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Hot spot gene mutations in splicing factor 3b subunit 1 (SF3B1) are observed in many types of cancer and create abundant aberrant mRNA splicing, which is profoundly implicated in tumorigenesis. Here, we identified that the SF3B1 K700E (SF3B1K700E) mutation is strongly associated with tumor growth in pancreatic ductal adenocarcinoma (PDAC). Knockdown of SF3B1 significantly retarded cell proliferation and tumor growth in a cell line (Panc05.04) with the SF3B1K700E mutation. However, SF3B1 knockdown had no notable effect on cell proliferation in two cell lines (BxPC3 and AsPC1) carrying wild‐type SF3B1. Ectopic expression of SF3B1K700E but not SF3B1WT in SF3B1‐knockout Panc05.04 cells largely restored the inhibitory role induced by SF3B1 knockdown. Introduction of the SF3B1K700E mutation in BxPC3 and AsPC1 cells also boosted cell proliferation. Gene set enrichment analysis demonstrated a close correlation between SF3B1 mutation and aerobic glycolysis. Functional analyses showed that the SF3B1K700E mutation promoted tumor glycolysis, as evidenced by glucose consumption, lactate release, and extracellular acidification rate. Mechanistically, the SF3B1 mutation promoted the aberrant splicing of PPP2R5A and led to the activation of the glycolytic regulator c‐Myc via post‐translational regulation. Pharmacological activation of PP2A with FTY‐720 markedly compromised the growth advantage induced by the SF3B1K700E mutation in vitro and in vivo. Taken together, our data suggest a novel function for SF3B1 mutation in the Warburg effect, and this finding may offer a potential therapeutic strategy against PDAC with the SF3B1K700E mutation. SF3B1 mutations have been previously implicated in tumorigenesis. Here, we investigate the role of SF3B1K700E mutation in pancreatic ductal adenocarcinoma (PDAC). SF3B1K700E led to aberrant splicing of PPP2R5A, coupled with c‐Myc activation higher aerobic glycolysis rate and growth advantage of tumor cells. Taken together, our data describe a novel function for SF3B1 K700E mutations in the Warburg effect. Inhibition of SF3B1 K700E mutation may potentially serve as a novel therapeutic strategy for patients with PDAC.

Due to the therapy resistance and frequent metastasis, pancreatic ductal adenocarcinoma(PDAC) remains one of the most malignant carcinoma. WNT7A, an important ligand of Wnt/β-catenin signaling pathways, has a controversial role in tumor development. The role of WNT7A in PDAC remains unclear. In this study, we analyzed the expression pattern of WNT7A at mRNA and protein levels. We found pancreatic cancer tissue demonstrated a significant high WNT7A expression compared with the adjacent non-tumor tissue and the expression of WNT7A positively correlates with poor prognosis and lymph node metastasis. Then, we performed transwell assays and wound healing assays in vitro and found that WNT7A promotes the migration capacity of cancer cells. Furthermore, we explored the underlying mechanism of the WNT7A inducing cell migration. Results showed that up-regulated WNT7A expression inducing higher expression of N-cadherin and lower expression of E-cadherin while the contrast result was shown in the WNT7A knock-down group, which suggested that WNT7A might contribute to an epithelial–mesenchymal transition. Finally, we found that the hypoxia culture condition remarkably increased the WNT7A expression. In conclusion, our work demonstrated that hypoxia induced high expression of WNT7A might promote the cell migration via enhancing the epithelial–mesenchymal transition in PDAC.

Pancreatic cancer (PC) is a highly malignant digestive tract tumor, with a dismal 5-year survival rate. Recently, cuproptosis was found to be copper-dependent cell death. This work aims to establish a cuproptosis-related lncRNA signature which could predict the prognosis of PC patients and help clinical decision-making. Firstly, cuproptosis-related lncRNAs were identified in the TCGA-PAAD database. Next, a cuproptosis-related lncRNA signature based on five lncRNAs was established. Besides, the ICGC cohort and our samples from 30 PC patients served as external validation groups to verify the predictive power of the risk signature. Then, the expression of CASC8 was verified in PC samples, scRNA-seq dataset CRA001160, and PC cell lines. The correlation between CASC8 and cuproptosis-related genes was validated by Real-Time PCR. Additionally, the roles of CASC8 in PC progression and immune microenvironment characterization were explored by loss-of-function assay. As showed in the results, the prognosis of patients with higher risk scores was prominently worse than that with lower risk scores. Real-Time PCR and single cell analysis suggested that CASC8 was highly expressed in pancreatic cancer and related to cuproptosis. Additionally, gene inhibition of CASC8 impacted the proliferation, apoptosis and migration of PC cells. Furthermore, CASC8 was demonstrated to impact the expression of CD274 and several chemokines, and serve as a key indicator in tumor immune microenvironment characterization. In conclusion, the cuproptosis-related lncRNA signature could provide valuable indications for the prognosis of PC patients, and CASC8 was a candidate biomarker for not only predicting the progression of PC patients but also their antitumor immune responses.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most aggressive and lethal malignancies. Long non-coding RNAs (lncRNAs) are a novel class of non-protein-coding transcripts that have been implicated in cancer biogenesis and prognosis. By repurposing microarray probes, we herein analysed the lncRNA expression profiles in two public PDAC microarray datasets and identified 34 dysregulated lncRNAs in PDAC. In addition, the expression of 6 selected lncRNAs was confirmed in Ren Ji cohort and pancreatic cell lines, and their association with 80 PDAC patients’ clinicopathological features and prognosis was investigated. Results indicated that AFAP1-AS1, UCA1 and ENSG00000218510 might be involved in PDAC progression and significantly associated with overall survival of PDAC. UCA1 and ENSG00000218510 expression status may serve as independent prognostic biomarkers for overall survival of PDAC. Gene set enrichment analysis (GSEA) analysis suggested that high AFAP1-AS1, UCA1 and low ENSG00000218510 expression were correlated with several tumorigenesis related pathways. Functional experiments demonstrated that AFAP1-AS1 and UCA1 were required for efficient invasion and/or proliferation promotion in PDAC cell lines, while ENSG00000218510 acted the opposite. Our findings provide novel information on lncRNAs expression profiles which might be beneficial to the precise diagnosis, subcategorization and ultimately, the individualized therapy of PDAC.

Cloud task scheduling is a hot technology today, how to effectively improve the utilization of resources, time efficiency, load balancing, is the focus and difficult of the study. The time efficiency, load balancing of K-Min algorithm still need to be improved, so this paper proposes cloud computing task scheduling algorithm based on modified K-Means (Improved K-Min), firstly, This paper improves the k-means algorithm using the BFA and PSO,then according to the length attribute of the task, resource requirements, the algorithm uses the improved K-means for packet processing tasks, then performs Min-Min scheduling algorithm within the group. Through theoretical research and simulation of Cloud-sim platform, when the number of tasks is 300, experimental result is best, comparing with Min-Min algorithm, the total task completion time improved 17.13%.

Ant colony algorithm as an intelligent bionic optimization algorithm, Because of its use of positive feedback mechanism, the result will be prone to premature, stagnation and slow speed of solving the problem etc. For this shortcoming is proposed based on chaos theory adaptive dynamic parameters ant colony algorithm (PDSACA Dynamic Parameters Self-adaptive Ant Colony Algorithm).In the process of the dynamic algorithm solving, introducing chaotic disturbance technique, the parameters of the algorithm design of dynamic changes to affect the algorithm quality and global parameters are adjusted adaptively to improve the global search capability. By using the TSPLABs reference example to test the algorithm. Experimental results show that the convergence of the algorithm, robustness and efficiency have been improved to Compare with the basic ant colony algorithm.

This paper develops a new scalable and efficient model for the design of p -cycles with the differentiated levels of node protection. The proposed model allows the indicated level of node survivability ranging from 0% to 100%, which could facilitate a carrier offer node-failure survivability (and hence availability) on a differentiated service basis. To design p -cycles, an integer linear program (ILP) is usually formulated with the prerequisite of a prior enumeration of all possible p -cycle candidates. A huge number of candidates may exist in a large-scale network. Thus, the resulting ILP becomes intractable. We propose a new design and solution method based on large-scale optimization techniques, known as column generation (CG). With CG, our design method generates p -cycle candidates dynamically when needed. Extensive experiments have been conducted for evaluation. The numerical results show that, with the spare capacity used only for link protection, up to 50% node-failure survivability can be achieved for free. Full node protection can be achieved at a marginal cost in comparison with those for link protection only.

This paper develops a new scalable and efficient model for the design of p-cycles with the differentiated levels of node protection. The proposed model allows the indicated level of node survivability ranging from 0% to 100%, which could facilitate a carrier offer node-failure survivability (and hence availability) on a differentiated service basis. To design p-cycles, an integer linear program (ILP) is usually formulated with the prerequisite of a prior enumeration of all possible p-cycle candidates. A huge number of candidates may exist in a large-scale network. Thus, the resulting ILP becomes intractable. We propose a new design and solution method based on large-scale optimization techniques, known as column generation (CG). With CG, our design method generates p-cycle candidates dynamically when needed. Extensive experiments have been conducted for evaluation. The numerical results show that, with the spare capacity used only for link protection, up to 50% node-failure survivability can be achieved for free. Full node protection can be achieved at a marginal cost in comparison with those for link protection only.

Paclitaxel (trade name Taxol) is a rare diterpenoid with anticancer activity isolated from Taxus . At present, paclitaxel is mainly produced by the semi-synthetic method using extract of Taxus tissues as raw materials. The studies of regulatory mechanisms in paclitaxel biosynthesis would promote the production of paclitaxel through tissue/cell culture approaches. Here, we systematically identified 990 transcription factors (TFs), 460 microRNAs (miRNAs), and 160 phased small interfering RNAs (phasiRNAs) in Taxus chinensis to explore their interactions and potential roles in regulation of paclitaxel synthesis. The expression levels of enzyme genes in cone and root were higher than those in leaf and bark. Nearly all enzyme genes in the paclitaxel synthesis pathway were significantly up-regulated after jasmonate treatment, except for GGPPS and CoA Ligase . The expression level of enzyme genes located in the latter steps of the synthesis pathway was significantly higher in female barks than in male. Regulatory TFs were inferred through co-expression network analysis, resulting in the identification of TFs from diverse families including MYB and AP2. Genes with ADP binding and copper ion binding functions were overrepresented in targets of miRNA genes. The miRNA targets were mainly enriched with genes in plant hormone signal transduction, mRNA surveillance pathway, cell cycle and DNA replication. Genes in oxidoreductase activity, protein-disulfide reductase activity were enriched in targets of phasiRNAs. Regulatory networks were further constructed including components of enzyme genes, TFs, miRNAs, and phasiRNAs. The hierarchical regulation of paclitaxel production by miRNAs and phasiRNAs indicates a robust regulation at post-transcriptional level. Our study on transcriptional and posttranscriptional regulation of paclitaxel synthesis provides clues for enhancing paclitaxel production using synthetic biology technology.

Purpose To develop a nomogram model for predicting local progress-free survival (LPFS) in esophageal squamous cell carcinoma (ESCC) patients treated with concurrent chemo-radiotherapy (CCRT). Methods We collected the clinical data of ESCC patients treated with CCRT in our hospital. Eligible patients were randomly divided into training cohort and validation cohort. The least absolute shrinkage and selection operator (LASSO) with COX regression was performed to select optimal radiomic features to calculate Rad-score for predicting LPFS in the training cohort. The univariate and multivariate analyses were performed to identify the predictive clinical factors for developing a nomogram model. The C-index was used to assess the performance of the predictive model and calibration curve was used to evaluate the accuracy. Results A total of 221 ESCC patients were included in our study, with 155 patients in training cohort and 66 patients in validation cohort. Seventeen radiomic features were selected by LASSO COX regression analysis to calculate Rad-score for predicting LPFS. The patients with a Rad-score ≥ 0.1411 had high risk of local recurrence, and those with a Rad-score < 0.1411 had low risk of local recurrence. Multivariate analysis showed that N stage, CR status and Rad-score were independent predictive factors for LPFS. A nomogram model was built based on the result of multivariate analysis. The C-index of the nomogram was 0.745 (95% CI 0.7700–0.790) in training cohort and 0.723(95% CI 0.654–0.791) in validation cohort. The 3-year LPFS rate predicted by the nomogram model was highly consistent with the actual 3-year LPFS rate both in the training cohort and the validation cohort. Conclusion We developed and validated a prediction model based on radiomic features and clinical factors, which can be used to predict LPFS of patients after CCRT. This model is conducive to identifying the patients with ESCC benefited more from CCRT.