Minor hepatectomy combined with cholangioplasty and cholangiojejunostomy for Bismuth II hilar cholangiocarcinoma: A propensity score matching analysis.

BACKGROUND: The optimal surgical approach for Bismuth II hilar cholangiocarcinoma (HCCA) remains controversial. This study compared perioperative and oncological outcomes between minor and major hepatectomy. MATERIALS AND METHODS: One hundred and seventeen patients with Bismuth II HCCA who underwent hepatectomy and cholangiojejunostomy between January 2018 and December 2022 were retrospectively investigated. Propensity score matching created a cohort of 62 patients who underwent minor (n = 31) or major (n = 31) hepatectomy. Perioperative outcomes, complications, quality of life, and survival outcomes were compared between the groups. Continuous data are expressed as the mean ± standard deviation, categorical variables are presented as n (%). RESULTS: Minor hepatectomy had a significantly shorter operation time (245.42 ± 54.31 vs. 282.16 ± 66.65 min; P = 0.023), less intraoperative blood loss (194.19 ± 149.17 vs. 315.81 ± 256.80 mL; P = 0.022), a lower transfusion rate (4 vs. 11 patients; P = 0.038), more rapid bowel recovery (17.77 ± 10.00 vs. 24.94 ± 9.82 h; P = 0.005), and a lower incidence of liver failure (1 vs. 6 patients; P = 0.045). There were no significant between-group differences in wound infection, bile leak, bleeding, pulmonary infection, intra-abdominal fluid collection, and complication rates. Postoperative laboratory values, length of hospital stay, quality of life scores, 3-year overall survival (25.8 % vs. 22.6 %; P = 0.648), and 3-year disease-free survival (12.9 % vs. 16.1 %; P = 0.989) were comparable between the groups. CONCLUSION: In this propensity score-matched analysis, overall survival and disease-free survival were comparable between minor and major hepatectomy in selected patients with Bismuth II HCCA. Minor hepatectomy was associated with a shorter operation time, less intraoperative blood loss, less need for transfusion, more rapid bowel recovery, and a lower incidence of liver failure. Besides, this findings need confirmation in a large-scale, multicenter, prospective randomized controlled trial with longer-term follow-up.

Effects of long-term closed and socially isolating spaceflight analog environment on default mode network connectivity as indicated by fMRI.

Long-term manned spaceflight and extraterrestrial planet settlement become the focus of space powers. However, the potential influence of closed and socially isolating spaceflight on the brain function remains unclear. A 180-day controlled ecological life support system integrated experiment was conducted, establishing a spaceflight analog environment to explore the effect of long-term socially isolating living. Three crewmembers were enrolled and underwent resting-state fMRI scanning before and after the experiment. We performed both seed-based and network-based analyses to investigate the functional connectivity (FC) changes of the default mode network (DMN), considering its key role in multiple higher-order cognitive functions. Compared with normal controls, the leader of crewmembers exhibited significantly reduced within-DMN and between-DMN FC after the experiment, while two others exhibited opposite trends. Moreover, individual differences of FC changes were further supported by evidence from behavioral analyses. The findings may shed new light on the development of psychological protection for space exploration.

LncTUG1 promotes hepatocellular carcinoma immune evasion via upregulating PD-L1 expression.

HCC is one of the most common malignant tumors worldwide. Although traditional treatment methods have been improved in recent years, the survival rate of HCC patients has not been significantly improved. Immunotherapy has shown extremely high clinical value in a variety of tumors. In this study, we found that TUG1 could regulate the expression of PD-L1 through JAK2/STAT3 to mediate immunosuppression. Here, The expression of TUG1 and PD-L1 in HCC tissues was evaluated through analysis of databases and verified in HCC tissue and HCC cancer cells by qRT-PCR. The effect of TUG1 on tumor immune escape was detected by coculture, and cell viability was detected with a CCK8 assay. The results demonstrated that TUG1 was closely associated with anticancer immunity. TUG1 and PD-L1 were highly expressed in HCC tissues and HCC cancer cells, and high expression of TUG1 and PD-L1 was related to the poor prognosis of HCC patients. In addition, knocking down TUG1 expression could reduce PD-L1 expression and enhance the cancer cell-killing capability of T cells. Downregulating TUG1 expression could also decrease the mRNA and protein expression of JAK2 and STAT3. To sum up, TUG1 and PD-L1 are overexpressed in patients with liver cancer and are related to the poor prognosis of these patients. Silencing TUG1 expression reduced the mRNA and protein expression of PD-L1 by affecting the JAK2/STAT3 pathway.

Pancreatic adenosquamous carcinoma: A population level analysis of epidemiological trends and prognosis.

BACKGROUND: The incidence and mortality of pancreatic adenosquamous carcinoma (PASC) have received little attention. The goal of our study was to explore the overall epidemiological trend of PASC at the population level. METHODS: The Surveillance, Epidemiology, and End Results database was used to collect the incidence, incidence-based (IB) mortality, and patient details for PASC from 2000 to 2017. The Joinpoint regression tool was used to examine the trends in incidence and IB mortality. The Kaplan-Meier approach was used for survival analysis. Univariate and multivariate Cox regression analyses were used to determine the independent prognostic factors. RESULTS: We included 815 patients with PASC in the study. The incidence of PASC continuously increased from 2000 to 2017, with an annual percentage change (APC) of 3.9% (95% CI: 2.2%-5.7%, p < 0.05). IB mortality also increased continuously, with an APC of 5.0% (95% CI: 2.5%-7.6%, p < 0.05). Multivariate Cox regression analysis revealed that age, treatment, regional lymph node involvement, and tumor size were independent prognostic factors. Nomograms were created for PASC to predict 1- and 2-year survival probabilities, respectively. CONCLUSIONS: The incidence and IB mortality of PASC had a sustained and rapid increase, indicating that the preventive and treatment measures for PASC were not ideal. We must identify the significance of this condition as soon as possible, and commit greater attention and resources to PASC research.

Integrating bioinformatic strategies in spatial life science research.

As space exploration programs progress, manned space missions will become more frequent and farther away from Earth, putting a greater emphasis on astronaut health. Through the collaborative efforts of researchers from various countries, the effect of the space environment factors on living systems is gradually being uncovered. Although a large number of interconnected research findings have been produced, their connection seems to be confused, and many unknown effects are left to be discovered. Simultaneously, several valuable data resources have emerged, accumulating data measuring biological effects in space that can be used to further investigate the unknown biological adaptations. In this review, the previous findings and their correlations are sorted out to facilitate the understanding of biological adaptations to space and the design of countermeasures. The biological effect measurement methods/data types are also organized to provide references for experimental design and data analysis. To aid deeper exploration of the data resources, we summarized common characteristics of the data generated from longitudinal experiments, outlined challenges or caveats in data analysis and provided corresponding solutions by recommending bioinformatics strategies and available models/tools.

Effects of 10-Day Complete Fasting on Physiological Homeostasis, Nutrition and Health Markers in Male Adults.

Fasting shows great potential in preventing chronic diseases and has to be surmounted under some extraordinary circumstances. This study aimed to investigate the safety, time effects of metabolic homeostasis and health indexes during prolonged fasting. Thirteen participants were recruited to conduct a 10-day complete fasting (CF) in a controlled health research building under medical supervision including 3-day Baseline (BL), 10-day CF, 4-day calorie restriction (CR) and 5-day full recovery (FR). Body healthy status was assessed by surveying pulse, blood pressure, body weight (BW), blood glucose and ketones, body composition and nutritional and biochemistry indexes at different times. BW declined about 7.28 kg (-9.8%) after 10-day CF, accompanied by increased pulse and decreased systolic blood pressure, but there were no changes to the myocardial enzymogram. Body composition analysis showed fat mass was constantly lost, but lean mass could recover after CR. The energy substrate switch from glucose to ketone occurred and formed a stable dynamic balance between 3-6 days of CF. The lipid metabolism presented increased total cholesterol, LDL-C, ApoA1 and almost no changes to TG and HDL-C. Prolonged CF did not influence liver function, but induced a slight decrease of kidney function. The interesting results came from the marked increase of lipid-soluble vitamins and a significant decrease of sodium and chlorine. Adults could well tol-erate a 10-day CF. A new metabolic homeostasis was achieved. No vitamins but NaCl supplement should be considered. These findings provide evidence to design a new fasting strategy for clinical practice.

Comprehensive analysis of the effects of KIF2C on prognosis, biological functions and immune infiltration in PAAD.

Kinesin family member 2 C (KIF2C), a modulator of microtubule depolymerization, bipolar spindle formation, and chromosome segregation, has been reported to play roles in cancer biology. Moreover, many articles have reported that KIF2C expression is closely associated with the progression and prognosis of a variety of tumors. However, the role of KIF2C in pancreatic cancer is unclear. In this study, we used various databases to investigate the correlation between KIF2C expression and pancreatic cancer. METHODS: First, we determined the location of KIF2C in tumour cell lines via The Human Protein Atlas and immunofluorescence staining. Then, we analysed the GEPIA2 and TISIDB databases to assess KIF2C expression in pancreatic cancer cells and its correlation with the prognosis of pancreatic cancer. Through the Linkdeomics database, we identified the mRNAs whose expression was correlated with KIF2C expression. Next, we used the miRDB, miRanda, miRTairBase, Targetscan and miRmap databases to predict miRNAs that interact with KIF2C. Furthermore, we performed PPI analysis of KIF2C using the STRING database and Cytoscape Mcode software. The TISIDB database was used to analyse the correlation between KIF2C expression and immunity in pancreatic cancer. Finally, datasets GSE62452 and PACA-UA(https://xenabrowser.net/datapages/?cohort=PACA-AU&removeHub=http%3 A%2 F%2F127.0.0.1%3A7222) were used to validate the results of survival analysis and immune analysis. RESULTS: It was revealed that KIF2C was mainly located in the nuclei of pancreatic cancer cells. We found that the KIF2C mRNA expression levels in pancreatic cancer tissues were higher than those in normal tissues. Notably, elevated KIF2C mRNA expression was significantly associated with decreased overall survival and disease-free survival in patients with pancreatic cancer. Furthermore, KIF2C expression was closely related to the expression of multiple proteins and miRNAs in pancreatic cancer tissues. In addition, KIF2C expression was closely related to CD4+T cells. These results indicated that the expression of KIF2C was closely related to the prognosis of pancreatic cancer. CONCLUSION: KIF2C expression is negatively correlated with the prognosis of pancreatic cancer patients, and one of the main mechanisms underlying this relationship may be related to the tumor immune microenvironment.

DNA Methylation Markers and Prediction Model for Depression and Their Contribution for Breast Cancer Risk.

Background: Major depressive disorder (MDD) has become a leading cause of disability worldwide. However, the diagnosis of the disorder is dependent on clinical experience and inventory. At present, there are no reliable biomarkers to help with diagnosis and treatment. DNA methylation patterns may be a promising approach for elucidating the etiology of MDD and predicting patient susceptibility. Our overarching aim was to identify biomarkers based on DNA methylation, and then use it to propose a methylation prediction score for MDD, which we hope will help us evaluate the risk of breast cancer. Methods: Methylation data from 533 samples were extracted from the Gene Expression Omnibus (GEO) database, of which, 324 individuals were diagnosed with MDD. Statistical difference of DNA Methylation between Promoter and Other body region (SIMPO) score for each gene was calculated based on the DNA methylation data. Based on SIMPO scores, we selected the top genes that showed a correlation with MDD in random resampling, then proposed a methylation-derived Depression Index (mDI) by combining the SIMPO of the selected genes to predict MDD. A validation analysis was then performed using additional DNA methylation data from 194 samples extracted from the GEO database. Furthermore, we applied the mDI to construct a prediction model for the risk of breast cancer using stepwise regression and random forest methods. Results: The optimal mDI was derived from 426 genes, which included 245 positive and 181 negative correlations. It was constructed to predict MDD with high predictive power (AUC of 0.88) in the discovery dataset. In addition, we observed moderate power for mDI in the validation dataset with an OR of 1.79. Biological function assessment of the 426 genes showed that they were functionally enriched in Eph Ephrin signaling and beta-catenin Wnt signaling pathways. The mDI was then used to construct a predictive model for breast cancer that had an AUC ranging from 0.70 to 0.67. Conclusion: Our results indicated that DNA methylation could help to explain the pathogenesis of MDD and assist with its diagnosis.

Insulin-resistance and depression cohort data mining to identify nutraceutical related DNA methylation biomarker for type 2 diabetes.

Insulin-resistance (IR) is one of the most important precursors of type 2 diabetes (T2D). Recent evidence suggests an association of depression with the onset of T2D. Accumulating evidence shows that depression and T2D share common biological origins, and DNA methylation examination might reveal the link between lifestyle, disease risk, and potential therapeutic targets for T2D. Here we hypothesize that integrative mining of IR and depression cohort data will facilitate predictive biomarkers identification for T2D. We utilized a newly proposed method to extract gene-level information from probe level data on genome-wide DNA methylation array. We identified a set of genes associated with IR and depression in clinical cohorts. By overlapping the IR-related nutraceutical-gene network with depression networks, we identified a common subnetwork centered with Vitamin D Receptor (VDR) gene. Preliminary clinical validation of gene methylation set in a small cohort of T2D patients and controls was established using the Sequenome matrix-assisted laser desorption ionization-time flight mass spectrometry. A set of sites in the promoter regions of VDR showed a significant difference between T2D patients and controls. Using a logistic regression model, the optimal prediction performance of these sites was AUC = 0.902，and an odds ratio = 19.76. Thus, monitoring the methylation status of specific VDR promoter region might help stratify the high-risk individuals who could potentially benefit from vitamin D dietary supplementation. Our results highlight the link between IR and depression, and the DNA methylation analysis might facilitate the search for their shared mechanisms in the etiology of T2D.

Mining the Selective Remodeling of DNA Methylation in Promoter Regions to Identify Robust Gene-Level Associations With Phenotype.

Epigenetics is an essential biological frontier linking genetics to the environment, where DNA methylation is one of the most studied epigenetic events. In recent years, through the epigenome-wide association study (EWAS), researchers have identified thousands of phenotype-related methylation sites. However, the overlaps of identified phenotype-related DNA methylation sites between various studies are often quite small, and it might be due to the fact that methylation remodeling has a certain degree of randomness within the genome. Thus, the identification of robust gene-phenotype associations is crucial to interpreting pathogenesis. How to integrate the methylation values of different sites on the same gene and to mine the DNA methylation at the gene level remains a challenge. A recent study found that the DNA methylation difference of the gene body and promoter region has a strong correlation with gene expression. In this study, we proposed a Statistical difference of DNA Methylation between Promoter and Other Body Region (SIMPO) algorithm to extract DNA methylation values at the gene level. First, by choosing to smoke as an environmental exposure factor, our method led to significant improvements in gene overlaps (from 5 to 17%) between different datasets. In addition, the biological significance of phenotype-related genes identified by SIMPO algorithm is comparable to that of the traditional probe-based methods. Then, we selected two disease contents (e.g., insulin resistance and Parkinson's disease) to show that the biological efficiency of disease-related gene identification increased from 15.43 to 44.44% (p-value = 1.20e-28). In summary, our results declare that mining the selective remodeling of DNA methylation in promoter regions can identify robust gene-level associations with phenotype, and the characteristic remodeling of a given gene's promoter region can reflect the essence of disease.

Ten days of complete fasting affected subjective sensations but not cognitive abilities in healthy adults.

PURPOSE: People may be unable to obtain anything edible for days under some circumstances, but they must maintain their calmness and cognition to navigate solutions. Our aim was to study changes in subjective sensations and cognition in healthy adults during a 10-day complete fasting experiment. METHODS: Thirteen healthy male volunteers voluntarily participated in the 22-day experiment comprising 4 phases: 3 days of baseline consumption, 10 days of complete fasting (only water ad libitum), 4 days of calorie restriction, and a 5-day recovery period. The volunteers' subjective sensations, cognitive performance, and serum energy substances were measured at 6 time points. RESULTS: Across the 6 time points, the trajectories of subjective sensations in response to fasting were "U"- or " ∩ "-shaped curves instead of progressive discomfort or mood enhancement. A significant fasting time effect was found on depression-dejection (baseline: 16.85 ± 2.88; highest score on the third day of completing fasting: 17.69 ± 3.97, P = 0.04) and self-rated anxiety (baseline: 26.23 ± 4.75; highest score on the sixth day of completing fasting: 30.85 ± 5.58, P = 0.01), and the change curves were consistent with the inflection point of the energy substrates shifting from serum glucose to ketone. In addition, basic cognitive functions appeared to be unaffected during the 10-day fast. CONCLUSIONS: Our study showed strong influences on the sensations from the third to sixth days of the prolonged fasting period but no significant effects on basic cognitive abilities associated with the energy substance switch. These findings could contribute to the development and understanding of survival strategies in food-shortage emergencies or of intermittent fasting programmes.

Blood Cell DNA Methylation of Aging-Related Ubiquitination Gene DZIP3 Can Predict the Onset of Early Stage Colorectal Cancer.

There is a body of evidence that the aging immune system is linked to cancer. In this study, with aging- and immune-related DNA methylation data, we investigated the DNA methylation regulation changes in promoters with other regions of genes during aging and their association with the immune-cell proportion in the circulating whole blood of individuals. The analyses for aging- and CD4+ T cell proportion-derived differential genes showed that ubiquitination plays an important role in the aging immune system and tumorigenesis. Therefore, starting from a set of pre-annotated ubiquitination genes, we found that among the differentially ubiquitinated genes, DZIP3, an E3 ubiquitin ligase with no reports on its function in immune cells and tumorigenesis, was significantly associated with both aging (P-value = 3.86e-06) and CD4+ T cell proportion (P-value = 1.97e-05) in circulating blood. By collecting a cohort of 100 colon cancer patients and 50 healthy individuals, we validated that the 1st exon DNA methylation of DZIP3 could predict the onset of early stage (AUC = 0.833, OR = 8.82) and all pTNM stages of colorectal cancer (AUC = 0.782, OR = 5.70). Thus, the epigenetically regulated ubiquitination machine plays an important role in immune aging and tumorigenesis.

Heat Diffusion Kernel Algorithm-Based Interpretation of the Disease Intervention Mechanism for DHA.

Docosahexaenoic acid (DHA) is effective in the prevention and treatment of cancer, congenital disorders, and various chronic diseases. According to the omnigenic hypothesis, these complex diseases are caused by disordered gene regulatory networks comprising dozens to hundreds of core genes and a mass of peripheral genes. However, conventional research on the disease intervention mechanism of DHA only focused on specific types of genes or pathways instead of examining genes at the network level, resulting in conflicting conclusions. In this study, we used HotNet2, a heat diffusion kernel algorithm, to calculate the gene regulatory networks of connectivity map (cMap)-derived agents (including DHA) based on gene expression profiles, aiming to interpret the disease intervention mechanism of DHA at the network level. As a result, significant gene regulatory networks for DHA and 676 cMap-derived agents were identified respectively. The biological functions of the DHA-regulated gene network provide preliminary insights into the mechanism by which DHA intervenes in disease. In addition, we compared the gene regulatory networks of DHA with those of cMap-derived agents, which allowed us to predict the pharmacological effects and disease intervention mechanism of DHA by analogy with similar agents with clear indications and mechanisms. Some of our analysis results were supported by experimental observations. Therefore, this study makes a significant contribution to research on the disease intervention mechanism of DHA at the regulatory network level, demonstrating the potential application value of this methodology in clarifying the mechanisms about nutrients influencing health.

Physiological Acclimatization of the Liver to 180-Day Isolation and the Mars Solar Day.

Physiological changes in humans are evident under environmental conditions similar to those on a Mars mission involving both a space factor (long-term isolation) and a time factor (the Mars solar day). However, very few studies have investigated the response of the liver to those conditions. Serum protein levels, bilirubin levels, aminotransferase activities, blood alkaline phosphatase, gamma-glutamyltransferase, lipid levels, and serum cytokines interleukin-6 and interferon-γ levels were analyzed 30 days before the mock mission; on days 2, 30, 60, 75, 90, 105, 120, 150, and 175 of the mission; and 30 days after the mission, in four subjects in 4-person 180-day Controlled Ecological Life Support System Experiment. Serum protein levels (total protein and globulin) decreased and bilirubin increased under the isolation environment from day 2 and exhibited chronic acclimatization from days 30 to 175. Effects of the Mars solar day were evident on day 75. Blood lipid levels were somewhat affected. No obvious peak in any enzyme level was detected during the mission. The change tendency of these results indicated that future studies should explore whether protein parameters especially globulin could serve as indicators of immunological function exposure to the stress of a Mars mission.

DNA Methylation Biomarkers Predict Objective Responses to PD-1/PD-L1 Inhibition Blockade.

Immune checkpoint inhibitor (ICI) treatment could bring long-lasting clinical benefits to patients with metastatic cancer. However, only a small proportion of patients respond to PD-1/PD-L1 blockade, so predictive biomarkers are needed. Here, based on DNA methylation profiles and the objective response rates (ORRs) of PD-1/PD-L1 inhibition therapy, we identified 269 CpG sites and developed an initial CpG-based model by Lasso to predict ORRs. Notably, as measured by the area under the receiver operating characteristic curve (AUC), our model can produce better performance (AUC = 0.92) than both a model based on tumor mutational burden (TMB) (AUC = 0.77) and a previously reported TMB model (AUC = 0.71). In addition, most CpGs also have additional synergies with TMB, which can achieve a higher prediction accuracy when joined with TMB. Furthermore, we identified CpGs that are associated with TMB at the individual level. DNA methylation modules defined by protein networks, Kyoto Encylopedia of Genes and Genomes (KEGG) pathways, and ligand-receptor gene pairs are also associated with ORRs. This method suggested novel immuno-oncology targets that might be beneficial when combined with PD-1/PD-L1 blockade. Thus, DNA methylation studies might hold great potential for individualized PD1/PD-L1 blockade or combinatory therapy.

Personalized Epigenome Remodeling Under Biochemical and Psychological Changes During Long-Term Isolation Environment.

It has been reported that several aspects of human health could be disturbed during a long-term isolated environment (for instance, the Mars-500 mission), including psychiatric disorders, circadian disruption, temporal dynamics of gut microbiota, immune responses, and physical-activity-related neuromuscular performance. Nevertheless, the mechanisms underlying these disturbances and the interactions among different aspects of human adaptation to extreme environments remain to be elucidated. Epigenetic features, like DNA methylation, might be a linking mechanism that explains the involvement of environmental factors between the human genome and the outcome of health. We conducted an exploration of personalized longitudinal DNA methylation patterns of the peripheral whole blood cells, profiling six subjects across six sampling points in the Mars-500 mission. Specifically, we developed a Personalized Epigenetic-Phenotype Synchronization Analysis (PeSa) algorithm to explore glucose- and mood-state-synchronized DNA methylation sites, focusing on finding the dynamic associations between epigenetic patterns and phenotypes in each individual, and exploring the underling epigenetic connections between glucose and mood-state disturbance. Results showed that DMPs (differentially methylated-probes) were significantly enriched in pathways related to glucose metabolism (Type II diabetes mellitus pathway), mood state (Long-term depression) and circadian rhythm (Circadian entrainment pathway) during the mission. Furthermore, our data revealed individualized glucose-synchronized and mood-state-synchronized DNA methylation sites, and PTPRN2 was found to be associated with both glucose and mood state disturbances across all six subjects. Our findings suggest that personalized phenotype-synchronized epigenetic features could reflect the effects on the human body, including the disturbances of glucose and mood-states. The association analysis of DNA methylation and phenotypes, like the PeSa analysis, could provide new possibilities in understanding the intrinsic relationship between phenotypic changes of the human body adapting to long-term isolation environmental factors.

Integrated Analysis of DNA Methylation and Biochemical/Metabolic Parameter During the Long-Term Isolation Environment.

Numerous studies have shown that changes in the epigenome are an important cause of human biochemical or metabolic parameter changes. Biochemical/metabolic parameter disorders of the human body are usually closely related to the occurrence of disease. Therefore, constructing credible DNA methylation site-biochemical/metabolic parameter associations are key in interpreting the pathogenesis of diseases. However, there is a lack of research on systematic integration analysis of DNA methylation with biochemical/metabolic parameter and diseases. In this study, we attempted to use the four-people, multiple time point detected data from the long-term isolation experiment to conduct a correlation analysis. We used the biclustering algorithm FABIA to cluster the DNA methylation site-parameter correlation matrixes into 28 biclusters. The results of the biological function analysis for these biclusters were consistent with the biochemical/metabolic parameter change characteristics of the human body during long-term isolation, demonstrating the reliability of the biclusters identified by our method. In addition, from these biclusters, we obtained highly credible biochemical/metabolic parameter-disease associations, which is supported by several studies. Our results indicate that there is an overlap of biochemical/metabolic parameter-disease associations derived from a small sample, multiple time point data in healthy populations and the associations obtained from a large sample data in patients during disease development. These findings provide insights into understanding the role of the epigenome in biochemical/metabolic parameter change and disease development and has potential applications in biology and medicine research.

Identifying cancer prognostic modules by module network analysis.

BACKGROUND: The identification of prognostic genes that can distinguish the prognostic risks of cancer patients remains a significant challenge. Previous works have proven that functional gene sets were more reliable for this task than the gene signature. However, few works have considered the cross-talk among functional gene sets, which may result in neglecting important prognostic gene sets for cancer. RESULTS: Here, we proposed a new method that considers both the interactions among modules and the prognostic correlation of the modules to identify prognostic modules in cancers. First, dense sub-networks in the gene co-expression network of cancer patients were detected. Second, cross-talk between every two modules was identified by a permutation test, thus generating the module network. Third, the prognostic correlation of each module was evaluated by the resampling method. Then, the GeneRank algorithm, which takes the module network and the prognostic correlations of all the modules as input, was applied to prioritize the prognostic modules. Finally, the selected modules were validated by survival analysis in various data sets. Our method was applied in three kinds of cancers, and the results show that our method succeeded in identifying prognostic modules in all the three cancers. In addition, our method outperformed state-of-the-art methods. Furthermore, the selected modules were significantly enriched with known cancer-related genes and drug targets of cancer, which may indicate that the genes involved in the modules may be drug targets for therapy. CONCLUSIONS: We proposed a useful method to identify key modules in cancer prognosis and our prognostic genes may be good candidates for drug targets.

Circulating microRNAs Correlated with Bone Loss Induced by 45 Days of Bed Rest.

The purpose of this study was to find the circulating microRNAs (miRNAs) co-related with bone loss induced by bed rest, and testify whether the selected miRNAs could reflect the bone mineral status of human after bed-rest. We analyzed plasma miRNA levels of 16 subjects after 45 days of -6° head-down tilt bed rest, which is a reliable model for the simulation of microgravity. We characterize the circulating miRNA profile in individuals after bed rest and identify circulating miRNAs which can best reflect the level of bone loss induced by bed rest. Expression profiling of circulating miRNA revealed significant downregulation of 37 miRNAs and upregulation of 2 miRNAs, while only 11 of the downregulated miRNAs were further validated in a larger volunteer cohort using qPCR. We found that 10 of these 11 miRNAs (miR-103, 130a, 1234, 1290, 151-5p, 151-3p, 199a-3p, 20a, 363, and 451a) had ROC curve that distinguished the status after bed rest. Importantly, significant positive correlations were identified between bone loss parameters and several miRNAs, eventually miR-1234 showed clinical significance in detecting the bone loss of individuals after 45 days of bed rest.