Nucleotide de novo synthesis increases breast cancer stemness and metastasis via cGMP-PKG-MAPK signaling pathway.

Metabolic reprogramming to fulfill the biosynthetic and bioenergetic demands of cancer cells has aroused great interest in recent years. However, metabolic reprogramming for cancer metastasis has not been well elucidated. Here, we screened a subpopulation of breast cancer cells with highly metastatic capacity to the lung in mice and investigated the metabolic alternations by analyzing the metabolome and the transcriptome, which were confirmed in breast cancer cells, mouse models, and patients' tissues. The effects and the mechanisms of nucleotide de novo synthesis in cancer metastasis were further evaluated in vitro and in vivo. In our study, we report an increased nucleotide de novo synthesis as a key metabolic hallmark in metastatic breast cancer cells and revealed that enforced nucleotide de novo synthesis was enough to drive the metastasis of breast cancer cells. An increased key metabolite of de novo synthesis, guanosine-5'-triphosphate (GTP), is able to generate more cyclic guanosine monophosphate (cGMP) to activate cGMP-dependent protein kinases PKG and downstream MAPK pathway, resulting in the increased tumor cell stemness and metastasis. Blocking de novo synthesis by silencing phosphoribosylpyrophosphate synthetase 2 (PRPS2) can effectively decrease the stemness of breast cancer cells and reduce the lung metastasis. More interestingly, in breast cancer patients, the level of plasma uric acid (UA), a downstream metabolite of purine, is tightly correlated with patient's survival. Our study uncovered that increased de novo synthesis is a metabolic hallmark of metastatic breast cancer cells and its metabolites can regulate the signaling pathway to promote the stemness and metastasis of breast cancer.

Genome-wide CRISPR/Cas9 knockout screening uncovers ZNF319 as a novel tumor suppressor critical for breast cancer metastasis.

Breast cancer is prone to relapse and metastasize to many vital organs, contributing to most of the breast cancer-related death and accentuating the importance of systematic identification of key factors regulating the metastasis of breast cancer. In this study, we performed a genome-wide CRISPR/Cas9 knock out screen in an orthotopic murine model of breast cancer for essential genes monitoring the progression and metastasis of breast cancer. We found one member of the zinc finger protein (ZNF) family, i.e., ZNF319, was among the top candidate genes. We further confirmed the lower expression of ZNF319 in the tumor tissue of breast cancer patients by analyzing tissue sections with IHC staining and TCGA database. Consistently, higher expression of ZNF319 correlates with better clinical outcome in almost all subtypes of breast cancer. Moreover, knocking down or overexpressing ZNF319 in breast cancer cells dramatically affects the breast cancer growth and metastasis capacity both in vitro and in vivo, suggesting ZNF319 functions as a strong suppressor of breast cancer progression. Lastly, the transcriptome analysis on ZNF319-silenced breast cancer cells shows that ZNF319 is involved in multiple crucial signaling pathways and biological processes, especially in cell cycle and proliferation. GO and KEGG analyses of our RNA-seq results reveal the up-regulation of E2F and G2/M related genes in ZNF319-silenced cells, suggesting that ZNF319 monitors the cell cycle during the breast cancer progression through the regulation of the E2F target genes and G2/M checkpoint. In summary, our study identifies ZNF319 as a novel metastasis suppressor gene arresting tumor cell cycle in breast cancer and thus presents a novel potential therapeutic target for breast cancer treatment.

Cytotoxic lymphocytes-related gene ITK from a systematic CRISPR screen could predict prognosis of ovarian cancer patients with distant metastasis.

BACKGROUND: Ovarian cancer, a highly metastatic malignancy, has benefited tremendously from advances in modern human genomics. However, the genomic variations related to the metastasis remains unclear. METHODS: We filtered various significant genes (n = 6722) associated with metastasis within a large-scale functional genomic CRISPR/Cas9 knock-out library including 122,756 single guide RNAs, and identified ITK (IL2 Inducible T Cell Kinase) as a potential cancer suppressor gene for ovarian cancer metastasis. Downstream bioinformatic analysis was performed for ITK using public databases. RESULTS: We found that patients in low-ITK group had poor prognosis and more distant metastasis than those in high-ITK group in TCGA and GEO databases. We also demonstrated that ITK combined with the clinical factors could accurately predict prognosis through multiple Cox regression analysis and ROC analysis. Moreover, alterations correlated with distant metastasis emereged with significantly increased expression in SAMRCD1 in low-ITK group, but CD244 and SOCS1 in high-ITK group. Integrated analysis revealed dysregulated molecular processes including predominantly oncogenic signaling pathways in low-ITK group but immune related pathways in high-ITK group, which suggested ITK might inhibit distant metastasis in ovarian cancer. Furtherly, deconvolution of the cellular composition of all samples validated the close correlation between ITK and immune related function especially for cytotoxic lymphocytes. CONCLUSIONS: Together, these data provide insights into the potential role of ITK, with implications for the future development of tansformative ovarian cancer therapeutics.

Is smoking a risk factor for bleeding in adult men with cerebral arteriovenous malformations? A single-center regression study from China.

OBJECTIVE: To assess whether smoking increases the risk of bleeding in patients with cerebral arteriovenous malformations (CAVM). MATERIAL AND METHODS: According to our research plan, 385 CAVM patients admitted to Beijing Tiantan Hospital from December 2015 to January 2018 were included in this study, including 210 bleeding patients and 175 non-bleeding patients. We divided patients into three subgroups of current smokers, ex-smokers (those who quit smoking for one year or more) and non-smokers. The relationship between smoking and the risk of CAVM rupture was assessed by univariate and multivariate regression analysis. RESULTS: Multivariate regression analysis showed that there was a statistically significant difference between current smoker and non-smoker (OR = 1.87, p = 0.019). Among the covariates of the multivariate regression analysis, the location, combined with blood flow-related intracranial aneurysms and size were related to the risk of CAVM bleeding. CONCLUSION: Current smoking may increase the risk of CAVM bleeding; however, there was no significant correlation between ex-smoking and CAVM bleeding.

CAME: identification of chromatin accessibility from nucleosome occupancy and methylome sequencing.

Motivation: Chromatin accessibility plays a key role in epigenetic regulation of gene activation and silencing. Open chromatin regions allow regulatory elements such as transcription factors and polymerases to bind for gene expression while closed chromatin regions prevent the activity of transcriptional machinery. Recently, Methyltransferase Accessibility Protocol for individual templates-Bisulfite Genome Sequencing (MAPit-BGS) and nucleosome occupancy and methylome sequencing (NOMe-seq) have been developed for simultaneously profiling chromatin accessibility and DNA methylation on single molecules. Therefore, there is a great demand in developing computational methods to identify chromatin accessibility from MAPit-BGS and NOMe-seq. Results: In this article, we present CAME (Chromatin Accessibility and Methylation), a seed-extension based approach that identifies chromatin accessibility from NOMe-seq. The efficiency and effectiveness of CAME were demonstrated through comparisons with other existing techniques on both simulated and real data, and the results show that our method not only can precisely identify chromatin accessibility but also outperforms other methods. Availability and Implementation: CAME is implemented in java and the program is freely available online at http://sourceforge.net/projects/came/. Contacts: jechoi@gru.edu or khryu@dblab.chungbuk.ac.kr. Supplementary information: Supplementary data are available at Bioinformatics online.

Positive Effect of Baby-Friendly Hospital Initiatives on Improving Mothers' Intention for Successful Breastfeeding in Korea.

BACKGROUND: In Korea, the breastfeeding (BF) rate of infants aged 6 months or more is drastically decreasing, and this phenomenon is particularly worrisome for the future health of the population. The present study aimed to identify an antenatal strategy for initiation and continuation of human BF, and to identify how Baby-Friendly Hospitals (BFHs) may positively influence the intention to breastfeed. METHODS: A total of 414 pregnant Korean antenatal women were surveyed using questionnaires to determine current knowledge of the benefits of human breast milk, whether they planned to breastfeed after delivery, to continue BF after reinstatement in the workforce, are willing to abide by rooming-in care for infants, and plan to give birth at BFHs. RESULTS: We found that planning room-in care, greater awareness of BF benefits for infant and mother, participation in antenatal education programs, and provision of BF facilities in the workplace were positively associated with plans for exclusive breastfeeding (EBF) and longer BF duration. The mothers who planned to give birth at BFHs also desired to breastfeed immediately after birth, implement in-room care, continue BF at their workplace, participate in antenatal BF educational programs, and were more aware of the benefits of BF. CONCLUSION: If the beneficial effects of BFHs were well known to individuals, these would enhance the success rate of BF in Korea. Antenatal education and consequent acquisition of better knowledge of the benefits of BF are important for increasing the rate of BF practices.

Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data.

BACKGROUND: Recently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments. RESULTS: In this paper, we compared eight non-abundance (RC, UQ, Med, TMM, DESeq, Q, RPKM, and ERPKM) and two abundance estimation normalization methods (RSEM and Sailfish). The experiments were based on real Illumina high-throughput RNA-Seq of 35- and 76-nucleotide sequences produced in the MAQC project and simulation reads. Reads were mapped with human genome obtained from UCSC Genome Browser Database. For precise evaluation, we investigated Spearman correlation between the normalization results from RNA-Seq and MAQC qRT-PCR values for 996 genes. Based on this work, we showed that out of the eight non-abundance estimation normalization methods, RC, UQ, Med, TMM, DESeq, and Q gave similar normalization results for all data sets. For RNA-Seq of a 35-nucleotide sequence, RPKM showed the highest correlation results, but for RNA-Seq of a 76-nucleotide sequence, least correlation was observed than the other methods. ERPKM did not improve results than RPKM. Between two abundance estimation normalization methods, for RNA-Seq of a 35-nucleotide sequence, higher correlation was obtained with Sailfish than that with RSEM, which was better than without using abundance estimation methods. However, for RNA-Seq of a 76-nucleotide sequence, the results achieved by RSEM were similar to without applying abundance estimation methods, and were much better than with Sailfish. Furthermore, we found that adding a poly-A tail increased alignment numbers, but did not improve normalization results. CONCLUSION: Spearman correlation analysis revealed that RC, UQ, Med, TMM, DESeq, and Q did not noticeably improve gene expression normalization, regardless of read length. Other normalization methods were more efficient when alignment accuracy was low; Sailfish with RPKM gave the best normalization results. When alignment accuracy was high, RC was sufficient for gene expression calculation. And we suggest ignoring poly-A tail during differential gene expression analysis.

Stimulation of mouse hair regrowth by exosomes derived from human umbilical cord mesenchymal stem cells.

BACKGROUND: There is an urgent need for new treatments to solve hair loss problem. As mesenchymal stem cells were proved to have effects on promoting tissue repair and regeneration, in which the exosome plays a vital role, we aim to investigate the influence of umbilical cord mesenchymal stem cells exosome (UCMSC-Exos) on hair growth and its mechanism. METHODS: The hUCMSC-Exos were extracted by ultracentrifugation. Primary fibroblasts were cultured with or without hUCMSC-Exos and cell proliferation was evaluated by CCK-8 assay. C57BL/6 mice model of depilation-induced hair regrowth was treated with either hUCMSC-Exos (200 µg/mL) or PBS on one side of the dorsal back. Real time quantitative PCR, flow cytometry analysis, immunohistochemistry and Immunofluorescent staining were used to analyze the regulative effect of hUCMSC-Exos on hair follicle stem/progenitor cells and Wnt/ß-catenin pathway. RESULTS: The proliferation of fibroblasts incubated with hUCMSC-Exos at the concentration of 200 µg/mL was greater than other groups. Treatment with hUCMSC-Exos resulted in rapid reentry into anagen. Hair follicle stem/progenitor cell markers (K15, Lgr5, Lgr6, CD34 and Lrig1) and Wnt/ß-catenin pathway related factors (Wnt5, Lef1, Lrp5 and ß-catenin) were increased in hUCMSC-Exos-injected region. CONCLUSION: hUCMSC-Exos promote fibroblasts proliferation and accelerate mouse hair regrowth by upregulating hair follicle stem/progenitor cell and Wnt/ß-catenin pathway, which suggests potential therapeutic approaches for hair loss disorders.

Comparative efficacy and safety of systemic steroids, oral JAK inhibitors and Contact Immunotherapy in the Treatment of severe alopecia areata: a systematic review and network meta-analysis.

Severe alopecia areata (AA) is a nonscarring hair loss for immune disorder and SALT score ≥ 50%. The guidelines for managing patients with severe AA suggest treatments: systemic steroids, JAK inhibitors, and contact immunotherapy. However, there is a lack of evidence indicating the superiority of one treatment over another. Therefore, this study aimed to identify the most effective treatment for severe AA through network meta-analysis. Following the PRISMA guidelines, we conducted a network meta-analysis. The literature search was retrieved across four databases. The Cochrane 5.1 risk of bias assessment tool and ROBINS-I tool assessed quality of the included studies. Subsequently, efficacy and safety comparisons among the three treatments were conducted using Stata 14.0 on account of the frequency method. The SUCRA rank indicated that oral dexamethasone (95.9%) > diphenylcyclopropenone(DPCP) (74.5%) > oral ritlecitinib (62.6%) > oral baricitinib (46.9%) > squaric acid dibutyl ester(SADBE) (20.1%) > placebo (0.0%) from high to low in the aspect of improving efficacy. As for safety, placebo(88.4%) > oral ritlecitinib (86.5%) > oral baricitinib (62.1%) > SADBE (37.0%) > oral dexamethasone(22.3%) > DPCP(3.8%) in the aspect of decreasing adverse events. Oral dexamethasone and DPCP showed superior efficacy compared to oral ritlecitinib and oral baricitinib. However, in terms of safety, oral ritlecitinib was preferable. Some adverse events associated with oral dexamethasone and DPCP were intolerable to patients, whereas those related to oral ritlecitinib and oral baricitinib were more manageable. Overall, ritlecitinib and baricitinib remain promising drugs in the future treatment of severe AA.

Development of a spontaneous preterm birth predictive model using a panel of serum protein biomarkers for early pregnant women: A nested case-control study.

OBJECTIVE: To develop a model based on maternal serum liquid chromatography tandem mass spectrometry (LC-MS/MS) proteins to predict spontaneous preterm birth (sPTB). METHODS: This nested case-control study used the data from a cohort of 2053 women in China from July 1, 2018, to January 31, 2019. In total, 110 singleton pregnancies at 11-13+6 weeks of pregnancy were used for model development and internal validation. A total of 72 pregnancies at 20-32 weeks from an additional cohort of 2167 women were used to evaluate the scalability of the model. Maternal serum samples were analyzed by LC-MS/MS, and a predictive model was developed using machine learning algorithms. RESULTS: A novel predictive panel with four proteins, including soluble fms-like tyrosine kinase-1, matrix metalloproteinase 8, ceruloplasmin, and sex-hormone-binding globulin, was developed. The optimal model of logistic regression had an AUC of 0.934, with additional prediction of sPTB in second and third trimester (AUC = 0.868). CONCLUSION: First-trimester modeling based on maternal serum LC-MS/MS identifies pregnant women at risk of sPTB, which may provide utility in identifying women at risk at an early stage of pregnancy before clinical presentation to allow for earlier intervention.

Combined lineage tracing and scRNA-seq reveal the activation of Sox9+ cells in renal regeneration with PGE2 treatment.

Uncovering mechanisms of endogenous regeneration and repair through resident stem cell activation will allow us to develop specific therapies for injuries and diseases by targeting resident stem cell lineages. Sox9+ stem cells have been reported to play an essential role in acute kidney injury (AKI). However, a complete view of the Sox9+ lineage was not well investigated to accurately elucidate the functional end state and the choice of cell fate during tissue repair after AKI. To identify the mechanisms of fate determination of Sox9+ stem cells, we set up an AKI model with prostaglandin E2 (PGE2) treatment in a Sox9 lineage tracing mouse model. Single-cell RNA sequencing (scRNA-seq) was performed to analyse the transcriptomic profile of the Sox9+ lineage. Our results revealed that PGE2 could activate renal Sox9+ cells and promote the differentiation of Sox9+ cells into renal proximal tubular epithelial cells and inhibit the development of fibrosis. Furthermore, single-cell transcriptome analysis demonstrated that PGE2 could regulate the restoration of lipid metabolism homeostasis in proximal tubular epithelial cells by participating in communication with different cell types. Our results highlight the prospects for the activation of endogenous renal Sox9+ stem cells with PGE2 for the regenerative therapy of AKI.

An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data.

MOTIVATION: Gene selection for cancer classification is one of the most important topics in the biomedical field. However, microarray data pose a severe challenge for computational techniques. We need dimension reduction techniques that identify a small set of genes to achieve better learning performance. From the perspective of machine learning, the selection of genes can be considered to be a feature selection problem that aims to find a small subset of features that has the most discriminative information for the target. RESULTS: In this article, we proposed an Ensemble Correlation-Based Gene Selection algorithm based on symmetrical uncertainty and Support Vector Machine. In our method, symmetrical uncertainty was used to analyze the relevance of the genes, the different starting points of the relevant subset were used to generate the gene subsets and the Support Vector Machine was used as an evaluation criterion of the wrapper. The efficiency and effectiveness of our method were demonstrated through comparisons with other feature selection techniques, and the results show that our method outperformed other methods published in the literature.

Whole exome sequencing reveals novel variants associated with diminished ovarian reserve in young women.

Background: Diminished ovarian reserve is one of the most important causes of female infertility. In the etiology study of DOR, besides age, it is known that chromosomal abnormality, radiotherapy, chemotherapy and ovarian surgery can result in DOR. For young women without obvious risk factors, gene mutation should be considered as a possible cause. However, the specific molecular mechanism of DOR has not been fully elucidated. Methods: In order to explore the pathogenic variants related to DOR, twenty young women under 35 years old affected by DOR without definite factors damaging ovarian reserve were recruited as the research subjects, and five women with normal ovarian reserve were recruited as the control group. Whole exome sequencing was applied as the genomics research tool. Results: As a result, we obtained a set of mutated genes that may be related to DOR, where the missense variant on GPR84 was selected for further study. It is found that GPR84Y370H variant promotes the expression of proinflammatory cytokines (TNF-α, IL12B, IL-1ß) and chemokines (CCL2, CCL5), as well as the activation of NF-κB signaling pathway. Conclusion: In conclusion, GPR84Y370H variant was identified though analysis for WES results of 20 DOR patients. The deleterious variant of GPR84 could be the potential molecular mechanism of non-age-related pathological DOR through its role in promoting inflammation. The findings of this study can be used as a preliminary research basis for the development of early molecular diagnosis and treatment target selection of DOR.

Thymol targeting interleukin 4 induced 1 expression reshapes the immune microenvironment to sensitize the immunotherapy in lung adenocarcinoma.

Immune checkpoint blockades are the most promising therapy in lung adenocarcinoma (LUAD). However, the response rate remains limited, underscoring the urgent need for effective sensitizers. Interleukin 4 induced 1 (IL4I1) is reported to have immunoinhibitory and tumor-promoting effects in several cancers. However, the targetable value of IL4I1 in sensitizing the immunotherapy is not clear, and there is a lack of effective small molecules that specifically target IL4I1. Here, we show that silencing IL4I1 significantly remodels the immune microenvironment via inhibiting aryl hydrocarbon receptor (AHR) signaling, thereby enhancing the efficacy of anti-PD-1 antibody in LUAD, which suggests that IL4I1 is a potential drug target for the combination immunotherapy. We then identify thymol as the first small molecule targeting IL4I1 transcription through a drug screening. Thymol inhibits the IL4I1 expression and blocks AHR signaling in LUAD cells. Thymol treatment restores the antitumor immune response and suppresses the progression of LUAD in an orthotopic mouse model. Strikingly, the combination treatment of thymol with anti-PD-1 antibody shows significant tumor regression in LUAD mice. Thus, we demonstrate that thymol is an effective small molecule to sensitize the PD-1 blockade in LUAD via targeting IL4I1, which provides a novel strategy for the immunotherapy of LUAD.

Fbxw11 impairs the repopulation capacity of hematopoietic stem/progenitor cells.

BACKGROUND: The ubiquitin-proteasome system plays important roles in maintaining the self-renewal and differentiation of stem and progenitor cells through highly ordered degradation of cellular proteins. Fbxw11, an E3 ligase, participates in many important biological processes by targeting a broad range of proteins. However, its roles in hematopoietic stem/progenitor cells (HSPCs) have not been established. METHODS: In this study, the effects of Fbxw11 on HSPCs were studied in vitro and in vivo by an overexpression strategy. Real-time PCR was performed to detect the expression of Fbxw11 in hematopoietic subpopulations. Colony-forming assays were performed to evaluate the in vitro function of Fbxw11 on HSPCs. Hoechst 33342 and Ki67 staining was performed to determine the cell-cycle distribution of HSPCs. Competitive transplantation experiments were used to evaluate the effect of Fbxw11 on the reconstitution potential of HSPCs. Single-cell RNA sequencing (scRNA-seq) was employed to reveal the transcriptomic alterations in HSPCs. RESULTS: The expression of Fbxw11 was higher in Lin-c-Kit+Sca-1+ (LSK) cells and myeloid progenitors than in lymphoid progenitors. Fbxw11 played negative roles in colony-forming and quiescence maintenance of HSPCs in vitro. Furthermore, serial competitive transplantation experiments revealed that Fbxw11 impaired the repopulation capacity of HSPCs. The proportion of granulocytes (Gr-1+CD11b+) in the differentiated mature cells was significantly higher than that in the control group, T cells and B cells were lower. Moreover, scRNA-seq revealed seven cell clusters in HSPCs. In addition, Fbxw11 downregulated the expression of Cebpa, Myc and Arid5b, which are significant regulators of HSPC activity, in most cell clusters. CONCLUSION: Our data demonstrate that Fbxw11 plays a negative role in the maintenance of HSPCs in vitro and repopulation capacity in vivo. Our data also provide valuable transcriptome references for HSPCs in homeostasis.

Perioperative Serum Scoring Systems Predict Early Recurrence and Poor Prognosis of Resectable Pancreatic Cancer.

Objective: Some patients with pancreatic ductal adenocarcinoma (PDAC) are prone to rapid recurrence or metastasis after radical resection. However, evaluation methods for effectively identifying these patients are lacking. In this study, we established perioperative serum scoring systems to screen patients with early recurrence and poor prognosis. Methods: We systematically analysed 44 perioperative serum parameters, including systemic inflammatory parameters, coagulation system parameters, tumor markers, and 18 clinicopathological characteristics of 218 patients with radical resection in our centre. Univariate Cox regression and LASSO regression models were used to screen variables. Kaplan-Meier survival analysis was used to compare relapse-free survival and overall survival. Multivariate Cox regression was used to evaluate the independent risk variables. AUC and C-index were used to reveal the effectiveness of the models. In addition, the effectiveness was also verified in an independent cohort of 109 patients. Results: Preoperative systemic immune coagulation cascade (SICC) (including increased neutrophil to lymphocyte ratio, decreased lymphocyte to monocyte ratio, increased platelet and fibrinogen) and increased postoperative tumor markers (TMs) (CA199, CEA and CA242) were independent risk factors for early recurrence of resectable pancreatic cancer. On this basis, we established the preoperative SICC score and postoperative TMs score models. The patients with higher preoperative SICC or postoperative TMs score were more likely to have early relapse and worse prognosis. The nomogram based on preoperative SICC, postoperative TMs, CACI, smoking index, vascular cancer embolus and adjuvant chemotherapy can effectively evaluate the recurrence rate (AUC1 year: 0.763, AUC2 year: 0.679, AUC3 year: 0.657) and overall survival rate (AUC1 year: 0.770, AUC3 year: 0.804, AUC5 year: 0.763). Conclusion: Preoperative SICC and postoperative TMs can help identify resectable PDAC patients with early recurrence and poor prognosis.

Identification of Neural Progenitor Cell-associated Chemoradiotherapy Resistance Gene Set (ARL4C, MSN, TNFAIP6) for Prognosis of Glioma.

BACKGROUND: Glioma is the most common malignant intracranial tumor with high lethality. Despite surgery combined with chemoradiotherapy, the prognosis for patients with glioma remains poor. This is primarily due to acquired chemoradiotherapy resistance. Therefore, to improve the prognosis of glioma, further study into the mechanism of chemoradiotherapy resistance is needed. OBJECTIVE: This study aimed to (1) evaluate the prognosis of patients with glioma by using a prognostic risk score model constructed by chemoradiotherapy resistance genes, (2) provide new targets and directions for precise treatment of glioma, and (3) discuss the tumor heterogeneity of tumor cells. METHODS: According to therapy class and overall survival (OS), we identified 53 genes associated with glioma chemoradiotherapy resistance in The Cancer Genome Atlas Glioblastoma (TCGA GBM) database. Considering the important role of chemoradiotherapy resistance-related genes in the prognosis of glioma, we preliminarily screened and identified vital prognostic factors among these genes by using the Cox regression model of absolute contraction and selection operators in the TCGA GBM lower-grade glioma (TCGA GBMLGG) dataset. Next, the heterogeneity of the chemoradiotherapy resistance-associated genes in different glioma cells was revealed by single-cell sequencing in the GSE117891 cohort. RESULTS: A prognostic risk score model consisting of three genes (ARL4C, MSN, TNFAIP6) was constructed. The expression of this model was high in glioma neural progenitor cells (NPCs) and low in glioma oligodendrocytes. The OS rates were significantly lower in the high- vs. low-risk group. CONCLUSION: Our 3 gene risk score complements the current glioma diagnosis and provides a novel insight into chemoradiotherapy resistance mechanisms for the prognosis of patients with glioma.

Clinical Significance of Inflammation Markers in Predicting the Severity of Acute Pancreatitis.

OBJECTIVES: Acute pancreatitis (AP) is an inflammatory disease of the pancreas. We analyzed changes in inflammation markers to explore the clinical significance of using these markers to predict the severity of AP. METHODS: The study included 169 patients (severe AP = 50 and nonsevere AP = 119) admitted to Yanbian University Hospital between January 2015 and July 2017. The neutrophil-to-lymphocyte ratio (NLR), prognostic nutrition index (PNI), lymphocyte-to-monocyte ratio, red blood cell distribution width coefficient of variation, mean platelet volume, platelet-to-lymphocyte ratio, and red blood cell distribution width-to-platelet ratio of the patients were detected after admission. Correlations between AP severity and various inflammatory markers were statistically analyzed. RESULTS: The results indicated that the NLR on the first day after admission (area under the curve, 0.824; 95% confidence interval, 0.753-0.896) and the PNI on the third day after admission (area under the curve, 0.814; 95% confidence interval, 0.753-0.896) had more significance than other inflammation markers in predicting the severity of AP. In AP patients, the NLR showed a gradual decline, and the PNI initially decreased and then increased. CONCLUSIONS: The NLR and PNI can provide new reference values for predicting the severity of AP.

Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification.

Hematopoietic cancer is a malignant transformation in immune system cells. Hematopoietic cancer is characterized by the cells that are expressed, so it is usually difficult to distinguish its heterogeneities in the hematopoiesis process. Traditional approaches for cancer subtyping use statistical techniques. Furthermore, due to the overfitting problem of small samples, in case of a minor cancer, it does not have enough sample material for building a classification model. Therefore, we propose not only to build a classification model for five major subtypes using two kinds of losses, namely reconstruction loss and classification loss, but also to extract suitable features using a deep autoencoder. Furthermore, for considering the data imbalance problem, we apply an oversampling algorithm, the synthetic minority oversampling technique (SMOTE). For validation of our proposed autoencoder-based feature extraction approach for hematopoietic cancer subtype classification, we compared other traditional feature selection algorithms (principal component analysis, non-negative matrix factorization) and classification algorithms with the SMOTE oversampling approach. Additionally, we used the Shapley Additive exPlanations (SHAP) interpretation technique in our model to explain the important gene/protein for hematopoietic cancer subtype classification. Furthermore, we compared five widely used classification algorithms, including logistic regression, random forest, k-nearest neighbor, artificial neural network and support vector machine. The results of autoencoder-based feature extraction approaches showed good performance, and the best result was the SMOTE oversampling-applied support vector machine algorithm consider both focal loss and reconstruction loss as the loss function for autoencoder (AE) feature selection approach, which produced 97.01% accuracy, 92.60% recall, 99.52% specificity, 93.54% F1-measure, 97.87% G-mean and 95.46% index of balanced accuracy as subtype classification performance measures.