Characterization of diagnostic and therapeutic biomarkers using differential expression analysis technique

The search for effective diagnostic and therapeutic markers in oncology has emerged as an essential tool for guiding medicine to new frontiers in recent years. This thesis explores the crucial search for those markers toward more precise diagnoses and more targeted treatments, with the goal of increasing therapy success in the near future. Among the many challenges in oncology is that some diseases are difficult to diagnose and treat. Pleural mesothelioma and lung adenocarcinoma are two emblematic examples of these challenging diseases. Pleural mesothelioma is a rare tumor located in the lung pleura, and, although there are also rare asbestos-independent cases associated with mutations in tumor suppressors like BAP1, it is mainly related to asbestos exposure. Its early diagnosis is complicated not only by vague and nonspecific symptoms but also by the challenge of distinguishing it from benign, precancerous lesions, such as mesothelial hyperplasia. This similarity can delay proper diagnosis. The search for specific diagnostic markers for pleural mesothelioma is therefore essential, thus offering hope for early diagnosis to patients. Lung adenocarcinoma represents the most common form of lung cancer. Like many others, this tumor is not a monolithic entity but rather a complex ecosystem in which tumor cells and the surrounding tumor microenvironment can interact. A deeper understanding of this microenvironment is essential for developing targeted therapeutic strategies against the microenvironment to promote an anti-tumor response. In light of this complexity, the search for specific therapeutic markers to guide these targeted treatments gains greater relevance, with the aspiration to offer patients more effective therapies. The first part of this thesis, the Main Project, aims to identify variations in genetic transcripts through differential expression analysis between malignant pleural mesothelioma and mesothelial hyperplasia to identify potential effective diagnostic and therapeutic biomarkers. The first chapter of this section explores bioinformatics as a key tool for identifying molecular differences between two conditions. The essential steps in the planned bioinformatics pipeline will be presented, from data collection and preprocessing to differential expression analysis and biological interpretation of the results. Chapter 2 outlines the key objectives of our research, while Chapter 3 outlines the bioinformatics methodologies and tools used to guide the study toward identifying differentially expressed genes. In Chapter 4, we present the results that emerged from the analysis. The fifth Chapter, meanwhile, reflects on the conclusions drawn, followed by Chapter 6, which looks ahead, exploring future prospects and suggesting possible directions for further research and applications based on the results of this study. The second part of this thesis, the Side project, aims to identify variations in genetic transcripts through differential expression analysis within the lung adenocarcinoma immune population compared to control samples to identify potential therapeutic markers for innovative and targeted future therapies. The chapter 1 of this section introduces lung adenocarcinoma and provides insight into the importance of the relationship between the neoplasm and its microenvironmental context. Chapter 2 defines the objective, followed by the third chapter describing the bioinformatics methods and approaches used. Chapter 4 lays out the results, and Chapter 5 reports the techniques and future prospects of this study.