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Cellular and Viral Oncogenes
Published in Pimentel Enrique, Oncogenes, 2020
Many molecular phenomena at both genetic and epigenetic levels could occur between the transfection of DNA and the appearance of foci of transformed cells, and the nature of these events remains unknown. DNA transfected to mammalian cells suffers an extraordinarily high mutation frequency, the mutations including base substitutions and deletions as well as insertions from the recipient genome.127 Such mutations are introduced early, rather than continually, during replication, and they may be the explanation, at least in some cases, for the detection of mutated oncogenes after DNA transfection. The increased susceptibility of c-ras genes derived from tumor cells for mutagenic processes occurring during DNA transfection experiments could be related to some conformational changes present in genes which are actively transcribed in the population of tumor cells.
The health of the nation
Published in Brendan Curran, A Terrible Beauty is Born, 2020
We have seen how cancer normally requires mutations to occur in two or more genes concerned with the regulation of cell division. It therefore follows that any factors increasing the frequency of mutations will also increase the tendency for cancer cells to arise; conversely, anything decreasing mutation frequency delays cancer cell formation. This consideration underlies concern about excessive exposure to bright sunlight and the depletion of the ozone layer resulting in more skin cancer. The ozone layer normally absorbs ultraviolet light from the sun. In its absence, these rays in sunlight cause DNA damage much more often than the repair system can accommodate. As a result, mutations are generated, some of which induce oncogenes and subsequently trigger the development of a skin cancer.
The Molecular Model for Cellular Effects
Published in K. H. Chadwick, Understanding Radiation Biology, 2019
Equation 2.5 describes a curve which shows a linear–quadratic bend upwards from the origin, passes through a maximum mutation frequency and decreases at higher doses because of increased cell inactivation. The peak mutation frequency is related to the values of the probabilities (p) and (q) which relate DNA double strand breaks to cell inactivation and the specific mutation, respectively.
RNA methylation-related genes of m6A, m5C, and m1A predict prognosis and immunotherapy response in cervical cancer
Published in Annals of Medicine, 2023
Yan Wang, Yiwen Mao, Caizhi Wang, Xuefeng Jiang, Qionglan Tang, Lingling Wang, Jialei Zhu, Mengqiu Zhao
The methylation model revealed that the genes highly expressed in the high-risk group were SLC2A1, PTBP1, COL4A6, CUX1, and CA2; the genes highly expressed in the low-risk group were CHAF1A, DUOX1, STAC3, and IGBP1 (Figure 6(A)); The genes highly expressed in the cancer tissue were PTBP1, CA2, DUOX1, IQGAP3, CHAF1A, and STAC3; and genes with high expression in the normal adjacent tissue were CUX1, IGBP1, and COL4A6 (Figure 6(B)). Meanwhile, We have made a Pearson correlation analysis for the 10 MEGs genes. Deep red color indicates a strong positive correlation and a deep blue color indicates a strong negative correlation, and the results showed that SLC2A1 was significantly correlated with PTBP1, CA2, DUOX1, CHAF1A and COL4A6, respectively, and PTBP1 was significantly correlated with IGBP1, IQGAP3, CHAF1A, CLO4A6 and CUX1, respectively (Figure 6(C)). The comparison of survival time in different clinical stages revealed that the survival time in stages I-II and III-IV in the high-risk group was lower (Figure 6(D–E)). Mutation analysis of model genes revealed no statistically significant difference in mutation frequency between the high- and low-risk groups However, the low mutation group had a worse prognosis than the high mutation group, and the high-risk low mutation rate group had a shorter survival time and a worse prognosis than the other groups (Figure 6(F)). A prognostic analysis of the model genes is presented in Supplementary Figure 1.
A computational prognostic model of lncRNA signature for clear cell renal cell carcinoma with genome instability
Published in Expert Review of Molecular Diagnostics, 2022
Tingting Cui, Jiantao Guo, Zhixia Sun
The computational framework combining lncRNA expression and somatic mutation data in ccRCC was developed with Perl and R-version 4.0.3 using the following parameters: (i) the mutation frequency of each patient was obtained from the downloaded mutation data and arranged in descending order; (ii) the top 25% patients with the highest mutation frequency were defined as the high mutation [genomic instability (GU)] group; (iii) the bottom 25% of patients with the lowest mutation frequency were defined as the low mutation [genomic stability (GS)] group; (iv) the difference in lncRNAs between the GU and GS groups was analyzed by Wilcoxon test; and (v) lncRNAs with fold change > 1.0, FDR (false discovery rate) adjustment and P < 0.05 were defined as genomic instability–related lncRNAs.
Induced variability and assessment of mutagenic effectiveness and efficiency in sorghum genotypes [Sorghum bicolor (L.) Moench]
Published in International Journal of Radiation Biology, 2022
H. V. Kalpande, S. M. Surashe, Ashok Badigannavar, Ambika More, T. R. Ganapathi
In order to get high frequency of mutations, selection of mutagen is essential in mutation breeding experiments. A highly effective mutagen may not lead to high mutation frequency but show less biological damage (Shah et al. 2008). Therefore, knowledge of mutagenic effectiveness, a measure of mutation frequency per unit dose of mutagen and efficiency, mutation rate based on the biological damage are essential (Smith 1972). They are mainly dependent on genotype used and type of mutagen applied. Various factors including biological, environmental and chemical would modify mutation rate and effectiveness/efficiency of any mutagen (Kodym and Afza 2003). Owing to the toxicity of the chemical mutagens, methylating agents are highly toxic and have to be used at lower concentrations than ethylating agents (Khan et al. 2005). Combination of physical and chemical mutagens would lead to increased efficiency and effectiveness among the field crops. Ideally, a mutagen causing less biological damage is highly useful and lead to more frequency of favorable mutations (Khan and Tyagi 2010).