Bulk RNA-Seq is usually referred to for the collection of RNA transcripts of tumor tissue. Raw data of RNA-seq can be obtained with high-throughput sequencing and microarrays, or downloaded from open public databases, like the GEO and TCGA databases. To transform raw data into a standard expression matrix, multiples steps are required, including quality control, indexing, mapping, and abundance calculation. After acquiring the standard expression matrix following analysis, reveals the characteristics of the gene expression matrix. For example, differential expression of gene identification and biofunction prediction, like in GO/KEGG enrichment analysis and GSEA enrichment analysis, enable us to understand different gene expressions between groups in depth, as well as in preferential biofunction activation. The cluster algorithm or the WCGNA analysis can group samples based on gene expression for further analysis. Regression algorithms like LASSO or ENET analysis, can be applied to identify prognostic related biomarkers. Machine learning, like the support vector machine and the neural network, can distinguish features among different groups and characterize other data based on those features. Prognostic models, like the nomogram, enable clinical applications. However, most gene expression information cannot be fully analyzed in one study. Allowing for comparisons with traditional experimental research, bulk RNA-Seq analysis provides a comprehensive, informative reference. Therefore, integrating the application of bulk RNA-Seq analysis and experimental research can benefit future study.