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Forests ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 120
Yijie Li ◽  
Song Chen ◽  
Yuhang Liu ◽  
Haijiao Huang

Research Highlights: This study identified the cell cycle genes in birch that likely play important roles during the plant’s growth and development. This analysis provides a basis for understanding the regulatory mechanism of various cell cycles in Betula pendula Roth. Background and Objectives: The cell cycle factors not only influence cell cycles progression together, but also regulate accretion, division, and differentiation of cells, and then regulate growth and development of the plant. In this study, we identified the putative cell cycle genes in the B. pendula genome, based on the annotated cell cycle genes in Arabidopsis thaliana (L.) Heynh. It can be used as a basis for further functional research. Materials and Methods: RNA-seq technology was used to determine the transcription abundance of all cell cycle genes in xylem, roots, leaves, and floral tissues. Results: We identified 59 cell cycle gene models in the genome of B. pendula, with 17 highly expression genes among them. These genes were BpCDKA.1, BpCDKB1.1, BpCDKB2.1, BpCKS1.2, BpCYCB1.1, BpCYCB1.2, BpCYCB2.1, BpCYCD3.1, BpCYCD3.5, BpDEL1, BpDpa2, BpE2Fa, BpE2Fb, BpKRP1, BpKRP2, BpRb1, and BpWEE1. Conclusions: By combining phylogenetic analysis and tissue-specific expression data, we identified 17 core cell cycle genes in the Betulapendula genome.

2022 ◽  
Malvika Sudhakar ◽  
Raghunathan Rengaswamy ◽  
Karthik Raman

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy >0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at

2022 ◽  
Vol 11 ◽  
Mingming Hu ◽  
Jinjing Tan ◽  
Zhentian Liu ◽  
Lifeng Li ◽  
Hongmei Zhang ◽  

BackgroundYoung lung cancer as a small subgroup of lung cancer has not been fully studied. Most of the previous studies focused on the clinicopathological features, but studies of molecular characteristics are still few and limited. Here, we explore the characteristics of prognosis and variation in young lung cancer patients with NSCLC.MethodsA total of 5639 young lung cancer samples (NSCLC, age ≤40) were screened from the SEER and the same number of the old (NSCLC, age ≥60) were screened by propensity score matching to evaluate the prognosis of two groups. 165 treatment-naïve patients diagnosed with NSCLC were enrolled to explore the molecular feature difference between two age-varying groups. CCLE cell line expression data was used to verify the finding from the cohort of 165 patients.ResultsThe overall survival of the young lung cancer group was significantly better than the old. Germline analysis showed a trend that the young group contained a higher incidence of germline alterations. The TMB of the young group was lower. Meanwhile, the heterogeneity and evolutionary degrees of the young lung cancer group were also lower than the old. The mutation spectrums of two groups exhibited variance with LRP1B, SMARCA4, STK11, FAT2, RBM10, FANCM mutations, EGFR L858R more recurrent in the old group and EML4-ALK fusions, BCL2L11 deletion polymorphism, EGFR 19DEL, 20IN more recurrent in the young group. For the base substitution, the young showed a lower fraction of transversion. Further, we performed a pathway analysis and found the EGFR tyrosine kinase inhibitor resistance pathway enriched in the young lung cancer group, which was validated in gene expression data later.ConclusionsThere were significantly different molecular features of the young lung cancer group. The young lung cancer group had a more simple alteration structure. Alteration spectrums and base substitution types varied between two groups, implying the different pathogenesis. The young lung cancer group had more potential treatment choices. Although young lung patients had better outcomes, there were still adverse factors of them, suggesting that the young group still needs more caution for treatment choice and monitoring after the treatment to further improve the prognosis.

2022 ◽  
Kay Spiess ◽  
Timothy Fulton ◽  
Seogwon Hwang ◽  
Kane Toh ◽  
Dillan Saunders ◽  

The study of pattern formation has benefited from reverse-engineering gene regulatory network (GRN) structure from spatio-temporal quantitative gene expression data. Traditional approaches omit tissue morphogenesis, hence focusing on systems where the timescales of pattern formation and morphogenesis can be separated. In such systems, pattern forms as an emergent property of the underlying GRN. This is not the case in many animal patterning systems, where patterning and morphogenesis are simultaneous. To address pattern formation in these systems we need to adapt our methodologies to explicitly accommodate cell movements and tissue shape changes. In this work we present a novel framework to reverse-engineer GRNs underlying pattern formation in tissues experiencing morphogenetic changes and cell rearrangements. By combination of quantitative data from live and fixed embryos we approximate gene expression trajectories (AGETs) in single cells and use a subset to reverse-engineer candidate GRNs using a Markov Chain Monte Carlo approach. GRN fit is assessed by simulating on cell tracks (live-modelling) and comparing the output to quantitative data-sets. This framework outputs candidate GRNs that recapitulate pattern formation at the level of the tissue and the single cell. To our knowledge, this inference methodology is the first to integrate cell movements and gene expression data, making it possible to reverse-engineer GRNs patterning tissues undergoing morphogenetic changes.

BMC Genomics ◽  
2022 ◽  
Vol 23 (1) ◽  
Zherou Rong ◽  
Hongwei Chen ◽  
Zihan Zhang ◽  
Yue Zhang ◽  
Luanfeng Ge ◽  

Abstract Background Cardiomyopathy is a complex type of myocardial disease, and its incidence has increased significantly in recent years. Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common and indistinguishable types of cardiomyopathy. Results Here, a systematic multi-omics integration approach was proposed to identify cardiomyopathy-related core genes that could distinguish normal, DCM and ICM samples using cardiomyopathy expression profile data based on a human metabolic network. First, according to the differentially expressed genes between different states (DCM/ICM and normal, or DCM and ICM) of samples, three sets of initial modules were obtained from the human metabolic network. Two permutation tests were used to evaluate the significance of the Pearson correlation coefficient difference score of the initial modules, and three candidate modules were screened out. Then, a cardiomyopathy risk module that was significantly related to DCM and ICM was determined according to the significance of the module score based on Markov random field. Finally, based on the shortest path between cardiomyopathy known genes, 13 core genes related to cardiomyopathy were identified. These core genes were enriched in pathways and functions significantly related to cardiomyopathy and could distinguish between samples of different states. Conclusion The identified core genes might serve as potential biomarkers of cardiomyopathy. This research will contribute to identifying potential biomarkers of cardiomyopathy and to distinguishing different types of cardiomyopathy.

2022 ◽  
Yongsheng Zhang ◽  
Yunlong Wang ◽  
Jichuang Wang ◽  
Kaixiang Zhang

Abstract Bladder cancer (BLCA) is among the most frequent types of cancer. Patients with BLCA have a significant recurrence rate and a poor post-surgery survival rate. Recent research has found a link between tumor immune cell infiltration (ICI) and the prognosis of BLCA patients. However, the ICI picture of BLCA remains unclear. Common gene expression data was obtained by combining the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) expression databases. Two computational algorithms were proposed to unravel the ICI landscape of BLCA patients. The R package "limma" was applied to find differentially expressed genes (DEGs). Principal-component analysis (PCA) was used to calculate the ICI score. A total of 569 common gene expression data were retrieved from TCGA and GEO cohorts. CD8+ T cells were found to have a substantial positive connection with activated memory CD4+ T cells and immune score. On the contrary, CD8+ T cells were found to have a substantial negative connection with Macrophages M0. Thirty-eight DEGs were selected. Two ICI patterns were defined by unsupervised clustering method. Patients of BLCA were separated into two groups. The high ICI score group exhibits better outcome than the low one (p < 0.001). Finally, the group with a high tumor mutation burden (TMB) as well as a high ICI score had the best outcome. (p <0.001). Combining TMB and ICI score resulted in a more accurate survival prediction, suggesting that ICI score could be used as a prognostic marker for BLCA patients.

2022 ◽  
Vol 11 ◽  
Adrián Mosquera Orgueira ◽  
Miguel Cid López ◽  
Andrés Peleteiro Raíndo ◽  
Aitor Abuín Blanco ◽  
Jose Ángel Díaz Arias ◽  

Follicular Lymphoma (FL) has a 10-year mortality rate of 20%, and this is mostly related to lymphoma progression and transformation to higher grades. In the era of personalized medicine it has become increasingly important to provide patients with an optimal prediction about their expected outcomes. The objective of this work was to apply machine learning (ML) tools on gene expression data in order to create individualized predictions about survival in patients with FL. Using data from two different studies, we were able to create a model which achieved good prediction accuracies in both cohorts (c-indexes of 0.793 and 0.662 in the training and test sets). Integration of this model with m7-FLIPI and age rendered high prediction accuracies in the test set (cox c-index 0.79), and a simplified approach identified 4 groups with remarkably different outcomes in terms of survival. Importantly, one of the groups comprised 27.35% of patients and had a median survival of 4.64 years. In summary, we have created a gene expression-based individualized predictor of overall survival in FL that can improve the predictions of the m7-FLIPI score.

2022 ◽  
Vol 12 (1) ◽  
Juliana Albano de Guimarães ◽  
Bidossessi Wilfried Hounpke ◽  
Bruna Duarte ◽  
Ana Luiza Mylla Boso ◽  
Marina Gonçalves Monteiro Viturino ◽  

AbstractPterygium is a common ocular surface condition frequently associated with irritative symptoms. The precise identity of its critical triggers as well as the hierarchical relationship between all the elements involved in the pathogenesis of this disease are not yet elucidated. Meta-analysis of gene expression studies represents a novel strategy capable of identifying key pathogenic mediators and therapeutic targets in complex diseases. Samples from nine patients were collected during surgery after photo documentation and clinical characterization of pterygia. Gene expression experiments were performed using Human Clariom D Assay gene chip. Differential gene expression analysis between active and atrophic pterygia was performed using limma package after adjusting variables by age. In addition, a meta-analysis was performed including recent gene expression studies available at the Gene Expression Omnibus public repository. Two databases including samples from adults with pterygium and controls fulfilled our inclusion criteria. Meta-analysis was performed using the Rank Production algorithm of the RankProd package. Gene set analysis was performed using ClueGO and the transcription factor regulatory network prediction was performed using appropriate bioinformatics tools. Finally, miRNA-mRNA regulatory network was reconstructed using up-regulated genes identified in the gene set analysis from the meta-analysis and their interacting miRNAs from the Brazilian cohort expression data. The meta-analysis identified 154 up-regulated and 58 down-regulated genes. A gene set analysis with the top up-regulated genes evidenced an overrepresentation of pathways associated with remodeling of extracellular matrix. Other pathways represented in the network included formation of cornified envelopes and unsaturated fatty acid metabolic processes. The miRNA-mRNA target prediction network, also reconstructed based on the set of up-regulated genes presented in the gene ontology and biological pathways network, showed that 17 target genes were negatively correlated with their interacting miRNAs from the Brazilian cohort expression data. Once again, the main identified cluster involved extracellular matrix remodeling mechanisms, while the second cluster involved formation of cornified envelope, establishment of skin barrier and unsaturated fatty acid metabolic process. Differential expression comparing active pterygium with atrophic pterygium using data generated from the Brazilian cohort identified differentially expressed genes between the two forms of presentation of this condition. Our results reveal differentially expressed genes not only in pterygium, but also in active pterygium when compared to the atrophic ones. New insights in relation to pterygium’s pathophysiology are suggested.

2022 ◽  
Vol 12 (1) ◽  
Kyra N. Smit ◽  
Ruben Boers ◽  
Jolanda Vaarwater ◽  
Joachim Boers ◽  
Tom Brands ◽  

AbstractUveal melanoma (UM) is an aggressive intra-ocular cancer with a strong tendency to metastasize. Metastatic UM is associated with mutations in BAP1 and SF3B1, however only little is known about the epigenetic modifications that arise in metastatic UM. In this study we aim to unravel epigenetic changes contributing to UM metastasis using a new genome-wide methylation analysis technique that covers over 50% of all CpG’s. We identified aberrant methylation contributing to BAP1 and SF3B1-mediated UM metastasis. The methylation data was integrated with expression data and surveyed in matched UM metastases from the liver, skin and bone. UM metastases showed no commonly shared novel epigenetic modifications, implying that epigenetic changes contributing to metastatic spreading and colonization in distant tissues occur early in the development of UM and epigenetic changes that occur after metastasis are mainly patient-specific. Our findings reveal a plethora of epigenetic modifications in metastatic UM and its metastases, which could subsequently result in aberrant repression or activation of many tumor-related genes. This observation points towards additional layers of complexity at the level of gene expression regulation, which may explain the low mutational burden of UM.

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