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Discovery of molecular associations among aging, stem cells, and cancer based on gene expression profiling

The emergence of a huge volume of “omics” data enables a computational approach to the investigation of the biology of cancer. The cancer informatics approach is a useful supplement to the traditional experimental approach. I reviewed several reports that used a bioinformatics approach to analyze the associations among aging, stem cells, and cancer by microarray gene expression profiling. The high expression of aging- or human embryonic stem cell-related molecules in cancer suggests that certain important mechanisms are commonly underlying aging, stem cells, and cancer. These mechanisms are involved in cell cycle regulation, metabolic process, DNA damage response, apoptosis, p53 signaling pathway, immune/inflammatory response, and other processes, suggesting that cancer is a developmental and evolutional disease that is strongly related to aging. Moreover, these mechanisms demonstrate that the initiation, proliferation, and metastasis of cancer are associated with the deregulation of stem cells. These findings provide insights into the biology of cancer. Certainly, the findings that are obtained by the informatics approach should be justified by experimental validation. This review also noted that next-generation sequencing data provide enriched sources for cancer informatics study.

Cancer is closely related to aging in that the incidence of cancer increases exponentially with age. The molecular commonality between human embryonic stem cells (hESCs) and cancer cells suggests a strong linkage between hESCs and cancer cells [1,2] . Thus, to a certain extent, cancer is a developmental disease. In fact, the initiation, proliferation, and metastasis of cancer are often associated with abnormalities in various developmental signatures. An indepth investigation of the molecular mechanisms that connect aging, stem cells, and cancer could provide meaningful insights into the biology of cancer. The emergence of a huge amount of 野omics冶 data provides a platform for the investigation of the related mechanisms through a computational biology approach. In particular, microarraybased gene expression data that are related to aging, stem cells, and cancer are valuable resources whereby molecular associations among aging, stem cells, and cancer could be discovered.

Molecular Associations Between Aging and Cancer
To date, only one study has investigated the association between aging and cancer by an extensive examination of the expression profiling of agingrelated genes in various human tumor types [3] .
In addition, I obtained 261 human agingrelated genes (HAGs) from the Human Ageing Genomic Resources website (http://genomics.senescence.info/genes/allgenes. php) [14] , which is an important resource that collects HAGs. Within this 261gene list, certain genes play important roles in both aging and cancer. For example, FOS regulates cell proliferation, differentiation, apoptosis, and transformation, and has been associated with aging and cancer [15] . FOXO1 belongs to the forkhead family of transcription factors, which are strongly associated with human longevity [16] and cancer [17] . TP63 encodes a member of the P53 family of transcription factors and has been associated with aging and cancerrelated pathology [18,19] .
Expression profiling of human aging鄄 related genes in tumors I have identified 28 sets of differentially expressed genes by the 28 normal vs. tumor phenotypes class comparisons in 25 human tumor gene expression datasets; subsequently, I analyzed the overlap between each of these 28 gene sets and each of the aforementioned ASGs and HAGs [3] . Table 1 presents several representative genes that are highly overlapping between both classes of datasets, which include not only the aforementioned FOS, FOXO1, and TP63 but also certain other genes, such as APOD [20] , IGF1 [21] and FOXM1 [22] that have also been shown to strongly correlate aging with cancer. Gene function enrichment analyses suggested that these highly overlapping genes were primarily involved in metabolic process, cell cycle regulation, DNA damage response, apoptosis, cell proliferation, and transcriptional regulation. Based on these highly overlapping genes, I have identified several convergent pathways between aging and cancer. These pathways include pathways in cancer, ErbB signaling pathway, MAPK signaling pathway, cell cycle, Tcell receptor signaling pathway, mTOR signaling pathway, Bcell receptor signaling pathway, p53 signaling pathway, insulin signaling pathway, VEGF signaling pathway, and chemokine signaling pathway, among others.

Identification of human stem cell鄄 related molecular signatures
Based on a literature mining approach [1] , I have identi鄄 fied four types of hESCrelated molecular signatures: genes, pathways, transcription factors (TFs), and microRNAs (miRNAs). The gene signatures include 24 hESCrelated gene sets involving the targets of three core TFs (OCT4, SOX2, and NANOG) in hESCs, the targets of oncogene cMyc, the targets of the tumor suppressor p53, and the targets of polycomb group proteins; these gene sets are essential for controlling the development of hESCs. The pathway signatures include 54 hESCrelated pathways that essentially belong to developmental signaling pathways such as the Wnt, Notch, Hedgehog, and Bmi1 pathways that are necessary for the regulation of stem cell selfrenewal and differentiation. The TF signatures include 189 hESC related TFs that are primarily involved in the regulation of hESC selfrenewal and differentiation. Among these TFs, OCT4, SOX2, and NANOG have essential roles in the transcriptional control of the regulatory circuitry underlying pluripotency [23,24] . The miRNA signatures include 114 hESCrelated miRNAs that have important roles in regulating stem cell selfrenewal and differentiation [25] .
Expression profiling of human stem cell鄄 related molecular signatures in tumors I have analyzed 51 human gene expression datasets involving 23 tumor types and identified differentially expressed genes among either normal vs. tumor or good prognosis vs. poor prognosis phenotype classes [1] . Furthermore, I identified important tumorrelated path ways, TFs, and miRNAs by analyzing gene sets for differential expression among these predefined classes.
I have identified 72 sets of differentially expressed genes in the 51 cancerrelated human gene expression datasets and found considerable overlaps between each of the 72 cancerrelated gene sets and each of the 24 hESCrelated gene sets [1] . Figure 1 presents the proportion of genes in each of the 24 hESCrelated gene sets that have no less than 10 occurrences in the 72 cancerrelated gene sets and demonstrates that a substantial portion of the hESCrelated genes are also related to cancer. The highly overlapping genes are mainly involved in cell cycle regulation, DNA damage repair and replication, apoptosis, development and differentiation, cell adhesion, and TF activity.
I have identified 68 groups of pathways by 75 class comparisons and survival analyses of the cancerrelated datasets and found that 26 hESCrelated pathway signatures appeared in at least 8 groups [1] . Figure 2 presents the occurrence rate of each of the 26 pathways in the 68 groups of cancerrelated pathways. Among the highly overlapping pathways between hESCs and cancer, the cell cycle pathway and MAPK pathway are most prominent. The IGF, ERK, SHH, WNT, PRC2, Notch, PTEN, and TGFβ pathways are also important signaling pathways that link hESCs to cancer. I have identified 73 groups of TFs in the human cancer gene expression datasets [1] . Among the 189 hESC associated TFs, 42 TFs appeared in at least 3 groups. Figure 3 presents the occurrence rate of each of the 42 TFs in the 73 groups of cancerrelated TFs. The most frequently identified hESCrelated TF in tumor was MYC. In fact, MYC is an important TF in both hESCs and cancer cells [23,2635] , and its regulatory networks may account for most of the transcriptional similarity between embryonic stem cells (ESCs) and cancer cells [33] . Several families of hESCassociated TFs, such as MYB, E2F, PAX, SMAD, STAT, POU, SP, and GLI, were shown to be related to cancer (Figure 3).
I have identified 67 groups of miRNAs in the cancer datasets [1] . Among the 114 hESCassociated miRNAs, 50 miRNAs appeared in at least 20 different groups. The most frequently identified miRNA was miR29c, which occurred 34 times (51% occurrence rate), and the next most common miRNA was miR200b, which occurred 30 times (45% occurrence rate). Figure 4 lists the 50  Each number represents the occur鄄 rence rate of the correspond鄄 ing hESC related TF in the 68 cancer鄄 related TF sets.

Xiaosheng Wang
Aging, stem cells, and cancer 茁 资 Notch miRNAs for which the occurrence frequency is no less than 20, indicating that there is a broad range of overlap between 野stemness冶 miRNAs and oncogenic miRNAs. Certain miRNA families, such as the miR302 family and the miR200 family, appear to play an important role in the regulation of hESCs and tumorigenesis [3642] .

Other works identifying common molecular signatures between cancer and stem cells
In addition to the previously discussed work [1] , several other studies have examined the expression of hESCassociated genes in human cancer [2,29,30,43,44] . For instance, Murat . [43] provided the first clinical evidence for the existence of a 野glioma stem cell冶 or 野selfrenewal冶 phenotype in the context of the treatment resistance of glioblastoma. Santagata . [44] explored the expression levels of the ESC TFs OCT3/4, NANOG, and SOX2 in primary and metastatic germ cell tumors (GCTs). They confirmed NANOG and OCT3/4 as sensitive and specific markers for primary seminoma and embryonal carcinoma and demonstrated that NANOG was a marker for metastatic GCTs. Their findings showed that ESC TFs were useful in the diagnosis of tumors that were metastatic to the retroperitoneum . BenPorath . [2] identified a subset of hESC associated transcriptional regulators, including Nanog, Oct4, Sox2, and cMyc, that were highly expressed in poorly differentiated tumors. They found a novel link between genes associated with hESC identity and the histopathologic traits of tumors and suggested that these genes might contribute to the stem celllike phenotypes presented by many tumors. Hassan . [29] used microarray gene expression analysis to identify gene set enrichment patterns between human lung adenocar鄄 cinoma and squamous cell carcinoma. This analysis revealed that an increased expression of the ESC gene set and decreased expression of the Polycomb target gene set identified poorly differentiated lung adenocar鄄 cinoma and that this gene expression signature was associated with markers of poor prognosis and worse overall survival in lung adenocarcinoma. Schoenhals . [30] compared the expression levels of the pluripotency factors OCT4, SOX2, KLF4, and MYC in 40 human tumor types with the levels in the normal tissue counterparts using publicly available gene expression data and found significant overexpression of at least one of these factors in 18 out of the 40 investigated cancer types. Furthermore, they found that these genes were associated with tumor progression or poor prognosis. Overall, these studies revealed that 野stemness冶 gene expression signatures were associated with tumor malignancies and therefore might be informative molecular predictors for cancer therapy outcomes [45] .

Discussion
Cancer is related to aging. Many agingrelated genes demonstrate differential expression profiling between normal tissues and tumor. Two significant agingrelated pathways, pathways in cancer and MAPK signaling pathway, are also implicated in carcinogenesis. Certain critical molecular mechanisms underlie both aging and cancer; these mechanisms include cell cycle regulation, metabolic process, DNA damage response, apoptosis, p53 signaling pathway, immune/inflammatory response, etc. It is worthy of note that the immune/inflammatory response is an important link between aging and cancer, given that the convergent pathways between aging and cancer include multiple pathways that correlate with the immune/inflammatory response, such as T鄄 cell receptor signaling pathway, mTOR signaling pathway, B鄄 cell receptor signaling pathway, and chemokine signaling pathway (see Table 5

of reference [3]).
Cancer is also related to development. Various hESCspecific characteristics, including genes, path鄄 ways, TFs, and miRNAs, were often differentially expressed between normal and tumor phenotypes or among cancer subtypes with distinct clinical outcomes. The signaling pathways like the cell cycle, MAPK, SHH, WNT, PRC2, Notch, PTEN, and TGFβ pathways, that played an important role in control of the hESC fate were also strongly implicated in the initiation, proliferation, and metastasis of cancer. The hESCspecific TFs like OCT4 and cMyc were important in the maintenance of the undifferentiated state of cancer cells. The miRNAs that were overexpressed in undifferentiated hESCs like miRNA302 and miRNA200 families were also involved in cancerous pathology. The mechanism of cell cycle regulation provides the primary commonality between hESCs and cancer cells in that the hESCassociated signatures most frequently identified in tumors are primarily involved in the regulation of cell cycle [1] .
The huge amount of available 野omics冶 data facilitates the investigation of the associations among aging, stem cells, and cancer using the computational biology approach. This approach constitutes an important supplement to the traditional experimental approach. However, the findings obtained through the compu鄄 tational biology approach are only reliable after they have been experimentally validated.
With the development of nextgeneration sequencing (NGS) technology, increasing quantities of cancerrelated sequencing data are emerging. In contrast to microarray methods, NGS can directly determine the cDNA sequences and quantify the expression of diverse RNA molecules from genomes over a wide range of levels [46] . RNA sequencing (RNASeq) is useful for studying complex transcriptomes and revealing sequence variations, such as SNPs, in the transcribed regions [47] . RNASeq has a large dynamic range of expression levels over which transcripts can be detected and is therefore highly accurate for quantifying expression levels. Moreover, this approach also has high levels of reproducibility and requires less RNA samples. The bioinformatics investigation of NGS data will provide a promising direction for cancer informatics research.