Biomarkers最新文献

Background: Lung cancer is the cancer with the highest morbidity and mortality in the world. With the increasing diagnosis rate of patients with early-stage lung cancer, surgery treatment becomes an option for more patients. However, there is a lack of effective indicators to assess the risk of recurrence after lung cancer surgery.

Methods: We collected levels of serum autoantibodies and evaluated their roles as biomarkers especially for postoperative recurrence of lung cancer. In vitro experiments including antibody-dependent cellular cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP) and complement-dependent cytotoxicity (CDC) were performed to explore the functions of serum autoantibodies.

Results: Our study demonstrated that serum autoantibody-positive patients with early-stage lung cancer had a longer postoperative progression period. The levels of serum autoantibodies in patients with lung cancer were higher than that in patients with benign lung diseases. But all the serum autoantibodies had no difference between patients with stage I and II. In addition, the results of in vitro experiments indicated that serum autoantibodies can mediate immune responses and enhance anti-tumour effects.

Conclusion: This study proposed effective biomarkers for prognosis in lung cancer patients after surgery which is critical to reduce the recurrence.

Background: Dynamins are defined as a group of molecules with GTPase activity. Among them, DNM3 has gained recognition in oncology for its tumor suppressor role. Based on this, the aim of this study is to investigate the effects of the DNM3 gene in patients diagnosed with pancreatic cancer using bioinformatics databases.

Materials and methods: For differential gene expression analysis, TCGA TARGET GTEx study on the UCSC Xena and GEO datasets were utilized; for the analysis of changes in gene expression according to clinical and pathological characteristics, UALCAN was employed; for Overall Survival (OS) analysis, Kaplan-Meier Plotter was used; for gene alteration analysis, cBioPortal was utilized; for immune cell infiltration analysis, Tumor Immune Estimation Resource (TIMER) and TIMER2.0 were employed; for enrichment analyses Enrichr was used; for Gene Set Correlation Enrichment Analysis Gscore was used on GSE15471; for essentiality of DNM3 gene in pancratic cancer cell lines DepMap was used; and for the detection of miRNAs, miRDB was utilized; ENCORI was used for gene-miRNA correlation and miRNA prognosis analyses.

Results: In the pancreatic adenocarcinoma (PAAD) cohort, DNM3 gene expression was higher in tumor samples, and there was no significant difference in expression among cancer stages. High levels of DNM3 gene expression were associated with longer OS in PAAD. A weak positive correlation was observed between DNM3 gene expression and B-Cell and CD4+ T Cell infiltrations, while a moderate positive correlation was found with CD8+ T Cell, Macrophage, Neutrophil, and Dendritic Cell infiltrations in TIMER. NK cell by QUANTISEQ, CD 4+ T Cell by TIMER, T cell regulatory (Tregs) by CIBERSORT-ABS infiltrations were positively associated with DNM3 gene expression and decreased risk in prognosis. Common lymphoid progenitor by XCELL and MDSC by TIDE infiltrations were negatively associated with DNM3 gene expression and increased risk of prognosis. Macrophage M1 by QUANTISEQ was positively associated with DNM3 gene expression and increased risk in prognosis. DNM3 gene appears to be associated with various pathways related to inflammation and the immune system. Amplification of the DNM3 gene was detected in 5 out of 175 patients. Enrichment was observed in pathways such as bacterial invasion of epithelial cells, endocytosis, endocrine and other factor-regulated calcium reabsorption, synaptic vesicle cycle, and phospholipase D signaling pathway. According to Gscore, DNM3 gene was associated with Fc epsilon RI signaling pathway, HALLMARK MTORC1 SIGNALING, HALLMARK EPITHELIAL MESENCHYMAL TRANSITION gene sets. According to ENCORI, DNM3 gene was negatively correlated with hsa-miR-203a-3p and increased expression of this miRNA was associated with adverse prognosis in PAAD.

Conclusions: The DNM3 gene may play a tumor suppressor role in pancreatic cancer, similar to its r

Introduction: Elevated fluoride (F^-) exposure during childhood produces dental fluorosis (DF). Nails have been used for monitoring systemic F^- in relation to DF. The aim of this study was to evaluate F^- levels in toenails in association with DF severity in Mexican schoolchildren.

Materials and methods: 120 schoolchildren from nonendemic areas (NEAs) and endemic F^- areas (EAs) were screened for DF via the Thylstrup and Fejerskov index (TFI). Toenails were collected to quantify systemic F^-. The associations between the biomarker, DF severity, tap water intake, sex, and age were analyzed.

Results: The mean F^- in toenails in the NEAs and EAs were 0.63 ± 0.43 and 2.72 ± 1.38 mg/kg, respectively (p < 0.001). A positive correlation was observed between the biomarker and DF severity (r_s = 0.755, p < 0.001). Tap water consumption and the biomarker were associated with DF severity (p < 0.001). Within TFI7-8 the mean F^- level was higher in those ages 10-11 than in those ages 8-9 (p < 0.05).

Conclusion: Systemic F^- levels in toenails are associated with DF severity in Mexican schoolchildren from both the NEAs and the EAs, which reflects the ability of the biomarker to accurately record the exposure to the compound in relation to clinical damage.

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.