Recently Published Documents
The objective of this study was to identify potential biomarkers and possible metabolic pathways of malignant and benign thyroid nodules through lipidomics study. A total of 47 papillary thyroid carcinomas (PTC) and 33 control check (CK) were enrolled. Plasma samples were collected for UPLC-Q-TOF MS system detection, and then OPLS-DA model was used to identify differential metabolites. Based on classical statistical methods and machine learning, potential biomarkers were characterized and related metabolic pathways were identified. According to the metabolic spectrum, 13 metabolites were identified between PTC group and CK group, and a total of five metabolites were obtained after further screening. Its metabolic pathways were involved in glycerophospholipid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, glycosylphosphatidylinositol (GPI)—anchor biosynthesis, Phosphatidylinositol signaling system and the metabolism of arachidonic acid metabolism. The metabolomics method based on PROTON nuclear magnetic resonance (NMR) had great potential for distinguishing normal subjects from PTC. GlcCer(d14:1/24:1), PE-NME (18:1/18:1), SM(d16:1/24:1), SM(d18:1/15:0), and SM(d18:1/16:1) can be used as potential serum markers for the diagnosis of PTC.
A Computational Systems Analyses to Identify Biomarkers and Mechanistic Link in Psoriasis and Cutaneous Squamous Cell Carcinoma
Psoriasis is the most common and chronic skin disease that affects individuals from every age group. The rate of psoriasis is increasing over the time in both developed and developing countries. Studies have revealed the possibility of association of psoriasis with skin cancers, particularly non-melanoma skin cancers (NMSC), which, include basal cell carcinoma and cutaneous squamous cell carcinoma (cSCC). There is a need to analyze the disease at molecular level to propose potential biomarkers and therapeutic targets in comparison to cSCC. Therefore, the second analyzed disease of this study is cSCC. It is the second most common prevalent skin cancer all over the world with the potential to metastasize and recur. There is an urge to validate the proposed biomarkers and discover new potential biomarkers as well. In order to achieve the goals and objectives of the study, microarray and RNA-sequencing data analyses were performed followed by network analysis. Afterwards, quantitative systems biology was implemented to analyze the results at a holistic level. The aim was to predict the molecular patterns that can lead psoriasis to cancer. The current study proposed potential biomarkers and therapeutic targets for psoriasis and cSCC. IL-17 signaling pathway is also identified as significant pathway in both diseases. Moreover, the current study proposed that autoimmune pathology, neutrophil recruitment, and immunity to extracellular pathogens are sensitive towards MAPKs (MAPK13 and MAPK14) and genes for AP-1 (FOSL1 and FOS). Therefore, these genes should be further studied in gene knock down based studies as they may play significant role in leading psoriasis towards cancer.
Stomach adenocarcinoma (STAD) is a leading cause of cancer deaths, and the outcome of the patients remains dismal for the lack of effective biomarkers of early detection. Recent studies have elucidated the landscape of genomic alterations of gastric cancer and reveal some biomarkers of advanced-stage gastric cancer, however, information about early-stage biomarkers is limited. Here, we adopt Weighted Gene Co-expression Network Analysis (WGCNA) to screen potential biomarkers for early-stage STAD using RNA-Seq and clinical data from TCGA database. We find six gene clusters (or modules) are significantly correlated with the stage-I STADs. Among these, five hub genes, i.e., MS4A1, THBS2, VCAN, PDGFRB, and KCNA3 are identified and significantly de-regulated in the stage-I STADs compared with the normal stomach gland tissues, which suggests they can serve as potential early diagnostic biomarkers. Moreover, we show that high expression of VCAN and PDGFRB is associated with poor prognosis of STAD. VCAN encodes a large chondroitin sulfate proteoglycan that is the main component of the extracellular matrix, and PDGFRB encodes a cell surface tyrosine kinase receptor for members of the platelet-derived growth factor (PDGF) family. Consistently, Gene Ontology (GO) analysis of differentially expressed genes in the STADs indicates terms associated with extracellular matrix and receptor ligand activity are significantly enriched. Protein-protein network interaction analysis (PPI) and Gene Set Enrichment Analysis (GSEA) further support the core role of VCAN and PDGFRB in the tumorigenesis. Collectively, our study identifies the potential biomarkers for early detection and prognosis of STAD.
ObjectiveMicroorganisms play a key role in the initiation and progression of periodontal disease. Research studies have focused on seeking specific microorganisms for diagnosing and monitoring the outcome of periodontitis treatment. Large samples may help to discover novel potential biomarkers and capture the common characteristics among different periodontitis patients. This study examines how to screen and merge high-quality periodontitis-related sequence datasets from several similar projects to analyze and mine the potential information comprehensively.MethodsIn all, 943 subgingival samples from nine publications were included based on predetermined screening criteria. A uniform pipeline (QIIME2) was applied to clean the raw sequence datasets and merge them together. Microbial structure, biomarkers, and correlation network were explored between periodontitis and healthy individuals. The microbiota patterns at different periodontal pocket depths were described. Additionally, potential microbial functions and metabolic pathways were predicted using PICRUSt to assess the differences between health and periodontitis.ResultsThe subgingival microbial communities and functions in subjects with periodontitis were significantly different from those in healthy subjects. Treponema, TG5, Desulfobulbus, Catonella, Bacteroides, Aggregatibacter, Peptostreptococcus, and Eikenella were periodontitis biomarkers, while Veillonella, Corynebacterium, Neisseria, Rothia, Paludibacter, Capnocytophaga, and Kingella were signature of healthy periodontium. With the variation of pocket depth from shallow to deep pocket, the proportion of Spirochaetes, Bacteroidetes, TM7, and Fusobacteria increased, whereas that of Proteobacteria and Actinobacteria decreased. Synergistic relationships were observed among different pathobionts and negative relationships were noted between periodontal pathobionts and healthy microbiota.ConclusionThis study shows significant differences in the oral microbial community and potential metabolic pathways between the periodontitis and healthy groups. Our integrated analysis provides potential biomarkers and directions for in-depth research. Moreover, a new method for integrating similar sequence data is shown here that can be applied to other microbial-related areas.
In situ analysis of N-linked Glycans as Potential Biomarkers of Clinical Course in Human Prostate Cancer
Endometriosis is a common gynaecological disease that is characterized by endometrium-like tissue outside the uterine cavity. Endometriosis significantly compromises the quality of life of women and is a major cause of infertility. The gold standard for diagnosis of endometriosis is visual inspection by laparoscopy, which significantly prolongs the time to final diagnosis. This lack of non-invasive diagnostic approaches is why the discovery of biomarkers for endometriosis has been defined as a research priority. In this report, we describe hypothesis-driven and hypothesis-generating approaches for biomarker discovery, along with some important potential biomarkers of endometriosis and their diagnostic characteristics, sensitivities, and specificities. Finally, we present our perspective on the discovery of biomarkers for endometriosis, and discuss some results from our previous and more recent studies. Future studies must focus on improving patient quality of life rather than on discovering significant differences, and therefore close collaboration between clinicians and pre-clinical researchers is essential.
Serum Metabolomic Analysis of Coronary Heart Disease Patients with Stable Angina Pectoris Subtyped by Traditional Chinese Medicine Diagnostics Reveals Biomarkers Relevant to Personalized Treatments
To improve the treatment of patients with coronary heart disease (CHD), personalized treatments based on potential biomarkers could make a difference. To investigate if such potential biomarkers could be found for CHD inhomogeneous, we combined traditional Chinese medicine based diagnosis with untargeted and targeted metabolomics analyses. Shi and Xu patient subtype groups of CHD with angina pectoris were identified. Different metabolites including lipids, fatty acids and amino acids were further analyzed with targeted metabolomics and mapped to disease-related pathways. The long-chain unsaturated lipids ceramides metabolism, bile acid metabolism were differentially affected in the Xu subtype groups. While, Shi-subtype patients seemed to show inflammation, anomalous levels of bioactive phospholipids and antioxidant molecules. Furthermore, variations in the endothelial damage response and energy metabolism found based on ELISA analysis are the key divergence points between different CHD subtypes. The results showed Xu subtype patients might benefit from long-chain unsaturated lipids ceramides as therapeutic targets. Shi subtype patients might benefit more from levels of polyunsaturated fatty acid consumption and treatments that help in restoring energy balance. Metabolic differences can be essential for treatment protocols. Thus, patient group specific differences can serve as important information to refine current treatment approaches in a personalized manner.
Potential Biomarkers for Treatment Response to the BCL-2 Inhibitor Venetoclax: State of the Art and Future Directions
Intrinsic apoptotic pathway dysregulation plays an essential role in all cancers, particularly hematologic malignancies. This role has led to the development of multiple therapeutic agents targeting this pathway. Venetoclax is a selective BCL-2 inhibitor that has been approved for the treatment of chronic lymphoid leukemia and acute myeloid leukemia. Given the reported resistance to venetoclax, understanding the mechanisms of resistance and the potential biomarkers of response is crucial to ensure optimal drug usage and improved patient outcomes. Mechanisms of resistance to venetoclax include alterations involving the BH3-binding groove, BCL2 gene mutations affecting venetoclax binding, and activation of alternative anti-apoptotic pathways. Moreover, various potential genetic biomarkers of venetoclax resistance have been proposed, including chromosome 17p deletion, trisomy 12, and TP53 loss or mutation. This manuscript provides an overview of biomarkers that could predict treatment response to venetoclax.