Prognostic Marker Genes Research Articles

Bioinformatics reveal macrophages marker genes signature in breast cancer to predict prognosis

Background Breast cancer is a pivotal cause of global women cancer death. Immunotherapy has become a promising means to cure breast cancer. As constitutes of immune microenvironment of breast cancer, macrophages exert complicated functions in the tumour development and treatment. This study aims to develop a prognostic macrophage marker genes signature (MMGS). Methods Single cell RNA sequence data analysis was performed to identify macrophage marker genes in breast cancer. TCGA database was used to construct MMGS model as a training cohort, and GSE96058 dataset was used to validate the MMGS as a validation cohort. Results Genes included in the MMGS model were: SERPINA1, CD74, STX11, ADAM9, CD24, NFKBIA, PGK1. MMGS risk score stratified by overall survival of patients divided them into high- and low-risk groups. And MMGS risk score remained independent prognostic factor in multivariate analysis after adjusting for classical clinical factors in both training and validation cohorts. Besides, hormone receptors negative and human epidermal growth factor receptor 2 (HER2) positive patients had higher risk score. MMGS showed better distinguishing capability between high-risk and low-risk groups in hormone receptor positive and HER2 negative subgroup. Conclusion MMGS provides a new understanding of immune cell marker genes in breast cancer prognosis and may offer reference for immunotherapy decision for breast cancer patients.

The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data

BackgroundIn genomics, we often assume that continuous data, such as gene expression, follow a specific kind of distribution. However we rarely stop to question the validity of this assumption, or consider how broadly applicable it may be to all genes that are in the transcriptome. Our study investigated the prevalence of a range of gene expression distributions in three different tumor types from the Cancer Genome Atlas (TCGA).ResultsSurprisingly, the expression of less than 50% of all genes was Normally-distributed, with other distributions including Gamma, Bimodal, Cauchy, and Lognormal also represented. Most of the distribution categories contained genes that were significantly enriched for unique biological processes. Different assumptions based on the shape of the expression profile were used to identify genes that could discriminate between patients with good versus poor survival. The prognostic marker genes that were identified when the shape of the distribution was accounted for reflected functional insights into cancer biology that were not observed when standard assumptions were applied. We showed that when multiple types of distributions were permitted, i.e. the shape of the expression profile was used, the statistical classifiers had greater predictive accuracy for determining the prognosis of a patient versus those that assumed only one type of gene expression distribution.ConclusionsOur results highlight the value of studying a gene’s distribution shape to model heterogeneity of transcriptomic data and the impact on using analyses that permit more than one type of gene expression distribution. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of distribution in a patient cohort.

Identification of Prognostic Candidate Genes in Breast Cancer by Integrated Bioinformatic Analysis.

Breast cancer is one of the most common malignancies. However, the molecular mechanisms underlying its pathogenesis remain to be elucidated. The present study aimed to identify the potential prognostic marker genes associated with the progression of breast cancer. Weighted gene coexpression network analysis was used to construct free-scale gene coexpression networks, evaluate the associations between the gene sets and clinical features, and identify candidate biomarkers. The gene expression profiles of GSE48213 were selected from the Gene Expression Omnibus database. RNA-seq data and clinical information on breast cancer from The Cancer Genome Atlas were used for validation. Four modules were identified from the gene coexpression network, one of which was found to be significantly associated with patient survival time. The expression status of 28 genes formed the black module (basal); 18 genes, dark red module (claudin-low); nine genes, brown module (luminal), and seven genes, midnight blue module (nonmalignant). These modules were clustered into two groups according to significant difference in survival time between the groups. Therefore, based on betweenness centrality, we identified TXN and ANXA2 in the nonmalignant module, TPM4 and LOXL2 in the luminal module, TPRN and ADCY6 in the claudin-low module, and TUBA1C and CMIP in the basal module as the genes with the highest betweenness, suggesting that they play a central role in information transfer in the network. In the present study, eight candidate biomarkers were identified for further basic and advanced understanding of the molecular pathogenesis of breast cancer by using co-expression network analysis.

A 63 signature genes prediction system is effective for glioblastoma prognosis.

The present study aimed to explore possible prognostic marker genes in glioblastoma (GBM). Differentially expressed genes (DEGs) were screened by comparing microarray data of tumor and normal tissue samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) dataset GSE22866. Subsequently, the prognosis-associated DEGs were screened via Cox regression analysis, followed by construction of gene/protein/pathway interaction networks of these DEGs by calculating the correlation coefficient between the DEGs. Next, a prognostic prediction system was constructed using Bayes discriminant analysis, which was validated by the microarray data of samples from patients with good and bad prognosis from the TCGA and Chinese Glioma Genome Atlas (CGGA), as well as the GEO dataset. Finally, a co-expression network of the signature genes in the prediction system was constructed in combination with the significant pathways. A total of 288 overlapping DEGs (false discovery rate <0.5 and |log2 of fold change|>1) were screened, 123 of which were identified to be associated with the prognosis of GBM patients. The co-expression network of these prognosis-associated DEGs included 1405 interactions and 112 DEGs, and 6 functional modules were identified in the network. The prognostic prediction system was comprised of 63 signature genes with a specificity value of 0.929 and a sensitivity value of 0.948. GBM samples with good and bad prognosis in the TCGA, CGGA and GEO datasets were distinguishable by these signature genes (P=1.33×10−6, 1.63×10−4 and 0.00534, respectively). The co-expression network of signature genes with significant pathways was comprised of 56 genes and 361 interactions. Protein kinase Cγ (PRKCG), protein kinase Cβ (PRKCB) and calcium/calmodulin-dependent protein kinase IIα (CAMK2A) were important genes in the network, and based on the expression of these genes, it was possible to distinguish between samples with significantly different survival risks. In the present study, an effective prognostic prediction system for GBM patients was constructed and validated. PRKCG, PRKCB and CAMK2A may be potential prognostic factors for GBM.

TOP2A, HELLS, ATAD2, and TET3 Are Novel Prognostic Markers in Renal Cell Carcinoma

To identify and validate novel prognostic marker genes in clear cell renal cell carcinoma (RCC) that are increasingly expressed during tumor progression. Total RNA was isolated from normal renal tissue, primary G1 and G3 tumors, 14 samples each, and 32 metastases from RCC patients. Expression profiles were created using oligonucleotide microarrays. Significant gene expression differences (P < .05) were identified among normal kidney, primary tumor, and metastases. For all filtered genes, univariate survival analysis was carried out. Genes for which lower expression was significantly associated with longer survival were further analyzed using multivariate analysis. Expression of the best candidate markers was further validated in an independent cohort of 55 primary tumors using quantitative real-time polymerase chain reaction. Fifty-nine genes exhibited increased expression in primary RCC compared with normal kidney, and in metastases compared with primary tumors. In univariate or multivariate survival analysis, upregulation of 15 genes was significant. Expression of 8 genes was validated by quantitative real-time polymerase chain reaction. Survival analysis in an independent cohort of 55 RCC patients based on expression in primary RCC showed that TOP2A (hazard ratio [HR] = 4.3, P = .005), HELLS (HR = 3.7, P = .007), ATAD2 (HR = 3.7, P = .019), and TET3 (HR = 2.8, P = .035) represent independent predictors for cancer-specific survival. The proteins encoded by these genes function as topoisomerase, helicase, chromatin modifier, and methyl cytosine dioxygenase, respectively. They are involved in proliferation, transcription, and epigenetic modification. High mRNA levels of TOP2A, HELLS, ATAD2, and TET3 are independent predictors of poor outcome in RCC patients and may be used for individual risk-adapted therapy in the future.

Transcriptome analysis of non-small cell lung cancer and genetically matched adjacent normal tissues identifies novel prognostic marker genes

Lung cancer can be classified into two different types, non-small cell lung cancer (NSCLC) and small cell lung cancer. Up to 85% of lung cancer cases are NSCLC. To identify prognostic marker genes for NSCLC, we conducted 10 pairs of genetically matched transcriptome (NSCLC and adjacent normal tissues obtained from 10 patients) analysis using next-generation sequencing. Pathway analysis revealed that the genes associated with the cell cycle are highly upregulated in NSCLC. Integrative analysis of two independent NSCLC studies, 71 pairs (GSE40419) and 58 pairs (TCGA-LUAD), also confirmed that cell cycle–related genes are mostly upregulated in NSCLC. All three independent NSCLC transcriptomes concordantly identified 10 potential tumor-marker genes that are highly expressed as well as significantly altered in NSCLC. A quantitative real-time polymerase chain reaction experiment validated the altered expressions of the genes. Among them, downregulation of MFAP4 and AGER and upregulation of SPP1 genes in NSCLC compared to normal tissues were significantly associated with poor prognosis as evaluated by Kaplan–Meier survival and receiver operating characteristic analyses. A further analysis indicated that the expression ratio of MFAP4 and SPP1 or AGER and SPP1 can be used as potential prognostic marker genes for NSCLC.

Estimation of Prognostic Marker Genes by Public Microarray Data in Patients with Ovarian Serous Cystadenocarcinoma.

PurposeLymphatic invasion (LI) is regarded as a predictor of the aggressiveness of ovarian cancer (OC). However, LI is not always the major determinant of long-term patient survival. To establish proper diagnosis and treatment for OC, we analyzed differentially expressed genes (DEGs) for patients with serous epithelial OC, with or without LI, who did or did not survive for 5 years.Materials and MethodsGene expression data from 63 patients with OC and LI, and 35 patients with OC but without LI, were investigated using an Affymetrix Human Genome U133 Array and analyzed using The Cancer Genome Atlas (TCGA) database. Among these 98 patients, 16 survived for 5 years or more. DEGs were identified using the Bioconductor R package, and their functions were analyzed using the DAVID web tool.ResultsWe found 55 significant DEGs (p<0.01) from the patients with LI and 20 highly significant DEGs (p<0.001) from those without it. Pathway analysis showed that DEGs associated with carbohydrate metabolism or with renal cell carcinoma pathways were enriched in the patients with and without LI, respectively. Using the top five prognostic marker genes, we generated survival scores that could be used to predict the 5-year survival of patients with OC without LI.ConclusionThe DEGs identified in this study could be used to elucidate the mechanism of tumor progression and to guide the prognosis and treatment of patients with serous OC but without LI.

The microRNA-200 family targets multiple non-small cell lung cancer prognostic markers in H1299 cells and BEAS-2B cells

Lung cancer remains the leading cause of cancer-related mortality for both men and women. Tumor recurrence and metastasis is the major cause of lung cancer treatment failure and death. The microRNA-200 (miR-200) family is a powerful regulator of the epithelial-mesenchymal transition (EMT) process, which is essential in tumor metastasis. Nevertheless, miR-200 family target genes that promote metastasis in non-small cell lung cancer (NSCLC) remain largely unknown. Here, we sought to investigate whether the microRNA-200 family regulates our previously identified NSCLC prognostic marker genes associated with metastasis, as potential molecular targets. Novel miRNA targets were predicted using bioinformatics tools based on correlation analyses of miRNA and mRNA expression in 57 squamous cell lung cancer tumor samples. The predicted target genes were validated with quantitative RT-PCR assays and western blot analysis following re-expression of miR-200a, -200b and -200c in the metastatic NSCLC H1299 cell line. The results show that restoring miR-200a or miR-200c in H1299 cells induces downregulation of DLC1, ATRX and HFE. Reinforced miR-200b expression results in downregulation of DLC1, HNRNPA3 and HFE. Additionally, miR-200 family downregulates HNRNPR3, HFE and ATRX in BEAS-2B immortalized lung epithelial cells in quantitative RT-PCR and western blot assays. The miR-200 family and these potential targets are functionally involved in canonical pathways of immune response, molecular mechanisms of cancer, metastasis signaling, cell-cell communication, proliferation and DNA repair in Ingenuity pathway analysis (IPA). These results indicate that re-expression of miR-200 downregulates our previously identified NSCLC prognostic biomarkers in metastatic NSCLC cells. These results provide new insights into miR-200 regulation in lung cancer metastasis and consequent clinical outcome, and may provide a potential basis for innovative therapeutic approaches for the treatment of this deadly disease.

Therapeutic Delivery of MicroRNA-29b by Cationic Lipoplexes for Lung Cancer

MicroRNA-29b (miR-29b) expression has been shown to be reduced in non-small–cell lung cancer (NSCLC) tissues. Here, we have identified the oncogene cyclin-dependent protein kinase 6 (CDK6) as a direct target of miR-29b in lung cancer. We hypothesized that in vivo restoration of miR-29b and thus targeting of genes important to tumor initiation and progression may represent an option for lung cancer treatment. We developed a cationic lipoplexes (LPs)-based carrier that efficiently delivered miR-29b both in vitro and in vivo. LPs containing miR-29b (LP-miR-29b) efficiently delivered miR-29b to NSCLC A549 cells, reduced the expression of key targets CDK6, DNMT3B, and myeloid cell leukemia sequence 1 (MCL1), as well as cell growth and clonogenicity of A549 cells. In addition, the IC50 for cisplatin in the miR-29b–treated cells was effectively reduced. In a xenograft murine model, LPs efficiently accumulated at tumor sites. Systemic delivery of LP-miR-29b increased the tumor miR-29b expression by approximately fivefold, downregulated the tumor mRNA expression of CDK6, DNMT3B, and MCL1 by ~57.4, ~40.5, and ~52.4%, respectively, and significantly inhibited tumor growth by ~60% compared with LP-miR-NC (negative control). Our results demonstrate that cationic LPs represent an efficient delivery system that holds great potential in the development of miRNA-based therapeutics for lung cancer treatment.

Measurable impact of RNA quality on gene expression results from quantitative PCR

Compromised RNA quality is suggested to lead to unreliable results in gene expression studies. Therefore, assessment of RNA integrity and purity is deemed essential prior to including samples in the analytical pipeline. This may be of particular importance when diagnostic, prognostic or therapeutic conclusions depend on such analyses. In this study, the comparative value of six RNA quality parameters was determined using a large panel of 740 primary tumour samples for which real-time quantitative PCR gene expression results were available. The tested parameters comprise of microfluidic capillary electrophoresis based 18S/28S rRNA ratio and RNA Quality Index value, HPRT1 5′–3′ difference in quantification cycle (Cq) and HPRT1 3′ Cq value based on a 5′/3′ ratio mRNA integrity assay, the Cq value of expressed Alu repeat sequences and a normalization factor based on the mean expression level of four reference genes. Upon establishment of an innovative analytical framework to assess impact of RNA quality, we observed a measurable impact of RNA quality on the variation of the reference genes, on the significance of differential expression of prognostic marker genes between two cancer patient risk groups, and on risk classification performance using a multigene signature. This study forms the basis for further rational assessment of reverse transcription quantitative PCR based results in relation to RNA quality.

Enhanced expression of lymphomagenesis-related genes in peripheral blood B cells of chronic hepatitis C patients

Epidemiological data indicate a close relationship between chronic hepatitis C virus (HCV) infection and B-cell non-Hodgkin's lymphoma (B-NHL), suggesting that chronic HCV infection is, at least in part, associated with B-lymphomagenesis. However, experimental data concerning these conditions remains elusive. In this study, we confirmed that peripheral blood B cells of chronic hepatitis C (CHC) patients were infected with HCV. Expression levels of activation-induced cytidine deaminase (AID) which are thought to be associated with occurrence of B-NHL were analyzed in these CHC B cells. It was demonstrated that AID mRNA/protein levels in CHC B cells were dramatically increased compared with those of healthy subjects. Furthermore, expression levels of several previously reported prognostic B-NHL marker genes in the B cell subset of CHC patients were increased. These results suggest a possible relationship between chronic HCV infection and B-lymphomagenesis.

Gene expression analysis on a photodiode array-based bioluminescence analyzer by using sensitivity-improved SRPP

Most methods used for gene expression analysis are based on dye-labeling, which requires costly instruments. Recently a dye-free gene expression analysis method-SRPP (Sequence-tagged reverse-transcription polymerase chain reaction coupled with pyrosequencing) was developed to compare relative gene expression levels in different tissues, but the throughput of the SRPP assay is very limited due to the use of a photomultiplier tube (PMT)-based pyrosequencer for the detection. To increase the throughput of the SRPP assay, an inexpensive photodiode (PD) array-based bioluminescence analyzer (termed as "PD-based pyrosequencer") was coupled to SRPP; however the low sensitivity of PD limited the wide application of SRPP. To enable SRPP analyzing low abundance genes in clinical samples, sequence-tagged gene-specific primers instead of sequence-tagged poly (T)(n) primers were used for reverse-transcription, and the SRPP sensitivity was thus improved more than 10 times. This improvement compensates the sensitivity loss due to the use of PD in a pyrosequencer. The accurate determination of the expression levels of ten prognostic marker genes (AL080059, MMP9, EXT1, ORC6L, AF052162, C9orf30, FBXO31, IGFBP5, ESM1, and RUNDC1) differing between normal tissues and tumor tissues of breast cancer patients demonstrated that SRPP using gene-specific RT primers coupled with the PD array-based bioluminescence analyzer is reliable, inexpensive, and sensitive in gene expression analysis.

Genes Proximal and Distal to MYCN Are Highly Expressed in Human Neuroblastoma as Visualized by Comparative Expressed Sequence Hybridization

MYCN amplification is associated with poor prognosis in neuroblastoma disease. To improve our understanding of the influence of the MYCN amplicon and its corresponding expression, we investigated the 2p expression pattern of MYCN amplified (n = 13) and nonamplified (n = 4) cell lines and corresponding primary tumors (n = 3) using the comparative expressed sequence hybridization technique. All but one MYCN amplified cell line displayed overexpression at 2p. Expression peaks were observed frequently at 2pter and less frequently at 2p24 (MYCN locus), 2p23.3-23.2, and/or 2p23.1. Importantly, cell lines and two corresponding primary tumors displayed expression peaks at similar loci. No significant 2p24 expression level was observed for those cell lines displaying a low amplification rate (n = 3) by comparative genomic hybridization. Only the cell lines with an enhanced peak at 2p23.2-23.3 displayed coamplification of the ALK gene (2p23.2), reported to be associated with unfavorable prognosis. Finally, two of four cell lines without MYCN amplification, both derived from patients with poor outcome, also showed an expression peak at 2p23.2. These data indicate that, besides MYCN, other genes proximal and distal to MYCN are highly expressed in neuroblastoma. The prognostic significance of expression peaks at 2p23.2-23.3, independent of MYCN and ALK status, remains to be investigated.

Meta-analysis of several gene lists for distinct types of cancer: A simple way to reveal common prognostic markers

BackgroundAlthough prognostic biomarkers specific for particular cancers have been discovered, microarray analysis of gene expression profiles, supported by integrative analysis algorithms, helps to identify common factors in molecular oncology. Similarities of Ordered Gene Lists (SOGL) is a recently proposed approach to meta-analysis suitable for identifying features shared by two data sets. Here we extend the idea of SOGL to the detection of significant prognostic marker genes from microarrays of multiple data sets. Three data sets for leukemia and the other six for different solid tumors are used to demonstrate our method, using established statistical techniques.ResultsWe describe a set of significantly similar ordered gene lists, representing outcome comparisons for distinct types of cancer. This kind of similarity could improve the diagnostic accuracies of individual studies when SOGL is incorporated into the support vector machine algorithm. In particular, we investigate the similarities among three ordered gene lists pertaining to mesothelioma survival, prostate recurrence and glioma survival. The similarity-driving genes are related to the outcomes of patients with lung cancer with a hazard ratio of 4.47 (p = 0.035). Many of these genes are involved in breakdown of EMC proteins regulating angiogenesis, and may be used for further research on prognostic markers and molecular targets of gene therapy for cancers.ConclusionThe proposed method and its application show the potential of such meta-analyses in clinical studies of gene expression profiles.

Cancer diagnosis marker extraction for soft tissue sarcomas based on gene expression profiling data by using projective adaptive resonance theory (PART) filtering method.

BackgroundRecent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis and selection of treatment. To accomplish this objective, it is important to establish a sophisticated algorithm that can deal with large quantities of data such as gene expression profiles obtained by DNA microarray analysis.ResultsPreviously, we developed the projective adaptive resonance theory (PART) filtering method as a gene filtering method. This is one of the clustering methods that can select specific genes for each subtype. In this study, we applied the PART filtering method to analyze microarray data that were obtained from soft tissue sarcoma (STS) patients for the extraction of subtype-specific genes. The performance of the filtering method was evaluated by comparison with other widely used methods, such as signal-to-noise, significance analysis of microarrays, and nearest shrunken centroids. In addition, various combinations of filtering and modeling methods were used to extract essential subtype-specific genes. The combination of the PART filtering method and boosting – the PART-BFCS method – showed the highest accuracy. Seven genes among the 15 genes that are frequently selected by this method – MIF, CYFIP2, HSPCB, TIMP3, LDHA, ABR, and RGS3 – are known prognostic marker genes for other tumors. These genes are candidate marker genes for the diagnosis of STS. Correlation analysis was performed to extract marker genes that were not selected by PART-BFCS. Sixteen genes among those extracted are also known prognostic marker genes for other tumors, and they could be candidate marker genes for the diagnosis of STS.ConclusionThe procedure that consisted of two steps, such as the PART-BFCS and the correlation analysis, was proposed. The results suggest that novel diagnostic and therapeutic targets for STS can be extracted by a procedure that includes the PART filtering method.

Gene Expression Profiles of Breast Cancer Obtained from Core Cut Biopsies Before Neoadjuvant Docetaxel, Adriamycin, and Cyclophoshamide Chemotherapy Correlate with Routine Prognostic Markers and Could Be Used to Identify Predictive Signatures

Neoadjuvant administration of chemotherapy provides a unique opportunity to monitor response to treatment in breast cancer and assesses response exactly. Global gene expression profiling by microarrays has been used as a valuable tool for the identification of prognostic and predictive marker genes. Even though this technology is now wide spread and relatively standardized, there are only few data available which compare established parameters with expression values to determine reliability of this method. Therefore we analyzed gene expression data of pretreatment biopsies of breast cancer patients and compared them with the results of the immunohistochemical receptor expression for ER/ PR and Her-2, as well as FISH testing for HER-2 amplification. We analyzed the change of expression of these markers before and after neoadjuvant chemotherapy. Furthermore we evaluated the predictive significance of prognostic gene signatures as described by Sorlie, van't Veer and Ahr for response to neoadjuvant chemotherapy. Pretherapeutic core biopsies were obtained from 70 patients undergoing neoadjuvant TAC chemotherapy within the GEPARTRIO-trial. Samples were characterized according to standard pathology including ER, PR and HER2 IHC and amount of cancer cells. Only biopsies with more than 80 % tumor cells were considered for further examination. RNA was isolated and expression profiling performed using Affymetrix Hg U133 Arrays (22 500 genes). GeneData's Expressionist software was used for bioinformatic analyses. More than two thirds of the biopsies yielded sufficient amounts (> 5 microg) of RNA for expression profiling and high quality data were obtained for 50 samples. Unsupervised clustering broadly revealed a correlation with hormone receptor status. When ER-alpha, PR and HER2 as analyzed by immunohistochemistry were compared to the corresponding mRNA data from gene chips more than 90 % concordance was observed. We could observe a switch of receptor expression for ER, PR or HER-2 from positive to negative and vice versa in 16/35 cases (45.7 %) and 5/22 cases (22.7 %) respectively. The prognostic marker sets of Sorlie, van't Veer and Ahr could not discriminate responders from non-responders in our patient group. Our results demonstrate that reliable expression profiles can be achieved by using limited amounts of tissue obtained during neoadjuvant chemotherapy. Microarray data capture conventional prognostic markers but might contain additional informative gene sets correlated with treatment outcome. Prognostic marker sets are not suitable to predict tumor response in the neoadjuvant setting, suggesting the necessity of class prediction methods to identify marker sets predictive for the type of therapy used.

P-625 Quantitative PCR validation of putative non-small cell lungcancer (NSCLC) prognostic marker genes derived from multiple published microarray databases and reports

P-625 Quantitative PCR validation of putative non-small cell lungcancer (NSCLC) prognostic marker genes derived from multiple published microarray databases and reports

Validating the prognostic value of marker genes derived from a non-small cell lung cancer microarray study

We previously reported that our cDNA microarray analysis of primary non-small cell lung carcinoma (NSCLC) could predict for patients at increased risk of cancer recurrence. From the result of this analysis, we selected 11 genes that were considered candidate prognostic marker genes and used the realtime reverse transcription polymerase chain reaction (RT-PCR) to investigate their expression in the same set of NSCLC cases used in the microarray study. Cluster analysis of the realtime RT-PCR data separated these patients into two groups with significantly different disease-free survivals (log-rank test, P<0.017). In contrast, cluster analysis failed to confirm the prognostic significance of the realtime RT-PCR results for these 11 genes in a validation series of 92 NSCLC cases. In univariate analysis, hypoxia inducible factor 1α, Rho-GDP dissociation inhibitor (GDI) α (RhoGDI) and Citron/rho-interacting serine–threonine kinase 21 (Citron K21) were significant prognostic factors for disease-free survival in the entire cohort of 130 NSCLC patients, but none were significant in multivariate analysis. The results demonstrate that the prognostic significance of microarray (SAM) results can be partially validated using realtime RT-PCR, but secondary validation using larger and independent series of tumors is necessary to identify true prognostic marker genes.

Gene expression profiling and identification of novel prognostic marker genes in neuroblastoma.

To investigate the various genetic characteristics of and differences between early- and advanced-stage neuroblastoma (NB) and to identify candidate genes involved in NB progression, we performed DNA microarray analysis on 20 primary tumors. Two-way clustering analysis based on the expression pattern of approximately 500 of 1,700 genes revealed genetic subgroups in these NB tumors. Although 9 of the 13 early-stage tumors (69%) and 4 of the 6 advanced-stage tumors (67%) were classified as being in the same cluster, the remaining tumors showed different expression profiles. This indicates that both the early- and advanced-stage tumors were heterogeneous. Based on the microarray data, we identified the BIRC, CDKN2D, and SMARCD3 genes as those that are predominantly expressed in either the early or the advanced stage of NB. These genes have been reported to be associated with apoptosis, cell cycles, and the transcriptional activator, respectively. To better assess the prognostic value of the expression of these genes in NB, real-time polymerase chain reaction was carried out on 50 primary tumors. The expression of both the BIRC3 and CDKN2D genes was significantly higher in the early-stage group than in the advanced-stage group (P = 0.002 and 0.003, respectively), whereas the expression of the SMARCD3 gene was significantly reduced in the early-stage group (P = 0.02). Therefore, the BIRC, CDKN2D, and SMARCD3 genes are possible candidates for being novel prognostic markers for NB.