Multi-omic molecular profiling of lung cancer in COPD

Exploring the protective role of maternal lung cancer history on allergic rhinitis

BACKGROUND

The causal relationship between family history of lung cancer and allergic rhinitis remains unclear. This study aimed to explore the association between family history of lung cancer and allergic rhinitis, along with potential mediating mechanisms, using Mendelian randomization.

METHODS

A bidirectional two-sample Mendelian randomization analysis was conducted to assess the causal relationship between family history of lung cancer (including parental, paternal, maternal, and sibling histories) and allergic rhinitis, using genetic variants associated with family history of lung cancer as instrumental variables. Additionally, mediation Mendelian randomization analysis was performed to investigate the role of specific metabolites in mediating this relationship.

RESULTS

The analysis revealed a significant causal relationship between parental history of lung cancer and allergic rhinitis, with maternal lung cancer history showing a strong protective effect against allergic rhinitis (OR = 0.28, p<0.05). Mediation analysis further indicated that metabolites such as 1-linoleoyl-GPE (18:2) and N-palmitoyl-sphingosine exhibited negative mediating effects in the association between maternal lung cancer and allergic rhinitis. Lower levels of these metabolites enhanced the protective effect of maternal lung cancer history on allergic rhinitis.

CONCLUSION

This study demonstrates a significant causal relationship between maternal lung cancer history and allergic rhinitis, with specific metabolites potentially playing a mediating role. Changes in the levels of 1-linoleoyl-GPE (18:2) and N-palmitoyl-sphingosine are associated with the protective effect of maternal lung cancer history on allergic rhinitis, suggesting that metabolites may be crucial in regulating this relationship. These findings provide new insights into the relationship between family history of lung cancer and immune-related diseases, offering potential directions for future clinical prevention and treatment strategies.

Application of laboratory models for sarcoidosis research

This manuscript will review the implications and applications of sarcoidosis models towards advancing our understanding of sarcoidosis disease mechanisms, identification of biomarkers, and preclinical testing of novel therapies. Emerging disease models and innovative research tools will also be considered.

A model-free and distribution-free multi-omics integration approach for detecting novel lung adenocarcinoma genes

Detection of important genes affecting lung adenocarcinoma (LUAD) is critical to finding effective therapeutic targets for this highly lethal cancer. However, many existing approaches have focused on single outcomes or phenotypic associations, which may not be as thorough as investigating molecular transcript levels within cells. In this article, we apply a novel multivariate rank-distance correlation-based gene selection procedure (MrDcGene) to LUAD multi-omics data downloaded from The Cancer Genome Atlas (TCGA). MrDcGene provides additional opportunities for detecting novel susceptibility genes as it leverages information from multiple platforms, while efficiently handling challenges such as high dimensionality, low signal-to-noise ratio, unknown distributions, and non-linear structures, etc. Notably, the MrDcGene method is able to detect two different scenarios, i.e., strong association strength with a few gene expressions and weak association strength with several gene expressions. After thoroughly exploring the association between gene expression (GE) and multiple other platforms, including reverse phase protein array (RPPA), miRNA, copy number variation (CNV) and DNA methylation (ME), we detect several novel genes that may play an important role in LUAD (ZNF133, CCDC159, YWHAZ, HNRNPR. ITPR2, PTHLH, and WIPI2). In addition, we quantitatively validate several other susceptibility genes that were reported in the literature using different methods and studies. The accuracy of the MrDcGene approach is theoretically assured and empirically demonstrated by the simulation studies.

Prognostic Performance of Hematological and Serum Iron and Metabolite Indices for Detection of Early Iron Deficiency Induced Metabolic Brain Dysfunction in Infant Rhesus Monkeys

BACKGROUND

The current pediatric practice of monitoring for infantile iron deficiency (ID) via hemoglobin (Hgb) screening at one y of age does not identify preanemic ID nor protect against later neurocognitive deficits.

OBJECTIVES

To identify biomarkers of iron-related metabolic alterations in the serum and brain and determine the sensitivity of conventional iron and heme indices for predicting risk of brain metabolic dysfunction using a nonhuman primate model of infantile ID.

METHODS

Simultaneous serum iron and RBC indices, and serum and cerebrospinal fluid (CSF) metabolomic profiles were determined in 20 rhesus infants, comparing iron sufficient (IS; N = 10) and ID (N = 10) infants at 2 and 4 mo of age.

RESULTS

Reticulocyte hemoglobin (RET-He) was lower at 2 wk in the ID group. Significant IS compared with ID differences in serum iron indices were present at 2 mo, but Hgb and RBC indices differed only at 4 mo (P < 0.05). Serum and CSF metabolomic profiles of the ID and IS groups differed at 2 and 4 mo (P < 0.05). Key metabolites, including homostachydrine and stachydrine (4-5-fold lower at 4 mo in ID group, P < 0.05), were altered in both serum and CSF. Iron indices and RET-He at 2 mo, but not Hgb or other RBC indices, were correlated with altered CSF metabolic profile at 4 mo and had comparable predictive accuracy (area under the receiver operating characteristic curve scores, 0.75-0.80).

CONCLUSIONS

Preanemic ID at 2 mo was associated with metabolic alterations in serum and CSF in infant monkeys. Among the RBC indices, only RET-He predicted the future risk of abnormal CSF metabolic profile with a predictive accuracy comparable to serum iron indices. The concordance of homostachydrine and stachydrine changes in serum and CSF indicates their potential use as early biomarkers of brain metabolic dysfunction in infantile ID.

Extensive acute and sustained changes to neutrophil proteomes post-SARS-CoV-2 infection

BACKGROUND

Neutrophils are important in the pathophysiology of coronavirus disease 2019 (COVID-19), but the molecular changes contributing to altered neutrophil phenotypes following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are not fully understood. We used quantitative mass spectrometry-based proteomics to explore neutrophil phenotypes immediately following acute SARS-CoV-2 infection and during recovery.

METHODS

Prospective observational study of hospitalised patients with PCR-confirmed SARS-CoV-2 infection (May to December 2020). Patients were enrolled within 96 h of admission, with longitudinal sampling up to 29 days. Control groups comprised non-COVID-19 acute lower respiratory tract infection (LRTI) and age-matched noninfected controls. Neutrophils were isolated from peripheral blood and analysed using mass spectrometry. COVID-19 severity and recovery were defined using the World Health Organization ordinal scale.

RESULTS

Neutrophil proteomes from 84 COVID-19 patients were compared to those from 91 LRTI and 42 control participants. 5800 neutrophil proteins were identified, with >1700 proteins significantly changed in neutrophils from COVID-19 patients compared to noninfected controls. Neutrophils from COVID-19 patients initially all demonstrated a strong interferon signature, but this signature rapidly declined in patients with severe disease. Severe disease was associated with increased abundance of proteins involved in metabolism, immunosuppression and pattern recognition, while delayed recovery from COVID-19 was associated with decreased granule components and reduced abundance of metabolic proteins, chemokine and leukotriene receptors, integrins and inhibitory receptors.

CONCLUSIONS

SARS-CoV-2 infection results in the sustained presence of circulating neutrophils with distinct proteomes suggesting altered metabolic and immunosuppressive profiles and altered capacities to respond to migratory signals and cues from other immune cells, pathogens or cytokines.

RNA-Binding Proteins as a Molecular Link between COPD and Lung Cancer

Chronic obstructive pulmonary disease (COPD) represents an independent risk factor for lung cancer development. Accelerated cell senescence, induced by oxidative stress and inflammation, is a common pathogenic determinant of both COPD and lung cancer. The post transcriptional regulation of genes involved in these processes is finely regulated by RNA-binding proteins (RBPs), which regulate mRNA turnover, subcellular localization, splicing and translation. Multiple pro-inflammatory mediators (including cytokines, chemokines, proteins, growth factors and others), responsible of lung microenvironment alteration, are regulated by RBPs. Several mouse models have shown the implication of RBPs in multiple mechanisms that sustain chronic inflammation and neoplastic transformation. However, further studies are required to clarify the role of RBPs in the pathogenic mechanisms shared by lung cancer and COPD, in order to identify novel biomarkers and therapeutic targets. This review will therefore focus on the studies collectively indicating the role of RBPs in oxidative stress and chronic inflammation as common pathogenic mechanisms shared by lung cancer and COPD.

Intranasal insulin treatment partially corrects the altered gene expression profile in the hippocampus of developing rats with perinatal iron deficiency

Perinatal iron deficiency (FeD) targets the hippocampus and leads to long-term cognitive deficits. Intranasal insulin administration improves cognitive deficits in adult humans with Alzheimer's disease and type 2 diabetes and could provide benefits in FeD-induced hippocampal dysfunction. To objective was to assess the effects of intranasal insulin administration intranasal insulin administration on the hippocampal transcriptome in a developing rat model of perinatal FeD. Perinatal FeD was induced using low-iron diet from gestational day 3 until postnatal day (P) 7, followed by an iron sufficient (FeS) diet through P21. Intranasal insulin was administered at a dose of 0.3 IU twice daily from P8 to P21. Hippocampi were removed on P21 from FeS control, FeD control, FeS insulin, and FeD insulin groups. Total RNA was isolated and profiled using next-generation sequencing. Gene expression profiles were characterized using custom workflows and expression patterns examined using ingenuity pathways analysis (n = 7-9 per group). Select RNAseq results were confirmed via qPCR. Transcriptomic profiling revealed that mitochondrial biogenesis and flux, oxidative phosphorylation, quantity of neurons, CREB signaling in neurons, and RICTOR-based mTOR signaling were disrupted with FeD and positively affected by intranasal insulin treatment with the most benefit observed in the FeD insulin group. Both perinatal FeD and intranasal insulin administration altered gene expression profile in the developing hippocampus. Intranasal insulin treatment reversed the adverse effects of FeD on many molecular pathways and could be explored as an adjunct therapy in perinatal FeD.

Multiomic Investigations into Lung Health and Disease

Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.

Quantitative proteomics identifies tumour matrisome signatures in patients with non-small cell lung cancer

Introduction

The composition and remodelling of the extracellular matrix (ECM) are important factors in the development and progression of cancers, and the ECM is implicated in promoting tumour growth and restricting anti-tumour therapies through multiple mechanisms. The characterisation of differences in ECM composition between normal and diseased tissues may aid in identifying novel diagnostic markers, prognostic indicators and therapeutic targets for drug development.

Methods

Using tissue from non-small cell lung cancer (NSCLC) patients undergoing curative intent surgery, we characterised quantitative tumour-specific ECM proteome signatures by mass spectrometry.

Results

We identified 161 matrisome proteins differentially regulated between tumour tissue and nearby non-malignant lung tissue, and we defined a collagen hydroxylation functional protein network that is enriched in the lung tumour microenvironment. We validated two novel putative extracellular markers of NSCLC, the collagen cross-linking enzyme peroxidasin and a disintegrin and metalloproteinase with thrombospondin motifs 16 (ADAMTS16), for discrimination of malignant and non-malignant lung tissue. These proteins were up-regulated in lung tumour samples, and high PXDN and ADAMTS16 gene expression was associated with shorter survival of lung adenocarcinoma and squamous cell carcinoma patients, respectively.

Discussion

These data chart extensive remodelling of the lung extracellular niche and reveal tumour matrisome signatures in human NSCLC.

Identification of cancer risk assessment signature in patients with chronic obstructive pulmonary disease and exploration of the potential key genes

It is essential to assess the cancer risk for patients with chronic obstructive pulmonary disease (COPD). Comparing gene expression data from patients with lung cancer (a total of 506 samples) and those with cancer-adjacent normal lung tissues (a total of 370 samples), we generated a qualitative transcriptional signature consisting of 2046 gene pairs. The signature was verified in an evaluation dataset comprising 18 subjects with severe disease and 52 subjects with moderate disease (Wilcoxon rank-sum test; p = 7.33 × 10^-5). Similar results were obtained in other independent datasets. Among the gene pairs in the signature, 326 COPD stage-related gene pairs were identified based on Spearman's rank correlation tests and those gene pairs comprised 368 unique genes. Of these 368 genes, 16 genes were significantly dysregulated in COPD rat model data compared with control data. Some of these genes (Dhx16, Upf2, Notch3, Sec61a1, Dyrk2, and Hmmr) were altered when the COPD rat model was treated with traditional Chinese medicines (TCM), including Bufei Yishen formula, Bufei Jianpi formula, and Yiqi Zishen formula. Overall, the signature could predict the cancer incidence-risk of COPD and the identified key genes might provide guidance regarding both the treatment of COPD using TCM and the prevention of cancer in patients with COPD. KEY MESSAGESA cancer risk assessment signature was identified in patients with COPD.The signature is insensitive to batch effects and is well verified.COPD key genes identified in this study might play a crucial role in TCM treatment and cancer prevention.

Recent evidence from omic analysis for redox signalling and mitochondrial oxidative stress in COPD

COPD is driven by exogenous and endogenous oxidative stress derived from inhaled cigarette smoke, air pollution and reactive oxygen species from dysregulated mitochondria in activated inflammatory cells within the airway and lung. This is compounded by the loss in antioxidant defences including FOXO and NRF2 and other antioxidant transcription factors together with various key enzymes that attenuate oxidant effects. Oxidative stress enhances inflammation; airway remodelling including fibrosis and emphysema; post-translational protein modifications leading to autoantibody generation; DNA damage and cellular senescence. Recent studies using various omics technologies in the airways, lungs and blood of COPD patients has emphasised the importance of oxidative stress, particularly that derived from dysfunctional mitochondria in COPD and its role in immunity, inflammation, mucosal barrier function and infection. Therapeutic interventions targeting oxidative stress should overcome the deleterious pathologic effects of COPD if targeted to the lung. We require novel, more efficacious antioxidant COPD treatments among which mitochondria-targeted antioxidants and Nrf2 activators are promising.

sJIVE: Supervised Joint and Individual Variation Explained

Analyzing multi-source data, which are multiple views of data on the same subjects, has become increasingly common in molecular biomedical research. Recent methods have sought to uncover underlying structure and relationships within and/or between the data sources, and other methods have sought to build a predictive model for an outcome using all sources. However, existing methods that do both are presently limited because they either (1) only consider data structure shared by all datasets while ignoring structures unique to each source, or (2) they extract underlying structures first without consideration to the outcome. The proposed method, supervised joint and individual variation explained (sJIVE), can simultaneously (1) identify shared (joint) and source-specific (individual) underlying structure and (2) build a linear prediction model for an outcome using these structures. These two components are weighted to compromise between explaining variation in the multi-source data and in the outcome. Simulations show sJIVE to outperform existing methods when large amounts of noise are present in the multi-source data. An application to data from the COPDGene study explores gene expression and proteomic patterns associated with lung function.

Interpretive JIVE: Connections with CCA and an application to brain connectivity

Joint and Individual Variation Explained (JIVE) is a model that decomposes multiple datasets obtained on the same subjects into shared structure, structure unique to each dataset, and noise. JIVE is an important tool for multimodal data integration in neuroimaging. The two most common algorithms are R.JIVE, an iterative approach, and AJIVE, which uses principal angle analysis. The joint structure in JIVE is defined by shared subspaces, but interpreting these subspaces can be challenging. In this paper, we reinterpret AJIVE as a canonical correlation analysis of principal component scores. This reformulation, which we call CJIVE, (1) provides an intuitive view of AJIVE; (2) uses a permutation test for the number of joint components; (3) can be used to predict subject scores for out-of-sample observations; and (4) is computationally fast. We conduct simulation studies that show CJIVE and AJIVE are accurate when the total signal ranks are correctly specified but, generally inaccurate when the total ranks are too large. CJIVE and AJIVE can still extract joint signal even when the joint signal variance is relatively small. JIVE methods are applied to integrate functional connectivity (resting-state fMRI) and structural connectivity (diffusion MRI) from the Human Connectome Project. Surprisingly, the edges with largest loadings in the joint component in functional connectivity do not coincide with the same edges in the structural connectivity, indicating more complex patterns than assumed in spatial priors. Using these loadings, we accurately predict joint subject scores in new participants. We also find joint scores are associated with fluid intelligence, highlighting the potential for JIVE to reveal important shared structure.

Study on Chronic Obstructive Pulmonary Disease and Lung Cancer: Web of Science-Based Bibliometric and Visual Analysis

PURPOSE

Chronic obstructive pulmonary disease (COPD) is one of the main risk factors for lung carcinomas. This study aimed to analyze and construct a model to assess scientific publications on the relationship between COPD and lung carcinomas.

PATIENTS AND METHODS

A literature search of the Web of Science database was performed for publications until November 2, 2021. Microsoft Excel and CiteSpace software were used to perform bibliometric and visual analysis of source journals, countries/regions, institutions, authors, research areas, and hot topics of selected publications.

RESULTS

A total of 2175 publications on the relationship between COPD and lung carcinomas were identified. The annual number of papers published and the total annual citation frequency in the field of COPD and lung carcinoma show an upward trend, and the current research hot topics are health, disease risk factors, disease burden, prevention and serious complications. The top three countries/regions with the number of published articles are the United States, China, and the United Kingdom. The author with the most signatures was Castaldi PJ of USA, followed by Xian JF of China. The lack of multinational/regional multi-center research illustrated that the distribution of research forces is unbalanced.

CONCLUSION

According to this study, researchers can identify hot topics and explore new research directions in research of the relationship between COPD and lung carcinomas.

Molecular and Clinicopathological Characteristics of Lung Cancer Concomitant Chronic Obstructive Pulmonary Disease (COPD)

Introduction

Chronic obstructive pulmonary disease (COPD) and lung cancer often coexist, but its pathophysiology and genomics features are still unclear.

Methods

In this study, we retrospectively collected lung cancer concomitant COPD (COPD-LC) and non-COPD lung cancer (non-COPD-LC) patients, who performed next generation sequencing (NGS) and had clinicopathological information simultaneously. The COPD-LC data from the TCGA cohort were collected to conduct further analysis.

Results

A total of 51 COPD-LC patients and 88 non-COPD-LC patients were included in the study. Clinicopathological analysis showed that proportion of male gender, older age, and smoking patients were all substantially higher in COPD-LC group than in non-COPD-LC group (all P<0.01). Comparing the genomic data of the two groups in our cohort, COPD-LC had higher mutation frequency of LRP1B (43% vs 9%, P = 0.001), EPHA5 (24% vs 1%, P = 0.002), PRKDC (14% vs 1%, P = 0.039), PREX2 (14% vs 0%, P = 0.012), and FAT1 (14% vs 0%, P = 0.012), which had a relationship with improved tumor immunity. Immunotherapy biomarker of PD-L1 positive expression (62.5% vs 52.0%, P = 0.397) and tumor mutation burden (TMB, median TMB: 7.09 vs 2.94, P = 0.004) also were higher in COPD-LC. In addition, RNA data from TCGA further indicated tumor immunity increased in COPD-LC. Whereas, COPD-LC had lower frequency of EGFR mutation (19% vs 50%, P = 0.013) and EGFR mutant COPD-LC treated with EGFR-TKI had worse progression-free survival (PFS) (HR = 3.52, 95% CI: 1.27–9.80, P = 0.01).

Conclusion

In this retrospective study, we first explored molecular features of COPD-LC in a Chinese population. Although COPD-LC had lower EGFR mutant frequency and worse PFS with target treatment, high PD-L1 expression and TMB indicated these patients may benefit from immunotherapy.

Proteomic analysis of the IPF mesenchymal progenitor cell nuclear proteome identifies abnormalities in key nodal proteins that underlie their fibrogenic phenotype

IPF is a progressive fibrotic lung disease whose pathogenesis remains incompletely understood. We have previously discovered pathologic mesenchymal progenitor cells (MPCs) in the lungs of IPF patients. IPF MPCs display a distinct transcriptome and create sustained interstitial fibrosis in immune deficient mice. However, the precise pathologic alterations responsible for this fibrotic phenotype remain to be uncovered. Quantitative mass spectrometry and interactomics is a powerful tool that can define protein alterations in specific subcellular compartments that can be implemented to understand disease pathogenesis. We employed quantitative mass spectrometry and interactomics to define protein alterations in the nuclear compartment of IPF MPCs compared to control MPCs. We identified increased nuclear levels of PARP1, CDK1, and BACH1. Interactomics implicated PARP1, CDK1, and BACH1 as key hub proteins in the DNA damage/repair, differentiation, and apoptosis signaling pathways respectively. Loss of function and inhibitor studies demonstrated important roles for PARP1 in DNA damage/repair, CDK1 in regulating IPF MPC stemness and self-renewal, and BACH1 in regulating IPF MPC viability. Our quantitative mass spectrometry studies combined with interactomic analysis uncovered key roles for nuclear PARP1, CDK1, and BACH1 in regulating IPF MPC fibrogenicity.

SFPQ Promotes Lung Cancer Malignancy via Regulation of CD44 v6 Expression

Mesenchymal stem cells (MSCs) contribute to tumor pathogenesis and elicit antitumor immune responses in tumor microenvironments. Nuclear proteins might be the main players in these processes. In the current study, combining spatial proteomics with ingenuity pathway analysis (IPA) in lung non-small cell (NSC) cancer MSCs, we identify a key nuclear protein regulator, SFPQ (Splicing Factor Proline and Glutamine Rich), which is overexpressed in lung cancer MSCs and functions to promote MSCs proliferation, chemical resistance, and invasion. Mechanistically, the knockdown of SFPQ reduces CD44v6 expression to inhibit lung cancer MSCs stemness, proliferation in vitro, and metastasis in vivo. The data indicates that SFPQ may be a potential therapeutic target for limiting growth, chemotherapy resistance, and metastasis of lung cancer.

Multiomic profiling of iron-deficient infant monkeys reveals alterations in neurologically important biochemicals in serum and cerebrospinal fluid before the onset of anemia

The effects of iron deficiency (ID) during infancy extend beyond the hematologic compartment and include short- and long-term adverse effects on many tissues including the brain. However, sensitive biomarkers of iron-dependent brain health are lacking in humans. To determine whether serum and cerebrospinal fluid (CSF) biomarkers of ID-induced metabolic dysfunction are concordant in the pre/early anemic stage of ID before anemia in a nonhuman primate model of infantile iron deficiency anemia (IDA). ID (n = 7), rhesus infants at 4 mo (pre-anemic period) and 6 mo of age (anemic) were examined. Hematological, metabolomic, and proteomic profiles were generated via HPLC/MS at both time points to discriminate serum biomarkers of ID-induced brain metabolic dysfunction. We identified 227 metabolites and 205 proteins in serum. Abnormalities indicating altered liver function, lipid dysregulation, and increased acute phase reactants were present in ID. In CSF, we measured 210 metabolites and 1,560 proteins with changes in ID infants indicative of metabolomic and proteomic differences indexing disrupted synaptogenesis. Systemic and CSF proteomic and metabolomic changes were present and concurrent in the pre-anemic and anemic periods. Multiomic serum and CSF profiling uncovered pathways disrupted by ID in both the pre-anemic and anemic stages of infantile IDA, including evidence for hepatic dysfunction and activation of acute phase response. Parallel changes observed in serum and CSF potentially provide measurable serum biomarkers of ID that reflect at-risk brain processes prior to progression to clinical anemia.

Correcting iron deficiency anemia with iron dextran alters the serum metabolomic profile of the infant Rhesus Monkey

BACKGROUND

The effects of infantile iron deficiency anemia (IDA) extend beyond hematological indices and include short- and long-term adverse effects on multiple cells and tissues. IDA is associated with an abnormal serum metabolomic profile, characterized by altered hepatic metabolism, lowered NAD flux, increased nucleoside levels, and a reduction in circulating dopamine levels.

OBJECTIVES

The objective of this study was to determine whether the serum metabolomic profile is normalized after rapid correction of IDA using iron dextran injections.

METHODS

Blood was collected from iron-sufficient (IS; n = 10) and IDA (n = 12) rhesus infants at 6 months of age. IDA infants were then administered iron dextran and vitamin B via intramuscular injections at weekly intervals for 2 to 8 weeks. Their hematological and metabolomic statuses were evaluated following treatment and compared with baseline and a separate group of age-matched IS infants (n = 5).

RESULTS

Serum metabolomic profiles assessed at baseline and after treatment via HPLC/MS using isobaric standards identified 654 quantifiable metabolites. At baseline, 53 metabolites differed between IS and IDA infants. Iron treatment restored traditional hematological indices, including hemoglobin and mean corpuscular volume, into the normal range, but the metabolite profile in the IDA group after iron treatment was markedly altered, with 323 metabolites differentially expressed when compared with an infant's own baseline profile.

CONCLUSIONS

Rapid correction of IDA with iron dextran resulted in extensive metabolic changes across biochemical pathways indexing the liver function, bile acid release, essential fatty acid production, nucleoside release, and several neurologically important metabolites. The results highlight the importance of a cautious approach when developing a route and regimen of iron repletion to treat infantile IDA.

Multiple microarray analyses identify key genes associated with the development of Non-Small Cell Lung Cancer from Chronic Obstructive Pulmonary Disease

Introduction: Chronic obstructive pulmonary disease (COPD) is an independent risk factor of non-small cell lung cancer (NSCLC). This study aimed to analyze the key genes and potential molecular mechanisms that are involved in the development from COPD to NSCLC.

Methods: Expression profiles of COPD and NSCLC in GSE106899, GSE12472, and GSE12428 were downloaded from the Gene Expression Omnibus (GEO) database, followed by identification of the differentially expressed genes (DEGs) between COPD and NSCLC. Based on the identified DEGs, functional pathway enrichment and lung carcinogenesis-related networks analyses were performed and further visualized with Cytoscape software. Then, principal component analysis (PCA), cluster analysis, and support vector machines (SVM) verified the ability of the top modular genes to distinguish COPD from NSCLC. Additionally, the corrections between these key genes and clinical staging of NSCLC were studied using the UALCAN and HPA websites. Finally, a prognostic risk model was constructed based on multivariate Cox regression analysis. Kaplan-Meier survival curves of the top modular genes on the training and verification sets were generated.

Results: A total of 2350, 1914, and 1850 DEGs were obtained from GSE106899, GSE12472, and GSE12428 datasets, respectively. Following analysis of protein-protein interaction networks, the identified modular gene signatures containing H2AFX, MCM2, MCM3, MCM7, POLD1, and RPA1 were identified as markers for discrimination between COPD and NSCLC. The modular gene signatures were mainly enriched in the processes of DNA replication, cell cycle, mismatch repair, and others. Besides, the expression levels of these genes were significantly higher in NSCLC than in COPD, which was further verified by the immunohistochemistry. In addition, the high expression levels of H2AFX, MCM2, MCM7, and POLD1 correlate with poor prognosis of lung adenocarcinoma (LUAD). The Cox regression prognostic risk model showed the similar results and the predictive ability of this model is independent of other clinical variables.

Conclusions: This study revealed several key modules that closely relate to NSCLC with underlying disease COPD, which provide a deeper understanding of the potential mechanisms underlying the malignant development from COPD to NSCLC. This study provides valuable prognostic factors in high-risk lung cancer patients with COPD.

Early-Life Iron Deficiency and Its Natural Resolution Are Associated with Altered Serum Metabolomic Profiles in Infant Rhesus Monkeys

BACKGROUND

Iron deficiency is the most common nutrient deficiency in human infants aged 6 to 24 mo, and negatively affects many cellular metabolic processes, including energy production, electron transport, and oxidative degradation of toxins. There can be persistent influences on long-term metabolic health beyond its acute effects.

OBJECTIVES

The objective was to determine how iron deficiency in infancy alters the serum metabolomic profile and to test whether these effects persist after the resolution of iron deficiency in a nonhuman primate model of spontaneous iron deficiency.

METHODS

Blood was collected from naturally iron-sufficient (IS; n = 10) and iron-deficient (ID; n = 10) male and female infant rhesus monkeys (Macaca mulatta) at 6 mo of age. Iron deficiency resolved without intervention upon feeding of solid foods, and iron status was re-evaluated at 12 mo of age from the IS and formerly ID monkeys using hematological and other indices; sera were metabolically profiled using HPLC/MS and GC/MS with isobaric standards for identification and quantification at both time points.

RESULTS

A total of 413 metabolites were measured, with differences in 40 metabolites identified between IS and ID monkeys at 6 mo (P$\le $ 0.05). At 12 mo, iron-related hematological parameters had returned to normal, but the formerly ID infants remained metabolically distinct from the age-matched IS infants, with 48 metabolites differentially expressed between the groups. Metabolomic profiling indicated altered liver metabolites, differential fatty acid production, increased serum uridine release, and atypical bile acid production in the ID monkeys.

CONCLUSIONS

Pathway analyses of serum metabolites provided evidence of a hypometabolic state, altered liver function, differential essential fatty acid production, irregular uracil metabolism, and atypical bile acid production in ID infants. Many metabolites remained altered after the resolution of ID, suggesting long-term effects on metabolic health.

Distinct Cancer-Promoting Stromal Gene Expression Depending on Lung Function

Rationale: Chronic obstructive pulmonary disease is an independent risk factor for lung cancer, but the underlying molecular mechanisms are unknown. We hypothesized that lung stromal cells activate pathological gene expression programs that support oncogenesis.Objectives: To identify molecular mechanisms operating in the lung stroma that support the development of lung cancer.Methods: The study included subjects with and without lung cancer across a spectrum of lung-function values. We conducted a multiomics analysis of nonmalignant lung tissue to quantify the transcriptome, translatome, and proteome.Measurements and Main Results: Cancer-associated gene expression changes predominantly manifested as alterations in the efficiency of mRNA translation modulating protein levels in the absence of corresponding changes in mRNA levels. The molecular mechanisms that drove these cancer-associated translation programs differed based on lung function. In subjects with normal to mildly impaired lung function, the mammalian target of rapamycin (mTOR) pathway served as an upstream driver, whereas in subjects with severe airflow obstruction, pathways downstream of pathological extracellular matrix emerged. Consistent with a role during cancer initiation, both the mTOR and extracellular matrix gene expression programs paralleled the activation of previously identified procancer secretomes. Furthermore, an in situ examination of lung tissue showed that stromal fibroblasts expressed cancer-associated proteins from two procancer secretomes: one that included IL-6 (in cases of mild or no airflow obstruction), and one that included BMP1 (in cases of severe airflow obstruction).Conclusions: Two distinct stromal gene expression programs that promote cancer initiation are activated in patients with lung cancer depending on lung function. Our work has implications both for screening strategies and for personalized approaches to cancer treatment.

Interleukin-1 Beta-A Friend or Foe in Malignancies?

Interleukin-1 beta (IL-1β) is induced by inflammatory signals in a broad number of immune cell types. IL-1β (and IL-18) are the only cytokines which are processed by caspase-1 after inflammasome-mediated activation. This review aims to summarize current knowledge about parameters of regulation of IL-1β expression and its multi-facetted role in pathophysiological conditions. IL-1 signaling activates innate immune cells including antigen presenting cells, and drives polarization of CD4+ T cells towards T helper type (Th) 1 and Th17 cells. Therefore, IL-1β has been attributed a largely beneficial role in resolving acute inflammations, and by initiating adaptive anti-tumor responses. However, IL-1β generated in the course of chronic inflammation supports tumor development. Furthermore, IL-1β generated within the tumor microenvironment predominantly by tumor-infiltrating macrophages promotes tumor growth and metastasis via different mechanisms. These include the expression of IL-1 targets which promote neoangiogenesis and of soluble mediators in cancer-associated fibroblasts that evoke antiapoptotic signaling in tumor cells. Moreover, IL-1 promotes the propagation of myeloid-derived suppressor cells. Using genetic mouse models as well as agents for pharmacological inhibition of IL-1 signaling therapeutically applied for treatment of IL-1 associated autoimmune diseases indicate that IL-1β is a driver of tumor induction and development.