Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, with limited biomarkers available for early diagnosis and risk stratification. In this study, we performed an integrative analysis of tissue and circulating microRNA (miRNA) expression profiles to identify candidates linked to disease progression and clinical outcomes. Tumor miRNA data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) and serum miRNA data from the Gene Expression Omnibus (GSE113740) were analyzed using differential expression, survival analysis, functional enrichment, and clinical subgroup evaluation. We identified 16 significantly dysregulated miRNAs in HCC tissues, including hsa-miR-187 and hsa-miR-6718, which were associated with poor survival, and hsa-miR-5589, which showed a protective effect. Clinical analyses revealed stage-specific upregulation of hsa-miR-106b and downregulation of the hsa-miR-124 family in metastatic tumors. Functional enrichment highlighted pathways such as PI3K-Akt, MAPK signalling, and nucleocytoplasmic transport. Circulating miRNAs, including hsa-miR-3619-3p, hsa-miR-1290, and hsa-miR-1185-2-3p, correlated with AFP levels and disease stage, underscoring their value as non-invasive biomarkers. These findings demonstrate that integrated analysis of tissue and serum miRNAs can identify clinically relevant biomarkers and potential therapeutic targets in HCC.

Breast cancer is a multifaceted disease that has many subcategories characterized by unique genetic features. This research focuses on two important subgroups, including ER+ and HER2-. We conducted an analysis of gene expression data obtained from reliable sources (Array Express: E-GEOD-52194, E-GEOD-75367, and E-GEOD-58135) in order to reveal the complex molecular details of these subtypes. The computational pipeline we used identified 396 genes that exhibited distinct patterns of gene expression in ER+ and HER2- breast cancers. The diagnostic and prognostic significance of these genes was evaluated using machine learning methods, namely SVM and decision tree models. Metrics like as accuracy, sensitivity, and specificity provide insights into their usefulness. Furthermore, the use of the STRING database for network analysis revealed significant signaling pathways and biological processes associated with the development of ER+ and HER2- breast cancer. The results of our research enhance our comprehension of these subcategories, which might possibly facilitate more accurate diagnoses and focused treatment interventions. To summarize, this work provides valuable information on the genetic foundations of ER+ and HER2- breast cancer, which has potential implications for enhancing patient treatment and outcomes.

Breast cancer is a life-threatening disease and has serious health implications. It is categorized based on receptors, including the estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2), which are the focus of the present research We analyzed gene expression from data obtained from a functional genomics repository called Array Express. The accession numbers are E-GEOD-52194, E-GEOD-75367, and E-GEOD-58135, and the molecular details of these subsets of cancer receptors. Upon following a predefined computational pipeline, we identified 369 genes that had distinct patterns of gene expression profiles in cases of ER-positive (ER + ) and HER2-negative (HER2-) breast cancer. The support vector machine (SVM) and decision tree models of machine learning were used to evaluate the prognostic and diagnostic significance. Accuracy, sensitivity, and specificity were examined to gauge the effectiveness of these models. Then, a network analysis was performed to assess the significant biological process and signaling pathways of HER2- and ER+ breast cancer development. The present study facilitates an enhanced approach to these subcategories of breast cancer so that precise diagnoses can be made, and better and more focused treatment plans can be provided. The current research provides valuable information on the molecular and genetic basis of ER+ and HER2- breast cancer and has great potential for improving patients' treatment.