Potential Diagnostic Biomarkers Research Articles

Blood-based microRNAs as Potential Diagnostic Biomarkers for Melanoma: A Meta-Analysis.

Circulating microRNAs (miRNAs) serve as noninvasive diagnostic markers in many cancers. This meta-analysis aims to evaluate the diagnostic efficacy of circulating microRNAs for melanoma. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and ROC curve were evaluated using the Meta-Disc V.1.4 and Comprehensive Meta-Analysis V.3.3 software packages. To investigate the heterogeneity, the I2 and Chi-square tests were used. The publishing bias was evaluated using Begg's rank correlation and Egger regression asymmetry tests. A total of 9 articles covering 13 studies (more than 50 miRs individually and in combination) were included, containing 1,355 participants (878 cases and 477 controls). The overall pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and AUC were 0.78 (95% CI: 0.76-0.81), 0.80 (95% CI: 0.77-0.83), 4.32 (95% CI: 3.21-5.82), 0.17 (95% CI: 0.09-0.32), 28.0 (95% CI: 15.34-51.09), and 0.91, respectively. According to Begg's and Egger's tests, there was no publication bias (Begg's p = 0.160 and Egger's p = 0.289). Circulating miRNAs can serve as fair and non-invasive diagnostic biomarkers for melanoma. Additionally, specific miRNAs still need to be discovered for diagnosing melanoma.

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data

Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. Machine learning (ML) models were developed using selected features from blood-secretory proteins collected from the curated databases. The logistic regression (LR) model demonstrated the optimal performance. Transcriptome analysis across 7 LC cohorts revealed 231 common differentially expressed genes (DEGs). The encoded proteins of these DEGs were compared with the ML dataset, revealing 29 proteins overlapping with the blood-secretory dataset. The LR model also predicted 29 additional proteins as blood-secretory with the remaining protein-coding genes. As a result, 58 potential blood-secretory proteins were obtained. Among the top 20 genes, 13 common hub genes were identified. Further, area under the receiver operating characteristic curve (ROC AUC) analysis was performed to assess the genes as potential diagnostic blood biomarkers. Six genes, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6, exhibited an AUC value higher than 0.85 and were predicted as blood-secretory. This study highlights the potential of an integrative computational approach for discovering non-invasive blood-based biomarkers in LC, facilitating for further validation and clinical translation. SignificanceLiver cancer is one of the leading causes of premature death worldwide, with its prevalence and mortality rates projected to increase. Although current diagnostic methods are highly sensitive, they are invasive and unsuitable for repeated testing. Blood biomarkers offer a promising non-invasive alternative, but their wide dynamic range of protein concentration poses experimental challenges. Therefore, utilizing available omics data to develop a diagnostic model could provide a potential solution for accurate diagnosis. This study developed a computational method integrating machine learning and bioinformatics analysis to identify potential blood biomarkers. As a result, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6 biomarkers were identified, holding significant promise for improving diagnosis and understanding of liver cancer. The integrated method can be applied to other cancers, offering a possible solution for early detection and improved patient outcomes.

Proteomic analysis of peripheral blood mononuclear cells in first episode psychosis – Protein and peptide-centered approaches to elucidate potential diagnostic biomarkers

Diagnosing patients suffering from psychotic disorders is far from being achieved with molecular support, despite all the efforts to study these disorders from different perspectives. Characterizing the proteome of easily obtainable blood specimens, such as the peripheral blood mononuclear cells (PBMCs), has particular interest in biomarker discovery and generating pathophysiological knowledge. This approach has been explored in psychiatry, and while generating valuable information, it has not translated into meaningful biomarker discovery. In this project, we report the proof-of-concept of a methodology that aims to explore further information obtained with classical proteomics approaches that is easily overlooked. PBMC samples from first-episode psychosis and control subjects were subjected to a SWATH-MS approach, and the classical protein relative quantification was performed, where 389 proteins were found to be important to distinguish the two groups. Individual analysis of the quantified peptides was also performed, highlighting peptides of unchanged proteins that were significantly altered. With the combination of protein- and peptide-centered proteomics approaches, it is possible to highlight that information about proteoforms, namely regulation at the peptide level possibly due to post-translational modifications, is routinely overlooked and that its diagnostic potential should be further explored. SignificanceOur exploratory findings highlight the potential of MS-based proteomics strategies, combining protein- and peptide-centered approaches, to aid clinical decision-making in first-episode psychosis, helping to establish early biomarkers for schizophrenia and other psychotic disorders. Particularly, the less popular peptide-centered approach allows the identification/measurement of overlooked modulated peptides that may have potential biomarker characteristics. The application in parallel of protein- and peptide-centered strategies is transversal to research of other diseases, potentially allowing a more comprehensive characterization of the metabolic/pathophysiological alterations related to a specific disease.

Isolation and characterization of ssDNA aptamers against BipD antigen of Burkholderia pseudomallei

BackgroundMelioidosis is difficult to diagnose due to its wide range of clinical symptoms. The culture method is time-consuming and less sensitive, emphasizing the importance of rapid and accurate diagnostic tests for melioidosis. Burkholderia invasion protein D (BipD) of Burkholderia pseudomallei is a potential diagnostic biomarker. This study aimed to isolate and characterize single-stranded DNA aptamers that specifically target BipD. MethodsThe recombinant BipD protein was produced, followed by isolation of BipD-specific aptamers using Systematic Evolution of Ligands by EXponential enrichment. The binding affinity and specificity of the selected aptamers were evaluated using Enzyme-Linked Oligonucleotide Assay. ResultsThe fifth SELEX cycle showed a notable enrichment of recombinant BipD protein-specific aptamers. Sequencing analysis identified two clusters with a total of seventeen distinct aptamers. AptBipD1, AptBipD13, and AptBipD50 were chosen based on their frequency. Among them, AptBipD1 exhibited the highest binding affinity with a Kd value of 1.0 μM for the recombinant BipD protein. Furthermore, AptBipD1 showed significant specificity for B. pseudomallei compared to other tested bacteria. ConclusionAptBipD1 is a promising candidate for further development of reliable, affordable, and efficient point-of-care diagnostic tests for melioidosis.

Cell Polarity-related Gene PTK7, a Potential Diagnostic Biomarker in Pan-cancer.

PTK7 (Protein Tyrosine Kinase 7), a member of the receptor protein tyrosine kinase family, was originally discovered in colon cancer cells. It plays a pivotal role in numerous developmental and physiological processes, particularly in the regulation of cell polarity. Despite accumulating evidence of PTK7's significant influence on tumor development, a comprehensive pan-cancer analysis of PTK7 has yet to be conducted. We conducted a comprehensive analysis of PTK7's expression, prognostic value, and mutational patterns across various tumor types. We further explored the correlations between PTK7 expression and tumor stemness, immune-related genes, immune scores, and immune cell infiltration. Enrichment analysis revealed PTK7's critical involvement in pan-cancer functions and processes, including the WNT pathway, Epithelial-Mesenchymal Transition (EMT), and cell polarity regulation. Additionally, we validated PTK7's expression in gastric cancer via immunohistochemistry. Our study indicates that PTK7 holds promise as an ideal pan-cancer biomarker due to its involvement in tumor progression and tumor immunity.

Amino acid metabolism in glioma: in vivo MR-spectroscopic detection of alanine as a potential biomarker of poor survival in glioma patients.

Reprogramming of amino acid metabolism is relevant for initiating and fueling tumor formation and growth. Therefore, there has been growing interest in anticancer therapies targeting amino acid metabolism. While developing personalized therapeutic approaches to glioma, in vivo proton magnetic resonance spectroscopy (MRS) is a valuable tool for non-invasive monitoring of tumor metabolism. Here, we evaluated MRS-detected brain amino acids and myo-inositol as potential diagnostic and prognostic biomarkers in glioma. We measured alanine, glycine, glutamate, glutamine, and myo-inositol in 38 patients with MRI-suspected glioma using short and long echo-time single-voxel PRESS MRS sequences. The detectability of alanine, glycine, and myo-inositol and the (glutamate + glutamine)/total creatine ratio were evaluated against the patients' IDH mutation status, CNS WHO grade, and overall survival. While the detection of alanine and non-detection of myo-inositol significantly correlated with IDH wildtype (p = 0.0008, p = 0.007, respectively) and WHO grade 4 (p = 0.01, p = 0.04, respectively), glycine detection was not significantly associated with either. The ratio of (glutamate + glutamine)/total creatine was significantly higher in WHO grade 4 than in 2 and 3. We found that the overall survival was significantly shorter in glioma patients with alanine detection (p = 0.00002). Focusing on amino acids in MRS can improve its diagnostic and prognostic value in glioma. Alanine, which is visible at long TE even in the presence of lipids, could be a relevant indicator for overall survival.

KRT18 as a Novel Biomarker of Urothelial Papilloma while Evaluating Low-Grade Papillary Urothelial Neoplasms: Bi-Center Analysis.

Although urothelial papilloma (UP) is an indolent papillary neoplasm that can mimic the morphology of low-grade papillary urothelial carcinoma (PUC), there is no immunomarker to differentiate reliably these two entities. In addition, the molecular characteristics of UP are not fully understood. We conducted an in-depth proteomic analysis of papillary urothelial lesions (n = 31), including UP and PUC along with normal urothelium. Protein markers distinguishing UP and PUC were selected with machine learning analysis, followed by internal and external validation using immunohistochemistry. In the proteomic analysis, UP and PUC showed overlapping proteomic profiles. We identified EHD4 and KRT18 as candidate diagnostic biomarkers of UP. Through immunohistochemical validation in two independent cohorts (n = 120), KRT18 was suggested as a novel UP diagnostic marker, able to differentiate UP from low-grade PUC. We also found that 3.5% of patients with UP developed urothelial carcinoma in subsequent resections, supporting the malignant potential of UP. KRT18 downregulation was significantly associated with UPs subsequently progressing to urothelial carcinoma, following their initial diagnosis. This is the first study that successfully revealed UPs comprehensive proteomic landscape, while it also identified KRT18 as a potential diagnostic biomarker of UP.

Unveiling circRNA-mediated ceRNA networks in ischemic stroke by integrative analysis of multi-source gene expression profiling

BackgroundCircular RNAs (circRNAs) are emerging as potential therapeutic targets for ischemic stroke (IS) due to their regulatory roles in inflammation and apoptosis. This study aimed to develop a comprehensive and robust IS-specific competing endogenous RNA (ceRNA) network to facilitate the identification of novel diagnostic and therapeutic targets. MethodsWe integrated expression data from 15 IS studies using the Rank-In algorithm to minimize batch effects. Differentially expressed circRNAs, miRNAs, and mRNAs were identified by comparing IS and control samples. Functional enrichment analysis of differentially expressed circRNA host genes revealed significantly enriched pathways and Gene Ontology (GO) terms relevant to IS pathogenesis. We further predicted miRNA-circRNA and mRNA-miRNA interactions, enabling the construction of a comprehensive ceRNA network to identify circRNA-related genes with diagnostic potential for IS. ResultsIntegrated analysis revealed 199 differentially expressed circRNAs, 103 miRNAs, and 1736 mRNAs in IS patients. Functional enrichment analysis implicated these molecules in relevant pathways like the neurotrophin signaling pathway and p53 signaling pathway. The constructed circRNA-miRNA-mRNA regulatory network provided insights into potential mechanisms underlying IS. Three circRNA-related genes (RGS2, CDK5R1, and NSF) displayed promising diagnostic potential for IS when combined. ConclusionsWe successfully constructed a robust and informative IS-specific ceRNA network by integrating data from diverse sources. This network identified differentially expressed RNAs and revealed enriched pathways potentially involved in IS pathogenesis. Notably, our analysis identified CDK5R1, RGS2, and NSF as potential diagnostic biomarkers for IS. This study sheds light on a circRNA-mediated regulatory network with potential diagnostic and therapeutic implications for ischemic stroke.

Potential diagnostic biomarkers and Mapk14 protein expression: Autophagy-related genes linking immune infiltration in acute respiratory distress syndrome

The pathogenesis of this condition is intricate, characterized by the aberrant activation of numerous cytokines and signaling pathways. This study aimed to delve into the association between the expression of the MAPK14 protein and immune cell infiltration in patients suffering from Acute Respiratory Distress Syndrome (ARDS). Additionally, it sought to assess the viability of autophagy-related genes as potential diagnostic biomarkers. To achieve this, the researchers employed various techniques such as immunohistochemistry, real-time quantitative PCR, and western blotting to measure the MAPK14 protein levels in the lung tissues of ARDS patients. These measurements were then correlated with clinical data to provide a comprehensive analysis.In this study, the researchers conducted a gene expression profile analysis to identify genes associated with autophagy. The relationship between these genes, MAPK14 expression, and immune cell infiltration was thoroughly evaluated. The findings revealed a marked increase in the expression of MAPK14 protein in the lung tissues of ARDS patients. This increased expression was found to be positively correlated with the extent of immune cell infiltration. The study's further analysis highlighted that several genes associated with autophagy exhibited expression levels that were correlated with both MAPK14 expression and the degree of immune infiltration. This suggests a complex interplay between MAPK14 protein levels, autophagy-related genes, and immune responses in the pathogenesis of ARDS. The results underscore the potential of these molecular markers in understanding the disease mechanisms and possibly aiding in the diagnosis and treatment of ARDS.

Up-regulated DSG2 promotes tumor growth and reduces immune infiltration in cervical cancer

BackgroundDesmoglein-2 (DSG2) has been reported to play pivotal roles in various diseases. However, its roles in cervical cancer (CC) remain insufficiently elucidated. Here, we aimed to comprehensively explore the functional mechanisms of DSG2 in CC using bioinformatics and experimental methods. MethodsSeveral online databases, including Gene Expression Profiling Interactive Analysis (GEPIA), ONCOMINE, LinkedOmics, MetaScape, Human protein atlas (HPA), OMICS and single-cell RNA sequencing (scRNA-seq) data were used to explore the expression, prognosis, gene mutations, and potential signaling pathway of DSG2 in CC. Quantitative real-time PCR (qRT-PCR) and western blotting were used to measure DSG2 expression in collected samples. Experimental assays were conducted to verify the effects of dysregulated DSG2 on cervical cell lines in vitro. ResultsBioinformatic analyses revealed that DSG2 was significantly up-regulated in CC compared to normal cervical tissues at both mRNA and protein levels. Elevated DSG2 levels were also associated with poor prognosis and clinical parameters (e.g., cancer stages, tumor grade, nodal metastasis status, etc.). DSG2 expression was predominantly observed in epithelial cells, increasing with disease progression on a single-cell resolution. Additionally, up-regulation of DSG2 significantly enhanced tumor purity by reducing the infiltration of immune cells (e.g., B cells, T cells, NK cells, etc.). Over-expression of DSG2 was further validated in collected CC samples at both mRNA and protein levels. Knockdown of DSG2 markedly reduced the proliferation and invasion of CC cell lines in vitro. ConclusionsIn summary, elevated levels of DSG2 were significantly associated with poor prognosis and diminished immune infiltration in CC. Thus, DSG2 may serve as a potential therapeutic and diagnostic biomarker for CC.

Identifying biomarkers for nasopharyngeal carcinoma diagnosis and chemoradiotherapy response using serum endogenous small molecules profiling

Changes in metabolic characteristics are important features of tumor progression and prognosis, including nasopharyngeal carcinoma (NPC). Identifying serum metabolites as potential diagnostic and chemoradiotherapy response biomarkers for NPC is therefore crucial. In this study, ultra-performance liquid chromatography coupled with linear ion trap quadrupole orbitrap high-resolution mass spectrometry (UPLC-LTQ-Orbitrap MS) was used to analyze metabolic variations among controls, NPC patients, and NPC patients undergoing chemoradiotherapy (CRT). Univariate and multivariate analyses revealed seven differential metabolites between the control and NPC groups and eleven metabolites between the CRT and NPC groups. Five common metabolites, gluconic acid, palmitic acid, LysoPC (15:0/0:0), stearic acid, and LysoPC (20:2(11Z,14Z)/0:0), were consistently altered across groups. Notably, the first four metabolites were adjusted closer to normal after chemoradiotherapy, while this change is not reflected at LysoPC (20:2(11Z,14Z)/0:0). These common metabolites were enriched in five pathways. These findings underscore the importance of serum metabolite profiling in NPC diagnosis and treatment response assessment and offer a promising foundation for further clinical research.

Potential Diagnostic Biomarkers of tRNA-Derived Small RNAs in PBMCs for Nonproliferative Diabetic Retinopathy in Patients With Type 2 Diabetes Mellitus.

This study aimed to reveal the altered expressions of transfer RNA (tRNA)-derived small RNAs (tsRNAs) in peripheral blood mononuclear cells and identify potential diagnostic biomarkers for nonproliferative diabetic retinopathy (NPDR) from patients with type 2 diabetes mellitus. Fifty-three patients diagnosed with type 2 diabetes mellitus were enrolled, including 25 patients with NPDR and 28 patients without diabetic retinopathy (DR) as the control group. A small RNA microarray was performed to screen the differentially expressed tsRNAs. Reverse transcriptase quantitative polymerase chain reaction was used to validate the significantly altered tsRNAs in a screening cohort and a verification cohort. The target genes, their enriched functions, and signaling pathways were predicted by bioinformatics analyses. In total, 668 upregulated and 485 downregulated tsRNAs were found in the NPDR group by microarray. Eight tsRNAs were validated preliminarily to be altered significantly by reverse transcriptase quantitative polymerase chain reaction, and their target genes were enriched in cellular macromolecule metabolic process and ubiquitin-mediated proteolysis. The verification experiments confirmed the increased levels of 5'tiRNA-35-PheGAA-8, tRF3-28-PheGAA-1, and tRF3b-PheGAA-6, and the decreased levels of mt-tRF3-19-ArgTCG, mt-tRF3-20-ArgTCG, and mt-tRF3-21-ArgTCG in patients with NPDR, which may serve as potential biomarkers with clinical significance. The study recognized the tsRNA expression changes in peripheral blood mononuclear cells from patients with NPDR and discovered potential diagnostic biomarkers that hold clinical significance. The significantly altered tsRNAs identified in the study may serve as potential diagnostic biomarkers for patients with NPDR as well as possible molecular targets of the occurrence and development of DR.

Identification of copper death-associated molecular clusters and immunological profiles for lumbar disc herniation based on the machine learning

Lumbar disc herniation (LDH) is a common clinical spinal disorder, yet its etiology remains unclear. We aimed to explore the role of cuproptosis-related genes (CRGs) and identify potential diagnostic biomarkers. Our analysis involved interrogating the GSE124272 and GSE150408 datasets for differential gene expression profiles associated with CRGs and immune characteristics. Molecular clustering was performed on LDH samples, followed by expression and immune infiltration analyses. Using the WGCNA algorithm, specific genes within CRG clusters were identified. After selecting the most predictive genes from the optimal model, four machine learning models were constructed and validated. This study identified nine CRGs associated with copper-regulated cell death. Two copper-containing molecular clusters linked to death were detected in LDH samples. Elevated expression and immune infiltration levels were found in LDH patients, particularly in CRG cluster C2. Utilizing XGB, five genes were identified for constructing a diagnostic model, achieving an area under the curve values of 0.715. In conclusion, this research provides valuable insights into the association between LDH and copper-regulated cell death, alongside proposing a promising predictive model.

Increased UBD Is a Potential Diagnostic and Prognostic Biomarker in Glioma.

Various studies have demonstrated that ubiquitin D (UBD) is overexpressed in different cancer types and may serve as a potential prognostic factor. However, additional research is necessary to establish the prognostic significance and possible role of UBD in glioma. Transcriptomic expression data from The Cancer Genome Atlas database (TCGA) and Chinese Glioma Genome Atlas (CGGA) were analyzed to identify UBD expression differences in tumor and normal tissues. The relative levels of UBD in glioma and normal tissues were determined using qRT-PCR and WB. Logistic regression analysis was performed to investigate the association between UBD expression and clinicopathological characteristics of glioma patients. To evaluate the diagnostic and prognostic predictive values of UBD, we used Kaplan-Meier survival curves, Cox regression analysis, diagnostic receiver operating characteristic (ROC) curves, and nomogram model. We also conducted wound healing assays, transwell assays, EdU assays, and colony formation assays to verify the UBD function. Gene ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, as well as gene set enrichment analysis (GSEA), were employed to determine the functions of UBD. Finally, we performed the western blot assays to assess changes in EMT markers as well as p-PI3K, p-AKT, and p-mTOR expressions. Our study revealed a remarkable increase of UBD expression in glioma samples. Cox regression analysis demonstrated that high expression of UBD mRNA was an independent prognostic factor for overall survival (OS) in TCGA. ROC curve analysis showed that UBD expression levels could differentiate glioma from adjacent normal tissues accurately. Additionally, knockdown of UBD reduced the migration, invasion, and proliferation ability of glioma cells while UBD overexpression had the opposite effect. GSEA showed that the expression of UBD involved with various pathways including epithelial-mesenchymal transition (EMT), PI3K-AKT-mTOR signaling, P53 pathway, angiogenesis, inflammatory response, KRAS signaling, hypoxia, as well as TGF-β signaling. Furthermore, our findings suggest that UBD accelerates the activation of EMT and PI3K/AKT/mTOR pathway.

Olink and gut microbial metabolomics reveal new biomarkers for the prediction and diagnosis of PMOP.

Postmenopausal osteoporosis (PMOP) can cause postmenopausal women to experience pain and interference. Identifying and exploring potential early diagnostic biomarkers of PMOP is of substantial clinical value and social significance. This study aimed to screen for potential novel diagnostic biomarkers of PMOP through a multiomics approach, providing new directions and ideas for the early prevention and treatment of this disease. Fifteen postmenopausal women with osteoporosis and 12 without were recruited. Clinical information was collected, and various clinical biochemical parameters were tested. Plasma and fecal samples were collected and analyzed using Olink proteomics and gut microbial metabolomics. The functions of the differentially abundant metabolites were mainly related to autophagy and arginine and proline metabolism and were involved in immunoinflammatory metabolic processes. Olink showed significant differences in the expression of seven inflammation-related proteins between the two groups. We demonstrated that metabolic differences between PMOP patients and healthy controls were associated with inflammatory responses and found seven proteins with significant differences. Among these proteins, CDCP1, IL10, and IL-1alpha combined with clinical indicators had high discriminant efficiency in identifying PMOP. This is also the first study to demonstrate noteworthy changes in CDCP1 levels in patients with PMOP.

BRD4 expression and its regulatory interaction with miR-26a-3p, DLG5-AS1, and JMJD1C-AS1 lncRNAs in gastric cancer progression

Gastric cancer remains a significant health challenge despite advancements in diagnosis and treatment. Early detection is critical to reducing mortality, necessitating the investigation of molecular mechanisms underlying gastric cancer progression. This study focuses on BRD4 expression and its correlation with miR-26a-3p, DLG5-AS1, and JMJD1C-AS1 lncRNAs in gastric cancer. Analysis of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets revealed significant upregulation of BRD4 in gastric cancer tissues compared to normal tissues, correlating negatively with miR-26a-3p and positively with DLG5-AS1 and JMJD1C-AS1 lncRNAs. Quantitative RT-PCR confirmed these findings in 25 gastric cancer tissue samples and 25 normal samples. BRD4's overexpression was associated with reduced survival rates and older patient age. MiR-26a-3p, a known tumor suppressor, showed decreased expression in gastric cancer tissues, with ROC analysis suggesting it, alongside BRD4, as a potential diagnostic biomarker. Additionally, bioinformatics predicted miR-26a-3p’s interaction with BRD4 mRNA. Upregulated lncRNAs DLG5-AS1 and JMJD1C-AS1 likely act as competing endogenous RNAs, sponging miR-26a-3p, thus promoting BRD4 dysregulation. These lncRNAs have not been previously studied in gastric cancer. The findings propose a novel BRD4/lncRNA/miRNA regulatory axis in gastric cancer, highlighting the potential of BRD4, DLG5-AS1, and JMJD1C-AS1 as biomarkers for early diagnosis. Further studies with larger sample sizes and in vivo and in vitro experiments are needed to elucidate this regulatory mechanism’s role in gastric cancer progression.

The Association between Long Non-Coding RNAs and Alzheimer's Disease.

Neurodegeneration occurs naturally as humans age, but the presence of additional pathogenic mechanisms yields harmful and consequential effects on the brain. Alzheimer's disease (AD), the most common form of dementia, is a composite of such factors. Despite extensive research to identify the exact causes of AD, therapeutic approaches for treating the disease continue to be ineffective, indicating important gaps in our understanding of disease mechanisms. Long non-coding RNAs (lncRNAs) are an endogenous class of regulatory RNA transcripts longer than 200 nucleotides, involved in various regulatory networks, whose dysregulation is evident in several neural and extraneural diseases. LncRNAs are ubiquitously expressed across all tissues with a wide range of functions, including controlling cell differentiation and development, responding to environmental stimuli, and other physiological processes. Several lncRNAs have been identified as potential contributors in worsening neurodegeneration due to altered regulation during abnormal pathological conditions. Within neurological disease, lncRNAs are prime candidates for use as biomarkers and pharmacological targets. Gender-associated lncRNA expression is altered in a gender-dependent manner for AD, suggesting more research needs to be focused on this relationship. Overall, research on lncRNAs and their connection to neurodegenerative disease is growing exponentially, as commercial enterprises are already designing and employing RNA therapeutics. In this review we offer a comprehensive overview of the current state of knowledge on the role of lncRNAs in AD and discuss the potential implications of lncRNA as potential therapeutic targets and diagnostic biomarkers in patients with Alzheimer's disease.

LncRNA NEAT1 and miRNA 101 as potential diagnostic biomarkers in patients with alopecia areata

BackgroundAlopecia areata (AA) commonly displays as non-scarring, irregular hair loss. Experimental and clinical research have specifically implicated autoimmunity and genetics in the disruption of anagen hair follicles. AA patients' scalp lesions and peripheral blood mononuclear cells (PBMCs) exhibited an immune state imbalance. Numerous studies attempt to establish a connection between the occurrence and prognosis of AA and the epigenetic modulation of gene expression by long noncoding RNA (lncRNA) and microRNA (miRNA). The current study aimed to examine the serum levels of nuclear enriched abundant transcript 1 (NEAT1) and its target miRNA101 (miR-101) in AA and investigate the ability to use them as diagnostic biomarkers in the disease. MethodsSeventy-two AA patients were included in this prospective cohort study. Demographics, patient history, laboratory characteristics, and treatments were recorded. The miR-101 and NEAT1 levels were evaluated. ResultsSerum NEAT1 levels were lower in AA patients, but there was no significant difference. However, there was no substantial disparity in NEAT1 level regarding other disease characteristics. There was a substantial positive association between NEAT1 and miR-101 levels among cases. On the other hand, the results showed a markedly low mean of miR-101 levels among patients, but the miR-101 marker shows no significant difference regarding different disease characteristics. The specificity and sensitivity test for the miR-101 marker shows a significant specificity of 60 % and sensitivity of 75 % with a p-value of 0.001 and a cut-off value of 0.897. ConclusionsThe current research determined that miR-101 works as a diagnostic biomarker for AA.

Identification and Validation of Aging Related Genes Signature in Chronic Obstructive Pulmonary Disease

Purpose Chronic Obstructive Pulmonary Disease (COPD) is regarded as an accelerated aging disease. Aging-related genes in COPD are still poorly understood. Method Data set GSE76925 was obtained from the Gene Expression Omnibus (GEO) database. The “limma” package identified the differentially expressed genes. The weighted gene co-expression network analysis (WGCNA) constructes co-expression modules and detect COPD-related modules. The least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE) algorithms were chosen to identify the hub genes and the diagnostic ability. Three external datasets were used to identify differences in the expression of hub genes. Real-time reverse transcription polymerase chain reaction (RT-qPCR) was used to verify the expression of hub genes. Result We identified 15 differentially expressed genes associated with aging (ARDEGs). The SVM-RFE and LASSO algorithms pinpointed four potential diagnostic biomarkers. Analysis of external datasets confirmed significant differences in PIK3R1 expression. RT-qPCR results indicated decreased expression of hub genes. The ROC curve demonstrated that PIK3R1 exhibited strong diagnostic capability for COPD. Conclusion We identified 15 differentially expressed genes associated with aging. Among them, PIK3R1 showed differences in external data sets and RT-qPCR results. Therefore, PIK3R1 may play an essential role in regulating aging involved in COPD.

METTL14-mediated m6A mRNA modification of G6PD promotes lung adenocarcinoma

METTL14 functions as an RNA methyltransferase involved in m6A modification, influencing mRNA biogenesis, decay, and translation processes. However, the specific mechanism by which METTL14 regulates glucose-6-phosphate dehydrogenase (G6PD) to promote the progression of lung adenocarcinoma (LUAD) is not well understood. Quantitative measurement and immunohistochemistry (IHC) analysis have demonstrated higher levels of m6A in LUAD tissues compared to adjacent normal tissues. Additionally, the expression of METTL14 was significantly increased in LUAD tissues. In LUAD cell lines, both METTL14 and m6A levels were elevated compared to normal human lung epithelial cells. Knockdown of METTL14 markedly reduced LUAD cell proliferation, migration, and invasion. Conversely, overexpression of METTL14, but not the mutant form, significantly enhanced these cellular processes in LUAD. In vivo studies using nude mice with subcutaneously transplanted LUAD cells demonstrated that stable METTL14 knockdown led to notably reduced tumor volume and weight, along with fewer Ki67-positive cells and lung metastatic sites. Importantly, METTL14 knockdown reduced glycolytic activity in LUAD cells. Through a combination of RNA sequencing and MeRIP-sequencing, we identified numerous altered genes and confirmed that IGF2BP2 enhances G6PD mRNA stability after METTL14-mediated m6A modification, thereby promoting tumor growth and metastasis. Moreover, LUAD patients with higher levels of G6PD had poorer overall survival (OS). In conclusion, our study indicates that METTL14 upregulates G6PD expression post-transcriptionally through an m6A-IGF2BP2-dependent mechanism, thereby stabilizing G6PD mRNA. These findings propose potential diagnostic biomarkers and effective targets for anti-metabolism therapy in LUAD.