Comparative Analysis of Protein-Protein Interactions in Cancer-Associated Genes 25

Protein-protein interactions (PPIs) have been widely studied to understand the bi-ological processes or molecular functions associated with different disease systems like cancer. While focused studies on individual cancers have generated valuable in-formation, global and comparative analysis of datasets from different cancer types has not been done. In this work, we carried out bioinformatic analysis of PPIs corresponding to differentially expressed genes from microarrays of various tumor tissues (belonging to bladder, colon, kidney and thyroid cancers) and compared their associated biological processes and molecular functions (based on Gene On-tology terms). We identified a set of processes or functions that are common to all these cancers, as well as those that are specific to only one or partial cancer types. Similarly, protein interaction networks in nucleic acid metabolism were compared to identify the common/specific clusters of proteins across different cancer types. Our results provide a basis for further experimental investigations to study protein interaction networks associated with cancer. The methodology developed in this work can also be applied to study similar disease systems.     

Identification and validation of colorectal neoplasia-specific methylation markers for accurate classification of disease

Aberrant DNA methylation occurs early in oncogenesis, is stable, and can be assayed in tissues and body fluids. Therefore, genes with aberrant methylation can provide clues for understanding tumor pathways and are attractive candidates for detection of early neoplastic events. Identification of sequences that optimally discriminate cancer from other diseased and healthy tissues is needed to advance both approaches. Using well-characterized specimens, genome-wide methylation techniques were used to identify candidate markers specific for colorectal neoplasia. To further validate 30 of these candidates from genome-wide analysis and 13 literature-derived genes, including genes involved in cancer and others with unknown functions, a high-throughput methylation-specific oligonucleotide microarray was used. The arrays were probed with bisulfite-converted DNA from 89 colorectal adenocarcinomas, 55 colorectal polyps, 31 inflammatory bowel disease, 115 extracolonic cancers, and 67 healthy tissues. The 20 most discriminating markers were highly methylated in colorectal neoplasia (area under the receiver operating characteristic curve > 0.8; P < 0.0001). Normal epithelium and extracolonic cancers revealed significantly lower methylation. Real-time PCR assays developed for 11 markers were tested on an independent set of 149 samples from colorectal adenocarcinomas, other diseases, and healthy tissues. Microarray results could be reproduced for 10 of 11 marker assays, including eight of the most discriminating markers (area under the receiver operating characteristic curve > 0.72; P < 0.009). The markers with high specificity for colorectal cancer have potential as blood-based screening markers whereas markers that are specific for multiple cancers could potentially be used as prognostic indicators, as biomarkers for therapeutic response monitoring or other diagnostic applications, compelling further investigation into their use in clinical testing and overall roles in tumorigenesis.     

Identification and Comparison of Aberrant Key Regulatory Networks in Breast,Colon, Liver,Lung, and Stomach Cancers through Methylome Database Analysis

Aberrant methylation of specific CpG sites at the promoter is widely responsible for genesis and development of various cancer types. Even though the microarray-based methylome analyzing techniques have contributed to the elucidation of the methylation change at the genome-wide level, the identification of key methylation markers or top regulatory networks appearing common in highly incident cancers through comparison analysis is still limited. In this study, we in silico performed the genome-wide methylation analysis on each 10 sets of normal and cancer pairs of five tissues: breast, colon, liver, lung, and stomach. The methylation array covers 27,578 CpG sites, corresponding to 14,495 genes, and significantly hypermethylated or hypomethylated genes in the cancer were collected (FDR adjusted p-value <0.05; methylation difference >0.3). Analysis of the dataset confirmed the methylation of previously known methylation markers and further identified novel methylation markers, such as GPX2, CLDN15, and KL. Cluster analysis using the methylome dataset resulted in a diagram with a bipartite mode distinguishing cancer cells from normal cells regardless of tissue types. The analysis further revealed that breast cancer was closest with lung cancer, whereas it was farthest from colon cancer. Pathway analysis identified that either the “cancer” related network or the “cancer” related bio-function appeared as the highest confidence in all the five cancers, whereas each cancer type represents its tissue-specific gene sets. Our results contribute toward understanding the essential abnormal epigenetic pathways involved in carcinogenesis. Further, the novel methylation markers could be applied to establish markers for cancer prognosis.     

In silico analysis of DND1 and its co-expressed genes in human cancers

Dead-End (DND1) is an RNA-binding protein involved in translational regulation. Defects in DND1 gene causes germ cell tumors and sterility in rodents. Experimental studies with human somatic cancer cells indicate that DND1 has anti-proliferative and pro-apoptotic function in some while oncogenic function in other cells. We examined The Cancer Genome Atlas data for gene alterations and gene expression changes in DND1 in a variety of human cancers. We found that DND1 is amplified, deleted or mutated in multiple human cancers. In different cancers, DND1 alteration correlates with increased diagnosis age of patients, shift in tumor spectrum or change of tumor sites and in some cases is significantly associated with worse survival for cancer patients. For 15 cancers, we retrieved expression data of thousands of genes that co-expressed with DND1. We found that these cancers contain different percentage of genes that are positively or negatively co-expressed with DND1. Ingenuity Pathway Analysis was performed to explore the biological implications of these genes. More than 10 canonical pathways were identified and each cancer type exhibits unique pathway profiles. Comparison analysis across all 15 cancer types showed that some cancers exhibit strikingly similar profiles of DND1-correlated signaling pathway activation or suppression. Our data reinforce the notion that the biological role of DND1 is cell-type specific and suggest that DND1 may play opposing role by exerting anti-proliferative effects in some cancer cells while being pro-proliferative in others. Our study provides valuable insights to direct experimental investigations of DND1 function in somatic cancers.     

The Identification of Specific Methylation Patterns across Different Cancers

Abnormal DNA methylation is known as playing an important role in the tumorgenesis. It is helpful for distinguishing the specificity of diagnosis and therapeutic targets for cancers based on characteristics of DNA methylation patterns across cancers. High throughput DNA methylation analysis provides the possibility to comprehensively filter the epigenetics diversity across various cancers. We integrated whole-genome methylation data detected in 798 samples from seven cancers. The hierarchical clustering revealed the existence of cancer-specific methylation pattern. Then we identified 331 differentially methylated genes across these cancers, most of which (266) were specifically differential methylation in unique cancer. A DNA methylation correlation network (DMCN) was built based on the methylation correlation between these genes. It was shown the hubs in the DMCN were inclined to cancer-specific genes in seven cancers. Further survival analysis using the part of genes in the DMCN revealed high-risk group and low-risk group were distinguished by seven biomarkers (PCDHB15, WBSCR17, IGF1, GYPC, CYGB, ACTG2, and PRRT1) in breast cancer and eight biomarkers (ZBTB32, OR51B4, CCL8, TMEFF2, SALL3, GPSM1, MAGEA8, and SALL1) in colon cancer, respectively. At last, a protein-protein interaction network was introduced to verify the biological function of differentially methylated genes. It was shown that MAP3K14, PTN, ACVR1 and HCK sharing different DNA methylation and gene expression across cancers were relatively high degree distribution in PPI network. The study suggested that not only the identified cancer-specific genes provided reference for individual treatment but also the relationship across cancers could be explained by differential DNA methylation.     

Comparison of Breast Cancer to Healthy Control Tissue Discovers Novel Markers with Potential for Prognosis and Early Detection

This study was initiated to identify biomarkers with potential value for the early detection of poor-outcome breast cancer. Two sets of well-characterized tissues were utilized: one from breast cancer patients with favorable vs. poor outcome and the other from healthy women undergoing reduction mammaplasty. Over 46 differentially expressed genes were identified from a large list of potential targets by a) mining publicly available expression data (identifying 134 genes for quantitative PCR) and b) utilizing a commercial PCR array. Three genes show elevated expression in cancers with poor outcome and low expression in all other tissues, warranting further investigation as potential blood markers for early detection of cancers with poor outcome. Twelve genes showed lower expression in cancers with poor outcome than in cancers with favorable outcome but no differential expression between aggressive cancers and most healthy controls. These genes are more likely to be useful as prognostic tissue markers than as serum markers for early detection of aggressive disease. As a secondary finding was that, when histologically normal breast tissue was removed from a distant site in a breast with cancer, 7 of 38 specimens displayed a cancer-like expression profile, while the remaining 31 were genetically similar to the reduction mammaplasty control group. This finding suggests that some regions of ipsilateral histologically ‘normal’ breast tissue are predisposed to becoming malignant and that normal-appearing tissue with malignant signature might warrant treatment to prevent new primary tumors.     

Comparative Gene Expression Analyses Identify Luminal and Basal Subtypes of Canine Invasive Urothelial Carcinoma That Mimic Patterns in Human Invasive Bladder Cancer

More than 160,000 people are expected to die from invasive urothelial carcinoma (iUC) this year worldwide. Research in relevant animal models is essential to improving iUC management. Naturally-occurring canine iUC closely resembles human iUC in histopathology, metastatic behavior, and treatment response, and could provide a relevant model for human iUC. The molecular characterization of canine iUC, however, has been limited. Work was conducted to compare gene expression array results between tissue samples from iUC and normal bladder in dogs, with comparison to similar expression array data from human iUC and normal bladder in the literature. Considerable similarities between enrichment patterns of genes in canine and human iUC were observed. These included patterns mirroring basal and luminal subtypes initially observed in human breast cancer and more recently noted in human iUC. Canine iUC samples also exhibited enrichment for genes involved in P53 pathways, as has been reported in human iUC. This is particularly relevant as drugs targeting these genes/pathways in other cancers could be repurposed to treat iUC, with dogs providing a model to optimize therapy. As part of the validation of the results and proof of principal for evaluating individualized targeted therapy, the overexpression of EGFR in canine bladder iUC was confirmed. The similarities in gene expression patterns between dogs and humans add considerably to the value of naturally-occurring canine iUC as a relevant and much needed animal model for human iUC. Furthermore, the finding of expression patterns that cross different pathologically-defined cancers could allow studies of dogs with iUC to help optimize cancer management across multiple cancer types. The work is also expected to lead to a better understanding of the biological importance of the gene expression patterns, and the potential application of the cross-species comparisons approach to other cancer types as well.     

A suite of DNA methylation markers that can detect most common human cancers

Cancer-specific DNA methylation from the tumor derived fraction of cell free DNA found in blood samples could be used for minimally invasive detection and monitoring of cancer. The knowledge of marker regions with cancer-specific DNA methylation is necessary to the success of such a process. We analyzed the largest cancer DNA methylation dataset available—TCGA Illumina HumanMethylation450 data with over 8,500 tumors—in order to find cancer-specific DNA methylation markers for most common human cancers. First, we identified differentially methylated regions for individual cancer types and those were further filtered against data from normal tissues to obtain marker regions with cancer-specific methylation, resulting in a total of 1,250 hypermethylated and 584 hypomethylated marker CpGs. From hypermethylated markers, optimal sets of six markers for each TCGA cancer type were chosen that could identify most tumors with high specificity and sensitivity [area under the curve (AUC): 0.969-1.000] and a universal 12 marker set that can detect tumors of all 33 TCGA cancer types (AUC >0.84). In addition to hundreds of new DNA methylation markers, our approach also identified markers that are in current clinical use, SEPT9 and GSTP1, indicating the validity of our approach and a significant potential utility for the newly discovered markers. The hypermethylated markers are linked to polycomb associated loci and a significant fraction of the discovered markers is within noncoding RNA genes; one of the best markers is MIR129-2. Future clinical testing of herein discovered markers will confirm new markers that will improve minimally invasive diagnosis and monitoring for multiple cancers.     

Average rank-based score to measure deregulation of molecular pathway gene sets

Background

Deregulation of biological pathways has been shown to be involved in the turmorigenesis of a variety of cancers. The co-regulation of pathways in tumor and normal tissues has not been studied in a systematic manner.

Results

In this study we propose a novel statistic named AR-score (average rank based score) to measure pathway activities based on microarray gene expression profiles. We calculate and compare the AR-scores of pathways in microarray datasets containing expression profiles for a wide range of cancer types as well as the corresponding normal tissues. We find that many pathways undergo significant activity changes in tumors with respect to normal tissues. AR-scores for a small subset of pathways are capable of distinguishing tumor from normal tissues or classifying tumor subtypes. In normal tissues many pathways are highly correlated in their activities, whereas their correlations reduce significantly in tumors and cancer cell lines. The co-expression of genes in the same pathways was also significantly perturbed in tumors.

Conclusions

The co-regulation of genes in the same pathways and co-regulation of different pathways are significantly perturbed in tumors versus normal tissues. Our method provides a useful tool for better understanding the mechanistic changes in tumors, which can also be used for exploring other biological problems.     

Integrative genomic data mining for discovery of potential blood-borne biomarkers for early diagnosis of cancer

Background

With the arrival of the postgenomic era, there is increasing interest in the discovery of biomarkers for the accurate diagnosis, prognosis, and early detection of cancer. Blood-borne cancer markers are favored by clinicians, because blood samples can be obtained and analyzed with relative ease. We have used a combined mining strategy based on an integrated cancer microarray platform, Oncomine, and the biomarker module of the Ingenuity Pathways Analysis (IPA) program to identify potential blood-based markers for six common human cancer types.

Methodology/Principal Findings

In the Oncomine platform, the genes overexpressed in cancer tissues relative to their corresponding normal tissues were filtered by Gene Ontology keywords, with the extracellular environment stipulated and a corrected Q value (false discovery rate) cut-off implemented. The identified genes were imported to the IPA biomarker module to separate out those genes encoding putative secreted or cell-surface proteins as blood-borne (blood/serum/plasma) cancer markers. The filtered potential indicators were ranked and prioritized according to normalized absolute Student t values. The retrieval of numerous marker genes that are already clinically useful or under active investigation confirmed the effectiveness of our mining strategy. To identify the biomarkers that are unique for each cancer type, the upregulated marker genes that are in common between each two tumor types across the six human tumors were also analyzed by the IPA biomarker comparison function.

Conclusion/Significance

The upregulated marker genes shared among the six cancer types may serve as a molecular tool to complement histopathologic examination, and the combination of the commonly upregulated and unique biomarkers may serve as differentiating markers for a specific cancer. This approach will be increasingly useful to discover diagnostic signatures as the mass of microarray data continues to grow in the ‘omics’ era.     

基因芯片筛选新疆维吾尔妇女宫颈癌差异表达基因

新疆南部维吾尔族聚居区是宫颈癌高发区. 本文旨在利用基因芯片技术筛选与维吾尔族妇女宫颈癌发生相关的基因. 首先,分别提取5例新疆维吾尔妇女宫颈癌和5例子宫肌瘤组织(对照)的mRNA,逆转录成cDNA,并用Cy3-dUTP标记子宫肌瘤组织的cDNA,用Cy5 dUTP标记宫颈癌组织的cDNA,制成芯片杂交探针.为筛选出宫颈癌组织中差异表达的基因,上述标记探针分别与含有20 000条人类基因的Affymetrix基因芯片进行杂交,杂交信号用GeneChip Scanner 3000扫描仪扫描,并用芯片图像分析软件(SAM software)分析扫描结果.筛选出的差异表达基因经GO(Gene Ontology)分析和KEGG（Kyoto Encyclopedia of Genes and Genomes）信号通路分析,确定其在宫颈癌中的作用.基因芯片筛选结果显示,在宫颈癌组织中发现2 758个差异表达基因,其中1 326个上调基因,1 432个下调基因.GO分析和KEGG信号通路分析表明,表达差异在两倍以上的基因涉及168个信号通路,包括细胞粘附分子、细胞周期以及MAPK和mTOR信号通路等.上述结果表明,基因芯片技术筛选出大量与宫颈癌发生相关的基因,其中表达差异显著的基因涉及细胞粘附分子、细胞周期和mTOR等信号通路.     

Microarray analysis of bleomycin-exposed lymphoblastoid cells for identifying cancer susceptibility genes

The uncovering of genes involved in susceptibility to the sporadic cancer types is a great challenge. It is well established that the way in which an individual deals with DNA damage is related to the chance to develop cancer. Mutagen sensitivity is a phenotype that reflects an individual's susceptibility to the major sporadic cancer types, including colon, lung, and head and neck cancer. A standard test for mutagen sensitivity is measuring the number of chromatid breaks in lymphocytes after exposure to bleomycin. The aim of the present study was to search for the pathways involved in mutagen sensitivity. Lymphoblastoid cell lines of seven individuals with low mutagen sensitivity were compared with seven individuals with a high score. RNA was isolated from cells exposed to bleomycin (4 hours) and from unexposed cells. Microarray analysis (19K) was used to compare gene expression of insensitive and sensitive cells. The profile of most altered genes after bleomycin exposure, analyzed in all 14 cell lines, included relatively many genes involved in biological processes, such as cell growth and/or maintenance, proliferation, and regulation of cell cycle, as well as some genes involved in DNA repair. When comparing the insensitive and sensitive individuals, other differentially expressed genes were found that are involved in signal transduction and cell growth and/or maintenance (e.g., BUB1 and DUSP4). This difference in expression profiles between mutagen-sensitive and mutagen-insensitive individuals justifies further studies aimed at elucidating the genes responsible for the development of sporadic cancers.     

Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues

A critical and difficult part of studying cancer with DNA microarrays is data interpretation. Besides the need for data analysis algorithms, integration of additional information about genes might be useful. We performed genome-wide expression profiling of 36 types of normal human tissues and identified 2503 tissue-specific genes. We then systematically studied the expression of these genes in cancers by reanalyzing a large collection of published DNA microarray datasets. We observed that the expression level of liver-specific genes in hepatocellular carcinoma (HCC) correlates with the clinically defined degree of tumor differentiation. Through unsupervised clustering of tissue-specific genes differentially expressed in tumors, we extracted expression patterns that are characteristic of individual cell types, uncovering differences in cell lineage among tumor subtypes. We were able to detect the expression signature of hepatocytes in HCC, neuron cells in medulloblastoma, glia cells in glioma, basal and luminal epithelial cells in breast tumors, and various cell types in lung cancer samples. We also demonstrated that tissue-specific expression signatures are useful in locating the origin of metastatic tumors. Our study shows that integration of each gene's breadth of expression (BOE) in normal tissues is important for biological interpretation of the expression profiles of cancers in terms of tumor differentiation, cell lineage, and metastasis.     

Variation in gene expression patterns in human gastric cancers

Gastric cancer is the world's second most common cause of cancer death. We analyzed gene expression patterns in 90 primary gastric cancers, 14 metastatic gastric cancers, and 22 nonneoplastic gastric tissues, using cDNA microarrays representing approximately 30,300 genes. Gastric cancers were distinguished from nonneoplastic gastric tissues by characteristic differences in their gene expression patterns. We found a diversity of gene expression patterns in gastric cancer, reflecting variation in intrinsic properties of tumor and normal cells and variation in the cellular composition of these complex tissues. We identified several genes whose expression levels were significantly correlated with patient survival. The variations in gene expression patterns among cancers in different patients suggest differences in pathogenetic pathways and potential therapeutic strategies.     

Distinct Expression Levels and Patterns of Stem Cell Marker,Aldehyde Dehydrogenase Isoform 1 (ALDH1), in Human Epithelial Cancers

Aldehyde dehydrogenase isoform 1 (ALDH1) has been proved useful for the identification of cancer stem cells. However, our knowledge of the expression and activity of ALDH1 in common epithelial cancers and their corresponding normal tissues is still largely absent. Therefore, we characterized ALDH1 expression in 24 types of normal tissues and a large collection of epithelial tumor specimens (six cancer types, n = 792) by immunohistochemical staining. Using the ALDEFUOR assay, ALDH1 activity was also examined in 16 primary tumor specimens and 43 established epithelial cancer cell lines. In addition, an ovarian cancer transgenic mouse model and 7 murine ovarian cancer cell lines were analyzed. We found that the expression levels and patterns of ALDH1 in epithelial cancers are remarkably distinct, and they correlate with their corresponding normal tissues. ALDH1 protein expression levels are positively correlated with ALDH1 enzymatic activity measured by ALDEFLUOR assay. Long-term in vitro culture doesn''t significantly affect ALDH1 activity in epithelial tumor cells. Consistent with research on other cancers, we found that high ALDH1 expression is significantly associated with poor clinical outcomes in serous ovarian cancer patients (n = 439, p = 0.0036). Finally, ALDH^br tumor cells exhibit cancer stem cell properties and are resistant to chemotherapy. As a novel cancer stem cell marker, ALDH1 can be used for tumors whose corresponding normal tissues express ALDH1 in relatively restricted or limited levels such as breast, lung, ovarian or colon cancer.