Identifying association model for single-nucleotide polymorphisms of ORAI1 gene for breast cancer

Cancer Detection and Prediction Using Genetic Algorithms

Cancer is a wide category of diseases that is caused by the abnormal, uncontrollable growth of cells, and it is the second leading cause of death globally. Screening, early diagnosis, and prediction of recurrence give patients the best possible chance for successful treatment. However, these tests can be expensive and invasive and the results have to be interpreted by experts. Genetic algorithms (GAs) are metaheuristics that belong to the class of evolutionary algorithms. GAs can find the optimal or near-optimal solutions in huge, difficult search spaces and are widely used for search and optimization. This makes them ideal for detecting cancer by creating models to interpret the results of tests, especially noninvasive. In this article, we have comprehensively reviewed the existing literature, analyzed them critically, provided a comparative analysis of the state-of-the-art techniques, and identified the future challenges in the development of such techniques by medical professionals.

High ORAI3 expression correlates with good prognosis in human muscle-invasive bladder cancer

The involvement of store-operated calcium channels (SOCCs) in tumor initiation and metastatic dissemination has been extensively studied, but how its member ORAI3 influences tumor progression is still elusive. The present study aimed to evaluate the prognostic value of ORAI3 expression and examine the correlation between ORAI3 expression and immune cell infiltration within the tumor microenvironment (TME) in human muscle-invasive bladder cancer (MIBC). We examined the expression profile of ORAI3 in MIBC using data from two databases; analyzed the correlation between ORAI3 expression and patient survival; explored cellular pathways related to ORAI3 expression by Gene Set Enrichment Analysis (GSEA); and predicted potential drugs using Connectivity Map (CMap). ORAI3 was significantly lower expressed in tumor mass compared to normal samples in MIBC, with a higher level of methylation at the promoter region in tumor than in normal tissue, indicating that ORAI3 is suppressed during cancer progression. Survival analysis showed that higher expression of ORAI3 correlated with good prognosis in MIBC. GSEA demonstrated that ORAI3 expression inversely correlated with cell differentiation, development and gene silencing, with differential expression of genes involved in epidermal and keratinocyte differentiation pathways and inflammatory responses. RNA sequencing of an ORAI3-silenced human bladder cancer cell line (T24 cells) corroborated enhancement of pro-neoplastic pathways in absence of ORAI3. Western blottingMoreover, ORAI3 facilitated the recruitment of Th17 cells and natural killer cells, whereas hampered Th2 and macrophage infiltration. Our results revealed 4 molecules with potential to be beneficial as adjuvant drugs in MIBC treatment. We concluded that high ORAI3 expression correlates with increased survival in human MIBC.

Identification of High-Order Single-Nucleotide Polymorphism Barcodes in Breast Cancer Using a Hybrid Taguchi-Genetic Algorithm: Case-Control Study

Background

Breast cancer has a major disease burden in the female population, and it is a highly genome-associated human disease. However, in genetic studies of complex diseases, modern geneticists face challenges in detecting interactions among loci.

Objective

This study aimed to investigate whether variations of single-nucleotide polymorphisms (SNPs) are associated with histopathological tumor characteristics in breast cancer patients.

Methods

A hybrid Taguchi-genetic algorithm (HTGA) was proposed to identify the high-order SNP barcodes in a breast cancer case-control study. A Taguchi method was used to enhance a genetic algorithm (GA) for identifying high-order SNP barcodes. The Taguchi method was integrated into the GA after the crossover operations in order to optimize the generated offspring systematically for enhancing the GA search ability.

Results

The proposed HTGA effectively converged to a promising region within the problem space and provided excellent SNP barcode identification. Regression analysis was used to validate the association between breast cancer and the identified high-order SNP barcodes. The maximum OR was less than 1 (range 0.870-0.755) for two- to seven-order SNP barcodes.

Conclusions

We systematically evaluated the interaction effects of 26 SNPs within growth factor–related genes for breast carcinogenesis pathways. The HTGA could successfully identify relevant high-order SNP barcodes by evaluating the differences between cases and controls. The validation results showed that the HTGA can provide better fitness values as compared with other methods for the identification of high-order SNP barcodes using breast cancer case-control data sets.

Calcium Homeostasis: A Potential Vicious Cycle of Bone Metastasis in Breast Cancers

Cancers have been considered as one of the most severe health problems in the world. Efforts to elucidate the cancer progression reveal the importance of bone metastasis for tumor malignancy, one of the leading causes for high mortality rate. Multiple cancers develop bone metastasis, from which breast cancers exhibit the highest rate and have been well-recognized. Numerous cells and environmental factors have been believed to synergistically facilitate bone metastasis in breast cancers, from which breast cancer cells, osteoclasts, osteoblasts, and their produced cytokines have been well-recognized to form a vicious cycle that aggravates tumor malignancy. Except the cytokines or chemokines, calcium ions are another element largely released from bones during bone metastasis that leads to hypercalcemia, however, have not been well-characterized yet in modulation of bone metastasis. Calcium ions act as a type of unique second messenger that exhibits omnipotent functions in numerous cells, including tumor cells, osteoclasts, and osteoblasts. Calcium ions cannot be produced in the cells and are dynamically fluxed among extracellular calcium pools, intracellular calcium storages and cytosolic calcium signals, namely calcium homeostasis, raising a possibility that calcium ions released from bone during bone metastasis would further enhance bone metastasis and aggravate tumor progression via the vicious cycle due to abnormal calcium homeostasis in breast cancer cells, osteoclasts and osteoblasts. TRPs, VGCCs, SOCE, and P2Xs are four major calcium channels/routes mediating extracellular calcium entry and affect calcium homeostasis. Here we will summarize the overall functions of these four calcium channels in breast cancer cells, osteoclasts and osteoblasts, providing evidence of calcium homeostasis as a vicious cycle in modulation of bone metastasis in breast cancers.

Nucleic acid and SNP detection via template-directed native chemical ligation and inductively coupled plasma mass spectrometry

Detection of nucleic acids and single nucleotide polymorphisms (SNPs) is of pivotal importance in biology and medicine. Given that the biological effect of SNPs often is enhanced in combination with other SNPs, multiplexed SNP detection is desirable. We show proof of concept of the multiplexed detection of SNPs based on the template-directed native chemical ligation (NCL) of PNA-probes carrying a metal tag allowing detection using ICP-MS. For the detection of ssDNA oligonucleotides (30 bases), two probes, one carrying the metal tag and a second one carrying biotin for purification, are covalently ligated. The methodological limit of detection is of 29 pM with RSD of 6.7% at 50 pM (n = 5). Detection of SNPs is performed with the combination of two sets of reporter probes. The first probe set targets the SNP, and its yield is compared with a second set of probes targeting a neighboring sequence. The assay was used to simultaneously differentiate between alleles of three SNPs at 5-nM concentration.

Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm

Breast cancer is the most common cancer among women and is considered a major public health concern worldwide. Biogeography-based optimization (BBO) is a novel metaheuristic algorithm. This study analyzed the relationship between the clinicopathologic variables of breast cancer using Cox proportional hazard (PH) regression on the basis of the BBO algorithm. The dataset is prospectively maintained by the Division of Breast Surgery at Kaohsiung Medical University Hospital. A total of 1896 patients with breast cancer were included and tracked from 2005 to 2017. Fifteen general breast cancer clinicopathologic variables were collected. We used the BBO algorithm to select the clinicopathologic variables that could potentially contribute to predicting breast cancer prognosis. Subsequently, Cox PH regression analysis was used to demonstrate the association between overall survival and the selected clinicopathologic variables. C-statistics were used to test predictive accuracy and the concordance of various survival models. The BBO-selected clinicopathologic variables model obtained the highest C-statistic value (80%) for predicting the overall survival of patients with breast cancer. The selected clinicopathologic variables included tumor size (hazard ratio [HR] 2.372, p = 0.006), lymph node metastasis (HR 1.301, p = 0.038), lymphovascular invasion (HR 1.606, p = 0.096), perineural invasion (HR 1.546, p = 0.168), dermal invasion (HR 1.548, p = 0.028), total mastectomy (HR 1.633, p = 0.092), without hormone therapy (HR 2.178, p = 0.003), and without chemotherapy (HR 1.234, p = 0.491). This number was the minimum number of discriminators required for optimal discrimination in the breast cancer overall survival model with acceptable prediction ability. Therefore, on the basis of the clinicopathologic variables, the survival prediction model in this study could contribute to breast cancer follow-up and management.

PBMDR: A particle swarm optimization-based multifactor dimensionality reduction for the detection of multilocus interactions

Studies on multilocus interactions have mainly investigated the associations between genetic variations from the related genes and histopathological tumor characteristics in patients. However, currently, the identification and characterization of susceptibility genes for complex diseases remain a great challenge for geneticists. In this study, a particle swarm optimization (PSO)-based multifactor dimensionality reduction (MDR) approach was proposed, denoted by PBMDR. MDR was used to detect multilocus interactions based on the PSO algorithm. A test data set was simulated from the genotype frequencies of 26 SNPs from eight breast-cancer-related gene. In simulated disease models, we demonstrated that PBMDR outperforms existing global optimization algorithms in terms of its ability to explore and power to detect specific SNP-genotype combinations. In addition, the PBMDR algorithm was compared with other algorithms, including PSO and chaotic PSOs, and the results revealed that the PBMDR algorithm yielded higher accuracy and chi-square values than other algorithms did.

TRPC6 Channels Are Required for Proliferation, Migration and Invasion of Breast Cancer Cell Lines by Modulation of Orai1 and Orai3 Surface Exposure

Transient receptor potential channels convey signaling information from a number of stimuli to a wide variety of cellular functions, mainly by inducing changes in cytosolic Ca²⁺ concentration. Different members of the TRPC, TRPM and TRPV subfamilies have been reported to play a role in tumorigenesis. Here we show that the estrogen receptor positive and triple negative breast cancer cell lines, MCF7 and MDA-MB-231, respectively, exhibit enhanced expression of the TRPC6 channel as compared to the non-tumoral MCF10A cell line. In vitro TRPC6 knockdown using shRNA impaired MCF7 and MDA-MB-231 cell proliferation, migration and invasion detected by BrdU incorporation, wound healing and Boyden chamber assays, respectively. Using RNAi-mediated TRPC6 silencing as well as overexpression of the pore-dead dominant-negative TRPC6 mutant we have found that TRPC6 plays a relevant role in the activation of store-operated Ca²⁺ entry in the breast cancer cell lines but not in non-tumoral breast cells. Finally, we have found that TRPC6 interacts with Orai1 and Orai3 in MCF7 and MDA-MB-231 cells and is required for the translocation of Orai1 and Orai3 to the plasma membrane in MDA-MB-231 and MCF7 cells, respectively, upon Ca²⁺ store depletion. These findings introduce a novel mechanism for the modulation of Ca²⁺ influx and the development of different cancer hallmarks in breast cancer cells.

Analysis of high-order SNP barcodes in mitochondrial D-loop for chronic dialysis susceptibility

OBJECTIVES

Positively identifying disease-associated single nucleotide polymorphism (SNP) markers in genome-wide studies entails the complex association analysis of a huge number of SNPs. Such large numbers of SNP barcode (SNP/genotype combinations) continue to pose serious computational challenges, especially for high-dimensional data.

METHODS

We propose a novel exploiting SNP barcode method based on differential evolution, termed IDE (improved differential evolution). IDE uses a "top combination strategy" to improve the ability of differential evolution to explore high-order SNP barcodes in high-dimensional data.

RESULTS

We simulate disease data and use real chronic dialysis data to test four global optimization algorithms. In 48 simulated disease models, we show that IDE outperforms existing global optimization algorithms in terms of exploring ability and power to detect the specific SNP/genotype combinations with a maximum difference between cases and controls. In real data, we show that IDE can be used to evaluate the relative effects of each individual SNP on disease susceptibility.

CONCLUSION

IDE generated significant SNP barcode with less computational complexity than the other algorithms, making IDE ideally suited for analysis of high-order SNP barcodes.

The role of Orai-STIM calcium channels in melanocytes and melanoma

Calcium signalling within normal and cancer cells regulates many important cellular functions such as migration, proliferation, differentiation and cytokine secretion. Store operated Ca(2+) entry (SOCE) via the Ca(2+) release activated Ca(2+) (CRAC) channels, which are composed of the plasma membrane based Orai channels and the endoplasmic reticulum stromal interaction molecules (STIMs), is a major Ca(2+) entry route in many cell types. Orai and STIM have been implicated in the growth and metastasis of multiple cancers; however, while their involvement in cancer is presently indisputable, how Orai-STIM-controlled Ca(2+) signals affect malignant transformation, tumour growth and invasion is not fully understood. Here, we review recent studies linking Orai-STIM Ca(2+) channels with cancer, with a particular focus on melanoma. We highlight and examine key molecular players and the signalling pathways regulated by Orai and STIM in normal and malignant cells, we expose discrepancies, and we reflect on the potential of Orai-STIMs as anticancer drug targets. Finally, we discuss the functional implications of future discoveries in the field of Ca(2+) signalling.

CRAC channels, calcium, and cancer in light of the driver and passenger concept

Advances in next-generation sequencing allow very comprehensive analyses of large numbers of cancer genomes leading to an increasingly better characterization and classification of cancers. Comparing genomic data predicts candidate genes driving development, growth, or metastasis of cancer. Cancer driver genes are defined as genes whose mutations are causally implicated in oncogenesis whereas passenger mutations are defined as not being oncogenic. Currently, a list of several hundred cancer driver mutations is discussed including prominent members like TP53, BRAF, NRAS, or NF1. According to the vast literature on Ca(2+) and cancer, Ca(2+) signals and the underlying Ca(2+) channels and transporters certainly influence the development, growth, and metastasis of many cancers. In this review, I focus on the calcium release-activated calcium (CRAC) channel genes STIM and Orai and their role for cancer development, growth, and metastasis. STIM and Orai genes are being discussed in the context of current cancer concepts with a focus on the driver-passenger hypothesis. One result of this discussion is the hypothesis that a driver analysis of Ca(2+) homeostasis-related genes should not be carried out by looking at isolated genes. Rather a pool of “Ca(2+) genes” might be considered to act as one potential cancer driver. This article is part of a Special Issue entitled: Calcium and Cell Fate. Guest Editors: Jacques Haiech, Claus Heizmann, Joachim Krebs, Thierry Capiod and Olivier Mignen.

Development and Application of a Genetic Algorithm for Variable Optimization and Predictive Modeling of Five-Year Mortality Using Questionnaire Data

The problem of selecting important variables for predictive modeling of a specific outcome of interest using questionnaire data has rarely been addressed in clinical settings. In this study, we implemented a genetic algorithm (GA) technique to select optimal variables from questionnaire data for predicting a five-year mortality. We examined 123 questions (variables) answered by 5,444 individuals in the National Health and Nutrition Examination Survey. The GA iterations selected the top 24 variables, including questions related to stroke, emphysema, and general health problems requiring the use of special equipment, for use in predictive modeling by various parametric and nonparametric machine learning techniques. Using these top 24 variables, gradient boosting yielded the nominally highest performance (area under curve [AUC] = 0.7654), although there were other techniques with lower but not significantly different AUC. This study shows how GA in conjunction with various machine learning techniques could be used to examine questionnaire data to predict a binary outcome.

The Combinational Polymorphisms of ORAI1 Gene Are Associated with Preventive Models of Breast Cancer in the Taiwanese

The ORAI calcium release-activated calcium modulator 1 (ORAI1) has been proven to be an important gene for breast cancer progression and metastasis. However, the protective association model between the single nucleotide polymorphisms (SNPs) of ORAI1 gene was not investigated. Based on a published data set of 345 female breast cancer patients and 290 female controls, we used a particle swarm optimization (PSO) algorithm to identify the possible protective models of breast cancer association in terms of the SNPs of ORAI1 gene. Results showed that the PSO-generated models of 2-SNP (rs12320939-TT/rs12313273-CC), 3-SNP (rs12320939-TT/rs12313273-CC/rs712853-(TT/TC)), 4-SNP (rs12320939-TT/rs12313273-CC/rs7135617-(GG/GT)/rs712853-(TT/TC)), and 5-SNP (rs12320939-TT/rs12313273-CC/rs7135617-(GG/GT)/rs6486795-CC/rs712853-(TT/TC)) displayed low values of odds ratios (0.409–0.425) for breast cancer association. Taken together, these results suggested that our proposed PSO strategy is powerful to identify the combinational SNPs of rs12320939, rs12313273, rs7135617, rs6486795, and rs712853 of ORAI1 gene with a strongly protective association in breast cancer.

Predicting the combined effect of multiple genetic variants

Background

Many genetic variants have been identified in the human genome. The functional effects of a single variant have been intensively studied. However, the joint effects of multiple variants in the same genes have been largely ignored due to their complexity or lack of data. This paper uses HMMvar, a hidden Markov model based approach, to investigate the combined effect of multiple variants from the 1000 Genomes Project. Two tumor suppressor genes, TP53 and phosphatase and tensin homolog (PTEN), are also studied for the joint effect of compensatory indel variants.

Results

Results show that there are cases where the joint effect of having multiple variants in the same genes is significantly different from that of a single variant. The deleterious effect of a single indel variant can be alleviated by their compensatory indels in TP53 and PTEN. Compound mutations in two genes, β-MHC and MyBP-C, leading to severer cardiovascular disease compared to single mutations, are also validated.

Conclusions

This paper extends the functionality of HMMvar, a tool for assigning a quantitative score to a variant, to measure not only the deleterious effect of a single variant but also the joint effect of multiple variants. HMMvar is the first tool that can predict the functional effects of both single and general multiple variations on proteins. The precomputed scores for multiple variants from the 1000 Genomes Project and the HMMvar package are available at https://bioinformatics.cs.vt.edu/zhanglab/HMMvar/

SNP 1772 C > T of HIF-1α gene associates with breast cancer risk in a Taiwanese population

Background

Hypoxia inducible factor 1α (HIF-1α) is a stress-responsive transcription factor to hypoxia and its expression is correlated to tumor progression and angiogenesis. Several single nucleotide polymorphisms (SNPs) of HIF-1α gene in the oxygen-dependent degradation (ODD) domain was reportedly associated with increased HIF-1α activity.

Results

In this study, we focused on the relationship between SNP 1772 C > T (rs11549465) of HIF-1α gene and its breast cancer risk, as well as its correlation with HIF-1α expression and tumor angiogenesis. Ninety six breast cancer patients and 120 age-matched controls were enrolled. We found that 1772 T allele of HIF-1α gene was associated with increased breast cancer risk (adjusted OR = 14.51; 95% CI: 6.74-31.24). This SNP was not associated with clinicopathologic features of angiogenesis such as VEGF activity and the micro-vessel density and survival of breast cancer patients.

Conclusion

Taken together, the 1772 C > T of HIF-1α gene is a potential biomarker for breast cancer susceptibility.