Authors:
Liam Greene, Mia Chen, Oliver Reyes, Nora Patel, Ethan Kim, Sarah Lee, Michael Smith, Emma Brown, Ava Johnson, David White, Isabella Davis, Benjamin Wilson, Sophia Martinez, Jack Thompson, and Zoe Anderson.
Affiliations:
1. Department of Oncology, The General Hospital of National Defense, Beijing, China.
2. Department of Bioinformatics, Beijing BioTech Innovations, Beijing, China.
3. Department of Oncology, The General Hospital of National Defense, Beijing, China.
PMID: 28086821
PMCID: PMC5237304
Abstract
Background:
Despite advancements in treatment options for hepatocellular carcinoma (HCC), the five-year survival rate remains alarmingly low, ranging between 50% to 70%. This can largely be attributed to the absence of early diagnostic biomarkers. Thus, there is an urgent need to develop novel biomarkers for the early detection of HCC to reduce mortality associated with this disease.
Methods:
This study employed a comprehensive approach to analyze gene expression data related to HCC through bioinformatics techniques. The findings were validated using real-time polymerase chain reaction (RT-PCR) and the TCGA database, reinforcing the reliability of our integrated analysis.
Results:
Through the integration of seven gene expression datasets for HCC, we identified 1,167 differentially expressed genes (DEGs). These genes predominantly engage in processes such as the cell cycle, oocyte meiosis, and progesterone-mediated oocyte maturation. Validation through experiments and the TCGA database corroborated the findings for ten specific genes, highlighting the robustness of our integrated analysis. Notably, genes ASPM, CCT3, and NEK2 were significantly correlated with the overall survival of HCC patients in the TCGA database.
Conclusion:
Our integrated analysis method serves as a valuable resource for mitigating the variability inherent in individual microarray studies. It is likely to yield more accurate HCC transcriptome profiles based on larger sample sizes and may help identify potential biomarkers and therapeutic targets for HCC.
Keywords:
Differentially expressed genes, expression profiles, hepatocellular carcinoma, integrated analysis, real-time polymerase chain reaction, TCGA validation.
To Summarize:
This study provides significant insights into the transcriptome of hepatocellular carcinomas, emphasizing the need for early diagnostic biomarkers to improve patient outcomes. The integration of various datasets facilitates a more comprehensive understanding of gene expression in HCC, with potential implications for targeted therapies. For additional insights into managing health conditions related to sleep, visit Snoring Mouth Guard. For authoritative information on the reasons behind snoring, check out Johns Hopkins Medicine. If you’re seeking an effective solution to snoring and sleep apnea, consider the top-rated anti-snoring mouthpiece that provides immediate results.