Cancer Biomarker Discovery: The Entropic Hallmark

Berretta, Regina; Moscato, Pablo

doi:10.1371/journal.pone.0012262

Cited by 49 publications

(27 citation statements)

References 749 publications

(803 reference statements)

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“…Most of these lncRNAs do not have an official Human Genome Nomenclature Committee symbol and their function is unknown. But some have been reported to play a role in cancer, including LINC00152 [21] , taurine upregulated 1 (TUG1) [22] , urothelial cancer associated 1 (UCA1) [23,24] , Pvt1 oncogene (PVT1) [25] , small nucleolar RNA host gene 1 (SNHG1) [26] , and LINC00261 [27] .…”

Section: Identification Of Differentially Expressed Lncrnas In Gastrimentioning

confidence: 99%

Analysis of long non-coding RNA expression profiles in gastric cancer

Cao

Wu²,

He³

et al. 2013

WJG

181

159

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AIM:To investigate the expression patterns of long non-coding RNAs (lncRNAs) in gastric cancer. METHODS:Two publicly available human exon arrays for gastric cancer and data for the corresponding normal tissue were downloaded from the Gene Expression Omnibus (GEO). We re-annotated the probes of the human exon arrays and retained the probes uniquely mapping to lncRNAs at the gene level. LncRNA expression profiles were generated by using robust multi-array average method in affymetrix power tools. The normalized data were then analyzed with a Bioconductor package linear models for microarray data and genes with adjusted P -values below 0.01 were considered differentially expressed. An independent data set was used to validate the results. RESULTS:With the computational pipeline established to re-annotate over 6.5 million probes of the Affymetrix Human Exon 1.0 ST array, we identified 136053 probes uniquely mapping to lncRNAs at the gene level. These probes correspond to 9294 lncRNAs, covering nearly 76% of the GENCODE lncRNA data set. By analyzing GSE27342 consisting of 80 paired gastric cancer and normal adjacent tissue samples, we identified 88 lncRNAs that were differentially expressed in gastric cancer, some of which have been reported to play a role in cancer, such as LINC00152, taurine upregulated 1, urothelial cancer associated 1, Pvt1 oncogene, small nucleolar RNA host gene 1 and LINC00261. In the validation data set GSE33335, 59% of these differentially expressed lncRNAs showed significant expression changes (adjusted P -value < 0.01) with the same direction. CONCLUSION:We identified a set of lncRNAs differentially expressed in gastric cancer, providing useful information for discovery of new biomarkers and therapeutic targets in gastric cancer. © 2013 Baishideng. All rights reserved.Key words: Long non-coding RNA; Gastric cancer; Microarray analysis; Data mining Core tip: Long non-coding RNAs (lncRNAs) have risen to prominence with important roles in a broad range of biological processes. LncRNA expression patterns and their biological functions in gastric cancer still remain unknown. We re-annotated the probes from an Affymetrix Human Exon 1.0 ST array and identified probes uniquely mapping to lncRNAs at the gene level. These probes correspond to 9294 lncRNAs, covering nearly 76% of the GENCODE lncRNA data set. We identified a set of lncRNAs that were differentially expressed in gastric cancer. In an independent data set, 59% of these differentially expressed lncRNAs showed significant expression changes with the same direction.

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Section: Identification Of Differentially Expressed Lncrnas In Gastrimentioning

confidence: 99%

Analysis of long non-coding RNA expression profiles in gastric cancer

Cao

Wu²,

He³

et al. 2013

WJG

181

159

View full text Add to dashboard Cite

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“…This concept receives strong support from two quite different but detailed bio-mathematical modelling [114,115]. In one [114], signal transduction in cancer metastases is more "entropic" ie.…”

Section: Energy Sensing and Anaplasticitymentioning

confidence: 98%

Energy management – a critical role in cancer induction?

Garland

2013

Critical Reviews in Oncology/Hematology

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“…The loss of coherence will result in excessive energy consumption with the proper dissipation. The consequence of this "energy overload" will be the distortion in the dissipative structures, distorted metabolic networks' selforganizing potential, increased thermal noise, and higher entropy of the biological system, as shown in [72].…”

Section: Classical Semiclassical and Quantum Modeling Of Tumor And mentioning

confidence: 99%

Quantum-Mechanical Modeling of Mutations, Aging, Evolution, Tumor, and Cancer Development

Djordjević

2016

Quantum Biological Information Theory

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In this chapter, we will describe the quantum-mechanical models to accurately describe the process of creation of spontaneous, induced, and adaptive mutations. These models will be used to describe the processes of evolution and aging. The various theories of quantum evolution and epievolution will be studied as well. We then describe the Markovian chain-like classical and quantummechanical modeling of mutations and aging. In the same section, the hybrid quantum-classical biological channel model with memory is described as well. After that, various classical, semiclassical, and quantum models of cancer development are studied. The final section concludes the chapter. Quantum-Mechanical and Quantum Mechanics-Like Models for Mutations and EvolutionWe have already discussed the role of mutations in evolution process in Chap. 3; see Sect. 3.4. Based on several recent publications [1][2][3][4][5][6][7][8][9][10][11][12], it has become evident that both Darwinian type of evolution (random mutations followed by selection process) [13] and Lamarckian type of evolution (selected mutations beneficial to the organism) [14] are important. The modernized Lamarckian mechanism, which can also be called neo-Lamarckism, can be described as follows: environmental factors are introducing genomic changes, the mutations are targeted to specific genes, and the mutations provide the adaptation to the original cause. Clearly, this mechanism is well aligned with adaptive mutation mechanism discussed in Sect. 4.3 of the previous chapter (see also [15][16][17][18][19][20][21][22]). The Darwinian mechanism of evaluation is less demanding as no specialized mechanism is required to direct the change of the relevant genomic locus and restrict it to the specific mutations.Based on mutation models we discussed in previous chapter as well as references [1][2][3][4][5][6][7][8][9][10][11][12], it appears that both mechanisms can contribute to the evolution. For low stress levels, the spontaneous mutations seem to be a dominant source to evolution, suggesting Darwinian evolution mode is more relevant. On the other hand, for strong stress levels, the adaptive mutations dominate, indicating that Lamarckian

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Cancer Biomarker Discovery: The Entropic Hallmark

Cited by 49 publications

References 749 publications

Analysis of long non-coding RNA expression profiles in gastric cancer

Analysis of long non-coding RNA expression profiles in gastric cancer

Energy management – a critical role in cancer induction?

Quantum-Mechanical Modeling of Mutations, Aging, Evolution, Tumor, and Cancer Development

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