Soluble form of suppression of tumorigenicity-2 predicts clinical stability of inpatients with community-acquired pneumonia

The soluble form of the suppression of tumorigenicity-2 (sST2) is a biomarker for risk classification and prognosis of heart failure, and its production and secretion in the alveolar epithelium are significantly correlated with the inflammation-inducing in pulmonary diseases. However, the predictive value of sST2 in pulmonary disease had not been widely studied. This study investigated the potential value in prognosis and risk classification of sST2 in patients with community-acquired pneumonia. Clinical data of ninety-three CAP inpatients were retrieved and their sST2 and other clinical indices were studied. Cox regression models were constructed to probe the sST2’s predictive value for patients’ restoring clinical stability and its additive effect on pneumonia severity index and CURB-65 scores. Patients who did not reach clinical stability within the defined time (30 days from hospitalization) have had significantly higher levels of sST2 at admission (P < 0.05). In univariate and multivariate Cox regression analysis, a high sST2 level (≥72.8 ng/mL) was an independent reverse predictor of clinical stability (P < 0.05). The Cox regression model combined with sST2 and CURB-65 (AUC: 0.96) provided a more accurate risk classification than CURB-65 (AUC:0.89) alone (NRI: 1.18, IDI: 0.16, P < 0.05). The Cox regression model combined with sST2 and pneumonia severity index (AUC: 0.96) also provided a more accurate risk classification than pneumonia severity index (AUC:0.93) alone (NRI: 0.06; IDI: 0.06, P < 0.05). sST2 at admission can be used as an independent early prognostic indicator for CAP patients. Moreover, it can improve the predictive power of CURB-65 and pneumonia severity index score.     

A Seven-microRNA Expression Signature Predicts Survival in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the fifth common cancer. The differential expression of microRNAs (miRNAs) has been associated with the prognosis of various cancers. However, limited information is available regarding genome-wide miRNA expression profiles in HCC to generate a tumor-specific miRNA signature of prognostic values. In this study, the miRNA profiles in 327 HCC patients, including 327 tumor and 43 adjacent non-tumor tissues, from The Cancer Genome Atlas (TCGA) Liver hepatocellular carcinoma (LIHC) were analyzed. The associations of the differentially expressed miRNAs with patient survival and other clinical characteristics were examined with t-test and Cox proportional regression model. Finally, a tumor-specific miRNA signature was generated and examined with Kaplan–Meier survival, univariate\multivariate Cox regression analyses and KEGG pathway analysis. Results showed that a total of 207 miRNAs were found differentially expressed between tumor and adjacent non-tumor HCC tissues. 78 of them were also discriminatively expressed with gender, race, tumor grade and AJCC tumor stage. Seven miRNAs were significantly associated with survival (P value <0.001). Among the seven significant miRNAs, six (hsa-mir-326, hsa-mir-3677, hsa-mir-511-1, hsa-mir-511-2, hsa-mir-9-1, and hsa-mir-9-2) were negatively associated with overall survival (OS), while the remaining one (hsa-mir-30d) was positively correlated. A tumor-specific 7-miRNAs signature was generated and validated as an independent prognostic predictor. Collectively, we have identified and validated an independent prognostic model based on the expression of seven miRNAs, which can be used to assess patients’ survival. Additional work is needed to translate our model into clinical practice.     

Identification of a three-miRNA signature as a novel potential prognostic biomarker in patients with clear cell renal cell carcinoma

Current studies suggest that some microRNAs (miRNAs) are associated with prognosis in clear cell renal cell carcinoma (ccRCC). In this paper, we aimed to identify a miRNAs signature to improve prognostic prediction for ccRCC patients. Using ccRCC RNA-Seq data of The Cancer Genome Atlas (TCGA) database, we identified 177 differentially expressed miRNAs between ccRCC and paracancerous tissue. Then all the ccRCC tumor samples were divided into training set and validation set randomly. Three-miRNA signature including miR130b, miR-18a, and miR-223 were constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression model in training set. According to optimal cut-off value of three-miRNA signature risk score, all the patients could be classified into high-risk group and low-risk group significantly. Survival of patients was significantly different between two groups (hazard ratio, 5.58, 95% confidence interval, 3.17-9.80; P < 0.0001), and three-miRNA signature performed favorably prognostic and predictive accuracy. The results were further validated in the validation set and total set. Multivariate Cox regression analyses and subgroup analyses showed that three-miRNA signature was an independent prognostic factor. Two nomograms that integrated three-miRNA signature and three clinicopathological risk factors were constructed to predict overall survival and disease-free survival after surgery for ccRCC patients. Functional enrichment analysis showed the possible roles of three-miRNA signature in some cancer-associated biological processes and pathways. In conclusion, we developed a novel three-miRNA signature that performed reliable prognostic for patient survival with ccRCC, it might facilitate ccRCC patients counseling and individualize management.     

A novel microRNA signature predicts vascular invasion in hepatocellular carcinoma

Vascular invasion (VI) in hepatocellular carcinoma (HCC) is an important clinical parameter to predict survival. In this study, we collected microRNA (miRNA) expression data from HCC patients using The Cancer Genome Atlas database and identified a novel miRNA signature associated with VI. First, we categorized HCC patients into groups with or without VI (VI+ and VI−). We identified three miRNAs (miRNA-210, miRNA-10b, and miRNA-9-1) that were associated with VI according to a Kaplan–Meier analysis. This three-miRNA signature exhibited good predictive ability for VI in patients with HCC according to a receiver operating characteristic curve analysis at 1, 3, and 5 years. Patients with HCC with a high risk score exhibited a trend toward worse outcomes as determined by multivariable Cox regression and stratified analyses. This three-miRNA signature provides an accurate prediction of VI and can be used as an independent prognostic indicator for predicting VI in HCC patients.     

Activation of Akt/mTOR Pathway Is Associated with Poor Prognosis of Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma (NPC) is a malignant tumor of the head and neck region, which frequently occurs in Southeast Asia, especially in the south of China. It is known that the mammalian target of rapamycin (mTOR) pathway plays a central role in regulating cellular functions, including proliferation, growth, survival, mobility, and angiogenesis. Aberrant expression of the mTOR signaling pathway molecules has been found in many types of cancer. However, whether the alterations of p-Akt, p-p70S6K and p-4EBP1 protein expression are associated with clinicopathological features and prognostic implications in NPC have not been reported. The purposes of the present study are to investigate the association between the expression of p-Akt, p-p70S6K and p-4EBP1 proteins and clinicopathological features in NPC by immunohistochemistry. The results showed that the positive percentage of p-Akt, p-p70S6K and p-4EBP1 proteins expression in NPC (47.2%, 73.0% and 61.7%, respectively) was significantly higher than that in the non-cancerous nasopharyngeal control tissue (33.3%, 59.1% and 47.0%, respectively). There was a significantly higher positive expression of p-Akt in undifferentiated non-keratinizing nasopharyngeal carcinoma than that in differentiated non-keratinizing nasopharyngeal carcinoma (P = 0.014). Additionally, positive expression of p-p70S6K and p-4EBP1 proteins, and positive expression of either of p-Akt, p-p70S6K and p-4EBP1 were significantly correlated inversely with overall survival rates of NPC patients (P = 0.023, P = 0.033, P = 0.008, respectively). Spearman’s rank correlation test showed that expression of p-Akt in NPC was significantly associated with expression of p-p70S6K (r = 0.263, P<0.001) and p-4EBP1(r = 0.284, P<0.001). Also there was an obviously positive association between expression of p-p70S6K and p-4EBP1 proteins in NPC (r = 0.286, P<0.001). Multivariate Cox regression analysis further identified positive expression of p-4EBP1 and p-p70S6K proteins were the independent poor prognostic factors for NPC (P = 0.043, P = 0.027, respectively). Taken together, high expression of p-p70S6K and p-4EBP1 proteins may act as valuable independent biomarkers to predict a poor prognosis of NPC.     

The Glasgow Prognostic Score Predicts Poor Survival in Cisplatin-Based Treated Patients with Metastatic Nasopharyngeal Carcinoma

Background

Several inflammation-based prognostic scoring systems, including Glasgow Prognostic Score (GPS), neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) have been reported to predict survival in many malignancies, whereas their role in metastatic nasopharyngeal carcinoma (NPC) remains unclear. The aim of this study is to evaluate the clinical value of these prognostic scoring systems in a cohort of cisplatin-based treated patients with metastatic NPC.

Methods

Two hundred and eleven patients with histologically proven metastatic NPC treated with first-line cisplatin-based chemotherapy were retrospectively evaluated. Demographics, disease-related characteristics and relevant laboratory data before treatment were recorded. GPS, NLR and PLR were calculated as described previously. Response to first-line therapy and survival data were also collected. Survival was analyzed in Cox regressions and stability of the models was examined by bootstrap resampling. The area under the receiver operating characteristics curve (AUC) was calculated to compare the discriminatory ability of each scoring system.

Results

Among the above three inflammation-based prognostic scoring systems, GPS (P<0.001) and NLR (P = 0.019) were independently associated with overall survival, which showed to be stable in a bootstrap resampling study. The GPS consistently showed a higher AUC value at 6-month (0.805), 12-month (0.705), and 24-month (0.705) in comparison with NLR and PLR. Further analysis of the association of GPS with progression-free survival showed GPS was also associated independently with progression-free survival (P<0.001).

Conclusions

Our study demonstrated that the GPS may be of prognostic value in metastatic NPC patients treated with cisplatin-based palliative chemotherapy and facilitate individualized treatment. However a prospective study to validate this prognostic model is still needed.     

12 Survival-related differentially expressed genes based on the TARGET-osteosarcoma database

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project aims to determine molecular changes that drive childhood cancers, including osteosarcoma. The main purpose of the program is to use the open-source database to develop novel, effective, and less toxic therapies. We downloaded TARGET-OS RNA-Sequencing data through R studio and merged the mRNA expression of genes with clinical information (vital status, survival time and gender). Further, we analyzed differential gene expressions between dead and alive patients based on TARGET-OS project. By this study, we found 5758 differentially expressed genes between deceased and alive patients with a false discovery rate below 0.05; 4469 genes were upregulated in deceased patients compared to alive, whereas 1289 genes were downregulated. The survival-related genes were obtained using Kaplan–Meier survival analysis and Cox univariate regression (KM < 0.05 and Cox P-value < 0.05). Out of 5758 differentially expressed genes, only 217 have been associated with overall survival. Eight survival-related downregulated genes (ERCC4, CLUAP1, CTNNBIP1, GCA, RAB40C, SIRPA, USP11, and TCN2) and four survival-related upregulated genes (MUC1, COL13A1, JAG2 and KAZALD1) were selected for further analysis as potential independent prognostic candidate genes. This study may help to discover novel prognostic markers and potential therapeutic targets for osteosarcoma.     

Genes Influenced by the Non-Muscle Isoform of Myosin Light Chain Kinase Impact Human Cancer Prognosis

The multifunctional non-muscle isoform of myosin light chain kinase (nmMLCK) is critical to the rapid dynamic coordination of the cytoskeleton involved in cancer cell proliferation and migration. We identified 45 nmMLCK-influenced genes by bioinformatic filtering of genome–wide expression in wild type and nmMLCK knockout (KO) mice exposed to preclinical models of murine acute inflammatory lung injury, pathologies that are well established to include nmMLCK as an essential participant. To determine whether these nmMLCK-influenced genes were relevant to human cancers, the 45 mouse genes were matched to 38 distinct human orthologs (M38 signature) (GeneCards definition) and underwent Kaplan-Meier survival analysis in training and validation cohorts. These studies revealed that in training cohorts, the M38 signature successfully identified cancer patients with poor overall survival in breast cancer (P<0.001), colon cancer (P<0.001), glioma (P<0.001), and lung cancer (P<0.001). In validation cohorts, the M38 signature demonstrated significantly reduced overall survival for high-score patients of breast cancer (P = 0.002), colon cancer (P = 0.035), glioma (P = 0.023), and lung cancer (P = 0.023). The association between M38 risk score and overall survival was confirmed by univariate Cox proportional hazard analysis of overall survival in the both training and validation cohorts. This study, providing a novel prognostic cancer gene signature derived from a murine model of nmMLCK-associated lung inflammation, strongly supports nmMLCK-involved pathways in tumor growth and progression in human cancers and nmMLCK as an attractive candidate molecular target in both inflammatory and neoplastic processes.     

Identification of 6 gene markers for survival prediction in osteosarcoma cases based on multi-omics analysis

Multiple-omics sequencing information with high-throughput has laid a solid foundation to identify genes associated with cancer prognostic process. Multiomics information study is capable of revealing the cancer occurring and developing system according to several aspects. Currently, the prognosis of osteosarcoma is still poor, so a genetic marker is needed for predicting the clinically related overall survival result. First, Office of Cancer Genomics (OCG Target) provided RNASeq, copy amount variations information, and clinically related follow-up data. Genes associated with prognostic process and genes exhibiting copy amount difference were screened in the training group, and the mentioned genes were integrated for feature selection with least absolute shrinkage and selection operator (Lasso). Eventually, effective biomarkers received the screening process. Lastly, this study built and demonstrated one gene-associated prognosis mode according to the set of the test and gene expression omnibus validation set; 512 prognosis-related genes (P < 0.01), 336 copies of amplified genes (P < 0.05), and 36 copies of deleted genes (P < 0.05) were obtained, and those genes of the mentioned genomic variants display close associations with tumor occurring and developing mechanisms. This study generated 10 genes for candidates through the integration of genomic variant genes as well as prognosis-related genes. Six typical genes (i.e. MYC, CHIC2, CCDC152, LYL1, GPR142, and MMP27) were obtained by Lasso feature selection and stepwise multivariate regression study, many of which are reported to show a relationship to tumor progressing process. The authors conducted Cox regression study for building 6-gene sign, i.e. one single prognosis-related element, in terms of cases carrying osteosarcoma. In addition, the samples were able to be risk stratified in the training group, test set, and externally validating set. The AUC of five-year survival according to the training group and validation set reached over 0.85, with superior predictive performance as opposed to the existing researches. Here, 6-gene sign was built to be new prognosis-related marking elements for assessing osteosarcoma cases’ surviving state.     

Two Panels of Plasma MicroRNAs as Non-Invasive Biomarkers for Prediction of Recurrence in Resectable NSCLC

The diagnosis of non-small cell lung carcinoma (NSCLC) at an early stage, as well as better prediction of outcome remains clinically challenging due to the lack of specific and robust non-invasive markers. The discovery of microRNAs (miRNAs), particularly those found in the bloodstream, has opened up new perspectives for tumor diagnosis and prognosis. The aim of our study was to determine whether expression profiles of specific miRNAs in plasma could accurately discriminate between NSCLC patients and controls, and whether they are able to predict the prognosis of resectable NSCLC patients. We therefore evaluated a series of seventeen NSCLC-related miRNAs by quantitative real-time (qRT)-PCR in plasma from 52 patients with I-IIIA stages NSCLC, 10 patients with chronic obstructive pulmonary disease (COPD) and 20-age, sex and smoking status-matched healthy individuals. We identified an eleven-plasma miRNA panel that could distinguish NSCLC patients from healthy subjects (AUC = 0.879). A six-plasma miRNA panel was able to discriminate between NSCLC patients and COPD patients (AUC = 0.944). Furthermore, we identified a three-miRNA plasma signature (high miR-155-5p, high miR-223-3p, and low miR-126-3p) that significantly associated with a higher risk for progression in adenocarcinoma patients. In addition, a three-miRNA plasma panel (high miR-20a-5p, low miR-152-3p, and low miR-199a-5p) significantly predicted survival of squamous cell carcinoma patients. In conclusion, we identified two plasma miRNA expression profiles that may be useful for predicting the outcome of patients with resectable NSCLC.     

Unraveling the Hidden Heterogeneities of Breast Cancer Based on Functional miRNA Cluster

It has become increasingly clear that the current taxonomy of clinical phenotypes is mixed with molecular heterogeneity, which potentially affects the treatment effect for involved patients. Defining the hidden molecular-distinct diseases using modern large-scale genomic approaches is therefore useful for refining clinical practice and improving intervention strategies. Given that microRNA expression profiling has provided a powerful way to dissect hidden genetic heterogeneity for complex diseases, the aim of the study was to develop a bioinformatics approach that identifies microRNA features leading to the hidden subtyping of complex clinical phenotypes. The basic strategy of the proposed method was to identify optimal miRNA clusters by iteratively partitioning the sample and feature space using the two-ways super-paramagnetic clustering technique. We evaluated the obtained optimal miRNA cluster by determining the consistency of co-expression and the chromosome location among the within-cluster microRNAs, and concluded that the optimal miRNA cluster could lead to a natural partition of disease samples. We applied the proposed method to a publicly available microarray dataset of breast cancer patients that have notoriously heterogeneous phenotypes. We obtained a feature subset of 13 microRNAs that could classify the 71 breast cancer patients into five subtypes with significantly different five-year overall survival rates (45%, 82.4%, 70.6%, 100% and 60% respectively; p = 0.008). By building a multivariate Cox proportional-hazards prediction model for the feature subset, we identified has-miR-146b as one of the most significant predictor (p = 0.045; hazard ratios = 0.39). The proposed algorithm is a promising computational strategy for dissecting hidden genetic heterogeneity for complex diseases, and will be of value for improving cancer diagnosis and treatment.     

High atherogenic index of plasma and cardiovascular risk factors among Ghanaian breast cancer patients

Comorbidities impact negatively on breast cancer prognosis, especially in developing countries where cases are usually presented to clinics at advanced stages. This study aimed to determine the atherogenic index of plasma (AIP) and cardiovascular risk factors among Ghanaian women diagnosed with breast cancer. A total of 52 breast cancer patients were age-matched with 52 healthy controls. Sociodemographics of participants were obtained using a well-structured questionnaire. Pathological data of patients were obtained from medical records, and all clinical and anthropometric measurements were done using standard instruments. Lipid profile was determined from serum using enzymatic assays, and cardiovascular risk factors were calculated from estimated lipid parameters. Blood pressure, AIP, total cholesterol (T. chol), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-c) were significantly elevated (P < 0.05) in the breast cancer patients compared to the controls, but the reverse was observed for high-density lipoprotein cholesterol (HDL-c) (P < 0.01). Obesity (odds ratio [OR] = 2.51, P = 0.015), hypertension (OR = 4.04, P < 0.001), AIP (OR = 10.44, P < 0.001), and dyslipidemia (P < 0.01) were significantly associated with breast cancer. AIP correlated positively with age (r = 0.244, P < 0.05), body mass index (r = 0.225, P < 0.05), blood pressure (P < 0.01), T. chol (r =0.418, P< 0.01), and TG (r = 0.880, P < 0.01), but inversely correlated with HDL-c (r = −0.460, P < 0.01). A greater proportion (88%) of the patients presented with advanced breast cancer. AIP and cardiovascular risk factors were high in the breast cancer patients. Considering that AIP and cardiovascular disease risk factors are of interest in breast cancer patients, further studies are needed to understand the effect of AIP and cardiovascular risk factors on breast cancer outcomes.     

Upregulation of KLHDC4 Predicts a Poor Prognosis in Human Nasopharyngeal Carcinoma

Kelch proteins are implicated in the pathogenesis of many human diseases, including cancer. Nasopharyngeal carcinoma (NPC) is a rare malignancy in most countries, but prevalent in southern China and certain areas of Southeast Asia. In this study, we identified Kelch Domain Containing 4 (KLHDC4), an orphan member of the kelch repeat superfamily, as a prognosis marker for NPC. We examined the expression of KLHDC4 in 168 NPC cases by immunohistochemical staining and found a substantially higher level of KLHDC4 in NPC biopsies compared to adjacent normal nasopharyngeal mucosa. KLHDC4 expression was significantly related to the T classification (P <0.05), N classification (P <0.05) and total staging (P <0.01) in NPC, and patients with higher KLHDC4 expression had poorer overall (P <0.01) and metastasis-free survival (P <0.05) rates. Knockout (KO) of KLHDC4 via CRISPR/Cas9-mediated gene editing in NPC cell line dramatically inhibited cell proliferation, colony formation in soft agar and tumor formation in nude mice. In addition, cell migration and invasion were also impaired by KLHDC4 depletion as revealed by wound healing and Transwell assay. Mechanically, loss of KLHDC4 markedly induced spontaneous apoptosis in NPC cells, as evidenced by increased levels of cleaved caspase-3 and cleaved PARP. Consistently, KLHDC4 knockout cell-derived xenografts also showed elevated cleaved caspase-3 and PARP but reduced Ki-67 staining. In conclusion, our results suggest that KLHDC4 promotes NPC oncogenesis by suppressing cellular apoptosis. Thus, KLHDC4 may serve as a prognosis biomarker and a potential therapeutic target for NPC.     

Hypofunction of directed brain network within alpha frequency band in depressive patients: a graph-theoretic analysis

Directed brain networks may provide new insights into exploring physiological mechanism and neuromarkers for depression. This study aims to investigate the abnormalities of directed brain networks in depressive patients. We constructed the directed brain network based on resting electroencephalogram for 19 depressive patients and 20 healthy controls with eyes closed and eyes open. The weighted directed brain connectivity was measured by partial directed coherence for α, β, γ frequency band. Furthermore, topological parameters (clustering coefficient, characteristic path length, and et al.) were computed based on graph theory. The correlation between network metrics and clinical symptom was also examined. Depressive patients had a significantly weaker value of partial directed coherence at alpha frequency band in eyes-closed state. Clustering coefficient and characteristic path length were significantly lower in depressive patients (both p < .01). More importantly, in depressive patients, disruption of directed connectivity was noted in left-to-left (p < .05), right-to-left (p < .01) hemispheres and frontal-to-central (p < .01), parietal-to-central (p < .05), occipital-to-central (p < .05) regions. Furthermore, connectivity in LL and RL hemispheres was negatively correlated with depression scale scores (both p < .05). Depressive patients showed a more randomized network structure, disturbed directed interaction of left-to-left, right-to-left hemispheric information and between different cerebral regions. Specifically, left-to-left, right-to-left hemispheric connectivity was negatively correlated with the severity of depression. Our analysis may serve as a potential neuromarker of depression.     

ATIC inhibits autophagy in hepatocellular cancer through the AKT/FOXO3 pathway and serves as a prognostic signature for modeling patient survival

Background: Autophagy regulates many cell functions related to cancer, ranging from cell proliferation and angiogenesis to metabolism. Due to the close relationship between autophagy and tumors, we investigated the predictive value of autophagy-related genes.Methods: Data from patients with hepatocellular carcinoma were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. A regression analysis of differentially expressed genes was performed. Based on a prognostic model, patients were divided into a high-risk or low-risk group. Kaplan-Meier survival analyses of patients were conducted. The immune landscapes, as determined using single-sample gene set enrichment analysis (ssGSEA), exhibited different patterns in the two groups. The prognostic model was verified using the ICGC database and clinical data from patients collected at Zhongnan Hospital. Based on the results of multivariate Cox regression analysis, 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/inosine monophosphate (IMP) cyclohydrolase (ATIC) had the largest hazard ratio, and thus we studied the effect of ATIC on autophagy and tumor progression by performing in vitro and in vivo experiments.Results: Fifty-eight autophagy-related genes were differentially expressed (false discovery rate (FDR)<0.05, log₂ fold change (logFC)>1); 23 genes were related to the prognosis of patients. A prognostic model based on 12 genes (ATG10, ATIC, BIRC5, CAPN10, FKBP1A, GAPDH, HDAC1, PRKCD, RHEB, SPNS1, SQSTM1 and TMEM74) was constructed. A significant difference in survival rate was observed between the high-risk group and low-risk group distinguished by the model (P<0.001). The model had good predictive power (area under the curve (AUC)>0.7). Risk-related genes were related to the terms type II IFN response, MHC class I (P<0.001) and HLA (P<0.05). ATIC was confirmed to inhibit autophagy and promote the proliferation, invasion and metastasis of liver cancer cells through the AKT/Forkhead box subgroup O3 (FOXO3) signaling pathway in vitro and in vivo.Conclusions: The prediction model effectively predicts the survival time of patients with liver cancer. The risk score reflects the immune cell features and immune status of patients. ATIC inhibits autophagy and promotes the progression of liver cancer through the AKT/FOXO3 signaling pathway.     

The association of plasma biomarkers with computed tomography-assessed emphysema phenotypes

Rationale

Chronic obstructive pulmonary disease (COPD) is a phenotypically heterogeneous disease. In COPD, the presence of emphysema is associated with increased mortality and risk of lung cancer. High resolution computed tomography (HRCT) scans are useful in quantifying emphysema but are associated with radiation exposure and high incidence of false positive findings (i.e., nodules). Using a comprehensive biomarker panel, we sought to determine if there was a peripheral blood biomarker signature of emphysema.

Methods

114 plasma biomarkers were measured using a custom assay in 588 individuals enrolled in the COPDGene study. Quantitative emphysema measurements included percent low lung attenuation (%LAA) ≤ −950 HU, ≤ − 910 HU and mean lung attenuation at the 15^th percentile on lung attenuation curve (LP15A). Multiple regression analysis was performed to determine plasma biomarkers associated with emphysema independent of covariates age, gender, smoking status, body mass index and FEV₁. The findings were subsequently validated using baseline blood samples from a separate cohort of 388 subjects enrolled in the Treatment of Emphysema with a Selective Retinoid Agonist (TESRA) study.

Results

Regression analysis identified multiple biomarkers associated with CT-assessed emphysema in COPDGene, including advanced glycosylation end-products receptor (AGER or RAGE, p < 0.001), intercellular adhesion molecule 1 (ICAM, p < 0.001), and chemokine ligand 20 (CCL20, p < 0.001). Validation in the TESRA cohort revealed significant associations with RAGE, ICAM1, and CCL20 with radiologic emphysema (p < 0.001 after meta-analysis). Other biomarkers that were associated with emphysema include CDH1, CDH 13 and SERPINA7, but were not available for validation in the TESRA study. Receiver operating characteristics analysis demonstrated a benefit of adding a biomarker panel to clinical covariates for detecting emphysema, especially in those without severe airflow limitation (AUC 0.85).

Conclusions

Our findings, suggest that a panel of blood biomarkers including sRAGE, ICAM1 and CCL20 may serve as a useful surrogate measure of emphysema, and when combined with clinical covariates, may be useful clinically in predicting the presence of emphysema compared to just using covariates alone, especially in those with less severe COPD. Ultimately biomarkers may shed light on disease pathogenesis, providing targets for new treatments.

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-014-0127-9) contains supplementary material, which is available to authorized users.     

Downregulation of six microRNAs is associated with advanced stage, lymph node metastasis and poor prognosis in small cell carcinoma of the cervix

Background

Small cell carcinoma of the cervix (SCCC) is very rare, and due to the long time period required to recruit sufficient numbers of patients, there is a paucity of information regarding the prognostic factors associated with survival. MicroRNAs (miRNAs) have been used as cancer-related biomarkers in a variety of tumor types, and the objective of this study was to determine whether microRNA expression profiles can predict clinical outcome in SCCC.

Methodology/Principal Findings

Forty-four patients with SCCC who underwent radical hysterectomy between January 2000 and October 2009 were enrolled. Using the GeneCopoeia All-in-One™ Customized Human qPCR Primer Array, the expression profiles of 30 miRNAs associated with tumor metastasis was obtained from the formalin-fixed paraffin embedded samples of all 44 patients. Seven miRNAs, has-let-7c, has-miR-10b, has-miR-100, has-miR-125b, has-miR-143, has-miR-145 and has-miR-199a-5p were significantly down-regulated in advanced stage SCCCpatients (FIGO IB2-IV) compared to early stage SCCC patients (FIGOIB1). Among, downregulation of six miRNAs, has-let-7c, has-miR-100, has-miR-125b, has-miR-143, has-miR-145 and has-miR-199a-5p were significantly associated with lymph node metastasis and reduced survival in SCCC. Kaplan–Meier survival analyses revealed that SCCC patients with low expression of has-miR-100 (P = 0.019) and has-miR-125b (P = 0.020) projected a significant tendency towards poorer prognosis.

Conclusions/Significance

This study demonstrates that downregulation of 7 miRNA associated with advanced stage, 6 miRNAs with metastasis and 2 with poor prognosis in SCCC. Functional analysis of these miRNAs may enhance our understanding of SCCC, as altered expression of specific miRNAs may regulate the metastatic pathway and provide novel targets for therapy.     

Relationships between kinetic constants and the amino acid composition of enzymes from the yeast Saccharomyces cerevisiae glycolysis pathway

The kinetic models of metabolic pathways represent a system of biochemical reactions in terms of metabolic fluxes and enzyme kinetics. Therefore, the apparent differences of metabolic fluxes might reflect distinctive kinetic characteristics, as well as sequence-dependent properties of the employed enzymes. This study aims to examine possible linkages between kinetic constants and the amino acid (AA) composition (AAC) for enzymes from the yeast Saccharomyces cerevisiae glycolytic pathway. The values of Michaelis-Menten constant (KM), turnover number (kcat), and specificity constant (ksp = kcat/KM) were taken from BRENDA (15, 17, and 16 values, respectively) and protein sequences of nine enzymes (HXK, GADH, PGK, PGM, ENO, PK, PDC, TIM, and PYC) from UniProtKB. The AAC and sequence properties were computed by ExPASy/ProtParam tool and data processed by conventional methods of multivariate statistics. Multiple linear regressions were found between the log-values of kcat (3 models, 85.74% < Radj.2 <94.11%, p < 0.00001), KM (1 model, Radj.2 = 96.70%, p < 0.00001), ksp (3 models, 96.15% < Radj.2 < 96.50%, p < 0.00001), and the sets of AA frequencies (four to six for each model) selected from enzyme sequences while assessing the potential multicollinearity between variables. It was also found that the selection of independent variables in multiple regression models may reflect certain advantages for definite AA physicochemical and structural propensities, which could affect the properties of sequences. The results support the view on the actual interdependence of catalytic, binding, and structural residues to ensure the efficiency of biocatalysts, since the kinetic constants of the yeast enzymes appear as closely related to the overall AAC of sequences.