PeerJ

Our medical research library holds 5 studies published in PeerJ (2022–2025). Each entry links to the study's record on PubMed and, where there is one, to the publisher's page and a free full text. 5 have an abstract, the authors' own summary as PubMed records it: open "Abstract" to read it.

About the journal

• Publisher: PeerJ Inc
• Published: 2013 to the present, United States
• Language: English
• ISSN: 2167-8359 (electronic)
• Its record in the NLM Catalog

2020s

• Insights into the molecular mechanisms underlying the function of lysyl oxidase like 1 in cancers. X. Wang, X. Wang, Y. Li, et al. 2025;13:e19628. Review PubMed · Publisher · Free full text

  Abstract

  Cancer is one of the primary causes of human mortality and a significant barrier to increasing human life expectancy. The effective screening, early diagnosis, and treatment of cancer have long been clinical challenges, and thus new biomarkers or molecular targets must be identified to improve the diagnosis and treatment of cancer patients. Lysyl oxidase like 1 (LOXL1), a secreted copper-dependent amine oxidase, is commonly expressed in a variety of cell types. LOXL1 can maintain the steady state of elastin, engage in extracellular matrix (ECM) remodelling. LOXL1 has diverse biological functions, and its dysregulation is the basis of many clinical diseases. The abnormal expression or activation of LOXL1 can disrupt the cellular microenvironment, contributing to the development of various diseases, such as atherosclerosis, tissue damage, fibrosis, and cancer. Recent research has revealed that LOXL1 is often overexpressed in a majority of cancers, where it plays a role in regulating tumor growth and metastasis. However, some studies have also suggested that LOXL1 may have a tumor-suppressive function. Research has indicated that the LOXL1 protein is reduced in human renal cell carcinoma (RCC) and bladder cancer (BLCA), where it acts to suppress tumor growth. Conversely, it is upregulated in human salivary adenoid cystic carcinoma (SACC), non-small cell lung cancer (NSCLC), pleural mesothelioma (PM), brain glioma, prostate cancer (PRAD), gastric cancer (GC), breast cancer (BC), thyroid carcinoma (THCA), pancreatic adenocarcinoma (PAAD), and osteosarcoma (OS). The expression of LOXL1 in colorectal cancer (CRC) remains a topic of debate, as it may either be upregulated or downregulated. These findings imply that LOXL1 may have a dual role in cancer, either inhibiting or facilitating carcinogenesis. This article provides a comprehensive review of the structure and function of LOXL1, along with its associations with cancer. Additionally, it explores the role of LOXL1 in tumor microenvironment remodeling, tumorigenesis, metastasis, and the molecular mechanisms that underpin these processes.

  ©2025 Wang et al.

• Near-infrared spectroscopy combined with machine learning for plasma-based discriminant diagnosis of malignant mesothelioma: a retrospective study. Y. Gu, R. Wang, W. Mao, et al. 2025;13:e20503. PubMed · Publisher · Free full text

  Abstract

  This study developed a novel, non-invasive platform integrating near-infrared spectroscopy (NIRS) with machine learning (ML) to address the critical clinical challenge of misdiagnosing malignant mesothelioma (MM). We analyzed plasma samples from 99 individuals (29 MM, 41 lung cancer (LC), and 29 healthy controls). A support vector machine (SVM) model perfectly discriminated MM from LC (area under the curve (AUC) = 0.827), while a partial least squares (PLS) model differentiated MM from healthy control (HC) with high accuracy (AUC = 1.0). Despite the highly promising results, this single-center study is however limited by a small sample size, inherent to the rarity of MM and the associated difficulties in patient recruitment. Our findings demonstrate the potential of the NIRS-ML platform as a highly accurate tool for improving MM diagnosis and discriminant diagnosis, meriting further validation in larger cohorts.

  © 2025 Gu et al.

• The diagnostic utility of glycosaminoglycans (GAGs) in the early detection of cancer: a systematic review. S. Douglah, R. Khalil, R. Kanaan, et al. 2024;12:e18486. Systematic review PubMed · Publisher · Free full text

  Abstract

  Background: Glycosaminoglycans (GAGs) are a family of polysaccharides found abundantly in the extracellular matrix (ECM) of tissues. Research has indicated that the dysregulation of ECM, including changes and disruptions in GAGs, contributes to various cancer hallmarks such as metabolic reprogramming, persistent growth signals, immunosuppression, angiogenesis, tumor invasion, and metastasis.

  Objective: This systematic review aims to evaluate the diagnostic accuracy of GAGs, including heparan sulfate (HS), chondroitin sulfate (CS), and hyaluronic acid (HA), in early detection of cancer.

  Method: Four databases (PubMed, Scopus, EBSCO, and Ovid) were searched for studies in English within the last 15 years, involving at least 50 human participants. Using a two-stage process: identification and screening, 11 articles were selected and critically appraised using Critical Appraisal Skills Programme (CASP) checklists and Newcastle-Ottawa Scale (NOS) appropriate for each study design.

  Result: Eleven studies met the inclusion criteria, encompassing various cancers like renal cell carcinoma (RCC), upper GI cancer (UGI), ovarian cancer, prostate cancer, breast cancer, lung cancer, colorectal cancer and oral cancer. Methodological quality was assessed using two established tools, with no studies exhibiting a high risk of bias. Heparan sulfate levels showed diagnostic potential in renal cancer with a maximum accuracy of 98.9%, achieving 94.7% specificity and 100% sensitivity. Chondroitin sulfate disaccharides emerged as a promising diagnostic marker in ovarian cancer and showed potential as diagnostic markers in renal cancer. However, there were no statistically significant differences in urinary chondroitin sulfate levels between patients and controls in prostate cancer. In breast cancer, hyaluronic acid showed moderate accuracy (AUC = 0.792) in distinguishing metastatic from non-metastatic disease, and a composite score incorporating multiple markers, including HA, showed even higher accuracy (AUC = 0.901) in detecting metastatic breast cancer. HA demonstrated moderate diagnostic accuracy for UGI cancers. Serum HA levels were significantly elevated in patients with oral cancer and pleural malignant mesothelioma and associated with tumor progression in patients with lung cancer. Elevated low molecular weight form of hyaluronan (~6 k Da HA) levels were found in colorectal cancer tissues.

  Conclusion: GAGs hold potential as early cancer detection biomarkers. Further validation with larger, diverse populations is needed to validate their diagnostic accuracy and clinical utility.

  © 2024 Douglah et al.

• GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma. D. Wang, J. Zhu, N. Li, et al. 2023;11:e15302. PubMed · Publisher · Free full text

  Abstract

  Background: Malignant mesothelioma (MM) is a cancer caused mainly by asbestos exposure, and is aggressive and incurable. This study aimed to identify differential metabolites and metabolic pathways involved in the pathogenesis and diagnosis of malignant mesothelioma.

  Methods: By using gas chromatography-mass spectrometry (GC-MS), this study examined the plasma metabolic profile of human malignant mesothelioma. We performed univariate and multivariate analyses and pathway analyses to identify differential metabolites, enriched metabolism pathways, and potential metabolic targets. The area under the receiver-operating curve (AUC) criterion was used to identify possible plasma biomarkers.

  Results: Using samples from MM (n = 19) and healthy control (n = 22) participants, 20 metabolites were annotated. Seven metabolic pathways were disrupted, involving alanine, aspartate, and glutamate metabolism; glyoxylate and dicarboxylate metabolism; arginine and proline metabolism; butanoate and histidine metabolism; beta-alanine metabolism; and pentose phosphate metabolic pathway. The AUC was used to identify potential plasma biomarkers. Using a threshold of AUC = 0.9, five metabolites were identified, including xanthurenic acid, (s)-3,4-hydroxybutyric acid, D-arabinose, gluconic acid, and beta-d-glucopyranuronic acid.

  Conclusions: To the best of our knowledge, this is the first report of a plasma metabolomics analysis using GC-MS analyses of Asian MM patients. Our identification of these metabolic abnormalities is critical for identifying plasma biomarkers in patients with MM. However, additional research using a larger population is needed to validate our findings.

  © 2023 Wang et al.

• Metabolomics of a cell line-derived xenograft model reveals circulating metabolic signatures for malignant mesothelioma. Y. Gao, Z. Dai, C. Yang, et al. 2022;10:e12568. PubMed · Publisher · Free full text

  Abstract

  Background: Malignant mesothelioma (MM) is a rare and highly aggressive cancer. Despite advances in multidisciplinary treatments for cancer, the prognosis for MM remains poor with no effective diagnostic biomarkers currently available. The aim of this study was to identify plasma metabolic biomarkers for better MM diagnosis and prognosis by use of a MM cell line-derived xenograft (CDX) model.

  Methods: The MM CDX model was confirmed by hematoxylin and eosin staining and immunohistochemistry. Twenty female nude mice were randomly divided into two groups, 10 for the MM CDX model and 10 controls. Plasma samples were collected two weeks after tumor cell implantation. Gas chromatography-mass spectrometry analysis was conducted. Both univariate and multivariate statistics were used to select potential metabolic biomarkers. Hierarchical clustering analysis, metabolic pathway analysis, and receiver operating characteristic (ROC) analysis were performed. Additionally, bioinformatics analysis was used to investigate differential genes between tumor and normal tissues, and survival-associated genes.

  Results: The MM CDX model was successfully established. With VIP > 1.0 and P-value < 0.05, a total of 23 differential metabolites were annotated, in which isoleucine, 5-dihydrocortisol, and indole-3-acetamide had the highest diagnostic values based on ROC analysis. These were mainly enriched in pathways for starch and sucrose metabolism, pentose and glucuronate interconversions, galactose metabolism, steroid hormone biosynthesis, as well as phenylalanine, tyrosine and tryptophan biosynthesis. Further, down-regulation was observed for amino acids, especially isoleucine, which is consistent with up-regulation of amino acid transporter genes SLC7A5 and SLC1A3 in MM. Overall survival was also negatively associated with SLC1A5, SLC7A5, and SLC1A3.

  Conclusion: We found several altered plasma metabolites in the MM CDX model. The importance of specific metabolic pathways, for example amino acid metabolism, is herein highlighted, although further investigation is warranted.

  ©2022 Gao et al.

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