Biomarker Discovery for Immunotherapy of Pituitary Adenomas: Enhanced Robustness and Prediction Ability by Modern Computational Tools

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/multiclassmetabo/.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics

ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single-cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in-depth and high-quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well-performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (https://idrblab.org/anpela/), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights.

The immune microenviroment in somatotropinomas: from biology to personalized and target therapy

Pituitary tumors are rare neoplasms, with a heterogeneous biological and clinical behavior, due to their clinical course, local invasive growth, resistance to conventional therapies and the risk of disease progression. Recent studies on tumor microenvironment (TME) provided new knowledge on the biology of these neoplasia, that may explain the different phenotypes of these tumors and suggest new biomarkers able to predict the prognosis and the treatment outcome. The identification of molecular markers that act as targets for biological therapies may open new perspectives in the medical treatments of aggressive pituitary tumors.In this paper, we will review data of TME and target therapies in somatotropinomas.

The microenvironment of pituitary adenomas: biological, clinical and therapeutical implications

The microenvironment of pituitary adenomas (PAs) includes a range of non-tumoral cells, such as immune and stromal cells, as well as cell signaling molecules such as cytokines, chemokines and growth factors, which surround pituitary tumor cells and may modulate tumor initiation, progression, invasion, angiogenesis and other tumorigenic processes. The microenvironment of PAs has been actively investigated over the last years, with several immune and stromal cell populations, as well as different cytokines, chemokines and growth factors being recently characterized in PAs. Moreover, key microenvironment-related genes as well as immune-related molecules and pathways have been investigated, with immune check point regulators emerging as promising targets for immunotherapy. Understanding the microenvironment of PAs will contribute to a deeper knowledge of the complex biology of PAs, as well as will provide developments in terms of diagnosis, clinical management and ultimately treatment of patients with aggressive and/or refractory PAs.

Muti-omics integration analysis revealed molecular network alterations in human nonfunctional pituitary neuroendocrine tumors in the framework of 3P medicine

Nonfuctional pituitary neuroendocrine tumor (NF-PitNET) is highly heterogeneous and generally considered a common intracranial tumor. A series of molecules are involved in NF-PitNET pathogenesis that alter in multiple levels of genome, transcriptome, proteome, and metabolome, and those molecules mutually interact to form dynamically associated molecular-network systems. This article reviewed signaling pathway alterations in NF-PitNET based on the analyses of the genome, transcriptome, proteome, and metabolome, and emphasized signaling pathway network alterations based on the integrative omics, including calcium signaling pathway, cGMP-PKG signaling pathway, mTOR signaling pathway, PI3K/AKT signaling pathway, MAPK (mitogen-activated protein kinase) signaling pathway, oxidative stress response, mitochondrial dysfunction, and cell cycle dysregulation, and those signaling pathway networks are important for NF-PitNET formation and progression. Especially, this review article emphasized the altered signaling pathways and their key molecules related to NF-PitNET invasiveness and aggressiveness that are challenging clinical problems. Furthermore, the currently used medication and potential therapeutic agents that target these important signaling pathway networks are also summarized. These signaling pathway network changes offer important resources for insights into molecular mechanisms, discovery of effective biomarkers, and therapeutic targets for patient stratification, predictive diagnosis, prognostic assessment, and targeted therapy of NF-PitNET.

Construction of the Classification Model Using Key Genes Identified Between Benign and Malignant Thyroid Nodules From Comprehensive Transcriptomic Data

Thyroid nodules are present in upto 50% of the population worldwide, and thyroid malignancy occurs in only 5–15% of nodules. Until now, fine-needle biopsy with cytologic evaluation remains the diagnostic choice to determine the risk of malignancy, yet it fails to discriminate as benign or malignant in one-third of cases. In order to improve the diagnostic accuracy and reliability, molecular testing based on transcriptomic data has developed rapidly. However, gene signatures of thyroid nodules identified in a plenty of transcriptomic studies are highly inconsistent and extremely difficult to be applied in clinical application. Therefore, it is highly necessary to identify consistent signatures to discriminate benign or malignant thyroid nodules. In this study, five independent transcriptomic studies were combined to discover the gene signature between benign and malignant thyroid nodules. This combined dataset comprises 150 malignant and 93 benign thyroid samples. Then, there were 279 differentially expressed genes (DEGs) discovered by the feature selection method (Student’s t test and fold change). And the weighted gene co-expression network analysis (WGCNA) was performed to identify the modules of highly co-expressed genes, and 454 genes in the gray module were discovered as the hub genes. The intersection between DEGs by the feature selection method and hub genes in the WGCNA model was identified as the key genes for thyroid nodules. Finally, four key genes (ST3GAL5, NRCAM, MT1F, and PROS1) participated in the pathogenesis of malignant thyroid nodules were validated using an independent dataset. Moreover, a high-performance classification model for discriminating thyroid nodules was constructed using these key genes. All in all, this study might provide a new insight into the key differentiation of benign and malignant thyroid nodules.

Overview of omics biomarkers in pituitary neuroendocrine tumors to design future diagnosis and treatment strategies

Pituitary neuroendocrine tumors (PitNETs) are the second most common type of intracranial neoplasia. Since their manifestation usually causes hormone hypersecretion, effective management of PitNETs is indisputably necessary. Most of the non-functioning PitNETs pose a real challenge in diagnosis as they grow without giving any signs. Despite the good response of prolactinomas to dopamine agonist therapy, some of these tumors persist or recur; also, about 20% are resistant and 10% behave aggressively. The silent corticotropinomas may not cause symptoms until the tumor mass causes a complication. In somatotropinomas, the possibility of recurrence after transsphenoidal resection is more common in pediatric patients than in adult patients. Therefore, detection of tumors at early stages or identification of recurrence and remission after transsphenoidal surgery would allow wiser management of the disease. Extensive studies have been performed to uncover potential signatures that can be used for preventive diagnosis and/or prognosis of PitNETs as well as for targeted therapy. These molecular signatures at multiple biological levels hold promise for the convergence of preventive approaches and patient-centered disease management and offer potential therapeutic strategies. In this review, we provide an overview of the omics-based biomarker research and highlight the multi-omics signatures that have been proposed as pitNET biomarkers. In addition, understanding the multi-omics data integration of current biomarker discovery strategies was discussed in terms of preventive, predictive, and personalized medicine. The topics discussed in this review will help to develop broader visions for pitNET research, diagnosis, and therapy, particularly in the context of personalized medicine.

Genomic and molecular characterization of pituitary adenoma pathogenesis: review and translational opportunities

OBJECTIVE

Innovations in genomics, epigenomics, and transcriptomics now lay the groundwork for therapeutic interventions against neoplastic disease. In the past 30 years, the molecular pathogenesis of pituitary adenomas has been characterized. This enhanced understanding of the biology of pituitary tumors has potential to impact current treatment paradigms, and there exists significant translational potential for these results. In this review the authors summarize the results of genomics and molecular biology investigations into pituitary adenoma pathogenesis and behavior and discuss opportunities to translate basic science findings into clinical benefit.

METHODS

The authors searched the PubMed and MEDLINE databases by using combinations of the keywords "pituitary adenoma," "genomics," "pathogenesis," and "epigenomics." From the initial search, additional articles were individually evaluated and selected.

RESULTS

Pituitary adenoma growth is primarily driven by unrestrained cell cycle progression, deregulation of growth and proliferation pathways, and abnormal epigenetic regulation of gene expression. These pathways may be amenable to therapeutic intervention. A significant number of studies have attempted to establish links between gene mutations and tumor progression, but a thorough mechanistic understanding remains elusive.

CONCLUSIONS

Although not currently a prominent aspect in the clinical management of pituitary adenomas, genomics and epigenomic studies may become essential in refining patient care and developing novel pharmacological agents. Future basic science investigations should aim at elucidating mechanistic understandings unique to each pituitary adenoma subtype, which will facilitate rational drug design.

Novel Insights into Pituitary Tumorigenesis: Genetic and Epigenetic Mechanisms

Substantial advances have been made recently in the pathobiology of pituitary tumors. Similar to many other endocrine tumors, over the last few years we have recognized the role of germline and somatic mutations in a number of syndromic or nonsyndromic conditions with pituitary tumor predisposition. These include the identification of novel germline variants in patients with familial or simplex pituitary tumors and establishment of novel somatic variants identified through next generation sequencing. Advanced techniques have allowed the exploration of epigenetic mechanisms mediated through DNA methylation, histone modifications and noncoding RNAs, such as microRNA, long noncoding RNAs and circular RNAs. These mechanisms can influence tumor formation, growth, and invasion. While genetic and epigenetic mechanisms often disrupt similar pathways, such as cell cycle regulation, in pituitary tumors there is little overlap between genes altered by germline, somatic, and epigenetic mechanisms. The interplay between these complex mechanisms driving tumorigenesis are best studied in the emerging multiomics studies. Here, we summarize insights from the recent developments in the regulation of pituitary tumorigenesis.

MMEASE: Online meta-analysis of metabolomic data by enhanced metabolite annotation, marker selection and enrichment analysis

Large-scale and long-term metabolomic studies have attracted widespread attention in the biomedical studies yet remain challenging despite recent technique progresses. In particular, the ineffective way of experiment integration and limited capacity in metabolite annotation are known issues. Herein, we constructed an online tool MMEASE enabling the integration of multiple analytical experiments with an enhanced metabolite annotation and enrichment analysis (https://idrblab.org/mmease/). MMEASE was unique in capable of (1) integrating multiple analytical blocks; (2) providing enriched annotation for >330 thousands of metabolites; (3) conducting enrichment analysis using various categories/sub-categories. All in all, MMEASE aimed at supplying a comprehensive service for large-scale and long-term metabolomics, which might provide valuable guidance to current biomedical studies. SIGNIFICANCE: To facilitate the studies of large-scale and long-term metabolomic analysis, MMEASE was developed to (1) achieve the online integration of multiple datasets from different analytical experiments, (2) provide the most diverse strategies for marker discovery, enabling performance assessment and (3) significantly amplify metabolite annotation and subsequent enrichment analysis. MMEASE aimed at supplying a comprehensive service for long-term and large-scale metabolomics, which might provide valuable guidance to current biomedical studies.

Analysis of Cyclooxygenase 2, Programmed Cell Death Ligand 1, and Arginase 1 Expression in Human Pituitary Adenoma

BACKGROUND

Cyclooxygenase 2 (COX-2) is a key enzyme in the synthesis of prostaglandins. Recent studies have shown that overexpression of COX-2 can reduce the antitumor effect of the immune system by inhibiting the proliferation of B and T lymphocytes. Programmed cell death ligand 1 (PD-L1) was the first functionally characterized ligand of programmed cell death protein 1. It plays an important role in maintaining peripheral and central immune tolerance by combining with programmed cell death protein 1. Arginase 1 (ARG1) can process L-arginine in the local microenvironment and affect the function of T cells, resulting in immune escape. In this study, COX-2, PD-L1, and ARG1 expression in human pituitary adenoma (PA) and their relationship were investigated, which provided an initial theoretic basis for further study of the immune escape mechanism in PA in cellular and animal experiments.

METHODS

The protein expression of COX-2, PD-L1, and ARG1 in 55 PA samples was detected by immunohistochemistry, with 10 normal brain tissues as the control group. The location of COX-2, PD-L1, and ARG1 in PA cells was studied by double immunofluorescence colocalization. The results of immunohistochemistry were further verified by Western blot.

RESULTS

The expression of COX-2, PD-L1, and ARG1 in PA was significantly higher than that in normal brain tissue. In functional PA (FPA) and nonfunctional PA (NFPA), there was no significant difference in the expression of COX-2 and PD-L1, whereas ARG1 was higher in NFPA. Moreover, the protein expression level of COX-2 was positively correlated with that of PD-L1 and ARG1, and the expression of PD-L1 was positively correlated with that of ARG1. Immunofluorescence confocal imaging showed that COX-2, PD-L1, and ARG1 were all expressed in the cytoplasm of PA cells, and the physical positions of COX-2, PD-L1, and ARG1 were partially coincident.

CONCLUSIONS

These findings indicate that overexpression of COX-2, PD-L1, and ARG1 may be involved in the pathogenesis of PA. ARG1 plays a more important role in the development of NFPA. By upregulating the expression of PD-L1, COX-2 may promote the expression of ARG1, forming the COX-2/PD-L1/ARG1 signal pathway in promoting the occurrence and development of PA. Perhaps further study of the pathogenesis of PA can start with the mechanism of immune escape.

The tumour microenvironment of pituitary neuroendocrine tumours

The tumour microenvironment (TME) includes a variety of non-neoplastic cells and non-cellular elements such as cytokines, growth factors and enzymes surrounding tumour cells. The TME emerged as a key modulator of tumour initiation, progression and invasion, with extensive data available in many cancers, but little is known in pituitary tumours. However, the understanding of the TME of pituitary tumours has advanced thanks to active research in this field over the last decade. Different immune and stromal cell subpopulations, and several cytokines, growth factors and matrix remodelling enzymes, have been characterised in pituitary tumours. Studying the TME in pituitary tumours may lead to a better understanding of tumourigenic mechanisms, identification of biomarkers useful to predict aggressive disease, and development of novel therapies. This review summarises the current knowledge on the different TME cellular/non-cellular elements in pituitary tumours and provides an overview of their role in tumourigenesis, biological behaviour and clinical outcomes.

Differential Expression of MicroRNAs in Silent and Functioning Corticotroph Tumors

The potential role of miRNAs in the silencing mechanisms of pituitary neuroendocrine tumors (PitNETs) has not been addressed. The aim of the present study was to evaluate the expression levels and the potential associated role of some miRNAs, pathways, and transcription factors in the silencing mechanisms of corticotroph tumors (CTs). Accordingly, the expression of miR-375, miR-383, miR-488, miR-200a and miR-103; of PKA, MAP3K8, MEK, MAPK3, NGFIB, NURR1, PITX1, and STAT3 were analyzed via qRT-PCR in 23 silent and 24 functioning CTs. miR-200a and miR-103 showed significantly higher expression in silent than in functioning CTs, even after eliminating the bias of tumor size, therefore enabling the differentiation between the two variants. Additionally, miR-383 correlated negatively with TBX19 in silent CTs, a transcription factor related with the processing of POMC that can participate in the silencing mechanisms of CTs. Finally, the gene expression levels of miR-488, miR-200a, and miR-103 were significantly higher in macroadenomas (functioning and silent) than in microadenomas. The evidence from this study indicates that miRNAs could be involved in the pathophysiology of CTs. The translational implications of these findings suggest that pharmacological treatments specifically targeting these miRNAs could become a promising therapeutic option for these patients.

The Progress of Immunotherapy in Refractory Pituitary Adenomas and Pituitary Carcinomas

Most pituitary adenomas (PAs) are considered benign tumors, but approximately 0.2% can present metastasis and are classified as pituitary carcinomas (PCs). Refractory PAs lie between benign adenomas and true malignant PC and are defined as aggressive-invasive PAs characterized by a high Ki-67 index, rapid growth, frequent recurrence, and resistance to conventional treatments, including temozolomide. It is notoriously difficult to manage refractory PAs and PC because of the limited therapeutic options. As a promising therapeutic approach, cancer immunotherapy has been experimentally used for the treatment of many tumors, including pituitary tumors. The purpose of this review is to report the progress of immunotherapy in pituitary tumors, including refractory PAs and PCs. The tumor immune microenvironment has been recognized as a key contributor to tumorigenesis, progression, and prognosis. One study indicated that the number of CD68+ macrophages was positively correlated with tumor size and Knosp classification grade for tumor invasiveness. The infiltration of CD4+ and CD8+ T cells was relatively scant in these adenomas, but pituitary growth hormone (GH) adenomas exhibited significantly more CD4+ and CD8+ T cells than non-GH adenomas. These results suggest an association of CD68+ macrophage infiltration with an increase in pituitary tumor size and invasiveness. Another study suggested that a lower number of CD8+ lymphocytes is associated with cavernous sinus invasion and resistance to treatment with first-generation somatostatin analogs in acromegaly patients, highlighting a potential role of the tumor immune microenvironment in determining the prognosis of somatotroph pituitary tumors. Preclinical studies have indicated that widely varying degrees of programmed death-ligand 1 (PD-L1) expression and tumor-infiltrating lymphocytes (TILs) are found among different subtypes. Functional PAs and aggressive PAs express significantly higher levels of PD-L1 and TILs than other subtypes, indicating that PD-1 blockade might be a promising alternative therapy for patients with aggressive PAs. PD-L1 transcript and protein levels were found to be significantly increased in functioning (GH and prolactin-expressing) pituitary tumors compared to nonfunctioning (null cell and silent gonadotroph) adenomas. Moreover, primary pituitary tumors harbored higher levels of PD-L1 mRNA than recurrent tumors. These findings suggest the possibility of considering checkpoint blockade immunotherapy for functioning pituitary tumors refractory to conventional management. Animal models of Cushing's disease also demonstrated PD-L1 and TIL expression in cultured tumors and murine models, as well as the effectiveness of checkpoint blockade therapy in reducing the tumor mass, decreasing hormone secretion, and increasing the survival rate. Clinical studies show that immunotherapy may be an effective treatment in patients with pituitary tumors. One corticotroph carcinoma patient showed a significant reduction in hormone levels and shrinkage of the tumor size of primary and metastatic lesions immediately after investigational treatment with ipilimumab and nivolumab. However, another patient with corticotroph adenoma progressed rapidly after four cycles of anti-PD-1 (pembrolizumab) treatment. To date, there are two registered clinical trials of immunotherapy for pituitary tumors. One of them is the phase II clinical trial of nivolumab combined with ipilimumab for patients with aggressive pituitary tumors (NCT04042753). The other one is also a phase II clinical trial of the combination of nivolumab and ipilimumab for rare tumors, including pituitary tumors (NCT02834013). Both clinical trials are in the stage of recruiting patients and have not been completed. In summary, the results from preclinical research and clinical studies indicated that immunotherapy might be a promising alternative therapy for PCs and refractory PAs resistant to conventional treatments. The combination of immunotherapy and radiotherapy or temozolomide may have synergistic effects compared to a single treatment. More preclinical and clinical studies are needed to further indicate the exact efficacy of immunotherapy in pituitary tumors.

Transcriptome Analysis Identifies an Attenuated Local Immune Response in Invasive Nonfunctioning Pituitary Adenomas

BACKGROUND

Invasive nonfunctioning pituitary adenomas (NFPAs) remain challenging due to their high complication rate and poor prognosis. We aimed to identify the distinctive molecular signatures of invasive NFPAs, compared with noninvasive NFPAs, using gene expression profiling by RNA sequencing.

METHODS

We obtained frozen fresh tissue samples from 14 patients with NFPAs who underwent primary transsphenoidal surgery. Three non-invasive and 11 invasive NFPAs were used for RNA sequencing. The bioinformatics analysis included differential gene expression, gene ontology analysis, and pathway analysis.

RESULTS

A total of 700 genes were differentially expressed (59 up-regulated and 641 down-regulated genes) between invasive and non-invasive NFPAs (false discovery rate <0.1, and |fold change| ≥2). Using the down-regulated genes in invasive NFPAs, gene ontology enrichment analyses and pathway analyses demonstrated that the local immune response was attenuated and that transforming growth factor-β (TGF-β) RII-initiated TGF-β signaling was down-regulated in invasive NFPAs. The overexpression of claudin-9 (CLDN9) and the down-regulation of insulin-like growth factor-binding protein 5 (IGFBP5), death-associated protein kinase 1 (DAPK1), and tissue inhibitor of metalloproteinase-3 (TIMP3) may be related with invasiveness in NFPAs.

CONCLUSION

Invasive NFPAs harbor different gene expression profiles relative to noninvasive NFPAs. In particular, local suppression of the immune response and TGF-β signaling can make PAs prone to invasiveness.

Consistent Biomarkers and Related Pathogenesis Underlying Asthma Revealed by Systems Biology Approach

Asthma is a common chronic airway disease worldwide. Due to its clinical and genetic heterogeneity, the cellular and molecular processes in asthma are highly complex and relatively unknown. To discover novel biomarkers and the molecular mechanisms underlying asthma, several studies have been conducted by focusing on gene expression patterns in epithelium through microarray analysis. However, few robust specific biomarkers were identified and some inconsistent results were observed. Therefore, it is imperative to conduct a robust analysis to solve these problems. Herein, an integrated gene expression analysis of ten independent, publicly available microarray data of bronchial epithelial cells from 348 asthmatic patients and 208 healthy controls was performed. As a result, 78 up- and 75 down-regulated genes were identified in bronchial epithelium of asthmatics. Comprehensive functional enrichment and pathway analysis revealed that response to chemical stimulus, extracellular region, pathways in cancer, and arachidonic acid metabolism were the four most significantly enriched terms. In the protein-protein interaction network, three main communities associated with cytoskeleton, response to lipid, and regulation of response to stimulus were established, and the most highly ranked 6 hub genes (up-regulated CD44, KRT6A, CEACAM5, SERPINB2, and down-regulated LTF and MUC5B) were identified and should be considered as new biomarkers. Pathway cross-talk analysis highlights that signaling pathways mediated by IL-4/13 and transcription factor HIF-1α and FOXA1 play crucial roles in the pathogenesis of asthma. Interestingly, three chemicals, polyphenol catechin, antibiotic lomefloxacin, and natural alkaloid boldine, were predicted and may be potential drugs for asthma treatment. Taken together, our findings shed new light on the common molecular pathogenesis mechanisms of asthma and provide theoretical support for further clinical therapeutic studies.

Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence-based method of enhanced reproducibility

Aims

As one of the most fundamental questions in modern science, “what causes schizophrenia (SZ)” remains a profound mystery due to the absence of objective gene markers. The reproducibility of the gene signatures identified by independent studies is found to be extremely low due to the incapability of available feature selection methods and the lack of measurement on validating signatures’ robustness. These irreproducible results have significantly limited our understanding of the etiology of SZ.

Methods

In this study, a new feature selection strategy was developed, and a comprehensive analysis was then conducted to ensure a reliable signature discovery. Particularly, the new strategy (a) combined multiple randomized sampling with consensus scoring and (b) assessed gene ranking consistency among different datasets, and a comprehensive analysis among nine independent studies was conducted.

Results

Based on a first‐ever evaluation of methods’ reproducibility that was cross‐validated by nine independent studies, the newly developed strategy was found to be superior to the traditional ones. As a result, 33 genes were consistently identified from multiple datasets by the new strategy as differentially expressed, which might facilitate our understanding of the mechanism underlying the etiology of SZ.

Conclusion

A new strategy capable of enhancing the reproducibility of feature selection in current SZ research was successfully constructed and validated. A group of candidate genes identified in this study should be considered as great potential for revealing the etiology of SZ.

Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics

Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.

A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies

Microbial community (MC) has great impact on mediating complex disease indications, biogeochemical cycling and agricultural productivities, which makes metaproteomics powerful technique for quantifying diverse and dynamic composition of proteins or peptides. The key role of biostatistical strategies in MC study is reported to be underestimated, especially the appropriate application of feature selection method (FSM) is largely ignored. Although extensive efforts have been devoted to assessing the performance of FSMs, previous studies focused only on their classification accuracy without considering their ability to correctly and comprehensively identify the spiked proteins. In this study, the performances of 14 FSMs were comprehensively assessed based on two key criteria (both sample classification and spiked protein discovery) using a variety of metaproteomics benchmarks. First, the classification accuracies of those 14 FSMs were evaluated. Then, their abilities in identifying the proteins of different spiked concentrations were assessed. Finally, seven FSMs (FC, LMEB, OPLS-DA, PLS-DA, SAM, SVM-RFE and T-Test) were identified as performing consistently superior or good under both criteria with the PLS-DA performing consistently superior. In summary, this study served as comprehensive analysis on the performances of current FSMs and could provide a valuable guideline for researchers in metaproteomics.

Assessing the Effectiveness of Direct Data Merging Strategy in Long-Term and Large-Scale Pharmacometabonomics

Because of the extended period of clinic data collection and huge size of analyzed samples, the long-term and large-scale pharmacometabonomics profiling is frequently encountered in the discovery of drug/target and the guidance of personalized medicine. So far, integration of the results (ReIn) from multiple experiments in a large-scale metabolomic profiling has become a widely used strategy for enhancing the reliability and robustness of analytical results, and the strategy of direct data merging (DiMe) among experiments is also proposed to increase statistical power, reduce experimental bias, enhance reproducibility and improve overall biological understanding. However, compared with the ReIn, the DiMe has not yet been widely adopted in current metabolomics studies, due to the difficulty in removing unwanted variations and the inexistence of prior knowledges on the performance of the available merging methods. It is therefore urgently needed to clarify whether DiMe can enhance the performance of metabolic profiling or not. Herein, the performance of DiMe on 4 pairs of benchmark datasets was comprehensively assessed by multiple criteria (classification capacity, robustness and false discovery rate). As a result, integration/merging-based strategies (ReIn and DiMe) were found to perform better under all criteria than those strategies based on single experiment. Moreover, DiMe was discovered to outperform ReIn in classification capacity and robustness, while the ReIn showed superior capacity in controlling false discovery rate. In conclusion, these findings provided valuable guidance to the selection of suitable analytical strategy for current metabolomics.