Biomarkers最新文献

Background: Lung cancer is a primary global health concern, responsible for a considerable portion of cancer-related fatalities worldwide. Understanding its molecular complexities is crucial for identifying potential targets for treatment. The goal is to slow disease progression and intervene early to prevent the development of advanced lung cancer cases. Hence, there's an urgent need for new biomarkers that can detect lung cancer in its early stages.

Methods: The study conducted RNA-Seq analysis of lung cancer samples from the publicly available SRA database (NCBI SRP009408), including both control and tumour samples. The genes with differential expression between tumour and healthy tissues were identified using R and Bioconductor. Machine learning (ML) techniques, Random Forest, Lasso, XGBoost, Gradient Boosting and Elastic Net were employed to pinpoint significant genes followed by classifiers, Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbours (k-NN). Gene ontology and pathway analyses were performed on the significant differentially expressed genes (DEGs). The top genes from DEG and machine learning analyses were combined for protein-protein interaction (PPI) analysis, identifying 10 hub genes essential for lung cancer progression.

Results: The integrated analysis of ML and DEGs revealed the significance of specific genes in lung cancer samples, identified the top 5 upregulated genes (COL11A1, TOP2A, SULF1, DIO2, MIR196A2) and the top 5 downregulated genes (PDK4, FOSB, FLYWCH1, CYB5D2, MIR328), along with their associated genes implicated in pathways or co-expression networks were identified. Among the various algorithms employed, Random Forest and XGBoost proved effective in identifying common genes, underscoring their potential significance in lung cancer pathogenesis. The MLP exhibited the highest accuracy in classifying samples using all genes. Additionally, the protein-protein interaction (PPI) analysis identified 10 hub genes that are pivotal in lung cancer pathogenesis: COL1A1, SOX2, SPP1, THBS2, POSTN, COL5A1, COL11A1, TIMP1, TOP2A and PKP1.

Conclusion: The study contributes to the early prediction of lung cancer by identifying potential biomarkers that could enhance early diagnosis and pave the way for practical clinical applications in the future. Integrating DEGs and machine learning-derived significant genes for PPI analysis offers a robust approach to uncovering critical molecular targets for lung cancer treatment.

Background: Growing evidence indicates that noncombustible products could be a tobacco harm reduction tool for smokers who do not quit. The Tobacco Heating System (THS) emits substantially lower levels of harmful cigarette smoke constituents, and previous randomized clinical studies showed improved levels of biomarkers of potential harm (BoPH) linked to smoking-related disease.

Methods: In this cross-sectional study of healthy participants (n = 982) who (i) smoked cigarettes, (ii) had voluntarily switched from smoking to THS use, or (iii) formerly smoked, blood and urine samples were assayed for nine BoPH. The co-primary endpoints were carboxyhemoglobin, total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol, white blood cells, and 8-epi-prostaglandin-F_2α. The key secondary endpoints were high-density lipoprotein cholesterol, soluble intercellular adhesion molecule-1, 11-dehydrothromboxane B₂, central vascular augmentation index, and forced expiratory volume in 1 s (%predicted post-bronchodilator).

Results: THS users showed significant favorable differences in all nine BoPH compared to current smokers. Results in THS users were similar to those in former smokers.

Conclusion: Compared to current smokers, healthy participants who voluntarily switched from smoking to THS use for ≥2 years in the real world had favorable differences in BoPH related to oxygen delivery, genotoxicity, inflammation, oxidative stress, lipid metabolism, endothelial function, platelet activation, and cardiovascular and respiratory function. Clinicaltrials.gov Identifier: NCT05385055.

Background: Radon, a radioactive gas, is a significant risk factor for lung cancer, especially in non-smokers. This study examines the expression of exosomal microRNAs (miRNAs) as potential biomarkers for radon-induced effects.

Methods: A total of 109 participants from high- and low-radon areas in Kazakhstan were included. Exosomal hsa-miR-125b-5p and hsa-miR-320c levels were quantified using real-time PCR.

Results: Results revealed a 25.4-fold increase in hsa-miR-125b-5p and a 12.5-fold decrease in hsa-miR-320c in participants exposed to high-radon levels compared to controls. Bioinformatic analysis identified key target genes, such as PRDM1 and IRF4, which are implicated in cancer development.

Conclusion: These findings suggest that exosomal miRNAs could serve as non-invasive biomarkers for radon exposure, offering potential for early diagnosis and monitoring of radon-induced lung cancer. The study underscores the need for further research to validate these miRNAs as reliable diagnostic tools.

Background and objectives: Prior studies have shown that small non-coding RNAs (sncRNAs) are associated with cancer occurrence or development. Recently, a newly discovered class of small ncRNAs known as PIWI-interacting RNAs (piRNAs) have been found to play a vital role in physiological processes and cancer initiation. This study aims to utilize piRNAs as innovative, noninvasive diagnostic biomarkers for breast cancer. Our objective is to develop computational methods that leverage piRNA attributes for breast cancer prediction and its application in diagnostics.

Methods: We created a set of piRNA sequence descriptors using information extracted from the piRNA sequences. To ensure accuracy, we found a path to convert non-standard piRNA names to standard ones to enable precise identification of these sequences. Using these descriptors, we applied machine-learning (ML) techniques in WEKA (Waikato Environment for Knowledge Analysis) to a dataset of piRNA to assess the predictive accuracy of the following classifiers: Logistic Regression model, Sequential Minimal Optimization (SMO), Random Forest classifier, and Logistic Model Tree (LMT). Furthermore, we performed Shapley additive explanations (SHAP) Analysis to understand which descriptors were the most relevant to the prediction accuracy. The ML models were then validated on an independent dataset to evaluate their effectiveness in predicting breast cancer.

Results: The top three performing classifiers in WEKA were Logistic Regression, SMO, and LMT. The Logistic Regression model achieved an accuracy of 90.7% in predicting breast cancer, while SMO and LMT attained 89.7% and 85.65%, respectively.

Conclusions: Our study demonstrates the effectiveness of using ML-based piRNA classifiers in diagnosing breast cancer and contributes to the growing body of evidence supporting piRNAs as biomarkers in cancer diagnosis. However, additional research is needed to validate these findings and further assess the clinical applicability of this approach.

Purpose: Chimeric antigen receptor (CAR) T-cell CD19 therapy has changed the treatment paradigm for patients with relapsed/refractory B-cell acute lymphoblastic leukemia. It is frequently associated with potentially severe toxicities: cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), and admission to PICU is often required. Some biomarkers seem to correlate with CRS severity. Our goal is to elucidate the role of procalcitonin (PCT), C-reactive protein (CRP) and ferritin in the context of CRS following CAR T-cell infusion to predict its severity and PICU admission.

Methods: Prospective observational study (2016-2022) in children and young adult who received CAR T-cell therapy (Tisagenlecleucel/ARI-0001). We collected epidemiologic data, specific CAR T-cell toxicities, PICU admission, biomarker results (PCT, CRP and ferritin), length of stay and mortality. Biomarkers were analyzed considering two values: the highest value during ward admission, and the highest overall value including PICU admission.

Results: Seventy-seven patients were included. Median age at infusion was 9.1 years (IQR 6-13), 49.4% were females. Before CAR T-cell infusion, the median bone marrow blast was 9% (IQR 0-59). The most frequent toxicity was CRS in 62 patients (80.5%), it was severe in 18 cases (23.4%). Fourteen patients (18.1%) had ICANS. Thirty-one patients (40.3%) required admission to the PICU. PCT and ferritin were higher in patients admitted to PICU (PCT 0.8 ng/mL vs 0.15 ng/mL, p < 0.001, ferritin 5490 vs. 2900 µg/L, p < 0.019). The proposed cut-off for PCT to predict admission to PICU is 0.55 ng/mL, presenting a sensitivity of 67.7% and a specificity of 86.7%. The maximum value of three biomarkers was higher in those who presented any primary outcome: development of severe CRS, the need for admission to PICU, and in-hospital mortality. Biomarkers were higher in those who needed inotropic or respiratory support.

Conclusions: PCT levels increase after CAR-T cell therapy in the setting of systemic inflammation and could be a predictor of PICU admission and evolution to death. Further research studying its role in the context of CRS and the differential diagnosis between infection and CRS is needed to better understand the biology of this biomarker and to define its value in clinical practice.

Background: Despite the current diagnostic and therapeutic methods for colorectal cancer (CRC), patients are often diagnosed at advanced stages of colorectal cancer. Recently, numerous investigations have highlighted the role of lncRNAs in cancer development and progression. This study investigated less well-characterized genes in the colorectal cancer metastasis process.

Materials and methods: Genes expression profiles from CRC patients were downloaded from the TCGA database by the TCGAbiolinks R package. Differential gene expression analysis of miRNA, lncRNAs, and mRNAs was conducted for the M1 and M0 compared to control samples. Then, the DIANA lncbase3 tool was used to find M1-specific miRNA-LncRNA interactions. In addition, the expression of selected genes was evaluated by Real-time RT-PCR in forty-one CRC tissues.

Results: Our analysis showed that the expression levels of 77 lncRNAs, 12 miRNAs, and 627 mRNA were significantly changed only in metastatic tumors. In experimental study, significant overexpression of LncRNAs LINC00839, LINC01006, BACE1-AS and G2E3-AS1 was confirmed in metastatic tumors. Also, ROC analysis showed that these lncRNAs, especially lncRNAs G2E3-AS1 and BACE1-AS, are good prognostic biomarkers for metastatic colorectal tumors.

Conclusion: We demonstrated that the lncRNAs G2E3-AS1 and BACE1-AS expression upregulated in CRC tissues can be good potential biomarkers for metastatic colorectal cancer.

Introduction: Colorectal cancer (CRC) incidence and mortality before 50 have been rising alarmingly in the recent decades.

Methods: Using a cohort of 10,000 patients, this study investigates the clinical, mutational, and co-mutational features of CRC in early-onset (EOCRC, < 50 years) compared to late-onset (LOCRC, ≥ 50 years).

Results: EOCRC was associated with a higher prevalence of Asian and Hispanic patients, rectal or left-sided tumors (72% vs. 59%), and advanced-stage disease. Molecular analyses revealed differences in mutation patterns, with EOCRC having higher frequencies of TP53 (74% vs. 68%, p < 0.01) and SMAD4 (17% vs. 14%, p = 0.015), while BRAF (5% vs. 11%, p < 0.001) and NOTCH1 (2.7% vs. 4.1%, p = 0.01) mutations were more prevalent in LOCRC. Stratification by tumor site and MSI status highlighted significant location- and age-specific molecular differences, such as increased KRAS and CTNNB1 mutations in right-sided EOCRC and higher BRAF prevalence in MSI-H LOCRC (47% vs. 6.7%, p < 0.001). Additionally, co-occurrence analysis revealed unique mutational networks in EOCRC MSS, including significant co-occurrences of FBXW7 with NOTCH3, RB1, and PIK3R1.

Conclusion: This study highlights the significance of age-specific molecular profiling, offering insights into the unique biology of EOCRC and potential clinical applications.

Objective: To examine the role and diagnostic potential of miR-421 in prostate cancer (PCa).

Methods: Expression data and clinical information for miR-421 were obtained from the TCGA and Genotype-Tissue Expression (GTEx) databases. Experimental validation was performed at the cellular, blood, and tissue levels to confirm miR-421 expression and its association with clinicopathological features. ROC curves were drawn on the bioinformatic study using TCGA data. The target genes of miR-421 were predicted via four online databases, and protein interaction associations were analyzed for intersecting targets. Gene Ontology (GO) analysis was subsequently conducted to assess functional relevance.

Results: MiR-421 was significantly overexpressed in prostate cancer (PCa) patients, a finding validated in cell, blood, and tissue samples. ROC analysis on the bioinformatic study using TCGA data revealed that miR-421 reliably differentiated PCa tissues from normal tissues. Higher miR-421 expression was associated with an elevated Gleason score, advanced TNM stage, and metastasis. GO enrichment analysis indicated that the target genes of miR-421 were significantly related to diverse molecular functions.

Conclusions: MiR-421 is a promising biomarker for diagnosing and predicting PCa.

Introduction: Urine metabolomics offers a non-invasive approach to diagnose and manage inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), by identifying distinct metabolic signatures.

Objectives: This narrative review summarizes current findings on urinary metabolites in IBD, evaluating their roles in disease differentiation, assessment of activity, and monitoring therapeutic response.

Methods: A comprehensive literature search of PubMed and MEDLINE up to October 2023 was conducted using keywords, such as 'urine metabolomics', 'inflammatory bowel disease', 'Crohn's disease', 'ulcerative colitis', and 'urinary biomarkers'. Studies were included that described alterations to metabolic pathways, including those related to the urea cycle, central energy metabolism (Krebs cycle), amino acid metabolism, and neurotransmitters.

Results: Specific urinary metabolites differentiate IBD patients from healthy controls and between CD and UC. Decreased urinary levels of hippurate, acetate, methanol, formate, and methylamine are observed in IBD, indicating altered gut microbiota. In CD patients, urea cycle alterations include reduced urinary urea and ornithine with increased arginine. Changes in Krebs cycle intermediates show decreased citrate and succinate in adults, but increased fumarate and isocitrate in pediatric patients, reflecting energy metabolism differences. Amino acid metabolism differs by age: Adults exhibit decreased urinary asparagine, lysine, and histidine, while pediatric patients show increased methionine, proline, aspartic acid, and isoleucine. Elevated urinary neurotransmitters like dopamine are noted in pediatric IBD patients. Urine metabolomics also can monitor treatment efficacy by distinguishing responders from non-responders to therapies and differentiating active disease from remission.

Conclusion: Urine metabolomics provides promising, non-invasive biomarkers to enhance IBD diagnostics by distinguishing CD from UC and offering insights into underlying metabolic disturbances, paving the way for more precise, accessible patient care.

Background: Mortality in patients after carotid artery stenting (CAS), a treatment approach for atherosclerotic carotid artery stenosis, is influenced by numerous factors. This study aimed to investigate the prognostic value of the Naples prognostic score (NPS), which reflects nutritional and inflammatory status, in CAS patients.

Methods: We retrospectively included 697 patients who underwent CAS from January 2016 to December 2020 at our institute. The primary endpoint of the study was long-term all-cause mortality. The study population was divided into two groups based on the NPS value: Low NPS (NPS 0-2) and high NPS (NPS 3-4). Univariable and multivariable Cox regression analysis was used to identify independent predictors of death.

Results: The median follow-up time was 60.8 (46.36-75.36) months. During the follow-up period, all-cause mortality was higher in the high-NPS group compared to the low-NPS group [54% (n = 88) vs. 24% (n = 128) p < 0.001]. Advanced age (p = 0.003), diabetes (p = 0.023), and NPS (hazard ratio: 1.83, confidence interval: 1.58-2.12, p < 0.001) were found to be independent predictors of all-cause mortality at long-term follow-up.

Conclusion: Consequently, NPS as a marker of malnutrition and inflammation, was found to be associated with long-term mortality and serves as an independent predictor of long-term mortality in patients undergoing CAS.