Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis

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dc.contributor.authorOtálora-Otálora, Beatriz Andrea
dc.contributor.authorOsuna-Garzón, Daniel Alejandro
dc.contributor.authorCarvajal-Parra, Michael Steven
dc.contributor.authorCañas, Alejandra
dc.contributor.authorMontecino, Martín
dc.contributor.authorLópez-Kleine, Liliana
dc.contributor.authorRojas, Adriana
dc.date.accessioned2023-10-13T17:36:12Z
dc.date.available2023-10-13T17:36:12Z
dc.date.issued2022-07
dc.descriptionIndexación: Scopuses
dc.description.abstractThe bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels. © 2022 by the authors.es
dc.description.urihttps://www.mdpi.com/2079-7737/11/7/1082
dc.identifier.citationBiology Open Access Volume 11, Issue 7July 2022 Article number 1082es
dc.identifier.doi10.3390/biology11071082
dc.identifier.issn2079-7737
dc.identifier.urihttps://repositorio.unab.cl/xmlui/handle/ria/53457
dc.language.isoenes
dc.publisherMDPIes
dc.rights.licenseAtribución 4.0 Internacional (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subjectBreast cancer (BC)es
dc.subjectCoexpression networkses
dc.subjectDifferentially expressed genes (DEGs)es
dc.subjectEarly detection and prognosis biomarkerses
dc.subjectLeukemia (LK)es
dc.subjectLung cancer (LC)es
dc.titleIdentifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysises
dc.typeArtículoes
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