Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

Summary Great efforts have been made on the algorithms that deal with RNA‐seq data to enhance the accuracy and efficiency of differential expression (DE) analysis. However, no consensus has been reached on the proper threshold values of fold change and adjusted p‐value for filtering differentially expressed genes (DEGs). It is generally believed that the more stringent the filtering threshold, the more reliable the result of a DE analysis. Nevertheless, by analyzing the impact of both adjusted p‐value and fold change thresholds on DE analyses, with RNA‐seq data obtained for three different cancer types from the Cancer Genome Atlas (TCGA) database, we found that, for a given sample size, the reproducibility of DE results became poorer when more stringent thresholds were applied. No matter which threshold level was applied, the overlap rates of DEGs were generally lower for small sample sizes than for large sample sizes. The raw read count analysis demonstrated that the transcript expression of the same gene in different samples, whether in tumor groups or in normal groups, showed high variations, which resulted in a drastic fluctuation in fold change values and adjustedp‐values when different sets of samples were used. Overall, more stringent thresholds did not yield more reliable DEGs due to high variations in transcript expression; the reliability of DEGs obtained with small sample sizes was more susceptible to these variations. Therefore, less stringent thresholds are recommended for screening DEGs. Moreover, large sample sizes should be considered in RNA‐seq experimental designs to reduce the interfering effect of variations in transcript expression on DEG identification.

The Bradford assay is one of the most commonly used methods for protein quantification. However, in proteomic research, the lysis buffer generally used for dissolving proteins can cause some interference to the assay. The dye reagent of classical Bradford assay contains 8.50% (w/v) phosphoric acid, which is an important factor relating to the color yield of the assay. In this study, the phosphoric acid content in dye reagent was increased to 9.35% (w/v), 10.20% (w/v), and 11.05% (w/v) to evaluate the changes of interference and the effects of lysis buffer on the interaction between proteins and dye reagent. Results show that lysis buffer not only causes background interference but also affects the protein-dye chromogenic process. Analysis of different components in the lysis buffer showed that carrier ampholyte is the main factor that introduces interference to the Bradford assay. Detergents are well-known interfering compounds in the Bradford assay, but CHAPS and octyl b-D-glucopyranoside only cause slight interference. When the amount of phosphoric acid was increased from 8.50%(w/v) to 10.20% (w/v), the sensitivity of the Bradford assay to proteins in lysis buffer was increased, and the interference delivered by lysis buffer was considerably reduced.

The present study aimed to explore the genetic changes involved in the liver hepatocellular carcinoma (HCC) development. The RNA-Seq data of 212 HCC tissue samples and 50 normal tissue samples were downloaded using TCGA-Assembler. A total of 4 subgroups were obtained, and 4167, 6279, 5379, and 2548 DEGs were screened in group 1, group 2, group 3, and group 4, respectively. Enrichment analysis found that cell cycle, metabolism, and translation related terms were the most significantly changed functions and pathways. There were 454 genes (1114 pairs), 803 genes (722 pairs), and 788 genes (724 pairs), separately interacted in the condition specific PPI network of group 1, 2, 3, and 4, with MMP2, ATNXN1, F2, and HDAC1 as the hub genes. What's more, using these genes, total 7, 20, 198, and 1 subtype related miRNAs; 35, 50, 47, and 17 subtype related TFs; 1, 1, 0, and 2 subtype related drugs were screened in group 1, 2, 3, and 4, respectively. The integrated biological analysis on RNA-Seq data provided substantial of bio-molecular related to the HCC development. miR-147b, SP1, and Riboflavin were the subtype-related regulator/drug for HCC. The study about the big data of HCC RNA-Seq data reveals the intrinsic gene expression pattern of the tumor, which provides a novel perspective to understand the heterogeneity of pathogenesis in HCC tumorigenesis.