Pub Date : 2023-10-24DOI: 10.2174/0113892029264990231013112156
Amarinder Singh Thind, Somdatta Sinha
Background: Viruses have high mutation rates, facilitating rapid evolution and the emergence of new species, subspecies, strains and recombinant forms. Accurate classification of these forms is crucial for understanding viral evolution and developing therapeutic applications. Phylogenetic classification is typically performed by analyzing molecular differences at the genomic and sub-genomic levels. This involves aligning homologous proteins or genes. However, there is growing interest in developing alignment-free methods for whole-genome comparisons that are computationally efficient. Methods: Here we elaborate on the Chaos Game Representation (CGR) method, based on concepts of statistical physics and free of sequence alignment assumptions. We adopt the CGR method for classification of the closely related clades/lineages A and B of the SARS-Corona virus 2019 (SARS-CoV-2), which is one of the fastest evolving viruses. Results: Our study shows that the CGR approach can easily yield the SARS-CoV-2 phylogeny from the available whole genomes of lineage A and lineage B sequences. It also shows an accurate classification of eight different strains and the newly evolved XBB variant from its parental strains. Compared to alignment-based methods (Neighbour-Joining and Maximum Likelihood), the CGR method requires low computational resources, is fast and accurate for long sequences, and, being a K-mer based approach, allows simultaneous comparison of a large number of closely-related sequences of different sizes. Further, we developed an R pipeline CGRphylo, available on GitHub, which integrates the CGR module with various other R packages to create phylogenetic trees and visualize them. Conclusion: Our findings demonstrate the efficacy of the CGR method for accurate classification and tracking of rapidly evolving viruses, offering valuable insights into the evolution and emergence of new SARS-CoV-2 strains and recombinants.
{"title":"Using Chaos-Game-Representation for Analysing the SARS-CoV-2 Lineages, Newly Emerging Strains and Recombinants","authors":"Amarinder Singh Thind, Somdatta Sinha","doi":"10.2174/0113892029264990231013112156","DOIUrl":"https://doi.org/10.2174/0113892029264990231013112156","url":null,"abstract":"Background: Viruses have high mutation rates, facilitating rapid evolution and the emergence of new species, subspecies, strains and recombinant forms. Accurate classification of these forms is crucial for understanding viral evolution and developing therapeutic applications. Phylogenetic classification is typically performed by analyzing molecular differences at the genomic and sub-genomic levels. This involves aligning homologous proteins or genes. However, there is growing interest in developing alignment-free methods for whole-genome comparisons that are computationally efficient. Methods: Here we elaborate on the Chaos Game Representation (CGR) method, based on concepts of statistical physics and free of sequence alignment assumptions. We adopt the CGR method for classification of the closely related clades/lineages A and B of the SARS-Corona virus 2019 (SARS-CoV-2), which is one of the fastest evolving viruses. Results: Our study shows that the CGR approach can easily yield the SARS-CoV-2 phylogeny from the available whole genomes of lineage A and lineage B sequences. It also shows an accurate classification of eight different strains and the newly evolved XBB variant from its parental strains. Compared to alignment-based methods (Neighbour-Joining and Maximum Likelihood), the CGR method requires low computational resources, is fast and accurate for long sequences, and, being a K-mer based approach, allows simultaneous comparison of a large number of closely-related sequences of different sizes. Further, we developed an R pipeline CGRphylo, available on GitHub, which integrates the CGR module with various other R packages to create phylogenetic trees and visualize them. Conclusion: Our findings demonstrate the efficacy of the CGR method for accurate classification and tracking of rapidly evolving viruses, offering valuable insights into the evolution and emergence of new SARS-CoV-2 strains and recombinants.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135315919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.2174/0113892029265046231011100327
Ezgi Man, Serap EVRAN
abstract: Epigenetic changes play an important role in the pathophysiology of autoimmune diseases such as allergic asthma, multiple sclerosis, lung diseases, diabetes, cystic fibrosis, atherosclerosis, rheumatoid arthritis, and COVID-19. There are three main classes of epigenetic alterations: post-translational modifications of histone proteins, control by non-coding RNA and DNA methylation. Since histone modifications can directly affect chromatin structure and accessibility, they can regulate gene expression levels. Abnormal expression and activity of histone deacetylases (HDACs) have been reported in immune mediated diseases. Increased acetylated levels of lysine residues have been suggested to be related to the overexpression of inflammatory genes. This review focuses on the effect of HDAC modifications on histone and non–histone proteins in autoimmune diseases. Furthermore, we discuss the potential therapeutic effect of HDAC inhibitors (HDACi) used in these diseases.
{"title":"Deacetylation of Histones and Non-histone Proteins in Inflammatory Diseases and Cancer Therapeutic Potential of Histone Deacetylase Inhibitors","authors":"Ezgi Man, Serap EVRAN","doi":"10.2174/0113892029265046231011100327","DOIUrl":"https://doi.org/10.2174/0113892029265046231011100327","url":null,"abstract":"abstract: Epigenetic changes play an important role in the pathophysiology of autoimmune diseases such as allergic asthma, multiple sclerosis, lung diseases, diabetes, cystic fibrosis, atherosclerosis, rheumatoid arthritis, and COVID-19. There are three main classes of epigenetic alterations: post-translational modifications of histone proteins, control by non-coding RNA and DNA methylation. Since histone modifications can directly affect chromatin structure and accessibility, they can regulate gene expression levels. Abnormal expression and activity of histone deacetylases (HDACs) have been reported in immune mediated diseases. Increased acetylated levels of lysine residues have been suggested to be related to the overexpression of inflammatory genes. This review focuses on the effect of HDAC modifications on histone and non–histone proteins in autoimmune diseases. Furthermore, we discuss the potential therapeutic effect of HDAC inhibitors (HDACi) used in these diseases.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135322662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-19DOI: 10.2174/0113892029270191231013111911
Jianhua Jia, Xiaojing Cao, Zhangying Wei
Introduction: N4 acetylcytidine (ac4C) is a highly conserved nucleoside modification that is essential for the regulation of immune functions in organisms. Currently, the identification of ac4C is primarily achieved using biological methods, which can be time-consuming and laborintensive. In contrast, accurate identification of ac4C by computational methods has become a more effective method for classification and prediction Aim: To the best of our knowledge, although there are several computational methods for ac4C locus prediction, the performance of the models they constructed is poor, and the network structure they used is relatively simple and suffers from the disadvantage of network degradation. This study aims to improve these limitations by proposing a predictive model based on integrated deep learning to better help identify ac4C sites. Methods: In this study, we propose a new integrated deep learning prediction framework, DLCac4C. First, we encode RNA sequences based on three feature encoding schemes, namely C2 encoding, nucleotide chemical property (NCP) encoding, and nucleotide density (ND) encoding. Second, one-dimensional convolutional layers and densely connected convolutional networks (DenseNet) are used to learn local features, and bi-directional long short-term memory networks (Bi-LSTM) are used to learn global features. Third, a channel attention mechanism is introduced to determine the importance of sequence characteristics. Finally, a homomorphic integration strategy is used to limit the generalization error of the model, which further improves the performance of the model. Results: The DLC-ac4C model performed well in terms of sensitivity (Sn), specificity (Sp), accuracy (Acc), Mathews correlation coefficient (MCC), and area under the curve (AUC) for the independent test data with 86.23%, 79.71%, 82.97%, 66.08%, and 90.42%, respectively, which was significantly better than the prediction accuracy of the existing methods. Conclusion: Our model not only combines DenseNet and Bi-LSTM, but also uses the channel attention mechanism to better capture hidden information features from a sequence perspective, and can identify ac4C sites more effectively.
{"title":"DLC-ac4C: A Prediction Model for N4-acetylcytidine Sites in Human mRNA Based on DenseNet and Bidirectional LSTM Methods","authors":"Jianhua Jia, Xiaojing Cao, Zhangying Wei","doi":"10.2174/0113892029270191231013111911","DOIUrl":"https://doi.org/10.2174/0113892029270191231013111911","url":null,"abstract":"Introduction: N4 acetylcytidine (ac4C) is a highly conserved nucleoside modification that is essential for the regulation of immune functions in organisms. Currently, the identification of ac4C is primarily achieved using biological methods, which can be time-consuming and laborintensive. In contrast, accurate identification of ac4C by computational methods has become a more effective method for classification and prediction Aim: To the best of our knowledge, although there are several computational methods for ac4C locus prediction, the performance of the models they constructed is poor, and the network structure they used is relatively simple and suffers from the disadvantage of network degradation. This study aims to improve these limitations by proposing a predictive model based on integrated deep learning to better help identify ac4C sites. Methods: In this study, we propose a new integrated deep learning prediction framework, DLCac4C. First, we encode RNA sequences based on three feature encoding schemes, namely C2 encoding, nucleotide chemical property (NCP) encoding, and nucleotide density (ND) encoding. Second, one-dimensional convolutional layers and densely connected convolutional networks (DenseNet) are used to learn local features, and bi-directional long short-term memory networks (Bi-LSTM) are used to learn global features. Third, a channel attention mechanism is introduced to determine the importance of sequence characteristics. Finally, a homomorphic integration strategy is used to limit the generalization error of the model, which further improves the performance of the model. Results: The DLC-ac4C model performed well in terms of sensitivity (Sn), specificity (Sp), accuracy (Acc), Mathews correlation coefficient (MCC), and area under the curve (AUC) for the independent test data with 86.23%, 79.71%, 82.97%, 66.08%, and 90.42%, respectively, which was significantly better than the prediction accuracy of the existing methods. Conclusion: Our model not only combines DenseNet and Bi-LSTM, but also uses the channel attention mechanism to better capture hidden information features from a sequence perspective, and can identify ac4C sites more effectively.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135779408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-15DOI: 10.2174/0113892029258657231010065320
Heon-Jin Lee, Youngkyun Lee, Su-Hyung Hong, Jin-Woo Park
{"title":"Decoding the Link between Periodontitis and Neuroinflammation: The Journey of Bacterial Extracellular Vesicles","authors":"Heon-Jin Lee, Youngkyun Lee, Su-Hyung Hong, Jin-Woo Park","doi":"10.2174/0113892029258657231010065320","DOIUrl":"https://doi.org/10.2174/0113892029258657231010065320","url":null,"abstract":"<jats:sec>\u0000<jats:title />\u0000<jats:p />\u0000</jats:sec>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135759272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Plant breeding has made a significant contribution to increasing agricultural production. Conventional breeding based on phenotypic selection is not effective for crop improvement. Because phenotype is considerably influenced by environmental factors, which will affect the selection of breeding materials for crop improvement. The past two decades have seen tremendous progress in plant breeding research. Especially the availability of high-throughput molecular markers followed by genomic-assisted approaches significantly contributed to advancing plant breeding. Integration of speed breeding with genomic and phenomic facilities allowed rapid quantitative trait loci (QTL)/gene identifications and ultimately accelerated crop improvement programs. The advances in sequencing technology helps to understand the genome organization of many crops and helped with genomic selection in crop breeding. Plant breeding has gradually changed from phenotype-to-genotype-based to genotype-to-phenotype-based selection. High-throughput phenomic platforms have played a significant role in the modern breeding program and are considered an essential part of precision breeding. In this review, we discuss the rapid advance in plant breeding technology for efficient crop improvements and provide details on various approaches/platforms that are helpful for crop improvement. This review will help researchers understand the recent developments in crop breeding and improvements.
{"title":"The Era of Plant Breeding: Conventional Breeding to Genomics-assisted Breeding for Crop Improvement.","authors":"Thumadath Palayullaparambil Ajeesh Krishna, Duraipandiyan Veeramuthu, Theivanayagam Maharajan, Mariapackiam Soosaimanickam","doi":"10.2174/1389202924666230517115912","DOIUrl":"10.2174/1389202924666230517115912","url":null,"abstract":"<p><p>Plant breeding has made a significant contribution to increasing agricultural production. Conventional breeding based on phenotypic selection is not effective for crop improvement. Because phenotype is considerably influenced by environmental factors, which will affect the selection of breeding materials for crop improvement. The past two decades have seen tremendous progress in plant breeding research. Especially the availability of high-throughput molecular markers followed by genomic-assisted approaches significantly contributed to advancing plant breeding. Integration of speed breeding with genomic and phenomic facilities allowed rapid quantitative trait loci (QTL)/gene identifications and ultimately accelerated crop improvement programs. The advances in sequencing technology helps to understand the genome organization of many crops and helped with genomic selection in crop breeding. Plant breeding has gradually changed from phenotype-to-genotype-based to genotype-to-phenotype-based selection. High-throughput phenomic platforms have played a significant role in the modern breeding program and are considered an essential part of precision breeding. In this review, we discuss the rapid advance in plant breeding technology for efficient crop improvements and provide details on various approaches/platforms that are helpful for crop improvement. This review will help researchers understand the recent developments in crop breeding and improvements.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"1 1","pages":"24-35"},"PeriodicalIF":2.6,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10334699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41425964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract: Plants are a vital source of bioactive molecules for various drug development processes. Tetrastigma hemsleyanum is one of the endangered medicinal plant species well known to the world due to its wide range of therapeutic effects. Many bioactive molecules have been identified from this plant, including many classes of secondary metabolites such as flavonoids, phenols, terpenoids, steroids, alkaloids, etc. Due to its slow growth, it usually takes 3-5 years to meet commercial medicinal materials for this plant. Also, T. hemsleyanum contains low amounts of specific bioactive compounds, which are challenging to isolate easily. Currently, scientists are attempting to increase bioactive molecules' production from medicinal plants in different ways or to synthesize them chemically. The genomic tools helped to understand medicinal plants' genome organization and led to manipulating genes responsible for various biosynthesis pathways. Metabolic engineering has made it possible to enhance the production of secondary metabolites by introducing manipulated biosynthetic pathways to attain high levels of desirable bioactive molecules. Metabolic engineering is a promising approach for improving the production of secondary metabolites over a short time period. In this review, we have highlighted the scope of various biotechnological approaches for metabolic engineering to enhance the production of secondary metabolites for pharmaceutical applications in T. hemsleyanum. Also, we summarized the progress made in metabolic engineering for bioactive molecule enhancement in T. hemsleyanum. It may lead to reducing the destruction of the natural habitat of T. hemsleyanum and conserving them through the cost-effective production of bioactive molecules in the future.
{"title":"Insights into Metabolic Engineering of Bioactive Molecules in Tetrastigma Hemsleyanum Diels & Gilg: A Traditional Medicinal Herb","authors":"Ajeesh Krishna TP, Maharajan Theivanayagam, Adarsh Krishna, Antony Ceasar Stanislaus","doi":"10.2174/0113892029251472230921053135","DOIUrl":"https://doi.org/10.2174/0113892029251472230921053135","url":null,"abstract":"Abstract: Plants are a vital source of bioactive molecules for various drug development processes. Tetrastigma hemsleyanum is one of the endangered medicinal plant species well known to the world due to its wide range of therapeutic effects. Many bioactive molecules have been identified from this plant, including many classes of secondary metabolites such as flavonoids, phenols, terpenoids, steroids, alkaloids, etc. Due to its slow growth, it usually takes 3-5 years to meet commercial medicinal materials for this plant. Also, T. hemsleyanum contains low amounts of specific bioactive compounds, which are challenging to isolate easily. Currently, scientists are attempting to increase bioactive molecules' production from medicinal plants in different ways or to synthesize them chemically. The genomic tools helped to understand medicinal plants' genome organization and led to manipulating genes responsible for various biosynthesis pathways. Metabolic engineering has made it possible to enhance the production of secondary metabolites by introducing manipulated biosynthetic pathways to attain high levels of desirable bioactive molecules. Metabolic engineering is a promising approach for improving the production of secondary metabolites over a short time period. In this review, we have highlighted the scope of various biotechnological approaches for metabolic engineering to enhance the production of secondary metabolites for pharmaceutical applications in T. hemsleyanum. Also, we summarized the progress made in metabolic engineering for bioactive molecule enhancement in T. hemsleyanum. It may lead to reducing the destruction of the natural habitat of T. hemsleyanum and conserving them through the cost-effective production of bioactive molecules in the future.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"54 47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135533055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract: Circular RNAs (circRNAs) are a class of endogenous functional RNA generated by backsplicing. Recently, circRNAs have been found to have certain coding potential. Proteins/peptides translated from circRNAs play essential roles in various diseases. Here, we briefly summarize the basic knowledge and technologies that are usually applied to study circRNA translation. Then, we focus on the research progress of circRNA translation in cardiovascular diseases and discuss the perspective and future direction of translatable circRNA study in cardiovascular diseases.
{"title":"Circular RNA Translation in Cardiovascular Diseases","authors":"Lijun Wang, Xinxin Cui, Fei Jiang, Yuxue Hu, Wensi Wan, Guoping Li, Yanjuan Lin, Junjie Xiao","doi":"10.2174/1389202924666230911121358","DOIUrl":"https://doi.org/10.2174/1389202924666230911121358","url":null,"abstract":"Abstract: Circular RNAs (circRNAs) are a class of endogenous functional RNA generated by backsplicing. Recently, circRNAs have been found to have certain coding potential. Proteins/peptides translated from circRNAs play essential roles in various diseases. Here, we briefly summarize the basic knowledge and technologies that are usually applied to study circRNA translation. Then, we focus on the research progress of circRNA translation in cardiovascular diseases and discuss the perspective and future direction of translatable circRNA study in cardiovascular diseases.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135533444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Due to the heterogeneity of Hepatocellular carcinoma (HCC), there is an urgent need for reliable diagnosis and prognosis. Mitochondria-mediated abnormal lipid metabolism affects the occurrence and progression of HCC. Objective: This study aims to investigate the potential of mitochondrial lipid metabolism (MTLM) genes as diagnostic and independent prognostic biomarkers for HCC. Methods: MTLM genes were screened from the Gene Expression Omnibus (GEO) and Gene Set Enrichment Analysis (GSEA) databases, followed by an evaluation of their diagnostic values in both The Cancer Genome Atlas Program (TCGA) and the Affiliated Cancer Hospital of Guangxi Medical University (GXMU) cohort. The TCGA dataset was utilized to construct a gene signature and investigate the prognostic significance, immune infiltration, and copy number alterations. The validity of the prognostic signature was confirmed through GEO, International Cancer Genome Consortium (ICGC), and GXMU cohorts. Results: The diagnostic receiver operating characteristic (ROC) curve revealed that eight MTLM genes have excellent diagnostic of HCC. A prognostic signature comprising 5 MTLM genes with robust predictive value was constructed using the lasso regression algorithm based on TCGA data. The results of the Stepwise regression model showed that the combination of signature and routine clinical parameters had a higher area under the curve (AUC) compared to a single risk score. Further, a nomogram was constructed to predict the survival probability of HCC, and the calibration curves demonstrated a perfect predictive ability. Finally, the risk score also unveiled the different immune and mutation statuses between the two different risk groups. Conclusion: MTLT-related genes may serve as diagnostic and prognostic biomarkers for HCC as well as novel therapeutic targets, which may be beneficial for facilitating further understanding the molecular pathogenesis and providing potential therapeutic strategies for HCC.
{"title":"Mitochondrial Lipid Metabolism Genes as Diagnostic and Prognostic Indicators in Hepatocellular Carcinoma","authors":"Xuejing Li, Ying Tan, Bihan Liu, Houtian Guo, Yongjian Zhou, Jianhui Yuan, Feng Wang","doi":"10.2174/1389202924666230914110649","DOIUrl":"https://doi.org/10.2174/1389202924666230914110649","url":null,"abstract":"Background: Due to the heterogeneity of Hepatocellular carcinoma (HCC), there is an urgent need for reliable diagnosis and prognosis. Mitochondria-mediated abnormal lipid metabolism affects the occurrence and progression of HCC. Objective: This study aims to investigate the potential of mitochondrial lipid metabolism (MTLM) genes as diagnostic and independent prognostic biomarkers for HCC. Methods: MTLM genes were screened from the Gene Expression Omnibus (GEO) and Gene Set Enrichment Analysis (GSEA) databases, followed by an evaluation of their diagnostic values in both The Cancer Genome Atlas Program (TCGA) and the Affiliated Cancer Hospital of Guangxi Medical University (GXMU) cohort. The TCGA dataset was utilized to construct a gene signature and investigate the prognostic significance, immune infiltration, and copy number alterations. The validity of the prognostic signature was confirmed through GEO, International Cancer Genome Consortium (ICGC), and GXMU cohorts. Results: The diagnostic receiver operating characteristic (ROC) curve revealed that eight MTLM genes have excellent diagnostic of HCC. A prognostic signature comprising 5 MTLM genes with robust predictive value was constructed using the lasso regression algorithm based on TCGA data. The results of the Stepwise regression model showed that the combination of signature and routine clinical parameters had a higher area under the curve (AUC) compared to a single risk score. Further, a nomogram was constructed to predict the survival probability of HCC, and the calibration curves demonstrated a perfect predictive ability. Finally, the risk score also unveiled the different immune and mutation statuses between the two different risk groups. Conclusion: MTLT-related genes may serve as diagnostic and prognostic biomarkers for HCC as well as novel therapeutic targets, which may be beneficial for facilitating further understanding the molecular pathogenesis and providing potential therapeutic strategies for HCC.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135532118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.2174/1389202924666230102092822
C. Zhang, Yu-Xuan Ye, Dan-Ting Li, Si-Yu Zhang, Z. Shen
Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry In this study, we report a de novo assembly of the chromosome-level WBPH genome with characterized sex chromosomes using third-generation sequencing technologies, Hi-C data and full-length transcripts, and provide a dense landscape of sex-specific transcriptome. We generated a high-quality chromosome-level assembly with a contig N50 of 2.20 Mb and a scaffold N50 of 45.25 Mb. Fourteen autosomes and one X chromosomes were identified. More than 99.5% of the assembled bases located on the 15 chromosomes. 95.9% of the BUSCO complete genes were detected in the final assembly and 16,880 genes were annotated. The integrated genome, definite sex chromosomes, comprehensive transcriptome profiles, high efficiency of RNA interference and short life cycle substantially made WBPH an efficient research object for functional genomics.
{"title":"Chromosome-level genome assembly and sex-specific differential transcriptome of the white-backed planthopper, Sogatella furcifera","authors":"C. Zhang, Yu-Xuan Ye, Dan-Ting Li, Si-Yu Zhang, Z. Shen","doi":"10.2174/1389202924666230102092822","DOIUrl":"https://doi.org/10.2174/1389202924666230102092822","url":null,"abstract":"\u0000\u0000Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry\u0000\u0000\u0000\u0000In this study, we report a de novo assembly of the chromosome-level WBPH genome with characterized sex chromosomes using third-generation sequencing technologies, Hi-C data and full-length transcripts, and provide a dense landscape of sex-specific transcriptome.\u0000\u0000\u0000\u0000We generated a high-quality chromosome-level assembly with a contig N50 of 2.20 Mb and a scaffold N50 of 45.25 Mb. Fourteen autosomes and one X chromosomes were identified. More than 99.5% of the assembled bases located on the 15 chromosomes. 95.9% of the BUSCO complete genes were detected in the final assembly and 16,880 genes were annotated.\u0000\u0000\u0000\u0000The integrated genome, definite sex chromosomes, comprehensive transcriptome profiles, high efficiency of RNA interference and short life cycle substantially made WBPH an efficient research object for functional genomics.\u0000","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46717217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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