Key pathways and genes controlling the development and progression of clear cell renal cell carcinoma (ccRCC) based on gene set enrichment analysis

BACKGROUND: Clear-cell renal cell carcinoma (ccRCC) is one of the most common types of kidney cancer in adults; however, its causes are not completely understood. The study was designed to filter the key pathways and genes associated with the occurrence or development of ccRCC, acquaint its pathogenesis at gene and pathway level, to provide more theory evidence and targeted therapy for ccRCC. METHODS: Gene set enrichment analysis (GSEA) and meta-analysis (Meta) were used to screen the critical pathways and genes which may affect the occurrence and progression of ccRCC on the transcription level. Corresponding pathways of significant genes were obtained with the online website DAVID (http://david.abcc.ncifcrf.gov/). RESULTS: Thirty seven consistent pathways and key genes in these pathways related to ccRCC were obtained with combined GSEA and meta-analysis. These pathways were mainly involved in metabolism, organismal systems, cellular processes and environmental information processing. CONCLUSION: The gene pathways that we identified could provide insight concerning the development of ccRCC. Further studies are needed to determine the biological function for the positive genes

Personalized Adaptive Meta Learning for Cold-start User Preference Prediction

A common challenge in personalized user preference prediction is the cold-start problem. Due to the lack of user-item interactions, directly learning from the new users' log data causes serious over-fitting problem. Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot learning problem, where each user is the task and recommended items are the classes, and the gradient-based meta learning method (MAML) is leveraged to address this challenge. However, in real-world application, the users are not uniformly distributed (i.e., different users may have different browsing history, recommended items, and user profiles. We define the major users as the users in the groups with large numbers of users sharing similar user information, and other users are the minor users), existing MAML approaches tend to fit the major users and ignore the minor users. To address this cold-start task-overfitting problem, we propose a novel personalized adaptive meta learning approach to consider both the major and the minor users with three key contributions: 1) We are the first to present a personalized adaptive learning rate meta-learning approach to improve the performance of MAML by focusing on both the major and minor users. 2) To provide better personalized learning rates for each user, we introduce a similarity-based method to find similar users as a reference and a tree-based method to store users' features for fast search. 3) To reduce the memory usage, we design a memory agnostic regularizer to further reduce the space complexity to constant while maintain the performance. Experiments on MovieLens, BookCrossing, and real-world production datasets reveal that our method outperforms the state-of-the-art methods dramatically for both the minor and major users.Comment: Preprint Versio

Natural selection and functional diversification of the epidermal growth factor receptorEGFR family in vertebrates

AbstractBackgroundGenes that have been subject to adaptive evolution can produce varying degrees of pathology or differing symptomatology. ErbB family receptor activation will initiate a number of downstream signaling pathways, such as mitogen-activated protein kinase (MAPK), activator of transcription (STAT), the modulation of calcium channels, and so on, all of which lead to aggressive tumor behavior. However, the evolutionary mechanisms operating in the retention of ErbB family genes and the changes in selection pressures are not clear.ResultsSixty-two full-length cDNA sequences from 27 vertebrate species were extracted from the UniProt protein database, NCBI's GenBank and the Ensembl database. The result of phylogenetic analysis showed that the four ErbB family members in vertebrates might be formed by gene duplication. In order to determine the mode of evolution in vertebrates, selection analysis and functional divergence analysis were combined to explain the relationship of the site-specific evolution and functional divergence in the vertebrate ErbB family. Our results indicate that the acceleration of asymmetric evolutionary rates and purifying selection together were the main force for the production of ErbBs, and positive selections were detected in the ErbB family.ConclusionAn evolutional phylogeny of 27 vertebrates was presented in our study; the tree showed that the genes have evolved through duplications followed by purifying selection, except for seven sites, which evolved by positive selection. There was one common site with positive selection and functional divergence. In the process of functional differentiation evolving through gene duplication, relaxed selection may play an important part

Query-dominant User Interest Network for Large-Scale Search Ranking

Historical behaviors have shown great effect and potential in various prediction tasks, including recommendation and information retrieval. The overall historical behaviors are various but noisy while search behaviors are always sparse. Most existing approaches in personalized search ranking adopt the sparse search behaviors to learn representation with bottleneck, which do not sufficiently exploit the crucial long-term interest. In fact, there is no doubt that user long-term interest is various but noisy for instant search, and how to exploit it well still remains an open problem. To tackle this problem, in this work, we propose a novel model named Query-dominant user Interest Network (QIN), including two cascade units to filter the raw user behaviors and reweigh the behavior subsequences. Specifically, we propose a relevance search unit (RSU), which aims to search a subsequence relevant to the query first and then search the sub-subsequences relevant to the target item. These items are then fed into an attention unit called Fused Attention Unit (FAU). It should be able to calculate attention scores from the ID field and attribute field separately, and then adaptively fuse the item embedding and content embedding based on the user engagement of past period. Extensive experiments and ablation studies on real-world datasets demonstrate the superiority of our model over state-of-the-art methods. The QIN now has been successfully deployed on Kuaishou search, an online video search platform, and obtained 7.6% improvement on CTR.Comment: 10 page

Identification of Dof transcription factors in Dendrobium huoshanense and expression pattern under abiotic stresses

Introduction: DNA-binding with one finger (Dof) transcription factors (TFs) are a unique family of TFs found in higher plants that regulate plant responses to light, hormones, and abiotic stresses. The specific involvement of Dof genes in the response to environmental stresses remains unknown in D. huoshanense.Methods: A total of 22 Dof family genes were identified from the D. huoshanense genome.Results: Chromosome location analysis showed that DhDof genes were distributed on 12 chromosomes, with the largest number of Dof genes located on chromosome 8. The phylogenetic tree revealed that DhDofs could be categorized into 11 distinct subgroups. In addition to the common groups, DhDof4, DhDof5, DhDof17, and the AtDof1.4 ortholog were clustered into the B3 subgroup. Group E was a newly identified branch, among which DhDof6, DhDof7, DhDof8, and DhDof9 were in an independent branch. The conserved motifs and gene structure revealed the differences in motif number and composition of DhDofs. The dof domain near the N-terminus was highly conserved and contained a C2-C2-type zinc finger structure linked with four cysteines. Microsynteny and interspecies collinearity revealed gene duplication events and phylogenetic tree among DhDofs. Large-scale gene duplication had not occurred among the DhDofs genes and only in one pair of genes on chromosome 13. Synteny blocks were found more often between D. huoshanense and its relatives and less often between Oryza sativa and Arabidopsis thaliana. Selection pressure analysis indicated that DhDof genes were subject to purifying selection. Expression profiles and correlation analyses revealed that the Dof gene under hormone treatments showed several different expression patterns. DhDof20 and DhDof21 had the highest expression levels and were co-expressed under MeJA induction. The cis-acting element analysis revealed that each DhDof had several regulatory elements involved in plant growth as well as abiotic stresses. qRT-PCR analysis demonstrated that DhDof2 was the main ABA-responsive gene and DhDof7 was the main cold stress-related gene. IAA suppressed the expression of some Dof candidates, and SA inhibited most of the candidate genes.Discussion: Our results may provide new insights for the further investigation of the Dof genes and the screening of the core stress-resistance genes

Preoperative diagnosis of solitary pulmonary nodules with a novel hematological index model based on circulating tumor cells

ObjectivePreoperative noninvasive diagnosis of the benign or malignant solitary pulmonary nodule (SPN) is still important and difficult for clinical decisions and treatment. This study aimed to assist in the preoperative diagnosis of benign or malignant SPN using blood biomarkers.MethodsA total of 286 patients were recruited for this study. The serum FR+CTC, TK1, TP, TPS, ALB, Pre-ALB, ProGRP, CYFRA21-1, NSE, CA50, CA199, and CA242 were detected and analyzed.ResultsIn the univariate analysis, age, FR+CTC, TK1, CA50, CA19.9, CA242, ProGRP, NSE, CYFRA21-1, and TPS showed the statistical significance of a correlation with malignant SPNs (P <0.05). The highest performing biomarker is FR+CTC (odd ratio [OR], 4.47; 95% CI: 2.57–7.89; P <0.001). The multivariate analysis identified that age (OR, 2.69; 95% CI: 1.34–5.59, P = 0.006), FR+CTC (OR, 6.26; 95% CI: 3.09–13.37, P <0.001), TK1 (OR, 4.82; 95% CI: 2.4–10.27, P <0.001), and NSE (OR, 2.06; 95% CI: 1.07–4.06, P = 0.033) are independent predictors. A prediction model based on age, FR+CTC, TK1, CA50, CA242, ProGRP, NSE, and TPS was developed and presented as a nomogram, with a sensitivity of 71.1% and a specificity of 81.3%, and the AUC was 0.826 (95% CI: 0.768–0.884).ConclusionsThe novel prediction model based on FR+CTC showed much stronger performance than any single biomarker, and it can assist in predicting benign or malignant SPNs

Estrogen Induces Metastatic Potential of Papillary Thyroid Cancer Cells through Estrogen Receptor α

Estradiol (E2) promotes metastatic propensity. However, the detailed mechanism remains largely unknown. E-cadherin, vimentin, and MMP-9 play a dominant role in the metastatic process. We aimed to investigate the effects of E2 on metastatic potential of PTC cell line BCPAP and on E-cadherin, vimentin, and MMP-9 protein expression. PTC cell line BCPAP was evaluated for the presence of estrogen receptor (ER) by western blot analysis. The effects of E2, PPT (a potent ERα-selective agonist), and DPN (a potent ERβ-selective agonist) on modulation of metastatic phenotype were determined by using in vitro scratch wound assay and invasion assay. In addition, the effects on E-cadherin, vimentin, and matrix metalloproteinase-9 (MMP-9) protein expression were evaluated by Western blot analysis. We found that BCPAP cells expressed ERα and ERβ. E2 and PPT enhanced, but DPN inhibited, the migration and invasion of BCPAP cells in an in vitro experimental model system that is modulated by E-cadherin, vimentin, and MMP-9. These findings indicate that E2 induces the metastatic potential of BCPAP cells through ERα and ERβ. The two ER subtypes play differential roles in modulation of BCPAP cell metastasis and the related molecule expressions including E-cadherin, vimentin, and MMP-9

Demystifying DeFi MEV Activities in Flashbots Bundle

Decentralized Finance, mushrooming in permissionless blockchains, has attracted a recent surge in popularity. Due to the transparency of permissionless blockchains, opportunistic traders can compete to earn revenue by extracting Miner Extractable Value (MEV), which undermines both the consensus security and efficiency of blockchain systems. The Flashbots bundle mechanism further aggravates the MEV competition because it empowers opportunistic traders with the capability of designing more sophisticated MEV extraction. In this paper, we conduct the first systematic study on DeFi MEV activities in Flashbots bundle by developing ActLifter, a novel automated tool for accurately identifying DeFi actions in transactions of each bundle, and ActCluster, a new approach that leverages iterative clustering to facilitate us to discover known/unknown DeFi MEV activities. Extensive experimental results show that ActLifter can achieve nearly 100% precision and recall in DeFi action identification, significantly outperforming state-of-the-art techniques. Moreover, with the help of ActCluster, we obtain many new observations and discover 17 new kinds of DeFi MEV activities, which occur in 53.12% of bundles but have not been reported in existing studies