Role of transcription in the central ‘dogma’ of molecular biology. According to this ‘dogma’, the genetic information flows from DNA to messenger RNA (mRNA) to proteins. The gene CDKN2A/p16INK4a, for example, is located at position “21.3” of the short arm of the human chromosome 9, which resides inside the nucleus. Upon activation by the transcription factor (E2F1), its mRNA is transcribed and the corresponding proteins are translated in the cytoplasm (CDKN2A encodes three but only two variants are displayed). The interplay between the genome, transcriptome and proteome is oversimplified. 

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... 20 th century witnessed a great development of genetics and molecular biology, laying the foundations for a new era in medicine. The elucidation of the mechanism of heredity, for example, helped us understanding the connection between cells, chromosomes, DNA and the genetic code, an historical journey to the center of biology (Lander & Weinberg, 2000). This process strongly consolidated when “the central dogma of molecular biology” (Crick, 1970) was proposed long time ago, whereby the genetic information flows from DNA to RNA to protein. Since then, however, our understanding in the molecular and cellular organization, as well as physiology of living systems has radically changed, partially challenging the validity of the central ‘dogma’– by the way, dogma strictly means a belief that people are expected to accept without any doubts, a word to be expectedly seen outside the scientific method lexicon – of molecular biology (Shapiro, 2009). The main paradigm is that cells are able to make decisions based on actively sensing their environment; hence, information processing in living systems can be regarded at least bidirectional. In any case, the recent sequencing of the human genome is a great milestone (Human Genome Sequencing, 2004), whereby the language of the “common thread of humanity” in this new medicine era is just “the end of the beginning” (Stein, 2004). Genomics studies the total DNA sequence of an organism. Of the approximately 3,000 million base pairs that comprise the human genome, only 1% was firstly estimated to correspond to as low as 25,000 proteins (Southan, 2004), a number that has been changing since the initial sequence drafts of the Human Genome Project (HGP). One motivation behind genome-sequencing projects is the assumption that the nucleotide sequence of an organism provides a description of the genes, its products and interaction networks that orchestrate programs like those sustaining the metabolic activity of a cell or deploying a body plan. However, new discoveries in transcriptome functions significantly expand—and even challenge—the classical concept of the gene and how post-transcriptional molecular events are becoming key to understand gene regulation in higher eukaryotes. The success of the HGP has provided a blueprint of genes encoding the entire human protein set potentially expressed in any of the approximately 230 cell types comprising the human proteome. Considering that both the current and sometimes limited knowledge of only two-thirds of the 20,300 protein-coding human genes mapped through the HGP is at hand (Legrain et al. , 2011), the recently launched Human Proteome Project (HPP) aims to provide for the remaining one-third of proteins experimental evidence related to abundance, distribution, subcellular localization, functions, and interactions (Bustamante et al. , 2011). In the current "post-genomic era" scientists aim not only to build a catalog of all genes, but also to translate the knowledge obtained into benefits for humanity (Collins et al. , 2003). By examining tumors at the genomic, transcriptomic, and proteomic levels, for instance, it is possible to better understand cancer biology and improve patient care, diagnosis, prognosis, and therapy (Lin & Li, 2008). Importantly, one key development that has emerged between the interface of the HGP and the HPP is the area of functional genomics or transcriptomics, which aims to assign a function to all transcripts. But this is not a trivial task because talking about transcriptomes involves considering these as entities as diverse as the cell types, developmental stages, environmental conditions and pathological states that an organism harbors or faces. Therefore, we must include a global vision for the process of transcription, i.e. the process by which information contained in DNA is converted (or transcribed) into RNA and how this process is regulated by protein(s) (Fig. 1). Importantly, it should bear on mind that 57% - a scalable number up to 90% (Costa, 2010) of the genome is transcribed into RNA but does not code for proteins (Frith et al. , 2005). Moreover, very recently non-coding RNAs (microRNAs, small RNAs, small interfering RNAs or siRNAs as well as medium and large RNAs) have emerged as key elements in carcinogenesis. The amazing complexity of the transcriptome and its expansion (Mendes Soares & Valcarcel, 2006), has led to scientists eager to hunt transcriptomes. Fortunately, there are tools to examine the expression of genes at many levels, allowing us to globally understand complex diseases like cancer. The current manuscript introduces the most common techniques to study the transcription of the 1% protein-coding genes encoded in the human genome, followed by a review of microarray studies that had provided invaluable information of the carcinogenesis of cervical cancer (CC), the most and second most common cancer disease in women from the developing and developed world, respectively. The integration of all this information is very important to not only understand CC from a global perspective, but also to identify key tumor markers that could help for CC diagnosis, prognosis and/or therapy, as discussed in the last part of the manuscript. As for cancer progression involving noncoding RNAs – importantly considered the “masters of regulation” (Costa, 2010), the reader is encouraged to read an excellent recent review (Gibb et al. , 2011). Importantly, CC is largely associated to Human Papillomavirus (HPV) infection, from which there are over hundred types but of these 40 infecting the genital tract and 15 of high- risk related to the development of CC. Thus, HPV is a common sexually transmitted agent after a woman starts her first sexual relationship and responsible of ca. 30% of the global cancer burden associated to infective agents (20% of the total) (zur Hausen, 2009). The relationship between a particular molecule and cellular phenotype has allowed us to better understand the molecular mechanisms of complex diseases such as cancer. In the course of molecular biology many useful techniques to analyze DNA, RNA and proteins were developed. For about half century, reasonably, the practice of molecular biology was comfortable with its reductionism; however, in the coming era of genomics, the tendency to probe in a single experiment hundreds or thousands of biomolecules allows us talking of two mechanisms: (i) The “reductionist mechanism” employs tools to analyze one or few different molecules in a single experiment; it is a slow but comprehensive conclusions can be reliably obtained; (ii) The “holistic mechanism” allows the assessment of thousands of different molecules in a single experiment; it is a fast mechanism but the obtained hypotheses remained to be tested (Coulton, 2004). While single gene analyses gradually shifted towards large mutational screens and complete genome mapping, whole genome sequencing moved towards bioinformatics with exhaustive functional genomics and proteomics data. Systems biology aims to understand this complexity. Ironically, the holism in systems biology has re-emerged out of the traditional molecular biology, carrying with it the reductionism-holism debate since the past years (Gatherer, 2010). Interestingly, it has been boldly argued that traditional molecular biology represents a greedy reductionist approach (to some authors a naively reductionist one) that requires either extensive complementation from, or even replacement by systems biology. However, as we discuss along the text, it is more meaningful to combine both approaches. The study of transcription is important because the levels of mRNA transcripts in a cell correlate frequently with the expression levels of the corresponding proteins. There are several techniques used in transcriptomics, which are based on gene amplification by the polymerase chain reaction (PCR), hybridization and sequencing. All these tools permit analyzing differential expression, and determine what transcripts are mainly expressed in cancerous tissue in comparison with normal tissue and vice versa . This is important because knowing what and how genes are differentially expressed suggests that these may play an important role in carcinogenesis. This scenario can be found in the case of proto-oncogenes and anti-oncogenes (or tumor suppressor genes) that promote and prevent cell growth, respectively. In other words, the levels of expression of many oncogenes (normally known as proto-oncogenes) may be very high and the levels of expression of tumor suppressor genes may be low. Following the reductionist and holistic classification, the most common techniques used in transcriptomics can be classified into high, medium, and low performance, with respect to its ability to analyze different molecules in a single experiment. One of the first developed methods to detect a mRNA transcript was in ...