Genomic Selection Signals in Andean Highlanders Reveal Adaptive Placental Metabolic Phenotypes That Are Disrupted in Preeclampsia.

BACKGROUND: The chronic hypoxia of high-altitude residence poses challenges for tissue oxygen supply and metabolism. Exposure to high altitude during pregnancy increases the incidence of hypertensive disorders of pregnancy and fetal growth restriction and alters placental metabolism. High-altitude ancestry protects against altitude-associated fetal growth restriction, indicating hypoxia tolerance that is genetic in nature. Yet, not all babies are protected and placental pathologies associated with fetal growth restriction occur in some Andean highlanders. METHODS: We examined placental metabolic function in 79 Andeans (18-45 years; 39 preeclamptic and 40 normotensive) living in La Paz, Bolivia (3600-4100 m) delivered by unlabored Cesarean section. Using a selection-nominated approach, we examined links between putatively adaptive genetic variation and phenotypes related to oxygen delivery or placental metabolism. RESULTS: Mitochondrial oxidative capacity was associated with fetal oxygen delivery in normotensive but not preeclamptic placenta and was also suppressed in term preeclamptic pregnancy. Maternal haplotypes in or within 200 kb of selection-nominated genes were associated with lower placental mitochondrial respiratory capacity (PTPRD [protein tyrosine phosphatase receptor-δ]), lower maternal plasma erythropoietin (CPT2 [carnitine palmitoyl transferase 2], proopiomelanocortin, and DNMT3 [DNA methyltransferase 3]), and lower VEGF (vascular endothelial growth factor) in umbilical venous plasma (TBX5 [T-box transcription factor 5]). A fetal haplotype within 200 kb of CPT2 was associated with increased placental mitochondrial complex II capacity, placental nitrotyrosine, and GLUT4 (glucose transporter type 4) protein expression. CONCLUSIONS: Our findings reveal novel associations between putatively adaptive gene regions and phenotypes linked to oxygen delivery and placental metabolic function in highland Andeans, suggesting that such effects may be of genetic origin. Our findings also demonstrate maladaptive metabolic mechanisms in the context of preeclampsia, including dysregulation of placental oxygen consumption.

Delineating highly transcribed noncoding elements landscape in breast cancer.

Highly transcribed noncoding elements (HTNEs) are critical noncoding elements with high levels of transcriptional capacity in particular cohorts involved in multiple cellular biological processes. Investigation of HTNEs with persistent aberrant expression in abnormal tissues could be of benefit in exploring their roles in disease occurrence and progression. Breast cancer is a highly heterogeneous disease for which early screening and prognosis are exceedingly crucial. In this study, we developed a HTNE identification framework to systematically investigate HTNE landscapes in breast cancer patients and identified over ten thousand HTNEs. The robustness and rationality of our framework were demonstrated via public datasets. We revealed that HTNEs had significant chromatin characteristics of enhancers and long noncoding RNAs (lncRNAs) and were significantly enriched with RNA-binding proteins as well as targeted by miRNAs. Further, HTNE-associated genes were significantly overexpressed and exhibited strong correlations with breast cancer. Ultimately, we explored the subtype-specific transcriptional processes associated with HTNEs and uncovered the HTNE signatures that could classify breast cancer subtypes based on the properties of hormone receptors. Our results highlight that the identified HTNEs as well as their associated genes play crucial roles in breast cancer progression and correlate with subtype-specific transcriptional processes of breast cancer.

Inflammation biomarkers in OSA, chronic obstructive pulmonary disease, and chronic obstructive pulmonary disease/OSA overlap syndrome.

STUDY OBJECTIVES: The coexistence of obstructive sleep apnea (OSA) and chronic obstructive pulmonary disease (COPD) in a single individual, also known as overlap syndrome (OVS), is associated with higher cardiovascular risk and mortality than either OSA or COPD alone. However, the underlying mechanisms remain unclear. We hypothesized that patients with OVS have elevated systemic inflammatory biomarkers relative to patients with either disease alone, which could explain greater cardiovascular risk observed in OVS. METHODS: We included 255 participants in the study, 55 with COPD alone, 100 with OSA alone, 50 with OVS, and 50 healthy controls. All participants underwent a home sleep study, spirometry, and a blood draw for high-sensitivity C-reactive protein and total blood count analysis. In a randomly selected subset of 186 participants, inflammatory protein profiling was performed using Bio-Rad Bio-Plex Pro Human Cytokine 27-Plex Assays. Biomarker level differences across groups were identified using a mixed linear model. RESULTS: Levels of interleukin 6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), and granulocyte colony stimulating factor (G-CSF) were higher in participants with OVS and COPD compared with healthy controls and participants with OSA. Furthermore, participants with OVS had higher circulating levels of leukocytes and neutrophils than those with COPD, OSA, and controls. CONCLUSIONS: COPD and OVS are associated with higher systemic inflammation relative to OSA and healthy controls. This work proposes the potential utilization of interleukin 6, granulocyte colony stimulating factor, and high-sensitivity C-reactive protein as screening biomarkers for COPD in patients with OSA. Inflammatory pathways may not fully explain the higher cardiovascular risk observed in OVS, indicating the need for further investigation. CITATION: Sanchez-Azofra A, Gu W, Masso-Silva JA, et al. Inflammation biomarkers in OSA, chronic obstructive pulmonary disease, and chronic obstructive pulmonary disease/OSA overlap syndrome. J Clin Sleep Med. 2023;19(8):1447-1456.

Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics.

Background: This study aimed to reveal the heterogeneity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer (BC) and identify its prognosis values and molecular characteristics. Methods: Two radiogenomics cohorts (n = 246) were collected and tumor regions were segmented semi-automatically. A total of 174 radiomics features were extracted, and the imaging subtypes were identified and validated by unsupervised analysis. A gene-profile-based classifier was developed to predict the imaging subtypes. The prognostic differences and the biological and microenvironment characteristics of subtypes were uncovered by bioinformatics analysis. Results: Three imaging subtypes were identified and showed high reproducibility. The subtypes differed remarkably in tumor sizes and enhancement patterns, exhibiting significantly different disease-free survival (DFS) or overall survival (OS) in the discovery cohort (p = 0.024) and prognosis datasets (p ranged from <0.0001 to 0.0071). Large sizes and rapidly enhanced tumors usually had the worst outcomes. Associations were found between imaging subtypes and the established subtypes or clinical stages (p ranged from <0.001 to 0.011). Imaging subtypes were distinct in cell cycle and extracellular matrix (ECM)-receptor interaction pathways (false discovery rate, FDR < 0.25) and different in cellular fractions, such as cancer-associated fibroblasts (p < 0.05). Conclusions: The imaging subtypes had different clinical outcomes and biological characteristics, which may serve as potential biomarkers.

Predicting hormone receptors and PAM50 subtypes of breast cancer from multi-scale lesion images of DCE-MRI with transfer learning technique.

BACKGROUND: The recent development of artificial intelligence (AI) technologies coupled with medical imaging data has gained considerable attention, and offers a non-invasive approach for cancer diagnosis and prognosis. In this context, improved breast cancer (BC) molecular characteristics assessment models are foreseen to enable personalized strategies with better clinical outcomes compared to existing screening strategies. And it is a promising approach to developing models for hormone receptors (HR) and subtypes of BC patients from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. METHODS: In this institutional review board-approved study, 174 BC patients with both DCE-MRI and RNA-seq data in the local database were analyzed. Slice images from tumor lesions and multi-scale peri-tumor regions were used as model inputs, and five representative pre-trained transfer learning (TF) networks, such as Inception-v3 and Xception, were employed to establish prediction models. A comprehensive analysis was performed using five-fold cross-validation to avoid overfitting, and accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) to evaluate model performance. RESULTS: Xception achieved the superior results when using solely tumor regions, with highest AUROCs of 0.844 (95% CI: [0.841, 0.847]) and 0.784 (95% CI: [0.781, 0.788]) for estrogen receptor (ER) and progesterone receptor (PR), respectively, and best ACC of 0.467 (95% CI: [0.462, 0.470]) for PAM50 subtypes. A significant improvement in the model performance was observed when images of the peri-tumor region were included, with optimal results achieved using images of the tumor and the 10 mm peri-tumor regions. Xception-based TF models performed most effectively in predicting ER and PR statuses, with the AUROCs were 0.942 (95% CI: [0.940, 0.944]) and 0.920 (95% CI: [0.917, 0.922]), respectively, whereas for PAM50 subtypes, the Inception-v3-based network yielded the highest ACC as 0.742 (95% CI: [0.738, 0.746]). CONCLUSIONS: Transfer learning analysis based on DCE-MRI data of tumor and peri-tumor regions was helpful to the non-invasive assessment of molecular characteristics of BC.

Circular RNAs in peripheral blood mononuclear cells are more stable than linear RNAs upon sample processing delay.

Circular RNAs (circRNAs) are a novel class of RNAs with closed loop structure. Blood circRNAs are widely acknowledged to be more stable than linear mRNAs, which show promising prospect to be liquid biopsy biomarkers for clinical applications. However, accumulating studies have demonstrated that sample processing delays have profound effects on blood transcriptome expression profiles, wherein knowledge remains elusive about the impacts of prolonged sample processing on blood expression profiles of circRNAs. We collected whole blood samples from three donors and isolated peripheral blood mononuclear cells (PBMCs) at six different incubation time points. We measured total RNA expression profiles using RNA sequencing (RNA-seq) and investigated the differentially expressed circRNAs, mRNAs and lncRNAs upon blood processing delay. Meanwhile, we explored the underlying inducement of aberrant expression of circRNAs against their corresponding mRNA transcripts. Finally, we utilized rMATS-turbo and CIRI-AS, respectively, to screen out differential alternative splicing (AS) events in linear mRNAs and circRNAs. Sample incubation at 4°C lasting to 48 hours (h) led to minimal effects to circRNAs' expression. However, it induced extensive alterations for mRNAs and lncRNAs when the incubation time was beyond 12 h. Additionally, only 2 h processing delays may result in profound impacts on AS events of linear mRNAs, while less impact on the equivalence of circRNAs. Our results suggested that PBMC circRNAs are stable upon sample processing delay, which are more suitable to be liquid biopsy biomarkers.

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced-MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer.

Background: To investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively. Methods: Two radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial-temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis. Results: Expression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001). Conclusions: Our results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis.

Effects of mango and mint pod-based e-cigarette aerosol inhalation on inflammatory states of the brain, lung, heart, and colon in mice.

While health effects of conventional tobacco are well defined, data on vaping devices, including one of the most popular e-cigarettes which have high nicotine levels, are less established. Prior acute e-cigarette studies have demonstrated inflammatory and cardiopulmonary physiology changes while chronic studies have demonstrated extra-pulmonary effects, including neurotransmitter alterations in reward pathways. In this study we investigated the impact of inhalation of aerosols produced from pod-based, flavored e-cigarettes (JUUL) aerosols three times daily for 3 months on inflammatory markers in the brain, lung, heart, and colon. JUUL aerosol exposure induced upregulation of cytokine and chemokine gene expression and increased HMGB1 and RAGE in the nucleus accumbens in the central nervous system. Inflammatory gene expression increased in the colon, while gene expression was more broadly altered by e-cigarette aerosol inhalation in the lung. Cardiopulmonary inflammatory responses to acute lung injury with lipopolysaccharide were exacerbated in the heart. Flavor-specific findings were detected across these studies. Our findings suggest that daily e-cigarette use may cause neuroinflammation, which may contribute to behavioral changes and mood disorders. In addition, e-cigarette use may cause gut inflammation, which has been tied to poor systemic health, and cardiac inflammation, which leads to cardiovascular disease.

The use of e-cigarettes or 'vaping' has become widespread, particularly among young people and smokers trying to quit. One of the most popular e-cigarette brands is JUUL, which offers appealing flavors and a discrete design. Many e-cigarette users believe these products are healthier than traditional tobacco products. And while the harms of conventional tobacco products have been extensively researched, the short- and long-term health effects of e-cigarettes have not been well studied. There is even less information about the health impacts of newer products like JUUL. E-cigarettes made by JUUL are different relative to prior generations of e-cigarettes. The JUUL device uses disposable pods filled with nicotinic salts instead of nicotine. One JUUL pod contains as much nicotine as an entire pack of cigarettes (41.3 mg). These differences make studying the health effects of this product particularly important. Moshensky, Brand, Alhaddad et al. show that daily exposure to JUUL aerosols increases the expression of genes encoding inflammatory molecules in the brain, lung, heart and colon of mice. In the experiments, mice were exposed to JUUL mint and JUUL mango flavored aerosols for 20 minutes, 3 times a day, and for 4 and 12 weeks. The changes in inflammatory gene expression varied depending on the flavor. This suggests that the flavorings themselves contribute to the observed changes. The findings suggest that daily use of pod-based e-cigarettes or e-cigarettes containing high levels of nicotinic salts over months to years, may cause inflammation in various organs, increasing the risk of disease and poor health. This information may help individuals, clinicians and policymakers make more informed decisions about e-cigarettes. Further studies assessing the impact of these changes on long-term physical and mental health in humans are desperately needed. These should assess health effects across different e-cigarette types, flavors and duration of use.

Identifying Associations between DCE-MRI Radiomic Features and Expression Heterogeneity of Hallmark Pathways in Breast Cancer: A Multi-Center Radiogenomic Study.

BACKGROUND: To investigate the relationship between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic features and the expression activity of hallmark pathways and to develop prediction models of pathway-level heterogeneity for breast cancer (BC) patients. METHODS: Two radiogenomic cohorts were analyzed (n = 246). Tumor regions were segmented semiautomatically, and 174 imaging features were extracted. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to identify significant imaging-pathway associations. Random forest regression was used to predict pathway enrichment scores. Five-fold cross-validation and grid search were used to determine the optimal preprocessing operation and hyperparameters. RESULTS: We identified 43 pathways, and 101 radiomic features were significantly related in the discovery cohort (p-value < 0.05). The imaging features of the tumor shape and mid-to-late post-contrast stages showed more transcriptional connections. Ten pathways relevant to functions such as cell cycle showed a high correlation with imaging in both cohorts. The prediction model for the mTORC1 signaling pathway achieved the best performance with the mean absolute errors (MAEs) of 27.29 and 28.61% in internal and external test sets, respectively. CONCLUSIONS: The DCE-MRI features were associated with hallmark activities and may improve individualized medicine for BC by noninvasively predicting pathway-level heterogeneity.

The potential of using blood circular RNA as liquid biopsy biomarker for human diseases.

Circular RNA (circRNA) is a novel class of single-stranded RNAs with a closed loop structure. The majority of circRNAs are formed by a back-splicing process in pre-mRNA splicing. Their expression is dynamically regulated and shows spatiotemporal patterns among cell types, tissues and developmental stages. CircRNAs have important biological functions in many physiological processes, and their aberrant expression is implicated in many human diseases. Due to their high stability, circRNAs are becoming promising biomarkers in many human diseases, such as cardiovascular diseases, autoimmune diseases and human cancers. In this review, we focus on the translational potential of using human blood circRNAs as liquid biopsy biomarkers for human diseases. We highlight their abundant expression, essential biological functions and significant correlations to human diseases in various components of peripheral blood, including whole blood, blood cells and extracellular vesicles. In addition, we summarize the current knowledge of blood circRNA biomarkers for disease diagnosis or prognosis.

Denoising Autoencoder, A Deep Learning Algorithm, Aids the Identification of A Novel Molecular Signature of Lung Adenocarcinoma.

Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise to leverage very large datasets for making better predictions of disease biomarkers. Denoising autoencoder (DA) is one of the unsupervised deep learning algorithms, which is a stochastic version of autoencoder techniques. The principle of DA is to force the hidden layer of autoencoder to capture more robust features by reconstructing a clean input from a corrupted one. Here, a DA model was applied to analyze integrated transcriptomic data from 13 published lung cancer studies, which consisted of 1916 human lung tissue samples. Using DA, we discovered a molecular signature composed of multiple genes for lung adenocarcinoma (ADC). In independent validation cohorts, the proposed molecular signature is proved to be an effective classifier for lung cancer histological subtypes. Also, this signature successfully predicts clinical outcome in lung ADC, which is independent of traditional prognostic factors. More importantly, this signature exhibits a superior prognostic power compared with the other published prognostic genes. Our study suggests that unsupervised learning is helpful for biomarker development in the era of precision medicine.

Peripheral blood non-canonical small non-coding RNAs as novel biomarkers in lung cancer.

One unmet challenge in lung cancer diagnosis is to accurately differentiate lung cancer from other lung diseases with similar clinical symptoms and radiological features, such as pulmonary tuberculosis (TB). To identify reliable biomarkers for lung cancer screening, we leverage the recently discovered non-canonical small non-coding RNAs (i.e., tRNA-derived small RNAs [tsRNAs], rRNA-derived small RNAs [rsRNAs], and YRNA-derived small RNAs [ysRNAs]) in human peripheral blood mononuclear cells and develop a molecular signature composed of distinct ts/rs/ysRNAs (TRY-RNA). Our TRY-RNA signature precisely discriminates between control, lung cancer, and pulmonary TB subjects in both the discovery and validation cohorts and outperforms microRNA-based biomarkers, which bears the diagnostic potential for lung cancer screening.

Technical progress in circulating tumor DNA analysis using next generation sequencing.

Circulating tumor DNA (ctDNA) is tumor-derived, fragmented DNA that circulates freely in body fluids, predominantly in the peripheral blood. Recently, ctDNA analysis has been suggested as a complement to tissue biopsy in the detection and treatment of cancer. Genetic and epigenetic information specific to tumor cells, including single nucleotide variations, copy number variations, and modified methylation patterns, can be detected in ctDNA. Importantly, mutations in heterogenous tumors that could impart therapeutic resistance could be identified in ctDNA, which would aid in cancer diagnosis, prognosis, and real-time monitoring, and inform treatment with targeted therapies. However, ctDNA is still not a routinely used method for this purpose, because its detection techniques lack adequate sensitivity for reliable use in scientific studies and clinical trials. This review provides an up-to-date summary of ctDNA mutation detection methods based on next generation sequencing, highlighting their advantages and limitations, and focusing in particular on several optimized library preparation methods for improved sensitivity and specificity of ctDNA detection.

Two Distinct Subtypes Revealed in Blood Transcriptome of Breast Cancer Patients With an Unsupervised Analysis.

Background: Breast cancer (BC) is a highly heterogeneous cancer. The interaction between immune system and BC is complex, widespread yet unclear. In this study, we aimed to reveal the heterogeneity of host systemic immune response to BC and understand the possible mechanisms that may drive the heterogeneity using transcriptomic data from peripheral blood mononuclear cells (PBMCs). Methods: Transcriptome-wide gene expressions of PBMCs in 33 BC patients were generated by RNA sequencing. An unsupervised clustering algorithm was employed to discover PBMC transcriptome subtypes among BC patients. Association analysis between PBMC subtypes and age, clinical stage, abundance of immune cells, and other clinical factors was performed to understand the underlying biological processes that may drive this heterogeneity. Immune gene signature identification and in silico survival analysis were performed to investigate the potential clinical implications of these PBMC subtypes. The findings were validated using the whole blood transcriptomes of an independent cohort. Results: We observed that established BC subtypes were not associated with PBMC gene expression profiles. Instead, we discovered and validated two new BC subtypes using PBMC transcriptome, which have distinct immune cell proportions, especially for lymphocytes (P = 5.22 × 10-12) and neutrophils (P = 1.13 × 10-14). Enrichment analysis of differentially expressed genes revealed that these two subtypes had distinct patterns of immune responses, including osteoclast differentiation and interleukin-10 signaling pathway. We developed two immune gene signatures that can differentiate these two BC PBMC subtypes. Further analysis suggested they had the ability to predict the clinical outcome of BC patients. Conclusions: PBMC transcriptome profiles can classify BC patients into two distinct subtypes. These two subtypes are mainly shaped by different immune cell abundance, which may have implications on clinical outcomes.

A Pathway-Based Strategy to Identify Biomarkers for Lung Cancer Diagnosis and Prognosis.

Current research has identified several potential biomarkers for lung cancer diagnosis or prognosis. However, most of these biomarkers are derived from a relatively small number of samples using algorithms at the gene level. Hence, gene expression signatures discovered in these studies have little overlaps. In this study, we proposed a new strategy to identify biomarkers from multiple datasets at the pathway level. We integrated the genome-wide expression data of lung cancer tissues from 13 published studies and applied our strategy to identify lung cancer diagnostic and prognostic biomarkers. We identified a 32-gene signature that differentiates lung adenocarcinomas from other lung cancer subtypes. We also discovered a 43-gene signature that can predict the outcome of human lung cancers. We tested their performance in several independent cohorts, which confirmed their robust prognostic and diagnostic power. Furthermore, we showed that the proposed gene expression signatures were independent of several traditional clinical indicators in lung cancer management. Our results suggest that the pathway-based strategy is useful to identify transcriptomic biomarkers from large-scale gene expression datasets that were collected from multiple sources.

Identification of circular RNAs in human aortic valves.

With the wide application of RNA-Seq technology, thousands of circular RNAs (circRNAs) have been identified in different type of tissues and cells in many organisms, but little is known on the human aortic valve expressed circRNAs. In this study, we identified all circRNAs in two calcified human aortic valves, and characterized the features of all circRNAs. A total of 5476 circRNAs were identified in human aortic valves, including 1412 (25.79%) aortic valve specific circRNAs. Next, we showed that most aortic valve specific circRNAs were derived from the exonic regions of their host genes, and majority of the host genes contained less than three circRNAs. To predict the potential function of aortic valve specific circRNAs, we performed the GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis for the host genes, and identified both microRNA (miRNA) and RNA binding protein (RBP) binding sites inside aortic valve specific circRNAs. Results showed that these host genes were involved in some aortic valve related function pathways, such as ECM-receptor interaction pathway, ErbB signaling pathway, and vascular smooth muscle contraction pathway. We also found that most aortic valve specific circRNAs harbored abundant miRNA response elements (MREs), and some aortic valve specific circRNAs could bind to RBP of interest. Functional analysis suggested that these aortic valve specific circRNAs could act as post-transcriptional regulators.

Expression of nuclear factor, erythroid 2-like 2-mediated genes differentiates tuberculosis.

During infection and host defense, nuclear factor, erythroid 2-like 2 (Nrf2) dependent signaling is an efficient antioxidant defensive mechanism used by host cells to control the destructive effects of reactive oxygen species. This allows for effective defense responses against microbes while minimizing oxidative injury to the host cell itself. As a central regulator of antioxidant genes, Nrf2 has gained great attention in its pivotal role in infection, especially in tuberculosis (TB), the top infectious disease killer worldwide. To elucidate the genes potentially regulated by Nrf2 in TB, we conducted a meta-analysis on published gene expression datasets. Firstly, we compared the global gene expression profiles between control and Nrf2-deficient human cells. The differentially expressed genes were deemed as "Nrf2-mediated genes". Next, the whole blood gene expression pattern of TB patients was compared with that of healthy controls, pneumonia patients, and lung cancer patients. We found that the genes deregulated in TB significantly overlap with the Nrf2-mediated genes. Based on the intersection of Nrf2-mediated and TB-regulated genes, we identified an Nrf2-mediated 17-gene signature, which reflects a cluster of gene ontology terms highly related to TB physiology. We demonstrated that the 17-gene signature can be used to distinguish TB patients from healthy controls and patients with latent TB infection, pneumonia, or lung cancer. Also, the Nrf2-mediated gene signature can be used as an indicator of the anti-TB therapeutic response. More importantly, we confirmed that the predictive power of the Nrf2-mediated 17-gene signature is significantly better than the random gene sets selected from the human transcriptome. Also, the 17-gene signature performs even better than the random gene signatures selected from TB-associated genes. Our study confirms the central role of Nrf2 in TB pathogenesis and provides a novel and useful diagnostic method to differentiate TB patients from other human subjects.

Ion channel gene expression predicts survival in glioma patients.

Ion channels are important regulators in cell proliferation, migration, and apoptosis. The malfunction and/or aberrant expression of ion channels may disrupt these important biological processes and influence cancer progression. In this study, we investigate the expression pattern of ion channel genes in glioma. We designate 18 ion channel genes that are differentially expressed in high-grade glioma as a prognostic molecular signature. This ion channel gene expression based signature predicts glioma outcome in three independent validation cohorts. Interestingly, 16 of these 18 genes were down-regulated in high-grade glioma. This signature is independent of traditional clinical, molecular, and histological factors. Resampling tests indicate that the prognostic power of the signature outperforms random gene sets selected from human genome in all the validation cohorts. More importantly, this signature performs better than the random gene signatures selected from glioma-associated genes in two out of three validation datasets. This study implicates ion channels in brain cancer, thus expanding on knowledge of their roles in other cancers. Individualized profiling of ion channel gene expression serves as a superior and independent prognostic tool for glioma patients.

Expression profiling of mitochondrial voltage-dependent anion channel-1 associated genes predicts recurrence-free survival in human carcinomas.

BACKGROUND: Mitochondrial voltage-dependent anion channels (VDACs) play a key role in mitochondria-mediated apoptosis. Both in vivo and in vitro evidences indicate that VDACs are actively involved in tumor progression. Specifically, VDAC-1, one member of the VDAC family, was thought to be a potential anti-cancer therapeutic target. Our previous study demonstrated that the human gene VDAC1 (encoding the VDAC-1 isoform) was significantly up-regulated in lung tumor tissue compared with normal tissue. Also, we found a significant positive correlation between the gene expression of VDAC1 and histological grade in breast cancer. However, the prognostic power of VDAC1 and its associated genes in human cancers is largely unknown. METHODS: We systematically analyzed the expression pattern of VDAC1 and its interacting genes in breast, colon, liver, lung, pancreatic, and thyroid cancers. The genes differentially expressed between normal and tumor tissues in human carcinomas were identified. RESULTS: The expression level of VDAC1 was uniformly up-regulated in tumor tissue compared with normal tissue in breast, colon, liver, lung, pancreatic, and thyroid cancers. Forty-four VDAC1 interacting genes were identified as being commonly differentially expressed between normal and tumor tissues in human carcinomas. We designated VDAC1 and the 44 dysregulated interacting genes as the VDAC1 associated gene signature (VAG). We demonstrate that the VAG signature is a robust prognostic biomarker to predict recurrence-free survival in breast, colon, and lung cancers, and is independent of standard clinical and pathological prognostic factors. CONCLUSIONS: VAG represents a promising prognostic biomarker in human cancers, which may enhance prediction accuracy in identifying patients at higher risk for recurrence. Future therapies aimed specifically at VDAC1 associated genes may lead to novel agents in the treatment of cancer.

Ion channel gene expression in lung adenocarcinoma: potential role in prognosis and diagnosis.

Ion channels are known to regulate cancer processes at all stages. The roles of ion channels in cancer pathology are extremely diverse. We systematically analyzed the expression patterns of ion channel genes in lung adenocarcinoma. First, we compared the expression of ion channel genes between normal and tumor tissues in patients with lung adenocarcinoma. Thirty-seven ion channel genes were identified as being differentially expressed between the two groups. Next, we investigated the prognostic power of ion channel genes in lung adenocarcinoma. We assigned a risk score to each lung adenocarcinoma patient based on the expression of the differentially expressed ion channel genes. We demonstrated that the risk score effectively predicted overall survival and recurrence-free survival in lung adenocarcinoma. We also found that the risk scores for ever-smokers were higher than those for never-smokers. Multivariate analysis indicated that the risk score was a significant prognostic factor for survival, which is independent of patient age, gender, stage, smoking history, Myc level, and EGFR/KRAS/ALK gene mutation status. Finally, we investigated the difference in ion channel gene expression between the two major subtypes of non-small cell lung cancer: adenocarcinoma and squamous-cell carcinoma. Thirty ion channel genes were identified as being differentially expressed between the two groups. We suggest that ion channel gene expression can be used to improve the subtype classification in non-small cell lung cancer at the molecular level. The findings in this study have been validated in several independent lung cancer cohorts.