Identificação de Alterações na Expressão de Pseudogenes e seus Genes Parentais correspondentes em Adenocarcinoma Pulmonar
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Data
2019-02-27
Autores
Lapa, Rainer M.L.
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Universidade Estadual Paulista (Unesp)
Resumo
Introdução: O adenocarcinoma é o subtipo histológico mais comum de câncer de pulmão e leva à óbito milhões de pacientes a cada ano, mundialmente. Biomarcadores com utilidade clínica potencial têm sido identificados; entre estes, os RNAs não codificadores e os pseudogenes apresentam um potente papel na regulação de genes-alvo e genes parentais regulados por mRNAs respectivamente, os quais estão associados a vias moleculares de tumorigênese. Objetivos: Identificar alterações na expressão de pseudogenes em adenocarcinoma pulmonar, utilizando dados de transcritoma (RNA-Seq). Material e Métodos: Este estudo incluiu 27 tumores de adenocarcinoma pulmonar e 10 tecidos pulmonares histologicamente normais, adjacentes ao tumor, dos mesmos pacientes. Dados de RNA-Seq foram gerados na plataforma Illumina HiScan SQ e utilizados para a aplicação de uma estratégia de análise a fim de identificar sequências de pseudogenes com expressão anormal em tumores. Os pseudogenes com expressão significativamente alterada (p<0,05) foram validados no banco de dados The Cancer Genome Atlas (TCGA) e utilizados para identificação funcional, in silico, utilizando métodos computacionais incluindo o IID (Integrated Interactions Database), TOPPGENES (functional enrichment analysis), mirDip (microRNA Data Integration Portal) e NAViGaTOR (Network Analysis, Visualization, & GraphingTORonto). Resultados e Discussão: Foram identificados 60 pseudogenes desregulados em adenocarcinoma pulmonar, sendo que 34 destes foram validados no banco de dados do TCGA. Alguns pseudogenes mostraram associação estatisticamente significativa com dados clínicos: idade (FER1L4, AL390719.1 e MROH3P), tabagismo (AC140479.2), estadiamento (SLC44A3-ASI) e 7 pseudogenes estavam associados com sobrevida (ADAMTS7P3, CHIAP2, OR7E47P e ECEL1P2). Por meio de estratégias de investigação funcional, in silico, foram identificados e validados 21 pseudogenes com função de competidores endógenos de RNAs (ceRNAs); estes são preditos a atuarem na regulção de vias de tumorigenese, como a via de regulação da matriz extracelular. Conclusões: Este estudo contribui para a identificação de alterações na expressão de pseudogenes e fornece dados de validação funcional de pseudogenes. Os dados mostram o envolvimento de vias moleculares reguladoras incluindo pseudogenes-miRNAs-genes-alvo codificadores de proteínas. Sugere-se que os pseudogenes aqui identificados e validados devem desempenhar funções no desenvolvimento e progressão do adenocarcinoma pulmonar.
Background: Lung adenocarcinoma is the most common histological subtype of lung cancer and is associated with high rates of patient death (>1 million), every year. Clinically useful biomarkers have been identified; among these, non-coding RNAs and pseudogenes have a potent role in the regulation of miRNA target genes and parental genes, respectively, which are associated with tumorigenesis pathways. Objectives: To identify alterations in pseudogene expression in lung adenocarcinoma using transcriptome data (RNA-Seq). Material and Methods: This study included 27 lung adenocarcinoma and 10 histologically normal tissues, adjacent to the tumors, from the same patients. RNA-Seq data were generated on the Illumina HiScan SQ platform and utilized for application of a data analysis strategy (pipeline) in order to identify pseudogene sequences with abnormal expression in tumor compare to normal tissues. Pseudogenes with significantly altered expression (p<0,05) were validated using external dataset The Cancer Genome Atlas (TCGA) and subsequently used for in silico functional analysis, using computational tools including IID (Integrated Interactions Database), TOPPGENES (functional enrichment analysis), mirDip (microRNA Data Integration Portal) and NAViGaTOR (Network Analysis, Visualization, & Graphing TORonto). Results and Discussion: A total of 60 deregulated pseudogenes were identified in pulmonary adenocarcinoma, 34 of which were validated in the TCGA database. Some pseudogenes showed a statistically significant association with clinical data: age (FER1L4, AL390719.1 and MROH3P), smoking (AC140479.2), disease stage (SLC44A3-ASI) and 7 pseudogenes were associated with survival (ADAMTS7P3, CHIAP2, OR7E47P and ECEL1P2) . By means of functional, in silico research strategies, 21 pseudogenes with endogenous RNAs (ceRNAs) function were identified and validated; these are predicted to act in the regulation of tumorigenesis pathways, such as extracellular matrix regulation. Conclusions: This study contributes to the identification of changes in pseudogene expression and provides data of functional validation of pseudogenes. Data shown here indicate the involvement of regulatory pathways including pseudogenes-miRNAs-target genes. It is suggested that pseudogenes identified and validated herein should play a role in the development and progression of pulmonary adenocarcinoma.
Background: Lung adenocarcinoma is the most common histological subtype of lung cancer and is associated with high rates of patient death (>1 million), every year. Clinically useful biomarkers have been identified; among these, non-coding RNAs and pseudogenes have a potent role in the regulation of miRNA target genes and parental genes, respectively, which are associated with tumorigenesis pathways. Objectives: To identify alterations in pseudogene expression in lung adenocarcinoma using transcriptome data (RNA-Seq). Material and Methods: This study included 27 lung adenocarcinoma and 10 histologically normal tissues, adjacent to the tumors, from the same patients. RNA-Seq data were generated on the Illumina HiScan SQ platform and utilized for application of a data analysis strategy (pipeline) in order to identify pseudogene sequences with abnormal expression in tumor compare to normal tissues. Pseudogenes with significantly altered expression (p<0,05) were validated using external dataset The Cancer Genome Atlas (TCGA) and subsequently used for in silico functional analysis, using computational tools including IID (Integrated Interactions Database), TOPPGENES (functional enrichment analysis), mirDip (microRNA Data Integration Portal) and NAViGaTOR (Network Analysis, Visualization, & Graphing TORonto). Results and Discussion: A total of 60 deregulated pseudogenes were identified in pulmonary adenocarcinoma, 34 of which were validated in the TCGA database. Some pseudogenes showed a statistically significant association with clinical data: age (FER1L4, AL390719.1 and MROH3P), smoking (AC140479.2), disease stage (SLC44A3-ASI) and 7 pseudogenes were associated with survival (ADAMTS7P3, CHIAP2, OR7E47P and ECEL1P2) . By means of functional, in silico research strategies, 21 pseudogenes with endogenous RNAs (ceRNAs) function were identified and validated; these are predicted to act in the regulation of tumorigenesis pathways, such as extracellular matrix regulation. Conclusions: This study contributes to the identification of changes in pseudogene expression and provides data of functional validation of pseudogenes. Data shown here indicate the involvement of regulatory pathways including pseudogenes-miRNAs-target genes. It is suggested that pseudogenes identified and validated herein should play a role in the development and progression of pulmonary adenocarcinoma.
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Palavras-chave
Rna-seq, Pseudogenes, Gene parental