The expression of tumour suppressors and proto-oncogenes in tissues susceptible to their hereditary cancers

Advances in Genetic Reprogramming: Prospects from Developmental Biology to Regenerative Medicine

The foundations of cell reprogramming were laid by Yamanaka and co-workers, who showed that somatic cells can be reprogrammed into pluripotent cells (induced pluripotency). Since this discovery, the field of regenerative medicine has seen advancements. For example, because they can differentiate into multiple cell types, pluripotent stem cells are considered vital components in regenerative medicine aimed at the functional restoration of damaged tissue. Despite years of research, both replacement and restoration of failed organs/ tissues have remained elusive scientific feats. However, with the inception of cell engineering and nuclear reprogramming, useful solutions have been identified to counter the need for compatible and sustainable organs. By combining the science underlying genetic engineering and nuclear reprogramming with regenerative medicine, scientists have engineered cells to make gene and stem cell therapies applicable and effective. These approaches have enabled the targeting of various pathways to reprogramme cells, i.e., make them behave in beneficial ways in a patient-specific manner. Technological advancements have clearly supported the concept and realization of regenerative medicine. Genetic engineering is used for tissue engineering and nuclear reprogramming and has led to advances in regenerative medicine. Targeted therapies and replacement of traumatized , damaged, or aged organs can be realized through genetic engineering. Furthermore, the success of these therapies has been validated through thousands of clinical trials. Scientists are currently evaluating induced tissue-specific stem cells (iTSCs), which may lead to tumour-free applications of pluripotency induction. In this review, we present state-of-the-art genetic engineering that has been used in regenerative medicine. We also focus on ways that genetic engineering and nuclear reprogramming have transformed regenerative medicine and have become unique therapeutic niches.

Evaluation of Mutagenicity and Anti-Mutagenicity of Various Bean Milks Using Drosophila with High Bioactivation

The consumption of a nutritious diet including phytochemicals can minimize mutations as the primary cause of carcinogenesis. Bean consumption supplies calories, minerals and phytochemicals but their anti-mutagenic properties in vivo remain little understood. Hence, the present study aimed to study the mutagenicity and anti-mutagenic properties of five bean milks using the somatic mutation and recombination test (SMART) involving Drosophila with high bioactivation. Milk derived from five bean varieties, namely black bean (Phaseolus vulgaris), red kidney bean (Phaseolus vulgaris), mung bean (Phaseolus aureus), peanut (Arachis hypogaea) and soybean (Glycine max) did not induce DNA mutations in Drosophila with high bioactivation, indicating their genome-safe properties. All bean milks showed anti-mutagenicity against the food-derived mutagen, urethane, in vivo with different degrees of inhibition. In the co-administration study, larvae were treated with each bean milk together with urethane. Soybean milk showed the highest anti-mutagenicity at 27.75%; peanut milk exhibited the lowest at 7.51%. In the pre-feeding study, the larvae received each bean milk followed by urethane. Soybean milk exhibited the highest anti-mutagenic potential, followed by red kidney bean and black bean milks. Total phenolic and antioxidant data revealed that the anti-mutagenicity of both red kidney bean milk and black bean milk might be derived from their phenolic or antioxidant properties; other phytochemicals may contribute to the high anti-mutagenicity observed in soybean milk. Further investigations on the anti-mutagenicity of bean milks against other dietary mutagens are required to develop bean-based products with potent anti-mutagenic properties.

In silico study of cancer stage-specific DNA methylation pattern in White breast cancer patients based on TCGA dataset

BACKGROUND

Breast cancer is one of the most common types of cancer among women. As current breast cancer treatments are still ineffective, we assess the methylation pattern of White breast cancer patients across cancer stage based on The Cancer Genome Atlas (TCGA) dataset. Significant hypermethylation and hypomethylation can regulate the gene expression, thus becoming potential biomarkers in breast cancer tumorigenesis.

METHODS

DNA methylation data was downloaded using TCGA Assembler 2 based on race-specific metadata of TCGA - Breast Invasive Carcinoma (TCGA-BRCA) project from Genomic Data Commons (GDC) Data Portal. After the data was divided into each cancer stage, duplicated data of each patient was removed using OMICSBind, while differentially-expressed probes were identified using edgeR. The resulting probes were validated based on correlation and regression analysis with the gene expression, ANOVA between cancer stages, ROC curve per stage, as well as databases.

RESULTS

Based on the White dataset, we found 66 significant hypermethylated genes with logFC > 1.8 between Stage I-III. From this number, three epigenetic-regulated, stage-specific genes are proposed to be the detection biomarkers of breast cancer due to significant aberrant gene expression and/or low mutation ratio among breast cancer patients: ABCC9 (Stage III), SHISA3 (Stage II), and POU4F1 (Stage I-II).

CONCLUSIONS

Our study shows that ABCC9, SHISA3, and POU4F1 are potential stage-specific detection biomarkers of breast cancer for White individuals, whereas their roles in other races need to be studied further.

Resolving Peto's paradox: Modeling the potential effects of size-related metabolic changes, and of the evolution of immune policing and cancer suppression

The intrinsic risk of cancer increases with body size and longevity; however, big long-lived species do not exhibit this increase, a contradiction named Peto's paradox. Five hypotheses potentially resolving this paradox were modeled using the multistage model of carcinogenesis. The five hypotheses were based on (1) intrinsic changes in metabolic rate with body size; adaptive increase in immune policing of (2) cancer cells or (3) cells with driver mutations; or adaptive increase in cancer suppression via (4) decreased somatic mutation rate, or (5) increased genetic control. Parameter changes needed to stabilize cancer risk in three types of cancer were estimated for tissues scaled from mouse size and longevity to human and blue whale levels. The metabolic rate hypothesis alone was rejected due to a conflict between the required interspecific effect with the observed intraspecific effect of size on cancer risk, but some metabolic change was optionally incorporated in the other models. Necessary parameter changes in immune policing and somatic mutation rate far exceeded values observed; however, natural selection increasing the genetic suppression of cancer was generally consistent with data. Such adaptive increases in genetic control of cancers in large and/or long-lived animals raise the possibility that nonmodel animals will reveal novel anticancer mechanisms.

Gender Differential Transcriptome in Gastric and Thyroid Cancers

Cancer has an important and considerable gender differential susceptibility confirmed by several epidemiological studies. Gastric (GC) and thyroid cancer (TC) are examples of malignancies with a higher incidence in males and females, respectively. Beyond environmental predisposing factors, it is expected that gender-specific gene deregulation contributes to this differential incidence. We performed a detailed characterization of the transcriptomic differences between genders in normal and tumor tissues from stomach and thyroid using Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) data. We found hundreds of sex-biased genes (SBGs). Most of the SBGs shared by normal and tumor belong to sexual chromosomes, while the normal and tumor-specific tend to be found in the autosomes. Expression of several cancer-associated genes is also found to differ between sexes in both types of tissue. Thousands of differentially expressed genes (DEGs) between paired tumor-normal tissues were identified in GC and TC. For both cancers, in the most susceptible gender, the DEGs were mostly under-expressed in the tumor tissue, with an enrichment for tumor-suppressor genes (TSGs). Moreover, we found gene networks preferentially associated to males in GC and to females in TC and correlated with cancer histological subtypes. Our results shed light on the molecular differences and commonalities between genders and provide novel insights in the differential risk underlying these cancers.

Secretome profiling identifies neuron-derived neurotrophic factor as a tumor-suppressive factor in lung cancer

Clinical and preclinical studies show tissue-specific differences in tumorigenesis. Tissue specificity is controlled by differential gene expression. We prioritized genes that encode secreted proteins according to their preferential expression in normal lungs to identify candidates associated with lung cancer. Indeed, most of the lung-enriched genes identified in our analysis have known or suspected roles in lung cancer. We focused on the gene encoding neuron-derived neurotrophic factor (NDNF), which had not yet been associated with lung cancer. We determined that NDNF was preferentially expressed in the normal adult lung and that its expression was decreased in human lung adenocarcinoma and a mouse model of this cancer. Higher expression of NDNF was associated with better clinical outcome of patients with lung adenocarcinoma. Purified NDNF inhibited proliferation of lung cancer cells, whereas silencing NDNF promoted tumor cell growth in culture and in xenograft models. We determined that NDNF is downregulated through DNA hypermethylation near CpG island shores in human lung adenocarcinoma. Furthermore, the lung cancer-related DNA hypermethylation sites corresponded to the methylation sites that occurred in tissues with low NDNF expression. Thus, by analyzing the tissue-specific secretome, we identified a tumor-suppressive factor, NDNF, which is associated with patient outcomes in lung adenocarcinoma.

Insights into the nucleotide composition and codon usage pattern of human tumor suppressor genes

Tumor suppressor genes encode different proteins that inhibit the uncontrolled proliferation of cell growth and tumor development. To acquire clues for predicting gene expression level, it is essential to understand the codon usage bias (CUB) of genes to characterize genome which possesses its own compositional characteristics and unique coding sequences. We used bioinformatic tools to analyze the codon usage patterns of 637 human tumor suppressor genes as no work was reported earlier. The mean effective number of codons of these genes was 48, indicating low CUB. Our results exhibited a significant positive correlation among different nucleotide compositions and the codons ending with C base was most frequently used along with the most over-represented codon CTG and GTG codifying leucine and valine amino acid, respectively, in human tumor suppressor genes. The neutrality plot showed a significant positive correlation (Pearson, r = 0. 646; P < .01) suggesting that mutation on GC bias might affect the CUB. However, the linear regression coefficient of GC₁₂ on GC₃ in human tumor suppressor genes suggested that natural selection played a major role while mutation pressure played a minor role in the codon usage patterns of tumor suppressor genes in human. Our study would throw light into the factors that affect CUB and the codon usage patterns in the human tumor suppressor genes.

Peto's paradox and the hallmarks of cancer: constructing an evolutionary framework for understanding the incidence of cancer

An evolutionary perspective can help unify disparate observations and make testable predictions. We consider an evolutionary model in relation to two mechanistic frameworks of cancer biology: multistage carcinogenesis and the hallmarks of cancer. The multistage model predicts that cancer risk increases with body size and longevity; however, this is not observed across species (Peto's paradox), but the paradox is resolved by invoking the evolution of additional genetic mechanisms to suppress cancer in large, long-lived species. It is when cancer cells overcome these defence mechanisms that they exhibit the hallmarks of cancer, driving the ongoing evolution of these defences, which in turn is expected to create the differences observed in the genetics of cancer across species and tissues. To illustrate the utility of an evolutionary model we examined some recently published data linking stem-cell divisions and cancer incidence across a range of tissues and show why the original analysis was faulty, and demonstrate that the data are consistent with a multistage model varying from three to seven mutational hits across different tissues. Finally, we demonstrate how an evolutionary model can both define patterns of inherited (familial) cancer and explain the prevalence of cancer in post-reproductive years, including the dominance of epithelial cancers.