Abstract
Cancer is one of the leading causes of mortality worldwide, and in particular, breast cancer in women, prostate cancer in men, and lung cancer in both women and men. The present study aimed to identify a common set of genes which may serve as indicators of important molecular and cellular processes in breast, prostate and lung cancer. Six microarray gene expression profile datasets [GSE45827, GSE48984, GSE19804, GSE10072, GSE55945 and GSE26910 (two datasets for each cancer)] and one RNA-Seq expression dataset (GSE62944 including all three cancer types), were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in each individual cancer type using the LIMMA statistical package in R, and then a comparison of the resulting gene lists was performed to identify common DEGs across cancer types. This analysis was performed for microarray and RNA-Seq datasets individually, revealing a set of 62 and 1,290 differentially expressed genes respectively, which may be associated with the three cancers. Out of these genes, 44 were common to both analyses, and hence termed key genes. Gene Ontology functional annotation, Kyoto Encyclopedia of Genes and Genomes pathway mapping and literature citations were used to confirm the role of the key genes in cancer. Finally, the heterogeneity of expression of the key genes was explored using the I2 statistic (meta package in R). The results demonstrated non-heterogeneous expression of 6 out of the 44 key genes, whereas the remaining genes exhibited significant heterogeneity in expression across microarray samples. In conclusion, the identified DEGs may play important roles in the pathogenesis of breast, prostate and lung cancer and may be used as biomarkers for the development of novel diagnostic and therapeutic strategies.
Keywords: microarray, RNA-Seq, LIMMA, cancer, heterogeneity, meta-analysis, differentially expressed genes
Introduction
The highest rates of cancer-related mortality are associated with breast, prostate and lung cancer, as reported by the World Health Organization (1), the World Cancer Report (2) and Cancer facts and figures (3) A plethora of cancer microarray and RNA sequencing (RNA-Seq) studies are publicly available in databases, including the Gene Expression Omnibus (GEO) (4), Array Express (5) and The Cancer Genome Atlas (TCGA; http://cancergenome.nih.gov/). Recently, simultaneous analysis and comparison of the results from microarray and RNA-Seq data has been explored (6–8). These studies have indicated that RNA-Seq has more benefits compared with microarray platforms, including broader dynamic range and increased specificity and sensitivity, however using the samples belonging to the same biological conditions from both the platforms produces highly correlated gene expression profiles. However, microarrays remain a popular choice amongst researchers when conducting transcriptional profiling experiments, because RNA-Seq technology is novel, more expensive, and requires extensive and complex data storage and analysis. When analysis is conducted on both platforms, strongly concordant and highly correlated results are obtained (6,7). The present study focused on microarray analysis, but additionally performed analysis on RNA-Seq data, so as to validate the significance of the results obtained. Several studies in recent years have reported meta-analysis of such data, where the analyses are performed on integrated samples from multiple microarray datasets (9–13). The majority of the articles focusing on meta-analysis use the following strategies: assembling published differential expressed gene (DEG) lists from experimental studies and then articulating the consistently reported DEGs (14–16); or integrating multiple datasets from different microarray platforms and then executing statistical tests to discover consistently expressed DEGs (9–13). However, inconsistencies in the results are observed due to technical limitations, such as variance in expression measurements and differences in laboratory protocols for different microarray platforms. One major inconsistency reported in meta-signature studies is the overrepresentation of genes common to various platforms, and the underrepresentation of genes which are not common to different platforms (11). In addition, meta-analysis that uses previously published DEG lists when raw data are unavailable, has the limitation that it is difficult to assign a confidence for combined P-values and fold change measurements for each gene (14).
With a purview to improve the understanding of cancer pathogenesis, and based on the methods from the published literature, the present study applied differential gene expression analysis individually to six microarray datasets and one RNA-Seq dataset, representing three different cancer types, breast, lung, and prostate. The aim of the present study was to discover a common set of genes, which may demonstrate a significant expression pattern across these three cancer types. A common subset of DEGs was then explored by comparing the gene lists obtained from microarray and RNA-Seq analysis results. The resulting gene set was further analyzed by Gene Ontology (GO) functional annotations using GENECODIS (17), DAVID (18), Cancer Genetics Web (19), OMIM (20) and number of literature citations using TARGETgene (21). Furtemore, a meta-analysis of the combined samples was performed to identify the heterogeneity in expression of the obtained DEGs in all the six microarray datasets analyzed. This helped in observing the change in expression of the DEGs under different cancer conditions. It is an important implication that some genes always exhibit a consistent expression change, irrespective of the cancer type, whereas some genes exhibit inconsistency in expression change. This may aid oncologists in understanding the behavior of genes in cancer in terms of their heterogeneous expression.
Materials and methods
Outline of data and preprocessing
Six cancer microarray datasets and one RNA-Seq dataset were downloaded from the GEO database (www.ncbi.nlm.nih.gov/geo) (22–28). The information extracted from each identified study is illustrated in Table I. The microarray analysis was restricted to datasets derived from two platforms, Affymetrix HGU-133A (GPL96) and Affymetrix HGU-133APlus2 (GPL570), which characterize probe sets with unique genes for Homo-Sapiens. The RNA-Seq dataset, GSE62944, comprises data from 24 cancer types from The Cancer Genome Atlas, and it is already processed using Rsubread R package and featureCounts() function in order to summarize the gene level expression values as integer numbers. In the present study, integer-based read counts were extracted for only the three cancer types of interest (breast, prostate and lung) out of the data matrix for 24 cancer types. The total number of samples analyzed was 454 (311 tumor samples/143 normal samples) and 2,333 (2,120 tumor samples/213 normal samples) for the microarray and RNA-Seq datasets, respectively. To ensure unregulated, unbiased, and consistent screening of the expression values from the different microarray datasets, the raw CEL files of the experiments were used. The Robust Multichip Average (RMA) technique, which performs quantile normalization, was the expression normalization technique used in the present study (29). This technique was applied to all individual raw microarray datasets in order to minimize inconsistencies due to normalization. This method of normalization was selected due to its good differential change detection, stable variance on log scale and reduced production of false positives. A comparison between different normalization methods has reported that RMA outperformed other methods in terms of specificity and sensitivity when dealing with fold change criteria in the detection of differential expression (30). The box plots of the RMA normalized intensity were plotted (data not shown), demonstrating that measurements of data were closely aligned towards a central mean, and were thus comparable.
Table I.
Characteristics of the individual datasets used in the present study.
| Type of dataset | Type of cancer | Dataset identification number | Platform | Number of probes/genes | Number of samples (tumor/normal) |
|---|---|---|---|---|---|
| Microarray | Breast | GSE45827 | GPL570 | 54,675 | 174 (163/11) |
| gene expression | GSE48984 | GPL96 | 22,283 | 22 (13/9) | |
| Lung | GSE19804 | GPL570 | 54,675 | 120 (60/60) | |
| GSE10072 | GPL96 | 22,283 | 107 (57/50) | ||
| Prostate | GSE55945 | GPL570 | 54,675 | 19 (12/7) | |
| GSE26910 | GPL570 | 54,675 | 12 (6/6) | ||
| RNA-Seq | Breast | GSE62944 | GPL9052 | 23,368 | 1,230 (1,118/112) |
| gene expression | Lung squamous cell carcinoma | 551 (501/50) | |||
| Prostate adenocarcinoma | 552 (501/51) |
Identification of potentially significant target genes
The Bioconductor Linear Model for Microarray Analysis (LIMMA) package was used (31) to calculate the differential expression of each gene in the microarray and RNA-Seq datasets included in the present study. LIMMA remains highly recommended for such analyses (32). In a previous study comparing eight microarray analysis methods [Welch's t-test, analysis of variance (ANOVA), Wilcoxon's test, significance analysis of microarrays (SAM), Randomized Variance Model (RVM), LIMMA, variance mixture (VarMixt) and structural model for variances (SMVar)], LIMMA performed the best in terms of statistical power, false-positive rate, execution time and ease of use (33). In LIMMA, fitting of a linear model to the expression data for each probe is performed and the coefficients obtained describe the design matrix. Instead of simple t-statistics, it provides results for moderated t-statistic, moderated F-statistic, and B-statistic (which demonstrates the log-odds of differential expression), by applying the Empirical Bayes method and shrinking the standard errors towards a common value. Hence, LIMMA produces stable and reproducible results even with a small number of arrays. It also has the advantages of fast computation, simultaneous error rate control across multiple contrasts and genes, and effective prioritizing of results by applying a particular cutoff for fold change. For analysis of RNA-seq data, LIMMA with voom was used (34). The fitting of the mean-variance association into the differential expression analysis as a modification of limma's empirical Bayes procedure, and then converting it into a precision weight for each individual normalized observation is termed as limma-trend and voom. The performance of this method is best even when the sequencing depths are different for each RNA-sample.
Functional annotation of DEGs
In an effort to infer the biological functions and signals involving the DEGs, GO enrichment analysis was performed. The online tool GENECODIS (http://genecodis.cnb.csic.es) was used for this purpose (17), which also provides pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The DAVID functional annotation tool was used for expounding the results of annotation (18).
Literature citations for the DEGs
To confirm that the list of DEGs obtained in the present study is associated with cancer, the National Center for Biotechnology Information (NCBI; www.ncbi.nlm.nih.gov) literature was searched to identify published reports relating these genes to cancer. The TARGETgene tool was used for this purpose (21). This tool identifies probable therapeutic targets in cancer by constructing a whole genome network using integration of heterogeneous data at the genomic and proteomic level. Upon the construction of the gene network, TARGETgene evaluates network-based parameters to detect potential therapeutic targets and displays the number of literature citations in all and individual cancer types for each gene, as reported in the NCBI database.
Meta-analysis of expression heterogeneity of DEGs
Meta-analysis can refer to either the analysis of collectively published lists of DEGs, or the integration of diverse microarray datasets to perform a novel combined differential expression analysis. The meta-analysis performed in the present study investigated the diversity in expression of DEGs in six microarray datasets, collectively, with the aim to discover whether they display inconsistent expression changes in multiple studies, or whether they display consistent changes in all the analyzed studies. This is termed as heterogeneous or non-heterogonous behavior, respectively. This statistical heterogeneity implies genuine significant difference in between study variations, rather than within study variance which may be because of chance alone. Q and I2 statistic tests remain the most widely used measures of heterogeneity for which computation modules are available in standard statistical software for meta-analysis, such as Stata and R (35). I2 statistic is preferred among all measures of heterogeneity as it is a sample size and scale-invariant measure and has finite upper bounds and precise confidence intervals (36). For each gene obtained in the DEG list, analysis of heterogeneity was performed across cancer types using the meta package in R (37). A confidence interval of 95% was selected with the degree of freedom 5. The metacont function estimates the heterogeneity statistic score I2, along with the values, Q, df, and P-value. The seven suggested steps by Ramasamy et al (38), in conducting the meta-analysis of microarray datasets, were followed.
Results
Extracting significant gene markers relative to breast, lung and prostate cancer
The LIMMA R package was used to elucidate potential gene targets by adjusting the P-values using Benjamini-Hochberg correction. Genes were termed significantly differentially expressed if the adjusted P-value was <0.05 and the fold change was >2. DEGs for each microarray dataset of lung, breast and prostate cancer, were obtained individually, with results illustrated in Table II. Since datasets belonged to two platforms, GPL570 and GPL96, the number of probes was not equal in all datasets. Probes in GPL96 are a subset of probes in GPL570. Therefore, while combining the DEGs within the same cancer type, a union (merging) of the two individual lists of DEGs was performed, to get a single list of DEGs. The main aim of the present study was to find a common subset of DEGs across the three cancer types. Hence, an intersection of the DEG lists was performed to find members of the joint subset of genes across the three cancer types. Up to this stage of the analysis, mapping of probe IDs with the corresponding gene symbols was not performed. Therefore, the number of DEGs represented the unique probe IDs. In total, 75 differentially expressed probe IDs were discovered in common between the three cancer types. Following the removal of probes with no available annotation and the removal of repeated gene symbols, a list of 62 unique gene symbols was obtained as a result of the microarray data analysis.
Table II.
Differential expression analysis results for each microarray dataset.
| Cancer | Breast | Lung | Prostate | |||
|---|---|---|---|---|---|---|
| GEO dataset | GSE45827 | GSE48984 | GSE19804 | GSE10072 | GSE26910 | GSE55945 |
| Platform | GPL570 | GPL96 | GPL570 | GPL96 | GPL570 | GPL570 |
| Number of probes | 54,675 | 22,283 | 54,675 | 22,283 | 5,4675 | 5,4675 |
| Number of samples | 174 | 22 | 120 | 107 | 12 | 19 |
| Number of | 7,006 | 3,513 | 2,026 | 829 | 77 | 539 |
| differentially | Union of the two | Union of the two | Union of the two | |||
| expressed genes | 9,248 | 2,215 | 603 | |||
A similar analysis was performed on the RNA-Seq data for the three individual cancer types. An integer-based raw gene count data matrix of breast, lung and prostate cancer samples was used with LIMMA and voom to explore the DEGs (34). The voom method estimates the mean variance relationship of the log counts, generates a precision weight for each observation and enters these into the LIMMA empirical Bayes analysis pipeline. Using this method, 1,290 genes were obtained in common across the three cancer types.
To confirm the consistency of the results obtained, genes appearing in both the microarray and RNA-Seq analysis results were identified. Following removal of all the duplicate gene symbols, a list of 44 genes was generated. The overlap of DEGs across the three cancers obtained from microarray analysis, from RNA-Seq analysis and from the combined microarray and RNA-Seq analysis is illustrated in Fig. 1A-C respectively. The complete list of the genes identified by the combined microarray and RNA-Seq analysis, along with links to their description from the cancer genetics web (19) and OMIM database (20), is depicted in Table III.
Figure 1.
Overlap of differentially expressed genes in the three cancer types obtained from (A) microarray, (B) RNA-Seq and (C) combined microarray and RNA-Seq dataset analysis.
Table III.
Gene symbols of the common differentially expressed genes in breast, lung and prostate cancer.
Determination of functional annotation
The GENECODIS web software tool was used for functional annotation, which displays biological processes, molecular functions and cellular components that may be significantly enriched in a given gene list (17). The software also lists the KEGG pathways that may be significantly enriched in the gene list. The significance threshold of P<0.05 was selected. The results are illustrated in Figs. 2–4. The terms involving two or more genes were retained in the graphs. The significantly enriched biological processes were multicellular organismal development, cell adhesion, axon guidance, cell differentiation, blood coagulation, muscle contraction, cell death, negative regulation of apoptotic process and anti-apoptosis (Fig. 2). The significantly enriched molecular functions included protein, actin, calmodulin and syntaxin binding (Fig. 3). The significantly enriched cellular components were the nucleus, cytoplasm, plasma membrane, cytosol, caveola, stress fiber, focal adhesion, extracellular matrix, extracellular region and cystoskeleton (Fig. 4). Enriched KEGG pathways are listed in Table IV. The detailed GO enrichment was also obtained by use of the DAVID functional annotation tool (data not shown) (18). Several functional predictions were provided by DAVID, including the presence of BIRC5 in cell survival pathway, TIMP3 in p53 signaling pathway, CAV1 in integrin signaling pathway, and CFD in alternative complement pathway given by BIOCARTA. COG (Clusters of Orthologous Group) Ontology predicted KIF4A involved in cell division and chromosome partitioning, and MYL9 involved in signal transduction mechanisms/cytoskeleton/cell division and chromosome. Significantly enriched biological processes were sensory perception, angiogenesis, cell cycle checkpoint, nuclear division, cytokinesis, apoptosis, cell death, and cell adhesion. Cellular components included extracellular region, cytosol, cell surface, cytoskeleton, nucleolus, cell fraction. Enriched KEGG pathways included pathways in cancer, transcriptional misregulation in cancer, focal adhesion, vascular smooth muscle contraction, MAPK signaling pathway, and the neurotrophin signaling pathway. In summary, the results from the function annotation analysis demonstrate a significant association of the discovered DEGs with cancer pathogenesis.
Figure 2.
Enriched biological processes in differentially expressed genes as predicted by GENECODIS software analysis.
Figure 4.
Enriched cellular components in differentially expressed genes as predicted by GENECODIS software analysis.
Figure 3.
Enriched molecular functions in differentially expressed genes as predicted by GENECODIS software analysis.
Table IV.
Enriched KEGG pathways in differentially expressed genes as predicted by GENECODIS analysis.
| KEGG pathway | Class | Number of genes | P-value (adjusted) | Gene symbols |
|---|---|---|---|---|
| Regulation of actin cytoskeleton | Cellular processes; cell motility | 3 | 0.016092 | MYLK, IQGAP3, MYL9 |
| Vascular smooth muscle contraction | Organismal systems; circulatory system | 3 | 0.005475 | MYLK, MYH11, MYL9 |
| Focal adhesion | Cellular processes | 4 | 0.003144 | CAV2, MYLK, CAV1, MYL9 |
| Tight junction | Cellular processes | 2 | 0.039699 | MYH11, MYL9 |
| Bacterial invasion of epithelial cells | Human diseases; infectious diseases | 2 | 0.016007 | CAV2, CAV1 |
| Tryptophan metabolism | Metabolism; amino acid metabolism | 2 | 0.01113 | INMT, AOX1 |
| Viral myocarditis | Human diseases; Cardiovascular diseases | 2 | 0.015622 | CAV1, MYH11 |
KEGG, Kyoto Encyclopedia of Genes and Genomes.
Listing the literature citations
To explore the cancer-specific citations for these genes, and in particular the distribution of number of relevant citations in individual and/or all cancer types addressed in the present study, the TARGETgene tool was used (21). The results demonstrated a high ranking in literature from NCBI for the candidate key genes. These rankings are reported in Table V. Notably, the maximum number of citations in all cancers for these genes ranged from 1–326, with no gene having zero number of citations, suggesting that the key genes are relevant to cancer. When number of citations in individual cancers was considered, several genes had no relevant citations. For example, NTRK2 has zero NCBI citation in prostate cancer, whereas several studies report a role for this gene in prostate cancer (39,40). Similarly, ID4 has been reported to have a role in lung cancer (41). A summary of the roles of these key genes in cancer is provided by cancer-genetics web database (19) and OMIM database (20) and listed in Table III.
Table V.
TARGETgene results for differentially expressed gene ranking and their number of citations in all and individual cancer types.
| Rank | Gene symbol | Citation numbers for all cancers | Citation numbers for breast cancer | Citation numbers for prostate cancer | Citation numbers for lung cancer |
|---|---|---|---|---|---|
| 1 | MYLK | 4 | 3 | 1 | 0 |
| 2 | NTRK2 | 23 | 0 | 0 | 5 |
| 3 | CAV1 | 137 | 46 | 24 | 22 |
| 4 | MCAM | 22 | 3 | 6 | 1 |
| 5 | ANGPT1 | 35 | 3 | 0 | 3 |
| 6 | CAV2 | 24 | 6 | 4 | 2 |
| 7 | BIRC5 | 326 | 47 | 18 | 46 |
| 8 | EFEMP1 | 4 | 1 | 0 | 2 |
| 9 | EZH2 | 68 | 37 | 33 | 6 |
| 10 | HSPB8 | 14 | 3 | 1 | 2 |
| 11 | ERG | 35 | 0 | 67 | 2 |
| 12 | MYH11 | 16 | 1 | 1 | 0 |
| 13 | TIMP3 | 34 | 9 | 3 | 2 |
| 14 | MYL9 | 1 | 1 | 0 | 0 |
| 15 | SDPR | 2 | 0 | 0 | 0 |
| 16 | PGM5 | 1 | 0 | 0 | 0 |
| 17 | CLU | 48 | 11 | 18 | 8 |
| 18 | FHL1 | 5 | 1 | 1 | 0 |
| 19 | FXYD6 | 4 | 0 | 0 | 0 |
| 20 | KIF4A | 8 | 0 | 1 | 0 |
| 21 | KCNAB1 | 2 | 0 | 0 | 0 |
| 22 | GPM6A | 3 | 0 | 0 | 1 |
| 23 | CFD | 1 | 0 | 0 | 0 |
| 24 | FAM107A | 9 | 0 | 0 | 1 |
| 25 | PTRF | 3 | 1 | 1 | 0 |
| 26 | DPT | 3 | 0 | 0 | 0 |
| 27 | ID4 | 21 | 4 | 0 | 0 |
| 28 | FERMT2 | 4 | 1 | 0 | 2 |
| 29 | MAMDC2 | 4 | 0 | 0 | 0 |
| 30 | CCDC69 | 2 | 0 | 0 | 0 |
| 31 | IQGAP3 | 1 | 0 | 0 | 0 |
| 32 | PCDH9 | 3 | 1 | 0 | 0 |
| 33 | SYNPO2 | 7 | 0 | 3 | 0 |
| 34 | STIL | 21 | 0 | 0 | 1 |
| 35 | GLDN | 2 | 0 | 0 | 0 |
| 36 | CCDC85A | 1 | 0 | 0 | 0 |
| 37 | GPM6B | 4 | 0 | 0 | 0 |
| 38 | ITIH5 | 5 | 4 | 1 | 1 |
| 39 | AOX1 | 3 | 0 | 0 | 0 |
| 40 | NUSAP1 | 2 | 0 | 0 | 0 |
| 41 | ACSS3 | 1 | 0 | 0 | 0 |
| 42 | TCEAL2 | 1 | 0 | 0 | 0 |
| 43 | INMT | 3 | 0 | 0 | 1 |
Meta-analysis of the common set of DEGs
The I2 statistic describes the % of variation across studies that is due to heterogeneity with a confidence interval constructed using the iterative Chi-squared distribution method. The I2 statistic ensures that better consistency measure between the trials would be obtained in meta-analysis (35). The calculation of I2 is obtained from I2=100×(Q−df)/Q, where Q denotes the Cochran's heterogeneity statistic and df denotes degree of freedom. The I2 value lies between 0 and 100%, with all negative values set to zero. The grading of heterogeneity based on I2 value is categorized at 25, 50 and 75% as low, moderate and high heterogeneity respectively. For each DEG, heterogeneity analysis was performed using the meta package in R (37), by extracting RMA normalized values from the six microarray datasets. However, these values could not be retrieved for all the 44 genes, as some probes were not present in data derived from the GPL96 platform. Therefore, heterogeneity analysis was performed only for those DEGs for which the probe ID measurements were available in all six datasets. The results of this analysis are listed in Table VI. In this analysis, the P-value does not adequately describe the extent of heterogeneity in the results of the trials, whereas the I2 value does. Low I2 values indicate little variability between studies, with I2=0 meaning no heterogeneity. This non-heterogeneous behavior was observed in 6 genes out of the list of DEGs, namely CLU, EFEMP1, ID4, MCAM/MIR6756, PPAP2B, and DPT. The gene DPT was mapped by two different probe IDs, and therefore two different I2 values were obtained: one showed considerable heterogeneity, while the other showed no heterogeneity. The forest plots for some non-heterogeneous genes are illustrated in Figs. 5 and 6 as an example. These plots demonstrated that the mean difference of individual studies is very close to, or almost similar to the mean of all the studies, which is depicted by the dashed vertical line. Similar forest plots were observed for all heterogeneous genes (data not shown).
Table VI.
Meta-analysis of differentially expressed genes in the six microarray datasets.
| Gene symbol | Probe ID | I2 (%) | Q | df | P-value |
|---|---|---|---|---|---|
| ANGPT1 | 205608_s_at | 96.10 | 129.21 | 5 | <0.0001 |
| AOX1 | 205083_at | 86.20 | 36.33 | 5 | <0.0001 |
| BIRC5 | 202095_s_at | 97.00 | 167.98 | 5 | <0.0001 |
| CAV1 | 212097_at | 91.80 | 60.9 | 5 | <0.0001 |
| CAV2 | 203323_at | 90.10 | 50.61 | 5 | <0.0001 |
| CDKN1C | 213348_at | 92.60 | 67.89 | 5 | <0.0001 |
| CFD | 205382_s_at | 95.90 | 120.82 | 5 | <0.0001 |
| CLU | 208791_at | 0.00 | 2.97 | 5 | 0.7051 |
| DPT | 213068_at | 76.10 | 20.93 | 5 | 0.0008 |
| DPT | 207977_s_at | 0.00 | 4.25 | 5 | 0.5133 |
| EFEMP1 | 201843_s_at | 1.10 | 5.05 | 5 | 0.4094 |
| ERG | 213541_s_at | 96.20 | 131.24 | 5 | <0.0001 |
| EZH2 | 203358_s_at | 97.00 | 164.74 | 5 | <0.0001 |
| FAM107A | 209074_s_at | 99.00 | 507.18 | 5 | <0.0001 |
| FERMT2 | 209209_s_at | 89.10 | 46 | 5 | <0.0001 |
| FHL1 | 210299_s_at | 86.80 | 37.87 | 5 | <0.0001 |
| FXYD6 | 217897_at | 27.10 | 6.86 | 5 | 0.2311 |
| GPM6A | 209469_at | 97.90 | 235.98 | 5 | <0.0001 |
| GPM6B | 209168_at | 86.10 | 35.99 | 5 | <0.0001 |
| HSPB8 | 221667_s_at | 65.50 | 14.47 | 5 | 0.0129 |
| ID4 | 209292_at | 0.00 | 3.43 | 5 | 0.6338 |
| KCNAB1 | 210078_s_at | 64.50 | 14.1 | 5 | 0.015 |
| KIF4A | 218355_at | 95.80 | 119.67 | 5 | <0.0001 |
| LAPTM4B | 208767_s_at | 96.90 | 163.18 | 5 | <0.0001 |
| MCAM /// MIR6756 | 210869_s_at | 0.00 | 4.61 | 5 | 0.4657 |
| MYH11 | 201496_x_at | 91.50 | 58.49 | 5 | 0.001 |
| MYL9 | 201058_s_at | 73.80 | 19.12 | 5 | 0.0018 |
| MYLK | 202555_s_at | 90.00 | 49.86 | 5 | <0.0001 |
| NTRK2 | 221796_at | 88.60 | 43.8 | 5 | <0.0001 |
| NUSAP1 | 218039_at | 97 | 177.64 | 5 | <0.0001 |
| PCDH9 | 219737_s_at | 89.30 | 46.86 | 5 | <0.0001 |
| PPAP2B | 212226_s_at | 0.00 | 4.65 | 5 | 0.4606 |
| PTRF | 208789_at | 82.20 | 28.16 | 5 | <0.0001 |
| STIL | 205339_at | 95.20 | 103.46 | 5 | <0.0001 |
| TCEAL2 | 211276_at | 76.30 | 21.11 | 5 | 0.0008 |
| TIMP3 | 201147_s_at | 86.80 | 37.77 | 5 | <0.0001 |
Figure 5.
Forest plots illustrating the results from heterogeneity analysis for the (A) MCAM and (B) PPAP2B genes. MCAM, melanoma cell adhesion molecule; PPAP2B, phosphatidic acid phosphatase type 2B.
Figure 6.
Forest plots illustrating the results from heterogeneity analysis for the (A) EFEMP1 and (B) ID4 genes. EFEMP1, EGF-containing fibulin extracellular matrix protein 1; ID4, inhibitor of DNA-binding 4.
Discussion
The present analysis was motivated by previous research studies (42–44), where noteworthy genes were identified through bioinformatics analysis. The objective of the present study was to recognize common genetic indicators/biomarkers in lung, breast and prostate cancer, and to confirm their relevance in cancer by exploring NCBI citations using TARGETgene and by functional annotations using GENECODIS and DAVID. A robust gene set involved in the three cancer types was obtained, as microarray and RNA-Seq data were analyzed in combination in the present study. The RNA-Seq analysis proposed more genes compared with the microarray analysis to be involved in the process of oncogenesis. Further analysis would be required to classify these additional genes so that normal physiology could be attained by targeting cancer biomarkers. Further inspection of the obtained gene set for their inter-experiment behavior was performed to identify heterogeneity in expression. This is termed as meta-analysis as the normalized expression values from all available microarray data are combined. From this examination, it was evident that their comportment is subject to change in different types of cancers. A systematic review of between-study variance analysis demonstrated that some genes had no observed heterogeneity. These genes were CLU, EFEMP1, ID4, MCAM, PPAP2B and DPT, with I2 values 0, 1.1, 0, 0, 0 and 0% respectively. This indication of non-heterogeneous behavior across studies has inordinate importance from a biological perspective. Furthermore, some genes exhibited moderate heterogeneity, HSPB8, KCNAB1 and FXYD6 with I2 values 65.50, 64.50 and 27.10%, respectively. The DPT gene exhibited both types of behavior, which suggests that further experimental validation is required. The remaining genes had I2 values >70%, suggesting considerable heterogeneity. Thus, the present analysis demonstrated the mining of noteworthy gene markers by analysis of both microarray and RNA-Seq data and by identifying a common set of genes relevant in the three cancer conditions. By ensuring that the Affymetrix gene chip platforms used for all the microarray data were similar, technical variation between platforms were avoided. In addition, by applying a similar method for normalizing expression and detecting differential genes to all datasets, the present investigation led to the discovery of a common subset of genes which displayed significantly variable expression between tumor and normal samples from microarray data analysis. Further analysis of RNA-Seq data from the same cancer types to obtain overlapping results, resulted in a more robust gene list. The e roles of these genes in carcinogenesis were further confirmed by the results from GENECODIS (17), DAVID (18), cancer genetics web (19), OMIM (20) and literature citations (by using TARGETgene) (21). Finally, statistical analysis of heterogeneity led to novel conclusions about their performance in the three different cancer types. Further studies would be of interest, including how the deregulation of apoptotic pathways may be one of the major roles the genes discovered in the present study may have.
Acknowledgements
Authors would like to acknowledge CSIR-National Environmental Engineering Research Institute (Nagpur, India) for providing essential resources and constant support for the present research study. The authors would like to thank Dr Dhananjay Raje for his guidance and feedback.
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