scRNA-seq) technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose a new drug recommendation system called: A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD). ASGARD defines a novel drug score predicting drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. We tested ASGARD on multiple diseases, including breast cancer, acute lymphoblastic leukemia, and coronavirus disease 2019 (COVID-19). On single-drug therapy, ASGARD shows significantly better average accuracy (AUC of 0.92) compared to two other bulk-cell-based drug repurposing methods (AUC of 0.80 and 0.76). It is also considerably better (AUC of 0.82) than other cell cluster level predicting methods (AUC of 0.67 and 0.55). In addition, ASGARD is also validated by the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. Many top-ranked drugs are either approved by FDA or in clinical trials treating corresponding diseases. In silico cell-type specific drop-out experiments using triple-negative breast cancers show the importance of T cells in the tumor microenvironment in affecting drug predictions. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD.

Lilikoi V2.0: a deep learning-enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data.

BACKGROUND: previously we developed Lilikoi, a personalized pathway-based method to classify diseases using metabolomics data. Given the new trends of computation in the metabolomics field, it is important to update Lilikoi software. RESULTS: here we report the next version of Lilikoi as a significant upgrade. The new Lilikoi v2.0 R package has implemented a deep learning method for classification, in addition to popular machine learning methods. It also has several new modules, including the most significant addition of prognosis prediction, implemented by Cox-proportional hazards model and the deep learning-based Cox-nnet model. Additionally, Lilikoi v2.0 supports data preprocessing, exploratory analysis, pathway visualization, and metabolite pathway regression. CONCULSION: Lilikoi v2.0 is a modern, comprehensive package to enable metabolomics analysis in R programming environment.

Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data.

Annotating cell types is a critical step in single-cell RNA sequencing (scRNA-seq) data analysis. Some supervised or semi-supervised classification methods have recently emerged to enable automated cell type identification. However, comprehensive evaluations of these methods are lacking. Moreover, it is not clear whether some classification methods originally designed for analyzing other bulk omics data are adaptable to scRNA-seq analysis. In this study, we evaluated ten cell type annotation methods publicly available as R packages. Eight of them are popular methods developed specifically for single-cell research, including Seurat, scmap, SingleR, CHETAH, SingleCellNet, scID, Garnett, and SCINA. The other two methods were repurposed from deconvoluting DNA methylation data, i.e., linear constrained projection (CP) and robust partial correlations (RPC). We conducted systematic comparisons on a wide variety of public scRNA-seq datasets as well as simulation data. We assessed the accuracy through intra-dataset and inter-dataset predictions; the robustness over practical challenges such as gene filtering, high similarity among cell types, and increased cell type classes; as well as the detection of rare and unknown cell types. Overall, methods such as Seurat, SingleR, CP, RPC, and SingleCellNet performed well, with Seurat being the best at annotating major cell types. Additionally, Seurat, SingleR, CP, and RPC were more robust against downsampling. However, Seurat did have a major drawback at predicting rare cell populations, and it was suboptimal at differentiating cell types highly similar to each other, compared to SingleR and RPC. All the code and data are available from https://github.com/qianhuiSenn/scRNA_cell_deconv_benchmark.

Evaluation of STAR and Kallisto on Single Cell RNA-Seq Data Alignment.

Alignment of scRNA-Seq data are the first and one of the most critical steps of the scRNA-Seq analysis workflow, and thus the choice of proper aligners is of paramount importance. Recently, STAR an alignment method and Kallisto a pseudoalignment method have both gained a vast amount of popularity in the single cell sequencing field. However, an unbiased third-party comparison of these two methods in scRNA-Seq is lacking. Here we conduct a systematic comparison of them on a variety of Drop-seq, Fluidigm and 10x genomics data, from the aspects of gene abundance, alignment accuracy, as well as computational speed and memory use. We observe that STAR globally produces more genes and higher gene-expression values, compared to Kallisto, as well as Bowtie2, another popular alignment method for bulk RNA-Seq. STAR also yields higher correlations of the Gini index for the genes with RNA-FISH validation results. Using 10x genomics PBMC 3K scRNA-Seq and mouse cortex single nuclei RNA-Seq data, STAR shows similar or better cell-type annotation results, by detecting a larger subset of known gene markers. However, the gain of accuracy and gene abundance of STAR alignment comes with the price of significantly slower computation time (4 folds) and more memory (7.7 folds), compared to Kallisto.

PLAGL2 and POFUT1 are regulated by an evolutionarily conserved bidirectional promoter and are collaboratively involved in colorectal cancer by maintaining stemness.

BACKGROUND: Our previous study revealed that PLAGL2 or POFUT1 can promote tumorigenesis and maintain significant positive correlations in colorectal cancer (CRC). However, the mechanism leading to the co-expression and the underlying functional and biological implications remain unclear. METHODS: Clinical tumor tissues and TCGA dataset were utilized to analyze the co-expression of PLAGL2 and POFUT1. Luciferase reporter assays, specially made bidirectional promoter vectors and ectopic expression of 3'UTR were employed to study the mechanisms of co-expression. In vitro and in vivo assays were performed to further confirm the oncogenic function of both. The sphere formation assay, immunofluorescence, Western blot and qRT-PCR were performed to investigate the effect of both genes in colorectal cancer stem cells (CSCs). FINDINGS: PLAGL2 and POFUT1 maintained co-expression in CRC (r = 0.91, p < .0001). An evolutionarily conserved bidirectional promoter, rather than post-transcriptional regulation by competing endogenous RNAs, caused the co-expression of PLAGL2 and POFUT1 in CRC. The bidirectional gene pair PLAGL2/POFUT1 was subverted in CRC and acted synergistically to promote colorectal tumorigenesis by maintaining stemness of colorectal cancer stem cells through the Wnt and Notch pathways. Finally, PLAGL2 and POFUT1 share transcription factor binding sites, and introducing mutations into promoter regions with shared transcription regulatory elements led to a decrease in the PLAGL2/POFUT1 promoter activity in both directions. INTERPRETATION: Our team identified for the first time a bidirectional promoter pair oncogene, PLAGL2-POFUT1, in CRC. The two genes synergistically promote the progression of CRC and affect the characteristics of CSCs, which can offer promising intervention targets for clinicians and researchers. FUND: National Nature Science Foundation of China, the Hunan province projects of Postgraduate Independent Exploration and Innovation of Central South University.

Overexpressed PLAGL2 transcriptionally activates Wnt6 and promotes cancer development in colorectal cancer.

Researchers hold the view that PLAGL2 is overexpressed in many malignancies and that it can promote tumor proliferation, migration, invasion and self­renewal; however, there is no evidence revealing a relationship between PLAGL2 and colorectal cancer (CRC). In the present study, genes that are overexpressed in CRC were screened using the COSMIC database and GEPIA database and the expression of PLAGL2 in carcinoma tissues and pericarcinomatous tissues was detected by RT­qPCR and western blot assays. A Cell Counting Kit­8 assay, a cell cycle analysis experiment and a xenograft model were used to explore the influence of PLAGL2 on CRC after knocking down PLAGL2 expression in HCT116 and SW480 cells. Using ChIP assays and Dual­Luciferase Reporter assays, the promoter regions to which PLAGL2 binds were discovered. It was observed that PLAGL2 was overexpressed in colorectal cancer and that it influenced the colorectal cancer cell cycle and promoted colorectal cancer proliferation in vivo and in vitro. The expression of some genes in the Wnt/ß­catenin pathway, were downregulated after knocking down the expression of PLAGL2; Wnt6 was altered the most. PLAGL2 could bind to the promoter region of Wnt6 and promote its expression. These results indicated that PLAGL2 was overexpressed in CRC as a proto­oncogene and that it could active the Wnt/ß­catenin pathway as a transcription factor by binding with the promoter region of Wnt6. PALGL2 was revealed to play an important role in colorectal cancer and may be a new therapeutic target for targeted medicine.

POFUT1 promotes colorectal cancer development through the activation of Notch1 signaling.

Copy number variations (CNVs) are key drivers of colorectal cancer (CRC). Our previous studies revealed that protein O-fucosyltransferase 1 (POFUT1) overexpression is driven by CNVs during CRC development. The potential role and underlying mechanisms of POFUT1 in CRC were not investigated. In this study, we analyzed the expression of POFUT1 in CRC from cosmic and TCGA databases and confirmed that POFUT1 is highly expressed in CRC. We used well characterized CRC cell lines, including SW620 and HCT116 to establish a model POFUT1 knockdown cell line. Using these cells, we investigated the role of POFUT1 in CRC. Our data revealed that silencing POFUT1 in CRC cells inhibits cell proliferation, decreases cell invasion and migration, arrests cell cycle progression, and stimulates CRC cell apoptosis in vitro. We further demonstrate that POFUT1 silencing dramatically suppresses CRC tumor growth and transplantation in vivo. We additionally reveal new mechanistic insights into the role of POFUT1 during CRC, through demonstrating that POFUT1 silencing inhibits Notch1 signaling. Taken together, our findings demonstrate that POFUT1 is a tumor activating gene during CRC development, which positively regulates CRC tumor progression through activating Notch1.

Studying the mechanism of PLAGL2 overexpression and its carcinogenic characteristics based on 3'-untranslated region in colorectal cancer.

Pleomorphic adenoma gene like-2 (PLAGL2) is a zinc finger protein transcription factor, which is upregulated and serves an oncogenic function in multiple human malignancies, including colorectal cancer (CRC). First, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression levels of PLAGL2 in CRC tissues and normal tissues. Then, bioinformatics analysis, RT-qPCR, western blotting, luciferase reporter assays and RNA-binding protein immunoprecipitation assays were performed to explore whether the underlying mechanisms, including copy number variation (CNV), microRNAs (miRNAs/miRs) and RNA-binding proteins (RBPs) led to the abnormal expression of PLAGL2. Finally, cell counting kit-8 assays, Transwell assays and xenograft models were used to detect carcinogenesis-associated characteristics based on the 3'-untranslated region (3'-UTR) of PLAGL2. In the present study, PLAGL2 was revealed to be upregulated in CRC tissues compared with normal CRC tissues. CNV was one of the causes leading to the upregulation of PLAGL2. miRNA, including downregulated miR-486-5p, and RBPs, including upregulated human antigen R (HuR), were other key underlying causes. In addition, PLAGL2 3'-UTR was revealed to promote the progression of CRC in vitro and in vivo, and to regulate the expression of C-MYC and CD44. To conclude, these results suggested that high expression of PLAGL2 in CRC was associated with CNV, miR-486-5p and HuR expression, whose 3'-UTR may promote colon carcinogenesis and serve as a novel potential biomarker for CRC therapies.

Comprehensive bioinformatics analysis of the characterization and determination underlying mechanisms of over-expression and co-expression of genes residing on 20q in colorectal cancer.

The Long arm of chromosome 20 (20q) is closely related to the development of colorectal cancer, so identifying the expression profile of genes on 20q through a comprehensive overview is indispensable. In this article, preliminar experimental data, several available databases and bioinformatics tools such as the Cancer Genome Atlas, the Encyclopedia of DNA Elements, the JASPAR database and starBase were combined to analyze the correlation between genes and chromosomal aberrations, microRNA and transcription factors, as well as to explore the expression feature and potential regulative mechanism. The results showed that the most frequently unregulated genes in colorectal cancer arelocated on chromosome 20q, present a significant CNA-mRNA correlation.Furthermore, the genes with mRNA overexpression showed co-expression features and tended to be clustered within the same genomic neighborhoods. Then, several genes were selected to carry out further analysis and demonstrated that shared transcription factors, a conserved bidirectional promoter, and competition for a limited pool of microRNAin the 3'UTR of mRNA may be the underlying mechanisms behind the co-expression of physically adjacent genes.Finally, the databases, Lentivirus shRNA, and qPCR were used to find that these adjacent genes with co-expression cooperatively participated in the same biological pathways associated with the pathogenesis and development of colorectal cancer.