Publicação
Identification of potential biomarkers for early detection of Colorectal Cancer
| dc.contributor.advisor | Lima, Luis | |
| dc.contributor.advisor | Brandão, Andreia | |
| dc.contributor.advisor | Faria, Brígida Mónica | |
| dc.contributor.author | Pereira, Luis Gonçalo Teixeira | |
| dc.date.accessioned | 2026-02-23T14:44:37Z | |
| dc.date.available | 2026-02-23T14:44:37Z | |
| dc.date.issued | 2025-12-02 | |
| dc.description.abstract | Colorectal cancer (CRC) is globally classified as the second cancer type with the highest mortality rate index and the third most prevalent, which demonstrates the need to improve diagnostic procedures, making them less invasive and more efficient. The most effective screening method is, still, the colonoscopy. The fact that it is rather invasive in its nature could explain why participation rates for screening remain low. Other methods, like faecal occult blood test or tumour marker analysis (e.g. Carcinoembryonic Antigen and CA 19-9 Antigen) are less invasive but also considerably less effective. The objective of this study is to identify potential biological markers for the early detection of CRC through the analysis of public RNA-sequencing data. Samples from 54 patients with CRC and 46 healthy individuals were examined utilising several bioinformatics techniques and tools. Two separate workflows, made with different programming languages (R and Python), were developed to process and analyse genetic sequencing data. Analysing the data involves the evaluation of the quality of raw reads, removing unwanted sequences (adapters or low-quality regions), performing the alignment of processed sequences to the reference genome, followed by differential expression analysis, enrichment pathway analysis and the use of statistical techniques together with machine learning to identify genes or groups of genes with predictive potential to be used as biomarkers. Differential expression analysis identified 4,718 significantly differentially expressed genes (adjusted p-value<0.05), with a predominant pattern of downregulation in CRC samples. Notably, multiple ribosomal proteins were among the most significantly down-regulated genes. Pathway enrichment analysis revealed significant upregulation of haemostasis, blood coagulation and platelet activation processes in CRC, while ribosomal pathways, extracellular matrix organisation, and neuronal signalling were prominently down-regulated. Through machine learning approaches incorporating both statistical significance and predictive power, genes including RNU1-27P (RNA processing/small nuclear RNA pseudogene), RPL37A (ribosomal protein), MAGI2-AS3, ADGRG5, USP35 and LSMEM1 (growth and invasion regulators), SERPINF1 (coagulation and haemostasis related), IL3RA and IGF2R (among others), showed the most potential to be classified as CRC biomarkers. The identified biomarkers align with observed pathway dysregulations and still require future validation to be deemed viable in early CRC detection, therefore, potentially improving screening methods. | eng |
| dc.identifier.tid | 204178428 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/31881 | |
| dc.language.iso | eng | |
| dc.rights.uri | N/A | |
| dc.subject | Bioinformatics | |
| dc.subject | Biomarkers | |
| dc.subject | Colorectal cancer | |
| dc.subject | RNA | |
| dc.subject | Sequencing | |
| dc.title | Identification of potential biomarkers for early detection of Colorectal Cancer | eng |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| thesis.degree.name | Master’s degree in Biostatistics and Bioinformatics Applied to Health |
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