Integrative omics analysis identifies biomarkers of idiopathic pulmonary fibrosis

Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers

Glioma is currently one of the most prevalent types of primary brain cancer. Given its high level of heterogeneity along with the complex biological molecular markers, many efforts have been made to accurately classify the type of glioma in each patient, which, in turn, is critical to improve early diagnosis and increase survival. Nonetheless, as a result of the fast-growing technological advances in high-throughput sequencing and evolving molecular understanding of glioma biology, its classification has been recently subject to significant alterations. In this study, we integrate multiple glioma omics modalities (including mRNA, DNA methylation, and miRNA) from The Cancer Genome Atlas (TCGA), while using the revised glioma reclassified labels, with a supervised method based on sparse canonical correlation analysis (DIABLO) to discriminate between glioma types. We were able to find a set of highly correlated features distinguishing glioblastoma from lower-grade gliomas (LGGs) that were mainly associated with the disruption of receptor tyrosine kinases signaling pathways and extracellular matrix organization and remodeling. Concurrently, the discrimination of the LGG types was characterized primarily by features involved in ubiquitination and DNA transcription processes. Furthermore, we could identify several novel glioma biomarkers likely helpful in both diagnosis and prognosis of the patients, including the genes PPP1R8, GPBP1L1, KIAA1614, C14orf23, CCDC77, BVES, EXD3, CD300A, and HEPN1. Collectively, this comprehensive approach not only allowed a highly accurate discrimination of the different TCGA glioma patients but also presented a step forward in advancing our comprehension of the underlying molecular mechanisms driving glioma heterogeneity. Ultimately, our study also revealed novel candidate biomarkers that might constitute potential therapeutic targets, marking a significant stride toward personalized and more effective treatment strategies for patients with glioma.

The mechanism of action of Botrychium (Thunb.) Sw. for prevention of idiopathic pulmonary fibrosis based on 1H-NMR-based metabolomics

OBJECTIVES

This study aimed to reveal the anti-fibrotic effects of Botrychium ternatum (Thunb.) Sw. (BT) against idiopathic pulmonary fibrosis (IPF) and to preliminarily analyze its potential mechanism on bleomycin-induced IPF rats.

METHODS

The inhibition of fibrosis progression in vivo was assessed by histopathology combined with biochemical indicators. In addition, the metabolic regulatory mechanism was investigated using 1H-nuclear magnetic resonance-based metabolomics combined with multivariate statistical analysis.

KEY FINDINGS

Firstly, biochemical analysis revealed that BT notably suppressed the expression of hydroxyproline and transforming growth factor-β1 in the pulmonary tissue. Secondly, Masson's trichrome staining and hematoxylin and eosin showed that BT substantially improved the structure of the damaged lung and significantly inhibited the proliferation of collagen fibers and the deposition of extracellular matrix. Finally, serum metabolomic analysis suggested that BT may exert anti-fibrotic effects by synergistically regulating tyrosine metabolism; phenylalanine, tyrosine and tryptophan biosynthesis; and synthesis and degradation of ketone bodies.

CONCLUSIONS

Our study not only clarifies the potential anti-fibrotic mechanism of BT against IPF at the metabolic level but also provides a theoretical basis for developing BT as an effective anti-fibrotic agent.

Idiopathic pulmonary fibrosis-specific Bayesian network integrating extracellular vesicle proteome and clinical information

Idiopathic pulmonary fibrosis (IPF) is a progressive disease characterized by severe lung fibrosis and a poor prognosis. Although the biomolecules related to IPF have been extensively studied, molecular mechanisms of the pathogenesis and their association with serum biomarkers and clinical findings have not been fully elucidated. We constructed a Bayesian network using multimodal data consisting of a proteome dataset from serum extracellular vesicles, laboratory examinations, and clinical findings from 206 patients with IPF and 36 controls. Differential protein expression analysis was also performed by edgeR and incorporated into the constructed network. We have successfully visualized the relationship between biomolecules and clinical findings with this approach. The IPF-specific network included modules associated with TGF-β signaling (TGFB1 and LRC32), fibrosis-related (A2MG and PZP), myofibroblast and inflammation (LRP1 and ITIH4), complement-related (SAA1 and SAA2), as well as serum markers, and clinical symptoms (KL-6, SP-D and fine crackles). Notably, it identified SAA2 associated with lymphocyte counts and PSPB connected with the serum markers KL-6 and SP-D, along with fine crackles as clinical manifestations. These results contribute to the elucidation of the pathogenesis of IPF and potential therapeutic targets.

Dasatinib: a potential tyrosine kinase inhibitor to fight against multiple cancer malignancies

Dasatinib is the 2nd generation TKI (Tyrosine Kinase Inhibitor) having the potential to treat numerous forms of leukemic and cancer patients and it is 300 times more potent than imatinib. Cancer is the major cause of death globally and need to enumerate novel strategies to coping with it. Various novel therapeutics introduced into the market for ease in treating various forms of cancer. We reviewed and evaluated all the related aspects of dasatinib, which can enhance the knowledge about dasatinib therapeutics methodology, pharmacodynamic and pharmacokinetics, side effects, advantages, disadvantages, various kinds of interactions and its novel formulations as well.

Proteomic Fingerprint of Lung Fibrosis Progression and Response to Therapy in Bleomycin-Induced Mouse Model

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterized by the aberrant accumulation of extracellular matrix in the lungs. nintedanib is one of the two FDA-approved drugs for IPF treatment; however, the exact pathophysiological mechanisms of fibrosis progression and response to therapy are still poorly understood. In this work, the molecular fingerprint of fibrosis progression and response to nintedanib treatment have been investigated by mass spectrometry-based bottom-up proteomics in paraffin-embedded lung tissues from bleomycin-induced (BLM) pulmonary fibrosis mice. Our proteomics results unveiled that (i) samples clustered depending on the tissue fibrotic grade (mild, moderate, and severe) and not on the time course after BLM treatment; (ii) the dysregulation of different pathways involved in fibrosis progression such as the complement coagulation cascades, advanced glycation end products (AGEs) and their receptors (RAGEs) signaling, the extracellular matrix-receptor interaction, the regulation of actin cytoskeleton, and ribosomes; (iii) Coronin 1A (Coro1a) as the protein with the highest correlation when evaluating the progression of fibrosis, with an increased expression from mild to severe fibrosis; and (iv) a total of 10 differentially expressed proteins (p_adj-value ≤ 0.05 and Fold change ≤-1.5 or ≥1.5), whose abundance varied in the base of the severity of fibrosis (mild and moderate), were modulated by the antifibrotic treatment with nintedanib, reverting their trend. Notably, nintedanib significantly restored lactate dehydrogenase B (Ldhb) expression but not lactate dehydrogenase A (Ldha). Notwithstanding the need for further investigations to validate the roles of both Coro1a and Ldhb, our findings provide an extensive proteomic characterization with a strong relationship with histomorphometric measurements. These results unveil some biological processes in pulmonary fibrosis and drug-mediated fibrosis therapy.

Detection of interstitial pneumonia with autoimmune features and idiopathic pulmonary fibrosis are enhanced by involvement of matrix metalloproteinases levels and clinical diagnosis

BACKGROUND

Higher detection of interstitial pneumonia with autoimmune features (IPAF), and idiopathic pulmonary fibrosis (IPF), has significant diagnostic and therapeutic implications. Some matrix metalloproteinases (MMPs) have become reliable diagnostic biomarkers in IPAF and IPF in previous studies, yet relevant reliability remains to be recognized.

MATERIALS AND METHODS

In this study, 36 ILDs patients, including 31 IPAF patients (Mean ± SD, 50.20 ± 5.10 years; 16 [51.6%] females) and five IPF patients (Mean ± SD, 61.20 ± 6.73 years; one [20.0%] females) were retrospectively enrolled. Serial serum samples were collected from patients with IPAF and IPF between January 2019 and December 2020. Notably, Serum MMPs levels were measured by U-PLEX Biomarker Group 1(Human) Multiplex Assays (MSD, USA).

RESULTS

A combination of MMPs and combinatorial biomarkers was strongly associated with clinical subjects in this study (AUC, 0.597 for Stability vs. Improvement and 0.756 for Stability vs. Exacerbation). Importantly, the AUC of MMP-12 reaches 0.730 (p < 0.05, Stability AUC vs. Improvement AUC) while MMP-13 reaches 0.741 (p < 0.05, Stability AUC vs. Exacerbation AUC) showed better performance than other MMPs in two comparisons.

CONCLUSIONS

Clinical risk factors and MMPs are strongly associated with either stratification of the disease of progression of IPAF or in two IPAF and IPF independent cohorts. To our knowledge, this is the first to illustrate that MMP-12 and MMP-13 may be expected to become typical promising biomarkers in Improvement - IPAF and Exacerbation - IPAF, respectively.