RESUMO
Non-small cell lung cancer (NSCLC) is a significant public health concern with high mortality rates. Recent advancements in genomic data, bioinformatics tools, and the utilization of biomarkers have improved the possibilities for early diagnosis, effective treatment, and follow-up in NSCLC. Biomarkers play a crucial role in precision medicine by providing measurable indicators of disease characteristics, enabling tailored treatment strategies. The integration of big data and artificial intelligence (AI) further enhances the potential for personalized medicine through advanced biomarker analysis. However, challenges remain in the impact of new biomarkers on mortality and treatment efficacy due to limited evidence. Data analysis, interpretation, and the adoption of precision medicine approaches in clinical practice pose additional challenges and emphasize the integration of biomarkers with advanced technologies such as genomic data analysis and artificial intelligence (AI), which enhance the potential of precision medicine in NSCLC. Despite these obstacles, the integration of biomarkers into precision medicine has shown promising results in NSCLC, improving patient outcomes and enabling targeted therapies. Continued research and advancements in biomarker discovery, utilization, and evidence generation are necessary to overcome these challenges and further enhance the efficacy of precision medicine. Addressing these obstacles will contribute to the continued improvement of patient outcomes in non-small cell lung cancer.
RESUMO
It has been hypothesised that oral bacteria can migrate, through the blood, from the mouth to the arterial plaques, thus exacerbating atherosclerosis. This study compared bacteria present in the peripheral blood of individuals with and without coronary artery disease (CAD). RNA sequences obtained from blood were downloaded from GEO (GSE58150). Eight patients with coronary artery calcification (CAC) scoring > 500 and eight healthy individuals were analysed. After conducting quality control, the sequences were aligned to the hg38 reference genome using Hisat2. Bacterial taxa were analysed by inputting the unmapped sequences into Kraken. Ecological indices were calculated using Vegan. The package DESeq2 was used to compare the counts of bacteria per standard rank between groups. A total of 51 species were found only in patients with CAD and 41 were exclusively present in healthy individuals. The counts of one phylum, one class, three orders, two families and one genus were significantly different between the analysed groups (p < 0.00032, FDR < 10%), including the orders Cardiobacteriales, Corynebacteriales and Fusobacteriales. Twenty-three bacterial species belonging to the subgingival plaque bacterial complexes were also identified in the blood of individuals from both the groups; Fusobacterium nucleatum was significantly less frequent in patients with CAD (p = 0.0012, FDR = 4.8%). Furthermore, the frequency of another 11 bacteria differed significantly among patients with CAD than that among healthy individuals (p < 0.0030, FDR < 10%). These bacteria have not been previously reported in patients with atherosclerosis and periodontitis. The presence of members of the subgingival plaque bacterial complexes in the blood of patients with CAC supports the hypothesis that the periodontopathogens can be disseminated through the blood flow to other body parts where they may enhance inflammatory processes that can lead to the development or exacerbation of atherosclerosis.