In search of differentially expressed genes and proteins

Five Novel Oncogenic Signatures Could Be Utilized as AFP-Related Diagnostic Biomarkers for Hepatocellular Carcinoma Based on Next-Generation Sequencing

BACKGROUND

Alpha-fetal protein (AFP) is an important conventional clinical diagnostic indicator of hepatocellular carcinoma (HCC). However, the utilization of AFP alone might yield deceptive results due to its limited sensitivity and accuracy.

AIMS

Our study was designed to investigate latent diagnostic biomarkers that could function as auxiliary clinical indicators of HCC and enhance the accuracy of joint diagnosis with AFP.

METHODS

We analyzed gene expression profiles and clinical data from HCC patients in The Cancer Genome Atlas database. Differentially expressed genes were identified, and a gene set enrichment analysis was conducted to uncover their biological functions and molecular processes. A weighted correlation network analysis and a protein-protein interaction analysis were performed to detect AFP-related biomarkers. The diagnostic performance of these biomarkers was verified using datasets from the GEO database. A diagnostic nomogram was established using the expression levels of potential biomarkers. Quantitative real-time PCR was performed using tissues from 16 HCC patients to validate the results.

RESULTS

Five AFP-related diagnostic biomarkers, CDT1, MCM7, NUDT1, CENPM, and HDAC11, were discovered. The diagnostic performance of these biomarkers and the nomogram were demonstrated to be excellent according to receiver operating characteristic curves. CDT1, MCM7, and NUDT1 were shown to be up-regulated in HCC tissues through quantitative real-time PCR.

CONCLUSIONS

We discovered five diagnostic biomarkers and established a nomogram as a complement to AFP in the diagnosis of HCC. Our results provide a more accurate diagnostic plan for HCC patients based on next-generation sequencing compared with AFP alone.

Multi-gene expression-based statistical approaches to predicting patients' clinical outcomes and responses

Gene expression profiling technique now enables scientists to obtain a genome-wide picture of cellular functions on various human disease mechanisms which has also proven to be extremely valuable in forecasting patients' prognosis and therapeutic responses. A wide range of multivariate techniques have been employed in biomedical applications on such expression profiling data in order to identify expression biomarkers that are highly associated with patients' clinical outcome and to train multi-gene prediction models that can forecast various human disease outcome and drug toxicities. We provide here a brief overview on some of these approaches, succinctly summarizing relevant basic concepts, statistical algorithms, and several practical applications. We also introduce our recent in vitro molecular expression-based algorithm, the so-called COXEN technique, which uses specialized gene profile signatures as a Rosetta Stone for translating the information between two different biological systems or populations.

A precise mRNA quantification method using CE-based SSCP

Even though mRNA quantification provides significant information for biological analysis, current methods such as Northern blot analysis and real-time PCR are known to be laborious and lacking in precision. In this study, we demonstrate a new precise mRNA quantification method using CE based on SSCP (CE-SSCP) coupled with reverse transcription. mRNA samples could be simply analyzed for the quantification directly with reverse transcript obtained from a single reaction. This helps to avoid considerable errors generated by a series of the tedious manual steps. Also, unlike real-time PCR, reverse transcripts can be directly quantified by CE-SSCP in this method without further data estimation. Reproducibility and accuracy of CE-SSCP for mRNA quantification was examined using enhanced green fluorescent protein (eGFP) mRNA transcribed in vitro. Specific reverse transcription primer was determined for the accurate quantification of eGFP mRNA from total RNA obtained from the recombinant Escherichia coli. Using elongation factor Tu mRNA as an internal standard, it was shown that sample-to-sample variation could be minimized. Expression kinetics at both mRNA level and protein level was studied and the potential of CE-SSCP in expression analysis was demonstrated by comparison with the eGFP activity assay.

Proteome study of colorectal carcinogenesis

Development of cancer is a complex process involving multiple changes in gene expression. To unravel these alterations, a proteome approach aimed at the identification of qualitative and quantitative changes in protein composition, including their post-translational modifications, attracts great attention. Our study was focused on the identification of proteins whose amount is altered in the course of malignant transformation of colon mucosa. Proteins extracted from tissue specimens or cell lysates were separated by two-dimensional gel electrophoresis (2-DE). Comparative analyses of 2-DE protein patterns were done using computerized image analysis. Selected proteins exhibiting statistically significant abundance alterations comparing healthy and diseased tissues were identified by mass spectrometry. Globally, we have found 57 proteins that exhibited either a significant decrease or increase in amount in pathological tissues, and 18 of these were annotated by mass spectrometry. The alterations in the expression of nine proteins were common for both precancerous and neoplastic tissues suggesting their role in colon tumorigenesis. The epithelial origin of all identified spots was checked in two cell lines Caco-2 and DLD-1 originating from well-differentiated and poorly differentiated colon carcinoma, respectively.

High-resolution quantification of specific mRNA levels in human brain autopsies and biopsies

Quantification of mRNA levels in human cortical brain biopsies and autopsies was performed using a fluorogenic 5' nuclease assay. The reproducibility of the assay using replica plates was 97%-99%. Relative quantities of mRNA from 16 different genes were evaluated using a statistical approach based on ANCOVA analysis. Comparison of the relative mRNA levels between two groups of samples with different time postmortem revealed unchanged relative expression levels for most genes. Only CYP26A1 mRNA levels showed a significant decrease with prolonged time postmortem (p = 0.00004). Also, there was a general decrease in measured mRNA levels for all genes in autopsies compared to biopsies; however, on comparing mRNA levels after adjusting with reference genes, no significant differences were found between mRNA levels in autopsies and biopsies. This observation indicates that studies of postmortem material can be performed to reveal the relative in vivo mRNA levels of genes. Power calculations were done to determine the number of individuals necessary to detect differences in mRNA levels of 1.5-fold to tenfold using the strategy described here. This analysis showed that samples from at least 50 individuals per group, patients and controls, are required for high-resolution ( approximately twofold changes) differential expression screenings in the human brain. Experiments done on ten individuals per group will result in a resolution of approximately fivefold changes in expression levels. In general, the sensitivity and resolution of any differential expression study will depend on the sample size used and the between-individual variability of the genes analyzed.

High throughput analysis of gene expression in the human brain

The human brain is thought to have the greatest complexity of gene expression of any region of the body, reflecting the diverse functions of neurons and glia. Studies of gene expression in the human brain may yield fundamental information about the phenotype of brain cells in different stages of development, in different brain regions, and in different physiological and pathological states. As the human genome project nears completion, several technological advances allow the analysis of thousands of expressed genes in a small brain sample. This review describes available sources of human brain material, and several high throughput techniques used to measure the expression of thousands of genes. These techniques include expressed sequence tag (EST) sequencing of cDNA libraries; differential display; subtractive hybridization; serial analysis of gene expression (SAGE); and the emerging technology of high density DNA microarrays. Measurement of gene expression with microarrays and other technologies has potential applications in the study of human brain diseases, including cognitive disorders for which animal models are typically not available. Gene expression measurements may be used to identify genes that are abnormally regulated as a secondary consequence of a disease state, or to identify the response of brain cells to pharmacological treatments.

Identification of differentially expressed genes during a wool follicle growth cycle induced by prolactin

The wool follicles of New Zealand Wiltshire sheep can be induced to undergo growth cycles by manipulating circulating prolactin levels. Altered patterns of gene expression through this cycle were examined using differential display, and nine sequence tags for differentially expressed genes were isolated. Four of these tags were identified as fragments of known genes, encoding a wool keratin, KRTAP3.2, a desmosome component, desmoglein 1, an epithelial cell marker, stratifin, and a protein kinase, Clk3. All four genes were shown to be downregulated in telogen skin compared with anagen. In situ hybridization showed that all had localization patterns which included cells that are absent in telogen. The stratifin tag was used to clone a cDNA that incorporated a complete open-reading frame for ovine stratifin. Ovine stratifin is similar to the human form, showing only six single residue differences in the predicted amino acid sequence. Stratifin probably acts as a regulator of other proteins involved in trichocyte cell cycling and differentiation. Clk3 is involved in regulating RNA splicing. KRTAP3.2 and Dsg1 both play structural roles in hair follicles. The other five tags, including two representing genes that were upregulated during catagen, could not be identified by homology. Differential display is an effective means of identifying genes involved in follicle function and, potentially, of genes controlling the growth cycle.

Identification of genes regulated by muscarinic acetylcholine receptors: application of an improved and statistically comprehensive mRNA differential display technique

In order to identify genes that are regulated by muscarinic acetylcholine receptors, we developed an mRNA differential display technique (DD) approach. By increasing redundancy and by evaluating optimised reagents and conditions for reverse transcription of total RNA, PCR and separation of PCR products, we generated a DD protocol that yields highly consistent results. A set of 64 distinct random primers was specifically designed in order to approach a statistically comprehensive analysis of all mRNA species in a defined cell population. This modified DD protocol was applied to total RNA of HEK293 cells stably expressing muscarinic m1 acetylcholine receptors and cells stimulated with the receptor agonist carbachol were compared to identical but non-stimulated cells. In 81 of 192 possible PCR experiments, 38 differential bands were identified. Sequence analysis followed by northern blot analyses confirmed differentially expressed genes in 19 of 23 bands analysed. These represented 10 distinct immediate-early genes that were up-regulated by m1AChR activation: Egr-1, Egr-2, Egr-3, NGFi-B, ETR101, c- jun, jun -D, Gos-3 and hcyr61, as well as the unknown gene Gig-2. These data show that this improved DD protocol can be readily applied to reliably identify differentially expressed genes.

Predicting function: from genes to genomes and back

Predicting function from sequence using computational tools is a highly complicated procedure that is generally done for each gene individually. This review focuses on the added value that is provided by completely sequenced genomes in function prediction. Various levels of sequence annotation and function prediction are discussed, ranging from genomic sequence to that of complex cellular processes. Protein function is currently best described in the context of molecular interactions. In the near future it will be possible to predict protein function in the context of higher order processes such as the regulation of gene expression, metabolic pathways and signalling cascades. The analysis of such higher levels of function description uses, besides the information from completely sequenced genomes, also the additional information from proteomics and expression data. The final goal will be to elucidate the mapping between genotype and phenotype.