Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity by enabling gene expression profiling at the individual cell level. In this study, we apply scRNA-seq analysis to publicly available data from The Cancer Genome Atlas (TCGA), aiming to dissect tumor microenvironment composition, identify rare cell populations, and uncover novel biomarkers associated with cancer progression. By integrating scRNA-seq data with bulk transcriptomic data from TCGA, we explore the transcriptional diversity within tumors, distinguishing cancer cells from stromal, immune, and other non-malignant cells. Through differential expression and clustering analysis, we identify key gene signatures corresponding to distinct cellular phenotypes and tumor subtypes. Additionally, we investigate the role of specific cell types in treatment resistance and disease prognosis. Our findings provide new insights into tumor biology and highlight potential therapeutic targets, emphasizing the utility of scRNA-seq in cancer research.