In a living organism, tens of thousands of genes are expressed and interact with each other to achieve necessary cellular functions. Gene regulatory networks contain information on regulatory ...mechanisms and the functions of gene expressions. Thus, incorporating network structures, discerned either through biological experiments or statistical estimations, could potentially increase the selection and estimation accuracy of genes associated with a phenotype of interest. Here, we considered a gene selection problem using gene expression data and the graphical structures found in gene networks. Because gene expression measurements are intermediate phenotypes between a trait and its associated genes, we adopted an instrumental variable regression approach. We treated genetic variants as instrumental variables to address the endogeneity issue. We proposed a two‐step estimation procedure. In the first step, we applied the LASSO algorithm to estimate the effects of genetic variants on gene expression measurements. In the second step, the projected expression measurements obtained from the first step were treated as input variables. A graph‐constrained regularization method was adopted to improve the efficiency of gene selection and estimation. We theoretically showed the selection consistency of the estimation method and derived the
L
∞ bound of the estimates. Simulation and real data analyses were conducted to demonstrate the effectiveness of our method and to compare it with its counterparts.
Ischemic stroke causes irreversible damage to the brain. The hippocampus is a vulnerable region and plays an important role in cognition and locomotor activity. Puerarin is a phytoestrogen that has ...beneficial effects in treating neurological disorders. How puerarin protects against hippocampal injury and its molecular mechanisms remain to be elucidated. Transient global brain ischemia was induced by 4-vessel occlusion in adult male Sprague-Dawley rats. The rats were pretreated with puerarin alone or together with LY294002 (an PI3K inhibitor) before ischemia/reperfusion (I/R). The open- and closed-field tasks and Morris water maze (MWM) test were used to assess the effects of puerarin on anxiety-like behavioral and cognitive impairment following I/R. Hematoxylin-eosin staining(HE) was used to examine the survival of hippocampal CA1 pyramidal neurons, and immunoblotting was performed to examine the expression of the related proteins. By using the rat model for transient I/R, we demonstrated that puerarin pretreatment significantly increased the travelling distance and number of crossings in the open- and closed-field tests, reduced latency and increased the proportion of distance and time in zone IV in the MWM. The number of live cells in the hippocampus is sharply increased by puerarin pretreatment.We further observed that the levels of phosphorylated Akt1, GSK-3β and MCL-1were elevated and those of cleaved-caspase-3 were reduced in the puerarin-treatment group. Notably, the PI3K inhibitor LY294002 counteracted all of the effects of puerarin. Our findings suggest that puerarin protects the hippocampus from I/R damage by activating the PI3K/Akt1/GSK-3β/MCL-1 signaling pathway.
•In this paper, we found that the weighted random forest (WRF) model can better predict the incidence of hepatic encephalopathy (HE) in cirrhotic patients.•For unbalanced data, we use the random ...forest (RF) and support vector machine (SVM) algorithms to construct a risk prediction model for liver cirrhosis complicated by HE to improve the efficiency of its prediction.•This work is the first study to predict cirrhosis in patients with hepatic encephalopathy based on unbalanced data.
Hepatic encephalopathy (HE) is among the most common complications of cirrhosis. Data for cirrhosis with HE is typically unbalanced. Traditional statistical methods and machine learning algorithms thus cannot identify a few classes. In this paper, we use machine learning algorithms to construct a risk prediction model for liver cirrhosis complicated by HE to improve the efficiency of its prediction.
We collected medical data from 1,256 patients with cirrhosis and performed preprocessing to extract 81 features from these irregular data. To predict HE in cirrhotic patients, we compared several classification methods: logistic regression, weighted random forest (WRF), SVM, and weighted SVM (WSVM). We also used an additional 722 patients with cirrhosis for external validation of the model.
The WRF, WSVM, and logistic regression models exhibited better recognition ability for patients with HE than traditional machine learning models (sensitivity> 0.70), but their ability to identify patients with uncomplicated HE was slightly lower (specificity approximately 85%). The comprehensive evaluation index of the traditional model was higher than those of other models (G-means> 0.80 and F-measure> 0.40). For the WRF, the G-means (0.82), F-measure (0.46), and AUC (0.82) were superior to those of the logistic regression and WSVM models, which means that it can better predict the incidence of HE in patients.
The WRF model is more suitable for the classification of unbalanced medical data and can be used to construct a risk prediction and evaluation system for liver cirrhosis complicated with HE. The probabilistic prediction models of WRF can help clinicians identify high-risk patients with HE.
Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis for patients. Integrating multi-omics data can offer a comprehensive landscape of cancer ...biological process and provide promising ways for cancer diagnosis and treatment. Taking the heterogeneity of different omics data types into account, we propose a hierarchical multi-kernel learning (hMKL) approach, a novel cancer molecular subtyping method to identify cancer subtypes by adopting a two-stage kernel learning strategy. In stage 1, we obtain a composite kernel borrowing the cancer integration via multi-kernel learning (CIMLR) idea by optimizing the kernel parameters for individual omics data type. In stage 2, we obtain a final fused kernel through a weighted linear combination of individual kernels learned from stage 1 using an unsupervised multiple kernel learning method. Based on the final fusion kernel, k-means clustering is applied to identify cancer subtypes. Simulation studies show that hMKL outperforms the one-stage CIMLR method when there is data heterogeneity. hMKL can estimate the number of clusters correctly, which is the key challenge in subtyping. Application to two real data sets shows that hMKL identified meaningful subtypes and key cancer-associated biomarkers. The proposed method provides a novel toolkit for heterogeneous multi-omics data integration and cancer subtypes identification.
Abstract Mediation analysis has been a useful tool for investigating the effect of mediators that lie in the path from the independent variable to the outcome. With the increasing dimensionality of ...mediators such as in (epi)genomics studies, high-dimensional mediation model is needed. In this work, we focus on epigenetic studies with the goal to identify important DNA methylations that act as mediators between an exposure disease outcome. Specifically, we focus on gene-based high-dimensional mediation analysis implemented with kernel principal component analysis to capture potential nonlinear mediation effect. We first review the current high-dimensional mediation models and then propose two gene-based analytical approaches: gene-based high-dimensional mediation analysis based on linearity assumption between mediators and outcome (gHMA-L) and gene-based high-dimensional mediation analysis based on nonlinearity assumption (gHMA-NL). Since the underlying true mediation relationship is unknown in practice, we further propose an omnibus test of gene-based high-dimensional mediation analysis (gHMA-O) by combing gHMA-L and gHMA-NL. Extensive simulation studies show that gHMA-L performs better under the model linear assumption and gHMA-NL does better under the model nonlinear assumption, while gHMA-O is a more powerful and robust method by combining the two. We apply the proposed methods to two datasets to investigate genes whose methylation levels act as important mediators in the relationship: (1) between alcohol consumption and epithelial ovarian cancer risk using data from the Mayo Clinic Ovarian Cancer Case-Control Study and (2) between childhood maltreatment and comorbid post-traumatic stress disorder and depression in adulthood using data from the Gray Trauma Project.
The high consumption of soy isoflavones in Asian diets has been correlated to a lower incidence of clinically important cases of prostate cancer. This study characterized the effects of a soy-derived ...isoflavone concentrate (ISF) on growth and gene expression profiles in the LNCaP, an androgen-sensitive human prostate cancer cell line. ISF caused a dose-dependent decrease in viability (P < 0.05) and DNA synthesis (P < 0.01), as well as an accumulation of cells in G(2)/M, and G(0)/G(1) phases of the cell cycle compared with controls. Using Affymetrix oligonucleotide DNA microarrays (U133A), we determined that ISF upregulated 80 genes and downregulated 33 genes (P < 0.05) involving androgen-regulated genes and pathways controlling cell cycle, metabolism, and intracellular trafficking. Changes in the expression of the genes of interest, identified by microarrays, were validated by Western immunoblot, Northern blot, and luciferase reporter assays. Prostate-specific antigen, homeobox protein NKX3, and cyclin B mRNA were significantly reduced, whereas mRNA was significantly upregulated for p21(CIP1), a major cell cycle inhibitory protein, and fatty acid and cholesterol synthesis pathway genes. ISF also significantly increased cyclin-dependent kinase inhibitor p27(KIP1) and FOXO3A/FKHRL1, a forkhead transcription factor. A differential pattern of androgen-regulated genes was apparent with genes involved in prostate cancer progression being downregulated by ISF, whereas metabolism genes were upregulated. In summary, we found that ISF inhibits the growth of LNCaP cells through the modulation of cell cycle progression and the differential expression of androgen-regulated genes. Thus, ISF treatment serves to identify new therapeutic targets designed to prevent proliferation of malignant prostate cells.
Background
Previous studies on the effects of intergenerational social mobility on cognitive outcomes were not consistent, additionally, underlining brain anatomical variation is unclear. In the ...present study, we aim to examine the associations of social mobility with brain structure and cognitive trajectories in older adults.
Methods
The study included 771 participants, ages 69.8 ±5.2 years, of the Whitehall II ‐ MRI substudy (2012‐2016) with data on their father’s occupation, their own occupation in mid‐life, lifestyle (smoking, drinking, physical activity, social activity, sleep disturbances, auditory difficulty, depression, obesity, hypertension), neuroimaging (T1 structural MRI) and cognitive abilities (global cognition, crystallized cognitive ability, and fluid intelligence). Social mobility was classified into stable high social class, stable low social class, upward mobility and downward mobility group based on occupational shifts from father. We used the ANCOVA tests to assess the association of social mobility with gray matter volume (GMV) and cortical thickness (CT) in various brain regions, and the growth curve modeling (GCM) to evaluate its relations to cognition over 20 years, adjusting for social lifestyle covariates and associated GMV/CT regions identified above.
Results
Social mobility was significantly related to 48 out of the 136 regions of GMV across the frontal lobe, limbic lobe, and temporal lobe; and to 4 out of the 68 regions of CT at the whole‐brain level. Stable high social class preserved the largest GMV and CT value; pair‐wise results suggest upward mobility preserved a larger GMV value than the downward mobility group (average diff. in cm3: 0.121, P<0.05); and similarly a larger CT value (average diff. in cm:0.029, P = 0.051). In GCMs, stable high social class (ref. downward mobility) had a higher entry score of fluid intelligence (b = 2.617, P<0.05), all other comparisons including the entry score and decline rate of cognitive abilities were non‐significant.
Conclusions
Our results suggest a lack of direct association between social mobility and trajectories of global cognition and crystallized cognitive ability; instead, an indirect effect through preserving the value of brain GMV and CT may occur. Additionally, an adverse causal relationship between social mobility and fluid intelligence might happen.
Background
Previous studies on the effects of intergenerational social mobility on cognitive outcomes were not consistent, additionally, underlining brain anatomical variation is unclear. In the ...present study, we aim to examine the associations of social mobility with brain structure and cognitive trajectories in older adults.
Method
The study included 771 participants, ages 69.8 ±5.2 years, of the Whitehall II ‐ MRI substudy (2012‐2016) with data on their father’s occupation, their own occupation in mid‐life, lifestyle (smoking, drinking, physical activity, social activity, sleep disturbances, auditory difficulty, depression, obesity, hypertension), neuroimaging (T1 structural MRI) and cognitive abilities (global cognition, crystallized cognitive ability, and fluid intelligence). Social mobility was classified into stable high social class, stable low social class, upward mobility and downward mobility group based on occupational shifts from father. We used the ANCOVA tests to assess the association of social mobility with gray matter volume (GMV) and cortical thickness (CT) in various brain regions, and the growth curve modeling (GCM) to evaluate its relations to cognition over 20 years, adjusting for social lifestyle covariates and associated GMV/CT regions identified above.
Result
Social mobility was significantly related to 48 out of the 136 regions of GMV across the frontal lobe, limbic lobe, and temporal lobe; and to 4 out of the 68 regions of CT at the whole‐brain level. Stable high social class preserved the largest GMV and CT value; pair‐wise results suggest upward mobility preserved a larger GMV value than the downward mobility group (average diff. in cm3: 0.121, P<0.05); and similarly a larger CT value (average diff. in cm:0.029, P = 0.051). In GCMs, stable high social class (ref. downward mobility) had a higher entry score of fluid intelligence (b = 2.617, P<0.05), all other comparisons including the entry score and decline rate of cognitive abilities were non‐significant.
Conclusion
Our results suggest a lack of direct association between social mobility and trajectories of global cognition and crystallized cognitive ability; instead, an indirect effect through preserving the value of brain GMV and CT may occur. Additionally, an adverse causal relationship between social mobility and fluid intelligence might happen.
Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with poor prognosis and lacks effective targeted therapies. The microRNA-200 (miR-200) family is found to inhibit or ...promote breast cancer metastasis; however, the underlying mechanism is not well understood. This study was performed to investigate the effect and mechanism of miR-200b on TNBC metastasis and identify targets for developing more efficient treatment for TNBC. We found that miR-200 family expression levels are significantly lower in highly migratory TNBC cells and metastatic TNBC tumors than other types of breast cancer cells and tumors. Ectopically expressing a single member (miR-200b) of the miR-200 family drastically reduces TNBC cell migration and inhibits tumor metastasis in an orthotopic mouse mammary xenograft tumor model. We identified protein kinase Cα (PKCα) as a new direct target of miR-200b and found that PKCα protein levels are inversely correlated with miR-200b levels in 12 kinds of breast cancer cells. Inhibiting PKCα activity or knocking down PKCα levels significantly reduces TNBC cell migration. In contrast, forced expression of PKCα impairs the inhibitory effect of miR-200b on cell migration and tumor metastasis. Further mechanistic studies revealed that PKCα downregulation by miR-200b results in a significant decrease of Rac1 activation in TNBC cells. These results show that loss of miR-200b expression plays a crucial role in TNBC aggressiveness and that miR-200b suppresses TNBC cell migration and tumor metastasis by targeting PKCα. Our findings suggest that miR-200b and PKCα may serve as promising therapeutic targets for metastatic TNBC.
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