Alternative ribonucleic acid(RNA)splicing can lead to the assembly of different protein isoforms with distinctive functions.The outcome of alternative splicing(AS)can result in a complete loss of function or the acqui...Alternative ribonucleic acid(RNA)splicing can lead to the assembly of different protein isoforms with distinctive functions.The outcome of alternative splicing(AS)can result in a complete loss of function or the acquisition of new functions.There is a gap in knowledge of abnormal RNA splice variants promoting cancer stem cells(CSCs),and their prospective contribution in cancer progression.AS directly regulates the self-renewal features of stem cells(SCs)and stem-like cancer cells.Notably,octamer-binding transcription factor 4A spliced variant of octamerbinding transcription factor 4 contributes to maintaining stemness properties in both SCs and CSCs.The epithelial to mesenchymal transition pathway regulates the AS events in CSCs to maintain stemness.The alternative spliced variants of CSCs markers,including cluster of differentiation 44,aldehyde dehydrogenase,and doublecortin-like kinase,α6β1 integrin,have pivotal roles in increasing selfrenewal properties and maintaining the pluripotency of CSCs.Various splicing analysis tools are considered in this study.LeafCutter software can be considered as the best tool for differential splicing analysis and identification of the type of splicing events.Additionally,LeafCutter can be used for efficient mapping splicing quantitative trait loci.Altogether,the accumulating evidence re-enforces the fact that gene and protein expression need to be investigated in parallel with alternative splice variants.展开更多
Background:Accumulating evidence shows that long non-coding RNAs(lncRNAs)play critical roles in cancer progression.The possible association between lncRNAs and herbal medicine is yet to be known.This study aims to ide...Background:Accumulating evidence shows that long non-coding RNAs(lncRNAs)play critical roles in cancer progression.The possible association between lncRNAs and herbal medicine is yet to be known.This study aims to identify medicinal herbs associated with lncRNAs by RNA-seq data for breast and prostate cancer.Methods:To develop the optimal approach for identifying cancer-related lncRNAs,we implemented two steps:(1)applying protein–protein interaction(PPI),Gene Ontology(GO),and pathway analyses,and(2)applying attribute weighting and finding the efficient classification model of the machine learning approach.Results:In the first step,GO terms and pathway analyses on differential co-expressed mRNAs revealed that lncRNAs were widely co-expressed with metabolic process genes.We identified two hub lncRNA-mRNA networks that implicate lncRNAs associated with breast and prostate cancer.In the second step,we implemented various machine learning-based prediction systems(Decision Tree,Random Forest,Deep Learning,and Gradient-Boosted Tree)on the non-transformed and Z-standardized differential co-expressed lncRNAs.Based on five-fold cross-validation,we obtained high accuracy(91.11%),high sensitivity(88.33%),and high specificity(93.33%)in Deep Learning which reinforces the biomarker power of identified lncRNAs in this study.As data originally came from different cell lines at different durations of herbal treatment intervention,we applied seven attribute weighting algorithms to check the effects of variables on identifying lncRNAs.Attribute weighting results showed that the cell line and time had little or no effect on the selected lncRNAs list.Besides,we identified one known lncRNAs,downregulated RNA in cancer(DRAIC),as an essential feature.Conclusions:This study will provide further insights to investigate the potential therapeutic and prognostic targets for prostate cancer(PC)and breast cancer(BC)in common.展开更多
文摘Alternative ribonucleic acid(RNA)splicing can lead to the assembly of different protein isoforms with distinctive functions.The outcome of alternative splicing(AS)can result in a complete loss of function or the acquisition of new functions.There is a gap in knowledge of abnormal RNA splice variants promoting cancer stem cells(CSCs),and their prospective contribution in cancer progression.AS directly regulates the self-renewal features of stem cells(SCs)and stem-like cancer cells.Notably,octamer-binding transcription factor 4A spliced variant of octamerbinding transcription factor 4 contributes to maintaining stemness properties in both SCs and CSCs.The epithelial to mesenchymal transition pathway regulates the AS events in CSCs to maintain stemness.The alternative spliced variants of CSCs markers,including cluster of differentiation 44,aldehyde dehydrogenase,and doublecortin-like kinase,α6β1 integrin,have pivotal roles in increasing selfrenewal properties and maintaining the pluripotency of CSCs.Various splicing analysis tools are considered in this study.LeafCutter software can be considered as the best tool for differential splicing analysis and identification of the type of splicing events.Additionally,LeafCutter can be used for efficient mapping splicing quantitative trait loci.Altogether,the accumulating evidence re-enforces the fact that gene and protein expression need to be investigated in parallel with alternative splice variants.
文摘Background:Accumulating evidence shows that long non-coding RNAs(lncRNAs)play critical roles in cancer progression.The possible association between lncRNAs and herbal medicine is yet to be known.This study aims to identify medicinal herbs associated with lncRNAs by RNA-seq data for breast and prostate cancer.Methods:To develop the optimal approach for identifying cancer-related lncRNAs,we implemented two steps:(1)applying protein–protein interaction(PPI),Gene Ontology(GO),and pathway analyses,and(2)applying attribute weighting and finding the efficient classification model of the machine learning approach.Results:In the first step,GO terms and pathway analyses on differential co-expressed mRNAs revealed that lncRNAs were widely co-expressed with metabolic process genes.We identified two hub lncRNA-mRNA networks that implicate lncRNAs associated with breast and prostate cancer.In the second step,we implemented various machine learning-based prediction systems(Decision Tree,Random Forest,Deep Learning,and Gradient-Boosted Tree)on the non-transformed and Z-standardized differential co-expressed lncRNAs.Based on five-fold cross-validation,we obtained high accuracy(91.11%),high sensitivity(88.33%),and high specificity(93.33%)in Deep Learning which reinforces the biomarker power of identified lncRNAs in this study.As data originally came from different cell lines at different durations of herbal treatment intervention,we applied seven attribute weighting algorithms to check the effects of variables on identifying lncRNAs.Attribute weighting results showed that the cell line and time had little or no effect on the selected lncRNAs list.Besides,we identified one known lncRNAs,downregulated RNA in cancer(DRAIC),as an essential feature.Conclusions:This study will provide further insights to investigate the potential therapeutic and prognostic targets for prostate cancer(PC)and breast cancer(BC)in common.
