Extracellular matrix cues regulate the differentiation of pluripotent stem cell-derived endothelial cells

The generation of endothelial cells (ECs) from human pluripotent stem cells (PSCs) has been a promising approach for treating cardiovascular diseases for several years. Human PSCs, particularly induced pluripotent stem cells (iPSCs), are an attractive source of ECs for cell therapy. Although there is a diversity of methods for endothelial cell differentiation using biochemical factors, such as small molecules and cytokines, the efficiency of EC production varies depending on the type and dose of biochemical factors. Moreover, the protocols in which most EC differentiation studies have been performed were in very unphysiological conditions that do not reflect the microenvironment of native tissue. The microenvironment surrounding stem cells exerts variable biochemical and biomechanical stimuli that can affect stem cell differentiation and behavior. The stiffness and components of the extracellular microenvironment are critical inducers of stem cell behavior and fate specification by sensing the extracellular matrix (ECM) cues, adjusting the cytoskeleton tension, and delivering external signals to the nucleus. Differentiation of stem cells into ECs using a cocktail of biochemical factors has been performed for decades. However, the effects of mechanical stimuli on endothelial cell differentiation remain poorly understood. This review provides an overview of the methods used to differentiate ECs from stem cells by chemical and mechanical stimuli. We also propose the possibility of a novel EC differentiation strategy using a synthetic and natural extracellular matrix

Association of paternal age at birth and the risk of breast cancer in offspring: a case control study

BACKGROUND: Older paternal age may increase the germ cell mutation rate in the offspring. Maternal age may also mediate in utero exposure to pregnancy hormones in the offspring. To evaluate the association between paternal and maternal age at birth with the risk of breast cancer in female offspring, a case-control study was conducted in Korea. METHODS: Histologically confirmed breast cancer cases (n = 1,011) and controls (n = 1,011) with no present or previous history of cancer, matched on year of birth and menopausal status, were selected from several teaching hospitals and community in Seoul during 1995-2003. Information on paternal and maternal ages and other factors was collected by interviewed questionnaire. Odds ratio (OR) and 95% confidence interval (95% CI) were estimated by unconditional logistic regression model adjusting for family history of breast cancer in 1st or 2nd degree relatives, and lifetime estrogen exposure duration. RESULTS: The risk of breast cancer significantly increased as the paternal age increased (p for trend = 0.025). The association was stronger after controlling for maternal age; women whose fathers were aged >or=40 years at their birth had 1.6-fold increased risk of breast cancer compared with fathers aged or=40 vs. or=35 yrs at birth compared to women whose mothers were aged <25 years, were 1.2, 1.4, and 0.8, respectively, the trend was not significant (p for trend = 0.998). CONCLUSION: These findings suggest that older paternal age increases the risk of breast cancer in their female offspring

Antioxidant Vitamins Intake, Ataxia Telangiectasia Mutated (ATM) Genetic Polymorphisms, and Breast Cancer Risk

Ataxia telangiectasia mutated (ATM) cells exist under a constant state of oxidative stress with high levels of reactive oxygen species, which are removed by cellular antioxidant vitamins. We investigated the independent and combined effect of antioxidant vitamins intake and the ATM genotype or diplotype on the breast cancer risk. Analyses included 323 cases and age-matched controls who participated in the Korean Breast Cancer Study during 2001-2003 with complete dietary information. The vitamin A (P 0.01) and -tocopherol (P 0.01) were associated with lower breast cancer risk as well as some water-soluble vitamins including vitamin B2 (P = 0.01), vitamin C (P 0.01), and folic acid (P = 0.02) intake. No five single nucleotide polymorphisms (ATM-5144A T (rs228589), IVS21 + 1049T C (rs664677), IVS33-55T C (rs664982), IVS34+60G A (rs664143), and 3393T G (rs4585)) studied showed significant differences in their allele frequencies between the cases and controls. On the other hand, compared with the diploid of ATTGT/ATTGT, as the number of ATTGT haplotype decreased, the risk of breast cancer increased (P = 0.04). The association between ATM diplotype and the breast cancer risk was predominantly among women with low intake of antioxidant vitamins including vitamin A, vitamin C, and folic acid. This study suggested that some antioxidant vitamins intake may modify the effect of ATM diplotype on the breast cancer risk among Korean women.This research was supported by the BRL (Basic Research Laboratory) program through theNational Research Foundation of Korea funded by the Ministry of Education, Science and Technology (2009-0087452).Kabat GC, 2008, BRIT J CANCER, V99, P816, DOI 10.1038/sj.bjc.6604540*NAT CANC INF CTR, 2008, REP NAT CANC INF CTRMichels KB, 2007, CANCER, V109, P2712, DOI 10.1002/cncr.22654Erker L, 2006, FREE RADICAL BIO MED, V41, P590, DOI 10.1016/j.freeradbiomed.2006.04.032Bingham S, 2006, P NUTR SOC, V65, P19, DOI 10.1079/PNS2005472Powers HJ, 2005, J NUTR, V135, p2960SLee KM, 2005, CANCER EPIDEM BIOMAR, V14, P821Kim YI, 2004, CANCER EPIDEM BIOMAR, V13, P511Sommer SS, 2003, CANCER GENET CYTOGEN, V145, P115, DOI 10.1016/S0165-4608(03)00119-5Bretsky P, 2003, CANCER EPIDEM BIOMAR, V12, P733Dutta A, 2003, J AM COLL NUTR, V22, P258LEE SA, 2003, J KOREAN BREAST CANC, V6, P271Marcelain K, 2002, REV MED CHILE, V130, P957de Jong MM, 2002, J MED GENET, V39, P225Barzilai A, 2002, DNA REPAIR, V1, P3BERNSTEIN JL, 2002, BREAST CANCER RES, V4, P249SPURDLE AB, 2002, BREAST CANCER RES, V4, pNIL49Benhar M, 2001, MOL CELL BIOL, V21, P6913Ames BN, 2001, MUTAT RES-FUND MOL M, V475, P7, DOI 10.1016/S0027-5107(01)00070-7Kamsler A, 2001, CANCER RES, V61, P1849Shiloh Y, 2001, BIOCHEM SOC T, V29, P661Takao N, 2000, FEBS LETT, V472, P133Gandini S, 2000, EUR J CANCER, V36, P636, DOI 10.1016/S0959-8049(00)00022-8Reichenbach J, 1999, CLIN EXP IMMUNOL, V117, P535Barlow C, 1999, P NATL ACAD SCI USA, V96, P9915Rotman G, 1997, BIOESSAYS, V19, P911*WORLD CANC RES FU, 1997, FOOD NUTR PREV CANCShiloh Y, 1997, ANNU REV GENET, V31, P635Barlow C, 1996, CELL, V86, P159SAVITSKY K, 1995, SCIENCE, V268, P1749KEY T, 1994, P NUTR SOC, V53, P605EASTON DF, 1994, BRIT MED BULL, V50, P527

Genetic polymorphisms of TGF-beta1 & TNF-beta and breast cancer risk

OBJECTIVE: The proliferation of malignant breast epithelial cells is regulated by various stimuli including cytokines and growth factors, thus the variants of those genes may modify the breast cancer risk. To evaluate the potential influences of TGF-beta1 T29C and TNF-beta A252G gene polymorphisms on breast cancer risk, a case-control study was conducted in Korea. METHODS: Histologically confirmed breast cancer cases (n=560) and controls (n=509) with no previous history of cancer were recruited from three teaching hospitals in Seoul, Korea. Genotypes were determined by PCR-CTPP (polymerase chain reaction with confronting two-pair primers) method. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by unconditional logistic regression model adjusting for age, body mass index, education, parity, age at first full-term pregnancy, and family history of breast cancer. RESULTS: The TGF-beta1 29C-allele containing genotypes posed an increased risk of breast cancer (OR=1.3, 95% CI=1.02-1.79), especially in postmenopausal women (OR=1.6, 95% CI=1.01-2.44). Similarly, the TNF-beta 252G-allele containing genotypes posed an increased risk of postmenopausal breast cancer (OR=1.7, 95% CI=1.09-2.55). The risk of postmenopausal breast cancer increased in parallel with the number of the risk genotypes (p for trend 22.8 kg/m2) (OR=1.9, 95% CI=1.04-3.37). CONCLUSION: The results of this study therefore suggest that polymorphisms of TGF-beta1 and TNF-beta genes may modify individual susceptibility to breast cancer in Korean women

Genetic polymorphisms of interleukin-1 beta (IL-1B) and IL-1 receptor antagonist (IL-1RN) and breast cancer risk in Korean women

OBJECTIVE: To evaluate the potential role of genetic polymorphisms of interleukin-1 beta (IL-1B) and IL-1 receptor antagonist (IL-1RN) on breast cancer development, a hospital-based case-control study was conducted in Korea. METHODS: Histologically confirmed breast cancer cases (n = 560) and controls (n = 509) without cancer history were recruited from three teaching hospitals in Seoul between September 1998 and January 2002. Information on risk factors of breast cancer were collected by interviewed questionnaire. Genotypes of IL-1B (-31C/T) and IL-1RN (86 bp variable number tandom repeats in intron 2) were determined by PCR-CTPP (confronting two-pair primers) and PCR, respectively. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by unconditional logistic regression model. RESULTS: The IL-1RN *2-allele was associated with decreased breast cancer risk with marginal significance (OR = 0.7, 95% CI = 0.48-1.05). The IL-1B CC or TC genotype was not associated with decreased risk of breast cancer (OR = 0.9, 95% CI = 0.65-1.16). However, combination of IL-1B C-allele (CT or CC) and IL-1RN *2-allele containing genotypes significantly decreased the risk of breast cancer (OR = 0.6, 95% CI = 0.39-0.99). A moderately decreasing trend of risk was observed as the number of 'putative low risk' allele increased (p for trend = 0.07). Suggestive combined effect on breast cancer risk was also observed between body mass index (BMI) and IL-1RN non-*2 allele: women with higher BMI and IL-1RN non-*2 allele had 1.7-fold higher risk than women with lower BMI and IL-1RN*2 genotypes. CONCLUSION: Our results suggest that genetic polymorphisms of interleukin-1 may play a role in the individual susceptibility for breast cancer development in Korean women

Machine learning model for predicting excessive muscle loss during neoadjuvant chemoradiotherapy in oesophageal cancer

Abstract Background Excessive skeletal muscle loss during neoadjuvant concurrent chemoradiotherapy (NACRT) is significantly related to survival outcomes of oesophageal cancer. However, the conventional method for measuring skeletal muscle mass requires computed tomography (CT) images, and the calculation process is labour‐intensive. In this study, we built machine‐learning models to predict excessive skeletal muscle loss, using only body mass index data and blood laboratory test results. Methods We randomly split the data of 232 male patients treated with NACRT for oesophageal cancer into the training (70%) and test (30%) sets for 1000 iterations. The naive random over sampling method was applied to each training set to adjust for class imbalance, and we used seven different machine‐learning algorithms to predict excessive skeletal muscle loss. We used five input variables, namely, relative change percentage in body mass index, albumin, prognostic nutritional index, neutrophil‐to‐lymphocyte ratio, and platelet‐to‐lymphocyte ratio over 50 days. According to our previous study results, which used the maximal χ2 method, 10.0% decrease of skeletal muscle index over 50 days was determined as the cut‐off value to define the excessive skeletal muscle loss. Results The five input variables were significantly different between the excessive and the non‐excessive muscle loss group (all P < 0.001). None of the clinicopathologic variables differed significantly between the two groups. The ensemble model of logistic regression and support vector classifier showed the highest area under the curve value among all the other models [area under the curve = 0.808, 95% confidence interval (CI): 0.708–0.894]. The sensitivity and specificity of the ensemble model were 73.7% (95% CI: 52.6%–89.5%) and 74.5% (95% CI: 62.7%–86.3%), respectively. Conclusions Machine learning model using the ensemble of logistic regression and support vector classifier most effectively predicted the excessive muscle loss following NACRT in patients with oesophageal cancer. This model can easily screen the patients with excessive muscle loss who need an active intervention or timely care following NACRT