Biology subjects

Guimaraes, K. S. A. P.

Publications and source records attributed to Guimaraes, K. S. A. P..

3 recordsLinked to original sources

bioRxiv2025

Transcriptional Signatures of Field Cancerization in Gastric Cancer

The high rate of local recurrence in gastric adenocarcinoma (GA) suggests that carcinogenesis is not a focal event but a field-wide process. This phenomenon, known as "field cancerization," posits that histologically normal peritumoral tissue is, in fact, a pre-neoplastic field harboring incipient molecular alterations that confound genomic studies using it as a normal control. To overcome this limitation, we performed a three-way comparative transcriptomic analysis of tumor, peritumoral, and true-normal gastric tissues using a deep learning framework. We identified a stable 138-gene signature established within the peritumoral field and conserved in the tumor, which was absent in healthy controls. Within this signature, three key COSMIC-listed driver genes were highlighted: the Hippo pathway component FAT4 and the p53-inhibitor MDM4 were upregulated, while the EMT-suppressor NDRG1 was repressed. Co-expression analysis revealed a dynamic rewiring of these drivers, with a significant positive correlation between FAT4 and MDM4 emerging exclusively in the peritumoral field. In contrast, a negative correlation between FAT4 and NDRG1 was observed specifically in the tumor context. In public cohorts, high expression of FAT4 and MDM4 was significantly associated with poor patient prognosis, whereas NDRG1 showed no prognostic association. Critically, the prognostic power of MDM4 was validated in our local patient cohort. Our findings demonstrate that the peritumoral field is a molecularly distinct state in gastric carcinogenesis, characterized by a metabolic shift, and identify FAT4 and MDM4 as key drivers of this early transition, with significant potential as prognostic biomarkers.

genetics↗

bioRxiv2025

Integrated Expression Analysis of C-MYC Oncogene-Associated Pathways in Gastric Adenocarcinoma and its Correlation with Clinicopathological Factors

BackgroundThe C-MYC oncogene is a well-established driver of gastric carcinogenesis, yet the integrated expression pattern of its complex regulatory network and its clinical implications in gastric adenocarcinoma (GAC) remain to be fully elucidated. This study aimed to perform an integrated bioinformatic analysis of C-MYC and its associated pathways in a cohort of GAC patients to delineate its expression profile, assess its potential as a biomarker, and correlate its patterns with clinicopathological factors such as Lauren classification and neoadjuvant treatment status. MethodsThe study included transcriptome data from 74 GAC tumor samples and 68 normal gastric tissue samples. Following RNA sequencing, a comprehensive bioinformatic analysis was conducted on a curated list of 21 C-MYC-associated genes. The methodology included differential expression analysis (DESeq2), unsupervised hierarchical clustering, Principal Component Analysis (PCA), and Receiver Operating Characteristic (ROC) curve analysis to evaluate diagnostic performance. Gene expression levels were also statistically correlated with Lauren histological subtypes and neoadjuvant therapy status using the Wilcoxon rank-sum test. ResultsThe analysis revealed a profound dysregulation of the C-MYC network in GAC. While MYC itself was significantly upregulated, its transcriptional antagonists, particularly MXD4 and MXD3, were the most significantly downregulated genes. This gene signature robustly separated tumor from normal tissues in both hierarchical clustering and PCA. ROC analysis demonstrated the outstanding diagnostic potential of several genes, with MXD4 achieving a perfect Area Under the Curve (AUC) of 1.00, surpassing the diagnostic value of MYC (AUC=0.86). Stratification by Lauren classification showed that MYC and its stability regulator PTBP1 were significantly more expressed in the intestinal subtype, whereas the repressors MXD3 and MXI1 were higher in the diffuse subtype. No significant expression differences were observed based on neoadjuvant treatment status. ConclusionGastric adenocarcinoma is characterized by a coordinated dysregulation of the C-MYC network, marked by both oncogene activation and a concurrent loss of its key transcriptional repressors. The profound downregulation of antagonists like MXD4 serves as an exceptionally accurate molecular signature for GAC, suggesting its potential as a diagnostic biomarker superior to MYC alone. The divergent expression patterns between Lauren subtypes highlight distinct molecular pathobiology and may have implications for targeted therapies

genetics↗

bioRxiv2025

GASTRIC CANCER BASED ON TRANSCRIPTION FACTORS

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.

cancer biology↗