LC-MS based urine untargeted metabolomic analyses to identify and subdivide urothelial cancer

Magnetic 3D macroporous MOF oriented urinary exosome metabolomics for early diagnosis of bladder cancer

Bladder cancer (BCa) exhibits the escalating incidence and mortality due to the untimely and inaccurate early diagnosis. Urinary exosome metabolites, carrying critical tumor cell information and directly related to bladder, emerge as promising non-invasive diagnostic biomarkers of BCa. Herein, the magnetic 3D ordered macroporous zeolitic imidazolate framework-8 (magMZIF-8) is synthesized and used for efficient urinary exosome isolation. Notably, beyond retaining the single crystals and micropores of conventional ZIF-8, MZIF-8 is further enhanced with highly oriented and ordered macropores (150 nm) and the large specific surface area (973 m2·g-1), which could enable the high purity and yield separation of exosomes via leveraging the combination of size exclusion, affinity, and electrostatic interactions between magMZIF-8 and the surfaces of exosome. Furthermore, the magnetic and hydrophilic properties of magMZIF-8 will further simplify the process and enhance the efficiency of separation. After conditional optimization, a 50 mL of urine is sufficient for exosome metabolomics analysis, and the time for isolating exosomes from 42 urine samples was 2 hours only. Incorporating machine learning algorithms with LC-MS/MS analysis of the metabolic patterns obtained from isolated exosomes, early-stage BCa patients were differentiated from healthy controls, with area under the curve (AUC) value of 0.844-0.9970 in the training set and 0.875-1.00 in the test set, signifying its potential as a reliable diagnostic tool. This study offers a promising approach for the non-invasive and efficient diagnosis of BCa on a large scale via exosome metabolomics.

Improving platelet-RNA-based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification

Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community on which methods are the most effective or how to process the data. To circumvent this, we performed a large-scale study using various machine-learning techniques. First, we took a closer look at existing datasets and filtered out some patients to assert data collection quality. The final data collection included platelet RNA samples acquired from 1397 cancer patients (17 types of cancer) and 354 asymptomatic, presumed healthy, donors. Then, we assessed an array of different machine-learning models and techniques (e.g., feature selection of RNA transcripts) in pan-cancer detection and multiclass classification. Our results show that simple logistic regression performs the best, reaching a 68% cancer detection rate at a 99% specificity level, and multiclass classification accuracy of 79.38% when distinguishing between five cancer types. In summary, by revisiting classical machine-learning models, we have exceeded the previously used method by 5% and 9.65% in cancer detection and multiclass classification, respectively. To ease further research, we open-source our code and data processing pipelines (https://gitlab.com/jopekmaksym/improving-platelet-rna-based-diagnostics), which we hope will serve the community as a strong baseline.

BLCA prognostic model creation and validation based on immune gene-metabolic gene combination

BACKGROUND

Bladder cancer (BLCA) is a prevalent urinary system malignancy. Understanding the interplay of immunological and metabolic genes in BLCA is crucial for prognosis and treatment.

METHODS

Immune/metabolism genes were extracted, their expression profiles analyzed. NMF clustering found prognostic genes. Immunocyte infiltration and tumor microenvironment were examined. Risk prognostic signature using Cox/LASSO methods was developed. Immunological Microenvironment and functional enrichment analysis explored. Immunotherapy response and somatic mutations evaluated. RT-qPCR validated gene expression.

RESULTS

We investigated these genes in 614 BLCA samples, identifying relevant prognostic genes. We developed a predictive feature and signature comprising 7 genes (POLE2, AHNAK, SHMT2, NR2F1, TFRC, OAS1, CHKB). This immune and metabolism-related gene (IMRG) signature showed superior predictive performance across multiple datasets and was independent of clinical indicators. Immunotherapy response and immune cell infiltration correlated with the risk score. Functional enrichment analysis revealed distinct biological pathways between low- and high-risk groups. The signature demonstrated higher prediction accuracy than other signatures. qRT-PCR confirmed differential gene expression and immunotherapy response.

CONCLUSIONS

The model in our work is a novel assessment tool to measure immunotherapy's effectiveness and anticipate BLCA patients' prognosis, offering new avenues for immunological biomarkers and targeted treatments.