bioRxivCancer BiologyPreprint
GASTRIC CANCER BASED ON TRANSCRIPTION FACTORS
Aline Gonçalves da CostaiD, Kaue Sant Ana Pereira GuimaraesiD, Ronald Matheus da Silva MourãoiD, Juliana Barreto Albuquerque PintoiD, Jéssica Manoelli Costa da SilvaiD, Diego PereiraiD, Valéria Cristiane Santos da SilvaiD, Sâmia DemachkiiD, Williams Fernandes BarraiD, Samir Mansour Moraes CassebiD, Fabiano Cordeiro MoreiraiD, Rommel Mário Rodríguez BurbanoiD, Paulo Pimentel AssumpcãoiD
Abstract
IntroductionGastric adenocarcinoma (GA) is responsible for thousands of deaths annually and significantly affects patients quality of life. To increase knowledge about this disease, several studies have focused on the transcriptional control mechanism by analyzing the role of transcription factors (TFs) in GA. MethodsThis study identified differential expression (DE) of TFs in GA samples from 74 patients using NGS, biostatistical analysis in R, and comparison with 68 normal tissue samples in the genetic databases of the National Center for Biotechnology Information (NCBI). Results1,564 human transcription factors were DE. Of these, 87 were selected using the established cutoff value of AUC [≥] 0.95, and 25 were analyzed for their influence on GA carcinogenesis. Based on their role in biological processes, nine TFs were found to be underexpressed due to their growth suppression and cell differentiation properties, notably the TFs IRF3 and CXXC1. Sixteen TFs were overexpressed, associated with drug resistance, disease promotion, metastasis, and poor prognosis in GA, such as AHR, NCOA3, RRB1, and STAT3, respectively. ConclusionThis study integrated systematic bioinformatics and NGS, which revealed novel TFs with important oncogenic and suppressive functions in GA. Therefore, it provides new insights into genes with potential biomarkers for prognostic prediction in GA, which may offer important implications for clinical practice and may effectively target treatment, monitoring, or disease recurrence.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
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