Biomarkers For Diagnosis Research Articles

Untargeted metabolomic profiling of small extracellular vesicles reveals potential new biomarkers for triple negative breast cancer.

Breast Cancer (BC) is one of the most diagnosed malignancies among women and the second leading cause of cancer related death in North America. Triple Negative BC (TNBC), one of the most severe subtypes of BC, is extremely aggressive and has a higher chance of occurrence in women under 50 years of age. Due to a lack of regular mammographic testing in women under 50, many individuals with TNBC are diagnosed late which can decrease their survival rate. Currently, liquid biopsy is being investigated as a potentially less-invasive alternative to traditional breast tissue biopsy, but this approach is not completely reliable. Blood contains extracellular vesicles (EVs), which carry biomolecular cargo and play a role in BC progression and metastasis. Examination of small EVs could potentially yield metabolite biomarkers for early BC diagnosis. We aim to study metabolites in small EVs to find biomarkers for BC diagnosis. In this work, an untargeted nano-LC MS/MS metabolomics approach was used to analyze metabolites from small EVs derived from metastatic MDA-MB-231 and compare it with a non-cancerous MCF10A cell line. Two metabolites, LysoPC 22:6/0:0 and N-acetyl-L-Phenylalanine, unique to sEVs of MDA-MB-231, were identified, validated, and proposed as potential BC biomarkers. Metabolites from sEVs may be used for BC diagnosis.

Erythrocytic α-Synuclein in Parkinson’s Disease and Progressive Supranuclear Palsy—A Pilot Study

Background/Objectives: The current research examines the accuracy of α-synuclein in RBCs as a diagnostic biomarker for PD and PSP, despite their distinct molecular etiologies. Methods: We used ELISA to measure total, oligomeric, and p129-α-synuclein levels in erythrocytes from 8 PSP patients, 19 PD patients, and 18 healthy controls (HCs). The classification performances of RBC α-synuclein levels were investigated by receiver operator characteristic (ROC) curve. We also evaluated a possible correlation between RBC α-synuclein level and the biological and clinical features of our cohorts. Results: RBC total α-synuclein was higher in PSP patients compared to both PD patients and HCs, achieving good classification performance (AUC: 0.853) in distinguishing PSP patients from PD patients, with a sensitivity of 100% and a specificity of 70.6%; moreover, the levels of this biomarker positively correlated with disease severity in PSP group. Regarding oligomeric α-synuclein and p129-α-synuclein, the latter was slightly increased in RBCs from PSP patients compared to HCs, but no correlations were detected. Conclusions: Although these findings need to be confirmed in larger studies, our pilot work suggests that RBC total α-synuclein may represent a potential molecular biomarker for the differential diagnosis and clinical staging of PSP.

Neuroinflammatory Proteins in Huntington's Disease: Insights into Mechanisms, Diagnosis, and Therapeutic Implications.

Huntington's disease (HD) is a hereditary neurodegenerative disorder caused by a CAG tract expansion in the huntingtin gene (HTT). HD is characterized by involuntary movements, cognitive decline, and behavioral changes. Pathologically, patients with HD show selective striatal neuronal vulnerability at the early disease stage, although the mutant protein is ubiquitously expressed. Activation of the immune system and glial cell-mediated neuroinflammatory responses are early pathological features and have been found in all neurodegenerative diseases (NDDs), including HD. However, the role of inflammation in HD, as well as its therapeutic significance, has been less extensively studied compared to other NDDs. This review highlights the significantly elevated levels of inflammatory proteins and cellular markers observed in various HD animal models and HD patient tissues, emphasizing the critical roles of microglia, astrocytes, and oligodendrocytes in mediating neuroinflammation in HD. Moreover, it expands on recent discoveries related to the peripheral immune system's involvement in HD. Although current immunomodulatory treatments and inflammatory biomarkers for adjunctive diagnosis in HD are limited, targeting inflammation in combination with other therapies, along with comprehensive personalized treatment approaches, shows promising therapeutic potential.

Exosomal Non-coding RNAs: A New Approach to Melanoma Diagnosis and Therapeutic Strategy.

Malignant melanoma (MM) is a highly aggressive cancer with a poor prognosis. Currently, although a variety of therapies are available for treating melanoma, MM is still a serious threat to the patient's life due to numerous factors, such as the recurrence of tumors, the emergence of drug resistance, and the lack of effective therapeutic agents. Exosomes are biologically active lipid-bilayer extracellular vesicles secreted by diverse cell types that mediate intercellular signal communication. Studies found that exosomes are involved in cancer by carrying multiple bioactive molecules, including non-- coding RNAs (ncRNAs). The ncRNAs have been reported to play an important role in regulating proliferation, angiogenesis, immune regulation, invasion, metastasis, and treatment resistance of tumors. However, the functional role of exosomal ncRNAs in MM remains unknown. Therefore, this review summarizes the current state of melanoma diagnosis, treatment, and the application of exosomal ncRNAs in MM patients, which may provide new insights into the mechanisms involved in melanoma progression and serve as biomarkers for diagnosis and therapeutic targets.

GUCA2A dysregulation as a promising biomarker for accurate diagnosis and prognosis of colorectal cancer

Colorectal cancer is a leading cause of global mortality and presents a significant barrier to improving life expectancy. The primary objective of this study was to discern a unique differentially expressed gene (DEG) that exhibits a strong association with colorectal cancer. By achieving this goal, the research aims to contribute valuable insights to the field of translational medicine. We performed analysis of colorectal cancer microarray and the TCGA colon adenoma carcinoma (COAD) datasets to identify DEGs associated with COAD and common DEGs were selected. Furthermore, a pan-cancer analysis encompassing 33 different cancer types was performed to identify differential genes significantly expressed only in COAD. Then, comprehensively in-silico analysis including gene set enrichment analysis, constructing Protein–Protein interaction, co-expression, and competing endogenous RNA (ceRNA) networks, investigating the correlation between tumor-immune signatures in distinct tumor microenvironment and also the potential interactions between the identified gene and various drugs was executed. Further, the candidate gene was experimentally validated in tumoral colorectal tissues and colorectal adenomatous polyps by qRael-Time PCR. GUCA2A emerged as a significant DEG specific to colorectal cancer (|log2FC|> 1 and adjusted q-value < 0.05). Importantly, GUCA2A exhibited excellent diagnostic performance for COAD, with a 99.6% and 78% area under the curve (AUC) based on TCGA-COAD and colon cancer patients. In addition, GUCA2A expression in adenomatous polyps equal to or larger than 5 mm was significantly lower compared to smaller than 5 mm. Moreover, low expression of GUCA2A significantly impacted overall patient survival. Significant correlations were observed between tumor-immune signatures and GUCA2A expression. The ceRNA constructed included GUCA2A, 8 shared miRNAs, and 61 circRNAs. This study identifies GUCA2A as a promising prognostic and diagnostic biomarker for colorectal cancer. Further investigations are warranted to explore the potential of GUCA2A as a therapeutic biomarker.

LOXL1 promotes gastric cancer progression by β-catenin-cyclinD mediated proliferation.

Although much progress has been made in chemotherapy or target therapy for advanced gastric cancer, the prognosis is still poor. It is necessary to screen biomarkers for early diagnosis and prognosis prediction. However, the prognostic value of LOX family in gastric cancer and the underlying molecular mechanisms for promoting the progression of gastric cancer remains unclear. Among five members of LOX family, LOXL1 was the unique independent prognostic risk factor. The nomogram established based on the expression of LOXL1 and other clinical parameters could predict the overall survival rate of gastric cancer. Knockdown (KD) of LOXL1 decreased cell proliferation and led to G1 phase arrest in gastric cells. According to GSEA analysis that LOXL1 was positively correlated with the WNT signaling pathway, in vitro experiment proved that LOXL1-KD reduced the phosphorylation level of β-catenin and the expression of the downstream G1 phase checkpoint CCND1.In Conclusion, LOXL1 has been identified as a potential risk prognostic biomarker for gastric cancer by promoting gastric cancer proliferation via WNT/β-catenin/cyclinD1 pathway.

A Systematic Review and Meta-Analysis Assessing the Accuracy of Blood Biomarkers for the Diagnosis of Ischemic Stroke in Adult and Elderly Populations.

This study aims to elucidate the methodology and compare the accuracy of different blood biomarkers for diagnosing ischemic stroke (IS). We reviewed 29 articles retrieved from PubMed, MEDLINE, Web of Science, and CINAHL Plus with Full Text. Among these, 23 articles involving 3,494 participants were suitable for meta-analysis. The pooled area under the curve (AUC) of all studies for meta-analysis was 0.89. The pooled sensitivity and specificity were 0.76 (0.74-0.78) and 0.84 (0.83-0.86), respectively. Blood biomarkers from noninpatient settings demonstrated better diagnostic performance than those in inpatient settings (AUC 0.91 vs 0.88). Smaller sample sizes (<100) showed better performance than larger ones (≥100; AUC 0.92 vs 0.86). Blood biomarkers from acute IS (AIS) patients showed higher diagnostic values than those from IS and other stroke types (AUC 0.91 vs 0.87). The diagnostic performance of multiple blood biomarkers was superior to that of a single biomarker (AUC 0.91 vs 0.88). The diagnostic value of blood biomarkers from Caucasians was higher than that from Asians and Africans (AUC 0.90 vs 0.89, 0.75). Blood biomarkers from those with comorbidities (AUC 0.92) showed a better diagnostic performance than those not reporting comorbidities (AUC 0.84). All the subgroups analyzed, including setting, sample size, target IS population, blood biomarker profiling, ethnicity, and comorbidities could lead to heterogeneity. Blood biomarkers have demonstrated sufficient diagnostic accuracy for diagnosing IS and hold promise for integration into routine clinical practice. However, further research is recommended to refine the optimal model for utilizing blood biomarkers in IS diagnosis.

HERC5: a comprehensive in silico analysis of its diagnostic, prognostic, and therapeutic potential in cancer.

HERC5, a vital protein in the HERC family, plays crucial roles in immune response, cancer progression, and antiviral defense. This bioinformatic study comprehensively assessed HERC5's significance across various malignancies by analyzing its gene expression, immune and molecular subtype expressions, target proteins, biological functions, and prognostic and diagnostic values in pan-cancer. We further examined its correlation with clinical features, co-expressed and differentially expressed genes, and prognosis in clinical subgroups, focusing on endometrial cancer (UCEC). Our findings showed that HERC5 RNA is expressed at low levels in most cancers and significantly differs across immune and molecular subtypes. HERC5 accurately predicts cancer and correlates with most cancer prognoses. In UCEC, HERC5 was significantly associated with age, hormonal status, clinical stage, treatment status, and metastasis. Elevated HERC5 expression was linked to worse progression-free interval, disease-specific survival, and overall survival in UCEC, particularly in diverse clinical subgroups. Significant differences in HERC5 expression were also observed in various human cancer cell line validations. In summary, HERC5 may be a critical biomarker for pan-cancer prognosis, progression, and diagnosis, as well as a promising new target for cancer therapy.

The circular RNA circNFIX regulates MEF2C expression in muscle satellite cells in spastic cerebral palsy.

Cerebral palsy (CP) is a pediatric onset disorder with poorly understood molecular causes and progression, making early diagnosis difficult. Circular RNAs (circRNAs) are regulatory RNAs that show promise as biomarkers in various diseases but the role of circRNAs in CP is beginning to be understood. This study identified the role of circNFIX in regulating the expression of MEF2C, an important transcription factor for sarcomere development. We found that circNFIX is downregulated in the muscle cells of individuals with CP, and its localization shifts towards the nucleus as visualized using single molecule resolution imaging. The decreased expression of circNFIX, MEF2C, and MEF2C targets persisted throughout myoblasts to myotubes differentiation, and in the skeletal muscle tissue. Bioinformatic and experimental validation confirmed that circNFIX acts as a sponge for miR373-3p, a microRNA that represses MEF2C translation. In normal muscle, circNFIX de-represses MEF2C translation by sponging miR373-3p, allowing for normal sarcomere generation. In CP, reduced circNFIX expression results in loss of miRNA sponging, leading to lower MEF2C expression and downregulation of sarcomere genes, potentially causing shortened and dysfunctional muscle fibers. Knockdown of circNFIX (KD) reduced myogenic capacity of myoblasts to fuse and form myotubes similar to CP cells evident from the lower fusion index in CP and KD as compared to control myotubes. This the first study reporting reduction of MEF2C in CP and single molecule resolution imaging of circNFIX's subcellular distribution and its role in CP, suggesting circNFIX as a potential therapeutic target and biomarker for early CP diagnosis.

Discovery of novel metabolic biomarkers in blood serum for diagnosis of Alzheimer's disease.

Blood metabolites have emerged as promising candidates in the search for biomarkers for Alzheimer's disease (AD), as evidence shows that various metabolic derangements contribute to neurodegeneration in AD. We aim to identify metabolic biomarkers for AD diagnosis. We conducted an in-depth analysis of the serum metabolome of AD patients and age, sex-matched cognitively unimpaired older adults using ultra-high-performance liquid chromatography-high resolution mass spectrometry. The biomarkers associated with AD were identified using machine learning algorithms. Using the discovery dataset and support vector machine (SVM) algorithm, we identified a panel of 14 metabolites predicting AD with a 1.00 area under the curve (AUC) of receiver operating characteristic (ROC). The SVM model was tested against the verification dataset using an independent cohort and retained high predictive accuracy with a 0.97 AUC. Using the random forest (RF) algorithm, we identified a panel of 13 metabolites that predicted AD with a 0.96 AUC when tested against the verification dataset. These findings pave the way for an efficient, blood-based diagnostic test for AD, holding promise for clinical screenings and diagnostic procedures.

Immune Regulatory Circular RNAs, circRasGEF1B and circHIPK3, are Upregulated in Peripheral Blood Mononuclear Cells of COVID-19 Patients.

Background: COVID-19 is one of the worst pandemics worldwide, and its diagnosis and treatment are of great importance. Recent evidence has shown that circular RNA (circRNA deregulation is involved in different infectious diseases. In the present study, we tried to investigate the expression of cirRNAs RasGEF1B (hsa_circ_0127052), HIPK3 (hsa_circ_100783), and GATAD2A (hsa_circ_0050236) in COVID-19 patients. Methods: Using quantitative real-time polymerase chain reaction, the expression profiles of candidate circRNAs were detected in 57 COVID-19 patients and 51 healthy controls. As part of the process of identifying a candidate circRNA that is sensitive and specific, receiver operating characteristic (ROC) curves were also utilized. Results: Our results showed higher expression levels of circRasGEF1B and circHIPK3 in COVID-19 patients, however, circGATAD2A showed no statistical difference between patients and controls. ROC curves showed that circRasGEF1B (hsa_circ_0127052), and HIPK3 (hsa_circ_100783) had favorable specificity and sensitivity, whereas GATAD2A (hsa_circ_0050236) did not. Conclusion: In summary, our study highlights the potential of CircRasGEF1B (hsa_circ_0127052) and HIPK3 (hsa_circ_100783) as biomarkers for COVID-19 diagnosis due to their high expression levels and demonstrated diagnostic accuracy. These findings suggest that circRNAs could play a crucial role in the development of diagnostic tools for COVID-19, providing a new avenue for early detection and management of the disease.

Top-Down Proteomics Identifies Plasma Proteoform Signatures of Liver Cirrhosis Progression

Cirrhosis, advanced liver disease, affects 2-5 million Americans. While most patients have compensated cirrhosis and may be fairly asymptomatic, many decompensate and experience life-threatening complications such as gastrointestinal bleeding, confusion (hepatic encephalopathy), and ascites, reducing life expectancy from 12 to less than 2 years. Among patients with compensated cirrhosis, identifying patients at high risk of decompensation is critical to optimize care and reduce morbidity and mortality. Therefore, it is important to preferentially direct them towards specialty care which cannot be provided to all patients with cirrhosis. We used discovery Top-down Proteomics (TDP) to identify differentially expressed proteoforms (DEPs) in the plasma of patients with progressive stages of liver cirrhosis with the ultimate goal to identify candidate biomarkers of disease progression. In this pilot study, we identified 209 DEPs across three stages of cirrhosis (compensated, compensated with portal hypertension, and decompensated), of which 115 derived from proteins enriched in the liver at a transcriptional level and discriminated the three stages of cirrhosis. Enrichment analyses demonstrated DEPs are involved in several metabolic and immunological processes known to be impacted by cirrhosis progression. We have preliminarily defined the plasma proteoform signatures of cirrhosis patients, setting the stage for ongoing discovery and validation of biomarkers for early diagnosis, risk stratification, and disease monitoring.

Role of mir-32-3p in the diagnosis and risk assessment of osteoporotic fractures

BackgroundOsteoporotic fractures (OPF) are fractures that occur with low-energy injuries or during daily activities, representing a serious consequence of osteoporosis (OP). With the worsening of population aging, the number of OPF patients continues to expand, causing a significant burden on families and society. Consequently, it is significant to diagnose and analyze OPF at the molecular level.ObjectiveThe aim of this research was to explore the diagnostic value of miR-32-3p in OPF patients and to exploit new biomarkers for clinical applications.MethodsThe miR-32-3p expression level of patients was detected by RT-qPCR. Diagnostic accuracy of miR-32-3p analyzed adopting ROC curve. Additionally, the risk factors correlation with the occurrence of OPF were assessed by logistic analysis. The effect of miR-32-3p on BMSCs was verified by in vitro transfection experiments.ResultsmiR-32-3p expression was lower in OPF patients than in OP patients. ROC curve implied that miR-32-3p exhibits commendable sensitivity (88.9%) and specificity (75.6%) to differentiate between OP and OPF patients (AUC = 0.905, P < 0.001). Furthermore, miR-32-3p was correlated with the development of OPF and was a risk factor for OPF (P < 0.001). Functional assays revealed that transfection with miR-32-3p mimic could promote proliferation and inhibit apoptosis, whereas transfection with miR-32-3p inhibitor had the opposite effect.ConclusionmiR-32-3p demonstrates significant diagnostic potential for OPF patients. It is likely that miR-32-3p probably is a new diagnosis biomarker for OPF, offering promising therapeutic avenues through targeted interventions.

Utilization of miRNAs as Biomarkers for the Diagnosis, Prognosis, and Metastasis in Gynecological Malignancies.

Gynecological cancer is a term referring to malignancies that typically involve ovarian, cervical, uterine, vaginal, and vulvar cancer. Combined, these cancers represent major causes of morbidity and mortality in women with a heavy socioeconomic impact. MiRNAs are small non-coding RNAs that are intensively studied in the field of cancer and changes in them have been linked to a variety of processes involved in cancer that range from tumorigenesis to prognosis and metastatic potential. This review aims to summarize the existing literature that has linked miRNAs with each of the female malignancies as potential biomarkers in diagnosis (circulating miRNAs), in tumor histology and prognosis (as tissue biomarkers), and for local (lymph node) and distant metastatic disease.

AI-Enabled Ultra-large Virtual Screening Identifies Potential Inhibitors of Choline Acetyltransferase for Theranostic Purposes.

Alzheimer's disease (AD) and related dementias are among the primary neurological disorders and call for the urgent need for early-stage diagnosis to gain an upper edge in therapeutic intervention and increase the overall success rate. Choline acetyltransferase (ChAT) is the key acetylcholine (ACh) biosynthesizing enzyme and a legitimate target for the development of biomarkers for early-stage diagnosis and monitoring of therapeutic responses. It is also a theranostic target for tackling colon and lung cancers, where overexpression of non-neuronal ChAT leads to the production of acetylcholine, which acts as an autocrine growth factor for cancer cells. Theranostics is a hybrid of diagnostics and therapeutics that can be used to locate cancer cells using radiotracers and kill them without affecting other healthy tissues. Traditional virtual screening protocols have a lot of limitations; given the current rate of chemical database expansion exceeding billions, much faster screening protocols are required. Deep docking (DD) is one such platform that leverages the power of deep neural network (DNN)-based virtual screening, empowering researchers to dock billions of molecules in a speedy, yet explicit manner. Here, we have screened 1.3 billion compounds library from the ZINC20 database, identifying the best-performing hits. With each iteration run where the first iteration gave ∼116 million hits, the second iteration gave ∼3.7 million hits, and the final third iteration gave 168,447 hits from which further refinement gave us the top 5 compounds as potential ChAT inhibitors. The discovery of novel ChAT inhibitors will enable researchers to develop new probes that can be used as novel theranostic agents against cancer and as early-stage diagnostics for the onset of AD, for timely therapeutic intervention to halt the further progression of AD.

Metabolomic biomarkers in amniotic fluid for early diagnosis of preterm birth and fetal growth restriction.

Preterm birth, affecting about 10% of pregnancies, significantly contributes to perinatal morbidity and mortality. Recent research indicates that metabolomics could enhance pregnancy outcomes and reduce costs by identifying biomarkers related to common pregnancy complications. Our team focused on analyzing amniotic fluid collected during the second trimester to identify potential biomarkers for preterm birth using 1H-NMR metabolomic analysis. We compared amniotic fluid samples from women who delivered prematurely with those who delivered at term. Multivariate principal component analysis revealed dimethylglycine, glucose, myo-inositol, and succinic acid as potential biomarkers for preterm birth prognosis and early diagnosis. Further analysis demonstrated distinct regulation patterns of these metabolites in relation to fetal growth centiles. For instance, dimethylglycine and glucose were upregulated in fetuses above the 20th centile, while citrate and succinate were upregulated in those below it. With Area Under the Curve (AUROC) values over 0.75 and p-values less than 0.05, these metabolites show promise as reliable biomarkers for predicting fetal growth restriction. This approach could significantly impact maternal-fetal medicine by facilitating early diagnosis and personalized interventions. Future research should focus on validating these findings in larger populations and exploring the underlying mechanisms of metabolite regulation.

Diagnostic Potential of NEAT1, hsa-let-7a-5p, and miR-506-3p in Early-stage Parkinson's Disease.

Parkinson's disease (PD) is a multifaceted disease that is influenced by both genetic and environmental parameters. Non-coding RNAs have been shown to be ideal biomarkers for several diseases, including PD. This study was conducted to evaluate the expression levels of NEAT1, hsa-let-7a-5p, and miR-506-3p in individuals with PD to assess their efficacy for early-stage PD diagnosis. Twenty-four patients with PD and 29 healthy individuals participated in this study. The IntaRNA tool was used to predict potential base pairings between NEAT1 and let-7a-5p, and NEAT1 and miR-506-3p. RT-qPCR was employed to measure the relative expression of tyrosine hydroxylase (TH), as well as nuclear enriched abundant transcript 1 (NEAT1), hsa-let-7a-5p, and miR-506-3p levels in both groups. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the diagnostic value. The PD group exhibited significantly elevated NEAT1 expression levels compared to the healthy control group. While the PD group exhibited an insignificant decreased TH expression level relative to the healthy group. Furthermore, the levels of hsa-let-7a-5p and miR-506-3p expression were seen to be decreased in patients with PD in comparison to the control group. Integration of NEAT1, hsa-let-7a-5p, and miR-506-3p levels significantly enhanced diagnostic capabilities and increased the AUC to 0.9501 (95% confidence interval: 0.8978-1.000, p < .0001). The elevated NEAT1 expression and the decreased expression of hsalet- 7a-5p and miR-506-3p in PD patients indicate that these factors might contribute to the disease's pathogenesis. Combining the ROC curves of NEAT1 and hsa-let-7a-5p with miR-506-3p showed improved sensitivity and specificity, facilitating more accurate PD diagnosis. More importantly, they may contribute as promising non-invasive biomarkers for PD diagnosis.

FTO in oral diseases: Functions, mechanisms, and therapeutic potential.

Fat mass and obesity-associated protein (FTO) is the first identified N6-methyladenosine (m6A) demethylase widely distributed in various tissues in adults and children. It plays an essential role in diverse mRNA-associated processes including transcriptional stability, selective splicing, mRNA translocation, and also protein translation. Recently, emerging studies have shown that FTO is involved in the genesis and development of oral diseases. However, the correlation between FTO and oral diseases and its specific regulatory mechanism still needs further study. In this review, we will summarize the discovery, distribution, gene expression, protein structure, biological functions, inhibitors, and quantifying methods of FTO, as well as its regulatory role and mechanism in oral diseases. Notably, FTO genetic variants are strongly associated with periodontal diseases (PDs), temporomandibular joint osteoarthritis (TMJOA), and obstructive sleep apnea (OSA). Besides, the latest studies that describe the relationship between FTO and PDs, head and neck squamous cell carcinoma (HNSCCs), TMJOA, and OSA will be discussed. We elaborate on the regulatory roles of FTO in PDs, HNSCCs, and TMJOA, which are modulated through cell proliferation, cell migration, apoptosis, bone metabolism, and immune response. The review will enrich our understanding of RNA epigenetic modifications in oral diseases and present a solid theoretical foundation for FTO to serve as a novel diagnosis and prognostic biomarker for oral diseases.

Serum Levels of Protein Z, Laminin Subunit Alpha 2, Mixed Lineage Leukemia 4, and Plexin Domain Containing 2 in Newly Diagnosed and Metformin-Treated Type 2 Diabetes Patients

Diabetes mellitus is a globally prevalent disease with several parameters that may be involved in diagnosis or altered throughout disease progression. In this study, 120 patients with type 2 diabetes and healthy controls were recruited, with ages ranging from 30-65 years old. The research focused on determining levels of Protein Z (PROZ), Laminin Subunit Alpha 2 (LAMA2), Mixed Lineage Leukemia 4 (MLL4), and Plexin Domain Containing 2 (PLXDC2). Participants were divided into four groups: group I (n=30) with long-term metformin treatment, group II (n=30) with short-term metformin treatment, group III (n=30) newly diagnosed without medication, and group IV (n=30) healthy controls. Protein markers were measured by ELISA, while fasting blood glucose, lipid profiles, and HbA1c were determined by spectrophotometry and HPLC. ROC curve analysis revealed PLXDC2 had high sensitivity and specificity as a potential biomarker for diabetes diagnosis. LAMA2, MLL4, and PROZ also distinguished between diabetes patients and controls. Overall, these results identify promising biomarkers for diabetes detection and monitoring.

Identification of m5C-Related gene diagnostic biomarkers for sepsis: a machine learning study.

Sepsis is a serious condition that occurs when the body's response to infection becomes uncontrolled, resulting in a high risk of death. Despite improvements in healthcare, identifying sepsis early is difficult because of its diverse nature and the absence of distinct biomarkers. Recent studies suggest that 5-methylcytosine (m5C)-related genes play a significant role in immune responses, yet their diagnostic potential in sepsis remains unexplored. This research combined and examined four sepsis-related datasets (GSE95233, GSE57065, GSE100159, and GSE65682) sourced from the Gene Expression Omnibus (GEO)database to discover m5C-related genes with differential expression. Various machine learning methods, such as decision tree, random forest, and XGBoost, were utilized in identifying crucial hub genes. Receiver Operating Characteristic (ROC) curve analysis was used to assess the diagnostic accuracy of these genetic markers. Additionally, single-gene enrichment and immune infiltration analyses were conducted to investigate the underlying mechanisms involving these hub genes in sepsis. Three hub genes, DNA Methyltransferase 1 (DNMT1), tumor protein P53 (TP53), and toll-like receptor 8 (TLR8), were identified and validated for their diagnostic efficacy, showing area under the curve (AUC) values above 0.7 in both test and validation sets. Enrichment analyses revealed that these genes are involved in key pathways such as p53 signaling and Toll-like receptor signaling. Immune infiltration analysis indicated significant correlations between hub genes and various immune cell types, suggesting their roles in modulating immune responses during sepsis. The study highlights the diagnostic potential of m5C-related genes in sepsis and their involvement in immune regulation. These findings offer new insights into sepsis pathogenesis and suggest that DNMT1, TP53, and TLR8 could serve as valuable biomarkers for early diagnosis. Further studies should prioritize validating these biomarkers in clinical settings and investigating their potential for therapy.