Biology subjects

Casseb, S. M. M.

Publications and source records attributed to Casseb, S. M. M..

3 recordsLinked to original sources

bioRxiv2025

Cancer-Testis Antigens as Clinical and Prognostic Biomarkers in Gastric Adenocarcinoma: Integration of Differential Expression, Clinical Associations, Survival, and Co-Expression Networks

Gastric adenocarcinoma (GAC) remains one of the leading causes of cancer-related mortality, characterized by marked molecular and clinical heterogeneity, which underscores the need for robust biomarkers. In this study, we aimed to evaluate the role of cancer-testis antigens (CTAs) in GAC through integrated analyses of differential expression, clinical associations, survival impact, and gene co-expression networks. The cohort included 156 patients with complete clinical data, comprising a total of 362 tissue samples (tumor, peritumoral, and metaplastic). We identified 541 differentially expressed genes, of which 49 corresponded to previously described CTAs. Between GAC and peritumoral tissues, 14 CTAs exhibited significant differential expression, with MAGEA3, MAGEA6, GOLGA6L1, and MAGEA2 among the most highly expressed in tumors. Relevant associations were observed between the expression of MAGEA3, MAGEA6, POTEF, and CTCFL with TNM staging, as well as IGF2BP1, DAZ1/3/4, YBX2, and PCDHA4 in relation to variables such as tumor depth, metastasis, and TCGA subtypes. Survival analysis demonstrated that high expression of IGF2BP1, CTCFL, CT45A5, and LIN28B was strongly associated with worse prognosis (HR > 2.7), whereas SYCE1L, PIK3R3, and ZNF683 showed a protective effect. The co-expression network revealed five main clusters, highlighting germline-related modules (MAGEA, DAZ, CSAG), adhesion and transcriptional regulation (YBX2, POTEF, PCDHA, TAF1L), and immune-related genes (IRAK3, CXCR1, PIK3R3), evidencing functional integration between proliferation, adhesion, and immune microenvironment modulation. Collectively, these findings reinforce CTAs as potential clinical and prognostic biomarkers in GAC, with direct implications for risk stratification and the development of personalized therapeutic strategies.

cancer biology↗

bioRxiv2025

Transcriptomic Mutational Profiling of Gastric Adenocarcinoma in Northern Brazil

Gastric cancer (GC) remains among the neoplasms with the worst prognosis, partly due to its biological heterogeneity and the scarcity of robust biomarkers. The characterization of mutational profiles from transcripts can reveal specific tumor signatures and point to therapeutic targets. This study investigated the landscape of mutations expressed in 102 samples of gastric adenocarcinoma from northern Brazil, which were sequenced using NGS. Readings were aligned to the reference genome using STAR (two-pass mode), and variants were called with GATK and VarDict. Annotations and impact predictions were generated using VEP, SIFT, and PolyPhen. We identified >90,000 variants; among the most frequently mutated genes, FTH1 stood out. The mutational profile was described using maftools, and signatures were inferred with MutationalPatterns. We observed a predominant distribution of SNVs, with C>T transitions as the most common event, in addition to patterns compatible with signatures related to replication damage and DNA repair. To mitigate biases inherent to RNA-seq, we applied filters for coverage, strand bias, and RNA editing hotspots. Together, the data outline a regional landscape of mutations expressed in GC and reinforce the usefulness of the transcriptome for prioritizing biomarkers and functional hypotheses that may guide genomic validations and subsequent clinical studies.

bioinformatics↗

bioRxiv2025

Treasure: A Sensitive Pipeline for Species-Level and Functional Microbiome Profiling

Next Generation Sequencing (NGS) methods, such as 16S rRNA amplicon sequencing and Whole Genome Sequencing (WGS), enable taxonomic analyses but have limitations. This project proposes the development of a computational tool capable of performing functional analysis of the most abundant microorganisms within a microbiome based on taxonomic analysis. The proposed method integrates the tools Kraken, Gffread, and Salmon. Compared to Samsa 2, a commonly used pipeline for RNA-Seq Total samples, the new approach demonstrated superior performance across all evaluated scenarios (p < 0.01). The tool aims to functionally characterize the microbiome of regions affected by Gastric Cancer (GC) and adjacent areas, assess associations between survival, expression/abundance, and identify potential microbial biomarkers for GC.

bioinformatics↗